Achieving business goals starts with having the best metrics in place to understand and respond to customers.

Facebook has acquired emotion detection start-up FacioMetrics to push its artificial intelligence (AI) research into building facial gesture controls.

More often than not in traditional management consulting engagements involving operations, there is usually room for setting up or revising goals, metrics & reporting, and service level agreements. Depending on the phase of development of a company (often couched as Stage 0 [Ad-Hoc], Stage 1 [Functional], Stage 2 [Intrabusiness Cross-Functional], etc. maturity levels by a consulting firm), the next higher-level of metrics and reporting may apply and be a goal for the client organization to strive for.

Where management has to be somewhat careful about applying metrics, however, is how people and company culture play a role in getting everything to work together right.

To put a finer point on things, one can put in place a perfectly appropriate set of metrics and reporting systems for a company yet miss the boat completely on a management-level as to how the results should be applied because of the culture.

As an example in sales operations, I have frequently seen a competitive environment laid out. People's individual performance levels are exposed, and poor performing people are weeded out. This environment can work out in some cases. On the other hand, in a collaborative engineering or product development environment, cooperation may be encouraged.

On a related topic, a friend of my wife and I, Dr. Uri Gneezy at the University of Chicago, has some great research on the relationship between competition and gender. From the research summary:

Gneezy suggests that CEOs creating incentives in their firms should be aware that making the internal environment more competitive might create a bias that helps men, while putting women at a relative disadvantage.

My take on applying this type of research would be to recognize and raise one's sense of awareness to the environment one is implementing metrics in. Although metrics may be encouraged to be tracked to an individual-level doesn't automatically equate to that the environment has to be a competitive one. Choosing a collaborative versus dictatorial management style that is compatible with the desired company culture is a separate choice from metric implementation.

Steve Shu Managing Director S4 Management Group Email: sshu@s4management.com Web: http://www.s4management.com

Your small business is up and running, and you have a product or service that you are excited about. How do you boost your chances of building a venture that will last for the long term? A government statistic shows roughly 40% of Australian businesses fail within the first four years.

Three Key Success Metrics Every Small Business Owner Should Focus On

As 2019 moves to a close, Spotify is giving its yearly version of the personalized Spotify Wrapped, as well as a unique one that showcases your listening records through the last decade.

That indicates your Wrapped will add the songs, artists, albums, and podcasts you found on Spotify in 2019, plus the artists you streamed across the past decade, by a My Decade Wrapped option.

Spotify Wrapped extends to add your favorite music from the decade, plus podcaster metrics

In the digital technology era, mobile devices have an important rule to deploy a copy of data and information through the network. An electronic reader (eReader) allows readers to read written materials in an electronic manner that is available in many models. The objective of this study is to evaluate the usage of eReader by higher education students. We firstly identified the most frequently used eReader by surveying higher education students. The survey results showed that Apple iPad, Amazon Kindle, and Samsung Tablet are the most popular eReader devices used by higher education students. We presented these results, and then we analyzed the surveyed results in detail in order to develop an evaluation metric of the eReader in a mobile platform that clearly allows the selection of the most suitable eReader for higher education students. The main contribution of this paper is the development of a set of criteria that can be used by students in the selection of an eReader that matches their specific needs and requirements.

