Reviewerly/exHarmony huggingface dataset:
Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning

Neurons refer to nerve cells. Each neuron is connected with thousands of other neurons to form a corresponding functional area and carry out complex communication with other functional areas. Its importance to the human body is self-evident. There are also many scholars studying the mechanism of apoptosis. This paper proposes a study of neuronal apoptosis in patients with liver cirrhosis and neuronal epileptiform discharge models based on multi-modal fusion deep learning, aiming to study the influencing factors of abnormal neuronal discharge in the brain. The method in this paper is to study multi-modal information fusion methods, perform Bayesian inference, and analyze multi-modal medical data. The function of these research methods is to obtain the relationship between the independence of information and the intersection of information among modalities. In the neuronal epileptiform discharge model, the mRNA expression level of the necroptotic signaling pathway related protein was detected, and the mechanism of neuronal necrosis in patients with liver cirrhosis was explored. Experiments show that the neuron recognition rate has been increased from 67.2% to 84.5%, and the time has been reduced, proving the effectiveness of deep learning.
SIBYL: A Method for Detecting Similar Binary Functions Using Machine Learning

Binary code similarity comparison methods are mainly used to find bugs in software, to detect software plagiarism, and to reduce the workload during malware analysis. In this paper, we propose a method to compare the binary code similarity of each function by using a combination of Control Flow Graphs (CFGs) and disassembled instruction sequences contained in each function, and to detect a function with high similarity to a specified function. One of the challenges in performing similarity comparisons is that different compile-time optimizations and different architectures produce different binary code. The main units for comparing code are instructions, basic blocks and functions. The challenge of functions is that they have a graph structure in which basic blocks are combined, making it relatively difficult to derive similarity. However, analysis tools such as IDA, display the disassembled instruction sequence in function units. Detecting similarity on a function basis has the advantage of facilitating simplified understanding by analysts. To solve the aforementioned challenges, we use machine learning methods in the field of natural language processing. In this field, there is a Transformer model, as of 2017, that updates each record for various language processing tasks, and as of 2021, Transformer is the basis for BERT, which updates each record for language processing tasks. There is also a method called node2vec, which uses machine learning techniques to capture the features of each node from the graph structure. In this paper, we propose SIBYL, a combination of Transformer and node2vec. In SIBYL, a method called Triplet-Loss is used during learning so that similar items are brought closer and dissimilar items are moved away. To evaluate SIBYL, we created a new dataset using open-source software widely used in the real world, and conducted training and evaluation experiments using the dataset. In the evaluation experiments, we evaluated the similarity of binary codes across different architectures using evaluation indices such as Rank1 and MRR. The experimental results showed that SIBYL outperforms existing research. We believe that this is due to the fact that machine learning has been able to capture the features of the graph structure and the order of instructions on a function-by-function basis. The results of these experiments are presented in detail, followed by a discussion and conclusion.
Blind Image Restoration with Horizontal Motion Blur Based on Point Spread Function Estimation in Frequency Domain

To improve the quality of horizontal motion blurred image, a blind restoration method based on point spread function estimation is proposed by estimating the horizontal motion parameter automatically in frequency domain. Firstly, horizontal motion blurred image is transformed from spatial domain to frequency domain. Secondly, the distance between the center point and the nearest vertical black strip is automatically estimated by setting thresholds, and the parameter of horizontal motion point spread function of the blurred image is estimated. Thirdly, utilizing the estimated horizontal point spread function, the image is restored by the constrained least squares algorithm. Experimental results show that the proposed horizontal motion point spread function estimation algorithm has high accuracy, and the restored image has better visual effect and higher PSNR.
The Role of Emerging Technologies in Science Teacher Preparation

There is an international impetus to incorporate emerging technologies in K–12 science preservice teacher (PST) education. Yet, to date, there is no consensus on emerging technology use for PST science teaching and learning. A qualitative research synthesis method was employed to examine 54 selected research articles published within a 11-year period (2010–2021). This analysis of literature focused on the affordances and accessibility of emerging technology utilization in science PST education. Nearly a quarter (n = 13) of the articles employed virtual simulations as the chosen emerging technology for science PST education. Over half originated in the United States (n = 20) or Turkey (n = 14), and over half focused on supporting primary-level PSTs (n = 28). Twenty-four sampled articles described how emerging technologies provided opportunities for PSTs to learn science content or use it in teaching practices. Access to emerging technologies was the most common issue discussed in the articles, with studies citing concerns about initial purchases (n = 6) and needs for infrastructure and maintenance (n = 7). Recommendations focus on strengthening practices in integrating emerging technologies into classroom practices, promoting safety and convenience, and consideration of accessibility, equity, and global perspectives of emerging technologies to support science PSTs.
Low-Complexity Chase Decoding of Reed–Solomon Codes Using Channel Evaluation

