Reviewerly/exHarmony huggingface dataset:
Social robots with gamification principles to increase long-term user interaction, engagement and retention

Social robots hold significant promise to help individuals achieve their goals. Most individuals can benefit from a social robot when they interact with it over multiple sessions or a prolonged period. However, this can run well beyond the initial novelty period with a robot, signifying a challenging area for sustainable long-term user interest and engagement. Gamification is a key domain that holds promise to overcome uptake and retention concerns. Grounded in gamification theory in combination with the ‘Computers As Social Actors’ (CASA) paradigm, this paper presents a set of guidelines for recommended practice on creating and implementing social robot interactions for long-term human-robot interactions.
Best Practices for Maintaining Cybersecurity for Your New Remote Workforce

This article explores issues related to cybersecurity due to the increased need of remote work during the current COVID-19 crisis.
THE «ACTIVATION-LULL» METHOD FOR MEDIUM-STRENGTH EARTHQUAKES

The quality and depth of content Web of Science offers to researchers, authors, publishers, and institutions sets it apart from other research databases. The inclusion of News of NAS RK. Series of geology and technical sciences in the Emerging Sources Citation Index demonstrates our dedication to providing the most relevant and influential content of geology and engineering sciences to our community.Қазақстан Республикасы Ұлттық ғылым академиясы "ҚР ҰҒА Хабарлары.Геология жəне техникалық ғылымдар сериясы" ғылыми журналының
One-Dimensional Chaotic Function for Financial Applications Using Soft Computing Techniques

Cash forecasting is essential as a delay in process could lead to dissatisfaction of customers in financial organization. To maintain the actual cash requirement without any financial loss is difficult. Particle swarm optimization (PSO) enhanced the efficiency to converge the dataset and it will be easier to fall into local optima might prove fatal. Hence, chaotic system has been introduced to significantly enhance the interpretation toward cash management model for any financial organization. In the proposed study, one-dimensional chaotic-particle swarm optimization (CPSO) functions were used to improve the search strategy in the given problem space to yield the optimal values. The experimental analysis was made using chaotic sequence which shows the effectiveness of our proposed approach. Hence, the proposed methodology has the ability to increase the convergence rate without premature convergence.
Towards Automatic Clustering Analysis using Traces of Information Gain: The InfoGuide Method

Clustering analysis has become a ubiquitous information retrieval tool in a wide range of domains, but a more automatic framework is still lacking. Though internal metrics are the key players towards a successful retrieval of clusters, their effectiveness on real-world datasets remains not fully understood, mainly because of their unrealistic assumptions underlying datasets. We hypothesized that capturing {\it traces of information gain} between increasingly complex clustering retrievals---{\it InfoGuide}---enables an automatic clustering analysis with improved clustering retrievals. We validated the {\it InfoGuide} hypothesis by capturing the traces of information gain using the Kolmogorov-Smirnov statistic and comparing the clusters retrieved by {\it InfoGuide} against those retrieved by other commonly used internal metrics in artificially-generated, benchmarks, and real-world datasets. Our results suggested that {\it InfoGuide} can enable a more automatic clustering analysis and may be more suitable for retrieving clusters in real-world datasets displaying nontrivial statistical properties.
Summarizing Diverging String Sequences, with Applications to Chain-Letter Petitions

Algorithms to find optimal alignments among strings, or to find a parsimonious summary of a collection of strings, are well studied in a variety of contexts, addressing a wide range of interesting applications. In this paper, we consider chain letters, which contain a growing sequence of signatories added as the letter propagates. The unusual constellation of features exhibited by chain letters (one-ended growth, divergence, and mutation) make their propagation, and thus the corresponding reconstruction problem, both distinctive and rich. Here, inspired by these chain letters, we formally define the problem of computing an optimal summary of a set of diverging string sequences. From a collection of these sequences of names, with each sequence noisily corresponding to a branch of the unknown tree T representing the letter’s true dissemination, can we efficiently and accurately reconstruct a tree T' ≈ T? In this paper, we give efficient exact algorithms for this summarization problem when the number of sequences is small; for larger sets of sequences, we prove hardness and provide an efficient heuristic algorithm. We evaluate this heuristic on synthetic data sets chosen to emulate real chain letters, showing that our algorithm is competitive with or better than previous approaches, and that it also comes close to finding the true trees in these synthetic datasets.
"CELLU-LIT" : : a pragmatic study of Body Positive Language on Instagram

In this paper, two female-led Instagram accounts within the Body Positivity movementwere analyzed to see what type of linguistic strategies are employed in order to buildsolidarity with their follo ...
Blogs as Platforms for Disseminating and Popularizing Literature

