Reviewerly/exHarmony huggingface dataset:
Identity Verification Using Face Recognition Improved by Managing Check-in Behavior of Event Attendees

This is an extension from a selected paper from JSAI2019. This paper proposes an identity-verification system using continuous face recognition improved by managing check-in behavior of event attendees such as facial directions and eye contact (eyes are open or closed). We previously developed Ticket ID system for identifying the purchaser and hold-er of a ticket. This system carries out face recognition after attendant’s check-in using their membership cards. The average face-recognition accuracy was 90%, and the average time for identity verification from check-in to admission was 7 seconds per person. The system was proven effective for preventing illegal resale by verifying attendees of large concerts. The problem with this system is regarding face-recognition accuracy. This can be mitigated by securing clear facial photos because face recognition fails when unclear facial photos are obtained, i.e., when event attendees have their eyes closed, are not looking directly forward, or have their faces covered with hair or items such as facemasks and mufflers. In this paper, we propose a system for securing facial photos of attendees directly facing a camera by leading them to scan their check-in codes on a code-reader placed close to the camera just before executing face recognition. The system also takes two photos of attendees with this one camera after an interval of about 0.5 s to obtain facial photos with their eyes open. The system achieved 93% face-recognition accuracy with an average time of 2.7 seconds per person for identity verification when it was used for verifying 1,547 attendees of a concert of a popular music singer. The system made it possible to complete identity verification with higher accuracy with shorter average time than Ticket ID system.
Transductive non-linear semantic embedding for multi-class classification

This paper presents a method for semi-supervised multi-class classification based on matrix factorization. The method called semi-supervised online kernel matrix factorization (SS-OKMF) performs a semantic embedding by finding a non-linear mapping to a low-dimensional semantic space. An important characteristic of the SS-OKMF method is that the new low-dimensional semantic representation can be learned in a semi-supervised fashion. Therefore, the annotated instances can be used to maximize the discrimination between classes, but also, the non-annotated instances can be exploited to estimate the intrinsic manifold structure of the data. The non-linear modeling is based on kernel methods with a learning-in-a-budget strategy that allows keeping low memory requirements. This strategy, along with an online formulation based on stochastic gradient descent, reduces the computation time and keeps low computational requirements in large-scale problems. According to the experimental evaluation performed on several datasets of different nature (i.e. images, biosignals, and synthetic data), SS-OKMF, in comparison with several non-linear supervised, unsupervised and semi-supervised dimensionality reduction methods, presents a competitive performance in classification tasks under a transductive learning setup preserving a lower computational cost.
Beyond cultural dimensions

Today's global digital climate is characterized by a variety of UX communities that are emerging online for the first time. While many technology companies have developed design systems and practices to address the needs of global users, there is a noticeable gap in understanding of global UX in technical communication and even in UX education. The purpose of such experience report is to outline a framework for establishing a shared understanding of global UX. The framework relies on inclusive design as a guiding philosophy and is based on my experience developing and teaching a global UX course in an undergraduate UX program. This paper briefly outlines the purpose and key topics of a global UX course at [school name omitted intentionally]. The paper concludes with a call for UX educators to engage with the topic of global UX through curriculum development and research.
Security Analysis and Improvement of Hybrid Signcryption Scheme Based on Heterogeneous System

The heterogeneous hybrid signcryption scheme can not only solve the problem that only the short message can be encrypted in the traditional public key signcryption, but also realize the secure communication between different cryptosystems. Firstly, the security analysis of a heterogeneous hybrid signcryption scheme shows that the scheme does not satisfy confidentiality and unforgeability. In order to enhance security, an improved heterogeneous hybrid signcryption scheme is proposed. Based on the bilinear Diffie-Hellman problem and discrete logarithm problem, the new scheme is proved to satisfy confidentiality and unforgeability. Compared with the original solution, the new solution reduces the computational overhead while improving security.
Taking HDR Cinema Mainstream

Abstract Cinematic display solutions still lag behind consumer display technologies in terms of brightness and contrast, but new improvements could help them catch up. In this article, the second of a two‐part series, we explore challenges that must be addressed and potential solutions that could be incorporated to enable a mainstream rollout of HDR in cinema.
WebQMon.ai: Threshold-Oblivious On-Line Web QoE Assessment Using LSTM Neural Networks

