Reviewerly/exHarmony huggingface dataset:
Keystroke Dynamics as Part of Lifelogging

In this paper we present the case for including keystroke dynamics in lifelogging. We describe how we have used a simple keystroke logging application called Loggerman, to create a dataset of longitudinal keystroke timing data spanning a period of more than 6 months for 4 participants. We perform a detailed analysis of this data by examining the timing information associated with bigrams or pairs of adjacently-typed alphabetic characters. We show how there is very little day-on-day variation of the keystroke timing among the top-200 bigrams for some participants and for others there is a lot and this correlates with the amount of typing each would do on a daily basis. We explore how daily variations could correlate with sleep score from the previous night but find no significant relation-ship between the two. Finally we describe the public release of this data as well including as a series of pointers for future work including correlating keystroke dynamics with mood and fatigue during the day.
STUDENTS’ PERCEPTION ON QUIZZIZ AS GAME BASED LEARNING IN LEARNING GRAMMAR IN WRITEN DISCOURSE

The study is entitled the students’ perception on Quizizz as Game Based Learning as a in learning grammar in Written discourse. The aimed of this research was to investigate students’ opinion on Quizizz as Game Based Learning as a in learning grammar in Written discourse and their obstacles in the implementation. The data for this study were gained through qualitative method that used questionnaire and interview, and the respondents were four semester students who used quizziz as their learning activity. And created 10 questions relevant to the topics which are students already learned before by using Quizizz as games based learning for 20 minutes before finishing grammar in written discourse class once the students feel very tired, sleeping and bored for their grammar in written discourse class. The study revealed that the students had positive perception on the use of quizziz, and all students were very active to answer the questions which provided by researches, and more concentrated on the topic. The result of the study expected to give contribution in improving EFL teaching and learning activity, especially in learning grammar in Written discourse.
Roos bound for skew cyclic codes in Hamming and rank metric

In this paper, a Roos like bound on the minimum distance for skew cyclic codes over a general field is provided. The result holds in the Hamming metric and in the rank metric. The proofs involve arithmetic properties of skew polynomials and an analysis of the rank of parity-check matrices. For the rank metric case, a way to arithmetically construct codes with a prescribed minimum rank distance, using the skew Roos bound, is also given. Moreover, some examples of MDS codes and MRD codes over finite fields are built, using the skew Roos bound.
Lab work activities of computer network using virtual reality application

Lab work activities are one of the hearts of science learning, but several students have limited use and time to explore computer network tools.This study proposes a virtual reality (VR) application used to support lab work activities of a computer network so that students may conduct lab work activities by themselves at any time.The method used in this study is a multimedia development life cycle (MDLC) that consists of five stages, they are concept, design, material collecting, assembly, and testing & evaluation.A formative evaluation carried out by involving two experts, namely software expert and material expert, to measure the feasibility of application before conducting user testing.Meanwhile, a summative evaluation used to evaluate the VR application by involving 25 students who were taking the course of computer networking.Statistical analysis shows that the score of Cronbach's alpha test is higher than 0.6 for aspects of material subject and experience.Hence, the VR application can be used to support the lab work activities of a computer network.
Compressing Deep Networks by Neuron Agglomerative Clustering

In recent years, deep learning models have achieved remarkable successes in various applications, such as pattern recognition, computer vision, and signal processing. However, high-performance deep architectures are often accompanied by a large storage space and long computational time, which make it difficult to fully exploit many deep neural networks (DNNs), especially in scenarios in which computing resources are limited. In this paper, to tackle this problem, we introduce a method for compressing the structure and parameters of DNNs based on neuron agglomerative clustering (NAC). Specifically, we utilize the agglomerative clustering algorithm to find similar neurons, while these similar neurons and the connections linked to them are then agglomerated together. Using NAC, the number of parameters and the storage space of DNNs are greatly reduced, without the support of an extra library or hardware. Extensive experiments demonstrate that NAC is very effective for the neuron agglomeration of both the fully connected and convolutional layers, which are common building blocks of DNNs, delivering similar or even higher network accuracy. Specifically, on the benchmark CIFAR-10 and CIFAR-100 datasets, using NAC to compress the parameters of the original VGGNet by 92.96% and 81.10%, respectively, the compact network obtained still outperforms the original networks.
Design and Implementation of an Intelligent Gaming Agent Using A* Algorithm and Finite State Machines

