Reviewerly/exHarmony huggingface dataset:
Differential Privacy for Coverage Analysis of Software Traces

This work considers software execution traces, where a trace is a sequence of run-time events. Each user of a software system collects the set of traces covered by her execution of the software, and reports this set to an analysis server. Our goal is to report the local data of each user in a privacy-preserving manner by employing local differential privacy, a powerful theoretical framework for designing privacy-preserving data analysis. A significant advantage of such analysis is that it offers principled built-in privacy with clearly-defined and quantifiable privacy protections. In local differential privacy, the data of an individual user is modified using a local randomizer before being sent to the untrusted analysis server. Based on the randomized information from all users, the analysis server computes, for each trace, an estimate of how many users have covered it. Such analysis requires that the domain of possible traces be defined ahead of time. Unlike in prior related work, here the domain is either infinite or, at best, restricted to many billions of elements. Further, the traces in this domain typically have structure defined by the static properties of the software. To capture these novel aspects, we define the trace domain with the help of context-free grammars. We illustrate this approach with two exemplars: a call chain analysis in which traces are described through a regular language, and an enter/exit trace analysis in which traces are described by a balanced-parentheses context-free language. Randomization over such domains is challenging due to their large size, which makes it impossible to use prior randomization techniques. To solve this problem, we propose to use count sketch, a fixed-size hashing data structure for summarizing frequent items. We develop a version of count sketch for trace analysis and demonstrate its suitability for software execution data. In addition, instead of randomizing separately each contribution to the sketch, we develop a much-faster one-shot randomization of the accumulated sketch data. One important client of the collected information is the identification of high-frequency (hot) traces. We develop a novel approach to identify hot traces from the collected randomized sketches. A key insight is that the very large domain of possible traces can be efficiently explored for hot traces by using the frequency estimates of a visited trace and its prefixes and suffixes. Our experimental study of both call chain analysis and enter/exit trace analysis indicates that the frequency estimates, as well as the identification of hot traces, achieve high accuracy and high privacy.
Graph Neural Network for Generative Furniture Arrangement

<p indent=0mm>Autonomous furniture arrangement plays an important role in many computer vision and graphics applications, such as indoor scene design and dynamic scene generation. The traditional approaches leverage the common spatial, semantical and functional object-object relationships to understand the inner structure of the indoor scene and facilitate the scene generation task. Benefited from the large-scale indoor scene dataset, we propose to embed the unstructured furniture into the graph structure, and leverage the graph neural network to iteratively learn the distribution of scene layout. In order to satisfy the diversity of furniture arrangement, we propose to incorporate the graph neural network into a conditional variational autoencoder. It leverages an encoder to input the scene information into a latent vector that represent the Gaussian distribution, and employ a generator to decode the scene layout from the sampled Gaussian noise for conditional new scene generation. Experimentally, we observe better quality of our algorithm compared to various baselines via the minimum matching distance on the public Fu-floor benchmark. The proposed algorithm is important for many practical applications, including scene completion, interior design based on scene graph and so on.
Employee Health Monitoring System with Attendance Tracking

The well-being of employees in the working environment relates to all faces of working life, from the safety of the physical or institutional environment to how workers perceive their work, the atmosphere at work organization, etc. One of the major concerns in companies is workers' Well-being which is an important factor for determining the organization’s long-term performance. Here we are introducing an employee health monitoring system that helps the institution to take care of their employees’ health. This system includes a temperature sensor, pulse oximeter sensor, attendance marker using OCR technology, and employee-friendly app system. This employee monitoring system has two phases: system development phase and app development phase. The system development phase involves the implementation of various sensors and measuring the health parameters and recording those values in the database. In the app development phase, data is made available to employees based on respective dates through the developed app. A feedback system in-app will give the average working environment of the company based on the feedback collected from employees. The main aim of this system is to monitor the health of employees and thus, improve the working environment of an organization.
A Virtual Assistant for Predicting Defective Software Module

One of the important costly items in software projects is effort and time to fix the found problems. Several reporting software tools are used to record and track software bugs. In general, the file or files where the code change is made for the solution of the problem is/are associated with the created bug record and tracked through the software version control systems. At this point, it would be time-saving to utilize Artificial Intelligence methods to make use of the solution of past problems and predict which code file will need to be touched for the fixing of newly found problem in future. In this study, the design and implementation of an artificial intelligence supported virtual assistant which predicts defective software files will be explained.
The Role of Social Capital and Social Innovation in Economic Growth