Aim/Purpose: The aim of this study was to analyze various performance metrics and approaches to their classification. The main goal of the study was to develop a new typology that will help to advance knowledge of metrics and facilitate their use in machine learning regression algorithms Background: Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. A performance metric can be defined as a logical and mathematical construct designed to measure how close are the actual results from what has been expected or predicted. A vast variety of performance metrics have been described in academic literature. The most commonly mentioned metrics in research studies are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), etc. Knowledge about metrics properties needs to be systematized to simplify the design and use of the metrics. Methodology: A qualitative study was conducted to achieve the objectives of identifying related peer-reviewed research studies, literature reviews, critical thinking and inductive reasoning. Contribution: The main contribution of this paper is in ordering knowledge of performance metrics and enhancing understanding of their structure and properties by proposing a new typology, generic primary metrics mathematical formula and a visualization chart Findings: Based on the analysis of the structure of numerous performance metrics, we proposed a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set. For each component, implementation options have been identified. The suggested new typology has been shown to cover a total of over 40 commonly used primary metrics Recommendations for Practitioners: Presented findings can be used to facilitate teaching performance metrics to university students and expedite metrics selection and implementation processes for practitioners Recommendation for Researchers: By using the proposed typology, researchers can streamline development of new metrics with predetermined properties Impact on Society: The outcomes of this study could be used for improving evaluation results in machine learning regression, forecasting and prognostics with direct or indirect positive impacts on innovation and productivity in a societal sense Future Research: Future research is needed to examine the properties of the extended metrics, composite metrics, and hybrid sets of metrics. Empirical study of the metrics is needed using R Studio or Azure Machine Learning Studio, to find associations between the properties of primary metrics and their “numerical” behavior in a wide spectrum of data characteristics and business or research requirements

Global biodiversity loss is intensifying. But it is hard to assess progress towards the Aichi Biodiversity Targets for 2011–20 set by the Convention on Biological Diversity (CBD). Target 5, for instance, aims to halve global deforestation rates by 2020; but reliable indicators for deforestation that can be monitored remotely have not been developed or agreed on. National biodiversity monitoring programmes differ widely, most data sets are inconsistent, and few data are shared openly.

Read more on the topic in the original commentary article.

How can we better measure our safety culture?

More businesses are becoming aware of biometrics for identification and access control — and security professionals who help educate them will reap the benefits..

Interface introduces project-specific embodied carbon metrics on all floor plans created by its Design Studio. This industry-first initiative allows customers to easily understand the carbon impact of their flooring choices, comparing Interface products to industry averages.

The supercritical water oxidation (SCWO) system is a secondary waste processing reactor of the Blue Grass Chemical Agent Destruction Pilot Plant (BGCAPP). It is perhaps second in importance behind the agent neutralization reactors, which perform base hydrolysis of chemical warfare agents stored at the Blue Grass Army Depot.

The National Academies today released Criteria for Selecting the Leading Health Indicators for Healthy People 2030, the first of two reports that will help inform the development of Healthy People 2030 (HP2030).

Fastmetrics, the Official Business Internet Service Provider of the San Jose Earthquakes and a leading B2B ISP, will be the presenting sponsor for their Major League Soccer match on Saturday, March 5 (2:30 p.m. PT).

BodiMetrics is extremely proud to announce the addition of Dr. Meir Kryger to its Medical Advisory Board.

Deal reaffirms Zain's commitment and regional leadership in transparency, accountability, and ESG innovation. The centralized data platform will empower Zain to seamlessly manage ESG data in adherence to complex regulatory requirements.

PAXIO, a dedicated fiber provider primarily serving business and carrier clients, has acquired Fastmetrics, a business-to-business Internet Service Provider (ISP).

Marie Boran is a PhD candidate at the INSIGHT Centre for Data Analytics, the National

Metrics Matter: Assessing Progress towards Women’s Empowerment in Agriculture and Beyond

The Philippine Institute for Development Studies (PIDS), in partnership with the International Food Policy Research Institute (IFPRI), will hold a public seminar featuring three studies on women empowerment on August 14, 2024, 9:00 AM to 11:30 AM (Asia/Manila) / August 13, 2024, 9:00 PM to 11:30 PM (US/Eastern) at the PIDS Conference Hall and via Zoom. […]

The post Metrics Matter: Assessing Progress towards Women’s Empowerment in Agriculture and Beyond appeared first on IFPRI.

Though FaciliWorks CMMS is already used by hundreds of Mexican companies, there is still an enormous number of potential clients who are in need of a system like FaciliWorks.

Psychometrics - origins, definitions, theory, instruments and procedures.

