Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition Using The Human Skeleton Provided by Kinect

In this paper, a new method is proposed for people tracking using the human skeleton provided by the Kinect sensor, Our method is based on skeleton data, which includes the coordinate value of each joint in the human body. For data classification, the Support Vector Machine (SVM) and Random Forest techniques are used. To achieve this goal, 14 classes of movements are defined, using the Kinect Sensor to extract data containing 46 features and then using them to train the classification models. The system was tested on 12 subjects, each of whom performed 14 movements in each experiment. Experiment results show that the best average accuracy is 90.2 % for the SVM model and 99 % for the Random forest model. From the experiments, we concluded that the best distance between the Kinect sensor and the human body is one meter.
Fixed point property of amenable planar vortexes

This article introduces free group representations of planar vortexes in a CW space that are a natural outcome of results for amenable groups and fixed points found by M.M. Day during the 1960s and a fundamental result for fixed points given by L.E.J. Brouwer.
Novel Key Recovery Attack on Secure ECDSA Implementation by Exploiting Collisions between Unknown Entries

In this paper, we propose a novel key recovery attack against secure ECDSA signature generation employing regular table-based scalar multiplication. Our attack exploits novel leakage, denoted by collision information, which can be constructed by iteratively determining whether two entries loaded from the table are the same or not through side-channel collision analysis. Without knowing the actual value of the table entries, an adversary can recover the private key of ECDSA by finding the condition for which several nonces are linearly dependent by exploiting only the collision information. We show that this condition can be satisfied practically with a reasonable number of digital signatures and corresponding traces. Furthermore, we also show that all entries in the pre-computation table can be recovered using the recovered private key and a sufficient number of digital signatures based on the collision information. As case studies, we find that fixed-base comb and T_SM scalar multiplication are vulnerable to our attack. Finally, we verify that our attack is a real threat by conducting an experiment with power consumption traces acquired during T_SM scalar multiplication operations on an ARM Cortex-M based microcontroller. We also provide the details for validation process.
Rotation Invariance in Graph Convolutional Networks

Convolution filters in deep convolutional networks display rotation variant behavior.While learned invariant behavior can be partially achieved, this paper shows that current methods of utilizing rotation variant features can be improved by proposing a grid-based graph convolutional network.By performing spectral graph convolutions on features extracted from subareas of images, we are able to take advantage of the geometric nature of relational machine learning in graph neural networks to be able to overcome rotation variant features to perform object localization.We demonstrate that Grid-GCN heavily outperforms existing models on rotated images, and through a set of ablation studies, we show how the performance of Grid-GCN implies that there exist more performant methods to utilize fundamentally rotation variant features and we conclude that the inherit nature of spectral graph convolutions is able to learn invariant behavior.
A Comparative Analysis of Scalability Issues within Blockchain-based Solutions in the Internet of Things

Recently, enormous interest has been shown by both academia and industry around concepts and techniques related to connecting heterogeneous IoT devices.It is now considered a rapidly evolving technology with billions of IoT devices expected to be deployed in the upcoming years around the globe.These devices must be maintained, managed, traced, and secured in a timely and flexible manner.Previously, the centralized approaches constituted mainstream solutions to handle the ever-increasing number of connected IoT devices.However, these approaches may be inadequate to handle devices at a massive scale.Blockchain as a distributed approach that presents a promising solution to tackle the concerns of IoT devices connectivity.However, current Blockchain platforms face several scalability issues to accommodate diverse IoT devices without losing efficiency.This paper performs a comprehensive analysis of the recent blockchain-based scalability solutions applied to the Internet of Things domain.We propose an evaluation framework of scalability in IoT environments, encompassing critical criteria like throughput, latency, and block size.Moreover, we conduct an assessment of the notable scalability solutions and conclude the results by highlighting six overarching scalability issues of blockchain-based solutions in IoT that ought to be resolved by the industry and research community.
Evaluation Results of an Online Teacher Training Course Specialized in Engineering Education