A novel time-varying channel adaptive low-complexity chase (LCC) algorithm with low redundancy is proposed, where only the necessary number of test vectors (TVs) are generated and key equations are calculated according to the channel evaluation to reduce the decoding complexity. The algorithm evaluates the error symbol numbers by counting the number of unreliable bits of the received code sequence and dynamically adjusts the decoding parameters, which can reduce a large number of redundant calculations in the decoding process. We provide a simplified multiplicity assignment (MA) scheme and its architecture. Moreover, a multi-functional block that can implement polynomial selection, Chien search and the Forney algorithm (PCF) is provided. On this basis, a high-efficiency LCC decoder with adaptive error-correcting capability is proposed. Compared with the state-of-the-art LCC (TV = 16) decoding, the number of TVs of our decoder was reduced by 50.4% without loss of the frame error rate (FER) performance. The hardware implementation results show that the proposed decoder achieved 81.6% reduced average latency and 150% increased throughput compared to the state-of-the-art LCC decoder.
Improving Students' Listening Ability Using Edpuzzle Interactive Video

The problems faced by the students in this listening class were identified as the difficulty of recalling memory and managing concentration during listening. Therefore, this classroom action research solution is Edpuzzle interactive video because the teacher can easily embed questions, notes, and recording voices into the video. When students play the video until it reaches a particular minute of the timeline, it will automatically pause, and a question will appear. This study revealed that this media could increase the students' listening skills. The students were more motivated to listen using Edpuzzle because they liked the "rewatch" feature and the automatic feedback. The teacher's role in presenting a clear tutorial and directing the listening process is crucial to the success of this research. Moreover, the teacher paid more attention to the content of the video and the complexity of the questions adjusted to the students' listening ability level.
AviSense: A Real-time System for Detection, Classification, and Analysis of Aviation Signals

Wireless systems are an integral part of aviation. Apart from their apparent use in air-to-ground communication, wireless systems play a crucial role in avionic functions including navigation and landing. An interference-free wireless environment is therefore critical for the uninterrupted operation and safety of an aircraft. Hence, there is an urgency for airport facilities to acquire the capability to continuously monitor aviation frequency bands for real-time detection of interference and anomalies. To meet this critical need, we design and build AviSense, an SDR-based real-time , versatile system for monitoring aviation bands. AviSense detects and characterizes signal activities to enable practical and effective anomaly detection. We identify and tackle the challenges posed by a diverse set of critical aviation bands and technologies. We evaluate our methodology with real-world aviation signal measurements and two custom datasets of anomalous signals. We find that our signal classification capability achieves a true positive rate of ∼99%, with few exceptions, and a false positive rate of less than 4%. We also demonstrate that AviSense can effectively distinguish between different types of anomalies. We build and evaluate a prototype implementation of AviSense that supports distributed monitoring.
Benthik Android Physics Comic Effectiveness for Vector Representation and Crtitical Thinking Students’ Improvement

This research aims to reveal the effectiveness of the use of Kofie GeBoL media in improving (1) vector representation ability and (2) critical thinking ability in physics instruction.It is a descriptive quantitative study with the quasi-experiment design.It was conducted in two stages: empirical try out and implementation of Kofie GeboL to see the media effectiveness.It was conducted in four high schools in Sleman Regency, including State Senior High School (SSHS) 1 Mlati with 64 students, SSHS 1 Ngaglik with 96 students, SSHS 1 Ngemplak with 56 students, and SHS Bina Umat with 46 students on the learning materials of Parabolic Movement in October, Semester 1 of the academic year 2019/2020.The research instrument is a vector representation and critical thinking ability test which is valid and dan reliable according to the empirical test.The data analysis used the QUEST application to see the validity and reliability of the test items and the MANOVA test to see the media effectiveness.The research finding shows that the Kofie GeBoL is effective in improving students' vector representation and critical thinking abilities with the Cohen's value in each variable being 0.46 and 0.57 interpreted as Large Effect Size.This can also be seen in the N-gain scores of vector representation and critical thinking abilities of 0.7 and 0.8 respectively, which are in a high category.So similar research can be done for other physics theory to improve students' scientific abilities.
Improved GM-PHD Filter with Birth Intensity and Spawned Intensity Estimation Based on Trajectory Situation Feedback Control

