Reviewerly/exHarmony huggingface dataset:
General 4-GLV Lattice Reduction Algorithms

With two <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{Z}$</tex> -linear independence endomorphisms <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Phi$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Psi$</tex> satisfying <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Phi^{2}+r\Phi+s=0$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Psi^{2}-t_{\Psi}\Psi+n_{\Psi}=0$</tex> , we construct general 4-GLV lattice reduction algorithms with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{Z}[\Psi\vert$</tex> principal maximal orders of imaginary quadratic fields <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{Q}(\sqrt{-d})$</tex> . The algorithms can be used to calculate short bases for 4-GLV decompositions on elliptic curves (or Jacobians of genus 2 curves). Our algorithms have a theoretic upper bound of output <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Cn^{1/4}$</tex> , where \begin{align*} C=\begin{cases} {\frac{4+2\sqrt{d+1}}{3-d}} (\sqrt{1+\vert r\vert +\vert s\vert }),\ &\text{if}\ \mathbb{Z}[\Psi]=\mathbb{Z}[\frac{1+\sqrt{-d}}{2}],\\ \frac{4\sqrt{d}}{(4\sqrt{d}-(d+1)}(\sqrt{1+\vert r\vert +\vert s\vert }),\ &\text{if}\ \mathbb{Z}[\Psi]=\mathbb{Z}[\frac{1+\sqrt{-d}}{2}].\end{cases}\end{align*} Especially, our algorithms cover the case <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{Z}[\Psi]=\mathbb{Z}[\sqrt{-1}]$</tex> of Yi et al. (SAC 2017) and the case <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{Z}[\Psi]=\mathbb{Z}[\frac{1+\sqrt{-3}}{2}]$</tex> of Wang et al. (AMC 2021).
Integrating Information Flow Tracking into High-Level Synthesis Design Flow

Information flow analysis plays an important role in hardware security verification as it enables reasoning about properties related to confidentiality, integrity, and availability. This book chapter describes techniques for integrating information flow tracking into the high-level synthesis (HLS) design flow. Specifically, we develop precise information flow tracking methods that target the HLS backend and verify the security of HLS design outputs. We discuss the benefits of performing information flow tracking at the register transfer level, present fine-granularity information flow model formalizations in a common hardware description language, and illustrate how hardware security properties are formally verified using standard EDA verification tools. We present experimental results to show the effectiveness of our secure hardware design flow in proving security properties related to confidentiality, integrity, isolation, constant time, and malicious design modification. This provides a promising approach for enhancing the traditional functional HLS design flow to consider security as an additional design constraint.
Hovering and Contact Representation of Laser Contour-Based Hand with Swinging Tablet PC for Distant Communication

As covid-19 spread worldwide, the growing trend to reduce people’s mobility and the establishment of new lifestyles such as remote work, contactless communication systems have been required. Using a projected hand makes it possible to realize remote communication with a sense of presence without loading a user’s physical body. However, conventional projected hand systems have a problem that the relative brightness of the hand image in a bright living room is low. Therefore, we propose a configuration that combines a video call system and a contour-based projected hand by a laser scanning projector. In addition, we incorporate a haptic feedback system with a tablet PC to improve the sense of contact with remote objects. To enhance the sense of contact in remote areas, we focus on providing the transition between hovering and contact states of the contour-based projected hand and users’ hands. According to the experiment, we confirmed that there is a possibility to express hovering and contact even with simple swinging flat-panel feedback and visual effects of the contour-based projected hand.
Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments

The objective of the research is to develop a methodology to analyze a set of data extracted from a Learning Management System (LMS), in order to implement a Dashboard, which can be used by teachers to make timely and relevant decisions to improve the teaching-learning processes. The methodology used consisted of the analysis of 9,257 records extracted through simple random sampling from a population of 100,000 records. The indicators analyzed were: number of accesses, course grades, time spent, number of courses enrolled and number of activities developed. The results show the data analysis in the KNIME data mining analysis platform, the model was implemented in five phases: Requirements definition, model design, development, implementation and evaluation of results. The results are taken as a recommendation to design and implement a customized Dashboard for teachers to identify observable behavioral patterns that allow them to make decisions to improve the teaching-learning processes of students.
Pseudo-random noise generator based on FPGA

Abstract This paper proposes a novel pseudo-random noise generator based on FPGA. The system architecture is written by Verilog, and each sub-module is designed in Vivado software. Modelsim is employed to co-simulate the sub-modules and the saved data is imported to Matlab for spectrum analysis. Consequently, the proposed design can be applied to practical noise generating with great scalability and flexibility.
Machine learning algorithms used for short-term PV solar irradiation and temperature forecasting at microgrid