This paper highlights the dimensions of weblogs in promoting literature in this digital era. Journalism which had played a crucial role in the freedom struggle of India has become a strong force of social transformation in modern times. Today, blogs are present everywhere in and around journalism with individual journalists, corporate, celebrities and agencies. Weblog is a recent form of journalism where bloggers describe an event, express their views on daily happenings, promote a book or update articles. Blogs cover a wide range of writing from poetry to lengthy prose. There are a number of literature–oriented blogs which disseminate various genres such as poetry, fiction, reviews, criticism and language. ‘Shakespeare’ blog contains articles on Shakespeare’s works, his creative world and his plays in performance. ‘No Sweat Shakespeare’ blog tells the way that Shakespeare used mythology in his plays which brings audience closer to his imagination. Fiction blogs provide easy, accessible, informative data on established and current authors and their available books. ‘BookRiot’ blog helps the readers to take a quiz on authors’ famous works and also provides them a chance to win. Blogs like ‘The Blog Smugglers’ features a variety of different reviews dedicated to science fiction and fantasy short stories and novels. Indian English blogging is undergoing a sort of transformation in present scenario amidst its fewer audience for English language blogs. The Indian blogs include varied articles and reviews on literary works where we can find diverse voices and conversations on literature. ‘Literarism’ in its article on Indian English literature, gives the gradual evolution of English language in India from the arrival of captain William Hawkins to the flamboyance of fine Indian writers who have made considerable contributions to world literature. This blog also offers solved answers of UGC- NET questions to the aspirants. ‘Book Geeks’ is a book review blog dedicated to Indian authors. Blogs are more widely read than academic journals and textbooks as truth emerges from shared, collective knowledge. Book lovers on the web are a bunch where they are good writers too. Thus blogs not only inspire various talented individuals but also serves as a platform to promote literature.
DeSpin: a prototype system for detecting spin in biomedical publications

Improving the quality of medical research reporting is crucial to reduce avoidable waste in research and to improve the quality of health care.Despite various initiatives aiming at improving research reporting -guidelines, checklists, authoring aids, peer review procedures, etc. -overinterpretation of research results, also known as distorted reporting or spin, is still a serious issue in research reporting.
Anomaly Detection in an e-Transaction System using Data Driven Machine Learning Models : An unsupervised learning approach in time-series data

Background: Detecting anomalies in time-series data is a task that can be done with the help of data driven machine learning models. This thesis will investigate if, and how well, different machine ...
Bounded-Memory Criteria for Streams with Application Time.

Bounded-memory computability continues to be in the focus of those areas of AI and databases that deal with feasible computations over streams---be it feasible arithmetical calculations on low-level streams or feasible query answering for declaratively specified queries on relational data streams or even feasible query answering for high-level queries on streams w.r.t. a set of constraints in an ontology such as in the paradigm of Ontology-Based Data Access (OBDA). In classical OBDA, a high-level query is answered by transforming it into a query on data source level. The transformation requires a rewriting step, where knowledge from an ontology is incorporated into the query, followed by an unfolding step with respect to a set of mappings. Given an OBDA setting it is very difficult to decide, whether and how a query can be answered efficiently. In particular it is difficult to decide whether a query can be answered in bounded memory, i.e., in constant space w.r.t. an infinitely growing prefix of a data stream. This work presents criteria for bounded-memory computability of select-project-join (SPJ) queries over streams with application time. Deciding whether an SPJ query can be answered in constant space is easier than for high-level queries, as neither an ontology nor a set of mappings are part of the input. Using the transformation process of classical OBDA, these criteria then can help deciding the efficiency of answering high-level queries on streams.
Walk it like you talk it: Jira as a tool for documenting the curation process

April 2019 poster presented at the Research Data Alliance 13th Plenary in Philadelphia, Pennsylvania.
Representation of reality in filmmaking: philosophical and culturological aspects

This article explores the issues pertaining to perception of reality through cinematography, including modern film editing. Analysis is conducted on modern surrealistic portraying of the world in movies, highlighting the importance of created reality in movies and its perception by the viewers. The article presents brief analysis of the works of such renowned philosophers as Andr&amp;eacute; Bazin, Henri Bergson, Jean Baudrillard, Maurice Merleau-Ponty and Walter Benjamin. Modern cinematography in form of &amp;ldquo;movie universes&amp;rdquo; and &amp;ldquo;movie franchises&amp;rdquo; is used as an example. This research covers modern films, which reflect popular demand for maximal realism as an opportunity to identify and reach maximal immersion into story on the screen. Cinematography began be showing real events as chronicles, later undergoing changes, and with development of technology becomes more distant from portrayal of specific reality and people. In the 1980s the conceptual idea changes, and cinematography becomes more realistic. However, in the early XXI century, while chasing realism modern movies became hyper-realistic, causing gradual degradation of sense of reality of the picture.
Lower Bounds on the Time/Memory Tradeoff of Function Inversion.

