Reviewerly/exHarmony huggingface dataset:
Research and Implementation of Human-Computer Interaction System Based on Human Body Attitude Recognition Algorithm

Aiming at a series of problems in the application of visual human-computer interaction, such as complicated recognition process and low interaction efficiency, a gesture recognition algorithm based on human body structure is proposed. Using this algorithm and the design idea of hardware and software cooperation, a robot control system based on visual perception is implemented on FPGA platform. The attitude sensor is used to obtain the human attitude information and solve it. The attitude is determined by the information fusion method based on D-S evidence theory. combine the complex actions, simplify the operation, obtain the attitude of the remote robot through the “double virtual model” and correct the motion trajectory. Aiming at the defect of single sensor in information detection dimension and the sensitivity to noise data, a multi-sensor fusion method for human posture estimation is proposed. In this paper, an algorithm of human posture recognition (i.e. human joint position recognition) based on model constraints is proposed to solve the problem that the robot system can accurately extract the spatial coordinates of human joints before the transfer and multiplication operation.
Elderly Fall Detection Based on GCN-LSTM Multi-Task Learning Using Nursing Aids Integrated with Multi-Array Flexible Tactile Sensors

Due to the frailty of elderly individuals' physical condition, falling can lead to severe bodily injuries. Effective fall detection can significantly reduce the occurrence of such incidents. However, current fall detection methods heavily rely on visual and multi-sensor devices, which incur higher costs and complex wearable designs, limiting their wide-ranging applicability. In this paper, we propose a fall detection method based on nursing aids integrated with multi-array flexible tactile sensors. We design a kind of multi-array capacitive tactile sensor and arrange the distribution of tactile sensors on the foot based on plantar force analysis and measure tactile sequences from the sole of the foot to develop a dataset. Then we construct a fall detection model based on a graph convolution neural network and long-short term memory network (GCN-LSTM), where the GCN module and LSTM module separately extract spatial and temporal features from the tactile sequences, achieving detection on tactile data of foot and walking states for specific time series in the future. Experiments are carried out with the fall detection model, the Mean Squared Error (MSE) of the predicted tactile data of the foot at the next time step is 0.0716, with the fall detection accuracy of 96.36%. What is more, the model can achieve fall detection on 5-time steps with 0.2-s intervals in the future with high confidence results. It exhibits outstanding performance, surpassing other baseline algorithms. Besides, we conduct experiments on different ground types and ground morphologies for fall detection, and the model showcases robust generalization capabilities.
Automatic Search of Meet-in-the-Middle Differential Fault Analysis on AES-like Ciphers

Fault analysis is a powerful technique to retrieve secret keys by exploiting side-channel information. Differential fault analysis (DFA) is one of the most powerful threats utilizing differential information between correct and faulty ciphertexts and can recover keys for symmetric-key cryptosystems efficiently. Since DFA usually targets the first or last few rounds of the block ciphers, some countermeasures against DFA only protect the first and last few rounds for efficiency. Therefore, to explore how many rounds DFA can affect is very important to make sure how many rounds to protect in practice. At CHES 2011, Derbez et al. proposed an improved DFA on AES based on MitM approach, which covers one more round than previous DFAs. To perform good (or optimal) MitM DFA on block ciphers, the good (or optimal) attack configurations should be identified, such as the location where the faults inject, the matching point with differential relationship, and the two independent computation paths where two independent subsets of the key are involved. In this paper, we formulate the essential ideas of the construction of the attack, and translate the problem of searching for the best MitM DFA into optimization problems under constraints in Mixed-Integer-Linear-Programming (MILP) models. With the models, we achieve more powerful and practical DFA attacks on SKINNY, CRAFT, QARMA, PRINCE, PRINCEv2, and MIDORI with faults injected in 1 to 9 earlier rounds than the best previous DFAs.
Analog Analogies: <i>Intel v. Hamidi</i> and the Future of Trespass to Chattels

Abstract A symposium on great torts cases of the twenty-first century must include Intel Corp. v. Hamidi , the canonical case about whether unwanted e-mail spam sent to a company’s server could give rise to a trespass to chattels claim. While much has been written about Intel , in this Essay, we argue that Intel is as much of a classic for what it reveals about the old-fashioned tort as it is for its more closely examined ruling on “cybertrespass.” The dueling personal property analogies chosen by the majority and dissenting opinions in Intel reveal basic and fundamental disagreements about what sorts of conduct the traditional tort prohibits: specifically, when a plaintiff may obtain nominal damages or an injunction against a defendant’s contact with personal property when that contact does not have lasting physical effects. As we point out, this question arose in cases long before Intel and generated some discussion during the drafting of the First and Second Restatements of Torts. Now, the same question arises in Fourth Amendment law and the law of Article III standing, areas in which recent Supreme Court decisions have elevated trespass-to-chattels analyses to renewed significance. Our Essay indicates the need for further development on open questions in the law of trespass to chattels, suggesting some ways that central tort-law notions like intentionality and custom might provide firmer bases for recognizing the harm in unwanted contact with things.
ENTANGLEMENT BETWEEN TWO SUPERCONDUCTING CHARGE&#x0D; QUBITS