Microgrids (MGs) are considered an important part of the evolving power grid in terms of reliability, cost, and environmental impact of distributed generation systems. The main production resource in MGs is renewable energy (RE) sources which have grown even more recently. The energy management system in MGs has many features such as decreasing the power losses in transmission lines and flexibility of monitoring the distributed energy resources (DERs). However, the power output from the DERs may get badly affected due to the intermittent nature of RE connected with the MGs system. Therefore accurate short-term solar generation forecasting is an important issue in MG to predict the required amount of power to be dispatched by DERs, which can lead to economic advantages for end-users. In this chapter, a comparative study of different machine learning (ML) approaches has been applied for forecasting solar radiation and temperature. In the proposed method, the most applied models in ML such as linear regression, random forest (RF) regression, K-nearest neighbors, support vector machines (SVMs) models are compared. We assessed the performance of ML proposed by evaluation of the root mean square error, and then investigated the influences of the parameter’s techniques. The simulation result shows that RF and SVM perform well in the short-term forecasting of solar irradiation and temperature.
Learning Depth Estimation From Memory Infusing Monocular Cues: A Generalization Prediction Approach

Depth estimation from a single image is a challenging task, yet this field has a promising prospect in automatic driving and augmented reality. However, the prediction accuracy is degraded significantly when the trained network is transferred from the training dataset to real scenarios. To solve this issue, we propose MonoMeMa, a novel deep architecture based on the human monocular cue, which means humans can perceive depth information with one eye through the relative size of objects, light and shadow, etc. based on previous visual experience. Our method simulates the process of the formation and utilization of human monocular visual memory, including three steps: Firstly, MonoMeMa perceives and extracts real-world objects feature vectors (encoding). Then, it maintains and replaces the extracted feature vector over time (storing). Finally, MonoMeMa combines query objects feature vectors and memory to inference depth information (retrieving). According to the simulation results, our model shows the state-of-the-art results on the KITTI driving dataset. Moreover, MonoMema exhibits remarkable generalization performance when our model is migrated to other driving datasets without any finetune.
Implementation of the Spiral Optimization Algorithm in the Support Vector Machine (SVM) Classification Method (Case Study: Diabetes Prediction)

Classification is a data mining method that is formulated to estimate group membership for data samples, this process is used to analyze the connections between data in a large data set. One of the classification methods that are often used is Support Vector Machine (SVM), in the SVM method there is a kernel function that helps in solving classification problems that cannot be separated linearly, one of which is the Radial Basis Function (RBF) Kernel. In using the SVM method with the RBF kernel function, Gamma and C parameters can affect the shape of the hyperplane in producing a good classification model, so that optimal Gamma and C parameter values are needed to produce a good classification. This study using the Spiral optimization Algorithm in optimizing Gamma and C parameters, by conducting several experimental stages in determining the best parameters of the Spiral optimization Algorithm to determine the Gamma and C parameters, SVM classification method with RBF kernel function can produce the highest accuracy is 86.15% with an average accuracy is 80.12% based on Pima Indians Diabetes dataset.
DefenseNet-A Resilent Network Against Adverserail Attacks On Recommendation Systems

On internet, one of the most progressing applications is recommendation system (RS), such as, youtube, google, Amazon, Netflix, etc. It is a subset of information filtering systems wherein, information about certain products or services or a person are categorized and are recommended for the concerned individual. The most popular recommender systems leverage the power of deep neural networks (DNNs) to make recommendations. But these recommendation systems implementing object detection and classification, are known to be susceptible to adversarial attacks. Small barely perceptible perturbations to the input can cause these algorithms to incorrectly classify inputs that they would have otherwise classified correctly. In our research work, we present an ensemble architecture of recommender system which combines the power of popular images recognition DNNs and presents higher defense ratio against adversarial attack. We have also shown that our DefenseNet&#x2019;s accuracy is higher than the state-of-the-art recommendation under adversarial attacks on Amazon benchmark dataset.
Third Order Polynomial Derived Sentence Embedding

