Reviewerly/exHarmony huggingface dataset:
WAHC'22

The 10th Workshop on Encrypted Computing and Applied Homomorphic Cryptography is held in Los Angeles, CA, USA on November 7, 2022, co-located with the ACM Conference on Computer and Communications Security (CCS). The workshop aims to bring together professionals, researchers and practitioners from academia, industry and government in the area of computer security and applied cryptography with an interest in practical applications of homomorphic encryption, encrypted computing, functional encryption and secure function evaluation, private information retrieval and searchable encryption. The workshop will feature 6 exciting accepted talks on different aspects of secure computation and a forum to discuss current and future challenges. Additionally, the workshop will feature one keynote presentation, as well as a working session. The complete WAHC'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3560827.
Research on Emotion Classification for Social Media Texts Based on Affective Meta-Learning

Social media platforms are becoming increasingly popular worldwide with the rapid development of the Internet power. Therefore, many users have the opportunity to communicate in various forms such as texts, emoticons, and memes on the Internet. Meanwhile, they generate a lot of affective elements while discussing events and merchandise. In the process of classifying the user's language and text, if researchers can grasp those affective elements, they can accurately understand the user's main intentions and emotional tendencies in the discussion process. The above is a major point of the current research. This paper uses an advanced approach that combines natural language processing and affective meta-learning to automatically classify emotion text on social media to accurately understand the language habits of users. This approach achieves an ideally result by analyzing the determined parameters and using neural network algorithms for text-based emotion classification.
RESULTS OF THE SCIENTIFIC SEMINAR "THEORY AND PRACTICE OF APPLIED CULTURAL RESEARCH" ON 17TH JUNE 2022 (SIBERIAN FEDERAL UNIVERSITY, KRASNOYARSK)

This text presents a summary of the work of the educational, scientific and methodological seminar "Theories and Practices of Applied Cultural Research" (June 17, 2022, Krasnoyarsk, Siberian Federal University). The seminar was devoted to the presentation of current interest in art studies in accordance with memorable dates for the world of culture and art: 1) Art studies of works of architecture, dedicated to the 170th anniversary of the birth of Antoni Gaudí; 2) Current research of Russian art, dedicated to the 185th anniversary of the birth of Ivan Nikolaevich Kramskoy; 3) Animation art, dedicated to the 95th anniversary of the birth of Vyacheslav Mikhailovich Kotyonochkin, as well as the study of phenomena in the art of the XX-XXI centuries.
Creating a Community-Based Disaster Management System

&#x0D; &#x0D; &#x0D; &#x0D; This research is aimed into Malaysia's efforts to harness ICT's pervasiveness to manage natural and man-made calamities. These efforts have resulted in various agencies and programs ranging from forecasting to coordination and monitoring, but the number of fatalities during such inevitable catastrophes has been a major worry. Unfortunately, as seen by recent storms in Malaysia, the number of such calamities continues to rise. A proposal to create a crisis management joint platform will work alongside government departments, allowing for real-time sharing of knowledge, resources, and information. The building of the mosque drew the community into the system since they are seen as the closest to the people. These actions will surely bolster the government's attempts to meet its objectives. As a result, experts suggest that teaching these communities, which are expected to form a volunteer community, the science of gathering information and putting it to good use in the case of a disaster, would go ways away beyond maximizing tax dollars.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Reflective experiences with immersive heritage

The explosion in the development and communication of digital humanities has seen fascinating digital visualization projects. Some focus on slavery and massacre, such as the Monroe and Florence Work Today website (plaintalkhistory.com/monroeandflorencework, Unknown), a database and mapping platform of lynching in America, Slave voyages visualized by SLADE magazine (Kahn and Bouie 2015) and in Australia the Colonial Frontier Massacres in Australia 1788–1930 map (Allam and Evershed 2019; Ryan 2019). Some focus on outright horror, while others use digital technology to convey contestation and issues of ambiguity. Despite the growth and spread of these digital humanities visualization projects, parallel and accessible examples in immersive virtual heritage are harder to find. Over the last three decades, immersive technologies (especially as “new” media) have embraced digital heritage to create showstopping instant experiences, but existing, durable examples of virtual heritage (virtual reality applied to cultural heritage) are relatively rare, and examples of difficult heritage are far rarer. To review and address this gap, I will summarize dilemmas in present research on immersion, presence and immersivity and cover recent developments in virtual, augmented and mixed reality technology. Then, inspired by United Nations Educational, Scientific and Cultural Organization (UNESCO) charters, indigenous manifestos and ethical design principles in digital humanities (Hepworth and Church 2018), I will attempt to formulate a theoretical framework with criteria and guidelines to help immersive environment designers address the depiction or evocation of difficult pasts.
Strategies for establishing discourse coherence. The case of Slovene dialectal discourse

