Reviewerly/exHarmony huggingface dataset:
Transitioning Broadcast to Cloud

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">We analyze the differences between on-premise broadcast and cloud-based online video delivery workflows and identify means needed for bridging the gaps between them. Specifically, we note differences in the ingest protocols, media formats, signal-processing chains, codec constraints, metadata, transport formats, delays, and means for implementing operations such as ad-splicing, redundancy, and synchronization. To bridge the gaps, we suggest specific improvements in cloud ingest, signal processing, and transcoding stacks. Cloud playout is also identified as a critically needed technology for convergence. Finally, based on all such considerations, we offer sketches of several possible hybrid architectures, with varying degrees of offloading of processing in the cloud, which are likely to emerge in the future</i> .
Financial Innovation and Technology after COVID-19: a few Directions for Policy Makers and Regulators in the View of Old and New Disruptors

Innovation and technology have led to the redefinition of business models and development of new ones in many bricks and mortar sectors. Similarly, blockchain and fintech have impacted the finance and banking industries and are expected to further affect them in the future, leading some media to coin the expression “Uberization of banking”. The authors extrapolate from sharing economy models to conclude that while blockchain and fintech are poised to advance finance and banking, there are no disruptive features that corroborate the term. By analogy and successive approximations, this article identifies the limitations of the arguments for disruption in finance and banking. Besides, hinging upon stylized facts, the article establishes similarities with sharing economy models to identify potential threats stemming from financial innovations such as Tokenomics, tagged as “no-ABSs”. Eventually, the authors identify entry points and ways forward arising from the COVID-19 pandemic for policy makers and regulators to regain their pivotal role in policing the market and ensuring transparency while driving innovation.
Collusion By Blockchain And Smart Contracts

Blockchain may transform transactions the same way the Internet altered the dissemination and nature of information. If that were to be the case, all relationships between companies would change, including prohibited ones such as collusive agreements. For that reason, the stakes are crucial and the absence of academic studies entirely dedicated to this issue must be remedied.To this end, this article introduces the first taxonomy of collusion on blockchain. The discussion then moves on to explore their functioning, their robustness and their limits through the three fundamental stages of the existence of collusive agreements: their birth, life and death. The article further highlights how companies may use smart contracts and sophisticated algorithms to collude in the blockchain environment, thus contributing to the literature solely focused on algorithms.Using empirical studies, economic analyses and existing case law, we draw legal conclusions that we extend beyond the sole blockchain technology. Along the way, we propose methods of action for antitrust and competition agencies.
School–Family Relations: An Educational Challenge in Times of COVID-19

The COVID-19 pandemic has widened the gap regarding access to educational opportunities, which was included in the Millennium Development Goals (MDGs). This descriptive, quantitative study aims to examine the communication strategies employed by secondary schools in Spain during the lockdown, as well as to analyse the co-responsibility of the educational process between schools and families. An ad hoc questionnaire (GIESBAFCOV-19) was designed and implemented to gather information. The results show that, in most cases, mothers were responsible for assisting and supervising their children's homework as persons in charge of education-related matters. Additionally, before the lockdown was put in place, about half of the participating families received information from the educative centres regarding the disease and sanitary measures. Once the lockdown took place, families put the focus on their children's schoolwork, not without difficulties in academic and digital literacy. In general, the families were satisfied with the communication established with the educational centres. The present study has raised the necessity to improve communication between centres and families and to reflect on the tools and systems used for its exchange. Consequently, it seems that information and digital competences should be promoted to guarantee an equalitarian education for all.
Collaborating with Language Community Members to Enrich Ethnographic Descriptions in a Language Archive

Language archives connect users such as language communities, linguists, and other researchers, to language data. As the language archiving community develops, concerns have been raised about the ethics, ownership, accessibility, and context of archival materials. While there are no simple solutions to these questions, many language archives are seeking ways to involve language community members in these conversations as they continue. This presentation describes a pilot project undertaken at the Computational Resource for South Asian Languages (CoRSAL) which explores a collaborative archiving approach to enable language community members to tell their own stories by adding contextual information to archival materials.
Innovation and Development of University Education Management Informationization in the Environment of Wireless Communication and Big Data

