Reviewerly/exHarmony huggingface dataset:
Using web content analysis to create innovation indicators—What do we really measure?

This study explores the use of web content analysis to build innovation indicators from the complete texts of 79 corporate websites of Canadian nanotechnology and advanced materials firms. Indicators of four core concepts (R&amp;D, IP protection, collaboration, and external financing) of the innovation process were built using keywords frequency analysis. These web-based indicators were validated using several indicators built from a classic questionnaire-based survey with the following methods: correlation analysis, multitraits multimethods (MTMM) matrices, and confirmatory factor analysis (CFA). The results suggest that formative indices built with the questionnaire and web-based indicators measure the same concept, which is not the case when considering the items from the questionnaire separately. Web-based indicators can act either as complements to direct measures or as substitutes for broader measures, notably the importance of R&amp;D and the importance of IP protection, which are normally measured using conventional methods, such as government administrative data or questionnaire-based surveys.
Data Mining of New Snack E-commerce Reviews Based on Text Sentiment Analysis and Latent Dirichlet Allocation Topic Model

In the new retail era, in order to promote the development of new snack e-commerce enterprises, increase customer satisfaction with their products and services and the desire to repurchase, this article makes sentiment analysis on the 430,000 online reviews of new snack e-commerce as crawled online from the perspective of customers.Firstly, it makes an overall sentiment judgment on the online review texts of five stores.Secondly, it constructs the Latent Dirichlet Allocation (LDA) topic model, gets ten categories of keywords, analyzes the cluster analysis results and discusses for improving any consumer dissatisfaction point.Empirical analysis shows that in order to improve customer satisfaction and stickiness and attract more potential consumers, new snack e-commerce companies need to pay attention to optimizing brand reputation, product quality, service level, marketing strategy, and review information.
A Generalized Minimax Q-Learning Algorithm for Two-Player Zero-Sum Stochastic Games

We consider the problem of two-player zero-sum games. This problem is formulated as a min–max Markov game in this article. The solution of this game, which is the min–max payoff, starting from a given state is called the min–max value of the state. In this article, we compute the solution of the two-player zero-sum game, utilizing the technique of successive relaxation that has been successfully applied in this article to compute a faster value iteration algorithm in the context of Markov decision processes. We extend the concept of successive relaxation to the setting of two-player zero-sum games. We show that, under a special structure on the game, this technique facilitates faster computation of the min–max value of the states. We then derive a generalized minimax Q-learning algorithm, which computes the optimal policy when the model information is not known. Finally, we prove the convergence of the proposed generalized minimax Q-learning algorithm utilizing stochastic approximation techniques, under an assumption on the boundedness of iterates. Through experiments, we demonstrate the
Equivalence between Digital Well-Composedness and Well-Composedness in the Sense of Alexandrov on n-D Cubical Grids

Among the different flavors of well-composednesses on cubical grids, two of them, called, respectively, digital well-composedness (DWCness) and well-composedness in the sense of Alexandrov (AWCness), are known to be equivalent in 2D and in 3D. The former means that a cubical set does not contain critical configurations, while the latter means that the boundary of a cubical set is made of a disjoint union of discrete surfaces. In this paper, we prove that this equivalence holds in n-D, which is of interest because today images are not only 2D or 3D but also 4D and beyond. The main benefit of this proof is that the topological properties available for AWC sets, mainly their separation properties, are also true for DWC sets, and the properties of DWC sets are also true for AWC sets: an Euler number locally computable, equivalent connectivities from a local or global point of view. This result is also true for gray-level images thanks to cross section topology, which means that the sets of shapes of DWC gray-level images make a tree like the ones of AWC gray-level images.
Cloud Computing and Security in the IoT Era

Advancements in information technology, and transfer of data online leading to the vulnerabilities and giving an invitation to cyber threats.Security and privacy are important issues in cloud storage and networking fields.Cloud computing created an environment that facilitates the internet of things.The cloud computing technology consisted of servers, applications, and other storage devices, relying on the virtual connection.The stored information in clouds can be manipulated without the need for extensive physical activities.The internet of things devices provide information and services through information and communication technology, these end-node IoT devices are not very intelligent and resource-constrained thus they are vulnerable to cyber threats.It posed an issue of insecurity as malicious individuals learned ways of intruding these systems and causing intellectual and physical losses.The study proposes numerous solutions for the challenges of privacy and security in the cloud and IoT.The paper provides an in-depth examination of security challenges in cloud computing and IoT.The methodology uses the interpretive technique.The results depict the importance of the machine and deep learning in cyber-attack and mitigation.Analysis of variance (ANOVA) test statistics is used to test the reliability of the study.
Towards an Auditable Cryptographic Access Control to High-value Sensitive Data