The last decade has seen Artificial Intelligence (AI) seep into the game development industry, mainly for the purpose of developing more human-like non-player characters (NPCs) to improve player experience.This study focuses on solving the problem of player experience with respect to AI controlled gaming agents.The main aim of the study is to develop an intelligent gaming agent by implementing A* Pathfinding and Finite State Machines.For the implementation, Mixamo/Adobe Fuse was used to model the 3D characters (PC and NPCs), the A* Pathfinding component was implemented using a Unity 3D plugin known as "Arongranberg's A* Pathfinding Project".The FSM component on the other hand was implemented via C# scripts.Unity IDE was used to compile scripts and simulate the game.The end product of this project is a 3 rd Person survival shooter 3D game which fully implements the concepts of A* Pathfinding and Finite State Machines.Potentially, this project could be used as a guide to developing games of the same or different genre.With little improvements, the end product of this project (the game) could be made retail ready.
Document Recommendations and Feedback Collection Analysis within the Slovenian Open-Access Infrastructure

This paper presents a hybrid document recommender system intended for use in digital libraries and institutional repositories that are part of the Slovenian Open Access Infrastructure. The recommender system provides recommendations of similar documents across different digital libraries and institutional repositories with the aim to connect researchers and improve collaboration efforts. The hybrid recommender system makes use of document processing techniques, document metadata, and the similarity ranking function BM25 to provide content-based recommendations as a primary method. It also uses collaborative-filtering methods as a secondary method in a cascade hybrid recommendation technique. We also provide a real-world data feedback collection analysis for our hybrid recommender system on an academic digital repository in order to be able to identify suitable time-frames for direct feedback collection during the year.
Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement

We propose an unsupervised network with adversarial learning, the Raindrop-aware GAN, which enhances the quality of coastal video images contaminated by raindrops. Raindrop removal from coastal videos faces two main difficulties: converting the degraded image into a clean one by visually removing the raindrops, and restoring the background coastal wave information in the raindrop regions. The components of the proposed network—a generator and a discriminator for adversarial learning—are trained on unpaired images degraded by raindrops and clean images free from raindrops. By creating raindrop masks and background-restored images, the generator restores the background information in the raindrop regions alone, preserving the input as much as possible. The proposed network was trained and tested on an open-access dataset and directly collected dataset from the coastal area. It was then evaluated by three metrics: the peak signal-to-noise ratio, structural similarity, and a naturalness-quality evaluator. The indices of metrics are 8.2% (+2.012), 0.2% (+0.002), and 1.6% (−0.196) better than the state-of-the-art method, respectively. In the visual assessment of the enhanced video image quality, our method better restored the image patterns of steep wave crests and breaking than the other methods. In both quantitative and qualitative experiments, the proposed method more effectively removed the raindrops in coastal video and recovered the damaged background wave information than state-of-the-art methods.
Chatbot Kim: A Digital Tutor on AI. How Advanced Dialog Design Creates Better Conversational Learning Experiences

Conversational user interfaces, aka chatbots, offer new ways of interaction that can be used not only for task-led applications, but also for learning itself. Still, most conversational learning applications offer a predominantly one-directional dialog – either bot-led, with the user only confirming, or user-led, with the bot answering questions, but not going beyond. In contrast to these common approaches, learnbot Kim by time4you [1] not only conveys information, but wraps it in an equally entertaining and instructive chat, combining pre-defined dialog turns with a flexible dialog management. This article explains the design decisions made during the dialog development process and the underlying reasons. After having read this text, you will have a better idea of how dialog in conversational learning can be modeled to allow for a more natural conversational experience.
A hybrid naïve Bayes based on similarity measure to optimize the mixed-data classification

In this paper, a hybrid method has been introduced to improve the classification performance of naive Bayes (NB) for the mixed dataset and multi-class problems. This proposed method relies on a similarity measure which is applied to portions that are not correctly classified by NB. Since the data contains a multi-valued short text with rare words that limit the NB performance, we have employed an adapted selective classifier based on similarities (CSBS) classifier to exceed the NB limitations and included the rare words in the computation. This action has been achieved by transforming the formula from the product of the probabilities of the categorical variable to its sum weighted by numerical variable. The proposed algorithm has been experimented on card payment transaction data that contains the label of transactions: the multi-valued short text and the transaction amount. Based on K-fold cross validation, the evaluation results confirm that the proposed method achieved better results in terms of precision, recall, and F-score compared to NB and CSBS classifiers separately. Besides, the fact of converting a product form to a sum gives more chance to rare words to optimize the text classification, which is another advantage of the proposed method.
Realization of 3D Aqua Hologram Augmented Reality of Robot Fish

