The findings come from Third Sector’s inaugural Charity Employer Index

I am a firm believer in The 7 Habits of Highly Effective People, by Stephen Covey. If you've not read this book, it is worth the time. I mention this because my focus at BA Insight is around Covey's second habit, which is, "Begin with the end in mind." Seems simple, right? Well it is, but it's also quite rare. When approaching any enterprise search project, at any phase, I always try to come back to this idea. What is success? When are we done? What does finished look like? These are all different ways of saying, "Make sure you have goals!"

For decades, organizations have tried to unlock the collective knowledge contained within their people and systems. And the challenge is getting harder, since every year, massive amounts of additional information are created for people to share. We've reached a point at which individuals are unable consume, understand, or even find half the information that is available to them.

Awards recognizing best practices in local governance in Montenegro’s municipalities over the course of this year were presented at a ceremony organized on 28 December 2015 in Podgorica by the OSCE Mission to Montenegro in co-operation with the Union of Municipalities and the Ministry of Interior.

The annual Awards, now in their eighth year, are open to all local government units and allow them to showcase successful and innovative solutions and initiatives in providing services to their citizens.

Opening the award ceremony, Chairperson of the Executive Board of the Union of Municipalities of Montenegro and Mayor of Cetinje, Aleksandar Bogdanović, said the Awards were the product of the excellent co-operation of national authorities, local government and international organizations with the aim of supporting sustainable development at the local level.

Deputy Head of the OSCE Mission Dan Redford said: “The OSCE Mission to Montenegro has always and will continue to support each and every effort of local governance units in Montenegro to enhance principles of good governance in their communities. These are of fundamental importance because local government is closest to citizens and provides them with essential services. Our goal has always been to mobilize and stimulate action by local stakeholders so that the citizens may benefit from good democratic governance at the local level, through the continuously improving quality of local public services.”

General Director of Directorate for State and Local Authorities in the Ministry of Interior Dragana Ranitović said this programme is of great importance for local governance units as it enables the sharing of best practices among municipalities and that it could be expanded further to facilitate bilateral cross-border co-operation.

The winners of this year’s Awards are:

• Žabljak Municipality for its economical financial practices;
• Budva Municipality for its effective human resources management through an electronic personnel recording system;
• Petnjica Municipality for establishing institutional models to enhance co-operation with citizens and the diaspora;
• Bijelo Polje Municipality for its work in economically empowering women;
• Berane Municipality for establishing a Secretariat for Sports, Culture, Youth and Co-operation with NGOs, and a Youth Council.

Berane Municipality also received a special award for introducing a more efficient accounting management system for budget spending, and for establishing the post of an Internal controller.

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Piensa para reformar la Justicia en alguien que fue investigado por sexo con menores y tráfico de personas y al que el Congreso le ha abierto un expediente por conducta sexual inapropiada, consumo de drogas o uso inapropiado de fondos de su campaña Leer

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In the lab of Minna Roh-Johnson, PhD, great science and great mentorship are inextricable. Now, up to $250,000 in federal funding from the National Cancer Institute has made that philosophy concrete by advancing cancer research and building scientific community.

A long-term worker overseeing refugee relief work on Lesbos describes the people he’s met on the island, the chances he’s had to share his faith and how God has shown up during the crisis.

Spartan Net is excited to announce receiving national recognition for the first 10 Gigabit deployment in student housing! This new technology was deployed at Harbor Bay Real Estate Advisors Landmark on Grand River in partnership with Nokia and LightSpeed Technologies, Inc.

Once again Network Control is recognized as a key telecom expense management provider by Analyst firm Gartner in their 2020 Market Guide for Telecom Expense Management Services.

Telecom Expense Management company Network Control has been selected for the latest Gartner Market Guide for Telecom Expense Management Services.

Tokyo International Airport (HANEDA), the 4th largest airport in the world, chose the German SBD provider to equip their self-service installation with the Materna IPS One ID journey.

The International Lawyers Network (ILN) announces its continued distinction as a 'Leading Law Firm Network' in the prestigious Chambers Global Guide for 2024. This esteemed recognition highlights the ILN's unwavering dedication to excellence and its prominent position within the global legal community.

The NEA Foundation announced today that Kimberly Johnson, an interventionist, resource educator, and teacher lead at Auburn Junior High School in Auburn, Ala., is the recipient of the 2024 NEA Member Benefits Award for Teaching Excellence, one of public education's top honors. Johnson was presented with the award, which comes with $25,000, by NEA Member Benefits President and CEO Leona Lindner at the NEA Foundation Salute to Excellence in Education Gala on Friday, May 3.