The study assessed the role of social capital and social innovation in economic growth. Nonetheless, a panel of 147 countries was sampled from 2009 to 2017, and econometric panel techniques were utilized to arrive at a statistical conclusion. The techniques used in the estimation are contemporaneous correlation estimators; thus, panel corrected standard errors, panel generalized least square with correlation disturbances, and generalized linear model. Upon estimations, the study concluded that social capital and social innovation strongly play a positive role in economic growth. Therefore, in pursuit of sustainable economic growth, social capital accumulation; thus, social trust and social networking are essential. Moreover, social innovations that are internet-related positively contribute to economic growth sustainably. The study recommends that investment in digital communication technologies should be prioritized because it improves material living standards because it lowers the cost of maintaining and creating professional and personal ties, strengthening the diffusion of knowledge and ideas, and further creating productivity spill overs positively. Also, policymakers should enact policies that offer triple triumph; thus, triumph for government, society, and individuals characterised by affordability, benefits, and add value to citizens' standard of living.
Islamic vigilantism and women in social media

In Bangladesh, the number of cyber-citizens has been skyrocketing since the 2010s. Violence against women is also proliferating along with the presence of Islam in public spheres and discourses. Using thematic analysis, this study analyzes the discourse data collected from Facebook, the dominant social media of Bangladesh. The key aim of the research is to find out the bedrock of Islamic vigilantism and verbal aggressiveness against women in social media. Subsequently, three interlinked themes have been explored: women’s religiosity, women’s attire, and women’s virtue. The findings have shown that men mainly capitalize on these three conventional and stereotyped ideas of popular Islam to conduct vigilantism against women in social media, which is most often accompanied by different types of verbal aggressiveness. Further, this study considering deep-rooted misogyny and patriarchy in Bangladesh society argues that these factors might have contributed to directing online vigilantism against women. As little research has been done in this area, this research study would lead to further researches in this area.
A Trust Model Of Cloud Scheduling Based On Data Integrity Using Ant Lion Optimizer

In The Internet, A Number Of Services Have Become Flexible And Cost-Effective Because Of Cloud Computing.  Security Is The Major Hitch In Cloud Computing And Many Researchers Have Studied And Discussed The Problems Relating To This Issue. Various Techniques Are Requiring Ensuring The Integrity Of Data Which Is The Integral Part Of Cloud Storage Adoption.  Five Different Trust Attributes Are Collected From Third Party And Its Trust Model In This Work And Integrity Of Data Are Assured Through The Servers. Ant Lion Optimizer (Alo) Algorithm Uses Optimal Scheduling, Which Is Proposed And Contrasted With Particle Swarm Optimization (Pso).
A Mokken Scale Analysis of an English Reading Comprehension Test

Reading comprehension in English, as one of the most central skills, has a vital role in the process of learning English as a Foreign/Second Language. The current study used the Mokken Scale Analysis (MSA), a probabilistic-nonparametric approach to item response theory (IRT), to determine the unidimensionality and scalability of a 20-item reading comprehension test administered on 300 EFL university students in the Iranian context. The results showed no major concerns in terms of item scalability. Monoton Homogeneity Model (MHM) fitted all the items of the test very well as measured by the scalability coefficients and restscore groups method. Considering the IIO, it was concluded that the ordering of items according to their mean is invariant across examinees although HT was small. Dimensionality analysis results using the AISP showed that the test is unidimensional providing evidence of the validity of the test in measuring a single ability dimension.
Information Security of New Media Art Platform of Distributed System Based on Blockchain Technology

New media art is different from ready-made art, installation art, body art, and land art. New media art is a new art subject category with “optical” media and electronic media as the basic language. And, because of the continuous development of computer networks, distributed database management systems are becoming more and more popular. This article aims to study the information security control problem in the distributed new media system. Through comprehensive access control, reliable support, and many-to-many random mutual encryption, it solves the security requirements of supporting mobile computing in a distributed network environment, communication, and hierarchical grouping. Group key management and other issues have studied some key security technologies for building secure distributed information systems. This article proposes covering the behavior-based access control model ABAC (operation-based access control model), the architecture of the new Trusted Platform Module, the architecture of multithreaded crypto chips, and their service methods, as well as distributed information systems and key management solutions. Approximately obeying the chi-square distribution with 2 degrees of freedom, when the significance level is taken as 5%, the chi-square value for the skin is 5.99. Different initial values are selected for the chaotic sequence, 200 groups of chaotic sequences with a length of 2000 are selected for sequence inspection, and the pass rate is 97.5%. It can be seen that the autocorrelation and cross-correlation characteristics of the improved spatiotemporal chaotic sequence are still relatively ideal, so the usability of the platform is relatively high.
Annotated Corpora for Term Extraction Research (ACTER)