The purpose of this article is to present the results of the evaluation of the imple-mentation of a teaching training course in Engineering Pedagogy (EP) at a Chilean university of applied sciences. The research questions that guided the research process were: (i) How do the participants evaluate the course in general? (ii) How do the participants evaluate the didactic design of the course? (iii) How do they evaluate the teaching competencies? (iv) How do they value the usefulness of the learning outcomes for their teaching practice? (v) How they self-evaluate their participation and their learning process? Based on different authors a questionnaire with closed and open-ended questions was developed and implemented online. For the statistical analysis was applied an exploratory-descriptive analysis. The training course consists of two online modules with 90 working hours in LMS, and was designed by the Center of Engineering Education (CIEI) at the University of Talca, according to the IGIP Curricula of the IGIP center at the Technische Universität Dresden (TU Dresden, Germany). From the first results of the pilot project, it can be noted that there is a high level of motivation and interest to participate in a teaching training course based on EP, which has been specially oriented and designed to meet the specific requirements of the academic staff of engineering schools.
Precomputation for Rainbow Tables has Never Been so Fast

Cryptanalytic time-memory trade-offs (TMTOs) are techniques commonly used in computer security e.g., to crack passwords. However, TMTOs usually encounter in practice a bottleneck that is the time needed to perform the precomputation phase (preceding to the attack). We introduce in this paper a technique, called distributed filtration-computation, that significantly reduces the precomputation time without any negative impact the online phase. Experiments performed on large problems with a 128-core computer perfectly match the theoretical expectations. We construct a rainbow table for a space \(N=2^{42}\) in approximately 8 h instead of 50 h for the usual way to generate a table. We also show that the efficiency of our technique is very close from the theoretical time lower bound.
A Study on the Role of Sentence Structure Analysis in English Learning

In the introduction part, the paper points out that many students feel that long difficult sentences in English are really very difficult and bring great difficulties in understanding, but the long difficult sentences in English are ubiquitous, which is determined by the language characteristics of English itself.So in the methodology part, this paper first analyzes the structural features of English sentences which are demonstrated mainly in two aspects and introduces the English sentence structure analysis method which explores the different sentence structure types and uses a case study to demonstrate the detailed usage of this method.Then in the discussion part, the paper discusses the role of sentence structure analysis in English listening, reading comprehension, cloze test, translation, oral expression and writing before it finally works out the conclusion that the analysis of English sentence structure plays an universal role in English learning, and students of English major should master this method through certain practice like systematically mastering grammar, doing more analysis in reading and practicing and reading aloud so as to fundamentally improve their English levels.
Optimal Feature Manipulation Attacks Against Linear Regression

In this paper, we investigate how to manipulate the coefficients obtained via linear regression by adding carefully designed poisoning data points to the dataset or modify the original data points. Given the energy budget, we first provide the closed-form solution of the optimal poisoning data point when our target is modifying one designated regression coefficient. We then extend the analysis to the more challenging scenario where the attacker aims to change one particular regression coefficient while making others to be changed as small as possible. For this scenario, we introduce a semidefinite relaxation method to design the best attack scheme. Finally, we study a more powerful adversary who can perform a rank-one modification on the feature matrix. We propose an alternating optimization method to find the optimal rank-one modification matrix. Numerical examples are provided to illustrate the analytical results obtained in this paper.
Regenerative Documentary: Posthuman Art in the Context of the Philippine Drug War

Regenerative documentary is generated by a composite unity that comprises the living system of resistance in the Philippines. It takes into account the contingent, illustrated by artist groups—Respond and Break the Silence over the Killings (RESBAK), Sandata, and Gantala Press—that converged with the living system of resistance where and when human rights was curtailed by an environment of impunity, systemic poverty, social inequality, and injustice. The purpose of regenerative documentary is to identify and make known that a system of resistance exists and it is living. By harnessing the ideas of narrative, emergence, and ethicality espoused by autopoiesis and critical posthumanism, regenerative documentary situates its expediency within the context of the Philippine human rights violations under the Duterte regime as a transformative critical posthuman art.
Route Planning and Monitoring Design of Unmanned Aerial Vehicles Based on Global Particle Algorithm

Unmanned aerial vehicles (UAVs), possessing the characteristics of a sensor and a sensing function, are in great demand in emergency rescue, agricultural plant protection, police and law enforcement, geological exploration, environmental monitoring, film and television entertainment, and other industries.However, the UAV industry is faced with various bottlenecks, such as the low intelligence level of UAVs, the high technical threshold for completing complex tasks, the lack of autonomous cooperative ability between the formation of UAVs, and the lack of an intelligent operating system.To achieve the route planning and monitoring design of UAVs, we first designed a remote intelligent monitoring system for a UAV based on multimode communication and cloud computing, which consisted of an intelligent control module, a coordination module for the multimode communication and autonomous formation of UAVs, a UAV cloud operating system, and panoramic image dynamic fusion technology.Then, we presented an adaptive data transmission method based on priority in the multimode communication system.Finally, a route planning and monitoring design based on a global particle algorithm was designed for the remote intelligent monitoring system of the UAV.The simulation results show that the remote security system operates normally and meets urgent communication needs.At the same time, both data and video image transmission can be achieved, and the route of the UAV can be effectively planned.
Copy Move Forgery Detection Using Forensic Images