The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can effectively track multiple targets in a single scenario. However, for GM-PHD, unknown target behavior, e.g., target birth or target intersection, produces difficulties in terms of accurate estimation. First of all, GM-PHD assumes the model parameters about the birth target are prior information, which results in the inability to detect the birth target that occurs at random in complex scenarios. Then, since the measurements generated by the intersected targets overlap each other, GM-PHD cannot distinguish these targets, resulting in a biased estimation of the state and number of targets. To solve these problems, this paper proposes an improved GM-PHD filter with a birth intensity and spawned intensity updating method based on the trajectory situation feedback. In the filtering process, the trajectory initiation feedback formed by the rule-based correlation of Gaussian components is introduced to GM-PHD to adjust the birth intensity in real time, which is used to improve the detection of birth targets. Simultaneously, the analysis of trajectory situation is designed to determine the relative motion trend between targets. On this basis, the filter improves the recognition of the intersected targets by enhancing the spawned intensity. Simulation results demonstrate that the proposed algorithm achieves better performance on the state and number of targets in complex scenarios, and shows superiority to other GM-PHD filters.
A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing

Radar Warning Receiver (RWR) is one of the basic pieces of combat equipment necessary for the electromagnetic situational awareness of aircraft in modern operations and requires good rapid performance and accuracy. This paper proposes a data processing flow for radar warning devices based on a hierarchical processing mechanism to address the issue of existing algorithms’ inability to balance real-time and accuracy. In the front-level information processing module, multi-attribute decision-making under intuitionistic fuzzy information (IFS) is used to process radar signals with certain prior knowledge to achieve rapid performance. In the post-level information processing module, an improved tri-training method is used to ensure accurate recognition of signals with low pre-level recognition accuracy. To improve the performance of tri-training in identifying radar emitters, the original algorithm is combined with the modified Hyperbolic Tangent Weight (MHTW) to address the problem of data imbalance in the radar identification problem. Simultaneously, cross entropy is employed to enhance the sample selection mechanism, allowing the algorithm to converge rapidly.
Design of Pin On Disc Tribometer Test Equipment Using the Hatamura Method

o determine the rate of wear and coefficient of friction, a test instrument is used, namely a tribometer with a pin on disc type. Before manufacturing, it is necessary to have a design that must comply with existing standards. This study aims to design a pin on disc tribometer with circular motion and reciprocating linear motion using the Hatamura method and its main components, analyze the shaft and frame and produce working papers in the form of technical drawings and bill of materials in the manufacture of test equipment. Pin-on-disc tribometer. In this study, designing several concepts of the pin on disc Tribometer according to the Hatamura method and then evaluating the existing concepts and selecting concepts based on the decision matrix, then the concepts will be designed and analyzed by manual calculations and validation using Autodesk Inventor to determine stress, deflection, as well as safety factors on the axle and frame. The final result of this research is a technical document in the form of layout drawings and assembly drawings as well as detailed drawings of each component and bill of materials.
The Effect of Social Factors to Economic Growth

Social factors are great drivers of economic growth and it could result to tangible benefits that can trigger sustainable development. This paper argues that social factors can contribute to sustainable economic growth because social development leads to healthy, educated and productive citizens who can bring economic contributions. This paper intends to explore the effect of social factors to economic growth through a time series analysis among 58 countries (n=290 data) over the period 2014 to 2018. By conducting correlation and regression analyses, the study highlights that social factors such as population, health, education, development, labor force, environment, military, and geography have significant relationships and effect with the economic growth indicators such as GDP, GDP growth, GDP-per-capita, GNI, GNI-per-capita, manufacturing, and tourism.
On Molecular Bonding Logic and Matrix Representation of Constant and Balanced Boolean Functions

Representing a bonding manifold of a molecule or molecular cluster by a graph given by a set of vertices associated with atoms and a set of edges imitating bonds, the bonding edge encoding formalism is defined on n-tuples qubits in terms of the NOT logic gate acting on the "non-bonded" string. This formalism is illustrated by the simplest diatomic and triatomic molecules whose adjacency matrices generate different quadratic Boolean functions, among which the balanced function appears. In this regard, we review the Deutsch–Jozsa quantum algorithm, well-known in quantum computing, that discriminates between the balanced and constant Boolean functions. A novel matrix representation of the constant-balancedquantum oracle within this algorithm is elaborated. The proposed approach is generalized to distinguish between constant and evenly balanced Boolean functions.
ViRMA: Virtual Reality Multimedia Analytics at Video Browser Showdown 2022

