Reviewerly/exHarmony huggingface dataset:
SIXTH ALL-RUSSIAN ECONOMIC DICTATION. ENLIGHTENMENT TRADITIONS AND MODERN EDUCATIONAL TECHNOLOGIES

The review discusses the preliminary results of the all-Russian campaign (Economic Dictation) held at the site of the Faculty of Economics of the Russian State University for the Humanities (RGGU) to improve the economic literacy of the population and aimed at popularizing economic knowledge. The interpretation and coverage of the event is made also in a historical context, what allows to identify the role of traditions in modern educational technologies and productively use an interdisciplinary approach in their study
JPEG XS Standard (ISO/IEC 21122) for Lightweight, Low-Latency Image Coding

At its 94th meeting, ISO/IEC SC29WG1 (aka. JPEG committee) decided to create a third edition of the JPEG XS standard (ISO/IEC 21122) for lightweight, low-latency image coding. While existing coding tools already support the compression of natural content and CFA RAW Bayer pattern data well, this third edition will extend the previous editions with coding tools that improve its performance on compression of screen content and static background sequences. — First experiments demonstrate that the proposed coding tools can improve the average PSNR performance of JPEG XS on such sequences more than 10dB, and approximately 3dB on computer generated content. In this work, authors will present the proposed coding tools and will evaluate their performance objectively by PSNR.
Self-knowledge and ways of its improvement amongst university learners

In the prevailing era, to succeed in the professional area, an educator, in addition to a piece of particular knowledge should excel at the capacity to organize, examine and curb their actions separately, establish themselves extra educational assignments and resolve them, be keen on self-growth and able to boost it. The presented article is aimed at the improvement of self-knowledge amongst the students of pedagogical specialties. Considering the studies of the researchers of Russian, the writers of the survey illuminated the kinds of self-knowledge, and relevant kinds of educational assignments that are utilized in the expert preparation of prospective teachers. The complex comprises the subsequent parts: creating a positive, passionate atmosphere throughout training sessions with students; the application of reflexive pedagogical technologies in the process of education at the university, contributing to the improvement of self-knowledge between prospective educators and enhancing the status of their education.
Content Personalization; a Future Vision

Content personalization has the potential to generate compelling versions of documentaries, dramas, sports recaps, news summaries, movies, and advertisements that dynamically match each viewer's interests - the extreme relevance increasing audience engagement. Additionally, rich viewer-controlled personal preference data in turn increases the value of subscription and advertising revenue streams. — However, there is a growing and competing tension between content distributors' desire to serve precise and meaningful recommendations for viewers and viewers' willingness to allow access to their personal data, especially to unknown or untrusted organizations. The emerging need for content providers is to have a more comprehensive understanding of audience preferences and for viewers the ability to carry their preferences across all content providers. To address these needs, a secure privacy-assured mechanism to support rapid transparent use of data would address these competing tensions. — Today, neither a technical solution nor protocol standard exist to support this vision. The purpose of this paper is to explore the potential of a data rich, viewer controlled content personalization ecosystem.
Secure Collaboration in a Low-Trust Environment: Immutable Chain of Custody

Protecting against spoilers and theft of intellectual property isn't just the job of Security. For file-based workflows, the normal assumption (by outsiders) is that digital assets are just data, and handling is like traditional corporate IT, and Security mostly guarding infrastructure. The biggest challenge has been trying to regulate changes in every environment. Clearly, that effort is not sustainable or perhaps even achievable. Collaboration happens now in real-time, often with players in unknown locations. In the “content creation” world, it's about establishing tailored collaboration, and protecting the conditions of access. — What if data could protect itself, regardless of storage environment? Why can't we create and protect content using a zero-trust model? This paper reviews how the industry can create a content package of value with encryption and access security, transfer package to a collaboration space, track every access and contribution externally, maintain accountability through wide publication of activity and then transfer enhanced value back to content owner. — Learn how this low-trust model can free us from perpetual facility audits.
How Do We Make Media in the Cloud as Easy as on Prem?