In this study, we adopt third order polynomials to derive sentence embeddings for Internet of Things (IoT) pattern definitions. The aim was to use pre-identified (or baseline) IoT pattern definitions, and use third order polynomial regression to embed similar sentences to these definitions in vector spaces. While artificial neural networks (ARRs) have previously been shown to be effective in producing sentence embeddings, such embeddings are prone to multicollinearity. Generally speaking, in ARRs, the problem of multicollinearity becomes worse with each successive layer. This, in turn, causes convergence issues across all known sentence embedding techniques based on ARRs training. These issues are evident in some of the well-known ARRs based sentence embedding techniques such as Doc2Vec, InferSent, Universal Sentence Encoder, and SentenceBERT. We do not claim that polynomial regression is better suited than artificial neural networks for sentence embedding tasks, but rather that artificial neural networks (ANNs) are, in essence, polynomial regression. Thus, we treat the generation of an analytic function used with an analytic activation as a polynomial. In this way, the core problem in this context then becomes the searching for the coefficients of the polynomial, which is essentially a regression problem. In our model, similar sentences are approximated around an operating point (baseline vector) by third order polynomials (3OPs). In other words, we used 3OPs derived from the ANN model. The resulting 3OPs derived sentence embeddings show an accuracy of 83.33% which is almost on par with the 83.7% achieved by the prior state-of-the-art on Feng et al. [1].
Deviations in Efficiencies of Logarithmically Growing Sorting Algorithms at Infinitely Large Datasets

Asymptotic Notations have been very effective in representing the efficiencies of algorithms by analyzing the number of steps and the nature of traversals and operations that the algorithm is bound to perform. Though they represent the nature of growth of the time or space consumed by the algorithms, they only consider the fastest-growing term and not the other terms. The result from this kind of analysis often seems to be fruitful in grading the algorithms based on their efficiency, but theoretically, there seem to be evident deviations when these complexities are extrapolated to larger datasets, hypothetically &#x2018;infinitely large&#x2019; datasets. This deviation is tracked down when the constant and slow-growing terms involved are considered. The practical difficulty of empirically verifying this fact due to the ambiguity in defining a very large number of inputs is overcome by theoretically calculating the ratio between the time complexities of algorithms of interest and applying their limit to be infinity. In this study, the algorithms heapsort and merge sort are analyzed and the drastic deviation between them is shown mathematically though they both have the complexity of <tex>$n(log(n))$</tex>, concluding that the merge sort is actually twice as fast as heap sort when approaching infinitely large data elements.
Syntactic Error Analysis on Oral Classroom Discourse

Purpose: This qualitative research shed light on the syntactic errors frequently made by Grade 11 students in speaking. This research also explored and understood the probable linguistic reasons for these occurrences. The results of this study were intended for English teachers to obtain a deeper understanding of the existence and significance of errors in language learning. Methodology: This study is a qualitative research design using the Error Analysis approach. Twelve participants from three different private schools in the Davao Region were purposefully chosen. The following data collection techniques, namely observation, interview, and documentation were utilized. Error Analysis was carried out in four successive stages; a collection of a sample of learner language, identification of errors, description of errors, and explanation of errors. Findings: Results showed that among the syntactic errors committed by the participants in classroom discourse were omission, addition, selection, and ordering. Results further revealed that the causes of syntactic errors were intralingual, as the common cause of errors, and interlingual. Furthermore, the FGD results showed that there were other reasons for committing errors like fear to commit errors and lack of confidence, which impede the students in their speaking performance. Significance: As gleaned from the results of this qualitative study, English teachers are encouraged to focus on correcting high frequency and general errors rather than correcting every syntactic error committed by the students. Secondly, English teachers might consider pedagogical reforms in teaching speaking. Lastly, English teachers must mitigate anxiety while improving students’ oral proficiency.
Untangling Braids with Multi-Agent Q-Learning

We use reinforcement learning to tackle the problem of untangling braids. We experiment with braids with 2 and 3 strands. Two competing players learn to tangle and untangle a braid. We interface the braid untangling problem with the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. The results provide evidence that the more we train the system, the better the untangling player gets at untangling braids. At the same time, our tangling player produces good examples of tangled braids.
Design of multipliers for moduli$({2^{4k}} + {2^{2k + 1}} - {2^{3k + 1}} - {2^k} + 1)$ and $({2^{4k}} - {2^{3k + 1}} + {2^k} - 1)$

In this paper, the design of modulo multipliers for the moduli <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$({2^{4k}} + {2^{2k + 1}} - {2^{3k + 1}} - {2^k} + 1)$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$({2^{4k}} - {2^{3k + 1}} + {2^k} - 1)$</tex> is investigated. These moduli are useful for constructing four moduli sets with interesting properties resulting in simpler RNS to binary conversion architectures. The derived architectures of the multipliers can facilitate binary to RNS conversion as well. Hardware requirements and multiplication time are derived in tems of basic gates and ASIC implementation results are presented.
Determination of the Height of 3D Objects by Moire Measurement

