Reviewerly/exHarmony huggingface dataset:
A Post-quantum Cryptosystem with a Hybrid Quantum Random Number Generator

In recent years, a large amount of research has been conducted on quantum computers. If we ever get a large-scale quantum computer, they will be able to break many of the public key cryptosystems currently in use. Today, such cryptosystems are integrated into many commercial products. Alternatives are created that seems to protect us from quantum attacks, but due to safety and efficacy issues, they cannot be used in practice. We have presented an improved hash-based digital signature scheme that meets recommended standards. The scheme uses a secure quantum pseudo-random number generator as the small random initial values. It uses a random numbers that are generated using our hybrid quantum random number generator. We have discussed the method of random number generation. We have obtained a post-quantum cryptosystem, the initial random values of which are obtained using our generator. Our system is efficient and secure against quantum attacks.
Interval Type-2 Fuzzy Logic Controller Development for Coreless DC Micromotor Speed Control Applications

The objective of this work is to design, develop and demonstrate optimized fuzzy controllers for processes presenting nonlinearities such as coreless DC micromotors speed control problems, in order to improve the overall control system performance. One type-1 fuzzy logic controller (FLC) and one type-2 FLC (both Tagashi-Sugeno based) were designed and implemented in real time to improve motor speed control performance. For this purpose, a high-end microcontroller was used in which the developed controllers where embedded. The parameters of the input and the output membership functions of the proposed FLCs were optimally tuned using a well-known optimization algorithm, i.e., the particle swarm optimizer (PSO). A comparison of type-1 and type-2 Tagashi-Sugeno fuzzy control architectures is performed in different experimental scenarios and the relevant results are discussed and analyzed. The results demonstrate that type-2 FLC exhibits very satisfactory motor driving capability, providing zero overshoot, faster rise time and low settling time.
Detection of Malicious Sites Using Graph Machine Learning

Information and communication technology networks are developing rapidly, and many users’ daily activities, including e-banking and e-commerce, have moved to the internet. By using this anonymous framework, attackers establish new techniques such as phishing to trick victims into using fake sites to gather sensitive information. Detection of malicious sites is a challenging problem, due to its semantic-based attack framework, which exploits system and user vulnerabilities. While several approaches have been proposed to cope with phishing, new attacks constantly emerge requiring the improvement of existing techniques. This work proposes an approach to detect phishing, evaluating the structure of the graph from the connection of sites to extract features from the graph, in addition to the features commonly used in the literature such as features of URL and SSL certificate. We evaluate the impact of the use of graph features compared to existing features in the literature, from the point of view of machine learning metrics and of computational time required. The obtained results suggest that graph machine learning can be used effectively in phishing detection.
An Insight into EDGE-Based Solutions for Augmented Reality

The term “augmented reality” refers to a technology that combines digital and actual experiences. It is an immersive experience of a physical environment in which actual objects are enhanced with digital visual features, sound, or other sensory stimuli. The rapid advancement of augmented reality has piqued people’s interest in recent years. It is a rapidly developing area among businesses that deal with mobile computing and commercial apps. Using AR, digital information can be placed in reality to improve a human’s perspective of reality. This paper begins by defining augmented reality, its history, and its challenges. The paper then discusses some essential technology, development tools, and augmented reality applications in several industries. The main focus point of the paper is centered around the discussion on EDGE Technology as a solution to the limitations of AR. We have drawn a comparison between some frameworks that have been developed over time, merging AR with EDGE. Finally, it anticipates future advancements in augmented reality technologies, such as the Mobile AR.
An Anomaly Detection System for Intelligent Robot Vision Using LSTM

The automation is a very important in manufacturing industry to improve the efficiency of production processes. Recently, there are some research works that consider the use of robot vision for inspection and early recognition of malfunctions and abnormalities in order to prevent many accidents. In this paper, we propose and implement an anomaly detection system for intelligent robot vision using LSTM. We consider an anomaly when an object contacts the robot arm. The anomaly detection is determined based on the error of acceleration values between the measured value and predicted value by LSTM. We evaluate the proposed system by an experiment. The experimental results show that proposed system can detect anomalies.
Bonet Detection Mechanism Using Graph Neural Network