The shift of virtually every aspect of media to the cloud is in progress. Many extoll the wide-ranging benefits of this move, from increased agility, efficiency, freeing up physical space, switching from CapEx to OpEx, de-focusing staff on data center management, and the list goes on. While there are certainly potential benefits of such a move, they often come at a cost. Remember the old term “plug and play”? In the good old days of connecting physical equipment, as long as you had the right boxes, cables and connectors, you often could just plug equipment together, and be up and running in no time. Unfortunately, it's not quite so simple when trying to connect media products in the cloud. — In general, the world of software is far more flexible than the world of hardware, and this is where the real “gotchas” are. The places where integration in the cloud can be tripped up are seemingly endless. But does it really need to be so complex? Why can't we just connect up products from multiple vendors, across platforms, and have them “just work”? Who's working on making this a reality? The good news is that there are standardized solutions to these issues emerging. We'll dive into what they are, and how they realize the promise of making connecting media products in the cloud as easy as doing it on prem.
GAME SEJARAH UMRI SEBAGAI MEDIA PENGENALAN UMRI BERBASIS ANDROID

Muhammadiyah Riau University (UMRI) is one of private University on Riau – Pekanbaru. A University which is one of the business charities of Muhammadiyah Indonesia. To maintain the historical value of UMRI requires an interesting media to keep Umri citizen in particular keep in mind the history of UMRI development. for that made an interactive game that is able to tell the history of development of UMRI. In the development of this game used MDLC as a method in the development of interactive games. For the development of this game is used Unity as Game editor. With this game the history of UMRI development can be known by the public.
How Much to Invest, If Any, in Bitcoin?

With trust in financial institutions compromised by the turmoil of the global financial crisis of 2008–2009, Satoshi Nakamoto, a pseudonym for a person or a group, created Bitcoin on January 3, 2009, begetting the cryptocurrency movement. A medium of exchange not pegged to any financial intermediary or controlled by any organization, it relies on “blockchain” technology that resembles multilateral contractual obligations among its cooperative participants. The outcome has been a peer-to-peer network of transaction activity that, in turn, has fostered over 18,000 currently circulating cryptocurrencies. As the most widely used cryptocurrency, at 19 million units, Bitcoin comprises over 40% of the market.
Effects of democracy and natural resources on foreign direct investment in African natural resource-rich countries

This article aims to investigate the effects of democracy and natural resources on foreign direct investment (FDI) in African countries rich in natural resources. To this end, an econometric model was estimated using three methods, namely, fixed effects with sample heteroscedasticity correction, the instrumental variables method and the general method of moments, on a panel of 22 countries over the period 2000-2017. On the one hand, the results show that democracy has a positive effect on FDI. On the other hand, the analysis showed that natural resources do not help to attract FDI. Our findings suggest that concrete actions are needed in terms of strengthening and improving governance and democratic institutions to better attract FDI in African countries rich in natural resources.
New angular momentum state via the bosonic operator realization and its nonclassical property

Abstract We theoretically introduce a new angular momentum state via the bosonic operator realization of angular momentum operators on a number state, and study its nonclassicality based on the sub-Poissonian distribution, photon number distribution, entanglement entropy and Wigner distribution. The results show that the nonclassicality of the new state for odd q is more stronger than that for even q , and the nonclassicality for any q always enhances first and then weakens with increasing g . Besides, the entanglement always increases with the increase of q for all of g , and finally reaches a maximum when g and h are in certain value ranges and q is large enough.
Distances

Abstract Concepts of distance capture important musical intuitions in many spaces, intuitions not adequately captured by interval functions. This chapter describes distance functions and distance spaces, known to mathematicians as metrics and metric spaces. Some distance functions, especially those in real and modular distance spaces, are closely related to interval functions, but others are not. Among the concepts studied are isometries (distance-preserving mappings), distances derived from graphs or groups, relationships between distances and path lengths, and products and quotients of distance spaces. Several kinds of distance functions may be defined in OPTIC spaces, including the max distance, the Euclidean distance, and the voice-leading distance. As is often the case, spaces involving cardinality equivalence introduce complexities that do not arise in other spaces.
Hate Speech Detection in Social Media (Twitter) Using Neural Network

Hate speech recently became a real threat in social media, and almost all social media users are intended to in different ways. Hate speech is not limited to a group or society. It affects many people and can be classified as abusive, offensive, sexism, racism, political affiliation, religious hate, nationality, skin color, disability, gender-based, ethnicity, sexual orientation, immigrants, and others. Many researchers and authorities attempt to discover new procedures to sense hate speech in social media, especially on Facebook and Twitter, and many methods, models, and algorithms are used for this purpose. One of the most valuable models for detecting hate speech is Convolutional Neural Network (CNN). This review aims to assort academic studies on hate speech detection in Twitter using CNN-based models summarize the results of each model to expand the understanding of the recent circumstances of hate speech detection in Twitter. For this purpose, we implemented a broad, automated search using Boolean and Snowballing searching methods to find academic works in this area. Studies and papers have been distinguished, and the following information was obtained and aggregated from each article: authors, publication’s year, the journal name or the conference name, proposed model/method, the aim of the study, the outcome, and the quality of each study. According to the findings, the CNN and CNN-based models are standard models for hate speech detection. Besides, the findings show that other new models have a great compact on hate speech detection, and there is good progress in this field. However, the problems that still exist with hate speech detection models mainly are; most of the models cannot detect hate speech automatically. The methods are not suitable with all the languages, and they are working only with one language; most are best suited with the English language, and when they are used with datasets with other languages. Besides, the models are suffering from confusion in speech classification. Finally, most models are not considering a user-to-user speech in social media.
Marine expertise management services in big data environment