In this paper, we investigated the dynamics of entanglement of two identical charge qubits with Josephson junctions in the case when one of the qubits is exposed to a microwave field in a coherent or thermal state. We have found the exact solution of the quantum time equation of evolution of the system under consideration for the statistical operator in the case of initial separable and entangled states of qubits. The exact solution for the complete statistical operator is used to calculate the qubit entanglement criterion - concurrence. The results of numerical simulation of the time dependence of the concurrence in the case of the coherent field showed that, with a certain choice of model parameters, the system can realize long-lived entangled states. It is also shown that for the thermal state of the field and the entangled initial state of qubits, the qubits retain a certain degree of entanglement during evolution even in the case of very intensive fields. In this case, for any intensities of thermal noise, there is no effect of the sudden death of entanglement.
3D Visualization Trends in Science Learning: Content Analysis

In the current digital era, the development of science and technology is growing rapidly in the world of education. In learning the use of technology in the form of using smartphones and the internet which can be accessed by both teachers and students. Trends in learning by incorporating elements of technology in science learning. Science is a family of biology, physics, chemistry which has material characteristics that can be seen or observed by the senses and are not visible to the five senses and cannot be displayed in class. The natural aspect of science is quite broad because it relates to phenomena and natural phenomena, therefore 3D visualization is needed in learning so that abstract material can be more concrete in the meaning of learning. The purpose of this study is to analyze the content or content analysis of 20 articles related to 3D visualization in science learning. The results obtained are 3D visualization in learning used from elementary to high school levels. The 3D visualization is in the form of AR-based learning media and AR-based modules that affect the learning outcomes that teachers and students want to achieve. In making objects in the form of 3D visualization using several software, namely blender, unity, vuforia, AR, uniteAR.
Interrelation of learning model and peer interaction through motivation on achievement

Mathematics is often seen as a complex subject by most students. This has caused their achievement and motivation to be low. This study aims to see whether there is a direct or indirect influence from the independent and intervening variables on the dependent variables. This research is quantitative. The population is grade VIII of one of state junior high school, and the sample is class 8-B which consists of 28 students. The instruments used in data collection were questionnaires and tests. Data were analyzed using path analysis with the help of the Amos 25.0 program. The research result is that the P-value is 0.170.05. There is a direct effect of the learning approach on motivation; the P-value is 0.0490.05. Peer interaction immediately affects achievement; the P-value is 0.0350.05. There is a direct effect of the learning models on achievement; the P-value is 0.0880.05. There is no direct effect of peer interaction on achievement; the P-value of 0.0230.05 has a direct impact on motivation on achievement, the P-value of 0.4440.05 motivation cannot mediate peer interaction on achievement, and a P-value of 0.0240.05 with the conclusion that there is an indirect effect between learning model on achievement through motivation.
Pelatihan dan Pendampingan Blended Learning Berbasis Moodle dalam Pembelajaran Matematika di SMP Negeri 1 Magelang dan SMP Negeri 2 Magelang

Entering the new normal era, Mathematics learning is carried out using blended learning. However, in its implementation so far it has not run optimally. Discussions held online were not well facilitated, materials were only sent via whatsapp groups, and documentation was not good. Mathematics learning should be presented in an attractive manner by utilizing learning technology in accordance with the times. One of the platforms that can be used is the Moodle platform, which can be implemented simultaneously for face-to-face learning and online learning. Through Moodle, teachers can provide materials, videos, assignments, and quizzes that students can access anytime and anywhere, and create discussion forums with their students. Students who find it difficult to learn mathematics can ask the forum according to the topic of discussion. Thus, it is necessary to hold service activities in the form of training and mentoring of Moodle-based blended learning in learning mathematics at SMP Negeri 1 Magelang and SMP Negeri 2 Magelang. The purpose of this service activity is to provide training and mentoring for Moodle-based blended learning in mathematics learning at SMP Negeri 1 Magelang and SMP Negeri 2 Magelang as an innovation in learning mathematics. The target of this service is to transfer the science and technology of junior high school learning innovations to teachers at SMP Negeri 1 Magelang and SMP Negeri 2 Magelang in the form of training and mentoring, in the form of: (1) introduction to the Moodle platform and its features, (2) training in making teaching materials, assignments, quizzes, and videos using the Moodle platform, and (3) mentoring the use of Moodle in junior high school learning. The methods used in this service are lecture, question and answer, and practice methods. Service activities are carried out for 4 months in several stages, namely planning, implementing, evaluating, and reporting the results of service activities. The result of community service activities is an increase in skills using the Moodle platform for mathematics teachers at SMP Negeri 1 Magelang and SMP Negeri 2 Magelang in blended learning-based Mathematics learning.
An Approach of Federated Learning in Artificial Intelligence for Healthcare Analysis