Study on methods and metrics to evaluate energy efficiency for future 5G systems

GSTP-IPTV-QoS - Performance metrics for end-to-end IPTV video quality

HSTP-VID-WPOM - Working practices using objective metrics for evaluation of video coding efficiency experiments

Framework and metrics for digital human application systems

Energy efficiency metrics and measurement methods for telecommunication equipment

Deepfakes threats advance, and technology is challenged to keep up.

"There is power in quantity, but how we talk about our return on the nation’s investment is an area I’m looking to try to work on," Michael Sulmeyer said.

The post Pentagon’s first cyber policy chief targets better metrics for cybersecurity success first appeared on Federal News Network.

Public trust hinges on the resilience of critical infrastructure and government agencies against cyber threats.

The post Rethinking continuous risk metrics to fortify federal cybersecurity first appeared on Federal News Network.

Ahmed Elshafei, Ana Cristina Ferreira, Miguel Sánchez and Abdelghani Zeghib
Trans. Amer. Math. Soc. 377 (), 5837-5862.
Abstract, references and article information

Our objective is to explore quantitative imaging markers for early prediction of treatment response in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs) undergoing [¹⁷⁷Lu]Lu-DOTATATE therapy. By doing so, we aim to enable timely switching to more effective therapies in order to prevent time-resource waste and minimize toxicities. Methods: Patients diagnosed with unresectable or metastatic, progressive, well-differentiated, receptor-positive GEP-NETs who received 4 sessions of [¹⁷⁷Lu]Lu-DOTATATE were retrospectively selected. Using SPECT/CT images taken at the end of treatment sessions, we counted all visible tumors and measured their largest diameters to calculate the tumor burden score (TBS). Up to 4 target lesions were selected and semiautomatically segmented. Target lesion peak counts and spleen peak counts were measured, and normalized peak counts were calculated. Changes in TBS (TBS) and changes in normalized peak count (nPC) throughout treatment sessions in relation to the first treatment session were calculated. Treatment responses were evaluated using third-month CT and were binarized as progressive disease (PD) or non-PD. Results: Twenty-seven patients were included (7 PD, 20 non-PD). Significant differences were observed in TBS_second-first, TBS_third-first, and TBS_fourth-first (where second-first, third-first, and fourth-first denote scan number between the second and first, third and first, and fourth and first [¹⁷⁷Lu]Lu-DOTATATE treatment cycles), respectively) between the PD and non-PD groups (median, 0.043 vs. –0.049, 0.08 vs. –0.116, and 0.109 vs. –0.123 [P = 0.023, P = 0.002, and P < 0.001], respectively). nPC_second-first showed significant group differences (mean, –0.107 vs. –0.282; P = 0.033); nPC_third-first and nPC_fourth-first did not reach statistical significance (mean, –0.122 vs. –0.312 and –0.183 vs. –0.405 [P = 0.117 and 0.067], respectively). At the optimal threshold, TBS_fourth-first exhibited an area under the curve (AUC) of 0.957, achieving 100% sensitivity and 80% specificity. TBS_second-first and TBS_third-first reached AUCs of 0.793 and 0.893, sensitivities of 71.4%, and specificities of 85% and 95%, respectively. nPC_second-first, nPC_third-first, and nPC_fourth-first showed AUCs of 0.764, 0.693, and 0.679; sensitivities of 71.4%, 71.4%, and 100%; and specificities of 75%, 70%, and 35%, respectively. Conclusion: TBS and nPC can predict [¹⁷⁷Lu]Lu-DOTATATE response by the second treatment session.

Detecting illicit financial flows require much more than using traditional business methods. At this point, using centrality metrics in investigation and analytical models will provide wider detection approaches.

Using centrality metrics to detect illicit financial flows was published on SAS Users.

I'm working on the code coverage. Doing a metrics analysis by default we see overall average grade and overall covered. But when i do a block analysis on an instance i see overall covered grade, code covered grade, block covered grade, statement covered grade, expression covered grade, toggle covered grade.

As I dont know the difference I started to read the IMC user guide and came to know there are 3 things we come across while doing a code coverage local, covered, average

From my understanding

local - child instances metrics doesnt reach the parent level. For example, we have an instance Q and its sub instances like Q.a, Q.b. Block Local grade of Q can be 100% even when its instances Q.a and Q.b a block local grades isnt at 100%.

