Reviewerly/exHarmony huggingface dataset:
Keep Calm and Carry on with Cybersecurity @Home: A Framework for Securing Homeworking IT Environment

For the first time in modern history, businesses had to suddenly facilitate homeworking for a large proportion, if not all, of their workforce because of COVID-19 pandemic. The fact that employees access sensitive corporate data from non-corporate networks opens the door wide for many cybersecurity risks that could result in data loss, breaches and consequently huge financial loss. Since the move was sudden, most businesses, especially small ones, did not have the time to assess their homeworking cybersecurity requirements. This paper aims to bridge this gap and propose a multi-layered framework that is focused on businesses' requirements to guide cybersecurity @home activities. The framework can be also beneficial for businesses that currently have homeworking cybersecurity policies to assess their compliance with the framework and enrich it.
Comparison of mutual information and its point similarity implementation for image registration

Mutual information (MI) is one of the most popular and widely used similarity measures in image registration. In traditional registration processes, MI is computed in each optimization step to measure the similarity between the reference image and the moving image. The presumption is that whenever MI reaches its highest value, this corresponds to the best match. This paper shows that this presumption is not always valid and this leads to registration error. To overcome this problem, we propose to use point similarity measures (PSM) which in contrast to MI allows constant intensity dependence estimates called point similarity functions (PSF). We compare MI and PSM similarity measures in terms of registration misalignment errors. The result of the comparison confirms that the best alignment is not at the highest value of MI but near to it and it shows that PSM performs better than MI if PSF matches the correct intensity dependence between images. This opens a new direction of research towards the improvement of image registration.
Development of an Ontology-Based Knowledge Network by Interconnecting Soil/Water Concepts/Properties, Derived from Standards Methods and Published Scientific References Outlining Infiltration/Percolation Process of Contaminated Water

The present work deals with the development of an Ontology-Based Knowledge Network of soil/water physicochemical & biological properties (soil/water concepts), derived from ASTM Standard Methods (ASTM i,n ) and relevant scientific/applicable references (published papers-PP i,n ) to fill up/bridge the gap of the information science between cited Standards and infiltration discipline conceptual vocabulary providing accordingly a dedicated/internal Knowledge Base (KB).This attempt constitutes an innovative approach, since it is based on externalizing domain knowledge in the form of Ontology-Based Knowledge Networks, incorporating standardized methodology in soil engineering.The ontology soil/water concepts (semantics) of the developed network correspond to soil/water physicochemical & biological properties, classified in seven different generations that are distinguished/located in infiltration/percolation process of contaminated water through soil porous media.The interconnections with arcs between corresponding concepts/properties among the consecutive generations are defined by the relationship of dependent and independent variables.All these interconnections are documented according to the below three ways: 1) dependent and independent variables interconnected by using the logical operator "depends on" quoting existent explicit functions and equations; 2) dependent and independent variables interconnected by using the logical operator "depends on" quoting produced implicit functions, according to Rayleigh's method of indices; 3) dependent and independent variables interconnected by using the logical operator "re-How to cite this paper:
Positive operator-valued measure for two-photon detection via sum-frequency generation

Spontaneous parametric down conversion (PDC), in the perturbative limit, can be considered as a probabilistic splitting of one input photon into two output photons. Conversely, sum-frequency generation (SFG) implements the reverse process of combining two input photons into one. Here we show that a single-photon projective measurement in the temporal-mode basis of the output photon of a two-photon SFG process effects a generalized measurement on the input two-photon state. We describe the positive-operator-valued measure (POVM) associated with such a measurement, and show that its elements are proportional to the two-photon states produced by the time-reversed PDC process. Such a detection acts as a joint measurement on two photons, and is thus an important component of many quantum information processing protocols relying on photonic entanglement. Using the retrodictive approach, we analyze the properties of the two-photon POVM that are relevant for quantum protocols exploiting two-photon states and measurements.
Pedestrian Detection and Classification for Autonomous Train

In this paper, we present a combined approach for human localization and classification in Autonomous Train application. Our contribution is threefold. (a) The creation of a new dataset for workers wearing orange vests in a railway environment context. (b) A deep learning supervised YOLO object detector for persons detection combined with a linear SVM (Support Vector Machine) classifier for persons classification into workers wearing orange vests or travelers. (c) A realtime vision-based technique for the environment monitoring in a driverless train application. Experimental results evaluate the parameters of our two stages detection approach and show that our algorithm is robust in detecting and classifying railway workers for a real-time implementation on an embedded system. Our implementation on an embedded system allows a detection with a correct classification rate of 98.5 % of accuracy and a classification time of 1 ms per frame.
Anti-collision Trajectory Planning and Tracking Control based on MPC and Fuzzy PID Algorithm