During the previous several decades, data generation has an exponential growth due to increase in use of the Internet and smart devices. Also there is a huge increase in utilization of online media due to very cost-effective and high speed availability of the Internet which indirectly generates massive data. This increase in data leads to problems of data storage and processing of that data. For massive data storage, big data technologies and cloud storage resolved the problem to some extent. Nowadays, researchers started working on analyzing that data and producing useful results which will benefit the business, society, and to the world. Data processing for large-scale applications has been transformed by machine learning. Simultaneously, due to rising privacy concerns in various trending applications on online platforms, traditional data training techniques were redesigned. Classic machine learning, in particular, entails centralized model training, in which data is collected, and that central server is responsible for the whole training procedure. When information is sent to the main cloud server, it poses many privacy risks to participants. Here, federated learning can significantly overcome privacy issues over traditional centralized machine learning processes. The federated learning approach in machine learning enables users to train a model at local level without transmitting the user’s private data to the central data server. Here, we will implement an innovative federated learning model, also will evaluate output parameters of it with a traditional machine learning approach. For the implementation, we are using the publicly available healthcare dataset. Here we are safeguarding the privacy of participants. We believe that this study will demonstrate the advantages of federated learning and encourage its widespread adoption.
Enhancing Privacy-Preserving Intrusion Detection in Blockchain-Based Networks with Deep Learning

Data transfer in sensitive industries such as healthcare presents significant challenges due to privacy issues, which makes it difficult to collaborate and use machine learning effectively. These issues are explored in this study by looking at how hybrid learning approaches can be used to move models between users and consumers as well as within organizations. Blockchain technology is used, compensating participants with tokens, to provide privacy-preserving data collection and safe model transfer. The proposed approach combines Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) to create a privacy-preserving secure framework for predictive analytics. LSTM-GRU-based federated learning techniques are used for local model training. The approach uses blockchain to securely transmit data to a distributed, decentralised cloud server, guaranteeing data confidentiality and privacy using a variety of storage techniques. This architecture addresses privacy issues and encourages seamless cooperation by utilising hybrid learning, federated learning, and blockchain technology. The study contributes to bridging the gap between secure data transfer and effective deep learning, specifically within sensitive domains. Experimental results demonstrate an impressive accuracy rate of 99.01%.
STOCK PRICE ANALYSIS USING MARKOV CHAINS

The process of changing stock prices is described by a Markov chain with three states ("decline", "constancy", "growth" of the price). The parameters of the chain are estimated based on historical data on stock quotes. The problem of calculating the probabilistic characteristics of a random event associated with the transition from one state to another is solved. For the practical implementation of the methods considered, Python tools are used.
STAR-2 Content: Hearer’s Perspective

While a STAR-0 STAR-1 content conversion may apply to nonlanguage or language content, a hear mode STAR-1 STAR-2 conversion requires language. The STAR-1 content in a STAR-1 STAR-2 conversion is assumed insofar as it is the hearer’s interpretation of the STAR-1 surface, which in turn depends on who is assumed to be the intended addressee (Sect. 10.1).
Common Sense Reasoning

Given that logical and common sense reasoning are not separate in the natural prototype, there have been attempts to combine their artificial counterparts into one system. Several past attempts tried to assimilate common sense reasoning to substitution-driven sign-based logical reasoning, for example by using Predicate Calculus and its truth conditions as a convenient semantic notation (Fikes et al. 1971, Nilsson 1988, Hobbs et al. 1993).
THE EFFECT OF THE STORYTELLING METHOD ON THE PUBLIC SPEAKING SKILL OF CADETS SURABAYA AVIATION POLYTECHNIC

The ability of public speaking is very much needed by cadets in the face of competition in the world of work, so an appropriate method is needed to improve the public speaking skills of cadets, one of which is by using the storytelling method. This study aims to find out the effect of the storytelling method on the public speaking skills of cadets of the Aircraft Engineering study program at the Aviation Polytechnic of Surabaya. This study uses the design of the Explanatory Survey Method. The results showed that the effect of storytelling on the public speaking skills of cadets was in the high category. This means that more and more cadets in public speaking practice using the storytelling method will have an impact on their public speaking practice
Lexical Simplification in Foreign Language Learning: Creating Pedagogically Suitable Simplified Example Sentences