We discuss the challenge of achieving an auditable key management for cryptographic access control to high-value sensitive data.In such settings it is important to be able to audit the key management process -and in particular to be able to provide verifiable proofs of key generation.The auditable key management has several possible use cases in both civilian and military world.In particular, the new regulations for protection of sensitive personal data, such as GDPR, introduce strict requirements for handling of personal data and apply a very restrictive definition of what can be considered a personal data.Cryptographic access control for personal data has a potential to become extremely important for preserving industrial ability to innovate, while protecting subject's privacy, especially in the context of widely deployed modern monitoring, tracking and profiling capabilities, that are used by both governmental institutions and high-tech companies.However, in general, an encrypted data is still considered as personal under GDPR and therefore cannot be, e.g., stored or processed in a public cloud or distributed ledger.In our work we propose an identity-based cryptographic framework that ensures confidentiality, availability, integrity of data while potentially remaining compliant with the GDPR framework.
Realtime Fingerprint based Voting System

This report is based totally on ''REALTIME FINGERPRINT BASED VOTING SYSTEM''.The main intention of this proposal is to offer security and to overcome the limitations that are in the conventional balloting system.Initially, within the consumer registration procedure, the voter details, along with their fingerprint, are saved within the serial monitor.Here, the serial monitor acts as a database.The voter desires to area their finger at the module on the polling booth, thus allowing the acquisition of a finger impression from the voter, which serves as identification.Then the impression is passed on to the controlling unit for verification.The microcontroller fetches the statistics from the voter and compares it with the already present information stored at some stage in the registration of the voter.If the statistics fit with already existing statistics, then the voters are allowed to cast their votes.The voting mechanism is done manually through the use of pushbuttons.The welcome instruction and with the parties is displayed on the LCD.Whereas, the voter details and results are displayed on the screen.
A Semantic Question Answering through Heterogeneous Data Source in the Domain of Smart Factory

Manufacturing technologies have evolved with advancements of Industry 4.0 about big data systems, generation system for linked data from unstructured data sources, and streaming data pools.A heterogeneous data source is still problematic for restricted domain question answering due to the nature of unstructured data in manufacturing companies and data-intensive applications in smart factories.Smart factories have emerged with data-intensive operations that are occurring from manufacturing monitoring systems, hand terminals, mobile tablets, and assembly line controllers.Today, human operators experience an increased complexity of the data-intensive applications in the smart factory.Fetching data from various data sources brings a necessity of decreasing data size and derive an idea regarding what is happening by inductive reasoning at the smart factory.Heterogeneous data source occurs adversity in converting linked data to get answers asked questions by human operators, experts, workers through question answering systems.When dealing with a large amount of linked data, we need to design and implement a software solution that should enhance human operators' and experts' capabilities.In this study, we propose a semantic question answering that connects with heterogeneous data sources from different areas and devices of a smart factory.In the end, we will perform qualitative and quantitative evaluation regarding the semantic question answering that exploits heterogeneous data sources, as well as findings and conclude the main points concerning our research questions.
Pensamiento Computacional para la formación de maestros: Una revisión sistemática de literatura

The incorporation of computational thinking in the classroom has been identified as a fundamental strategy that could spread student participation and have an effective impact on learning. Although there are studies done on this subject, we do not find a systematic dissertation that compiles the fundamental practices of Computational Thinking in teacher training. That is why this article presents a systematic review of literature with the objective of summarizing studies on computational thinking for initial teacher training. Initially the search was conducted in five databases of scientific publications, 100 studies published in the last five years were obtained. After a method of analysis of each of them, verify that they match the requirements of the review, obtaining as a result the compilation of the main experiences and practices reported in the process of Computational Thinking for teachers. In addition, the determined systematic review will determine the associated problems, studies on computational thinking, characteristics, methodologies, teaching-learning strategies and trends. This article is an important starting point for future projects that wish to continue contributing to experiences that benefit both students and teachers in this field of TeachingComputational Thinking.
Gaussian phase sensitivity of boson-sampling-inspired strategies

