Reviewerly/exHarmony huggingface dataset:
Social Media in Teaching-Learning Process: Investigation of the Use of Whatsapp in Teaching and Learning in University of Port Harcourt

There is no doubt that digital technologies play major roles in education in modern times.Whatsapp mobile technology has been of immense value in this respect, as it is frequently and globally used in online instruction in universities.As well, it offers students increased choices and opportunities in the context of online instruction.Consequently, the purpose of this research work is to find out exactly to what extent the students and lecturers in the University of Port Harcourt, Faculty of Education are using the Whatsapp instant messaging tool in teaching and learning process in Faculty of Education, University of Port Harcourt, Nigeria.It also tried to find out if the aforesaid have enabling phones that are Internet ready to utilize the Whatsapp software in teaching and learning process.It attempts to discover major challenges, if any that are confronting its effective application in teaching and learning.Descriptive survey approach was adopted.The population for the study comprised of one hundred and twenty-eight (128) lecturers and one thousand six hundred and three (1603) first year undergraduate students of the University of Port Harcourt, Faculty of Education for the year 2017.Three (3) research questions guided the study.The research instrument used was a-12 item questionnaire for both lecturers and the students correspondingly.It was validated with a reliability index of 0.70 and considered appropriate for the study.The researchers also employed an in-depth interview method to cover any discrepancy in the structured questionnaire, which was administered to the lecturers and the students respectively.The research questionnaires so retrieved from both respondents were analyzed, using statistical percentile to answer the necessary research questions.The results revealed that regardless of the fact that both the lecturers and the students possess Internet enabled further revealed that 100% of both lecturers and students never received any form of training for the use of Whatsapp mobile technology.Recommendations were also made, which included among others, adequate training on Whatsapp skills and the purchase of Internet enabled phones for effective utilization of Whatsapp instant messaging in teaching and learning process.
Quantum key distribution and beyond: introduction

This feature issue presents a collection of recent theoretical and experimental developments in the field of quantum key distribution (QKD) and its extension to other quantum cryptography protocols and devices. It encompasses work on a variety of QKD protocols, including continuous-variable, measurement-device independent, and twin-field QKD, as well as other newly proposed protocols, in platforms ranging from optical fiber through to wireless indoor and satellite links. It covers examples of hacking strategies and their countermeasures as well as applications of machine learning techniques in designing quantum networks. It also includes new developments in efficient superconducting photon-number resolving detectors as well as fast quantum random number generators. Distinctively, this feature issue demonstrates how different expertise in science and engineering can come together to produce an outcome that hopefully takes us one step closer to the wide-scale deployment of quantum communications technologies.
Unifying the Clifford hierarchy via symmetric matrices over rings

The Clifford hierarchy of unitary operators is a foundational concept for universal quantum computation. It was introduced to show that universal quantum computation can be realized via quantum teleportation, given access to certain standard resources. While the full structure of the hierarchy is still not understood, Cui et al. [S. X. Cui et al., Phys. Rev. A 95, 012329 (2017)] recently described the structure of diagonal unitaries in the hierarchy. They considered diagonal unitaries whose action on a computational basis qudit state is described by a ${2}^{k}\mathrm{th}$ root of unity raised to some polynomial function of the state, and they established the level of such unitaries in the hierarchy as a function of $k$ and the degree of the polynomial. For qubit systems, we consider $k\mathrm{th}$-level diagonal unitaries that can be described just by quadratic forms of the state over the ring ${\mathbb{Z}}_{{2}^{k}}$ of integers modulo ${2}^{k}$. The quadratic forms involve symmetric matrices over ${\mathbb{Z}}_{{2}^{k}}$ that can be used to efficiently describe all two-local and certain higher locality diagonal gates in the hierarchy. We also provide explicit algebraic descriptions of their action on Pauli matrices, which establishes a natural recursion to diagonal unitaries from lower levels. The result involves symplectic matrices over ${\mathbb{Z}}_{{2}^{k}}$ and hence our perspective unifies a subgroup of diagonal gates in the Clifford hierarchy with the binary symplectic framework for gates in the Clifford group. We augment our description with simple examples for certain standard gates. In addition to demonstrating structure, these formulas might prove useful in applications such as (i) classical simulation of quantum circuits, especially via the stabilizer rank approach, (ii) synthesis of logical non-Clifford unitaries, specifically alternatives to expensive magic state distillation, and (iii) decomposition of arbitrary unitaries beyond the $\mathrm{Clifford}+T$ set of gates, perhaps leading to shorter depth circuits. Our results suggest that some nondiagonal gates in the hierarchy might also be understood by generalizing other binary symplectic matrices to integer rings.
Sequential Multi-Sensor JPDA for Target Tracking in Passive Multi-Static Radar With Range and Doppler Measurements