This study presents a lexical simplification (LS) methodology for foreign language (FL) learning purposes, a barely explored area of automatic text simplification (TS). The method, targeted at Spanish as a foreign language (SFL), includes a customised complex word identification (CWI) classifier and generates substitutions based on masked language modelling. Performance is calculated on a custom dataset by means of a new, pedagogically-oriented evaluation. With 43% of the top simplifications being found suitable, the method shows potential for simplifying sentences to be used in FL learning activities. The evaluation also suggests that, though still crucial, meaning preservation is not always a prerequisite for successful LS. To arrive at grammatically correct and more idiomatic simplifications, future research could study the integration of association measures based on co-occurrence data.
teamPN at TSAR-2022 Shared Task: Lexical Simplification using Multi-Level and Modular Approach

Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier-to-read (or understand) expressions while preserving the original information and meaning. This paper explains the work done by our team “teamPN” for the English track of TSAR 2022 Shared Task of Lexical Simplification. We created a modular pipeline which combines transformers based models with traditional NLP methods like paraphrasing and verb sense disambiguation. We created a multi-level and modular pipeline where the target text is treated according to its semantics (Part of Speech Tag). The pipeline is multi-level as we utilize multiple source models to find potential candidates for replacement. It is modular as we can switch the source models and their weighting in the final re-ranking.
READINESS AND INVOLVEMENT OF EARLY CHILDHOOD TEACHERS IN EAST JAVA DURING THE COVID-19 PANDEMIC THROUGH ONLINE LEARNING

Since the beginning of 2020, the world has been shaken by an incident, namely the spread of the Covid-19 virus. The spread of the Covid-19 virus is very widespread throughout the world, so the World Health Organization (WHO) conveyed this as a threat to the world on January 31, 2020 which was later announced to be a pandemic for the international community, namely on March 11, 2020 (Shan L. Pana et al, 2020). The Indonesian government finally took part in handling the spread of the Covid-19 virus, including by recording the area where the virus spread and mapping it into several zones (Kemenkes, 2020). Because of some areas in East Java are mostly in the red and orange zones, which are areas with a high and dangerous spread of the Covid-19 virus, the researchers wanted to know the readiness and involvement of early childhood teachers in East Java during the COVID-19 pandemic through online learning. This research is a qualitative descriptive study that uses data collection techniques through literature review and surveys. Furthermore, all data collected from both the literature review and survey results were analyzed for description. The survey results state that the activities that have been planned in the lesson plan can be carried out well. However, there are parents of students who often do not collect student assignments on time, resulting in teachers having difficulty in assessing each student. Good ICT mastery can facilitate the implementation of online learning activities and early childhood teachers also support the implementation of online learning from home imposed by the Government to suppress the spread of the Covid-19 virus.
Reading(,) with and without commas

All major writing systems mandate the use of commas to separate clauses and list items. However, casual writers often omit mandatory commas. Little empirical or theoretical research has been done on the effect that omitting mandatory commas has on eye movement control during reading. We present an eye-tracking experiment in Spanish, a language with a clear standard as to mandatory comma use. Sentences were presented with or without mandatory commas while readers’ eye movements were recorded. There was a local increase in the go-past time for the pre-comma region when commas were presented, which was balanced out by shorter first-pass and second-pass times on the subsequent regions. In global sentence reading time, there was no evidence for an advantage of presenting commas. These findings suggest that, even when commas are mandatory, their effect is primarily to shift when processing takes place rather than to facilitate processing overall.
Strengthening Security and Confidentiality in E-Health Systems through Quantum Encryption of Healthcare

This study presents a pioneering quantum encryption technique, the Generalized Novel Enhancement Quantum Representation (GNEQR), to ensure enhanced security for color medical images in E-health systems. The proposed method is seamlessly integrated into an ASP.NET Web API and Blazor Web Assembly environment. During the encryption process, the GNEQR algorithm utilizes advanced bit-plane scrambling to obscure the original color medical image. A 5D chaotic map is employed to generate an image key, which is then used to create a scrambled version of the key image. Applying an XOR operation between the scrambled image and the key image produces a highly secure quantum-encrypted color image. In the decryption process, the inverse of the encryption steps is implemented, leveraging the 5D chaotic map-generated key to accurately reconstruct the original color medical image. This research aims to significantly enhance E-health security by protecting patient data, including medical images and histories, through quantum encryption. The incorporation of GNEQR within an ASP.NET Web API and Blazor Web Assembly framework facilitates seamless deployment and underscores the potential of this method to revolutionize healthcare data privacy and integrity.