Reviewerly/exHarmony huggingface dataset:
BaaS Architecture for DApps and Application for Veterinary Medicine Case Study in Ireland

Blockchain technology provides promising solutions to problems where trust between participants is a significant concern. Blockchain technology eliminates centralised trusted authorities and provides decentralised trust assessments and improved transparency and immutability for transactions. Blockchain platforms have tightly connected decentralised consensus mechanisms and distributed ledgers. However, within DApps(decentralised applications), blockchain platforms should easily communicate with other systems, including data storage systems and front-end applications. Blockchain is not for storing big data. The transactions cost is a primary concern when developing DApps for commercial use. This paper proposes a BaaS(Blockchain as a Service) architecture enriched with a loosely coupled service API(Application Programming Interface) connecting IPFS(Interplanetary File System) as the information storage and smart contracts to govern the business logic. We realised the proposed architecture for prescriptions management in a veterinary medicine case study in Ireland. The solution DApp continuously monitors the updates of prescriptions, medicine dispenses, and medicine administrations while restricting the overuse of prescription and the use of counterfeit drugs. The traceability improves the transparency of treatment activities and information retrieval for users. Moreover, the proposed architecture is easily adaptable and maintains low-cost transactions with the support of IPFS and smart contract events.
Providing Humanitarian Relief Support through Knowledge Graphs

Disasters are often unpredictable and complex events, requiring humanitarian organizations to understand and respond to many different issues simultaneously and immediately. Often the biggest challenge to improving the effectiveness of the response is quickly finding the right expert, with the right expertise concerning a specific disaster type/disaster and geographic region. To assist in achieving such a goal, this paper demonstrates a knowledge graph-based search engine developed on top of an expert knowledge graph. It accommodates three modes of information retrieval, including a follow-your-nose search, an expert similarity search, and a SPARQL query interface. We will demonstrate utilizing the system to rapidly navigate from a hazard event to a specific expert who may be helpful, for example. More importantly, as the data is fully integrated including links between hazards and their abstract topics, we can find experts who have relevant expertise while navigating the graph.
Formal Modelling and Automated Trade-off Analysis of Enforcement Architectures for Cryptographic Access Control in the Cloud

To facilitate the adoption of cloud by organizations, Cryptographic Access Control (CAC) is the obvious solution to control data sharing among users while preventing partially trusted Cloud Service Providers (CSP) from accessing sensitive data. Indeed, several CAC schemes have been proposed in the literature. Despite their differences, available solutions are based on a common set of entities—e.g., a data storage service or a proxy mediating the access of users to encrypted data—that operate in different (security) domains—e.g., on-premise or the CSP. However, the majority of these CAC schemes assumes a fixed assignment of entities to domains; this has security and usability implications that are not made explicit and can make inappropriate the use of a CAC scheme in certain scenarios with specific trust assumptions and requirements. For instance, assuming that the proxy runs at the premises of the organization avoids the vendor lock-in effect but may give rise to other security concerns (e.g., malicious insiders attackers). To the best of our knowledge, no previous work considers how to select the best possible architecture (i.e., the assignment of entities to domains) to deploy a CAC scheme for the trust assumptions and requirements of a given scenario. In this article, we propose a methodology to assist administrators in exploring different architectures for the enforcement of CAC schemes in a given scenario. We do this by identifying the possible architectures underlying the CAC schemes available in the literature and formalizing them in simple set theory. This allows us to reduce the problem of selecting the most suitable architectures satisfying a heterogeneous set of trust assumptions and requirements arising from the considered scenario to a decidable Multi-objective Combinatorial Optimization Problem (MOCOP) for which state-of-the-art solvers can be invoked. Finally, we show how we use the capability of solving the MOCOP to build a prototype tool assisting administrators to preliminarily perform a “What-if” analysis to explore the trade-offs among the various architectures and then use available standards and tools (such as TOSCA and Cloudify) for automated deployment in multiple CSPs.
From Screenplays to Podcasts - New Perspectives on Improving Requirements Communication in Interdisciplinary Teams

