Reviewerly/exHarmony huggingface dataset:
Sample-Specific Output Constraints for Neural Networks

It is common practice to constrain the output space of a neural network with the final layer to a problem-specific value range. However, for many tasks it is desired to restrict the output space for each input independently to a different subdomain with a non-trivial geometry, e.g. in safety-critical applications, to exclude hazardous outputs sample-wise. We propose ConstraintNet—a scalable neural network architecture which constrains the output space in each forward pass independently. Contrary to prior approaches, which perform a projection in the final layer, ConstraintNet applies an input-dependent parametrization of the constrained output space. Thereby, the complete interior of the constrained region is covered and computational costs are reduced significantly. For constraints in form of convex polytopes, we leverage the vertex representation to specify the parametrization. The second modification consists of adding an auxiliary input in form of a tensor description of the constraint to enable the handling of multiple constraints for the same sample. Finally, ConstraintNet is end-to-end trainable with almost no overhead in the forward and backward pass. We demonstrate ConstraintNet on two regression tasks: First, we modify a CNN and construct several constraints for facial landmark detection tasks. Second, we demonstrate the application to a follow object controller for vehicles and accomplish safe reinforcement learning in this case. In both experiments, ConstraintNet improves performance and we conclude that our approach is promising for applying neural networks in safety-critical environments.
EEG çalışmalarında viral enfeksiyon bulaşma riskini azaltma yönergeleri

Protecting the wellbeing of both personnel and patient in medical and scientific activities is critical for continuing these activities uninterrupted. The SARS-CoV-2 (COVID-19) pandemic continues to disrupt many daily activities including medical and scientific. Electroencephalography (EEG) requires an extensive preparation and testing period that involves a highly trained experimenter being in close physical proximity with the participant, thus contains the risk of viral infection transmission. This report proposes suggestions on how to minimize virus transmission risks in EEG research and clinical use, without major compromises in data quality.
Split-Second Syndrome & Officer Created Jeopardy: Implications for Agency Policy

As a consequence of public safety, officers at times must use deadly force to protect themselves and others. Professional, respectful, effective and accountable policing demands that this use of force be subjected to and withstand not only legal, but ethical scrutiny regarding such a controversial topic. The ability to justify an act does not make it necessary and deadly force by police officers is the rule, not the exception. Police officers are often thrust into unpredictable and stressful situations, where their reactions have grave consequences. Police agencies have an obligation to ensure their officers are trained in best practices regarding force. Police agencies should implement policy that prohibits officer created jeopardy. Policy implementation which communicates a sanctity for life demonstrates that police agencies are sincere in preserving life whenever possible. This is best communicated by clear policy that promotes safety for citizens and officers. The purpose of this paper is to identify the need for policy governing officer behavior, which will promote uniformity, consistency, and best practices. Incorporating such policy will not adversely impact an officer’s ability to perform their duties and will enhance safety for all.
Scania V8 Sound R950 Konstantinidis [1.37]

Finally after the implementation of FMOD Sound System for SCS Games ETS2 and ATS, Rasta.Jr Productions proudly presents a new scania V8 sound. The sound ...
Investigating Factors that Influence Employees Creativity in Manufacturing Organizations: Empirical Analysis

The purpose of this article is to reveal the factorsinfluencing employees’ creativity in manufacturing organizations. This study discussed important factors that contribute to enhanceemployees’ creativity at manufacturing industries in Pakistan. Knowledge sharing, and supply chain strategy, onemployees’ creativity with mediating effect of motivation activities were found on previous studies. Creativity research has been generally studied in the different type of organizations;however, manufacturing businesseswere not considered seriously by scholars. As a matter of fact, the lack of coherent understanding of theoretical and empirical foundations on the influence of employees’ creativity in organizations. The framework of this paper has been tested empirically by collected data from 272 (employees) manufacturing industries working in Pakistan. Data analysis was performed by using Smart-PLS. This study provides a comprehensive finding analysis and discussion, to scholars, practitioners, and decision makerson the manufacturing sector.
Possibilities of using digital educational technologies in teaching analytical chemistry

The article examines the teaching of analytical chemistry in higher educational institutions using digital educational technologies as a pedagogical problem. The possibilities of digital educational technologies in teaching analytical chemistry in the formation of the professional competence of a chemist are highlighted.
Augmented Reality: Historical Figures of Tanah Melayu for Form 3 History