Baskits Inc. has announced its placement in the 2022 Report on Business ranking of Canada's Top Growing Companies for a third consecutive year.

Cognigen News and Current Events

Performance is everything on the job. If you're at the helm of any kind of business, you need to be on the lookout for exemplary employees, period. Lack of qualified team members can lead to all sorts of major issues. It can greatly interfere with your desire for pure achievement as well. If you want to reward outstanding team members who are part of your business, then you need to take action.

RecordForAll, audio recording and editing software for podcasters, took home top honors at the recent 2007 Shareware Industry Awards ceremony, by recieiving the award for the Best Sound Program. The Shareware Industry Awards are the Oscars of the software industry, recognizing outstanding software programs sold utilizing a marketing method that allows users to try the software prior to making a purchase decision.

While growing evidence suggests that there’s a link between blood iron levels and the development of Alzheimer’s disease, new research investigating the effects of an available iron-reducing drug has raised concerns about its use as a treatment for the condition.

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Category: Alzheimer's & Dementia, Brain Health, Body & Mind

Tags: Iron, clinical trials, Alzheimer's disease

Most people know about the incognito mode in web browsers, but fewer realize that YouTube offers a similar feature in its mobile app.

What started out as curiosity about water lilies has grown into full blown passion for Viji Abi of Thrissur

The State of Virginia was recently recognized by the Center for Digital Government with a third place ranking in the Best of the Web and Digital Government Achievement Awards. The recognition came largely for Virginia's new syndication and alert services. In accepting the award Governor Mark Warner said, "Our real-time online live help customer service continues to set the pace for the nation, and the portal's desktop alerts via live RSS feeds ensure that Virginia.gov users always have access to the most current information." The VIPNet portal and its RSS feeds are managed by the Virginia Information Providers Network. There are currently at least 34 feeds. Virginia uses RSS feeds not only for alerts, but also as a monitoring service that keeps citizens informed of new resources and services added to the portal.

If deception is so deceptive, how does one know if one is being deceived?

Fr. Ted reminds us that Palm Sunday is actually a tragic event because the people recognize the Messiah not as He is, but as they want him to be.

On the first Sunday after Pentecost, the feast of All Saints, Fr Thomas teaches us that, because we are created in the image of God, we have the calling to become Saints who have grown in the likeness of God.

Whether as individuals or as couples we too are each called in the words of Zacharias as set out in the Gospel of St. Luke to “prepare His ways”—that is, to prepare the way of Jesus Christ in our own lives and in the lives of others.

What comes to mind when you hear the term "cognitive dissonance"? Today Dr. Rossi shows how this reality is active in our lives as Orthodox Christians.

This work describes an online handwritten character recognition system working in combination with an offline recognition system. The online input data is also converted into an offline image, and in parallel recognized by both online and offline strategies. Features are proposed for offline recognition and a disambiguation step is employed in the offline system for the samples for which the confidence level of the classier is low. The outputs are then combined probabilistically resulting in a classier out-performing both individual systems. Experiments are performed for Kannada, a South Indian Language, over a database of 295 classes. The accuracy of the online recognizer improves by 11% when the combination with offline system is used.

In this paper, we propose a novel dexterous technique for fast and accurate recognition of online handwritten Kannada and Tamil characters. Based on the primary classifier output and prior knowledge, the best classifier is chosen from set of three classifiers for second stage classification. Prior knowledge is obtained through analysis of the confusion matrix of primary classifier which helped in identifying the multiple sets of confused characters. Further, studies were carried out to check the performance of secondary classifiers in disambiguating among the confusion sets. Using this technique we have achieved an average accuracy of 92.6% for Kannada characters on the MILE lab dataset and 90.2% for Tamil characters on the HP Labs dataset.

In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.

To improve the effectiveness of online training for music education, it was investigated how to improve the pulse-coupled neural network in image processing for spectral image segmentation. The study proposes a two-scale descent method to achieve oblique spectral correction. Subsequently, a convolutional neural network was optimised using a two-channel feature fusion recognition network for music theory notation recognition. The results showed that this image segmentation method had the highest accuracy, close to 98%, and the accuracy of spectral tilt correction was also as high as 98.4%, which provided good image pre-processing results. When combined with the improved convolutional neural network, the average accuracy of music theory symbol recognition was about 97% and the highest score of music majors was improved by 16 points. This shows that the method can effectively improve the teaching effect of online training in music education and has certain practical value.