A botnet is a group of computers that are infected by a malware that can be utilized to wreak havoc on other computers. Botnets have been around for quite some time and various techniques have been proposed in the literature to detect their presence in networks and systems. In today world, Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are capable of defending against botnets that create volumetric and fast paced traffic. But, these systems are not well suited to address prevalent real-time, long-term and stealth attacks. This paper proposes a Graph Neural Network (GNN)-based method for detecting botnets activity based on supervised learning. This work is the first ever application of AEN to build a GNN model for botnet detection purpose. The proposed model is evaluated using five different labelled datasets, namely, the CTU-MALWARE-CAPTURE-BOTNET-42, CTU-MALWARE- CAPTUREBOTNET-43, CTU-MALWARE-CAPTURE-BOTNET-48, ISCX Botnet Training and ISCX Botnet Test datasets, yielding preliminary promising results in terms of botnet prediction, using precision, recall, F1-score, and accuracy, as performance metrics.
OASL: SPARQL Query Language for OpenAPI Ontologies

OpenAPI descriptions detail the actions exposed by REST APIs in YAML or JSON syntax. The semantics of valid OpenAPI descriptions can be further exploited if they can be mapped to an OpenAPI ontology, as we have done in our previous work. However, queries become complex and require the user to be familiar with the peculiarities of the ontology. We introduce the OpenAPI SPARQL Language (OASL), an RDF query language for OpenAPI semantic descriptions. To formulate an OASL query, a user needs only a basic understanding of SPARQL and no knowledge of the OpenAPI ontology. OASL builds on top of SPARQL and simplifies query complexity, so even highly complex SPARQL queries can be expressed using only a few OASL statements. The run-time performance of OASL has been assessed experimentally on a Virtuoso database with OpenAPI ontologies of real-world services. A critical analysis of its performance is also presented along with several query examples.
Up- and Down-Correlations in Normal Variance Mixture Models

We study conditional correlations between pairs of risks in normal variance mixture models, which are widely used in risk management and finance. In particular, we examine up- and down-correlations defined as the conditional correlation between the sum of risks and an individual component, conditionally on the sum being large or small, respectively. We provide a characterization of conditional covariances and use this to derive explicit formulas for up- and down-correlations in important cases of the normal variance mixture model such as the normal, the Student-t, the logistic, and the Laplace model. Based on invariance properties of betas, we also provide a graphical test for assessing the validity of a normal variance mixture model.
The design of bungee cord type launcher system for Serindit V-2 UAV

The purposes of this study are to design, to produce, and to find out the performance of the launcher system for Serindit V-2 UAV. The design process was based on the Serindit V-2 specification, which are 1300 mm of wingspan, 2.5 kg Maximum Take-off Weight (MTOW), and 12 m/s of stall speed. The result of the design was a launcher system with dimensions of 2000 mm length, 310 mm wide, 658 mm height, and 15o inclination angle. The launcher system was a bungee cord type launcher with 25mm x 25mm x 1 mm squared size stainless steel as it's the main material, and has a total mass of 7 Kg. The structural strength calculation resulted in a criteria fulfillment of 9.398 safety factor, and a relatively small total deflection of 0.6624 mm. The calculation also result in a maximum launch speed of 15.6586 m/s. Next, practically the produced launcher result a maximum launch speed of 15.092 m/s which is relatively close to the theoretical calculation. The speed fulfills the initial criterion which is 2: 13.8 m/s. By these results, the launcher system was claimed successfully help Serindit V-2 UAV safely to take off.
Optimizing the utilization of grid connected rooftop solar photovoltaic (SPV) as green energy system development

The building of Faculty of Engineering, Semarang University is potential to install a solar photovoltaic (SPV) rooftop. It has emerged as a green energy technology to overcome climate change issues by reducing reliance on conventional fossil fuel energy. The energy produced by SPV can be used to fulfil self-consumption of electricity needs, so the fulfilment of electrical energy can be supplied by PLN network and rooftop SPV. The design of grid connected rooftop SPV needs optimization and prediction on how much power or energy can be produced. The power and energy produced are calculated based on meteorological data for solar irradiation and temperature in 2019 and depend on the dimensions of the building roof area. The results show the power and energy requirements in 103.87 kW and 1371.74 kWh with the dimensions of 441 m2. Roof of the building can accommodate the number of SPV modules as much as 356 units with a total power and energy amounted to 85.119 kW and 658.6 kWh. The utilization of grid-connected rooftop SPV is able to supply the energy needs of 81.9% of the total electric energy in Faculty of Engineering building. Another power loads supplied by PLN sources is 31.8%.
Institutional Repositories in the Linked Open Data Cloud

