Miracle-dz/UniFAR-FTUs huggingface dataset:
REAL-TIME STUDENT SURVEILLANCE SYSTEM USING MACHINE LEARNING AND COMPUTER VISION

In a classroom full of students, it is practically not possible for a single teacher to give personal attention to every student. Continuous monitoring and identification of students who show signs of lethargy, sadness or anger in classrooms can help management counsel them and advice some preventive measures – which if kept unchecked can lead the students to take adverse steps. Potential students who need help in some form can be identified. There are some unproductive activities that take place in a classroom which can be easily automated and the time saved can be devoted to productive activities. Manual attendance calling is one of them which can be automated using facial recognition algorithms. In this paper, a robust surveillance system using Machine Learning and Computer Vision algorithms is presented that can take on the above challenges. For facial recognition, Local Binary Pattern Histograms have been used and for emotion recognition, Deep Learning model has been used.
Exploring Saudi Computer Science Teachers’ Conceptual Mastery Level of Computational Thinking Skills

Abstract The Ministry of Education in Saudi Arabia implemented a new computer science curriculum in 2008. There was speculation that many computer science teachers did not possess sufficient knowledge of computational thinking needed to teach this subject. To investigate this topic, a quantitative research study was conducted with 55 male computer science teachers in Riyadh, Saudi Arabia. Results of the study revealed that most of the computer science teachers had a low conceptual level of computational thinking, and some of the teachers had misconceptions about the exact nature of computational thinking. Findings indicated that computer science teachers did indeed need more training in what computational thinking means and how to teach this subject.
A Comparative Research on Human Activity Recognition Using Deep Learning

In recent years, action recognition is becoming more popular in many fields such as person surveillance, human-robot interaction due to the widespread usage of various sensors. In this study, we aimed to develop an action recognition system that is intended to recognize human actions by using only accelerometer and gyroscope data. Various deep learning approaches like Convolutional Neural Network(CNN), Long-Short Term Memory (LSTM) with classical machine learning algorithms and their combinations were implemented and evaluated. A data augmentation method were applied while accuracy rates were increased noticeably. %98 accuracy rate obtained by using 3 layer LSTM network which means a solid contribution. Additionally, a realtime application was developed by using LSTM network.
Hierarchical Transfer Learning Architecture for Low-Resource Neural Machine Translation

Neural Machine Translation(NMT) has achieved notable results in high-resource languages, but still works poorly on low-resource languages. As times goes on, It is widely recognized that transfer learning methods are effective for low-resource language problems. However, existing transfer learning methods are typically based on the parent-child architecture, which does not adequately take advantages of helpful languages. In this paper, inspired by human transitive inference and learning ability, we handle this issue by proposing a new hierarchical transfer learning architecture for low-resource languages. In the architecture, the NMT model is trained in the unrelated high-resource language pair, the similar intermediate language pair and the low-resource language pair in turn. Correspondingly, the parameters are transferred and fine-tuned layer by layer for initialization. In this way, our hierarchical transfer learning architecture simultaneously combines the data volume advantages of high-resource languages and the syntactic similarity advantages of cognate languages. Specially, we utilize Byte Pair Encoding(BPE) and character-level embedding for data pre-processing, which effectively solve the problem of out of vocabulary(OOV). Experimental results on Uygur-Chinese and Turkish-English translation demonstrate the superiorities of the proposed architecture over the NMT model with parent-child architecture.
Hardware-Efficient Signal Detection for Ambient Backscattering Communications

Ambient Backscattering Communication (AmBC) is regarded as a promising approach for green Internet-of-Things (IoT) networks. However, the noise nature of the ambient radio-frequency (RF) signal poses great difficulty for the signal processing at the receiver, resulting in an unsatisfied Bit Error Rate (BER) performance at a long communication distance. Aiming at improving AmBC performance in practical noisy environments, this letter introduces the concept of nonlinear dynamics to AmBC signal processing and thereby designs a novel Stochastic Resonance (SR) based nonlinear AmBC receiver architecture. As such, the received Signal-to-Noise Ratio (SNR) can be greatly improved, as the analog nonlinear filtering module constructively utilizes useless noise to enhance the target signal. This further allows us to realise sub-symbol level sampling with low rate, i.e. avoiding high speed Analog-to-Digital Convertor (ADC) with high power expenditure. Signal detection is finally accomplished via simple averaging and automatic thresholding, which hence greatly facilitates the efficient hardware design. Simulation results demonstrate the superiority of our nonlinear AmBC signal receiving system.
Exploiting Symmetries When Proving Equivalence Properties for Security Protocols