Teaching advanced programming can be a challenge, especially when the students are pursuing different majors with diverse analytical and problem-solving capabilities. The purpose of this paper is to explore the efficacy of using a particular problem as a vehicle for imparting a broad set of programming concepts and problem-solving techniques. We present a classic brain teaser that is used to communicate and demonstrate advanced software development concepts and techniques. Our results show that students with varied academic experiences and goals, assuming at least one procedural/structured programming pre-requisite, can benefit from and also be challenged by such an exercise. Although this problem has been used by others in the classroom, we believe that our use of this problem in imparting such a broad range of topics to a diverse student population is unique.

Aim/Purpose: The purpose of this paper is to develop and evaluate a debiasing-based approach to assessing the learning materials in problem-based learning (PBL) environments. Background: Research in cognitive debiasing suggests nine debiasing strategies improve decision-making. Given the large number of decisions made in semester-long, problem-based learning projects, multiple tools and techniques help students make decisions. However, instructors may struggle to identify the specific tools or techniques that could be modified to best improve students’ decision-making in the project. Furthermore, a structured approach for identifying these modifications is lacking. Such an approach would match the debiasing strategies with the tools and techniques. Methodology: This debiasing framework for the PBL environment is developed through a study of debiasing literature and applied within an e-commerce course using the Model for Improvement, continuous improvement process, as an illustrative case to show its potential. In addition, a survey of the students, archival information, and participant observation provided feedback on the debiasing framework and its ability to assess the tools and techniques within the PBL environment. Contribution: This paper demonstrates how debiasing theory can be used within a continuous improvement process for PBL courses. By focusing on a cognitive debiasing-based approach, this debiasing framework helps instructors 1) identify what tools and techniques to change in an PBL environment, and 2) assess which tools and techniques failed to debias the students adequately, providing potential changes for future cycles. Findings: Using the debiasing framework in an e-commerce course with significant PBL elements provides evidence that this framework can be used within IS courses and more broadly. In this particular case, the change identified in a prior cycle proved effective and additional issues were identified for improvement. Recommendations for Practitioners: With the growing usage of semester-long PBL projects in business schools, instructors need to ensure that their design of the projects incorporates techniques that improve student learning and decision making. This approach provides a means for assessing the quality of that design. Recommendation for Researchers: This study uses debiasing theory to improve course techniques. Researchers interested in assessment, course improvement, and program improvement should incorporate debiasing theory within PBL environments or other types of decision-making scenarios. Impact on Society: Increased awareness of cognitive biases can help instructors, students, and professionals make better decisions and recommendations. By developing a framework for evaluating cognitive debiasing strategies, we help instructors improve projects that prepare students for complex and multifaceted real-world projects. Future Research: The approach could be applied to multiple contexts, within other courses, and more widely within information systems to extend this research. The framework might also be refined to make it more concise, integrated with assessment, or usable in more contexts.

Aim/Purpose: The aim of the study is to test whether the perception of self-regulated learning during text reading in online teacher education is improved by using a digital tool for the use of metacognitive strategies for planning, monitoring, and self-assessment. Background: The use of self-regulated learning is important in reading skills, and for students to develop self-regulated learning, their teachers must master it. Therefore, teaching strategies for self-regulated learning in teacher education is essential. Methodology: The sample size was 252 participants with the tool used by 42% or the participants. A quasi-experimental design was used in a pre-post study. ARATEX-R, a text-based scale, was used to evaluate self-regulated learning. The 5-point Likert scale includes the evaluation of five dimensions: planning strategies, cognition management, motivation management, comprehension assessment and context management. A Generalized Linear Model was used to analyse the results. Contribution: Using the tool to self-regulate learning has led to an improvement during text reading, especially in the dimensions of motivation management, planning management and comprehension assessment, key dimensions for text comprehension and learning. Findings: Participants who use the app perceive greater improvement, especially in the dimensions of motivation management (22,3%), planning management (19.9%) and comprehension assessment (24,6%), which are fundamental dimensions for self-regulation in text reading. Recommendations for Practitioners: This tool should be included in teacher training to enable reflection during the reading of texts, because it helps to improve three key types of strategies in self-regulation: (1) planning through planning management, (2) monitoring through motivation management and comprehension assessment, and (3) self-assessment through comprehension assessment. Recommendation for Researchers: The success of the tool suggests further study for its application in other use cases: other student profiles in higher education, other teaching modalities, and other educational stages. These studies will help to identify adaptations that will extend the tool’s use in education. Impact on Society: The use of Metadig facilitates reflection during the reading of texts in order to improve comprehension and thus self-regulate the learning of content. This reflection is crucial for students’ knowledge construction. Future Research: Future research will focus on enhancing the digital tool by adding features to support the development of cognition and context management. It will also focus on how on adapting the tool to help other types of learners.