The development of information technology has spawned a new type of demand and management analysis model of big data. The value of data is vividly reflected in the contemporary era, and all walks of life have devoted great energy to the construction and use of informatization. The object of college education and management is the group of college students who are most closely connected with contemporary trends. Their thoughts are active, their state is changeable, and they are very sensitive to external stimuli. Such an educational situation has greatly increased the difficulty of educational management in colleges and universities. This article takes the big data research as the breakthrough point, focuses on the promotion and innovation of contemporary college education management informatization, and strives to improve the college education management informatization system by solving existing problems. The innovation of this research is to use big data technology as the starting point for improving the current stage of the education informatization work of colleges and universities, through the combination of data analysis and the latter, to realize the comprehensive informatization innovation of education management. The study used questionnaire surveys to investigate and interview some students and related managers in 5 universities in the province and conducted an objective assessment of the construction of educational management informatization in these universities and found that these five universities have common problems in this field. Research experiments have found that among the five representative universities in the province, the service awareness of education administrators is not above the fifth level. This shows that in the big data environment, the quality of education administrators cannot be ignored.
Sentiment Analysis of Covid-19 Tweets by using LSTM Learning Model

Social media plays an important role in our lives due to the conditions of the age we live. Nowadays, the most popular social media platform that prioritizes meaningful content sharing is Twitter. In Twitter, which produces big data on an unprecedented scale, users have the opportunity to share their own perspectives, feelings, and experiences, as well as examine the opinions of other individuals. The Coronavirus-2019 (Covid-19) disease, transmitted through close contact and small droplets spread by people coughing, sneezing, or speaking, has created social and economic wounds worldwide. As of July 7, 2021, more than 185 million people worldwide have been diagnosed with the New Coronavirus (Covid-19), and approximately 4 million people have died from this infectious disease. This work focuses on the analysis of the sentiments that Covid-19 leaves on people, using the tweets that people share about the Covid-19 pandemic on the Twitter platform. Analyzes are based on deep learning algorithms. Sentiment analysis can provide serious benefits. In this study, we used a Long-short Term Memory (LSTM) based network model. Also, we compared the proposed model other machine learning algorithms: Support Vector Machine (SVM), Naïve Bayes and Logistic Regression. Experimental results show that our proposed method can effectively perform sentiment analysis on the Twitter dataset.
Making time/breaking time: critical literacy and politics of time in data visualisation

Purpose Representations of time are commonly used to construct narratives in visualisations of data. However, since time is a value-laden concept, and no representation can provide a full, objective account of “temporal reality”, they are also biased and political: reproducing and reinforcing certain views and values at the expense of alternative ones. This conceptual paper aims to explore expressions of temporal bias and politics in data visualisation, along with possibly mitigating user approaches and design strategies. Design/methodology/approach This study presents a theoretical framework rooted in a sociotechnical view of representations as biased and political, combined with perspectives from critical literacy, radical literacy and critical design. The framework provides a basis for discussion of various types and effects of temporal bias in visualisation. Empirical examples from previous research and public resources illustrate the arguments. Findings Four types of political effects of temporal bias in visualisations are presented, expressed as limitation of view, disregard of variation, oppression of social groups and misrepresentation of topic and suggest that appropriate critical and radical literacy approaches require users and designers to critique, contextualise, counter and cross beyond expressions of the same. Supporting critical design strategies involve the inclusion of multiple datasets and representations; broad access to flexible tools; and inclusive participation of marginalised groups. Originality/value The paper draws attention to a vital, yet little researched problem of temporal representation in visualisations of data. It offers a pioneering bridging of critical literacy, radical literacy and critical design and emphasises mutual rather than contradictory interests of the empirical sciences and humanities.
Burdens of Persuasion and Standards of Proof in Structured Argumentation

In this paper we provide an account of the burden of persuasion, in the context of structured argumentation. First, burdens of proof in legal proceedings are discussed in general, distinguishing the burdens of production and the burdens of persuasion. Then, we focus on burdens of persuasion, illustrating their role in civil and criminal law.A formal model for the burden of persuasion is then defined, discussed, and used to capture the role of the burden of persuasion in adjudicating conflicts between conflicting arguments and in determining the dialectical status of arguments. We consider how our model can also capture adversarial burdens of proof, namely, those cases in which failure to establish an argument for a proposition burdened with persuasion entails establishing the complementary proposition.Finally, we examine how burdens of proofs can be integrated with standards of proof defining the extent to which an argument for a proposition burdened with persuasion has to be stronger than arguments to the contrary, in order to meet the burden.
Blockchain Based Intelligent Transport System

