Reviewerly/exHarmony huggingface dataset:
The Development of a Web-based Credit Transfer Application (CTA) for Higher Academic Institution: From Feasibility Study to Testing Phase

Abstract Credit transfer is the process of transferring course credits of the subject to another programme or another institution to support and promote students’ flexibility in their study by reducing number of courses or subjects required to complete their studies. Currently, the enrolment of new degree students in Kolej Universiti Poly-Tech MARA Kuala Lumpur (KUPTMKL) is using the manual application forms to apply the subject credit transfer. Understandably, the manual process is tedious and students are pruned to make mistake. The objective of this paper is to develop a web-based Credit Transfer Application system (CTA) for Faculty of Computing and Multimedia (FCOM) of KUPTMKL that can be used for the FCOM degree programme Coordinator and student in managing the application of credit transfer with several security applications and mechanisms such as random password, restriction of unsupported file format and database security. The CTA is developed using Agile methodology and Visual Studio Code is used to build the CTA as the code editor to write the programming languages such as Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), Hypertext Pre-processor (PHP) and XAMMP to manage the MySQL databases. In the development of the CTA system, the feasibility study is performed and three methods are used namely, observation, conducting interviews and delivering questionnaires. Based on 100 respondents of FCOM degree students and 5 Coordinators, the result shows more than 95 percent of evidence indicates that the new system developed is able to store, display and manage all information successfully and performed efficiently compared to manual method. CTA can be easily accessed by students and management via the Web.
The “double being” of the human being: culture as the basis for becoming a personality

The main purpose of this article is to prove that one can become a personality only through culture. The authors propose to regard culture as a relatively autonomous spiritual sphere, which transcends the narrow requirements of the concrete society in its historical limitations. They argue that personality is the highest level of human development to emerge in culture, whereas the level of a biosocial organism is provided by society. The authors claim that only culture, be it religious or secular, contains an existential and spiritual imperative which demands each human being to realize one’s own potential humanity. They explain why in a postmodern world culture becomes an “existential choice” of each human being, depending on one’s free will: to inherit the culture, to become a human being of humanity or not. In the Free school of philosophy and culture seminars (Odesa, Ukraine), the authors propose to enter the field of culture through the mutual interpretation of a chosen masterpiece of world literature and to link its message with our lives.
RETRACTED: On Learning Styles Based on the Big Data from the Cross-cultural Perspective

Abstract Learning styles, as a key element of individual differences, have engaged more and more attention for it is found from the analysis of big data that awareness and appropriate manipulation of language learners’ learning styles can result in effectiveness in both teaching and learning. And judging from the cross-cultural perspective, culture or ethnicity exerts a great influence on learning styles. Thus, it is adovocated that language learners’ learning styles should be respected in order to achieve effectiveness in teaching and learning as well.
Structure from Motion Using Homography Constraints for Sequential Aerial Imagery

With the omnipresence of unmanned aerial vehicle (UAV), sequential aerial imagery is becoming more widely available in both civilian and military programs. Considering that the monocular imagery is sequentially ordered according to its acquisition time and a dominant ground plane is often visible in the scene, we present a novel and complete automatic Structure from Motion system based on homography constraints. The homography induced by the ground plane is used to recover the camera pose. The windowed bundle adjustment and automatic loop closure are performed in sequential reconstruction. The global bundle adjustment is performed over the complete reconstruction. The proposed SfM system has successfully been used for sequential aerial imagery taken from a UAV.
Deal Closure Prediction based on User's Browsing Behaviour of Sales Content

We present PrediTilk, a data-driven win prediction service based on user's browsing of sales content, such as quotes, competitor comparisons or product sheets. It makes part of our GDPR-compliant system of electronic document tracking designed for marketing and sales, and addresses win prediction problem (also known as deal closure prediction). The latter consists in estimating the probability of a given opportunity to close, becoming a customer. Given the information about user's consultation of documents issued from our tracking system, our service predicts win probability of this opportunity using machine learning models. Our evaluation shows that PrediTilk provides accurate predictions, while being purely based on automatically collected data about user's browsing behaviour. Besides, it can provide objective signals to a CRM system, where most of the prospects data are entered manually. The combination of such sources can become a highly valuable asset for win prediction.
Development of a framework for UX KPIs in Industry - a case study

This work addresses the needs companies face when assessing and tracking the UX quality of their products, which is necessary to ensure that the desired level of quality is met, clarify potential areas of improvement, and compare competitor products via benchmarking.
Improvement of Bit Distribution of Binary Sequence Generated by Alternative Trace Map Function