We study time/memory tradeoffs of function inversion: an algorithm, i.e., an inverter, equipped with an s-bit advice on a randomly chosen function \(f:[n] \mapsto [n]\) and using q oracle queries to f, tries to invert a randomly chosen output y of f, i.e., to find \(x\in f^{-1}(y)\). Much progress was done regarding adaptive function inversion—the inverter is allowed to make adaptive oracle queries. Hellman [IEEE transactions on Information Theory ’80] presented an adaptive inverter that inverts with high probability a random f. Fiat and Naor [SICOMP ’00] proved that for any s, q with \(s^3 q = n^3\) (ignoring low-order terms), an s-advice, q-query variant of Hellman’s algorithm inverts a constant fraction of the image points of any function. Yao [STOC ’90] proved a lower bound of \(sq\ge n\) for this problem. Closing the gap between the above lower and upper bounds is a long-standing open question.
Overfitting and Optimization in Offline Policy Learning.

We consider the task of policy learning from an offline dataset generated by some behavior policy. We analyze the two most prominent families of algorithms for this task: policy optimization and Q-learning. We demonstrate that policy optimization suffers from two problems, overfitting and spurious minima, that do not appear in Q-learning or full-feedback problems (i.e. cost-sensitive classification). Specifically, we describe the phenomenon of ``bandit overfitting'' in which an algorithm overfits based on the actions observed in the dataset, and show that it affects policy optimization but not Q-learning. Moreover, we show that the policy optimization objective suffers from spurious minima even with linear policies, whereas the Q-learning objective is convex for linear models. We empirically verify the existence of both problems in realistic datasets with neural network models.
PROBLEMS OF SELF-REGULATED LEARNING IN THE MULTIMEDIA ENVIRONMENT

The article considers self-regulated learning in the multimedia environment as one of the leading forms of solving the problems of modern education, especially in crisis situations. The purpose of this research is to identify and analyze the main problems of this form of learning. There are a number of pedagogical conditions for the productive organization of the educational process in an online format. The study of scientific and methodological literature on the main issues of this problem has shown, on the one hand, the powerful potential of online learning as an environment with a vast amount of information for learning and development, opportunities for social contact and communication, as well as the platform, impulse and motivating factor for the development of learning throughout life. On the other hand, the negative factors of organizing the educational process in the multimedia environment, as well as their lack of knowledge, require close attention. It is obvious that immersion in the multimedia environment predetermines the threat of "depersonalization" and "depersonalized communication", which limits the individualization of learning. The study shows that overabundance of information deserves special attention, which can quickly lead to cognitive overload of partners in cooperation in the field of training. It is emphasized that online learning is in no way an alternative to traditional forms of learning, and the multimedia environment is a useful addition and enrichment of the latter.
Proof of Concept Implementation of an Enterprise Service Bus for Health Information Exchanges

Abstract – Integration of health systems is one of the biggest problem in eHealth today. There are a lot of systems, yet they were developed using different platforms and technologies, making them virtually impossible to connect. In this paper, we discussed how to implement an ESB as the integration platform for health data. We identified use cases and functional requirements. Logical and deployment architecture were developed, and an actual proof of concept of an ESB is developed. Experiments were also done to determine the overhead caused by the ESB. Some of the functionalities of the ESB were examined to determine their individual overheads. Keywords : Enterprise Service Bus, Interoperability, Health Information Exchange, OpenHIE.
Gaussian Processes as Multiagent Reward Models

In multiagent problems that require complex joint actions, reward shaping methods yield good behavior by incentivizing the agents' potentially valuable actions. However, reward shaping often requires access to the functional form of the reward function and the global state of the system. In this work, we introduce the Exploratory Gaussian Reward (EGR), a new reward model that creates optimistic stepping stone rewards linking the agents potentially good actions to the desired joint action. EGR models the system reward as a Gaussian Process to leverage the inherent uncertainty in reward estimates that push agents to explore unobserved state space. In the tightly coupled rover coordination problem, we show that EGR significantly outperforms a neural network approximation baseline and is comparable to the system with access to the functional form of the global reward. Finally, we demonstrate how EGR improves performance over other reward shaping methods by forcing agents to explore and escape local optima.
BitcoinF: Achieving Fairness For Bitcoin In Transaction Fee Only Model

