Reviewerly/exHarmony huggingface dataset:
Ranked Window Query Retrieval over Video Repositories

Recent advances in Computer Vision have contributed to solid accuracy and efficiency improvements in many tasks such as object detection and tracking, enabling new opportunities for video analytics. In this paper, we initiate the study of ranked window queries that aim to retrieve clips from large video repositories in which objects co-occur in a query-specified fashion. For example, ranked window queries allow retrieval of clips of a set duration (e.g., 10 seconds) with the highest score from a long video, where at least the same 3 cars (with matching conditions based on suitably defined metadata) appear jointly. To answer such queries, we propose a two-phased approach, which builds indexes for all desired objects of the given videos during an Ingestion Phase and evaluates query answers efficiently in the Query Phase. During the Ingestion Phase, the proposed Partition-Based Index Construction (PBIC) algorithm builds indexes on partitions obtained by splitting each given video. Leveraging such indexes, queries are answered in the Query Phase using the Partition-Based Query Processing (PBQP) algorithm, which efficiently produces the desired (query-specified) number of results with the highest scores. We present the outcome of a thorough performance study on real videos that evaluates the performance of the proposed algorithms by varying parameters of interest. Our results indicate that the proposed set of techniques are capable of processing queries efficiently at scale, demonstrating multiple orders of magnitude speedups over other applicable approaches.
MPC: Minimum Property-Cut RDF Graph Partitioning

Scaling-out RDF processing to deal with graph size usually requires partitioning the RDF graph. Typical partitioning approaches minimize edge-cuts or vertex-cuts. In this paper we argue that these approaches do not avoid or reduce joins between different partitions (i.e., inter-partition join), and propose an approach based on minimizing the number of distinct crossing properties, which we call Minimum Property-Cut (MPC). This approach enables more queries to be independently evaluated without inter-partition join. However, the minimum property-cut partitioning is a NP-hard problem and we propose a heuristic greedy algorithm to address that. Extensive experiments over a variety of synthetic and real RDF graphs show that the proposed technique can significantly avoid joins and results in good performance.
Time Series Anomaly Detection Toolkit for Data Scientist

This tutorial presents a design and implementation of a scikit-compatible system for detecting anomalies from time series data for the purpose of offering a broad range of algorithms to the end user, with special focus on unsupervised/semi-supervised learning. Given an input time series, we discuss how data scientist can construct four categories of anomaly pipelines followed by an enrichment module that helps to label anomaly. The tutorial provides an hand-on-experience using a deployed system on IBM API Hub for developer communities that aim to support a wide range of execution engines to meet the diverse need of anomaly workloads such as Serveless for CPU intensive work, GPU for deep-learning model training, etc.
TwiCS: Twitter Stream Entity Mention Detection (Extended Abstract)

In this paper, we propose a system TwiCS for Entity Mention Detection (EMD) and Entity Detection (ED) in streaming environments. TwiCS employs a computationally light two-phase process: (1) exploit simple (low computation) syntactic cues to suggest Entity Mention (EM) candidates and (2) use occurrence mining to classify candidates according to their likelihood of being true EMs. Our experiments show that on average TwiCS improves effectiveness by 14.6%, while achieving at least 2.64 times higher throughput, when compared to several state-of-the-art systems.
Network Alignment with Holistic Embeddings (Extended Abstract)

Network alignment is the task of identifying topo-logically and semantically similar nodes across (two) different networks. However, existing alignment models either cannot handle large-scale graphs or fail to leverage different types of network information or modalities. In this paper, we pro-pose a novel end-to-end alignment framework that can lever-age different modalities to compare and align network nodes in an efficient way. A comprehensive evaluation on various datasets shows that our technique outperforms state-of-the-art approaches. Our source code is available at https://github.com/thanhtrunghuynh93/holisticEmbeddingsNA.
The Study and Importance of Media Ethics

The media serves as a conduit between the public and the government. It serves as a leader, motivator, or informant for a robust democracy at all levels. For this study, secondary data were gathered from books, journals, websites, and research papers. For proper broadcasting, preventing information misrepresentation, and avoiding conflicts of interest, media ethics are crucial. It is crucial to promote equality under the law, truthfulness, accountability, and universal ideals. Ethics maturity develops through the identification of values, subjects, and evaluation of several opinions while tolerating opposing points of view (Alia 2004). A person does not suddenly become ethical. People develop morally as well as physically and intellectually (Merrill 2011 p.30). To defend the interests of people and society, further research is required, and appropriate laws and standards for media ethics should be created globally.
INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “PIER PAOLO PASOLINI: PERSON, WORKS, PERSEPTION. ON HIS 100TH BIRTHDAY”