Abstract A discourse is considered coherent only if all its parts are semantically related to each other and if it makes sense. However, coherence cannot be attributed to a discourse in advance because it depends on how the participants understand what they hear/ read. So we can say that it is not the discourse that establishes coherence, but the participants according to their understanding of a discourse. In practise, this means that for the same discourse there are at least two coherences that may or may not match up. Usually they match up to some extent because participants follow the cooperative principle of mutual willingness to establish coherence in discourse. There are several strategies for establishing and maintaining discourse coherence; among them, repetition and inference form an extensive group. In this paper, however, I will focus only on the strategies that are typical of spontaneous spoken discourse. These are the following: questions, completion, contradiction and polyphonic talk. In the second part of the article I will present each of them in the examples of spontaneous dialectal discourse. The texts were recorded in Brda, a region in the far west of Slovenia. The dialect spoken there is called the Brda dialect. It belongs to the Littoral dialect group of the Slovene language.
Action-a-Bot: Exploring Human-Chatbot Conversations for Actionable Instruction Giving and Following

Conversation serves as one critical mechanism for knowledge-sharing and instruction-giving in collaborative work. Conversation allows people to take turns to make contributions, plan joint actions, align shared understanding of work status and resolve action failure. However, when such collaboration involves non-human AI actors like chatbots, there is a lack of understanding of how human participants may respond to the chatbot's prompts and guidance, and whether the interaction can similarly improve the actionability of instructions given to people. In this study, we prototyped a chatbot system, ActionaBot for providing task instructions to novice workers, and conducted an initial study to explore its effects on procedural instruction giving and following. Our results indicate that, novices although might perceive instructions to be inactionable due to prior experience and how instructions were authored, they were able to follow conversational guidance and willing to adapt to the chatbot through turn-taking to calibrate working states back-and-forth. Besides, users' awareness of the work status increased with the conversational prompts from the chatbot.
Regret Analysis of Certainty Equivalence Policies in Continuous-Time Linear-Quadratic Systems

This work theoretically studies a ubiquitous rein-forcement learning policy for controlling the canonical model of continuous-time stochastic linear-quadratic systems. We show that randomized certainty equivalent policy addresses the exploration-exploitation dilemma in linear control systems that evolve according to unknown stochastic differential equations and their operating cost is quadratic. More precisely, we establish square-root of time regret bounds, indicating that randomized certainty equivalent policy learns optimal control actions fast from a single state trajectory. Further, linear scaling of the regret with the number of parameters is shown. The pre-sented analysis introduces novel and useful technical approaches, and sheds light on fundamental challenges of continuous-time reinforcement learning.
Comparative Analysis on Effect of Different SVM Kernel Functions for Classification

Besides linear classification, Support Vector Machine (SVM) is proficient in non-linear classification by deploying kernel tricks that implicitly maps and transform input features to high dimensional feature space. Kernel-SVM, can be utilized to secure progressively complex connections on datasets with no push to do changes all alone. In this paper, 5 different SVM kernel functions are implemented on 4 datasets, viz., IRIS, Breast Cancer Wisconsin (diagnostic), Mushroom and Letter Recognition Dataset. The five kernel functions considered in this paper are: Linear kernel, Gaussian Radial Basis Function (RBF) kernel, Laplacian kernel, Polynomial kernel and Sigmoid kernel. Our goal is to locate the best non-linear kernel. The outcomes show that the precision of expectation for Laplacian kernel is most extreme with a forecast scope of (max 100%, min 97.53%) and least for the sigmoid kernel with a forecast scope of (max 100%, min 47.28%).
Review of Deep Learning: A New Era

Deep learning (DL) made surprising progress in different Artificial Intelligence (AI) and Computer vision applications. The learning permits different handling layers to learn highlights without help from anyone else inverse to traditional AI approaches, which could not handle the information in their normal structure. Deep convolution networks have shown incredible execution in handling pictures and recordings, though intermittent nets have shown extraordinary accomplishment for consecutive information. This paper surveys every one of the angles and investigates done work now around here alongside their future prospects.
INFORMATION SECURITY POLICY: ESTABLISHING RULES AND STANDARDS OF USE AMONG EMPLOYEES