Software engineering projects are highly collaborative. They require interdisciplinary teams to cooperate and exchange information efficiently and effectively to successfully achieve their project goals within time and budget. In this collaborative context, the communication of requirements plays an important role in enabling all team members to accomplish their engineering activities. However, establishing a shared understanding of the requirements is challenging. In this perspective paper, we reflect on such challenges, which we experienced in an ongoing software development project, and propose novel approaches that seem promising for tackling the identified challenges. These ideas stem from best practices of non- SE disciplines such as criminology, film studies & dramatics, journalism & authoring, and psychology. Moreover, we outline our future research agenda, which comprises research questions and activities that aim to further elaborate on the envisioned ideas and to apply, respectively evaluate, their effect on improving requirements communication. This research agenda is intended to motivate the RE community to contribute to our research activities and to encourage both practitioners and researchers to get inspired by other disciplines as this is surely beneficial when it comes to addressing current challenges.
Named Entity Recognition for the Azerbaijani Language

This research paper focuses on developing a Named Entity Recognition (NER) system for a low-resource language, namely Azerbaijani. The paper develops NER models with two different approaches which are rule-based and machine learning-based approaches and compares the performances of them with familiar and unfamiliar datasets to determine the best approach. The rule-based approach uses statistics as its main technique and brings sufficient results - 70% f-score for both datasets. The second method consists of three models. The first one is obtained by training a model from scratch with Convolution Neural Network (CNN) using Spacy library which results in the best outcome - above 90% f-score for each test dataset. Secondly, a pre-trained multilingual Spacy model is also used to contrast the results, which proves the importance of the domain in which a NER model is trained since this model scored less than 50% in testing. Additionally, a new model has also been trained on top of the multilingual model using training dataset and performs the best in its domain.
User Interface Design for Counseling Guidance Applications of Vocational High School Through a User-Centered Design Approach

Guidance and counseling services have an important role in moral assistance and directing students' talents and interests. The Covid-19 pandemic has hampered Counseling Guidance services because learning activities are carried out online. This study aims to conduct a UI and UX assessment of the counseling application based on the Cased Based Reasoning algorithm called B-Talk. This application was developed with a mobile platform design. Infernalities than the approach used is the User-Centered Design (UCD). The UCD approach is an approach that uses factors human, knowledge of usability techniques, and an interactive approach to developing design and systems. The UI(User Interface) and UX(User Experience) designs that have been produced will be tested with an expert assessment of Counseling Guidance which includes two aspects, namely usability testing and User Experience Question (UEQ). The media validation assessment was obtained from an average value of 89.2% of the standard feasibility without revision. In this case, it can be concluded that the B-Talk application design can be used as its function for Counseling Guidance media for adolescents.
Activity-aware privacy protection for smart water meters

Identifying water end-uses from household meter readings yields valuable insights of commercial and environmental value for both customers and water providers. But smart water meter data may expose sensitive information about the activities of metered households. This paper considers the case where a water provider wishes to publish a database of household water meter traces to be used by water analysts for research and planning purposes. We consider the risks to privacy should an adversary gain access to this database, with the threat of uniquely identifying a household of interest and exposing their water use activities. Previous work shows that the effectiveness of privacy protection techniques is strongly domain and application dependent, but few privacy studies have considered smart water metering. This paper introduces a framework for activity-aware privacy protection for databases of household smart water meters and evaluates its effectiveness using real-world and synthetic datasets. We found that privacy-protection is strongly dependent on the type of activity. Aggregating readings protects households from disclosure of small scale and common activities. For individualistic, infrequent activities such as garden watering or clothes washing, we found that data-aware sampling of households and seasons offers the best protection.
Using improved context-based code description for robust algorithmic binding to changing code

In a computer program, multiple problems are usually solved, both domain-specific and technical. The solution of a problem can be concentrated in a single decomposition element (e.g. class or method) or be implemented with multiple code fragments scattered throughout the codebase. With no supporting tools, scattered solutions are hard to explore and modify, because the fact that there is a the high-level semantic connection between some code fragments is not articulated in the code itself. To overcome this difficulty, programmers need a special tool to mark all the fragments that solve a single problem (to bind to them) and then use this markup to navigate through the project. The markup must be robust, i.e., all the fragments previously marked must be successfully found in the changed code. A method for achieving this robustness has recently been proposed. It consists in representing the marked code fragment as a set of structures called ”contexts”. Contexts capture valuable information about the fragment and its surrounding. This information is then used to find the fragment algorithmically in the edited code. In the present paper, the method is further improved. More informative contexts and more precise search algorithms are presented, allowing the tool to automatically find the vast majority of previously marked fragments existing in the edited code.
Impacts of HLS Optimizations on Side-Channel Leakage for AES Circuits