We believe that the wealth of digital humanity research resources that is available in Institutional Repositories in Southern Africa is largely over-looked because it is not machine explorable as defined by Berners-Lee (2006). We argue that extracting research from Institutional Repositories, which Berners-Lee (2006) describes as One-Star Linked Data, and then enriching this research and storing it as tuples in a triple store (RDF data store) will expose the research to a greater audience; make it machine explorable; and integrate it with a broader network of Linked Data, known as the Linked Open Data Cloud (LODC). Our approach is to utilize software building blocks licensed under Free (Libre) Open Source Software (FLOSS) licences and to create a pipeline that can potentially transform any Institutional Repository into into a triple store, which can be published on the Internet as part of the LODC. Our transformation process embraces under-resourced institutions by utilizing FLOSS com- ponents and utilizing digital resources already published on the internet in order to promote the research from One-Star Linked data to Five-Star Linked Data in Berners-Lee (2006) taxonomy of linked data.
Some Legal Aspects of Plagiarism Among Students

The paper describes the scope of the legal consequences of plagiarism among students in terms of copyright. The main question is connected with the scope and nature of the copyright protection against plagiarism. The issue is related to the specificity of the social role played by a student and to certain customs in the university community which enforce a certain behaviour and do not always require detailed references to the sources used. All university students have administrative and legal liabilities with regard to the university authorities, and in addition they bear full civil liability for their actions
Adaptive Multi-scale Fusion Siamese Network for Visual Tracking

The task of object tracking is to predict the size and position of a target in the subsequent frames of a video sequence, by giving the target's size and position in the initial frame. Target tracking has now become an important task in the field of computing and is widely used in numerous areas. Nowadays, deep based object tracking methods have outperformed traditional correlation filtering algorithms in terms of speed, where Siamese based trackers are not only simple in structure, better in performance, frame rate exceeding real time requirements but also very suitable for long-term tracking. In this paper, we expect to improve the performance of the Siamese network tracker by using multi-scale feature fusion, and to obtain a multi-scale feature map with complete semantic information and spatial-adaptive features by fusing deep and shallow network features through two mechanisms: weighted multi-scale fusion and adaptive multi-scale fusion modules. After training on the GOT-10K dataset and testing on the OTB2015 dataset, the experimental results demonstrate that the proposed SiamFC-ADF has significantly improved in terms of success and precision rate, with a 1.6% success rate and 2.1% precision rate improvement over SiamFC and the increase in computational effort and speed loss is negligible.
RGB-T Object Tracking Based on Cross-channel Local Response Normalization

Visible images are sensitive to light and are easily affected by imaging technology, while infrared images are not affected by illumination and have strong penetration in rain or smog, so the combination of infrared and visible images in the object tracking can significantly improve tracking performance. In this paper, we propose an RGB-T tracking method based on the cross-channel normalization of local response and incorporate some improvements to the ADRNet framework. On the one hand, we improve the structure of the backbone network to obtain richer semantic information and more robust features, and the dropout layer is added to prevent overfitting. On the other hand, we use 3D average pooling to process the feature data convolved with cross-channel operations in the part of local response normalization to extract the timing information between video frames. We conducted a large number of experiments on the corresponding dataset and compared our tracker with some of the most advanced trackers. The results show that our tracking algorithm can achieve better performance on RGB-T object tracking.
BlendPose: A New Linear Parametric Hand Model for High-quality 3D Hand Reconstruction

As 3D hand models have attracted extensive attention in human-computer interaction, VR and AR fields, building realistic hand models is becoming a long-term research hotspot. Performances of many existing 3D hand models may be limited due to their low resolution, sparsity and non-linearity. In this paper, we introduce a new linear parametric hand model termed BlendPose for hand reconstruction and prospose a new framework to redefine the relationship between a hand mesh and its 3D joints. BlendPose is a global parametric-driven model, reconstructing hand shape and pose simultaneously. The key idea of BlendPose is a hand mesh can be expressed by the linear combination of several hand posture models and a rest-pose model. Additionally, the new framework first reconstruct personalized hand mesh via BlendPose and then regress 3D joints from mesh vertices using a trainable joints regressor, which is quite different from other skeleton-driven models. We experimentally show that BlendPose can be applied to challenging datasets such as OneHand10k and Freihand while achieving high-quality reconstruction results.
Distribution Matched Low-bit Post-Training Quantization for Convolutional Neural Networks

Post-training quantization of deep convolutional neural networks is highly desirable since it does not require retraining or access to the full training dataset. Unlike many recent uniform quantization methods that quantize weights and activations by discretizing the domain of values to evenly-spaced low-precision integers, we propose one Distribution Matched Low-bit Quantization (DMLQ) scheme which uses different quantization strategies for weights and activations. For weight quantization, we firstly select one pair of breakpoints to divide the distribution regions, and then one uniform-for-dense and non-uniform-for-sparse quantization strategy is applied, aiming to achieve more effective quantization levels assignment. For activation quantization, one layer-wise log quantization strategy with maximum-value based offset is adopted, which mitigate the accuracy degradation induced by unreasonable assignment of quantization levels to the original activations equally and directly. Evaluation on large-scale image classification benchmark shows that the proposed scheme can achieve state-of-the-art performance, especially for 4-bit quantization.
Uncropped Siamese Fully Convolutional Network for Visual Tracking