Verification of privacy-type properties for cryptographic protocols in an active adversarial environment, modelled as a behavioural equivalence in concurrent-process calculi, exhibits a high computational complexity. While undecidable in general, for some classes of common cryptographic primitives the problem is coNEXP-complete when the number of honest participants is bounded. In this paper we develop optimisation techniques for verifying equivalences, exploiting symmetries between the two processes under study. We demonstrate that they provide a significant (several orders of magnitude) speed-up in practice, thus increasing the size of the protocols that can be analysed fully automatically.
AN OPEN RISK INDEX WITH LEARNING INDICATORS FROM OSM-TAGS, DEVELOPED BY MACHINE LEARNING AND TRAINED WITH THE WORLDRISKINDEX

Abstract. While climate change is already a real issue in many parts of the world, it is even more threatening the well-being of future generations. The SDG 1.5 explicitly aims to reduce the vulnerability and exposure to climate related hazards by 2030. TheWorld Risk Index (WRI) is one well-respected approach in profiling countries risk to natural hazard. To effectively monitor development and detect decision points on the climate resilience pathway, data of high resolution in space and time about the world’s countries is of urgent importance. The World Risk Index will guide the supervised learning part resulting in an indicator set derived from OpenStreetMap (OSM) tags, establishing on one hand an open risk index and adding deep explanatory power to its components by a qualitative discussion of the OSM themes. The second part explores with unsupervised algorithms the inherent characteristic of country groups classified by the open risk index and deduces common patterns of socio-economic vulnerability. Hence, the inherent challenge of this work is to substitute existing static indicators with new dynamic indicators, not only substituting them but also painting a more detailed picture. Moreover, new data sources still questioned often by their reliability compared to World Bank or census data, and therefore its opportunities are neglected instead of critically exploring the potential. This unique combination is not done yet and bares huge potential moreover united with the open source geo community to contribute a little piece of the puzzle for achieving the SDG 1.5.
Integrity Checking for Stored Data in Mobile Cloud Computing

: Nowadays cloud has become the jargon in computing. Cloud computing is a route for fresh software to increase ability or attach features without borrowing in infrastructure and operating costs. But this evolution and convenience of use revealed a amount of information quality (information correctness) and safety problems to the latest IT-based technology. Cloud storage data integrity is the cloud customers ' most critical issue. Data integrity assurance implies that information will stay as long as it is on the server. Client cannot obtain the information straight from the cloud server physically without all the understanding of the client. Cloud Service Provider (CSP) can change or delete information that is either long-term owned by the client or that uses up a big amount of storage. For its validity, information has to be rectified periodically. Checking correction information is called accuracy of information. Many methods are suggested under different schemes and safety designs to solve problems of data integrity, and all of these have one or many problems. To improve the protected data more security by using of MD6 and SHA3 algorithm to calculate the hash functions and hash values to secure the data integrity. This paper will be comparatively focused on challenges in data integrity techniques.
Capsule network‐based data pruning in wireless sensor networks

The development of the wireless sensor networks (WSN) being deployed among numerous application for its sensing capabilities is increasing at a very fast tread. Its distributed nature and ability to extend communication even to the inaccessible areas beyond communication range that lacks human intervention has made it even more attractive in a wide space of applications. Confined with numerous sensing nodes distributed over a wide area, the WSN incurs certain limitations as it is battery powered. Many developed routing enhancements with power and energy efficiency lacked in achieving the significant improvement in the performance. So, the paper proposes a machine learning system (capsule network) and technique (data pruning) for WSN involved in the real world observations to have knowledge‐based learning from the experience for an intelligent way of handling the dynamic and real environment without the intervention of the humans. The WSN cluster‐based routing aided with capsule network and data pruning proffered in paper enables the WSN to have a prolonged network lifetime, energy efficiency, minimized delay, and enhanced throughput by reducing the energy usage and extending communication within the limited battery availability. The proposed system is validated in the network simulator and compared with the WSN without ML to check for the performance enhancements of the WSN with ML inclusions in terms of quality of service enhancements, network lifetime, packet delivery ratio, and energy to evince the efficacy of the WSN with capsule network‐based data pruning.
Validation of a digital intelligence quotient questionnaire for employee of small and medium-sized Thai enterprises using exploratory and confirmatory factor analysis