There is a lot of historical figures who fought for our freedom against colonizers such as Dato Bahaman and many more for our freedom. The title for this research is “Augmented Reality: Historical Figures of Tanah Melayu for Form 3 History”. The purpose of this study is to create an augmented reality application that will help student specifically age 15 years old to learn more about history subject. This research will cover on History subject for Form 3 student on chapter 7. Finally, this research will also evaluate the level of understanding of student and teachers before and after using the application. This application will be used alongside an AR book which the researcher will provide. For this research, the ADDIE model is used since researcher will be dealing with lots of designing and handling a large group of target respondent. By using ADDIE model, it will help researcher to ease the work for him to identify his target respondent. Lastly, researcher hope by completing this application, student and teachers will have a new teaching method and can improve their understanding in learning History.
Best Extended Mouse Pad

While a mouse pad is great for protecting your mouse, an extended mouse pad protects your keyboard and mouse, while giving your desk a uniform ...
LibGuides: LibAnswers Training Guide for UL Employees: What is LibChat?

This is a guide for how to use LibAnswers at the CMU University Libraries. The guide includes materials for learning about the system, and our policies for use.
“Job Satisfaction and Organizational Commitment. A Case Study: Professors and Administrative Staff of the Al Dar University College”.

Job satisfaction and organizational commitment that play a very important role in the growth and survival of organizations and reduce delays and layoffs of employees and increase the level of efficiency and effectiveness in organizations. The study, entitled “Investigating the Relationship between Job Satisfaction and Organizational Commitment from the Perspective of Professors and Administrative Staff of the Al Dar University college is a descriptive –correlation al study. Its main purpose was to study the relations hip between job satisfaction and organizational commitment. The statistical population is the professors and staff of the Aldar University College. Due to the small number of professors and staff of this institution, all of them (53) have been included in the research. In this study, the required information was collected by two questionnaires of job satisfaction and organizational commitment and was analyzed using software and its descriptive and inferential results are described in the tables which in the Descriptive method describes the variables of satisfaction. Job (nature of work, co -workers, growth opportunities and payments) and organizational commitment variables (emotional commitment, continuous commitment) and in inferential analysis by correlation test or Pearson correlation to test hypotheses and analyze the relationship between Job satisfaction and organizational commitment are addressed. The results of the research show that the majority of the employees of this institution are satisfied with their work; but they are not satisfied with the behavior and attitude of the supervisors. On the contrary, the majority of the employees of this institution read the behavior of their colleagues as friendly and satisfied. The majority of the employees of this organization have said that there is no suitable opportunity for growth and promotion. The majorities of the employees of this institution are interested in this organization and consider the problems of the organization as their problems; but they do not see the current organization as suitable for securing their future, and they will leave the organization if they are offered a better job elsewhere. The results showed that the majority of the faculty and staff of this organization are below the level of job satisfaction (supervisors, growth opportunities and payments) and are below the level of continuous commitment.Regarding the hypotheses, the research showed that there is no relationship between the nature of work and emotional commitment. There is a significant relationship between the behavior of supervisors and normative commitment. There is no significant relationship between employee behavior and normative commitment. There is a significant and positive relationship between growth opportunities and pay and continuous commitment. There is a positive and significant relationship between job satisfaction and organizational commitment of the professors and staff of the Aldar University College.
Need Analysis of Pocket Dictionary of Dayak Ngaju Local Language for Elementary School Students

Some of Elementary Schools in Palangka Raya used Dayak Ngaju language as a local content subject. Unfortunately, there are many students who get problems in learning Dayak Ngaju language. This research aims to find out whether a pocket dictionary of Dayak Ngaju local language was needed for teaching vocabulary for Elementary School students. The research was conducted in descriptive-qualitative method, and the subjects were one teacher and 25 fifth grade students of Elementary School at SDN 8 Langkai in Palangka Raya. The data were in the form of qualitative data , obtained from the results of interview with the teacher and questionnaires distributed to the students in order to know the students’ need of pocket dictionary of Dayak Ngaju. From the results of interview and questionnaires, the researcher found that there were positive responses toward the needs of a pocket dictionary of Dayak Ngaju. They strongly agreed to have the pocket dictionary of Dayak Ngaju made in three languages, i.e., Dayak Ngaju, Indonesian, and English. Then, the findings become the basic for the researcher to further develop a pocket dictionary of Dayak Ngaju local language for teaching Dayak Ngaju as a local content subject and English vocabulary for Elementary School students.
LibGuides: Elementary Education: Ebooks and Databases

This subject guide is designed to support the Rogers State University and Cameron University joint Elementary Education programs.
Social Engineering Defense Mechanisms and InfoSec Policies: A Survey and Qualitative Analysis