Pseudo random binary sequences have been widely used in cryptographic applications due to their speedy construction, reproducibility and characteristics like true random sequence. Realization of modern cryptographic application and mathematical tools is inconceivable without random sequences. Previously, we proposed an alternative trace map function for generation of a pseudo random binary sequence that has long period, good correlation characteristics and high linear complexity that are attractive for security applications. However, it was found that the distribution of bits in sequence is not uniform which may lead to failure in randomness. In this paper, we propose a uniformization method using power of primitive root to improve uniformity of bit distribution in the sequence. Sequence of different lengths is generated using proposed sequence to verify the efficiency of the proposed uniformization method. By analyzing the bit distribution of generated sequences for different length of patterns, we show that the proposal achieves bit uniformity while maintaining randomness of the sequence.
On Talent Training and Cultural Development of Saxophone under Network Technology

Abstract Saxophone as a kind of musical instrument is unique in shape, playing method, playing skill, timbre and keeping way. The continuous development of network technology provides more possibilities for talent training, and popularizes Saxophone teaching, which can increase the choice of musical instruments for music lovers. Saxophone can further strengthen the performance of ensemble in group performance. This paper studies the relationship between talent training and cultural development of saxophone under the background of network technology, and deeply studies its important value in cultural shaping. After that, it explores how to popularize Saxophone more effectively and how to promote cultural production.
Generalized Likelihood-Ratio Enabled Machine Learning for UE Detection over Grant-free SCMA

In this work, we consider an uplink grant-free sparse coded multiple access (GF-SCMA) system that superimposes the transmissions from up to J user equipments (UEs) onto K resource elements (REs). A critical issue for GF-SCMA is that data retrieval is performed without the knowledge on the activeness of each UE. The detection accuracy of UE statuses thus becomes a dominant factor in data retrieval performance. At this background, a generalized likelihood ratio (GLR) enabled convolutional neural network (CNN) scheme is proposed for UE status detection. In order to reduce detection complexity, K parallel CNN structures are used, each of which decides the status of the UE associated with a respective RE. Mismatched decisions are resolved by the soft outputs from each CNN classifier. Simulation results show that the proposed GLR-enabled parallel CNNs with soft mismatch resolution can achieve 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> detection error probability at moderate to high SNRs, regardless of time correlation characteristic of channel gains, when the Rayleigh amplitude distortion of the channel can be removed by a sophisticate power control mechanism.
Triplet loss-based embeddings for forensic speaker identification in Spanish

With the advent of digital technology, it is more common that committed crimes or legal disputes involve some form of speech recording where the identity of a speaker is questioned [1]. In face of this situation, the field of forensic speaker identification has been looking to shed light on the problem by quantifying how much a speech recording belongs to a particular person in relation to a population. In this work, we explore the use of speech embeddings obtained by training a CNN using the triplet loss. In particular, we focus on the Spanish language which has not been extensively studies. We propose extracting the embeddings from speech spectrograms samples, then explore several configurations of such spectrograms, and finally, quantify the embeddings quality. We also show some limitations of our data setting which is predominantly composed by male speakers. At the end, we propose two approaches to calculate the Likelihood Radio given out speech embeddings and we show that triplet loss is a good alternative to create speech embeddings for forensic speaker identification.
A Convolution Neural Network Based on Residual Learning for Image Steganalysis

Image steganalysis is a very important technology for forensics. Recent studies show that the idea of steganalysis based on Convolutional Neural Network (CNN) is feasible. In this paper, we propose a novel digital image steganalysis model based on CNN. Compared with the existing CNN-based methods, the CNN model proposed to this paper has two characteristics. First, in the front of the network, high-pass filter in SRM is used to initialize the convolution kernels, which is beneficial to learning steganography noise in the image. Second, in the middle of the network, the residual learning mechanism is used to enhance the convergence speed and stability of the network. Experiments on the standard data set show that the proposed CNN model can detect S-UNIWARD steganography algorithm with high accuracy.
An algorithm acceleration framework for correlation-based feature selection

Repeated calculations lead to a sharp increase in the time of correlation-based feature selection. Incremental iteration has been applied in some algorithms to improve the efficiency. However, the computational efficiency of correlation has generally be ignored. An algorithm acceleration framework for correlation-based feature selection (AFCFS) is proposed. In AFCFS, the criterion of the feature selection will be analyzed and reconstructed based on entropy granularity, and the algorithm structure will also be adjusted accordingly. Specifically, all repeated part of calculation will be saved in mapping tables and can be accessed in next time directly, so as to further reduce the calculation repetition rate and improve the efficiency. The experimental results show that AFCFS can greatly reduce the cost time of these algorithms, and keep the corresponding classification accuracy basically unchanged.
Real-time Restoration of Quality Distortions in Mobile Images using Deep Learning