Considering the peculiarity of SiamCAR, which decomposes visual tracking into a Siamese subnetwork for feature extraction and a classification-regression subnetwork for bounding box prediction, we propose an empirical method to avoid cropping the high-level semantic information for further anchor-free tracking tasks. Based on the preprocessing of the training image pairs, we focus on the center region of the feature maps in the template branch, which includes the whole object while reducing background clutter. However, the position of the object in a sequence is always in constant motion. The loss of high-level features causes the large movement of the object to weaken the performance of the strategy for tracking the current object in accordance with the object location of the previous frame. Therefore, we remove the inappropriate cuts to obtain a better similarity map. Extensive experiments and comparisons show that our proposed simple but effective method achieves credible results with remarkable real-time speed on the UAV123, LaSOT and GOT-10k benchmarks.
A New Image Downscaling Algorithm based on a Circular Area Pixel Model

Image downscaling is a fundamental task for most image and video applications, and many research works have been proposed. Although most of them try to get a scaled image of higher quality and to reduce the processing time further, to develop more and more efficient algorithm is still challenging. In this paper, we propose a new method for image scaling based on a circular area pixel model rather than a rectangular area pixel model. In case of upscaling, both an original and a target pixel are treated as circular regions whereas the target pixel is treated as an elliptical region only for downscaling. A pixel's weight represents a spatial contribution of the original pixel to a target pixel by the area of their overlapped region. Compared with existing algorithms by experiments, the proposed algorithm produces downscaled images of better quality than those obtained by the others including Bicubic and Lanczos. The proposed filter kernel can be adopted as a spatial kernel for the existing edge-preserving image downscalers to improve their performance further.
Cross Knowledge Distillation for Image Super-Resolution

Convolutional neural networks (CNNs) have achieved great success in super-resolution (SR) tasks. However, the huge computational requirements and memory footprint limit the practical deployment. Knowledge distillation (KD) allows tiny student networks to obtain performance improvement by learning from over-parameterized teacher networks, thus completing model compression. Previous work has attempted to solve the SR distillation problem by using feature-based distillation while ignoring the supervisory role of the teacher module itself. In this paper, we introduce a novel cross knowledge distillation framework to compress and accelerate super-resolution models. Specifically, we propose to cascade the student into the teacher network for directly utilizing the teacher's well-trained parameters, which avoids manually transforming the features between different networks for alignment. In addition, by instructing the student to learn similarity relations between the teacher layers, we force student learning representation in its own space rather than directly mimic the teacher's intricate features. We also make the teacher's parameters trainable, for alleviating the inefficient distillation problem caused by the representation gap between teacher and student. We conduct experiments at 3 scales on two typical super-resolution networks, EDSR and RCAN. The results show that the generated images obtain better visual improvements and have competitive advantages in metrics such as PSNR and SSIM.
Information Bottleneck Enhanced Video-based Person Re-identification

Person re-identification has great potential in applications of public security. However, non-discriminative information like background noise still hinders further improvement of current person re-identification algorithms. This paper proposes an information bottleneck-enhanced video-based person re-identification algorithm concentrating on discriminative information. Specifically, the spatial-temporal information purification (STIP) module is designed to filter out irrelevant information and the variance of information is estimated to weigh the importance of each feature dimension. Our method is evaluated on MARS and results show that it pays more attention to discriminative information and suppresses the irrelevant feature.
Infrared and Visible Image Registration in UAV Inspection

Infrared and visible dual camera is commonly used in UAV inspection. As a crucial preprocess, accurate registration can promote subsequent multi-source image fusion, which can improve detectability and reduce the false positive rate. Due to significant geometric distortions, gray-scale differences and partial overlap between infrared image and visible image, feature-based methods or joint area-feature based methods cannot obtain satisfactory results. To solve this problem, this paper presents a novel registration method based on UAV imaging characteristics and intensity-structure similarity optimization. The reliable initial registration parameters are obtained by utilizing the UAV imaging parameters and approximate coaxial imaging principle. For further improving the accuracy of registration, this paper proposes an intensity-structure similarity metric and the final rectification parameters are obtained by maximizing the proposed metric via quantum particle swarm optimization (QPSO) method. The experimental results of infrared and visible images in UAV inspection of photovoltaic power station demonstrate that the proposed method is competitive against traditional feature-based methods (FBMs), such as SIFT and SURF, and the joint area-feature based methods (AFBMs) based on SIFT combined with regional mutual information.