Reviewerly/exHarmony huggingface dataset:
An empirical analysis of navigation behaviors across stock and cryptocurrency trading platforms: implications for targeting and segmentation strategies

This study examines and compares the behaviors of private investors in cryptocurrency and stock trading platforms. To this end, we propose a Tobit model for private investors who navigate across cryptocurrency and stock trading apps and identify factors associated with cryptocurrency and stock trading platform usage. We apply our model to unique mobile app usage data from August 2020 to March 2021, covering two waves of the COVID-19 cxpandemic in South Korea and the global speculative cryptocurrency bubble. We find that cryptocurrency and stock private investors differ considerably, in terms of demographics as well as such behaviors as loss-aversion, optimism, addiction, herding, cross-trading, and response to the pandemic. Our analysis also discovers potential competition between cryptocurrency and stock trading platforms in targeting their customers, e.g., private investors. Furthermore, we reveal behavioral profiles of segments of private investors of cryptocurrency and stocks, so the trading platforms can discover them. This study offers important managerial implications for trading platforms, to target and manage their private-investor customers.
IMPLEMENTATION OF ARAS ALGORITHM ON DECISION SUPPORT SYSTEM TO DETERMINE THE BEST LECTURER

In the world of higher education, lecturers are one of the main components in building quality and quantity. Good quality will give good results as well, to improve the quality of each lecturer, it is necessary to have an award given to lecturers from the campus so that it becomes a motivation for lecturers to improve the quality given to students and the community. Amik Mitra Gama is a private campus located in the Duri Riau area, in this case to improve the quality of education one of the steps taken is to give awards and appreciation to the best lecturers who will be selected every year. To realize this, we need an easy calculation system that is carried out in the form of ranking according to the final value, therefore a decision support system using the ARAS method is chosen because it is very appropriate for the selection process and provides convenience in the calculations which are determined based on ranking. The decision support system using the ARAS method uses 8 criteria that are set as a reference in determining the best lecturers, namely Recent Education, Lecturer Functional Position, Lecturer Certification, Number of Journal Publications, Roles in Research, Journal Publication History, Research Grants, and Community Service. There are 10 lecturers in the field of computers who will be used as alternative data with lecturer codes D01, D02, D03, D04, D05, D06, D07, D08, D09, D10. The results obtained from this study are the lecturer code D04 = 0.0974, D06 = 0.0965, D09 = 0.0932, D07 = 0.0903, D03 = 0.0901 was selected as the best lecturer in 2021/2022. So that the results of this study can help the campus to determine the best lecturers every year fairly and be selected based on rankings.
Multi-Scale Speaker Vectors for Zero-Shot Speech Synthesis

Recent advances in deep learning have allowed the task of synthesizing speech from text, otherwise known as Text-to-Speech (TTS), to be addressed with more powerful and effective techniques than previously possible. In this paper, we propose a novel speaker encoder model utilizing some of those techniques, such as self-attention and transformers. The proposed model results in a speaker embedding vector called Multi-Scale Speaker (MSS) vectors. MSS vectors aim to make improvements over the current state-of-the-art speaker embeddings for more natural and similar-sounding synthesized speech for unseen speakers in a zero-shot speech synthesis model. The proposed architecture relies on encoder layers which are coupled with a multi-scale approach for learning both local and global mel-spectra features of a reference speaker. We compared our proposed model against a modified generalized end-to-end (GE2E) speaker encoder using mel-cepstrum distortion and cosine similarity measures as well as mean opinion scores. Evaluation results confirm our encoder's advantages over a state-of-the-art speaker encoder model.
Online Multiple-Pedestrian Tracking With Detection-Pair-Based Graph Convolutional Networks

The typical Internet of Things application, unattended driving systems, will need the ability to recognize relevant traffic participants and detect dangerous situations ahead of time. An important component of these systems is one that is able to distinguish pedestrians and track their motion to make intelligent driving decisions. This article develops a high-accuracy multiple pedestrian tracking algorithm which is vital for intelligent transportation. Here, we use the off-the-shelf detectors and explore the benefits of modeling pedestrian interactions, such as the interaction of two pedestrians simultaneously matched to two pedestrians in another frame, for robust detection association. Explicitly studying interactions is nontrivial. Previous works often manually selected interacting detections (or “tracklets”) to simplify the association process. In this article, we propose a novel association method based on deep graph convolutional affinity networks (DGCANs) and extend detection-level interactions to the association-level, which treats a potential association of a detection pair as a node in the graph, and explicitly modeling the interactions among potential associations. Specifically, with the novel node, two corresponding edges are readily designed to model the compatible and colliding interactions between related associations. Our proposed method, by redefining nodes and edges, enables us to blend sufficient interaction cues from appearance and motion and learns a robust affinity measure in an end-to-end fashion. Using the Hungarian algorithm as an online tracker, our method archives state-of-the-art performance on benchmark data sets 2-D MOT15, MOT16, and MOT17.
Generating Expression Evaluation Learning Problems from Existing Program Code