Harmony between sectors of a company is something that adds a lot of value to the institution even more when it comes to the IT sector in relation to the others. Information security aims to adequately protect data from the various existing threats, and information security policy governs it. This is the basic theme of this article, in which the application proposal addressed aims to identify how it reflects in the area of information technology in relation to employees within a social concept of behavior in the face of some barriers imposed on sectors at the level of access. Within the context studied, the methodology applied to achieve the objective had a case study that provided the necessary data. Through the survey directly linked to the facts, the applied research raised the consequences of the theme. In addition to empirical and descriptive research, which through structured interviews used reports and documents, contributing to the understanding of the reflexes that occurred between the sectors related to the Security Policy.
Student Attendance Monitoring System Using Facial Recognition

With the furtherance of technology, the frauds and malpractices related to it have been on the verge of happening, and technology has been a kind of savior in so many cases. Facial recognition can be considered as such a savior in terms of numerous malpractices and fraud activities. Not only in the field of fraud prevention or detection, but facial recognition and automated face detection tools and technologies play an important role in the attendance management systems, detection of criminals, etc. Document image analysis is used in detecting frauds, but the proposed model relies on the image or video. In this paper, the implementation of facial recognition techniques along with their features and application has been explained. This paper also explains how facial recognition technology is now getting introduced and applied across numerous aspects of life. This paper also highlights the drawbacks or the limitations of facial recognition technologies, and in addition, it also presents the various methods and ideas using which facial recognition technology, and its performance can be enhanced and the limitations can be overcome. A novel framework for monitoring student attendance has been implemented. A Web application based upon the Django framework has been designed for easy monitoring and maintaining the attendance of the student using the facial landmark algorithm.
Cloud Ecosystem-Prevalent Threats and Countermeasures

Cloud-based services are in high demand because they give consumers and businesses a lot of flexibility in employing new applications and high-end infrastructure at a low cost. Despite the increased activity and interest, there are still worries about security vulnerabilities with cloud computing, resulting in hurdles for both consumers and service providers in terms of data protection, privacy, and service availability. As a result, cloud service providers and consumers must ensure that the cloud environment is secure from both external and internal threats. This chapter provides a comprehensive overview of key components of the cloud computing ecosystem and security concerns encompassing its impact on businesses. It focuses on understanding cloud computing technology, deployment environments, services, and usage considerations. The chapter identifies the most common security risks, allowing both end users and providers to identify the risks connected with the technology. Finally, different countermeasures to important security and privacy issues are presented.
Voice Synthesizer for Partially Paralyzed Patients

In this paper, we have created a voice synthesizer especially for the people who are partially paralyzed, and our aim is to retain their original voice of the targeted speaker. The model consists of orator encoder, synthesizer, spectrogram generator, and vocoder. The orator encoder is a trained model by using the voices of variety number of speakers including noisy speech and without caption. This is used to generate a stable proportional insertion vector from only a few seconds of a targeted speaker’s source speech. The synthesizer based on Tacotron 2 that is used to condition the generated Mel spectrogram based on the embedded speaker. An auto-regressive vocoder network is used to produce the waveform samples from the Mel spectrogram. Our model is very useful to retain the original voice of the speakers who have been partially paralyzed because they will be unable to raise their volumes of their voices and it is able to produce natural voice of the speaker that is unseen during the training purpose. The proposal is compared with several state-of-art methods and established a mean opinion score is 94%.
Judging the resilience of online learning for hospitality courses during COVID-19

In 2020, COVID-19, a novel coronavirus disease halted the operations of industries and educational institutions globally. During the lockdown, educational institutions looked for an alternative to face-to-face classes. The Institute of Hotel Management (IHM) and private colleges across India started offering online sessions for managing the curriculum. A close-ended questionnaire was distributed to various hotel management institutes. The survey focused on evaluating the effectiveness of online learning as a substitute for hospitality courses. This study analysed online studies from a student perspective to understand their effectiveness. The researcher received 711 responses from students enrolled in various IHM colleges. Findings suggest that 87.1% of students were familiar with online classes, and 33% reported problems in grasping concepts. Due to the reoccurrence of Coronavirus, students and faculty will have to remain prepared for hybrid education. The faculty’s commitment to online education is a significant factor; faculty should give personal attention to students as they face network issues, buffering, less visibility on mobile screens, chaos and connectivity problems.
Book review: Giusti, Giuliana, Di Caro, Vincenzo Nicolò &amp; Daniel Ross (eds). 2022. Pseudo-Coordination and Multiple Agreement Constructions. John Benjamins

