Reviewerly/exHarmony huggingface dataset:
Semantic Modeling of Events Using Linked Open Data

Significant happenings in terms of spatio-temporal factors are called events. In the digital age, these events and their associated features are scattered in various databases on the Internet. The event data are in heterogeneous formats, which are often not machine-readable. This leads to a lack of unification of event-related knowledge across different domains and results in a research gap in terms of event modeling and representation. Specialized event models are needed to overcome this gap and integrate relevant information of different similar events occurring worldwide. Our research explores the problem of heterogeneity in specialized event modeling and takes modeling for refugee registration and repatriation events as a case study. Our research explores the problem of heterogeneity at the data level and proposes a solution to this problem in the field of refugee registration and repatriation events. Considering refugee migration is one of the biggest crises in the world. The proposed model is designed according to Semantic Web standards to ensure reusability and machine readability. The project uses Protégé to model classes and ontology. Our ontology is called OntoEvent ontology and Karma is used for data mapping over ontology. Heterogeneity for the same concepts collected through the internet and through UNHCR (reports, excel sheets) is analyzed and resolved during the data modeling phase. As a result of this research, a timeline is designed to visualize events over time, along with a semantic data model and Linked Open Data representation of refugee data that we believe is of global significance. The W3C Ontology Validation Service has successfully validated the proposed OntoEvent ontology.
Planning Habit: Daily Planning Prompts with Alexa

The widespread adoption of intelligent voice assistants (IVAs), like Amazon’s Alexa or Google’s Assistant, presents new opportunities for designers of persuasive technologies to explore how to support people’s behavior change goals and habits with voice technology. In this work, we explore how to use planning prompts, a technique from behavior science to make specific and effective plans, with IVAs. We design and conduct usability testing (N = 13) on a voice app called Planning Habit that encourages users to formulate daily plans out loud. We identify strategies that make it possible to successfully adapt planning prompts to voice format. We then conduct a week-long online deployment (N = 40) of the voice app in the context of daily productivity. Overall, we find that traditional forms of planning prompts can be adapted to and enhanced by IVA technology.
Quantum Motions and Emotions for a Humanoid Robot Actor

No quantum robot exists in the world today, meaning a real physical robot, controlled by a “quantum brain”. In this paper a humanoid robot was programmed based on quantum circuits, in contrast to classical animation methods or classical logic circuit robot controllers. The robot actor executes deterministic, probabilistic and quantum probabilistic behaviors which makes him more entertaining to the robot theatre audience.
Data diet pills

Traffic reduction for bandwidth-hungry video streaming services, such as YouTube, benefits not only subscribers struggling to avoid going over their contracted data limit, but also service providers when the number of people who use video streaming services increase. Because not all stakeholders who want to reduce traffic usage are willing to conduct cumbersome operations, e.g., manually setting lower resolution, we argue here that network operators should introduce a traffic pacer for providing traffic reduction services as an optional plan for subscribers. This paper proposes NetPacer, an in-network traffic pacing system for reducing traffic usage by degrading the video quality. NetPacer has two features. The first is relative pacing, which degrades the video quality relative to the initial quality by traffic shaping, thus enabling flexible quality control. The second is in-network timely video quality identification via encrypted traffic analysis by using machine learning. Through experiments, we demonstrate that NetPacer successfully reduces traffic by 30.8% by degrading the resolution by one level while keeping the QoE (i.e., Mean Opinion Score (MOS)) degradation below 0.268 points on average for 50 YouTube videos.
Fact-Checking Reasoning System for Fake Review Detection Using Answer Set Programming

A rising number of people use online reviews to choose if they want to use or buy a service or product. Therefore, approaches for identifying fake reviews are in high request. This paper proposes a hybrid rule-based fact-checking framework based on Answer Set Programming (ASP) and natural language processing. The paper incorporates the behavioral patterns of reviewers combined with the qualitative and quantitative properties/features extracted from the content of their reviews. As a case study, we evaluated the framework using a movie review dataset, consisting of user accounts with their associated reviews, including the review title, content, and the star rating of the movie, to identify reviews that are not trustworthy and labeled them accordingly in the output. This output is then used in the front end of a movie review platform to tag reviews as fake and show their sentiment. The evaluation of the proposed approach showed promising results and high flexibility.
Infinite families of 2‐designs derived from affine‐invariant codes