When developing automated assessments and intelligent tutoring systems, a lot of routine effort goes into developing the bank of learning problems. Problem generation is the way to automate this process. In this paper, we present a method of generating expression-related problems for teaching introductory programming courses. The problems are generated from open-source software code which allows keeping learning problems similar to the production code the students should learn to analyze and write. Generated problems are automatically classified by their difficulties and the knowledge they need to solve, represented as sets of possible errors. This allows seamless integration with adaptive learning algorithms. The evaluation showed that the generated problems are indistinguishable from human-authored problems and suitable for use in the educational process.
Effectiveness of Data Augmentation to Identify Relevant Reviews for Product Question Answering

With the rapid growth of e-commerce and an increasing number of questions posted on the Question Answer (QA) platforms of e-commerce websites, there is a need for providing automated answers to questions. In this paper, we use transformer-based review ranking models which provide a ranked list of reviews as a potential answer to a new question. Since no explicit training data is available, we exploit the product reviews along with available QA pairs to learn a relevance function between a question and a review sentence. Further, we present a data augmentation technique by fine-tuning the T5 model to generate new questions from customer reviews by considering the summary of the review as an answer and the review as the document. We conduct experiments on a real-world dataset from three categories in Amazon.com. To assess the performance of the models, we use the annotated question review dataset from RIKER [13]. Experimental results show that Deberta-RR model with the augmentation technique outperforms the current state-of-the-art model by 5.84%, 4.38%, 3.96%, and 2.96% on average in [email protected], [email protected], [email protected], and [email protected], respectively.
Investigation into Common Errors in English Writing among Non-Academic Staff at a Malaysian Public University

The Malaysian government has long recognized the significance of mastering the English language among its citizenry. The government has planned, and subsequently, implemented many policies to ensure Malaysians master the English language. Although civil servants have a strong desire to master the English language to perform their duties more efficiently, poor English communication skills have become a major concern in this regard. Therefore, this research aims to investigate common errors in writing the English language among non-academic staff at Universiti Sultan Zainal Abidin (UniSZA), a public university in Malaysia. The study employed an Error Analysis approach by conducting document analysis where every respondent was required to write a paragraph in the English language in about 150-200 words on the topic“My greatest challenge as a UniSZA officer”. The findings revealed that a sizeable number of non-academic staff committed different types of errors in writing the English language, including errors related to subject-verb agreement, passive voice, plurality, choice of word, omission of word, use of article, tense sequence, word ordering, gerund, addition of word/redundancy, and comparison of adjective. It was observed that lack of linguistic skills, lack of exposure, and inadequate practices in written English were the main factors contributing to the English writing errors among the non-academic staff. These findings could help the university management to develop appropriate programs that can assist the non-academic staff to develop English writing competence. However, this study is limited to English writing errors. Further research may focus on other language skills such as speaking and listening.
Quantum group blind signature scheme based on measurement-based quantum computation

Digital signatures are widely used in electronic communications, for example in electronic commerce and e-mail. But classical digital signature scheme only offer security relying on computational assumptions. In contrast, quantum digital signatures provide security based on fundamental principles of quantum physics. Quantum group signatures provide anonymity for signatory, quantum blind signatures provide confidentiality for signed messages. In this paper we demonstrate a quantum group blind signature scheme which have the properties both of group signatures and blind signatures. Besides, the security of our scheme is guaranteed by the quantum mechanics, so without any computational assumption. the group property and blind property of our scheme profits from the properties of the measurement-based quantum computation (MBQC). Our quantum group blind signature scheme can be widely used in e-voting system, e-cash, e-commerce, etc.
Hierarchical Code-to-Architecture Mapping