A blockchain, such as Bitcoin, is an append-only, secure, transparent, distributed ledger. A fair blockchain is expected to have healthy metrics; high honest mining power, low processing latency, i.e., low wait times for transactions and stable price of consumption, i.e., the minimum transaction fee required to have a transaction processed. As Bitcoin matures, the influx of transactions increases and the block rewards become insignificant. We show that under these conditions, it becomes hard to maintain the health of the blockchain. In Bitcoin, under these mature operating conditions (MOC), the miners would find it challenging to cover their mining costs as there would be no more revenue from merely mining a block. It may cause miners not to continue mining, threatening the blockchain's security. Further, as we show in this paper using simulations, the cost of acting in favor of the health of the blockchain, under MOC, is very high in Bitcoin, causing all miners to process transactions greedily. It leads to stranded transactions, i.e., transactions offering low transaction fees, experiencing unreasonably high processing latency. To make matters worse, a compounding effect of these stranded transactions is the rising price of consumption. Such phenomena not only induce unfairness as experienced by the miners and the users but also deteriorate the health of the blockchain. We propose BitcoinF transaction processing protocol, a simple, yet highly effective modification to the existing Bitcoin protocol to fix these issues of unfairness. BitcoinF resolves these issues of unfairness while preserving the ability of the users to express urgency and have their transactions prioritized.
Mobile Services Recommendation

The overwhelming amount of services makes it difficult for users to find appropriate services to meet their functional and non-functional requirements. Therefore, the service recommendation technique becomes an important role in helping using services. Besides the typical methods driven by service properties, some external information can also be introduced to improve the recommendation of mobile services. This chapter proposes three different recommendation approaches that consider users’ context, trust and social information respectively to improve the recommendation quality.
Массовые открытые онлайн-курсы (МООК) как форма дистанционного обучения

Over the past few years, information and communication technologies have become particularly relevant in the field of education. E-learning forms are becoming part of the educational process along with the traditional system. New forms of distance education are emerging, such as Massive Open Online Courses (MOOCs) that reflect the main ideas of the Education 3.0 model. MOOCs can be implemented using two approaches that require additional research: cMOOCs that adhere to the principle of connective learning and continuous social networking, and xMOOCs that are based on a traditional academic approach to learning (Coursera, Udacity, EDx). MOOCs are a relatively new form of education that has not been sufficiently studied and arouses research interest. The paper identifies the advantages of two models of xMOOC and cMOOC: flexibility and openness, building your own educational trajectory and the ability to develop user skills in the network community. There are a number of problems: redundancy of information, the need for a high level of ownership of Web 2.0 services, the lack of a mentor (cMOOC); a low level of interpersonal interaction between students and teachers, the problem of objectivity of assessment, dropout of students (xMOOC). The author also suggests considering possible prospects for the development of MOOCs in the context of modern education.
Spoken dialect identification in Twitter using a multi-filter architecture

This paper presents our approach for SwissText & KONVENS 2020 shared task 2, which is a multi-stage neural model for Swiss German (GSW) identification on Twitter. Our model outputs either GSW or non-GSW and is not meant to be used as a generic language identifier. Our architecture consists of two independent filters where the first one favors recall, and the second one filter favors precision (both towards GSW). Moreover, we do not use binary models (GSW vs. not-GSW) in our filters but rather a multi-class classifier with GSW being one of the possible labels. Our model reaches F1-score of 0.982 on the test set of the shared task.
Hand modeling and grasping measurement by inertial sensors

Measurement and analysis of human movements in a real environment are important in various fields such as ergonomics, nursing care and medical care. In particular, considering the interaction between human work and the environment, it is necessary to analyze detailed finger movements. Therefore, we proposed a model construction method using inertial sensors to accurately obtain complex finger shapes and verified their accuracy. As a result, the finger movements with various grip shapes were measured, and it was confirmed that there was no unnatural intersection. From this, it was shown that finger movement measurement using inertial sensor system and link model construction method has sufficient position accuracy.
Video Representations of Goals Emerge from Watching Failure.

We introduce a video representation learning framework that models the latent goals behind observable human action. Motivated by how children learn to reason about goals and intentions by experiencing failure, we leverage unconstrained video of unintentional action to learn without direct supervision. Our approach models videos as contextual trajectories that represent both low-level motion and high-level action features. Experiments and visualizations show the model is able to predict underlying goals, detect when action switches from intentional to unintentional, and automatically correct unintentional action. Although the model is trained with minimal supervision, it is competitive with highly-supervised baselines, underscoring the role of failure examples for learning goal-oriented video representations. The project website is available at this https URL
Performance Evaluation and Comparison of Standard Cryptographic Algorithms and Chinese Cryptographic Algorithms

Background. China is regulating the import, export, sale, and use of encryption technology in China. If any foreign company wants to develop or release a product in China, they need to report their ...