Frames provided by camera on mobile devices may be distorted because of camera defects and/or weather conditions such as rain and snow. These distortions affect image classifiers. This paper proposes using deep-learning architectures to restore quality distortions in real-time mobile video for image classifiers. An iOS based app is developed using CoreML to show that deep convolutional auto-encoder (CAE) based methods can be used to restore picture quality.
Lower Bounds for Adversarially Robust PAC Learning under Evasion and Hybrid Attacks

In this work, we study probably approximately correct (PAC) learning under general perturbation-based adversarial attacks. In the most basic setting, referred to as an evasion attack, the adversary's goal is to misclassify an honestly sampled point x by adversarially perturbing it into x̃, i.e., h(x̃) = ≠ c(x̃), where c is the ground truth concept and h is the learned hypothesis. The only limitation on the adversary is that x̃ is not “too far” from x, controlled by a metric measure. We first prove that for many theoretically natural input spaces of high dimension n (e.g., isotropic Gaussian in dimension n under ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> perturbations), if the adversary is allowed to apply up to a sublinear amount of perturbations in the expected norm, PAC learning requires sample complexity that is exponential in the data dimension n. We then formalize hybrid attacks in which the evasion attack is preceded by a poisoning attack in which a poisoning phase is followed by specific evasion attacks. Special forms of hybrid attacks include so-called “backdoor attacks” but here we focus on the general setting in which adversary's evasion attack is only controlled by a pre-specified amount of perturbation based on data dimension and aim to misclassifying the perturbed instances. We show that PAC learning is sometimes impossible under such hybrid attacks, while it is possible without the attack (e.g., due to the bounded VC dimension).
Efficient Conversion Scheme Of Access Matrix In CP-ABE With Double Revocation Capability

To achieve a fine-grained access control function and guarantee the data confidentiality in the cloud storage environment, ciphertext policy attribute-based encryption (CP-ABE) has been widely implemented. However, due to the high computation and communication overhead, the nature of CP-ABE mechanism makes it difficult to be adopted in resource constrained terminals. Furthermore, the way of realizing varying levels of undo operations remains a problem. To this end, the access matrix that satisfies linear secret sharing scheme (LSSS) was optimized with Cauchy matrix, and then a user-level revocation scheme based on Chinese Remainder Theorem was proposed. Additionally, the attribute level revocation scheme which is based on the method of key encrypt key (KEK) and can help to reduce the storage overhead has also been improved.
Characterization and computation of feasible trajectories for an articulated probe with a variable-length end segment

An articulated probe is modeled in the plane as two line segments, ab and bc, joined at b, with ab being very long, and bc of some small length r. We investigate a trajectory planning problem involving the articulated two-segment probe where the length r of bc can be customized. Consider a set P of simple polygonal obstacles with a total of n vertices, a target point t located in the free space such that t cannot see to infinity, and a circle S centered at t enclosing P. The probe initially resides outside S, with ab and bc being collinear, and is restricted to the following sequence of moves: a straight line insertion of abc into S followed by a rotation of bc around b. The goal is to compute a feasible obstacle-avoiding trajectory for the probe so that, after the sequence of moves, c coincides with t. We prove that, for n line segment obstacles, the smallest length r for which there exists a feasible probe trajectory can be found in O(n2+ϵ) time using O(n2+ϵ) space, for any constant ϵ>0. Furthermore, we prove that all values r for which a feasible probe trajectory exists form O(n2) intervals, and can be computed in O(n5/2) time using O(n2+ϵ) space. We also show that, for a given r, the feasible trajectory space of the articulated probe can be characterized by a simple arrangement of complexity O(n2), which can be constructed in O(n2) time. To obtain our solutions, we design efficient data structures for a number of interesting variants of geometric intersection and emptiness query problems.
MEMS Plasmonics and Memristive Plasmonics for Optical Communications

Plasmonics allows for an unmatched miniaturization. Along with the shrinking comes power efficient operation. Here, we show how plasmonics allows for an unprecedented downscaling of the classical MEMS and even of emerging memristive approaches. Ultimately, we will demonstrate optical elements at the atomic scale.
Distance-Based Heuristic Solvers for Cooperative Path Planning with Heterogeneous Agents