The user perception of visiting a web page greatly affects the user willingness to continue surfing the website. However, it is difficult to construct a general Quality of Experience (QoE) model via traditional methods (e.g. deducing a formula or setting thresholds) because the contents of different websites are in endless varieties. To handle such an issue, we introduce WebQMon, a web QoE evaluation method using machine learning without deducing any formula or threshold, which is easy to deploy for different websites. WebQMon can evaluate the user experience through little application-layer data and most network-layer data, which makes it possible to deploy WebQMon on the gateway. Specifically, at the application layer, we use the referer field in the header to aggregate the packets generated by visiting a website, and in the network layer, we use the size and arrival time of packets to construct the WebQMon. In addition, we improved WebQMon by reversing the input data and evaluated two methods on three popular websites. The QoE prediction accuracy of the improved method can reach 99% for 16,000 samples.
A Taxonomy for Improving Industry-Academia Communication in IoT Vulnerability Management

Background: In software engineering, industryacademia is a symbiotic relationship. Researchers need to be aware of the industry to produce relevant research, while practitioners are educated in academia and could take advantage of empirical research. The SERP taxonomy architecture is designed to support communication between practitioners and researchers in software engineering. Objective: The purpose of this study is to analyze to what extent the SERP taxonomy architecture is useful for improving communication between researchers and practitioners in IoT vulnerability management. Method: We developed a SERP taxonomy for IoT vulnerability management, SERP-MENTION, in an incremental way. Along the development, we evaluated the developed taxonomy in a project of industry academia collaboration. Results: In addition to the taxonomy itself we elaborate on the taxonomy development process and the potential of SERP-MENTION to support communication between researchers and practitioners within the area. Conclusion: The SERP architecture can be used in a new field, it is perceived as useful by potential users to better describe and communicate research outputs and practical challenges in software vulnerability management.
Improving reliability of power plant equipment due to measurement information redundancy

Significant features of using the equipment installed at nuclear, hydraulic and thermal power plants are a multiyear cycle of continuous operation with minimum maintenance works and very high costs necessary to eliminate the consequences of possible crashes. Automatic checking of the state of the most important equipment units can become the optimal decision. In Russia, methods of building intelligent sensors and methods of intellectualization of multichannel measuring systems have been developed. Intellectualization of a measuring system enables to increase the reliability of equipment operation significantly. Examples are given.
QUALIMETRIC METHODS IN ASSESSING THE EFFECTIVENESS OF MANAGEMENT DECISIONS IN UNIVERSI-TIES

Abstract&#x0D; Purpose: Management decision in the university is a process of convergent analysis, forecasting and timely resolution of problem situations, aimed at achieving synergetic results. The research studies the advantages and feasibility of using qualimetric methods for evaluating effectiveness of the university’s management decisions.&#x0D; Methodology: Our proposal is to use the method, based on qualimetric analysis, in assessing the effectiveness of management decisions in the university. The theoretical propositions and the argumentation of the research findings have been grounded with the following general scientific methods system-thinking approach, structural and level-based, logical and economic-based, as well as statistical analysis.&#x0D; Main Findings: The management decision effectiveness is based on the level of collaboration in decision-making. The degree of collaboration in decision-making is determined by the involvement of various stakeholders in the process. Index of timeliness of a management decision implementation is the ratio of standard time to the actual time spent. The decision is effective if the index value is equal to 1 or exceeds it. Decision efficiency is evaluated based on the quality of its implementation. The integrated indicator of decision-making effectiveness is a combination of values for all four parameters. &#x0D; Implications/Applications: The practical significance of the research predetermined by the fact that application of this methodology contributes to optimizing the interaction of organizational structures on the management synergy basis, and, as a result, to improving the quality of educational services provided by the university. Using the method of qualimetric analysis raises the level of personnel involvement in the process of managerial decision-making, objectivity and decisions quality.
RECONSTRUCTION OF SOCIAL SCIENCE AND HUMANITIES THROUGH NARRATIVE

Purpose: The purpose of this paper is to present new directions and research strategies through critical analysis of the academic tendencies of existing social science and humanities. The narrative theory of human experience is adopted as a theoretical rationale for critical analysing existing social sciences and humanities. Since the 1970s and 1980s, the academic tendencies of the humanities and social sciences have been transformed into the narrative turn. We focus on the new integrity of humanities and social sciences in light of the narrative theory that approaches the totality of human life. The narrative theory for academic inquiry makes use of the position of Bruner, Polkinghorne, Ricoeur Methodology: We reviewed the literature related to the research topic and took an integrated approach to the philosophical analysis of core claims. Main Findings: As a result, the narrative theory has a characteristic approach to human life and experience as a whole, and it is possible to integrate by narrative ways of knowing. Implications/Applications: Based on this narrative theory, existing humanities and social sciences need to be reconstructed into narrative science. And a narrative method or narrative inquiry is useful as its specific inquiry method. As a narrative science, humanities and social sciences can be implemented by the integration of human experience and narrative epistemology. It has the advantage of integrating the atomized sub-sciences into the narrative of human experience according to this new method. Also, in-depth research on concrete exploration strategies is expected in the future.
Target Oriented Data Generation for Quality Estimation of Machine Translation