The proposed review is dedicated to the recent International Scientific and Practical Conference “Pier Paolo Pasolini: person, works, perception. On his 100th birthday”, which was held on March 01–02, 2022 at the Russian University for the Humanities and at the Gorky Institute of Word Literature of Russian Academy of Science (Moscow). The conference organized by the Department of Classical Literature of the West and Comparative Literature of the IW and the Department of European Languages of the IL RSUH, saw a large participation of literary scholars from universities in Russia, Italy, the Czech Republic and Germany, film critics, translators of fiction and poetry, etc. The purpose of the conference was to honor the memory of the great Italian poet, writer, thinker and director on the eve of a memorable date – the centenary of his birth (March 5, 2022). The reports made concerned both the literary and cinematic heritage of Pasolini, as well as the reception of his work in the contemporary culture.
Practical Method of the I&amp;C System Security Architecture Design Using Graph Models

In industrial control systems, the cybersecurity and cybersecurity measures are tightly bound to the technological and physical characteristic of a plant. It means that cybersecurity measures must be designed at the earliest stages of a control system lifecycle. The paper presents a method that implements the secure-by-design principle and provides conformance between target general cybersecurity requirements resulted from the risk assessment and the cybersecurity architecture of a designed system. The considered method uses graph models of both requirements and the architecture and gives the algorithms of mapping and comparison of the models. A criterion of proximity between two models is proposed. As an example, the paper describes cybersecurity architecture synthesis for a typical information channel in a instrumentation and control system.
The Intersection between Linguistic Theories and Computational Linguistics over time

ABSTRACT Recent achievements have turned Computational linguistics into a dynamic research area, and an important field of application development that is being explored by leading technology companies. Despite the advances, there is still much room for improvement to allow humans to interact with computational devices, through natural language, in the same way that humans interact with native speakers of the same language. How to make computers understand or create new metaphors, metonymies or other figures of language? Is it possible to develop natural language systems capable of producing lyrics or poetry on the same level as humans? Can we produce systems capable of assigning new meanings to syntactic elements that are immediately perceived as coherent by humans? In this paper, we account for the evolution of computational linguistics, drawing a parallel with the evolution of linguistic theories and speculating about its research opportunities and foreseeable limitations.
A Model of Information Regulation in the Presence of Competition Between Social Media Platforms

Content moderation policies vary widely across social media platforms, with many prominent actors expressing reluctance at imposing strict regulation. One important factor in this difference and reluctance may be the competitive advantage that the absence of regulation bestows to a platform. Indeed, a popular platform newly enforcing content moderation may fear that it will damage its user base by causing users to migrate to another less-heavily regulated platform. Moreover, since the migrating users can continue their harmful activities in the alternative platform, such regulation may end up being ineffective against information pollution. The goal of this article is to understand this competitive aspect of content moderation on platforms by considering the motivations of all players (platformer, news source, and social media users), characterizing the possible regulation policies that can be sustained in equilibrium, and evaluating the resulting quality of information in each platform. The model indicates that a popular platform can enforce any strict regulation without losing users if (1) the platform provides high social interaction quality, (2) news information is diffusive among users, (3) the social network structure has cohesive blocking clusters, and (4) many users are distant from the influencer.
Solar Power Production Forecasting with Solar Irradiance Estimated by Similar Days: A Case Study in Taiwan

The attempt made in this research is to tackle the problem associated with the forecast of the shortterm solar power production. To that end, the state-of-the-art machine learning and data mining techniques along with the weather data made publicly available for research were involved to do so. In particular, we established and compared various solar power production forecasting models with forecasting algorithms. The input features were the weather data collected from different weather bureaus. However, solar irradiance is not publicly available. We proposed a novel approach based on similar days to estimate solar irradiance for the target moment. Several techniques from the field of machine learning including linear regression, support vector regression, relevant vector regression (RVM), XGBoost and LightGBM were then employed to forecast solar power production. The best forecasting result came from RVM with nRMSE 34.26% and nMAE 23.62%, respectively. Various settings of ensemble were also investigated. The best result with nRMSE 32.59% and nMAE 22.48% was achieved by ensemble of estimated irradiances first and then ensemble of power outputs. We found that the inclusion of the result obtained with an inferior model in the ensemble could deteriorate the forecasting.
On the Minimal Adversarial Perturbation for Deep Neural Networks With Provable Estimation Error