In this paper we study the phase sensitivity of generic linear interferometric schemes using Gaussian resources and measurements. Our formalism is based on the Fisher information. This allows us to separate the contributions of the measurement scheme, the experimental imperfections, and auxiliary systems. We demonstrate the strength of this formalism using a broad class of multimode Gaussian states that includes well-known results from single- and two-mode metrology scenarios. Using this, we prove that input coherent states or squeezing improve upon the nonclassical states proposed in preceding boson-sampling-inspired phase-estimation schemes. We also develop a polychromatic interferometric protocol, demonstrating an enhanced sensitivity with respect to two-mode squeezed-vacuum states, for which the ideal homodyne detection is formally shown to be optimal.
Public Sentiment Insights Analysis using Word Sense Disambiguation Application on Twitter Data during a Pandemic – Covid’19

The entire world is affected because of the global pandemic Covid-19 due to the virus belongs to the family of Coronavirus.As the spread of infection and mortality rate is rapid people have started developing assorted emotions about the crisis.It is more significant to administer the mental health and Psychological wellbeing of public during a crisis like this.As many of the people broadly use the social media like twitter for sharing their opinions and thoughts, our work utilizes the Covid specific Tweets posted by the Tweeple and analyse them to understand the sentiments exhibited regarding the situation.After the tweets are collected and the real sentiments behind them are discovered using the classifier model developed using the Machine Learning methods.The experimental results may be used by the respective authorities to take necessary initiatives for addressing the concerns that affect the wellbeing of the society and the economic wellbeing as well.As our word uses Lexical based sentiment analysis, it is important to remove the ambiguities of a word which is a main challenge of this technique on sentiment analysis.To improve the performance of the Sentiment Analysis we have used the lexical dictionaries Wordnet and SentiWordNet along with Word Sense Disambiguation (WSD) to detect and remove the ambiguities understanding the context of the term used in the tweets.
Systematic review on evaluating planning process in agile development methods

Agile development methods have been catering the need of faster delivery of theever-demanding domain of software engineering. These methods are able to deliver value to users and businesses via fast, reliable, and repeatable process. Planning requirements and processes takes the driving seat in a dynamic environment because the value proposition rapidly changes. This paper exhibits asystematic literature review of planning processes implementedby various agile methods in order to find the best suited agile method in terms of robust planning. Keywords: It was found that Scrum is the best suited agile method for planning processes.
The Experience of Personal Time in Alzheimer’s Disease

Background. Alzheimer’s Disease (AD) is a neurodegenerative illness, which occurs with increasing frequency as people age, and is currently a disease beyond curative therapeutic possibilities. While progressive memory impairment is the upfront element associated with the disease, other neurocognitive problems are also associated with it, such as language impairment that tend to degenerate into aphasia. The paper presents singular representations of time in such patients’ lived experience, which with the course of the disease gradually move away from the real present around them. In consequence, they live in a bygone era. The dimension of the perceived world implies an anchoring in temporality in the present by using the resources of the past. This becomes impossible with the progression of the cognitive disorders. Multiple psychological tensions result from patient’s maladjustment to temporality.Objective. The article attempts to define the singular experience of the time of the demented patient according to the phases of the disease.Design. An analysis of the psychological tensions summoned at the various stages of the disease was carried out using a semiotic methodology.Results. The progression of the disease gives way to another mode of relation to the world, presentism. With its course, emotions can no longer unfold. At first, the patient seeks to increase his grip on the surrounding world, and the internal psychological tensions are accentuated. Suddenly the world ends up completely escaping him, and catastrophically the internal tensions collapse. The patient slides irreversibly towards regression.Conclusion. If the emotional intensity of what the patient experiences is enough, he remains engaged in the time of a presence in the world, even if it is distinct from the reality of those around him. When the intensity collapses, the demented patients disengage from it. The caregivers or families who take care of them can understand the mechanisms involved in the patients’ experience of time. This allows them to adapt their own behavior. Thus, they limit the risk of misinterpretation and induce certain behavioral disorders linked to their shift from the reality of the patients.
Online Attacks on Picture Owner Privacy