This paper is about the realization of robot fish motion along with 3D hologram fish in an aquarium, with the augmented reality of 3D aqua holograms to attract viewers.In this new proposed concept, the animated fish video with 3D hologram is projected into a robot fish aquarium.However, this process of projecting 3D hologram fish into a robot fish aquarium may cause the overlap of the robot fish with the animated fish.To avoid this problem of overlapping, sensors play an important role.Image sensors track the robot fish movement.The proposed concept can be realized by boundary detection and optical flow algorithms, which are used to detect the positions of the robot fish.The performance of this detection algorithm was evaluated and satisfactory results were obtained.This 3D hologram with augmented reality
Blockchain and Internet of Things for Business Process Management: Theory, Challenges, and Key Success Factors

The combination of business process management (BPM) and emerging technologies is a logical step.The evolving number of advanced technologies shows that contributions are with a high value for business processes.From a BPM point of view, value creation from the modern technologies such as Internet of things and Blockchain technology is pivotal on a progressively higher scale and affect the business process in different aspects.However, current research in this area still at the beginning.In order to close this research gap and the lack of experience in this area, the topic of integrating Blockchain and IoT technologies with BPM will play an essential role in a corporate context, particularly in the context of inter/intraorganizational information systems and their diverse design options.This review paper aims to survey the impact of tow emerging technologies: Internet of Things and Blockchain technology on BPM and illustrates the current state of the art in this research domain.Each technology was investigated through a design science research approach to provide as a descriptive theoretical overview, characterize its theoretical background, challenges, and key success factors.
A Hybrid POS Tagger for Khasi, an Under Resourced Language

Khasi is an Austro-Asiatic language spoken mainly in the state of Meghalaya, India, and can be considered as an under resourced and under studied language from the natural language processing perspective.Part-of-speech (POS) tagging is one of the major initial requirements in any natural language processing tasks where part of speech is assigned automatically to each word in a sentence.Therefore, it is only natural to initiate the development of a POS tagger for Khasi and this paper presents the construction of a Hybrid POS tagger for Khasi.The tagger is developed to address the tagging errors of a Khasi Hidden Markov Model (HMM) POS tagger by integrating conditional random fields (CRF).This integration incorporates language features which are otherwise not feasible in an HMM POS tagger.The results of the Hybrid Khasi tagger have shown significant improvement in the tagger's accuracy as well as substantially reducing most of the tagging confusion of the HMM POS tagger.
Comparison of distance education and smart education

In the context of the pandemic caused by the spread of COVID-19, the problems of organizing the educational process based on digital technologies came to the fore, and therefore close attention was paid to distance education. At the same time, to optimize the educational process, questions arise about the use of smart technologies. In this regard, there is often a replacement of concepts such as distance education and smart education. Smart education is often viewed as a form of distance education based on communication and information technology. The article compares two educational systems: distance education and smart education. As a result of the analysis, the main positions were identified that showed the integrity of each of the educational systems under consideration.
Digital Forensics of Internet of Things Smart Heating System Investigation

The Internet of Things (IoT) integrates the Internet and electronic devices with applicability to a large spectrum of domains, from smart home automation, industrial processes, military applications, to health and environmental monitoring. Despite the clear advantages, from a forensically sound perspective, IoT can improve the accuracy and integrity of forensic investigations, but still requires extensive scientific validation in practice. In this paper, we review selected stateof-the-art challenges corresponding to digital forensics of IoT environments, and we present an empirical method on how to investigate a security incident reported for an IoT specific case - Smart Heating system.
The link between the tonehole lattice cutoff frequency and clarinet sound radiation: a quantitative study

Musical instruments are said to have a personality, which we notice in the sound that they produce. The oscillation mechanism inside woodwinds is commonly studied, but the transmission from internal waveforms to radiated sound is often overlooked, although it is musically essential. It is influenced by the geometry of their resonators which are acoustical waveguides with frequency dependent behavior due in part to the lattice of open toneholes. For this acoustically periodic medium, wave propagation theory predicts that waves are evanescent in low frequency and propagate into the lattice above the cutoff frequency. These phenomena are generally assumed to impact the external sound perceived by the instrumentalist and the audience, however, a quantitative link has never been demonstrated. Here we show that the lattice shapes the radiated sound by inducing a reinforced frequency band in the envelope of the spectrum near the cutoff of the lattice. This is a direct result of the size and spacing between toneholes, independent of the generating sound source and musician, which we show using external measurements and simulations in playing conditions. As with the clarinet, the amplitude of the even harmonics increases with frequency until they match odd harmonics at the reinforced spectrum region.
COMPETITION LAW IN DIGITAL ERA – HOW TO DEFINE THE RELEVANT MARKET?