The Annotated Corpora for Term Extraction Research (ACTER) contain texts in four domains (corruption, dressage (horse riding), heart failure, and wind energy) and three languages (English, French, Dutch). For each corpus (combination of domain & language), around 50k tokens have been manually annotated to identify terminology and named entities (almost 600k annotated tokens in total). The results are presented as lists of annotations per corpus, with one, lowercased, unlemmatised, unique annotation per line, tab-separated by its label. In total, there are 19k unique annotations. The annotation process is transparant and well-documented, with freely available guidelines (http://hdl.handle.net/1854/LU-8503113) and several published papers for the validation of the dataset. It has also been used for the TermEval 2020 shared task on automatic term extraction, organised at the CompuTerm workshop at LREC 2020.
Phonology and phonetics in Functional Discourse Grammar: Interfaces, mismatches, and the direction of processing

In this paper we discuss the interfaces between phonological and phonetic representations in Functional Discourse Grammar, and the possible mismatches that occur at those interfaces. Firstly, we discuss different definitions of phonological opacity in the literature, and provide examples with these definitions. We argue that mismatches between phonological and phonetic representations can result from competing pressures of articulatory ease and perceptual distinctivity. In order to model these influences and the resulting mismatches adequately, the model should not be organised strictly top-down: we argue that FDG should incorporate bottom-up influences from the phonetics on the phonology. We show that these influences are language- specific, which entails that bottom-up feedback must involve the Grammatical Component. With this modification of the model’s architecture, language users’ tendency to speak efficiently can be incorporated into the model, explaining a wide array of phenomena such as (synchronic) reduction, the cross-linguistic frequency of phonological alternations, and (diachronic) grammaticalization.
The Effect of Knowledge Management on Organizational Performance: A Study in Technology Based Companies to Enhancing the Quality of Work

To control the course of the business with innovations and competitive advantages, the knowledge management needs to be incorporated into the organizational strategy. While studies show the relationship between Knowledge Management (KM), success and organizational strategy, practice differs. The value of knowledge management is well known to organizations but the area of debate needs to be left and actions translated in results. This thesis aims to determine the effect on organizational efficiency of knowledge management activities by carrying out quantitative research using a survey questionnaire in technologies focused companies. The analysis was carried out using the empirical model of Wu and Che, in order to validate the hypotheses identified in the study.
NEW APPROACH OF SIGNED BINARY NUMBERS MULTIPLICATION AND ITS IMPLEMENTATION ON FPGA

This paper proposes a new model of signed binary multiplication. This model is formulated mathematically and can handle four types of binary multipliers: signed positive numbers multiplied by signed positive numbers (SPN-by-SPN); signed positive numbers multiplied by signed negative numbers (SPN-by-SNN); signed negative numbers multiplied by signed positive numbers (SNN-by-SPN); and signed negative numbers multiplied by signed negative numbers (SNN-by-SNN). The proposed model has a low complexity algorithm, is easy to implement in software coding and integrated in a hardware FPGA (Field-Programmable Gate Array), and is more powerful compared to the modified Baugh-Wooley's model.
Use of SMS-linked electronic surveys for COVID-19 case investigation and contact tracing — Marin County, CA, USA

We sought to quantify the proportion of contacts reported by persons with COVID-19 through a short message service (SMS)-linked survey in comparison to the proportion of contacts reported during a follow-up phone-interview. We also sought to assess improvement in contact tracing timeliness associated with sending SMS-linked surveys. During December 4–15, 2020, persons identified as COVID-19 cases whose data was entered into Marin County's contact tracing database on even days received a SMS-linked survey and persons whose data was entered on odd days did not; all were called for case investigation and contact tracing. Chi-square test and Fisher's exact test were used to compare demographic data. Chi-square test was used to contrast categorical outcomes, and Wilcoxon's rank-sum test was used for continuous outcomes. Among 350 SMS-linked survey recipients, 85 (24%) responded and 4 (1%) reported contacts using the survey; an additional 303 contacts were reported during phone interviews. Without phone interviews, 99% of reported contacts would have been missed. There was no meaningful difference between study arms in the proportion of contacts notified within 48 h. This SMS-linked survey had low participation and was not useful for identifying contacts. Phone interviews remained crucial for COVID-19 contact tracing.
Implementation Of Recurrent Neural Network With Language Model For Automatic Articulation Identification System In Bangla