In this paper we describe the first iteration of the ViRMA prototype system, a novel approach to multimedia analysis in virtual reality, that is inspired by the M $$^3$$ data model. In this model, media is mapped into a multidimensional space, based on its metadata. ViRMA users can then interact with the media collection by dynamically projecting the metadata space to the 3D virtual space, through a variety of interactions, and exploring the resulting visualisations.
Electronic Collection of Public Revenue In The Light of the Corona Virus Pandemic in Jordanian Governmental Institutes

This study aims at identifying electronic payment systems used in Jordan as well as their components; the study also illustrates to which extent the use of these systems contributes to the stability of revenue collection processes in the Hashemite Kingdom of Jordan; in addition, it examines the extent of the availability of internal control procedures in electronic payment systems in order to ensure their continued use and cash flow of revenues during the Covid-19 pandemic. Furthermore, the study identifies the risks arising from electronic collection and payment systems affecting revenue collection process in the light of the Covid-19. Moreover, it illustrates the challenges which affect the use of electronic payment systems. To achieve the purposes of the study, the researcher has used inductive method as well as descriptive analytical method which examines accurately the phenomenon in a field side of study. In addition, a questionnaire has been designed in order to find out the responses of study sample composed of the employees of governmental departments which used electronic payment systems; 4 questionnaires have been distributed to employees of each department; they are financial manager, director of collection department, accountants responsible for records and settlements related to electronic payment. Total of 63 questionnaires have been distributed; 55 questionnaires have been retrieved and valid for statistical analysis; statistical package for the social sciences (SPSS) has been used. The study has reached a set of results. Most importantly, using electronic payment and collection systems during the Covid-19 pandemic has contributed to the stability of revenue collection processes; in addition, availability of internal control procedures in electronic collection processes has contributed to the continued use and cash flow of revenues during the Corona virus pandemic. Moreover, the study has illustrated the most important components of electronic payment and collection systems. Furthermore, the study has concluded that there are various risks arising from electronic collection and payment processes which affect revenue collection process during the Covid-19 pandemic. And the study has illustrated the most important challenges affecting the use of electronic systems of collecting the revenues in Jordan.
Strong Equivalence and Program Structure in Arguing Essential Equivalence between Logic Programs

Abstract Answer set programming is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing different knowledge representation formalisms. Frequently, several related and yet substantially different answer set programs exist for a given problem. Sometimes these encodings may display significantly different performance. Uncovering precise formal links between these programs is often important and yet far from trivial. This paper presents formal results carefully relating a number of interesting program rewritings. It also provides the proof of correctness of system projector concerned with automatic program rewritings for the sake of efficiency.
Mining human preference via self-correction causal structure learning

Spurred by causal structure learning (CSL) ability to reveal the cause-effect connection, significant research efforts have been made to enhance the scalability of CSL algorithms in various artificial intelligence applications. However, less effort has been made regarding the stability and the interpretability of CSL algorithms. Thus, this work proposes a self-correction mechanism that embeds domain knowledge for CSL, improving the stability and accuracy even in low-dimensional but high-noise environments by guaranteeing a meaningful output. The suggested algorithm is challenged against multiple classic and influential CSL algorithms in synthesized and field datasets. Our algorithm achieves a superior accuracy on the synthesized dataset, while on the field dataset, our method interprets the learned causal structure as a human preference for investment, coinciding with domain expert analysis.
DIGITAL PRACTICUM: EXPLORING AUGMENTED REALITY, REMOTE CLASSROOMS, AND VIRTUAL LEARNING FOR ONLINE MENTORING

J. Mena 1, M. Flores2, P. Hennissen3, M. Ulvik4, S. Kaldi5, A. Clarke6, T. Barkatsas7, L. Orland-Barak8 1University of Salamanca (SPAIN) 2University of Minho (PORTUGAL) 3Zuyd University of Applied Sciences (NETHERLANDS) 4University of Bergen (NORWAY) 5University of Thessaly (GREECE) 6University of British Columbia (CANADA) 7Royal Melbourne Institute of Technology (AUSTRALIA) 8University of Haifa (ISRAEL)