Cooperation among different vehicles is a promising concept for transportation services. For instance, vehicle platooning on highways with self-driving vehicles is known to decrease fuel consumption. This makes us construct paths on graphs, where many vehicles share sub-paths to form platoons. The platooning model has been recently generalized to model different types (e.g., trucks and UAVs) but existing exact solvers using Integer Programming (IP) are not experimentally evaluated on various settings and heuristic solvers are underdeveloped; hence solving large instances remains difficult. In this study, we propose heuristic solvers using a distance-based grouping of vehicles. Our solver first finds groups of vehicles and constructs paths in each group independently. We also experimentally investigate both exact and heuristic solvers. Experimental results suggest that IP-based solvers only solve small instances due to the overhead of compiling IP models. We observed that our solvers were almost fifth magnitude faster than the exact solver with at most 25% additional travel costs. Also, our method achieved roughly 15% additional costs if requests are clustered in terms of their locations, meaning that distance-based heuristic solvers could find moderate solutions within typically a few seconds.
Remote control system for the unmanned floating platform

Abstract The paper presents the results of modelling the coverage areas of a communication system in the Wi-Fi range for organizing a wireless data transmission channel between the coastal and onboard control subsystems of an unmanned floating platform, for which the main navigation zones near the coast are determined. It is shown that the use of a communication system based on the IEEE 802.11 standard makes it possible to ensure stable data exchange within the specified navigation zones of the unmanned platform. A generalized block diagram of a remote control system for an unmanned floating platform has been developed. It includes a video surveillance subsystem and a subsystem for engine control and collection of metrological data from various sensors. In this case, the floating platform is controlled both from a stationary post and using a mobile console. Functional diagrams of the onboard video surveillance and control system, as well as the coastal subsystem have been developed.
Comparative Assessment of Solar Radiation by Satellite-Based and Reanalysis Products Over Vietnam Regions

This study compares the daily solar irradiance estimated from Himawari-8 satellite and by the ERA-Interim reanalysis of the European Center for Medium-range Weather Forecasts (ECMWF) against qualified ground measurements in 5 stations in different regions of Vietnam. The result showed better agreement between satellite estimation and observed data with high correlation of values 0.92-0.94 compared to that of ERA-products which is 0.67-0.72. We compared satellite-based estimation with ERA-Interim reanalysis in the spatial scale of 7 climate zones and temporal scale of 4 seasons. The results showed that ERA-Interim products were well associated with satellite-based estimates in seasonal trends, but with different biases. The difference between two products was particularly pronounced in the months of DJF periods and in the north part of Vietnam because of unresolved cloud in ERA reanalysis. Thus, satellite estimation of solar irradiation could be used for producing reliable solar radiation maps for Vietnam in condition of limited surface observations.
MatchCloud: Service Matching for Multi Cloud Marketplace

The modern applications execute in the cloud via independent executable entities called virtual machines (VMs). In a typical multi-SP market with variable pricing and heterogeneous resource demands of VMs, resource allocation/placement is particularly challenging. To maximize the social welfare of the multi-SP markets, in this paper, we propose a resource allocation technique called MatchCloud formulated as a one-to-many matching game. Owing to the in-applicability of the classical deferred acceptance algorithm (DAA) due to size heterogeneity, we adopt a modified version of the algorithm. Moreover, preference generation is crucial for matching markets. Hence, we also present a simple yet efficient technique to assign preferences to two different stakeholders, i.e., VMs and SPs. Simulation results show that VM proposing RDA performs better compared to when SPs propose.
Development of Management System for Insulator UV Imaging Detection

Insulator is one of the most widely used electrical equipment in power system, and its insulation state is related to the safe and stable operation of power system. In order to realize the intelligent diagnosis of the insulation state of the field running insulators, an intelligent diagnosis framework based on cloud computing and deep learning algorithm is designed; the browser / server development mode manages the UV images and videos of insulator discharge, and develops a set of image and video management data According to the database system, the database management system of UV imaging samples is constructed, which integrates the function of insulator visible light recognition and insulator discharge severity assessment based on UV image. The developed software is applied to the inspection of transmission lines and substations, and the application result is good.
Ensemble approach for metadata extraction in Persian theses

Today with increasing number of scientific articles, collecting, managing, searching and retrieving of theses are one of the most important tasks that every scientific library or organization responsible for arranging articles, deals with. Information extraction is a controversial topic that is significant for retrieving and managing scientific theses but unfortunately it is mostly concentrated on English ones. Lack of meta-data extraction methods in Persian theses led us to create a system which uses machine learning methods to extract metadata from Persian theses. First, we tried different kinds of classifier algorithm such as SVM, KNN and decision tree to obtain metadata classes. Then, we combined classifiers based on ensemble learning as an examination. According to the results, methods implemented by ensemble techniques achieve higher F1-score than single classifier algorithm.