Target tracking in passive multi-static radar (PMSR) with bistatic range and Doppler frequency measurements from multiple transmit-receive pairs is gaining increasing interest.For the data association problem in this scenario, the parallel architecture of a multi-sensor joint probabilistic data association (P-MSJPDA) filter has been significantly investigated.As an alternative architecture, the sequential MSJPDA (S-MSJPDA) is rarely discussed in PMSR.In this paper, we evaluate the behaviors of S-MSJPDA in PMSR target tracking with bistatic range and Doppler frequency measurements.A comprehensive comparison between the S-MSJPDA and the P-MSJPDA in PMSR is provided.It can be found from the analysis that S-MSJPDA outperforms its parallel counterpart in terms of computational efficiency, given an acceptable degradation in position accuracy.The S-MSJPDA is further applied to an experimental passive multi-static radar for aircrafts tracking.The real data results obtained are rather close to the true trajectories of the targets.This demonstrates that the S-MSJPDA has great potentials in PMSR target tracking.
Deep learning enabled multi-wavelength spatial coherence microscope for the classification of malaria-infected stages with limited labelled data size

Malaria is a life-threatening mosquito-borne blood disease, hence early detection is very crucial for health. The conventional method for the detection is a microscopic examination of Giemsa-stained blood smears, which needs a highly trained skilled technician. Automated classifications of different stages of malaria still a challenging task, especially having poor sensitivity in detecting the early trophozoite and late trophozoite or schizont stage with limited labelled datasize. The study aims to develop a fast, robust and fully automated system for the classification of different stages of malaria with limited data size by using the pre-trained convolutional neural networks (CNNs) as a classifier and multi-wavelength to increase the sample size. We also compare our customized CNN with other well-known CNNs and shows that our network have a comparable performance with less computational time. We believe that our proposed method can be applied to other limited labelled biological datasets.
A Box Regularized Particle Filter for state estimation with severely ambiguous and non-linear measurements

The first stage in any control system is to be able to accurately estimate the system's state. However, some types of measurements are ambiguous (non-injective) in terms of state. Existing algorithms for such problems, such as Monte Carlo methods, are computationally expensive or not robust to such ambiguity. We propose the Box Regularized Particle Filter (BRPF) to resolve these problems. Based on previous works on box particle filters, we present a more generic and accurate formulation of the algorithm, with two innovations: a generalized box resampling step and a kernel smoothing method, which is shown to be optimal in terms of Mean Integrated Square Error. Monte Carlo simulations demonstrate the efficiency of BRPF on a severely ambiguous and non-linear estimation problem, that of Terrain Aided Navigation. BRPF is compared to the Sequential Importance Resampling Particle Filter (SIR-PF), Monte Carlo Markov Chain (MCMC), and the original Box Particle Filter (BPF). The algorithm outperforms existing methods in terms of Root Mean Square Error (e.g., improvement up to 42% in geographical position estimation with respect to the BPF) for a large initial uncertainty. The BRPF reduces the computational load by 73%and 90% for SIR-PF and MCMC, respectively, with similar RMSE values. This work offers an accurate (in terms of RMSE) and robust (in terms of divergence rate) way to tackle state estimation from ambiguous measurements while requiring a significantly lower computational load than classic Monte Carlo and particle filtering methods.
Credit card Fraud Detection based on Machine Learning Algorithms

Now a days online transactions have become an important and necessary part of our lives.As frequency of transactions is increasing, number of fraudulent transactions are also increasing rapidly.In order to reduce fraudulent transactions, machine learning algorithms like Naïve Bayes, Logistic regression, J48 and AdaBoost etc. are discussed in this paper.The same set of algorithms are implemented and tested using an online dataset.Through comparative analysis it can be concluded that Logistic regression and AdaBoost algorithms perform better in fraud detection.
Corruption and Its Implications for Development and Good Governance: A Perspective from Post-Colonial Africa

Abstract: One of the many impediments to a specific region, country and/or continents political, social and economic growth prospects is corruption, the aim of this paper is to unearth the drivers and consequences of corruption in post-colonial Africa. Corruption is a global phenomenon; however when observing global corruption statistics and/or trends, it seems to be more prominent in underdeveloped continents such as Africa. Corruption in Africa is purely driven by low levels of economic growth, bad governance structures weak constitutions political instability, high levels of poverty coupled with high and ever-increasing levels of unemployment. We argue that post the colonial era, there has been a rise of corruption activities within the continent where individuals including some African heads of states have looted the continent of its resources meant for the general populace. In this sense, corruption takes resources meant for the poor, limits foreign direct investments (FDI) and has severe effects on a continent that is already the least developed in the world.
Learning to See the Hidden Part of the Vehicle in the Autopilot Scene