Many IoT devices have been compromised by side-channel attacks. Among them, power analysis attack is a well-known technique to reveal the secret key of a circuit by measuring the power of the target device. In this study, we investigate the relationship between the performance and the side-channel attack resistance of cryptographic circuits. We synthesize AES circuits from a behavioral description written in the C language and analyze the power. In order to evaluate tolerance to side-channel attacks, we conduct T-test for the power consumed on the circuit. Experimental results show that there appears a trade-off between the side-channel leakage and the performance of the circuit.
Word Sense Disambiguation Using Prior Probability Estimation Based on the Korean WordNet

Supervised disambiguation using a large amount of corpus data delivers better performance than other word sense disambiguation methods. However, it is not easy to construct large-scale, sense-tagged corpora since this requires high cost and time. On the other hand, implementing unsupervised disambiguation is relatively easy, although most of the efforts have not been satisfactory. A primary reason for the performance degradation of unsupervised disambiguation is that the semantic occurrence probability of ambiguous words is not available. Hence, a data deficiency problem occurs while determining the dependency between words. This paper proposes an unsupervised disambiguation method using a prior probability estimation based on the Korean WordNet. This performs better than supervised disambiguation. In the Korean WordNet, all the words have similar semantic characteristics to their related words. Thus, it is assumed that the dependency between words is the same as the dependency between their related words. This resolves the data deficiency problem by determining the dependency between words by calculating the χ2 statistic between related words. Moreover, in order to have the same effect as using the semantic occurrence probability as prior probability, which is used in supervised disambiguation, semantically related words of ambiguous vocabulary are obtained and utilized as prior probability data. An experiment was conducted with Korean, English, and Chinese to evaluate the performance of our proposed lexical disambiguation method. We found that our proposed method had better performance than supervised disambiguation methods even though our method is based on unsupervised disambiguation (using a knowledge-based approach).
Recent Progress in Private Simultaneous Messages Protocols

The private simultaneous messages (PSM) model is a simple variant of the secure multiparty computation (MPC). In the k-party PSM model, each party $P_{\imath}$ has a private input $x_{i}$ for $i=1, \ldots, k$. For a function f, each $\lt p\gt P_{i}$ encrypts $x_{i}$ into a message $m_{i}$ with a random string r shared among $\lt p\gt P_{1}, \ldots, P_{k}$, and sends $m_{i}$ to the referee $R. R$ computes $f\left(x_{1}, \ldots, x_{k}\right)$ from their respective messages $m_{1}, \ldots, m_{k}$. Then, R learns nothing from $m_{1}, \ldots, m_{k}$ except for the output value $f\left(x_{1}, \ldots, x_{k}\right)$. This simple model provides interesting cryptographic applications and is essential for understanding the intrinsic costs (e.g., of communication $\left|m_{1}\right|+\ldots+\left|m_{k}\right|$ and randomness $|r|$) to achieve MPC. This study surveys recent results associated with the PSM and closely related models.
Feature-Level Fusion of Super-App and Telecommunication Alternative Data Sources for Credit Card Fraud Detection

Identity theft is a major problem for credit lenders when there’s not enough data to corroborate a customer’s identity. Among super-apps—large digital platforms that encompass many different services—this problem is even more relevant; losing a client in one branch can often mean losing them in other services. In this paper, we review the effectiveness of a feature-level fusion of super-app customer information, mobile phone line data, and traditional credit risk variables for the early detection of identity theft credit card fraud. Through the proposed framework, we achieved better performance when using a model whose input is a fusion of alternative data and traditional credit bureau data, achieving a ROC AUC score of 0.81. We evaluate our approach over approximately 90,000 users from a credit lender’s digital platform database. The evaluation was performed using not only traditional ML metrics but the financial costs as well.
On-stack replacement for program generators and source-to-source compilers

On-stack replacement (OSR) describes the ability to replace currently executing code with a different version, either a more optimized one (tiered execution) or a more general one (deoptimization to undo speculative optimization). While OSR is a key component in all modern VMs for languages like Java or JavaScript, OSR has only recently been studied as a more abstract program transformation, independent of language VMs. Still, previous work has only considered OSR in the context of low-level execution models based on stack frames, labels, and jumps.
A Study on Performance Diagnosis of College English Courses in General Education after Core Competency-Based Reform －The Case of I University