In the attached image there is formula 

The key difference between average and covered is the weights.

Average : Mathematically taking the above scenario where Q.a, and Q.b has 10 blocks each. Q.a has covered 8 blocks and q.b has covered 2 blocks. Now if we take the normal average it should be total covered/ totatl number = 8+2/10+10 yielding 50%. But when we add weights saying Q.a is 70% and Q.b is 30% the new number would be (8*0.7+2*0.3) / (10*0.7+10*0.3) resulting 62%. Because of the weights we see 12% bump.

Covered: there is no role of weights.

Among these 3 metrics i've changed my default view to this in the image to get more realistic picture when i do analyze metrics. Do you guys agree with the approach?

As noted in white papers, posts on the Team Specman Blog, and the Specman documentation, IntelliGen is a totally new stimulus generator than the original "Pgen" and, as a result, there is some amount of effort needed to migrate an existing verification environment to fully leverage the power of IntelliGen.  One of the main steps in migrating code is running the linters on your code and adressing the issues highlighted. 

Included below is a simple utility you can include in your environment that allows you to collect some valuable statistics about your code base to allow you to better gauge the amount of work that might be required to migrate from Pgen to IntelliGen.  The ICFS statistics reported are of particular benefit as the utility not only identifies the approximate number of ICFSs in the environment, it also breaks the total number down according to generation contexts (structs/units and gen-on-the-fly statements) allowing you to better focus your migration efforts. 

IMPORTANT: Sometimes a given environment can trigger a large number of IntelliGen linting messages right off the bat.  Don't let this freak you out!  This does not mean that migration will be a long effort as quite often some slight changes to an environment remove a large number of identified issues.  I recently encountered a situation where a simple change to three locations in the environment, removed 500+ ICFSs!

The methods included in the utility can be used to report information on the following:
- Number of e modules
- Number of lines in the environment (including blanks and comments)
- Number and type of IntelliGen Guidelines linting messages
- Number of Inconsistently Connected Field Sets (ICFSs)
- Number of ICFS contexts and how many ICFSs per context
- Number of soft..select overlays found in the envioronment
- Number of Laces identified in the environment

To use the code below, simply load it before/after loading e-code and then
you can execute any of the following methods:

- sys.print_file_stats()             : prints # of lines and files
- sys.print_constraint_stats()   : prints # of constraints in the environment
- sys.print_guideline_stats()    : prints # of each type of linting message
- sys.print_icfs_stats()            : prints # of ICFSs, contexts and #ICFS/context
- sys.print_soft_select_stats() : prints # of soft select overlay issues
- sys.print_lace_stats()           : *Only works for SPMNv6.2s4 and later* prints # of laces identified in the environment

Each of the above calls to methods produces it's own log files (stored in the current working directory) containing relevant information for more detailed analysis.
- file_stats_log.elog : Output of "show modules" command
- constraint_log.elog : Output of the "show constraint" command
- guidelines_log.elog : Output of "gen lint -g" (with notification set to MAX_INT in order to get all warnings)
- icfs_log.elog       : Output of "gen lint -i" command
- soft_select_log.elog: Output of the "gen lint -s" command
- lace_log.elog       : Output of the "show lace" command

Happy generating!

Corey Goss

It may be time to revisit RASK, CASK metrics; benchmarking based on cost drivers can boost an airline’s performance

Brands continue to invest more in social media marketing each year. In fact, HubSpot found that 74% of global marketers currently invest in social media marketing. And with this adoption, theres been enormous amounts of data collected in an effort to measure the success of social media campaigns.

But for many marketing teams, its becoming a challenge to sift through the wide range of metrics to understand if their social media campaigns are effective. Thats why we’ve asked social media experts how they determine success and what metrics you should track that actually matter.

The 2025 ranking of India’s top research institution and the assessment of other institutions raise questions about the objectivity and the credibility of the agency’s ranking system