Digital images are open to several manipulations and dropped cost of compact cameras and mobile phones due to the robust image editing tools. Image credibility is therefore become doubtful, particularly where photos have power, for instance, news reports and insurance claims in a criminal court. Images forensic methods therefore measure the integrity of image by apply different highly technical methods established in literatures. The present work deals with copy move forgery images of Media Integration and Communication Center Forgery (MICC-F2000) dataset for detecting and revealing the areas that have been tampered portion in the image, the image is sectioned into non overlapping blocks using Simple liner iterative clustering (SLIC) method. Then, Scale invariant feature transform (SIFT) descriptor is applied on the grey of the handled image to gives distinctive key points that classified by K-Nearest neighbor to detect and localize the forged portion in the tempered image. The forgery detection results gave a performance percent of about 98%, which reflects the ability of the KNN classifier that cooperated with SIFT descriptor to detect the forged portions even if the forged area is rotated or scaled or both of them.
Observability Study on Passive Target Localization by Conic–Angle Measurements

Information from a passive linear array sensor is related to the conic angle formed by a target and the sensor in three-dimensional (3D) space so that the target localization system using the sensor should be also designed in 3D space. This paper presents an observability study of a passive target localization system created using conic angle information. The study includes the analysis of the sensor maneuver requirement needed to achieve system observability and simulations to demonstrate the results of the analytic scheme. The proposed sensor maneuver requirements satisfy the system observability conditions by using the local linearization approach of the Fisher information matrix. It is also shown that this requirement can be mitigated for special cases in which the depth difference between the sensor and the target is given. Using the simulation, it is shown that sensors following the proposed scheme are able to obtain meaningful information that can be used to estimate 3D target states.
Analysis the Influence of Online Learning on Students' Learning Enthusiasm

This research was created to explain in detail how the objective conditions of decreased enthusiasm for learning experienced by students in online learning during the covid-19 pandemic. This research uses quantitative methods with survey techniques. This research was conducted by involving 10 high school or vocational high school students. The data collection technique used in this study was a survey technique with a Likert scale. The survey was created in Microsoft Word and then distributed via WhatsApp to make it easier to access. The data analysis technique used in this study was to calculate the percentage of data on each indicator, then interpret the presentation score and analyze each indicator in detail. The inal results of the study indicate that the decline in learning of high school or vocational high school students is considered to be less good, it is obtained from the percentage score which amounts to 45%. Hope this reseacrh can be an evaluation of various parties in implementing online learning, especially those involving motivation and enthusiasm for learning in it. In addition, it is hoped that it van be use as research material for other researchers in analyzing and developing students’ enthusiasm for learning in online learning during emergencies.
Semantic Prediction: Which One Should Come First, Recognition or Prediction?

The ultimate goal of video prediction is not forecasting future pixel-values given some previous frames.Rather, the end goal of video prediction is to discover valuable internal representations from the vast amount of available unlabeled video data in a self-supervised fashion for downstream tasks.One of the primary downstream tasks is interpreting the scene's semantic composition and using it for decision-making.For example, by predicting human movements, an observer can anticipate human activities and collaborate in a shared workspace.There are two main ways to achieve the same outcome, given a pre-trained video prediction and pre-trained semantic extraction model; one can first apply predictions and then extract semantics or first extract semantics and then predict.We investigate these configurations using the Local Frequency Domain Transformer Network (LFDTN) as the video prediction model and U-Net as the semantic extraction model on synthetic and real datasets.
Wasserstein Unsupervised Reinforcement Learning