This study conducted a basic learning performance diagnosis for each College English course falling under the purview of basic general education. In line with the five core competencies of I University, the area of foreign language in basic general education focused on communication competency. This study explored the reactions of students taking College English courses to examine whether each course matched the core competency focused on communication, especially the basic learning competence for English communication skills and experiences acquired through the knowledge provided and what changes were needed in the curriculum to improve students’ English communication competence.&lt;br/&gt;Through a survey, educational improvements recognized by students who participated in College English I (reading), College English II (listening), and College English III (speaking) classes were compared and analyzed. The performance diagnosis for the study aimed to measure learning performance obtained through each course and utilize it as a basic mean for improving curricula for College English courses. For performance diagnosis, the Learning Performance Diagnosis Tool developed by the Korea National Institute for General Education’s General Education Standards in 2016 was selected. Diagnosis questions from the areas of “basic learning competency” and “knowledge and experience” were selected, and each question was measured using a 5-point Likert scale. The performance diagnosis survey involved 1,366 of 2,063 students (66.2%) in all three courses.&lt;br/&gt;Based on the analysis result, students reported that their English communication competency had improved in each course. This result could be interpreted as indicating that the core competency pursed in the area of foreign language at I University and the direction of education pursued in College English courses were appropriate. However, slightly different from basic learning competence area, the performance indicators relating to class activities questions in knowledge and experience area were found to be lower than that of other questions. During the semester, online and offline classes had been conducted depending on the COVID-19 situation, and it can be assumed that this situation resulted in many inconveniences in terms of utilizing effective class activities where students could interact with the professor and classmates. It also seemed urgent to develop effective non-face-to-face or online class activities to prepare for the COVID-19 pandemic era.
An experimental reassessment of complex NP islands with NP-scrambling in Japanese

There is little consensus in the Japanese syntax literature on the question of whether complex NPs with a noun complement headed by toyuu ‘that.say’ are islands for NP-scrambling dependencies. To explore this question, we conducted two acceptability judgment experiments using the factorial definition of islands to test the status of noun complements, relative clauses (which are also complex NPs, and uniformly considered islands in the literature), and coordinated NP structures (which are also uniformly considered islands in the literature). Our first experiment yielded strong evidence that relative clauses and coordinated NPs are islands (as expected), and strong evidence that noun complements are not. Our second experiment also found strong evidence that relative clauses and coordinated NPs are islands, but yielded a small, non-significant, trend toward an effect with noun complements. Based on the sizes of our samples (89 and 90 participants, respectively), the sizes of the effects, and the details of the acceptability patterns, we conclude that noun complements in Japanese are not islands with respect to NP-scrambling. We also investigated between- and within-participant variability in our results. We observe no evidence of increased between-participant variability for noun complements relative to other islands, and no increase of within-participant variability for noun complements relative to scrambling out of (non-island) declarative CPs. Our results have consequences for a number of issues that have been encoded in current syntactic theories of island effects, including the correlation between syntactic constituent complexity and island status (e.g., number of bounding nodes or phase heads), and the correlation between complementizer deletion and island status (e.g., the complement/adjunct distinction).
GPU-based security of password hashing in cloud computing

Cloud computing paradigm, one of the rich standards for computing environment to deliver the services over the Internet with multiple features including robustness of services. The expeditious growth of cloud computing demand has raised a greater concern for security threats. Nowadays, security is considered as one of the major parameters for obtaining services in any Cloud platform. The discussion is on external attacks towards cryptographic systems in general. It includes password hashing schemes based on the hash functions then explore GPU's performance over MD5-crypt. Henceforth, the research proposal presents here with a GPU-based integrated technique for password hashing in cloud environment.
The CORSMAL Benchmark for the Prediction of the Properties of Containers

The contactless estimation of the weight of a container and the amount of its content manipulated by a person are key pre-requisites for safe human-to-robot handovers. However, opaqueness and transparencies of the container and the content, and variability of materials, shapes, and sizes, make this estimation difficult. In this paper, we present a range of methods and an open framework to benchmark acoustic and visual perception for the estimation of the capacity of a container, and the type, mass, and amount of its content. The framework includes a dataset, specific tasks and performance measures. We conduct an in-depth comparative analysis of methods that used this framework and audio-only or vision-only baselines designed from related works. Based on this analysis, we can conclude that audio-only and audio-visual classifiers are suitable for the estimation of the type and amount of the content using different types of convolutional neural networks, combined with either recurrent neural networks or a majority voting strategy, whereas computer vision methods are suitable to determine the capacity of the container using regression and geometric approaches. Classifying the content type and level using only audio achieves a weighted average F1-score up to 81% and 97%, respectively. Estimating the container capacity with vision-only approaches and estimating the filling mass with audio-visual multi-stage approaches reach up to 65% weighted average capacity and mass scores. These results show that there is still room for improvement on the design of new methods. These new methods can be ranked and compared on the individual leaderboards provided by our open framework.