Author(s): Alharthi, Dalal | Advisor(s): Regan, Amelia C | Abstract: Social engineering attacks can be severe and difficult to detect before considerable damage is done. Therefore, to prevent such attacks, organizations should be aware of social engineering defense mechanisms. The application of security policies is essential for mitigating the risk of social engineering attacks. However, incorporating and enforcing successful security policies in an organization is not a straightforward task. To that end, we developed a taxonomy of social engineering defense mechanisms and a customizable model of formal Social Engineering InfoSec Policies (SE-IPs) that can be adopted by a wide variety of organizations. We also designed and distributed a survey to measure employees awareness of social engineering defense mechanisms and a survey to measure the incorporation of formal SE-IPs in organizations. After collecting and analyzing the data from the two surveys which included over fifteen hundred responses, we found that more than half of employees are not aware of social engineering attacks, and on average, organizations incorporated just over fifty percent of the identified formal Social Engineering InfoSec Policies. Such worrisome results show that organizations are vulnerable to social engineering attacks, and serious steps need to be taken to elevate the awareness level against these emerging security threats.
Impact of Distance Metrics in Local Energy Oriented Pattern Based Image Retrieval

Algorithms in Content-Based Image Retrieval (CBIR) are being developed based on how the features of the images are represented to retrieve images from image archives. Local Binary pattern based CBIR gains attraction nowadays because of its simplicity and efficiency. But the effectiveness of CBIR also depends on the similarity measure used to retrieve the images. This work aims to analyze the impact of similarity measures concerning distance metrics in the performance of Local Energy Oriented Pattern (LEOP) based CBIR. LEOP is considered for the experiment since LEOP encodes pixel level energy orientations to find minute spatial features of an image, whereas other local patterns rely on only the neighborhood relationship. For each reference pixel in the image, LEOP maps four pixel progression orientations to find the top two maximum energy changes, i.e. two more 3X3 grids are derived from four pixel progressions for each reference pixel. The relationship of pixels derived from three 3 X 3 local patches is used to encode the LEOP pattern. The retrieval efficiency of the LEOP based CBIR system in the ORL image database and the UIUC image database is compared concerning Chi-square and D1 distance measures. The precision and recall measures show that Chi-square based LEOP CBIR system outperforms the D1 based LEOP CBIR system.
RETRA: Recurrent Transformers for Learning Temporally Contextualized Knowledge Graph Embeddings

Knowledge graph embeddings (KGE) are vector representations that capture the global distributional semantics of each entity instance and relation type in a static Knowledge Graph (KG). While KGEs have the capability to embed information related to an entity into a single representation, they are not customizable to a specific context.
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions

We introduce ParK, a new large-scale solver for kernel ridge regression. Our approach combines partitioning with random projections and iterative optimization to reduce space and time complexity while provably maintaining the same statistical accuracy. In particular, constructing suitable partitions directly in the feature space rather than in the input space, we promote orthogonality between the local estimators, thus ensuring that key quantities such as local effective dimension and bias remain under control. We characterize the statistical-computational tradeoff of our model, and demonstrate the effectiveness of our method by numerical experiments on large-scale datasets.
Attentive Transfer Learning via Self-supervised Learning for Cervical Dysplasia Diagnosis

Many deep learning approaches have been studied for image classification in computer vision. However, there are not enough data to generate accurate models in medical fields, and many datasets are not annotated. This study presents a new method that can use both unlabeled and labeled data. The proposed method is applied to classify cervix images into normal versus cancerous, and we demonstrate the results. First, we use a patch selfsupervised learning for training the global context of the image using an unlabeled image dataset. Second, we generate a classifier model by using the transferred knowledge from self-supervised learning. We also apply attention learning to capture the local features of the image. The combined method provides better performance than state-of-the-art approaches in accuracy and sensitivity.
Software reliability and quality assurance challenges in cyber physical systems security

Software Reliability is the probability of failure-free software operation for a specified period of time in a specified environment. Cyber threats on software security have been prevailing and have increased exponentially, posing a major challenge on software reliability in the cyber physical systems (CPS) environment. Applying patches after the software has been developed is outdated and a major security flaw. However, this has posed a major software reliability challenge as threat actors are exploiting unpatched and insecure software configuration vulnerabilities that are not identified at the design phase. This paper aims to investigate the SDLC approach to software reliability and quality assurance challenges in CPS security. To demonstrate the applicability of our work, we review existing security requirements engineering concepts and methodologies such as TROPOS, I*, KAOS, Tropos and Secure Tropos to determine their relevance in software security. We consider how the methodologies and function points are used to implement constraints to improve software reliability. Finally, the function points concepts are implemented into the CPS security components. The results show that software security threats in CPS can be addressed by integrating the SRE approach and function point analysis in the development to improve software reliability.
An Interview on the Utilization of E-instructional Materials for Teaching Extensive Reading Course During Pandemic Covid 19