Recent advances in deep learning have shown exciting promise in low-level artificial intelligence tasks such as image classification, speech recognition, object detection, and semantic segmentation, etc. Artificial intelligence has made an important contribution to autopilot, which is a complex high-level intelligence task. However, the real autopilot scene is quite complicated. The first accident of autopilot occurred in 2016. It resulted in a fatal crash where the white side of a vehicle appeared similar to a brightly lit sky. The root of the problem is that the autopilot vision system cannot identify the part of a vehicle when the part is similar to the background. A method called DIDA was first proposed based on the deep learning network to see the hidden part. DIDA cascades the following steps: object detection, scaling, image inpainting assuming a hidden part beside the car, object re-detection from inpainted image, zooming back to the original size, and setting an alarm region by comparing two detected regions. DIDA was tested in a similar scene and achieved exciting results. This method solves the aforementioned problem only by using optical signals. Additionally, the vehicle dataset captured in Xi’an, China can be used in subsequent research.
Developing an Expert System for Diagnosing Liver Diseases

The liver plays an important role in regulating the body's internal interactions as the largest and the most complex organs in the body. Such interactions are essential in preserving life and proper body function. The eaten foods are not consumed in the same way in the body. In fact, it should pass through the liver and after absorption and entering the blood, and the liver plays an important role in the storage of eaten substances. Accordingly, the speed of diagnosing and treating liver diseases are highly effective in improving patients' health; however, there is always a lack of immediate access to specialized physicians. The present study aims to provide an expert system using the VP-Expert shell for diagnosing liver disease. Also, block and Mockler charts have been used to provide the necessary knowledge in the expert system. In this regard, the factors affecting the diagnosis of the disease were firstly categorized into three groups of blood tests, clinical symptoms of illness and disease time on the block diagram for disease diagnose, and in the next step, the Mockler chart was designed according to the three groups of factors on the block chart. The three categories of disease diagnostic factors, questions corresponding sub-sections, and the response range are determined on Mockler chart, and finally, the tables are designed to express the system inference according to the user's answers to the questions.
Monitoring Student Activities with Smartwatches: On the Academic Performance Enhancement

Motivated by the importance of studying the relationship between habits of students and their academic performance, daily activities of undergraduate participants have been tracked with smartwatches and smartphones. Smartwatches collect data together with an Android application that interacts with the users who provide the labeling of their own activities. The tracked activities include eating, running, sleeping, classroom-session, exam, job, homework, transportation, watching TV-Series, and reading. The collected data were stored in a server for activity recognition with supervised machine learning algorithms. The methodology for the concept proof includes the extraction of features with the discrete wavelet transform from gyroscope and accelerometer signals to improve the classification accuracy. The results of activity recognition with Random Forest were satisfactory (86.9%) and support the relationship between smartwatch sensor signals and daily-living activities of students which opens the possibility for developing future experiments with automatic activity-labeling, and so forth to facilitate activity pattern recognition to propose a recommendation system to enhance the academic performance of each student.
Data Mining-based Design and Implementation of College Physical Education Performance Management and Analysis System

The purpose of this paper was to effectively apply data mining technology to sci-entifically analyze the students' physical education (PE) performance so as to serve the physical teaching. The methodology adopted in this paper was to apply ASP.NET 3-layer architecture and design and implement college PE performance management and analysis system under the premise of fully analyzing the system requirements based on Visual Studio2008 software development platform and using SQL Server 2005 database platform. Based on data mining technology, students' PE performances were analyzed, and decision tree algorithm was used to make valuable judgments on student performance. The results indicated that applying computer technology to the management and analysis of college PE per-formance can effectively reduce the teaching and managing workload of PE teachers so that the teachers concentrate more on the quality of physical educa-tion.
A Neural Approach to Entity Linking on Wikidata

We tackle \ac{NED} by comparing entities in short sentences with \wikidata{} graphs. Creating a context vector from graphs through deep learning is a challenging problem that has never been applied to \ac{NED}. Our main contribution is to present an experimental study of recent neural techniques, as well as a discussion about which graph features are most important for the disambiguation task. In addition, a new dataset (\wikidatadisamb{}) is created to allow a clean and scalable evaluation of \ac{NED} with \wikidata{} entries, and to be used as a reference in future research. In the end our results show that a \ac{Bi-LSTM} encoding of the graph triplets performs best, improving upon the baseline models and scoring an \rm{F1} value of $91.6\%$ on the \wikidatadisamb{} test set
Comparison of Heuristics for Optimization of Association Rules