This paper systematically describes the current situation of marine information resources management, analyzing the existing problems in the construction of marine information system. And it introduces the possibility of classification, standardization and integration of marine information resources based on knowledge service, and the construction of marine professional knowledge service framework based on big data. By studying the new knowledge service mode, it provides technical support for giving full play to the advantage of marine professional information resources.
Weak Supervision and Transformed-based Sentiment Analysis on Multi-lingual Data

Sentiment analysis on social media is an important research field with applications in many domains. This can be leveraged to predict unrest situations, mob violence, etc., on the occurrence of an event concerning national security. The paper aims to develop an automated intelligent framework to detect the sentiments of users in a multilingual and geospatial manner. This paper covers an important case study to obtain a comprehensive dataset in three languages, i.e., English, Hindi, and Urdu, from three regions. An automated approach for data annotation using weak supervision is presented to highlight the challenge of obtaining training data. We propose a BERT-based transformer model to effectively identify the sentiment considering the context. The study also presents an interactive visualization of results to obtain valuable insights.
Radian: Paperless Academic Testimonials Enabled for Long-Term Validation

Portable Document Format (PDF) are widely used throughout the world for exchanging documents. The main reason of their popularity is that this format maintains the structure of the original document without taking into consideration the software that was used to draft the document. PDFs also provide authenticity and integrity of documents by using and verifying digital signatures. Since this all happens digitally the security notion of the verification has to be stronger than in analog world. Nowadays digital certificates and digital contracts have equivalent importance as that of physical certificates and contracts. Digital signatures using cryptographic algorithms provide a way to verify a PDF's authenticity and integrity. We show how Public-Key Infrastructure (PKI) can be leveraged to seamlessly deploy a solution that can revolutionize the way academic testimonials are distributed [4]. The solution not only uses digital signatures using PKCS11 hardware tokens to provide authenticity to documents but also uses an interesting feature in PDF ISO standard [1] that allows embedding of revocation information in the document itself for allowing Long-Term Validation (LTV). All this is combined to develop Radian, a software conceived and designed in IIT Bhilai to provide digitally signed academic testimonials. These testimonials hold an immediate advantage over physical counterparts as they are tamper proof and self-contained in terms of their verification. Finally, this work showcases an interesting combination of PKI mechanism with advances in PDF technology to present a high impact use-case that can change the way academic testimonials will be issued in the future [4].
Bayesian Regression for Interpretable Network Dimensioning

This paper proposes a solution to learn interpretable regression models for the Telecommunications industry's network dimensioning problem. Domain knowledge in the form of expected model coefficients and desirable trends among them are incorporated into the modeling process, using an approach based on Bayesian Regression. This not only enables intuitive models for domain experts but simultaneously addresses known multicollinearity issues in the underlying data-set. The models thus trained are nearly as-performant as the best theoretical model for the data-set. The proposed solution has been deployed in production. Learnings from this paper are transferable across Telecommunications problem-contexts and application domains.
enVolve<sup>+</sup>: Inertial Sensing to Reinforce Involvement of Silent Listeners during an Online Interaction

Learning how engaged a remote participant is in an online meeting can lead to more productive discussions. If online meeting attendees turn off their mics and cameras, the presenter can't tell if they're paying attention. The research in [1] shows how earables could help remedy the issue at hand. While the prior study focused on offering speakers feedback of the classroom, the same results may be utilized to help students with their attention spans and bring back their attention to the screen during a lecture. Therefore, the proposed study adds a listener interface to previous work. The premise is, that if listeners start to doze off or drift away in an interaction, vibrations from the smartwatch might wake them up. Wearable IMU data from an in-lab user study was utilized to train machine learning models that obtained an F1 Score of 80% or above across the board. Additionally, we give persuasive evidence for the adoption of earables over other sensory modalities by analyzing experimental findings, user analysis, and current research needs in this field.
LiteEx: A Lightweight Feature Extraction Tool for Captured Network Traces