Extensive reading is a course taught at a higher education level. In this course, the learners read the texts for pleasure and for improving their general reading skills. In this case, the learners really need appropriate materials and a comfortable environment to achieve the goal of reading extensively. Unfortunately, in this crucial situation (pandemic covid 19) the teaching and learning should be conducted in online mode. Therefore, it is very important to select an effective instructional material for teaching the course. This study aims to find out a detailed description of the utilization of E-instructional Materials for Teaching Extensive Reading Course. This study applied the qualitative method and online interview, observation, and documentation were used as the research instrument. The data were analyzed by using Miles and Huberman's theory (2004). The result shows that in this crucial situation of pandemic Covid 19 the E-instructional material supports the online teaching and learning process. Here, the E-instructional materials are in the forms of texts or fun stories in PDF format. Moreover, the lecturer also explains the steps in teaching extensive reading course by using E-instructional materials.
Photonic Differential Privacy with Direct Feedback Alignment

Optical Processing Units (OPUs) -- low-power photonic chips dedicated to large scale random projections -- have been used in previous work to train deep neural networks using Direct Feedback Alignment (DFA), an effective alternative to backpropagation. Here, we demonstrate how to leverage the intrinsic noise of optical random projections to build a differentially private DFA mechanism, making OPUs a solution of choice to provide a private-by-design training. We provide a theoretical analysis of our adaptive privacy mechanism, carefully measuring how the noise of optical random projections propagates in the process and gives rise to provable Differential Privacy. Finally, we conduct experiments demonstrating the ability of our learning procedure to achieve solid end-task performance.
Big Data, Artificial Intelligence and Blockchain

1 - Big Data and regulatory Learning: How to supervise deep learning models?This seminar aims to present (i) The notions of Big Data, (ii) The architecture indispensable for the use of Big Data, (iii) The concept of artificial intelligence and the models associated, (iv) The question of the existence of a regulatory framework, (v) The study of a use case developed inside the banking system concerning the credit scoring.2 - Crypto-currencies and the Challenge for Financial Regulation: The example of Bitcoin after defining the notion of peer-to-peer lending, and distributed ledger, the Bitcoin concept is introduced. Regulation around the crypto-assets is analysed: remote and immediate risks as risks for the users.3 - The digital world: Blockchain and ICO: in this talk we analyse the concept of blockchain and the different classes of blockchain with their properties. The regulatory framework is presented. The Initial Coin offerings is also discussed with their interest and limits
2D Lists and Dictionaries

Building on their knowledge of lists, readers are shown how to create and amend two-dimensional lists and dictionaries. They complete nine challenges to practise creating two-dimensional lists and dictionaries, selecting specific rows, columns or individual values, adding new data or altering existing data.
Computational thinking, an educational strategy in times of pandemic

Education has the task of preparing individuals for challenges, needs and opportunities in a globalized world;now with technology permeating human activities, traditional learning theories without recognizing elements of digital literacy (behaviorism, cognitivism and constructivism). Education requires changes of thought and even more so with the presence of COVID-19, a pandemic that prompted social physical isolation;increasing the need to master digital scenarios in educational communities. The previous situation consolidates computational thinking (CT), as an effective strategy applied in the model presented by MIT-Harvard, with a defined theoretical framework and applied since 2012. The objective of the research is to diagnose the level of training of competencies of the computational thinking to solve problems;and establish from a quantitative study with a non-experimental transectional design the correlation between: the conceptual, practical and computational perspective dimensions, with the development of skills to solve problems, showed a positive correlation greater than 0.462 and significance of 0.000 between the theoretical foundation, the practice to consolidate learning and the development of capacities to make optimal decisions in the search for solutions;implemented in a public school in Colombia with 133 high school students, under training characterized by isolation and social distancing, from totally virtual settings. The diagnostic instrument, validated and categorized by 32 problems, measures the skills to generate competencies, with similarities to the Colombian educational system that promotes learning with: knowing, knowing how to do and knowing how to be;The above is consolidated as inputs for the generation of a virtual learning ecosystem.