Abstract In this paper, we first study a general type of affine‐invariant codes and their support 2‐designs. Then we derive infinite families of 2‐designs from a special class of affine‐invariant codes and obtain their parameters by determining the weight distribution of the linear codes.
WARChain: Blockchain-Based Validation of Web Archives

Background. Web archives store born-digital documents, which are usually collected from the Internet by crawlers and stored in the Web Archive (WARC) format. The trustworthiness and integrity of web archives is still an open challenge, especially in the news portal domain, which face additional challenges of censorship even in democratic societies. Aim. The aim of this paper is to present a light-weight, blockchain-based solution for web archive validation, which would ensure that the crawled documents are authentic for many years to come. Method. We developed our archive validation solution as an extension and continuation of our work in web crawler development, mainly targeting news portals. The system is designed as an overlay over a blockchain with a proof-of-stake (PoS) distributed consensus algorithm. PoS was chosen due to its lower ecological footprint compared to proof-of-work solutions (e.g. Bitcoin) and lower expected investment in computing infrastructure. Results. We implemented a prototype of the proposed solution in Python and C#. The prototype was tested on web archive content crawled from Hungarian news portals at two different timestamps which consisted of 1 million articles in total. Conclusions. We concluded that the proposed solution is accessible, usable by different stakeholders to validate crawled content, deployable on cheap commodity hardware, tackles the archive integrity challenge and is capable to efficiently manage duplicate documents.
Towards a corpus for credibility assessment in software practitioner blog articles

Background: Blogs are a source of grey literature which are widely adopted by software practitioners for disseminating opinion and experience. Analysing such articles can provide useful insights into the state–of–practice for software engineering research. However, there are challenges in identifying higher quality content from the large quantity of articles available. Credibility assessment can help in identifying quality content, though there is a lack of existing corpora. Credibility is typically measured through a series of conceptual criteria, with ’argumentation’ and ’evidence’ being two important criteria.
Social Media Debate Position 1: Against the Use of Social Media as a Credible Source of Information

There are ongoing conversations among information professionals on whether social media can be considered a credible source of information. The very features of social media platforms that make them attractive to users, such as their ability to create and share content, also make these platforms very open to false and misleading content, which calls all content on these platforms into question. The platforms themselves have not yet perfected moderation processes to filter out all misleading and harmful misinformation. This paper contends that for these and other reasons, the social media landscape in its current state cannot be considered a credible source of information. These issues negatively affect user expectations; however, the platforms continue to attract users and thereby present opportunities for misinformation to cause harm. All of these concerns affect the efforts of information professionals to instruct users on identifying false content, as well as the online behavior of these information organizations themselves. This paper discusses the issues of mis- and dis-information and lack of effective content moderation, and examines how information professionals and other users are affected by these issues.
Finite differences for higher order derivatives of low resolution data

The viability of finite difference stencils for higher-order derivatives of low-resolution data, encountered in various applications, is examined. This study’s illustrative example involves the evaluation of the fourth derivative of the digitized free surface radius obtained from pixelated images. Procedures to obtain an optimal approximation to the derivative are made from estimates of truncation and roundoff error, with emphasis on the analysis of roundoff error. A method of successive approximations to evaluate the optimal grid spacing is presented. Higher-order stencils allow for larger optimal grid spacing, thereby reducing the roundoff error that would otherwise dominate. Hence, higher-order stencils are effective for low precision data.
Heterogeneous Information Network based Adaptive Social Influence Learning for Recommendation and Explanation