Unsupervised reinforcement learning aims to train agents to learn a handful of policies or skills in environments without external reward. These pre-trained policies can accelerate learning when endowed with external reward, and can also be used as primitive options in hierarchical reinforcement learning. Conventional approaches of unsupervised skill discovery feed a latent variable to the agent and shed its empowerment on agent’s behavior by mutual information (MI) maximization. However, the policies learned by MI-based methods cannot sufficiently explore the state space, despite they can be successfully identified from each other. Therefore we propose a new framework Wasserstein unsupervised reinforcement learning (WURL) where we directly maximize the distance of state distributions induced by different policies. Additionally, we overcome difficulties in simultaneously training N(N&gt;2) policies, and amortizing the overall reward to each step. Experiments show policies learned by our approach outperform MI-based methods on the metric of Wasserstein distance while keeping high discriminability. Furthermore, the agents trained by WURL can sufficiently explore the state space in mazes and MuJoCo tasks and the pre-trained policies can be applied to downstream tasks by hierarchical learning.
The Development of Multiple Intelligence Based Textbook

This study aims to develop multiple intelligence-based textbooks seventh grade junior high school students with material on equations and inequalities. It is hoped that the results of the development of this textbook can improve student learning outcomes, especially in the ability of mathematical representation. The population used was students of SMP / MTs in Medan, by taking several research samples 108 students. The development model of this textbook uses the 4D development model (define, design, development, Dissemination), but due to the situation during Covid 19 and time constraints it only reaches the development of the expert validation section and limited trials. Thus in this development it only comes to the analysis of validation and practicality of textbooks, but for effectiveness will be continued in next research. The analysis shows that multiple intelligence-based textbooks on equations and inequalities in class VII are declared valid and practical.
Language Model Using Neural Turing Machine Based on Localized Content-Based Addressing

The performance of a long short-term memory (LSTM) recurrent neural network (RNN)-based language model has been improved on language model benchmarks. Although a recurrent layer has been widely used, previous studies showed that an LSTM RNN-based language model (LM) cannot overcome the limitation of the context length. To train LMs on longer sequences, attention mechanism-based models have recently been used. In this paper, we propose a LM using a neural Turing machine (NTM) architecture based on localized content-based addressing (LCA). The NTM architecture is one of the attention-based model. However, the NTM encounters a problem with content-based addressing because all memory addresses need to be accessed for calculating cosine similarities. To address this problem, we propose an LCA method. The LCA method searches for the maximum of all cosine similarities generated from all memory addresses. Next, a specific memory area including the selected memory address is normalized with the softmax function. The LCA method is applied to pre-trained NTM-based LM during the test stage. The proposed architecture is evaluated on Penn Treebank and enwik8 LM tasks. The experimental results indicate that the proposed approach outperforms the previous NTM architecture.
On the Robustness of Reading Comprehension Models to Entity Renaming

We study the robustness of machine reading comprehension (MRC) models to entity renaming-do models make more wrong predictions when the same questions are asked about an entity whose name has been changed?Such failures imply that models overly rely on entity information to answer questions, and thus may generalize poorly when facts about the world change or questions are asked about novel entities.To systematically audit this issue, we present a pipeline to automatically generate test examples at scale, by replacing entity names in the original test sample with names from a variety of sources, ranging from names in the same test set, to common names in life, to arbitrary strings.Across five datasets and three pretrained model architectures, MRC models consistently perform worse when entities are renamed, with particularly large accuracy drops on datasets constructed via distant supervision.We also find large differences between models: SpanBERT, which is pretrained with span-level masking, is more robust than RoBERTa, despite having similar accuracy on unperturbed test data.We further experiment with different masking strategies as the continual pretraining objective and find that entity-based masking can improve the robustness of MRC models. 1
Human Face and Facial Parts Detection using Template Matching Technique

Face recognition has become relevant in recent years because of its potential applications. The aim of this paper is to find out the relevant techniques which give not only better accuracy also the efficient speed. There are several techniques available for face detection which give much better accuracy but the execution speed is not efficient. In this paper, a normalized cross-correlation template matching technique is used to solve this problem. According to the proposed algorithm, first different facial parts are detected likes mouth, eyes, and nose. If any of the two facial parts are found successfully then the face can be detected. For matching the templates with the target image, the template rotates at a certain angle interval.
Investigation and Analysis on the Cultivation of Academic Competence of Postgraduates

Taking 201 graduate students from some universities in Anhui and Shaanxi as the research objects, using the method of the questionnaire survey, this paper analyzes the realistic characteristics and influencing factors of the academic competence of graduate students.At the same time, the corresponding countermeasures are put forward from the individual level and the school level, and the corresponding research conclusions are expected to provide the reference for improving the academic ability of graduate students.