In the past, the height of objects was mostly measured by contact methods, and it may damage the objects. Fringe projection, as a new emerging measurement, might effectively improve the problem with the advantages of non-contact, non-destructive, real-time, full-field, and simple installation. This paper mainly discusses the fringe projection measurement technology to measure the height of the objects accurately. With the application of moir&#x00E9; projection, this paper explains the operation mode and theory of the fringe projection system, and then introduces the phase extraction and phase expansion of the sine wave fringe in sequence. With the longitudinal depth correction, a polynomial fitting method is used to establish a "phase-depth relationship", which is applied to the fringe phase to improve the depth measurement accuracy.
Predicate-attention neural model for Chinese semantic role labeling

Semantic role labeling functions to convey the meaning of a sentence through forming a predicate-argument structure directed at the specific predicate. In recent years, end-to-end semantic role labeling methods associated with the deep neural network have received significant attention in the field of computational linguistics. Moreover, end-to-end semantic role labeling methods have demonstrated a beneficial capacity to reduce the incompleteness caused by handcrafted features, which is an observed short-coming of traditional Chinese role labeling methods. However, the critical focus of sentences are frequently lost as a result of existing semantic role labeling structures attributing equal importance to every single word, instead of the overall concept denoted by particular terms. Hence, the performance and function ability of deep neural network models is reduced. In this paper, we introduce a specific attention mechanism based on the established predicate. This mechanism would automatically calculate the weighted contributions of each word, and the corresponding Part-of-Speech, in order to accurately represent the general fundamental ideas of the sentence. In addition, we extended the Bidirectional LSTM using two different semantic role constraint methods, to effectively utilize the dependency and constraint relationships among different semantic role tags, hereby further improving the performance of the whole neural Chinese semantic role labeling model. Experimental results demonstrate the efficacy of our proposed model through providing a baseline that allows for meaningful comparisons, inferring that both weighted contributions of the predicate, and semantic role constraints can help significantly refine the overall model function.
Efficient Small-Object Detection in Airport Surface Based on Maintain Feature High Resolution

Detection of small objects is a challenge in computer vision, especially in the application of airport surface surveillance. Operation of convolution is the key to losing the semantic information of small objects. In this paper, a high-resolution network is designed to maintain the feature information of small objects. Channel restructuring is used to enlarge the feature map and merge it with the shallow feature map to enhance the feature position information, rather than bilinear interpolation upsampling in traditional methods. Experiments show that, in the self-built airport surface dataset, compared with the mainstream object detector Fully Convolutional One-Stage Object Detector, RetinaNet, and other methods, the proposed method can reach the state-of-the-art in various precision indexes. In terms of model size and number of parameters, the proposed method is significantly smaller than Faster Region-Convolutional Neural Networks. Under the same input size, compared with You Only Look Once v3, the method presented in this paper can obtain a lower number of parameters and a faster inference speed. In the case of using GTX1080Ti, our method can reach 76.7 frames per second.
Real-time global sentiment analysis system with custom augmentation of market intelligence

Due to the significant growth of internet access across the world, online presence and usage of social media of people have escalated. Among them, one of the most popular and contributing platforms is Twitter, which has more than 350 million users. Being a platform facilitating the opinions and views of such a huge population in every domain, Twitter qualifies itself as a great opinion mining source. Sentiment analysis – a process of discovering and identifying the emotion (positive or negative) from the text data, has been in significant rise for its use in decision-making. Implementing such a sentiment analysis model into the pool of tweets (text data of Twitter) is termed Twitter sentiment analysis. Also, the process of deriving public sentiment before decision-making has progressed significantly over the years. Intelligent systems work to create and implement a real-time model which can be functional on various aspects of commercial and non-commercial applications. This paper reviews and consists of such relevant systems.
Video Anomaly Detection Based on Frame Prediction of Generative Adversarial Network

With the development of society, the application of abnormal behavior detection in the field of public safety has become more and more extensive. We propose a frame prediction video behavior anomaly detection model based on Generative Adversarial Network (GAN). We use the U-net network with the feature storage module and variance attention mechanism as the generator, which not only increases the network&#x0027;s sensitivity to the movement part of the sample, but also reduces the network&#x0027;s learning ability and limits the network&#x0027;s ability to predict abnormal samples. For the discriminant model, we have added a channel and spatial attention mechanism to the Markov discriminator to improve the discrimination ability, which is conducive to improving the quality of future frame generation. Compared with the existing abnormal behavior detection methods, our proposed model achieves excellent detection performance.