Quality estimation (QE) is a non-trivial issue for machine translation (MT) and the neural approach appears a promising solution to this task. Annotating QE training corpora is a costly process but necessary for supervised QE systems. To provide informative large scale training data for the MT quality estimation model, this paper proposes an approach to generate pseudo QE training data. By leveraging the provided labeled corpus in this task, our method generates pseudo training samples with a purpose of similar distribution of translation error of the labeled corpus. It also describes a sentence specific data expansion strategy to incrementally boost the model performance. The experiments on the different open datasets and models confirm the effectiveness of the method, and indicate that our proposed method can significantly improve the QE performance.
Improving Fast Adaptive Stacking of Ensembles

The treatment of large data streams in the presence of concept drifts is one of the main challenges in the fields of machine learning and data mining. This article presents two ensemble algorithms designed to quickly adapt to concept drifts, both abrupt and gradual. Fast Stacking of Ensembles boosting the Best (FASEB) and Fast Ensemble boosting the Best with Combined Weighting Voting (FASEB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wv</sub> ) are adaptations of Fast Adaptive Stacking of Ensembles (FASE), designed to improve their execution time without causing considerable losses in the algorithm's accuracy. In order to achieve a more efficient model, adjustments were made in the update strategy and voting procedure of the ensemble. To evaluate the proposals against the state of the art, we used Naive Bayes (NB) and Hoeffding Tree (HT) as learners to compare the performance of the algorithms on artificial and real-world data sets. The experiments showed that FASEB and FASEB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wv</sub> are more efficient than FASE in many scenarios, often also achieving better results in accuracy.
FinRep: A Blockchain Approach for Financial Reputation

Financial reputation is the key to obtain credit from formal financial institutions. However, a reliable and customer-driven mechanism for proving one’s own financial mechanism does not exist. We propose FinRep—a sequential record of transactions involving a customer, and registered by that customer himself with the help of intermediary financial institutions. FinRep is implemented as a blockchain-based secure data storage and can be produced by the customer on demand to prove his financial reputation. FinRep provides authenticity, integrity and also power to the customer, on which transactions he wishes to record and share with a credit-issuing institution, thereby providing a certain amount of privacy to the customer. The institution then evaluates the FinRep only for its quality and either accepts or rejects. This paper details the methodology of constructing the FinRep along with the mechanisms for analysing the quality of customer submitted FinReps and discusses the implementation using Openchain.
Penerapan Applikasi Edukasi Komputer Untuk Meningkatkan Hapalan Abjad Di Tk Yapita Surabaya

This study aims to determine the effectiveness of the educational application that is applied in Yapita Kindergarten Surabaya. The author uses descriptive qualitative methods. The author focuses on how to apply computer education applications to help students memorize the alphabet. The author made observations during the 3x meeting. Each meeting, they get a different computer education application game but the contents of the material are the same. The application has been installed on every school computer that has been adapted to the material being taught. The subject of the research was TK A, amounting to 30 students. Each student uses the computer provided at each meeting for 30 minutes. The results showed that learning media through varied computer programs in a game will make children more excited and enthusiastic to learn because it is not boring. However, for children who do not understand the alphabetical order, they will experience difficulties and confusion. Also, children who have poorly trained physics will experience difficulties when using the mouse.&#x0D; Keywords: : computer application, alphabetical, enthusiastic, soft motoric
Efficient Protection for VDI Workstations

Many enterprises migrate to a private cloud VDI environment. In such an environment multiple workstations are served by a single powerful server. On such an environment each VDI workstation receives only a limited CPU power. An average of less than a quarter of a core per planned VDI workstation is a common setup. Under such cases, anti-virus and application control software load is multiplied by the number of VDI workstation running on each server. These security applications take merely a few percentages of a single core on a normal desktop. However, on a VDI server where the multiple VDI workstations run on a single server, they may consume 20-25 percent the load. Naturally, such an increase is significant. We present an application control system that is extremely useful in a VDI environment.
Is Bitcoin Trustworthy?