Collaborative filtering (CF)-based recommendation systems that rely on user-item history interactions often suffer from the data sparsity problem. Social-based recommendation methods have become one of the successful methods to address this problem. However, few works have focused on the sparsity problem of social data. As real-world social networks are usually sparse, the observed relationships in social networks can only represent a limited part of a person's real social network. The sparse social data will degrade the performance of the existing social-based algorithms. Also, the influence of a user's friends on their friends is dynamic: even the same friend may impact the target user in different decision-making processes. It is difficult for an the end-to-end deep learning-based model to provide underlying reasons for the recommendation results. To this end, we propose a novel deep learning-based model to extract useful missing links as auxiliary social information to enrich the users' features for item recommendation. The framework is composed of two major components: a missing links identifier module that generates useful social links from a heterogeneous information network to enrich the user's social profile and enhance the social-based recommendation model, and an attention-based recommendation module that assigns different scores for each friend with regard to different candidate items to adaptively evaluate the quality of different social links. An attention-based fusion strategy is proposed to improve the interpretability of the recommendation system by assigning non-uniform weight to different factors. Extensive experiments on three published datasets show that our proposed method achieves better performance than other state-of-the-art methods.
Non-Stochastic Private Function Evaluation

We consider private function evaluation to provide query responses based on private data of multiple untrusted entities in such a way that each cannot learn something substantially new about the data of others. First, we introduce perfect non-stochastic privacy in a two-party scenario. Perfect privacy amounts to conditional unrelatedness of the query response and the private uncertain variable of other individuals conditioned on the uncertain variable of a given entity. We show that perfect privacy can be achieved for queries that are functions of the common uncertain variable, a generalization of the common random variable. We compute the closest approximation of the queries that do not take this form. To provide a trade-off between privacy and utility, we relax the notion of perfect privacy. We define almost perfect privacy and show that this new definition equates to using conditional disassociation instead of conditional unrelatedness in the definition of perfect privacy. Then, we generalize the definitions to multi-party function evaluation (more than two data entities). We prove that uniform quantization of query responses, where the quantization resolution is a function of privacy budget and sensitivity of the query (cf., differential privacy), achieves function evaluation privacy.
Scalable, Confidential and Survivable Software Updates

Software update systems must guarantee high availability, integrity and security even in presence of cyber attacks. We propose the first survivable software update framework for the secure distribution of confidential updates that is based on a distributed infrastructure with no single points of failure. Previous works guarantee either survivability or confidentiality of software updates but do not ensure both properties. Our proposal is based on an original application of a multi-authority attribute-based encryption scheme in the context of decentralized access control management that avoids single-point-of-vulnerability. We describe the original framework, propose the protocols to implement it, and demonstrate its feasibility through a security and performance evaluation.
Universal Communication Efficient Quantum Threshold Secret Sharing Schemes

Quantum secret sharing (QSS) is a cryptographic protocol in which a quantum secret is distributed among a number of parties where some subsets of the parties are able to recover the secret while some subsets are unable to recover the secret. In the standard ((k, n)) quantum threshold secret sharing scheme, any subset of k or more parties out of the total n parties can recover the secret while other subsets have no information about the secret. But recovery of the secret incurs a communication cost of at least k qudits for every qudit in the secret. Recently, a class of communication efficient QSS schemes were proposed which can improve this communication cost to $\frac{d}{{d - k + 1}}$ by contacting d ≥ k parties where d is fixed prior to the distribution of shares. In this paper, we propose a more general class of ((k, n)) quantum secret sharing schemes with low communication complexity. In these schemes the combiner can contact any d parties at the time of recovery where k ≤ d ≤ n. This is the first such class of universal communication efficient quantum threshold schemes.
Generating Interesting Song-to-Song Segues With Dave

We introduce a novel domain-independent algorithm for generating interesting item-to-item textual connections, or segues. Pivotal to our contribution is the introduction of a scoring function for segues, based on their 'interestingness'. We provide an implementation of our algorithm in the music domain. We refer to our implementation as Dave. Dave is able to generate 1553 different types of segues, that can be broadly categorized as either informative or funny. We evaluate Dave by comparing it against a curated source of song-to-song segues, called The Chain. In the case of informative segues, we find that Dave can produce segues of the same quality, if not better, than those to be found in The Chain. And, we report positive correlation between the values produced by our scoring function and human perceptions of segue quality. The results highlight the validity of our method, and open future directions in the application of segues to recommender systems research.
On Conditional Cryptocurrency With Privacy