Automating the mapping of a system’s code to its architecture is important in improving the adoption of successful Software Architecture Consistency Checking (SACC) methods like Reflexion Modelling. InMap is an interactive and iterative code-to-architecture mapping recommendation approach that achieves a rather decent recall and precision of 0.97 and 0.82 respectively, using minimal architecture documentation to apply natural language techniques to a software’s codebase. Nevertheless, InMap like most other automated recommendations techniques maps to architectural modules, low-level source code units like classes. For large complex systems, this can still hinder adoption due to the review effort required by a software architect when accepting or rejecting the recommendations. In this paper, we present a hierarchical package mapping technique that provides recommendations for higher-level source code units, i.e. packages. It utilizes InMap’s information retrieval capabilities to recommend mappings between the software’s packages and its architectural modules. We show that using our proposed technique we are able to reduce the recommendation review effort required by an architect, by 95% on average, for the six systems tested, and still achieve a code coverage of 75%.
Big Data Pipeline Scheduling and Adaptation on the Computing Continuum

The Computing Continuum, covering Cloud, Fog, and Edge systems, promises to provide on-demand resource-as-a-service for Internet applications with diverse requirements, ranging from extremely low latency to high-performance processing. However, eminent challenges in automating the resources man-agement of Big Data pipelines across the Computing Continuum remain. The resource management and adaptation for Big Data pipelines across the Computing Continuum require significant research effort, as the current data processing pipelines are dynamic. In contrast, traditional resource management strategies are static, leading to inefficient pipeline scheduling and overly complex process deployment. To address these needs, we propose in this work a scheduling and adaptation approach implemented as a software tool to lower the technological barriers to the management of Big Data pipelines over the Computing Continuum. The approach separates the static scheduling from the run-time execution, em-powering domain experts with little infrastructure and software knowledge to take an active part in the Big Data pipeline adaptation. We conduct a feasibility study using a digital healthcare use case to validate our approach. We illustrate concrete scenarios supported by demonstrating how the scheduling and adaptation tool and its implementation automate the management of the lifecycle of a remote patient monitoring, treatment, and care pipeline.
Scalable Machine Learning Using PySpark

In this paper, we present a portable labware on Google CoLab for Scalable Machine Learning (SML) with PySpark for facilitating research in Science and Engineering (SML4SE) applications. This will allow researchers to access, share, collaborate, and practice hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning concepts and getting more experience in hands-on problem-solving skills for big data analytics.
Gamification as Support for Educational Inclusion: The Case of AR-mBot

Today’s education must offer multiple opportunities to address the individual needs of all students. In this context, educational innovation plays an important role in guiding the processes of creating strategies to achieve more inclusive learning and teaching. Many studies have analyzed the affordances of augmented reality, gamification, and the adoption of Universal Design for Learning as separate strategies to improve education. In turn, this paper presents the combination of these three strategies as a positive alternative to address diversity in the classroom. A gamified augmented educational application has been designed that considers the principles of Universal Design for Learning to teach robotics in secondary education. The results show the application facilitates the inclusion of students, improving the fulfillment of the academic challenges.
Classification of Encrypted Data Using Deep Learning and Legendre Polynomials

Problems involving the classification of sensitive cloud data, such as medical or financial records, can be solved using a convolutional neural network (CNN) approach. Data privacy and security, however, require extra care. Homomorphic encryption is a potential security solution to allow CNNs to classify cypher data. The convolution layer, for example, is a computation that can be fully homomorphically evaluated; however, non-linear functions, such as the activation layer, cannot be fully homomorphically assessed since they do not follow a linear pattern. This paper uses low-degree polynomials to approximate these non-linear functions. We compare various polynomial approximation approaches to achieve the optimal approximation function for activation functions such as Relu. Finally, we use a proposed CNN and the MNIST dataset to demonstrate the effectiveness of our approximation functions. The resulting proposed CNN achieved a 99.80% accuracy rate.
Domain-Specific Chatbot Development Using the Deep Learning-Based RASA Framework

Conversational agents are actively gaining popularity in research because of their ability to imitate human responses in almost every domain. As there are many research enhancements in deep learning models, it becomes challenging to incorporate all these enhancements while developing a conversational agent. One of the main advantages of conversational agents is their ability to answer frequently asked queries without any human involvement and automatically generate the conversation's story flow. In any educational institution, it becomes difficult for the teaching and non-teaching staff to answer all the students' queries regarding the course, exam, and other information regarding their daily activities in the institute. Using the deep learning framework, we developed a chatbot to answer various questions related to the education domain, such as exam(timetable, venue) and course-related queries(course handout). The questions are answered by querying databases which can be updated via an administrator's web browser. The system will first create intents for the use cases and entity recognition mechanisms after connecting the deep learning framework to the database using custom actions. We had created a user interface to allow updates to the database for exam timetable and course information via either file upload or a web page.
A Supply Chain Service Cybersecurity Certification Scheme based on the Cybersecurity Act