We present an online attribute inference attack by leveraging Facebook picture metadata (i) alt-text generated by Facebook to describe picture contents, and (ii) comments containing words and emojis posted by other Facebook users. Specifically, we study the correlation of the picture's owner with Facebook generated alt-text and comments used by commenters when reacting to the image. We concentrate on gender attribute that is highly relevant for targeted advertising or privacy breaking. We explore how to launch an online gender inference attack on any Facebook user by handling online newly discovered vocabulary using the retrofitting process to enrich a core vocabulary built during offline training. Our experiments show that even when the user hides most public data (e.g., friend list, attribute, page, group), an attacker can detect user gender with AUC (area under the ROC curve) from 87% to 92%, depending on the picture metadata availability. Moreover, we can detect with high accuracy sequences of words leading to gender disclosure, and accordingly, enable users to derive countermeasures and configure their privacy settings safely.
Scientometric methods of analysis of the effectiveness of scientific research according to Web of Sciense

The purpose of the study is to determine the factors for increasing the representation of the Ukrainian scientific community in the international scientific community and raising the international scientific authority of Ukraine.Used Results of the annual G20 evaluation - the effectiveness of research in 2019, conducted by the Institute of Scientific Information (ISI), part of the Web of Science Group.The principles of introduction of scientometric methods of research of publishing activity of scientists in the international and Ukrainian scientific space are considered. Based on the processing of statistical material and the obtained quantitative estimates, a comparative analysis of the effectiveness of research in the G20 and Ukraine according to the Web of Science. In this area, some differences between Ukraine and countries that are world leaders in the organization of the research process are shown. Recommendations for increasing the representation of the Ukrainian scientific community on the Web of Science platform and raising the international scientific authority of Ukraine are offered.
An Application of Knowledge Engineering to Mathematics Curricula Organization and Formal Verification

The authors present a theoretical proposal for the organization of mathematical contents, more precisely to curricula development formalization and formal verification, inspired by knowledge engineering techniques. The situation addressed is the following: the starting point is a mathematical “official curriculum” (or part of it), not necessarily completely detailed. In our proposal, a group of experts would have to first build a detailed formulation of this curriculum (including the “prerequisite” relation between contents), which we will denominate “preprocessed official curriculum.” We detail how any “official curriculum development” could then be rigorously formalized and formally verified in a way inspired by rule-based expert system formal verification. We have defined the following terms: “contents soundness,” “contents completeness,” “relation soundness,” “relation completeness,” and “absence of cycles.” We believe that this is a completely new formalization within mathematics teaching theory that, once computer is implemented, would be very helpful. That would be the case, for instance, in countries where government sets the “official curricula” for Primary and Secondary Education and textbook contents have to be manually checked and approved by academic authorities: evaluators would “only” have to extract the textbook contents and set the “prerequisite” relation among them and let the computer do the rest.
Frivolous Units: Wider Networks Are Not Really That Wide

A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network width is increased. Recent evidence suggests that developing compressible representations allows the complexity of large networks to be adjusted for the learning task at hand. However, these representations are poorly understood. A promising strand of research inspired from biology involves studying representations at the unit level as it offers a more granular interpretation of the neural mechanisms. In order to better understand what facilitates increases in width without decreases in accuracy, we ask: Are there mechanisms at the unit level by which networks control their effective complexity? If so, how do these depend on the architecture, dataset, and hyperparameters? We identify two distinct types of “frivolous” units that proliferate when the network’s width increases: prunable units which can be dropped out of the network without significant change to the output and redundant units whose activities can be expressed as a linear combination of others. These units imply complexity constraints as the function the network computes could be expressed without them. We also identify how the development of these units can be influenced by architecture and a number of training factors. Together, these results help to explain why the accuracy of DNNs does not degrade when width is increased and highlight the importance of frivolous units toward understanding implicit regularization in DNNs.
Adopted topic modeling for business process and software component conformity checking

Business processes and software components, especially class diagrams, have a firm connection. Considering software components support the business process in providing an excellent product and service. Besides, business process changes affect on software component design. One of them usually appears on the label or name of the software component or business process. Sometimes, a related business process and software component appears in the different label but the same meaning rather than using the same label. This situation is problematic when there are many changes to be made, in which the software component's modifying process becomes quite long. Therefore, the software maintainers should obtain an efficient procedure to shorten the modifying process. One solution is by using conformity checking, which helps the software maintainers know which software component is related to a specific business process. This paper compared two leading topic modeling techniques, namely probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), to determine which one has a better performancefor process traceability.
High-Dimensional Time-Frequency Entanglement and Schmidt Number Witnesses Using a Biphoton Frequency Comb

We demonstrated high-dimensional time-frequency entanglement with a mode-locked biphoton frequency comb. Hong-Ou-Mandel interference and joint spectral intensity measurements are performed, and we estimated Hilbert space dimensionality of at least 3481 via Schmidt mode decompositions.