Blockchain technology is widely studied in these days and has vital role in the ITS and Vehicular network. Intelligent Transport System (ITS) have resolved several issues of transportation like congestion, electronic toll collection, traffic light cameras, traffic updates, and environment forecasting. The vehicular network is the ever-increasing network it is not only facilitates us but also brings new challenges with it. The mobile nature of vehicular networks it is very important to collect and broadcast information of traffic events in real-time. A little delay to broadcast important information or deciding on this information can cause a serious situation in the mobile vehicular network. Moreover, malicious vehicles in the network broadcasting false information about these traffic events cause a disturbance in the network. In large-scale scenarios, the transmission of malicious messages offers a lot of danger to the system. They can wrongly claim the roads and provide false information about the incident. These traffic events can be life-threatening and cause unwanted situations like accidents, wastage of time and other resources. Therefore, it is very much important to provide real-time information on recent traffic events and real-time authentication of vehicles that broadcast information in the network. Traditional studies are unable to solve these security issues and contain a single point of failure issue. These studies are centralized and dependent on a single higher authority. Moreover, they have serious security concerns that are harmful for vehicular network. Moreover, any vehicles are unwilling to share their private information while broadcasting information about traffic events because they are strangers to each other. And if a vehicle does not want to share its private information like name, id, etc. It is not possible to authenticate this vehicle and manage trust in the network. It means that it is very crucial to prevent vehicles to broadcast wrong information in the network while preserving their privacy at the same time. Therefore, there is a need to authenticate vehicles and manage trust in the network while preserving their privacy simultaneously. Blockchain can offer better solution to solve these issues due to its secure distributed environment and features that ensure immutability about actions. The purpose of this report is to provide real-time security and privacy in the network. It is also ensured that vehicles get real-time authenticated information about traffic incidents from legitimate vehicles while simultaneously preserving their privacy. It means that only authenticated and legitimate entities (vehicles) can participate in vehicular network and privacy of both sender and receiver is secured in the network. Details of conducted experiments are given, and shreds of evidence are provided to evaluate the performance of architectures for authentication and trust management. The shreds of evidence show that these blockchain-based systems can solve security and trust issues more effectively.
A Review on Smart Materials, Types and Modelling: Need of the Modern Era

The need for usage of smart materials/structures in modern perspective in industries and societal applications at large, cannot be overemphasized as with regard to the propertied i.e. durability, cost reductions and performance consistency. Smart materials are now days being used in all spheres of human life and technology. Smart materials also termed as intelligent materials or active materials include matrix of material systems possessing predominantly a set of properties as per their functional requirements. Some of the examples of smart materials are: Piezoelectric materials, Shape memory alloys, Magnetostrictive materials, Electro-rheological materials, Magneto-rheological fluids, electro- and magneto theological fluids, Thermoresponsive and Electrochromic material. This paper highlights the different types of smart materials and their applications.
A SURVEY ON DIFFERENT ENCRYPTION TECHNIQUES FOR IMAGE, VIDEO, AUDIO AND DOCS

Encryption and Decryption of different files by various cryptography methods has been studied by the R&amp;D community for years. Cryptography methodology has advanced considerably throughout the years, and plenty of encrypting techniques are currently offered to be used as per the necessity. Also, there has recent development in these techniques to satisfy the ever increasing demand of providing higher and economical security of the info. Having such a big amount of cryptography ways makes it very exhausting to settle on one. Though selecting the correct encryption methodology as per the necessity is incredibly necessary. In this paper, we have analysed and studied different encryption methods like AES, DES, RSA, Blowfish and Twofish for different types of files like Docs, PDFs, Images, Audio, and Video. The end result is concluded based on the different performance parameters and a brief and clear conclusion is made for each method.
An Improved PixelHop Framework and its Application in Rolling Bearing Fault Diagnosis

The PixelHop framework based on successive subspace learning (SSL) has been widely used in signal processing and computer vision, which can effectively improve the classification accuracy in high spatial resolution scenes through successive subspace growth.To solve the problems of insufficient feature extraction and dependence on prior knowledge in the PixelHop framework, an improved PixelHop (I-PixelHop) framework is proposed.On the basis of PixelHop framework, I-PixelHop has made the following improvements.1) I-PixelHop fully extracts the continuous features in one-dimensional sequence data through the improved neighborhood expansion, which can provide a richer feature set.2) The improved label-assisted regression (ILAG) unit uses the Bi-K-Means clustering algorithm to enable more correct clustering of similar samples, and it adopts the cross-entropy threshold method to alleviate the negative effects caused by the improper setting of the number of pseudo-classes.3) The high-dimensional features are fully reused by adopting the pseudo dense connection structure to obtain a better feature set.Moreover, the proposed I-PixelHop framework is applied to the rolling bearing fault diagnosis.A series of experiments are carried out to verify the effectiveness of the proposed I-PixelHop.The experimental results show that the fault diagnosis accuracy of I-PixelHop can reach 98.91% and 98.74% on the two different rolling bearing fault datasets, and it also has satisfactory anti-noise ability, faster training speed, and smaller model size.
Recursive Gauss-Helmert model with equality constraints applied to the efficient system calibration of a 3D laser scanner