Since the provision of digital services in our days (e.g. container management, transport of COVID vaccinations or LNG) in most economic sectors (e.g. maritime, health, energy) involve national, EU and non-EU stakeholders compose complex Supply Chain Services (SCS). The security of the SCS is most important and it emphasized in the NIS 2 directive [3] and it is a shared responsibility of all stakeholders involved that will need to be compliant with a scheme. In this paper we present an overview of the proposed Cybersecurity Certification Scheme for Supply Chain Services (EUSCS) as proposed by the European Commission (EC) project CYRENE [1]. The EUSCS scheme covers all the three assurance levels defined in the Cybersecurity Act (CSA) [2] taking into consideration the criticality of SCS according to the NIS 2 directive [3], the ENISA Threat Landscape for Supply Chain Attacks [4] and the CYRENE extended online Information Security Management System (ISMS) that allows all SCS stakeholders to provide and access all information needed for certification purposes making the transition from current national schemes in the EU easier.
TUFA: A TOSCA extension for the specification of accelerator-aware applications in the Cloud Continuum

A Distributed Application Topology is a valuable commodity built on the strength of a long and iterative design process. A topology is generally refined over time, other topologies can use it as a component, and the community may share it. To reproduce a deployment, several properties must be recorded such as data origin, processing steps, configuration settings, and hardware requirements. This paper presents an extension to the TOSCA specification that allows for the definition of accelerator-aware services that can span from Cloud to Edge. Additionally, we introduce the concept of Abstract Applications that contain at least one abstract service definition. The process of Service Optimization replaces the abstract sertvices, creating an explicit topology deployable under hybrid deployment models (Virtual Machines, Containers, HPC) residing on the Cloud Continuum spectrum.
Privacy Preservation for Big Data Healthcare Management

In this era of advanced technology, where individuals, businesses and medical have become independent of digital technology, there is a need to increase privacy and security. Medical equipment, as well as other smart devices, have been combined with technology in recent years, allowing information to be acquired conveniently, swiftly, and efficiently. Some medical tools, such as patient monitors, infusion pumps, ventilators, and so on, produce, store, and handle a large amount of data on the patient. These records are personal to a patient and contain sensitive information about him or her. Because the data contains crucial information about a patient&#x2019;s health, even a minor leak could jeopardize the patient&#x2019;s personal life. Given the sensitive nature of the data, adequate security and privacy measures must be implemented to ensure its protection. As a result, there is a need for patient data storage and maintenance, as well as for privacy preservation. In this paper, we designed a model that provides more than 60% privacy than existing state-of-the-art techniques by using the pre-defined ways of privacy preservation. We present the simulation results and the layout of the privacy preservation tables that reflects the efficacy of the approach.
Quantum-Resistant 1-out-of-N Oblivious Signatures from Lattices

As business activities and information exchange increasingly move online, digital signatures, among other cryptographic techniques, have been developed to help authenticate the source and integrity of digital information when transferred. Various types of signature primitives, such as ring signatures and blind signatures, have been introduced to satisfy privacy protection needs spanning from ensuring anonymity of a signer to maintaining secrecy of the content to be signed from a signer. Among different signature schemes, the 1-out-of-N oblivious signature scheme, which was introduced by Chen (ESORICS’ 94) and later formalized by Tso et al. (ISPEC’ 08), provides a further basis of trust while preserving the signature requestor’s privacy as blind signatures do. In this scheme, a recipient first selects a set of messages, one of which being the message he or she intends to obtain a signature for. After interacting with a signer, while the recipient will be able to obtain a signature on the predetermined message, the signer only knows that he or she signed one of the messages but remains oblivious to exactly which message was signed. However, all existing oblivious signature schemes are built upon the hardness of number-theoretic problems, which, as Shor demonstrated in 1994, cannot withstand attacks from quantum adversaries. To address this problem, this work proposes a novel quantum-resistant 1-out-of-N oblivious signature scheme based on SIS hard assumption. We also provide security proofs to demonstrate that the security requirements of ambiguity and strong unforgeability are satisfied under the random oracle model. To the best of our knowledge, the proposed scheme is the first 1-out-of-N oblivious signature that is secure against quantum adversaries.