In this paper, seven greedy heuristics for construction of association rules are compared from the point of view of the length and coverage of constructed rules. The obtained rules are compared also with optimal ones constructed by dynamic programming algorithms. The average relative difference bet ween length of rules constructed by the best heuristic and minimum length of rules is at most 4%. The same situation is with coverage.
A Study on the Presence of Immersive User Interface in Collaborative Virtual Environments Application

This study proposes a collaboration-based interaction as a new method for providing an improved presence and a satisfying experience to various head-mounted display (HMD) users utilized in immersive virtual reality (IVR), and analyzes the experiences (improved presence, satisfying enjoyment, and social interaction) of applying collaboration to user interfaces. The key objective of the proposed interaction is to provide an environment where HMD users are able to collaborate with each other, based on their differentiated roles and behaviors. To this end, a collaboration-based interaction structured in three parts was designed, including a synchronization procedure and a communication interface that enable users to swiftly execute common goals with precision, based on immersive interactions that allow users to directly exchange information and provide feedback with their hands and feet. Moreover, experimental VR applications were built to systematically analyze the improved presence, enjoyment, and social interaction experienced by users through collaboration. Finally, by conducting a survey on the participants of the experiment, this study confirmed that the proposed interface indeed provided users with an improved presence and a satisfying experience, based on collaboration.
Compressed Indexes for Fast Search of Semantic Data

The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is to devise a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. In this work, we propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis, conducted over a wide range of publicly available real-world datasets, reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30-60 percent less space and speeding up query execution by a factor of 2 - 81×.
Galois Field-Based Image Encryption for Remote Transmission of Tumor Ultrasound Images

The remote transmission of tumor ultrasound images is an important link in telemedicine. Based on the Galois field (GF), this paper proposes an encryption algorithm that ensures the security of tumor ultrasound images (TUIs) during the transmission. First, the grayscale image of the TUI was generated by the secure hash algorithm 1 (SHA-1) algorithm, and the resulting hash value was taken as the medical record summary, which was used to monitor the transmission of the TUI. Second, the 2D ciphertext image was expanded into 1D vectors, and the TUI was encrypted with the non-repetitive scrambling algorithm, coupled with two diffusion algorithms. The simulation results show that the coupled algorithm can ensure the safety of the TUIs in remote transmission.
No-Reference Stereoscopic Image Quality Assessment Based on Visual Attention and Perception

In recent years, the methods of no-reference stereoscopic image quality assessment (NR-SIQA) have been well investigated, but there still remain challenges due to the inaccurate extraction of binocular perception information. In this paper, we propose an NR-SIQA method based on visual attention and perception. We combine saliency and just noticeable difference (JND) to model visual attention and perception, respectively, and weight the global and local features extracted from the left and right views. Meanwhile, in order to obtain the accurate binocular perception information, the global structural features reflecting spatial correlation are extracted from the cyclopean map that is synthesized by the left and right views. Then, a regression model is learned based on a support vector machine regression (SVR) to evaluate the quality of stereoscopic images. The experiments on popular SIQA datasets demonstrate that the proposed NR-SIQA method has better and more reliable performance than the state-of-the-art methods.
A Collective Anomaly Detection Approach for Multidimensional Streams in Mobile Service Security

Anomaly detection in many applications is becoming more and more important, especially for security and privacy in mobile service computing domains with the development of mobile internet and mobile cloud computing, in which data are typical multidimensional time series data.However, the collective anomaly detection for multidimensional streams exists lots of problems, owing to the differences between the anomaly detection in multidimensional time series and univariate time series data.For example, the temporal continuity of multidimensional time series is much weaker than univariate time series and it is unreasonable to judge the entire multidimensional data as an anomaly if a certain dimension is abnormal.In this paper, we consider the statistical features of the subsequence of streams, proposing a novel collective anomaly detection algorithm for multidimensional streams based on iForest in a cloud environment, namely iForestFS.When using different features about mobile cloud services' metrics suggested by domain knowledge, iForestFS could detect different kinds of anomalies for mobile service security.Furthermore, we implement a distributed iForestFS using spark framework in order to improve time performance and scalability.The experimental results performed on three datasets (mainly about network security) derived from the UCI repository demonstrate that the proposed algorithm can effectively detect a collective anomaly of multidimensional streams in the security domain.