The following topics are dealt with: road vehicles; vehicle dynamics; control system synthesis; predictive control; road safety; mobile robots; stability; steering systems; adaptive control; and driver information systems.
Consensus Control for Decentralized Deep Learning

Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers from the degradation in the quality of the model: the training and test performance of models trained in a decentralized fashion is in general worse than that of models trained in a centralized fashion, and this performance drop is impacted by parameters such as network size, communication topology and data partitioning. We identify the changing consensus distance between devices as a key parameter to explain the gap between centralized and decentralized training. We show in theory that when the training consensus distance is lower than a critical quantity, decentralized training converges as fast as the centralized counterpart. We empirically validate that the relation between generalization performance and consensus distance is consistent with this theoretical observation. Our empirical insights allow the principled design of better decentralized training schemes that mitigate the performance drop. To this end, we provide practical training guidelines and exemplify its effectiveness on the data-center setup as the important first step.
Foreign Journals of Information and Library Issues: Analysis of Publication Opportunities for Scientific and Pedagogical Staff

One of the performance indicators of higher education institutions is publication activity of scientific and pedagogical staff in journals indexed by the international scientometric databases Web of Science and Scopus. Of particular interest is the study of the possibility of publications in foreign journals with a high quartile. The issue of foreign publications is acute for domestic researchers of library and information activities. The study identified 227 foreign journals that reflect the category “Library and information sciences” and publish information in English. There were selected for study 130 journals indexed in the Web of Science. The analysis of indicators of professional foreign periodicals allowed to make the rating of journals that are relevant for publishing scientific articles on teaching the library disciplines. The detailed analysis of the topics of publications in the identified journals helped to determine specialized foreign periodicals on library and information issues. During the analysis, there were selected the professional journals that reflect the results of research in the field of principal disciplines of the mandatory part of the bachelor’s degree program in “Library and information activities”. The article considers the topics of specialized professional periodicals of foreign countries. For the selected publications, the author compiled the comparative table of the thematic distribution of publications with chronological coverage of 10 years that allowed identifying thematic headings with high publication activity. The article presents the analysis of territorial distribution of journals and identifies the leading foreign publishers. The author briefly highlights the features of publication placement in foreign journals. Based on information provided on the information and analytical portal eLIBRARY.ru, the author conducted the assessment of placement of publications by leading researchers in the field of library science in identified periodicals. The article presents the results of analytical review of the main foreign journals indexed by Web of Science and related to the first and second quartile, which publish articles in English and reflect issues in library and information sphere. The author established that publication in foreign professional periodicals can be implemented by domestic researchers and teachers of library and information disciplines, both in the analysed journals and other specialized publications and journals in related scientific fields.
An Efficient Analyses of the Behavior of One Dimensional Chaotic Maps using 0–1 Test and Three State Test

In this paper, a rigorous analysis of the behavior of the standard logistic map, Logistic Tent system (LTS), Logistic-Sine system (LSS) and Tent-Sine system (TSS) is performed using 0-1 test and three state test (3ST). In this work, it has been proved that the strength of the chaotic behavior is not uniform. Through extensive experiment and analysis, the strong and weak chaotic regions of LTS, LSS and TSS have been identified. This would enable researchers using these maps, to have better choices of control parameters as key values, for stronger encryption. In addition, this paper serves as a precursor to stronger testing practices in cryptosystem research, as Lyapunov exponent alone has been shown to fail as a true representation of the chaotic nature of a map.
A Systematic Review of the Continuous Professional Development for Technology Enhanced Learning Literature

There is a large body of international research on raising the quality of education, with particular emphasis on CPD to support professional and pedagogical growth. From an educator’s perspective, there is widespread agreement that effective CPD is an important component of educational success. Therefore, it is unsurprising that research interest in this area has grown, particularly in light of the digital agenda. In a TEL context, educators report one of the main barriers to effective use is the lack of training in this area. This review of literature will examine some of the key ideas that form successful TEL CPD delivery, more specifically with relation to transformative models of CPD. Likewise, the section attempts to understand the context in which educators are operating and make sense of the challenges that relate to continuing professional development (CPD). In order to fully explore this phenomenon, personal development (PD) frameworks are explored, with a specific focus on Aileen Kennedy’s (2005) 9 typologies.
Mobile VR and Marker Tracking Method Applied in Virtual Welding Simulation Kit for Welding Training