Competition law serves as an important tool for regulation of undertakings. In order to conduct a competition law analysis, one must first define the relevant market. However, this task is becoming more intricate in today’s digital era, especially in relation to so-called zero-price markets. These markets are characterised as markets where users of products or services do not pay for the use, at least they do not pay by money. This paper asks how to define relevant market in such case. Three methods of relevant market definition are presented, namely qualitative analysis, SSNIP test and SSNDQ test. The paper briefly explores positive and negative elements of these tests and compares the findings with the European Commission’s 2019 report. It leads to the answer that qualitative method might have certain advantages in this regard.
Implementasi Algoritma Apriori untuk Rekomendasi Kombinasi Produk Penjualan

Analyzing and systematically extracting essential information from recording transactions is important for a business, including online stores. Sometimes, some online stores offer a product package that is not suitable for the customer. It happens because they did not process the data transaction to observe the association between products on a package. A web-based recommendation system was build using the CodeIgniter framework with PHP programming language. The system developed using Market Basket analysis that can determine the combination of products. Apriori algorithm used as a technique to analyze the relationship between products based on the data transaction. The lift ratio value generated from the rule is 1.18, which means that the rule has the power of relationships between items. We evaluate the system using USE questionnaire with usefulness results is 90.83%, ease of use 89.09%, ease of learning 95%, and satisfaction 90.94%, which strongly agree in every aspect.
Entropic uncertainty relations for general symmetric informationally complete positive operator-valued measures and mutually unbiased measurements

We construct inequalities between R\'enyi $\ensuremath{\alpha}$-entropy and the indexes of coincidence of probability distributions, based on which we obtain improved state-dependent entropic uncertainty relations for general symmetric informationally complete positive operator-valued measures (SIC-POVMs) and mutually unbiased measurements (MUMs). We show that our uncertainty relations for general SIC-POVMs and MUMs can be tight for sufficiently mixed states, and, moreover, comparisons to the numerically optimal results are made via information diagrams.
Extended Truncated-differential Distinguishers on Round-reduced AES

Distinguishers on round-reduced AES have attracted considerable attention in the recent years. While the number of rounds covered in key-recovery attacks did not increase, subspace, yoyo, mixture-differential, and multiple-of-n cryptanalysis advanced the understanding of the properties of the cipher.For substitution-permutation networks, integral attacks are a suitable target for extension since they usually end after a linear layer sums several subcomponents. Based on results by Patarin, Chen et al. already observed that the expected number of collisions for a sum of permutations differs slightly from that for a random primitive. Though, their target remained lightweight primitives.The present work illustrates how the well-known integral distinguisher on three-round AES resembles a sum of PRPs and can be extended to truncated-differential distinguishers over 4 and 5 rounds. In contrast to previous distinguishers by Grassi et al., our approach allows to prepend a round that starts from a diagonal subspace. We demonstrate how the prepended round can be used for key recovery with a new differential key-recovery attack on six-round AES. Moreover, we show how the prepended round can also be integrated to form a six-round distinguisher. For all distinguishers and the key-recovery attack, our results are supported by implementations with Cid et al.’s established Small-AES version. While the distinguishers do not threaten the security of the AES, they try to shed more light on its properties.
Forensic Accounting: Breaking the Nexus between Financial Cybercrime and Terrorist Financing in Nigeria

This paper aims to examine forensic accounting (FA) investigation in tracking financial cybercrime (FC) and terrorist financing (TF) in Nigeria. It also verifies the associations between cyber criminality and terrorist financing. The sample composed of all Nigeria anti-graft agencies including the Nigeria Communication Commission (NCC). A quantitative method is used in a way to analyze the data. The results of the hypotheses revealed that forensic accounting has significant influence in disclosing the associations between financial cybercrime and terrorist financing. This study finds forensic accounting as a tool and deterrence to terrorist financing and associated crimes activities. The implications of this study emphasized that the government should strengthen enforcement on cybercrimes and provides security infrastructure (advanced technology) in tackling the precedence of financial cybercrime and terrorist financing in Nigeria.