Although Deep Neural Networks (DNNs) have shown incredible performance in perceptive and control tasks, several trustworthy issues are still open. One of the most discussed topics is the existence of adversarial perturbations, which has opened an interesting research line on provable techniques capable of quantifying the robustness of a given input. In this regard, the euclidean distance of the input from the classification boundary denotes a well-proved robustness assessment as the minimal affordable adversarial perturbation. Unfortunately, computing such a distance is highly complex due the non-convex nature of DNNs. Despite several methods have been proposed to address this issue, to the best of our knowledge, no provable results have been presented to estimate and bound the error committed. This paper addresses this issue by proposing two lightweight strategies to find the minimal adversarial perturbation. Differently from the state-of-the-art, the proposed approach allows formulating an error estimation theory of the approximate distance with respect to the theoretical one. Finally, a substantial set of experiments is reported to evaluate the performance of the algorithms and support the theoretical findings. The obtained results show that the proposed strategies approximate the theoretical distance for samples close to the classification boundary, leading to provable robustness guarantees against any adversarial attacks.
EXPERT ACTIVITIES IN HIGHER EDUCATION SPHERE: THE COMPETENCE DIMENSION

In the context of building state-public administration of higher education, when the state, society and the individual are equal subjects of the educational process, the problem of carrying out expert activities in higher education institutions by specialists who have the necessary level of knowledge, skills and general and special competencies. The purpose of the article is to study the competencies of experts in the field of higher education aimed at ensuring its quality. The author emphasizes the theoretical and practical aspects of the implementation of expert activities in the field of higher education and the requirements for the competencies of educational experts in higher education institutions. Competence is defined by the author as an integral characteristic of a person, which reflects his ability on the basis of a dynamic combination of knowledge, skills, personal traits and qualities, to conduct professional, educational and other activities that contribute to its development. Expertise in the field of higher education is considered in the article as a method of analysis and cognitive process aimed at finding out the cause-and-effect relationships. Educational expertise acts as a special mechanism for managing university education, when equal partners and participants in the educational process are jointly responsible for the quality of higher education, its monitoring and operation. Accordingly, expert activity in the field of higher education performs educational, methodological, consulting, recommendation, forecasting and innovation functions. It is emphasized that the implementation of expert activities in the higher education system requires from experts a wide range of general and professional competencies and soft skills for impartial assessment of the educational environment, quality of educational programs, management decisions and more. Educational analytics and consulting activities are aimed at directing the administration of higher education institutions and participants in the educational process to make optimal strategic decisions aimed at ensuring the quality of higher education.
Accelerating Surface Remeshing Through Gpu-Based Computation of the Restricted Tangent Face

High-quality mesh surfaces are crucial for geometric processing in a variety of applications. To generate these meshes, polyhedral remeshing techniques truncate Voronoi cells of the original surface and yield precise intersections, but the calculation is complicated. To simplify these techniques, some methods apply auxiliary points to construct Voronoi diagrams, thereby extracting co-planar facets to approximate the original surface. However, extracting these approximate facets from the constructed Voronoi diagram makes it inefficient and non-parallelizable. To this end, we propose an efficient GPU method for surface remeshing, where the restricted tangent face (RTF) is utilized to approximate the original surface. By intersecting the pre-clipped Voronoi cell with the tangent plane, this method directly calculates the RTF of each point without any auxiliary points or traversing Voronoi cells. Moreover, to restrict the movement of points, we introduce a projection method based on the KNN strategy, where each point is projected onto the triangular facet in the original surface. Owing to the independence and non-interference of the RTF computation and projection of each point, our method is implemented in parallel on the GPU. Experimental results on various mesh surfaces demonstrate the superior performance of our method in the viability, effectiveness, and efficiency.
Building An Authentication Infrastructure — Designing a Two Factor Authentication Hardware Token with Form Factor that Encourages Engagement