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Exploring Bitslicing Architectures for Enabling FHE-Assisted Machine Learning

Homomorphic encryption (HE) is the ultimate tool for performing secure computations even in untrusted environments. Application of HE for deep learning (DL) inference is an active area of research, given the fact that DL models are often deployed in untrusted environments (e.g., third-party servers) yet inferring on private data. However, existing HE libraries [somewhat (SWHE), leveled (LHE) or fully homomorphic (FHE)] suffer from extensive computational and memory overhead. Few performance optimized high-speed homomorphic libraries are either suffering from certain approximation issues leading to decryption errors or proven to be insecure according to recent published attacks. In this article, we propose architectural tricks to achieve performance speedup for encrypted DL inference developed with exact HE schemes without any approximation or decryption error in homomorphic computations. The main idea is to apply quantization and suitable data packing in the form of bitslicing to reduce the costly noise handling operation, Bootstrapping while achieving a functionally correct and highly parallel DL pipeline with a moderate memory footprint. Experimental evaluation on the MNIST dataset shows a significant ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$37\times$ </tex-math></inline-formula> ) speedup over the nonbitsliced versions of the same architecture. Low memory bandwidths (700 MB) of our design pipelines further highlight their promise toward scaling over larger gamut of Edge-AI analytics use cases.
The Importance of a Usage-Centric Approach

Karl Wiegers, principal consultant with Process Impact and author of 13 books, spoke with host Gavin Henry about common problems including technical debt; staff scaling; iron triangles; changes over the past 50 years; requirements elicitation with use cases; design iteration and abstraction; prototyping; modeling; project management; negotiating around constraints; product scopes; schedules, budgets, and staffing; product quality; teamwork and culture; defining quality; process improvement; and self-learning. This discussion was about some of the topics that I address in Software Development Pearls. This article provide summary excerpts; to hear the full interview, visit http://www.se-radio.net or access our archives via RSS at http://feeds.feedburner.com/se-radio. Robert Blumen
Explainable Artificial-Intelligence-Based Privacy Preservation Approach for Information Dissemination on Social Networks: An Incremental Technique

This article aims to address the issues above by defining a social network information transmission model with the amalgamation of explainable artificial intelligence (XAI) compatible with the paranormal connection. It suggests a way of information transmission called local greedy that aids in the preservation of user privacy. Its impact acts as a buffer between the conflicting interests of privacy protection and information dissemination. Aiming at the enumeration problem of seed set selection, an incremental technique is presented for constructing seed sets to minimize time overhead; a local influence subgraph method for computing nodes is also proposed to evaluate the influence of seed set propagation rapidly. The group meets privacy protection conditions. A strategy is presented to determine the upper bound on the likelihood of a node leaking state without resorting to the time-consuming Monte Carlo approach with XAI on the crawled Sina Weibo dataset. The suggested technique is validated experimentally and by example analysis, and the findings demonstrate its usefulness.
Scaling up public blockchains

Blockchain technology has emerged as the means to change the structure of the Internet and build more trustful and secure services. While deploying an application to a blockchain network brings significant development overhead, the level of decentralization and security offered by permissionless blockchain systems has triggered a huge interest in the technology and a high demand for using existing networks. This increased demand has highlighted the most significant problem of public blockchain systems, scalability in terms of the transaction processing rate. The first generation of blockchain systems is characterized by very low transaction processing rates that, in combination with high usage demand, trigger high latency and high usage fees.
Patent Litigation in the Games Space

I am not an attorney but a professor of engineering practice who has a large amount of experience in the patent litigation space as an expert witness in the game and computing industry domains. This column covers my understanding of patents and some of my experiences as an expert witness.
Distance Vocational Education and Training in the Pandemic

The COVID-19 pandemic has brought unprecedented challenges to all types and levels of learning, including vocational education and training. The chapter attempts to provide a preliminary analysis regarding the transition to ex distance education as a consequence of the closure of schools. Based on inputs from 14 informants, members of the Cedefop Ambassadors network for addressing early withdrawal from vocational education and training, covering five Member States and two non-EU countries, the work sheds light on aspects of this in order to highlight the challenges that trainees face in risk of early departure, but also their trainers, as well as the measures taken adopted for their support. Cedefop published a study in English, which the authors translated and summarized in the present work. The study shows that there is no digital integration without social integration. Vulnerable learners are less likely to participate in distance education, while their detachment from the learning process can lead to their early withdrawal from vocational education and training.