To nudge the state of the art of human-machine interacting applications, research in speech recognition systems has progressively been examining speech-to-text synthesis, but implementation has been done to minimal languages. Although the Bengali language has not been much of an object of interest, we present the automatic speech recognition (ASR) system solely based on this particular language since around 16% of the world’s population speak Bengali. It has been a demanding task to implement Bengali ASR because it consists of diacritic characters. We conduct a series of preprocessing and feature selection methods along with a convolutional neural net model in consideration of an automatic verbal communication recognition system. Furthermore, the researchers compared this method to a recurrent neural network that is based on an LSTM network and a vast data file of Google Inc. Investigation of these two models indicates such as the recurrent neural net outperforms the convolutional neural net: the former benefits from combining connectionist temporal classification (CTC) and language model (LM). A quantitative analysis of the output shows that the word error rate and validation loss can be affected by variation in dropout values. It also shows that the parameters are also affected by clean and augmented data.
Financial support for universities in the top of Project 5–100 and its impact on the quality of education

Analysis of the main trends in the development of the market for higher education services, affecting the transformation of university financing models, shows that improving the quality of educational services in Russian universities directly depends on an increase in the share of revenues received from research and development, as well as the ability of universities to present the results of their research and development in the form of a complete product from a marketing point of view and build a competent strategy for promoting innovations in the market. In the context of creating a long-term strategy for increasing the competitive advantages of leading universities, the author pays special attention to Project 5–100 of the Ministry of Education and Science of the Russian Federation, the purpose of which is to maximize the competitive position of a group of leading Russian universities in the global market of educational services and research programs. It is noted that university funding comes from various sources, while budgetary revenues dominate the structure of income, and the reduction in budgetary provision significantly affects the financial condition of Russian universities. The author comes to the conclusion that the forms and mechanisms of financial management of universities and research organizations are not strictly regulated, they independently choose the sources of funding.
Designing an adaptable benchmark and competition simulation for integrated planning and execution

Effectively using a planning system as the executive of an agent acting in real time poses a variety of challenges in integrating planning and execution. Many integrated systems have been developed with a focus on particular challenges, and it has been typically difficult to test, benchmark, and compare these systems. To do so requires a benchmark that has transparent and well-defined rules, and can be adapted to exhibit the problem characteristics of interest. In this paper, we propose a new benchmark simulation for integrated planning and execution, designed to be accessible and adaptive. We describe the simple core scenario of the simulation and how it can be configured to present more challenging scenarios. We describe our plans for the development of the simulation as a competition, benchmarking, and teaching tool, and encourage the community to contribute to its design.
Deep Neural Network Based Multi-Review Summarization System

E-commerce is prevalent everywhere now-a-days. While shopping from these sites, users generally go through the reviews of the product posted by other users. For a given product, thousands of reviews may be available and it is cumbersome for the user to analyze each and every review. This paper proposes a multi-review summarization method to get a summarized review of products. A deep neural network-based model is employed to create an extractive summary of the reviews collected from online e-commerce sites i.e. Amazon and Flipkart. The deep neural network has been used to obtain the features of the product from multi reviews and cluster the sentences based on learned features. After clustering, a ranking of sentences is done and hence, an extractive summary is generated by selecting top n sentences from each of the clusters formed.
Photovoltaic Power Prediction Considering the Influence of Smog on Solar Radiation

As smog significantly weakens the intensity of solar radiation, the impact of smog on photovoltaic power generation cannot be ignored. This article aims to improve the prediction accuracy of photovoltaic power generation under smog weather. The impact of main atmospheric meteorological factors on atmospheric aerosols under smog weather is studied, and radial basis function neural network is adopted to predict the optical thickness of atmospheric aerosols; then, the inclined plane radiation model is established to predict the radiation intensity received by the photovoltaic panel; finally, considering fully the factors affecting the photovoltaic power generation under the smog weather, the RBF neural network is used to predict the photovoltaic power. Experimental verification proved that the presented photovoltaic power prediction model has high accuracy.