The death of the password has been projected for decades, by a range of highly qualified experts. Yet passwords remain the dominant authentication technology. The dominance of passwords results in chronic vulnerability to social engineering. In this work we explore the reason for the lack of adoption of hardware security tokens that are considered best in class for authentication speed and security.In this three step evaluation we provided hardware tokens to students at Indiana University and then surveyed their reasons for use or non-use. These results aligned with previous work, indicating that the previously hypothesized solutions with similar results would be applicable.Following this we applied the Research Through Design(RtD) method(Zimmerman and Forlizzi, 2014 [32], Zimmerman et al., 2007 [33], Zimmerman et al.,2010 [34]) to design, prototype, and test a hardware security token based on previous work. We evaluated the prototype using the wizard of oz (Dahlbäck et al., 1993 [8]) technique during a pilot study of 7 participants. We found that the larger form factor and being able to attach the device to the smart phone did not result in a lower perceived cost of motivation of use. We also found that the biometric sensor is generally perceived to be more secure. Based on these interviews we conclude that drivers of lack of adoption goes beyond form factor, specifically lack of adoption is grounded in participant perception of low risk of account takeover. In addition, the ability to share authentication for mutual support and caregiving is identified as a strength of passwords. We close with a discussion, based on the survey of the literature and the design changes recommended in previous works, that design alone is inadequate for universal adoption of secure hardware authentication. We argue for integrating risk communication; beyond this, recognition that sharing of passwords can be a social good. The ability to enroll multiple keys for one account enables shared account practices with family and household settings: sharing Netflix accounts, assisting less technical parents, and managing shared household resources. We predict that strictly technical solutions may be inadequate, but rather that awareness, risk communication, and communication about the relationship between one-time password (OTP) hardware and account management are critical. We suggest that zero trust architectures that undermine or conflict with social practices may face similar friction in consumer applications.
Daze Computing Model Based Mobile Cloudlet- Zestful Vitality

The growth in mobile devices and applications has leveraged the emergence of mobile cloud computing, which allows the access to services at any place and extends mobile computing. Usually, the current mobile network consists of a restricting factor in supporting such access because, from a global perspective, cloud servers are distant from most mobile users, which introduces significant latency and results in considerably delays on applications in mobile devices. In this research author are going to develop Daze Computing Model Based Mobile Cloudlet- Zestful Vitality Optimization which is more efficient as compare to other existing model. On the other hand, Cloudlet is usually on the edge of Mobile Networks and can serve content to mobile users with high availability and high performance. This paper reviews both the traditional mobile cloud computing and the Cloudlet architecture. Taxonomy on the Cloudlet architecture is introduced and three related technologies are discussed. Based on the user needs in this environment, personal Model which is used to predict individual behavior and group model which considers caching popular data for several users are proposed. Making use of these two models and the Cloudlet architecture, two data access schemes are designed based on model distribution and data pre-distribution. We have conducted experiments and analysis for both the models and data access schemes. For the models, model efficiency and comparisons among different technologies are analyzed. Simulation results for the data access schemes show that the proposed schemes outperform the existing method from both battery consumption and performance aspects
Maximal Infix Codes and Some Related Classes of Codes

An infix code is a code such that the words are not infixes of each other. In the hierarchy of classes of codes, the solid codes lie below the class of infix codes. Solid codes have a remarkable resistance to insertion and deletion by their synchronization capabilities. This paper will study solid codes as anÂ independent subject in the theory of variable-length codes. Firstly, the sufficient and necessary conditions under which a maximal infix code becomes a maximal code and some descriptions of maximal infix codes will be given. Secondly, the sufficient and necessary conditions under which a maximal infix code that is a solid code becomes a maximal solid code will be provided. Finally, as a supplement to theory of infix codes and solid codes, we study the operations on infix codes and solid codes.
Intelligent Tutoring System of Linear Programming

There is a growing technological development in intelligent teaching systems.This field has become interesting to many researchers.In this paper, we present an intelligent tutoring system for teaching mathematics that helps students understand the basics of linear programming using Linear Program Solver and Service for Solving Linear Programming Problems, through which students will be able to solve economic problems.It comes down to determining the minimum or maximum value of a linear function, which is called the objective function, according to pre-set limiting conditions expressed by linear equations and inequalities.The goal function and the limiting conditions represent a mathematical model of the observed problem.Working as a professor of mathematics in high school, I felt the need for one such work and dealing with the study of linear programming as an integral part of mathematics.There are a number of papers in this regard, but exclusively related to traditional ways of working, as stated in the introductory part of the paper.The center of work as well as the final part deals with the study of linear programming using programs that deal with this topic.