Virtual Reality (VR) technology has become cheaper over time and makes it possible to own. However, interaction techniques in VR need to be natural as possible to be accepted by users. Compared to other computing devices (e.g., mobile and desktop), there is very few human-computer interactions (HCI) paradigms in VR and a lot of HCI issues and problems are yet to be solved. In this paper, we present a VR Welding Kit, a virtual reality welding training simulation developed for mobile smartphones using multiple marker images as an interaction method. The experimental results have revealed with comparison benchmark with high-end VR device, Oculus Quest to see the efficiency of multiple marker interaction with a current VR technology device. System Usability Scales (SUS) also used to see the acceptance between users with this new interaction. This is the first evaluation of combination VR with marker-based tracking method interaction. Simulator Sickness Questionnaire (SSQ) are used to check the cybersickness between participants toward multiple marker interaction. The results on performance shows that mobile VR have a large gap completion compared with Oculus Quest. The SUS score is 73.33, above 68 which is great. SSQ surveys result shows that participant almost feels none in simulation sickness.
Interactive metamaterials

Envisioning, designing, and implementing the user interface require a comprehensive understanding of interaction technologies. In this forum we scout trends and discuss new technologies with the potential to influence interaction design. --- Albrecht Schmidt, Editor
IFTS: A Location Privacy Protection Method Based on Initial and Final Trajectory Segments

Privacy protection problem is one of the most concerning issues related to Location-Based Services (LBS) in our daily life. Privacy protection of LBS often requires anonymizing customer's trajectory data. Currently available methods for trajectory anonymity often assume an entire trajectory as one anonymous unit, which may lead to low anonymity efficiency due to the massive amount of trajectory data, especially for customers travel through a long road. Considering people's routine activities, the starting and ending locations of a trip often uncover the user's request intent, which may lead to exposure of user privacy. In order to address the problem of inefficient trajectory anonymity, we propose a location privacy protection method that is based on the initial and final trajectory segmentation (IFTS) in this paper. In the IFTS method, the road network structure is first transformed into an edge cluster model based on its location type. Then, the user trajectory is divided to segments according to the temporal sequence of the ingress and egress nodes. The initial and final trajectory segments are identified and divided into equivalence classes, which then are used for constructing a trajectory graph and the corresponding k-anonymous set. Our experimental results show that the proposed method can reduce the anonymous area, improve anonymity efficiency, and enhance trajectory data utilization compared to the existing methods for trajectory anonymity.
Dynamically Grown Generative Adversarial Networks

Recent work introduced progressive network growing as a promising way to ease the training for large GANs, but the model design and architecture-growing strategy still remain under-explored and needs manual design for different image data. In this paper, we propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator. It enjoys the benefits of both eased training because of progressive growing and improved performance because of broader architecture design space. Experimental results demonstrate new state-of-the-art of image generation. Observations in the search procedure also provide constructive insights into the GAN model design such as generator-discriminator balance and convolutional layer choices.
Application of principal component analysis on chemical data for reservoir correlation: A case study from Cretaceous carbonate sedimentary rocks, Saudi Arabia

A new method is presented to identify chemostratigraphic zones by applying principal component (PC) analysis and to interpret geological boundaries based on PC curves. The PC data were calculated using inductively coupled plasma–optical emission spectrometry and inductively coupled plasma–mass spectrometry, with data acquired for 50 chemical elements in the range Na–U. In the present study, a total of 832 core and cuttings samples were analyzed from four wells penetrating the Cretaceous Wasia Formation, southeastern Saudi Arabia. The conventional workflow involves plotting profiles for elements and elemental ratios, with 250 to 300 profiles produced for each study section. With so many variables to consider, it is a challenging and time-consuming process to produce chemostratigraphic frameworks, requiring expert knowledge of chemostratigraphy. By using only a few PC curves, as opposed to element or ratio profiles, it is easier for nonexperts to identify correlative boundaries. The PC parameters reflect changes in more than one group of elements and summarize geological changes. For example, in this study, PC2 parameter values relate to changes in depositional redox conditions. A close association was revealed between boundaries identified using PC and boundaries from an existing conventional chemostratigraphic study on the same wells. For example, PC zone E1-3 relates to the conventional zones C3-1b. The fact that some PC and conventional chemostratigraphic boundaries do not always coincide (e.g., conventional boundaries C2-1:C2-2 and C2-2:C2-3 exist within PC zone E2-1) means that the integrated correlation scheme is of higher resolution if these techniques were employed in isolation.
Application of Blockchain Technology in Credit Management for Credit Bank System