Purpose The purpose of this paper is to develop a digital intelligence quotient (DIQ) scale questionnaire that encompasses the digital identity, digital use, digital safety, digital security, digital emotional intelligence, digital communication, digital literacy and digital rights. Design/methodology/approach DIQ research was conducted in two phases to develop an assessment scale. First, 33 questions were developed based on previous DIQ concepts and theories. These questions were then validated using exploratory factor analysis into eight dimensions as digital identity, digital use, digital safety, digital security, digital emotional intelligence, digital communication, digital literacy and digital rights. A survey was conducted comprising 409 admins and clerks in SMEs. Second, confirmatory factor analysis and convergent validity were tested along the eight digital dimensions. Findings This study extended the DIQ concept to provide theoretical contribution for DIQ with intelligence study. Eight dimensions were developed to measure DIQ, including aspects of digital identity, digital use, digital safety, digital security, digital emotional intelligence, digital communication, digital literacy and digital rights. Research limitations/implications The DIQ questionnaire was a single-source, self-assessed data collection, as the sample included only employees of SMEs in Thailand. Results showed a good fit but require further refinement and validation using a larger sample size and various supplementary sampling contexts. Practical implications The eight DIQ dimensions and questionnaire results will assist organisations and supervisors to focus on employees’ DIQ using both work and lifestyle parameters. This knowledge will help supervisors to encourage employees to increase their DIQ for more effective usage of digital literacy. Researchers and academics will be able to apply this instrument in future studies. Originality/value The DIQ questionnaire is a new instrument which comprehensively explores relevant dimensions to increase employee understanding of digital identity, digital use, digital safety, digital security, digital emotional intelligence, digital communication, digital literacy and digital rights.
Secure Transmission via Beamforming Optimization for NOMA Networks

Owing to the eminent performance gain, non-orthogonal multiple access (NOMA) has been regarded as a promising approach to achieve high throughput and low latency for the next-generation networks. Nevertheless, potential adversaries can still pose a threat to secure transmission in NOMA systems. Hence, in this article, secure transmission schemes based on beamforming optimization are designed to combat both internal and external eavesdropping for downlink multi-input single-output NOMA networks. Using the transmit beamforming design, we first propose two schemes to protect the privacy of a specific NOMA user in the presence of untrusted users. Then, the sum secrecy rate optimization problem is studied to combat the external eavesdropper through optimizing the precoding vectors, when the channel state information (CSI) is available. In addition, two secure beamforming strategies with the help of artificial noise and inter-user interference are proposed without eavesdropping CSI, respectively. Simulation results are presented to demonstrate the effectiveness of the proposed schemes, and some open issues are discussed as well.
An Entity Linking Method Based on Entity Category and Word Embedding

Named entity linking is a process of linking a given reference in a document to a knowledge base. In natural language processing, entity linking can enhance the computer’s understanding of unstructured text data. Applying traditional entity linking methods, especially entity linking methods for person names and organization names, has its limitations. Similar vocabulary as an entity to be linked is difficult to make full use of its contextual semantic information for ambiguity elimination. This paper makes full use of the entity’s category attribute and the semantic information contained in the context to design an entity linking method based on entity category and semantic word embedding. First, Training text classification model based on corpus to obtain entity attributes. Then the semantic feature is extracted by the word vector template to perform entity disambiguation through the semantic classification model. Finally, the results of the entity linking are predicted by means of model ensemble. Experiments show that the accuracy of the method after fusion on the entity linking dataset has improved.
PoM: Power-Efficient Multi-View Video Streaming Over Multi-Antenna Wireless Systems

Multi-view video streaming is becoming more popular, with increased potential for immersive applications. However, it poses a challenge in terms of energy consumption on the transmission devices with limited battery power due to the vast traffic volume. Previous literature has considered efficient video transmission but not exploited the full potential of joint design of 3D video coding and MIMO physical layer. In this paper, we present the first study on power consumption minimization for multi-view video streaming with a quality guarantee in 802.11 MIMO-OFDM systems. We propose an efficient algorithm to perform antenna assignment and transmission power allocation, by exploiting both the source coding characteristics of MVC and channel diversity of multiple antennas. A proof-of-concept system, namely PoM, is designed on the software-defined radio platform and is evaluated in realistic indoor environments. To the best of our knowledge, this is the first practical system for energy-efficient multi-view video streaming. Experimental results show that PoM can significantly save energy in the transmission by 88% ~ 26% on average for the PSNR requirement ranging from 35dB to 45dB, and 85% ~ 12% for SSIM from 0.80 to 0.95.
A comparative study between a computer-based and a mobile-based assessment