Network security researcher community widely uses network traffic analysis for various activities such as threat detection, threat prevention and security monitoring. Moreover, machine learning is widely used nowadays in various applications of network security. Therefore, it is important to extract features from network traffic traces which are then fed as input to machine learning algorithms to train the models and further these models are used for aimed specific tasks. However, there is a scarcity of freely available tools that can extract a wide variety of features from network packets and network flows without computationally overburdening the systems. To address this issue, we propose LiteEx which is a lightweight, flexible and GUI based feature extraction tool that can currently extract 68 packet-level features as well as 11 flow-level features from network traffic traces with low RAM and CPU utilization. We tested the performance of LiteEx on publicly available datasets having network traffic traces of PC, Smartphones, IoT devices etc. We tested the performance of LiteEx on two different machines in which one is high configuration and the other is a low configuration machine. After experiments, we found that LiteEx successfully extracted all kinds of features on 13.67 million packets with maximum CPU as 94.124% and maximum RAM utilization as just 8.775%.
Performance Measurement of Distributed Storage on Edge Devices

Edge computing and storage have been proposed as an alternative to current cloud computing infrastructures to address the latter's limitations. However, edge computing services and support for applications based on edge infrastructures are still in the incipient stages. There are numerous storage services for efficient data storage in the cloud. In contrast, few such services exist at the edge. We look at the performance of a cloud storage service Cassandra when deployed on an edge network. In this paper, we analyze Cassandra's performance in three different networks. We vary Cassandra's attributes and the overall conditions in each network to simulate a real-world scenario and study the effect on the total time taken for a particular request. We (a) establish trends for different configurations, (b) discuss the reasoning behind them, and (c) suggest optimal configurations for the networks used, which are important for deploying Cassandra in an edge setting.
Secure Onboarding and Management of Electronic Shelf Labels in Retail

Electronic shelf labels (ESLs) are seeing increased adoption in consumer retail, due to their many operational advantages and potential to enhance the shopping experience. However, ESLs can be vulnerable to cyberattacks that could severely disrupt many basic activities related to administering shops such as daily pricing updates, inventory tracking, promotions, and possible legal compliance with respect to in-store/online prices policies. More importantly, security vulnerabilities can be exploited to potentially inflict financial harm on shoppers who take advantage of pay-and-go services enabled by ESLs. In this work, ESL vulnerabilities are discussed along with the necessary capabilities that should be included into these devices as countermeasures.
EELAS: Energy Efficient and Latency Aware Scheduling of Cloud-Native ML Workloads

The widespread use of microservices and the use of cloud-native methodologies for the deployment of services have increased the service providers' flexibility and management efficiency. As the available resources for scheduling such work-loads have extended the boundaries of traditional datacenters to the fog, edge, and beyond -edge, the scheduling of challenging workloads must also account for energy efficiency, as these devices are typically battery-powered and resource-constrained, while maintaining acceptable performance. Specifically for ML inference workloads, provisioning and access latency plays a cru-cial role in their successful operation. Towards combating these issues, we design, develop, and evaluate our platform for Energy Efficient Latency-Aware Scheduling (EELAS) of workloads. First, we formulate the scheduling problem as an ILP problem, and then we develop a less complex heuristic method that allows the efficient allocation of resources within the continuum. Our EELAS prototype integrates with Kubernetes and is capable of reducing the overall energy consumption of cloud-to-things resources while accounting for latency of ML workloads. Our evaluation in real-world settings reveals significant energy gains for scheduling ML inference tasks, also reachable with the minimum possible latency from far-edge devices.
State of Internet Privacy and Tales of ECH-TLS

Security and Privacy are crucial in modern Internet services. Transport Layer Security (TLS) has largely addressed the issue of security. However, information about the type of service being accessed is transmitted in the initial handshakes of vanilla TLS, in plain-text mode, thus potentially revealing the activity of users and compromising privacy. The “Encrypted ClientHello” or ECH overcomes this issue by extending TLS 1.3 where all of the information that can potentially reveal the service type is masked, thus addressing the privacy issues in TLS 1.3. However, we notice that Internet services tend to use different versions of TLS for application data (primary connection/channel) and supporting data (side channels) such as scheduling information etc.. Although many internet services have migrated to TLS 1.3, we notice that it is only true for the primary connections which do benefit from TLS 1.3, while the side-channels continue to use lower version of TLS (e.g., 1.2) and continue to leak type of service accessed. We demonstrate that privacy information leaked from the side-channels can be used to affect the performance on the primary channels, like blocking or throttling specific service on the internet. Our work demonstrates that adapting ECH on primary channels alone is not sufficient to prevent the privacy leaks and attacks on primary channels. Further, we demonstrate that it is necessary for all of the associated side-channels also to migrate to TLS 1.3 and adapt ECH extension in order to offer complete privacy preservation.