In this paper, we present the design and implementation of a conditional cryptocurrency system with privacy protection. Unlike the existing approaches that often depend on smart contracts where cryptocurrencies are first locked in a vault, and then released according to event triggers, the conditional cryptocurrency system encodes event outcome as part of a cryptocurrency note in a UTXO based system. Without relying on any triggering mechanism, the proposed system separates event processing from conditional coin transaction processing where conditional cryptocurrency notes can be transferred freely in an asynchronous manner, only with their asset values conditional to the linked event outcomes. The main advantage of such design is that it enables free trade of conditional assets and prevents assets from being locked. In this work, we demonstrate a method of confidential conditional coin by extending the Zerocoin data model and protocol. The system is implemented and evaluated using xJsnark.
Distance learning at KazNPU Named after Abai: Models and Technologies

The article examines the models and technologies of the digital ecosystem of the educational environment of Abai KazNPU in the context of distance learning. The digital ecosystem of the university is defined as an integrated multicomponent system that includes training, measurement of educational results, research, extracurricular projects and movements, organizational and managerial decisions as well as ways of their implementation, designed to meet the information needs of all participants in the system. The analysis of projects and solutions in the context of the described components of the integrated ecosystem of the university is carried out. Models and technologies of distance learning - online, offline, flipped class, blended learning are considered. Also analyzed are innovative forms of control and assessment of educational achievements. The article also describes the results of a sociological study of the readiness of university teachers and students for distance learning, the advantages of using its various models and technologies.
Challenges in the Development of Mobile Online Services in the Automotive Industry - A Case Study

Today's automotive companies face new opportunities but also new challenges. With the development of new technologies, cars are increasingly integrated into the life of their owners for example by connecting their smartphones with their cars. This enables the development of new services for the owners of cars, e.g. monitoring of driving behavior. These so-called mobile online services demand an iterative and user-centered development and automotive companies have to align their processes with these demands. However, the necessary transition for that is difficult because automotive companies are often rigid in their structure, resistant to change, and still focused on the manufacturing of cars. Although mobile online services have become increasingly important for companies, there is, so far, only limited research in this field, especially about the challenges in the development of mobile online services. Therefore, we conduct a case study in a project that develops mobile online services to manage vehicle fleets in an automotive company. By using interviews, we investigate the challenges the project faces and the means that the project already implements to address these challenges. Our results show that the automotive company considers mobile services rather as a by-product and is still more focused on the manufacturing of cars. That is expressed by the number of external employees and a missing user-centric development that already starts in the manufacturing process.
Multi-Object Tracking with Tracked Object Bounding Box Association

The CenterTrack tracking algorithm achieves state-of-the-art tracking performance using a simple detection model and single-frame spatial offsets to localize objects and predict their associations in a single network. However, this joint detection and tracking method still suffers from high identity switches due to the inferior association method. To reduce the high number of identity switches and improve the tracking accuracy, in this paper, we propose to incorporate a simple tracked object bounding box and overlapping prediction based on the current frame onto the CenterTrack algorithm. Specifically, we propose an Intersection over Union (IOU) distance cost matrix in the association step instead of simple point displacement distance. We evaluate our proposed tracker on the MOT17 test dataset, showing that our proposed method can reduce identity switches significantly by 22.6% and obtain a notable improvement of 1.5% in IDF1 compared to the original CenterTrack’s under the same tracklet lifetime. The source code is released at https://github.com/Nanyangny/CenterTrack-IOU.
Chaotic Harmony Search based Multi-objective Feature Selection for Classification of Gene Expression Profiles

How to effectively select a subset of discriminant features from the high-dimensional low- sample-size microarray gene expression profiles remains crucial and meaningful for the bioinformatics analysis tasks such as locating disease genes and building classifiers for cancer diagnosis. Though meta-heuristic harmony search algorithm has been used for feature selection, it suffers from entrapment in local optima and low convergence speed. To this end, we propose a hybrid chaotic harmony search based multi-objective feature selection method, which uses the chaotic map to replace the parameter of harmony search during the optimization process. Specifically, the minimum redundancy maximum relevancy feature selector is first used to pre-select a subset of relevant features. Then, the chaotic harmony search is employed on the reduced feature set to find an optimal feature subset, where the fitness of a candidate solution is evaluated by a multi-objective formulation. Finally, extensive comparative experiments against its competitors, including six filter and four wrapper feature selection methods, are conducted on six public microarray datasets. Results show that the proposed method obtains higher classification accuracy. Besides, the convergence analysis indicates its efficiency.