Credit bank provides learning channels and direction for growing talents, but the current centralized management form of credit bank is not conducive to the management of learning results. The blockchain technology can solve the problem of credit management in credit bank system. The course, students and the results of their learning are linked to the block by the blockchain technology. It can realize the decentralization of management, the automation of transfer about learning credit and education, results are traceable and it cannot be tampered with, so blockchain technology can save management resources of credit bank and improve trust. It is feasible that using hyperledger fabric technology to realize the credit management of credit bank system by system design and experiment.
An Optimized Aho-Corasick Multi-Pattern Matching Algorithm for Fast Pattern Matching

Multi-pattern search is a critical task in many domains like DPI, NIDS, firewall and big data analytics. Aho-Corasick algorithm (AC) is one of the best multi-pattern search algorithms with linear complexity. Our goal is to optimize AC algorithm to reduce search time for multi-pattern match. Our optimized algorithm constructs AC DFA graph by adding up to K patterns for each input pattern and searches modified AC DFA by using every Kth bytes from input search text and skipping adjoining K-1 bytes. Here K is optimization factor. Our algorithm complexity is in order of n/K with input size n. Our algorithm requires less memory to construct optimized AC DFA compared to original AC DFA graph. Depending on number of patterns added in AC DFA, the evaluation results show 30-45% search performance gain for K = 2. With higher optimization factor, search performance gain close to 40-60% is achieved.
Pedagogical Requirements for the Organization of the Educational Process in Specialized State Educational Institutions

The article describes the pedagogical requirements for the organization of the educational process in state specialized educational institutions.
Observer Dependent Lossy Image Compression

Deep neural networks have recently advanced the state-of-the-art in image compression and surpassed many traditional compression algorithms. The training of such networks involves carefully trading off entropy of the latent representation against reconstruction quality. The term quality crucially depends on the observer of the images which, in the vast majority of literature, is assumed to be human. In this paper, we aim to go beyond this notion of compression quality and look at human visual perception and image classification simultaneously. To that end, we use a family of loss functions that allows to optimize deep image compression depending on the observer and to interpolate between human perceived visual quality and classification accuracy, enabling a more unified view on image compression. Our extensive experiments show that using perceptual loss functions to train a compression system preserves classification accuracy much better than traditional codecs such as BPG without requiring retraining of classifiers on compressed images. For example, compressing ImageNet to 0.25 bpp reduces Inception-ResNet classification accuracy by only 2%. At the same time, when using a human friendly loss function, the same compression system achieves competitive performance in terms of MS-SSIM. By combining these two objective functions, we show that there is a pronounced trade-off in compression quality between the human visual system and classification accuracy.
Building a Chatbot on a Closed Domain using RASA

In this study, we build a chatbot system in a closed domain with the RASA framework, using several models such as SVM for classifying intents, CRF for extracting entities and LSTM for predicting action. To improve responses from the bot, the kNN algorithm is used to transform false entities extracted into true entities. The knowledge domain of our chatbot is about the College of Information and Communication Technology of Can Tho University, Vietnam. We manually construct a chatbot corpus with 19 intents, 441 sentence patterns of intents, 253 entities and 133 stories. Experiment results show that the bot responds well to relevant questions.
Frequent Pattern Mining Based On Occupation and Correlation

Frequent itemset mining has been extensively studied in data mining for over the last two decade because of its numerous applications. However, the classic support-based mining framework used by most previous studies is not suitable for some real-world application, such as the travel landscapes recommendation, where occupancy besides support also plays a key role in evaluating the interestingness of an itemset. In this paper, we propose a new kind of tasks based on occupancy, namely high correlated occupancy mining, by introducing correlated occupancy into the support-based mining framework. We present the confidence constraint to filter redundant information and show the mining goal of top-k quality pattern combined with occupancy and correlation pattern mining algorithm to ensure the results credible.