Using a recent philosophical account of trustworthiness, the author evaluates whether Bitcoin can be considered trustworthy in each of its three most common uses: as a medium of exchange, as a store of value, and as a speculative investment. Primarily because the entity that controls Bitcoin, its community of users, does not take actions to stabilize and secure the cryptocurrency – that is, in the manner that nations’ monetary policies can stabilize and secure national currencies – the author evaluates Bitcoin as untrustworthy. The author then offer an argument that its untrustworthiness undermines Bitcoin’s ability to serve both as medium of exchange and as store of value. Bitcoin’s untrustworthiness does not, however, undermine its usefulness as a speculative investment. Finally, the author discuss the extent to which – and how – other cryptocurrencies can avoid the evaluation that they are untrustworthy.
From Tree Adjoining Grammars to Higher Order Representations of Abstract Meaning Representations via Abstract Categorial Grammars

We construct an Abstract Categorial Grammar (ACG) that interrelates Tree Adjoining Grammar (TAG) and Higher Order Logic (HOL) formulas encoding Abstract Meaning Representations (AMRs). We also propose another ACG that interrelates TAG and HOL formulas expressing neo-Davidsonian event semantics. Both of these encodings are based on the already existing ACG encoding of the syntax–semantics interface where TAG derivations are interpreted as HOL formulas representing Montague semantics. In particular, both of these encodings share the same abstract language coming from the ACG encoding of TAG with Montague semantics, which is second-order. For second-order ACGs, problems of parsing and generation are known to be of polynomial complexity. Thus we get the natural language generation and parsing with TAGs and HOL formulas modelling AMRs for free.
PROBLEMS OF MODERN HIGHER EDUCATION IN THE CONTEXT OF SOCIAL PHILOSOPHY

The article is devoted to analysing the situation of prevalence of pragmatic utilitarian orientations in the sphere of higher education. The modern society is characterized by the increasing role of the global network for obtaining ready-made knowledge, expansion of hyper-reality, processes of computerization of educational space. As a result, universities and the teaching staff lose their dominant role in the process of translating the existing values and forming new ones. The authors analyse the problem of teaching philosophy at higher school and identify the arising difficulties. The paper justifies the necessity to form learners’ fundamental philosophical competence as a basis for the critical analysis of consistent information streams.
Lossless Image Compression Using List Update Algorithms

We consider lossless image compression using a technique similar to bZip2 for sequential data. Given an image represented with a matrix of pixel values, we consider different approaches for linearising the image into a sequence and then encoding the sequence using the Move-To-Front list update algorithm. In both linearisation and encoding stages, we exploit the locality present in the images to achieve encodings that are as compressed as possible. We consider a few approaches, and in particular Hilbert space-filling curves, for linearising the image. Using a natural model of locality for images introduced by Albers et al. [J. Comput. Syst. Sci. 2015], we establish the advantage of Hilbert space-filling curves over other linearisation techniques such as row-major or column-major curves for preserving the locality during the linearisation. We also use a result by Angelopoulos and Schweitzer [J. ACM 2013] to select Move-To-Front as the best list update algorithm for encoding the linearised sequence. In summary, our theoretical results show that a combination of Hilbert space-filling curves and Move-To-Front encoding has advantage over other approaches. We verify this with experiments on a dataset consisting of different categories of images.
A Scope-Taking System with Dependent Types and Continuations

Different scope-taking mechanisms have been proposed for capturing quantifier scope alternation, including covert operations of quantifier movement, type-changing rules, storage devices. In this paper we propose a new scope-taking system with dependent types and continuations. The key elements of our formal framework are: (i) richly typed system; (ii) contexts for determining the relative scoping of quantifiers; (iii) recursive procedure by which the interpretation is computed and the dependently typed context is built along the surface structure tree. The main advantage of our proposal is that it does not overgenerate—it produces all and only the attested readings for the defined fragment. The core idea behind the proposal is that certain lexical elements are responsible for inverting scope: relational nouns and locative prepositions. This allows us to provide a principled solution to the question of why certain constructions missing such elements block inverse scope.
LWFD: A Simple Light-Weight Network for Face Detection

In the latest field of object detection, whatever it is one-stage approach or two-stage approach, both of them are using the CNNs with complex calculations to guide the detection performance better. But this also greatly limits our use of the platform (only available on the GPU), so we propose a simple light-weight network for face detection based on the well performance light-weight network backbone which can run on CPU or ARM. In our approach, we have a light-weight network that combine a simple but effective detection framework, a hyperparameter to control the number of channels. It makes our model allowed to have smaller model, faster speed and better accuracy. LWFD can perform CNN inference on mobile devices, and 1.0x run at 90 ms on 2.4 GHz CPU with f-score of 89% on FDDB dataset.