Abstract Sensors for environmental perception are nowadays applied in numerous vehicles and are expected to be used in even higher quantities for future autonomous driving. This leads to an increasing amount of observation data that must be processed reliably and accurately very quickly. For this purpose, recursive approaches are particularly suitable in terms of their efficiency when powerful CPUs and GPUs are uneconomical, too large, or too heavy for certain applications. If explicit functional relationships between the available observations and the requested parameters are used to process and adjust the observation data, complementary approaches exist. The situation is different for implicit relationships, which could not be considered recursively for a long time but only in the context of batch adjustments. In this contribution, a recursive Gauss-Helmert model is presented that can handle explicit and implicit equations and thus allows high flexibility. This recursive estimator is based on a Kalman filter for implicit measurement equations, which has already been used for georeferencing kinematic multi-sensor systems (MSS) in urban environments. Furthermore, different methods for introducing additional information using constraints and the resulting added value are shown. Practical application of the methodology is given by an example for the calibration of a laser scanner for a MSS.
SASTRA ANAK SEBAGAI MEDIA PENGENALAN NILAI SOSIAL DI MASA PANDEMI

Abstract&#x0D; Children's literature is literature that is made by children or adults and is aimed at children. The use of light sentences in children's literature makes it easier for children's literature to be absorbed and accepted by children. In children's literature there are also many social values ​​such as cooperation, love to help, love, like to pray for others, and say the words please and thank you. Children's literature can be used as a medium for the introduction of social values ​​because apart from being easy to convey, children's literature is very interesting to discuss with children. Social values ​​in children's literature can be used as learning and later applied by children in their lives. This article aims to introduce social values ​​through the medium of children's literature.
Sentence-Permuted Paragraph Generation.

Generating paragraphs of diverse contents is important in many applications. Existing generation models produce similar contents from homogenized contexts due to the fixed left-to-right sentence order. Our idea is permuting the sentence orders to improve the content diversity of multi-sentence paragraph. We propose a novel framework PermGen whose objective is to maximize the expected log-likelihood of output paragraph distributions with respect to all possible sentence orders. PermGen uses hierarchical positional embedding and designs new procedures for training, and decoding in the sentence-permuted generation. Experiments on three paragraph generation benchmarks demonstrate PermGen generates more diverse outputs with a higher quality than existing models.
Choices and their Consequences - Explaining Acceptable Sets in Abstract Argumentation Frameworks

We develop a notion of explanations for acceptance of arguments in an abstract argumentation framework. To this end we show that extensions returned by Dung's standard semantics can be decomposed into i) non-deterministic choices made on even cycles of the given argumentation graph and then ii) deterministic iteration of the so-called characteristic function. Naturally, the choice made in i) can be viewed as an explanation for the corresponding extension and thus the arguments it contains. We proceed to propose desirable criteria a reasonable notion of an explanation should satisfy. We present an exhaustive study of the newly introduced notion w.r.t. these criteria. Finally some interesting decision problems arise from our analysis and we examine their computational complexity, obtaining some surprising tractability results.
ISWR: An Implicit Sentiment Words Recognition Model Based on Sentiment Propagation

Considering that there have no efficient methods to detect implicit sentiment words, this paper proposes an implicit sentiment words recognition model based on sentiment propagation to automatically identify implicit sentiment words. In the model, we create part-of-speech trees based on Weibo texts. Based on these part-of-speech trees, we define some rules to generate word graphs. By borrowing the PolarityRank algorithm and SIS (Susceptible-Infected-Susceptible) epidemic model, we propose formulas of the sentiment propagation and non-propagation change rates for a word. Then we propagate the sentiment between the words in these word graphs to get the sentiment values of words. After normalizing the sentiment value of words, we get the implicit sentiment words. By some parameter sensitivity experiments, the optimal model parameters are chosen. Compared with SO-PMI, PolarityRank and PageRank models, the accuracy and micro-F1 of the proposed model are significantly better than the results of the three models for implicit sentiment words recognition.