The purpose of this study is to compare the overall usability and user experience of desktop computers and mobile-devices when used in a summative assessment in the context of a higher education course. ,The study follows a between-groups design. The participants were 110 first-year undergraduate students from a European university. Students in the experimental group participated in the assessment using mobile devices, whereas students in the control group participated using desktop computers. After the assessment, students self-reported their experiences with computer-based assessment (CBA) and mobile-based assessment (MBA), respectively. The instruments used were the user experience questionnaire and the system usability scale. ,Attractiveness and novelty were reported significantly higher in the experimental group (MBA), while no significant differences were found between the two groups in terms of efficiency, perspicuity, dependability and stimulation. The overall score for the system usability was not found to differ between the two conditions. ,The usability and user experience issues discussed in this study can inform educators and policymakers about the potential of using mobile devices in online assessment practices, as an alternative to desktop computers. ,The study is novel, in that it provides quantitative evidence for the usability and user experience of both desktop computers and mobile devices when used in a summative assessment in the context of a higher education course. Study findings can contribute towards the interchangeable usage of desktop computers and mobile devices in assessment practices in higher education.
Scale Adaptive Target Tracking Algorithm Based on Correlation Filtering

Focusing on the issue that the kernel correlation filter tracking method can not accurately adapt to the target scale change, a scale estimation method is added under the kernel correlation filter tracking frame, and a scale adaptive target tracking algorithm is proposed. The algorithm firstly uses the kernel function to obtain the kernel correlation filter, and estimates the position of the target for the candidate sample detection; then uses the scale estimation method to detect the target scale to further determine the optimal scale of the target to be tracked in the current frame; Finally, according to the occlusion judgment mechanism, the parameters of the target performance model and the scale model are updated online. The experimental results show that compared with kernel correlation filtering algorithm, the proposed algorithm can accurately target tracking process to adapt to changes in the target scale, and the center position error reduced by an average of 4.39 pixels.
Identifying Significant Components of Complex Software for Improving Reliability: Using Invocation Relationships and Component Characteristics

The scales of software systems are getting larger and larger, which may make the system reliability become low. To improve their reliability, a set of important components that are able to strongly influence the system reliability are usually determined first. However, the existing determining approaches only consider the components which are often called by other components as significant ones, but overlook the components which often call others also have a strong effect on the system. Besides, the approaches all require component invocation probabilities which cannot be obtained easily in large-scale systems. To attack the problems, we propose a novel approach for identifying the significant components in complex systems. This approach includes two component ranking algorithms, which take into account not only the components that are frequently invoked, but also the components which often invoke others. The two algorithms, which do not require component invocation probabilities, can either make significant component ranking only based on the component invocation relationships or consider not only the component invocation relationships but also the component characteristics to achieve results. The significant components are selected according to the two ranking results. Extensive experiments are provided to evaluate the approach and draw comparisons with existing methods.
A Practitioner’s Guide to Ethical Web Data Collection

The Web today offers a tremendous number of sites and services that can provide useful data for researchers. Websites ranging from online social networks (e.g., Facebook, Twitter), to online marketplaces (e.g., eBay, Craigslist), to recommendation services (e.g., Yelp, TripAdvisor) can be used to examine human behavior at scale. However, collecting data from these services brings up a host of technical, legal, and ethical issues for researchers, and many research communities are still grappling with the challenges that obtaining data from these services presents. This chapter provides an overview of many such issues, aiming to provide practitioners with specific examples of how various services can be accessed and how different communities have dealt with the ethical and legal challenges that they present.
A Framework for Visualizing the Dynamic Events of Carbon Nanocomposites using Virtual and Augmented Reality Tools

: Atomic interactions pertaining to carbon-nanocomposites can be elusive and hard to comprehend, and as such great benefit can be gained through a visualization of these interactions within a virtual or augmented reality setting. In this paper, we present a framework that can be used for Material Science research and education incorporating topics related to the dynamics in nanomaterials. We developed the proof of concept implementation of this framework for Virtual Reality (VR) and Augmented Reality (AR) settings using the Unity game engine. Throughout this paper, we discuss our framework as well as the related user experiences and performance measurements we gathered when using our framework with the Google Daydream, HTC Vive, and Microsoft HoloLens in introducing scientists to the use of AR and VR as a tool for nanocomposite and molecular dynamics research.