Action recognition plays a vital role in analysing human body behaviour and has significant implications for research and education. However, traditional recognition methods often suffer from issues such as inaccurate time and spatial feature vectors. Therefore, this study addresses the problem of inaccurate recognition of aerobic gymnastics action image data and proposes a visualised three-dimensional convolutional neural network algorithm-based action recognition model. This model incorporates unsupervised visualisation methods into the traditional network and enhances data recognition capabilities through the introduction of a random noise perturbation enhancement algorithm. The research results indicate that the data augmented with noise perturbation achieves the lowest mean square error, reducing the error value from 0.3352 to 0.3095. The use of unsupervised visualisation analysis enables clearer recognition of human actions, and the algorithm model is capable of accurately recognising aerobic movements. Compared to traditional algorithms, the new algorithm exhibits higher recognition accuracy and superior performance.

The conventional methods for identifying English online course learning behaviours have the problems of low recognition accuracy and high time cost. Therefore, a multimodal data fusion-based method for identifying English online course learning behaviours is proposed. Firstly, the analytic hierarchy process is used for decision fusion of multimodal data of learning behaviour. Secondly, based on the fusion results of multimodal data, weight coefficients are set to minimise losses and extract learning behaviour features. Finally, based on the extracted learning behaviour characteristics, the optimal classification function is constructed to classify the learning behaviour of English online courses. Based on the transfer information of learning behaviour status, the identification of online course learning behaviour is completed. The experimental results show that the recognition accuracy of the proposed method is above 90%, and its recognition accuracy is and can shorten the recognition time of learning behaviour, with high practical application reliability.

In order to improve the standardisation of mutual information index, accuracy rate and recall rate of student classroom behaviour recognition method, this paper proposes a student's classroom behaviour recognition method based on abstract hidden Markov model (HMM). After cleaning the students' classroom behaviour data, improve the data quality through interpolation and standardisation, and then divide the types of students' classroom behaviour. Then, in support vector machine, abstract HMM is used to calculate the output probability density of support vector machine. Finally, according to the characteristic interval of classroom behaviour, we can judge the category of behaviour characteristics. The experiment shows that normalised mutual information (NMI) index of this method is closer to one, and the maximum AUC-PR index can reach 0.82, which shows that this method can identify students' classroom behaviour more effectively and reliably.

In this article, we have attempted to study the ways in which the COVID-19 pandemic has gradually increased and impacted the world. The authors integrate the knowledge from cognitive psychology literature to illustrate how the limitations of the human mind might have a critical role in the decisions taken during the COVID-19 pandemic. The authors show the correlation between different biases in various contexts involved in the COVID-19 pandemic and highlight the ways in which we can nudge ourselves and various stakeholders involved in the decision-making process. This study uses a typology of biases to examine how different patterns of biases affect the decision-making behaviour of people during the pandemic. The presented model investigates the potential interrelations among environmental transformations, cognitive biases, and strategic decisions. By referring to cognitive biases, our model also helps to understand why the same performance improvement practices might incite different opinions among decision-makers.

A hybrid network system for mitigating errors due to jamming attacks in cognitive radio networks (CRNs) is named LeNet deep stacked autoencoder (LDSAE) and is developed. In this exploration, the sensing stage and decision-making are considered. The sensing unit is composed of four steps. First, the detected signal is forwarded to filtering progression. Here, BPF is utilised to filter the detected signal. The filtered signal is squared in the second phase. Third, signal samples are combined and jamming attacks occur by including false energy levels. Last, the attack is maliciously affecting the FC decision in the fourth step. On the other hand, FC initiated the decision-making and also recognised jamming attacks that affect the link amidst PU and SN in decision-making stage and it is accomplished by employing LDSAE-based trust model where the proposed module differentiates the malicious and selfish users. The analytic measures of LDSAE gained 79.40%, 79.90%, and 78.40%.

Numerous Internet of Things (IoT) applications require brain-empowered intelligence. This necessity has caused the emergence of a new area called cognitive IoT (CIoT). Reasoning, planning, and selection are typically involved in decision-making within the network bandwidth limit. Consequently, data minimisation is needed. Therefore, this research proposes a novel technique to extract conscious data from a massive dataset. First, it groups the data using k-means clustering, and the entropy is computed for each cluster. The most prominent cluster is then determined by selecting the cluster with the highest entropy. Subsequently, it transforms each cluster element into an informative element. The most informative data is chosen from the most prominent cluster that represents the whole massive data, which is further used for intelligent decision-making. The experimental evaluation is conducted on the 21.25 years of environmental dataset, revealing that the proposed method is efficient over competing approaches.