Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving in Double Deep-Q-Network with Differential Privacy in Continuous Spaces

With extensive applications and remarkable performance, deep reinforcement learning is becoming one of the most important technologies that researchers have been focusing on. Many applications have used reinforcement learning, such as robotics, recommendation systems, and healthcare systems. These systems could collect data about the environment or users, which may contain sensitive information that posed a real risk when these data were disclosed. In this work, we aim to preserve the privacy of the data used in deep reinforcement learning with Double Deep-Q-Network in continuous space by adopting the differentially private SGD method to inject a noise to the gradient. In our experiment, we used a different amount of noise on two separate settings to demonstrate how effective of using this method.
Improved AFSA for Solving Intelligent Test Problem

Intelligent test paper generation problem is a multi-objective parameter optimization problem under certain constraints, which has been realized by many algorithms. However, the existing intelligent test paper generation mostly adopts a single algorithm, and each algorithm has its own shortcomings. Sometimes it is inevitable to fall into the defects of the single algorithm in the process of test paper generation. Therefore, a mathematical model of intelligent test paper generation is proposed, which combines the advantages of artificial fish swarm algorithm and genetic algorithm to form a hybrid intelligent algorithm. At the beginning of intelligent test paper generation, the artificial fish swarm algorithm is used to quickly approach the test paper goal. In the process of test paper generation, when the optimal individual has no change or extremely small change in consecutive iterations, the genetic algorithm is used to jump the artificial fish individual to improve the convergence speed. The simulation results show that the hybrid intelligent algorithm can effectively optimize the effect of intelligent test paper generation by using a single algorithm alone.
Brain tumor segmentation and detection in magnetic resonance imaging (MRI) using convolutional neural network

Brain tumor refers to a tissue group that is prearranged by abnormal cells being slowly incorporated. These cells uncontrollably multiply, apparently unaffected by the processes that regulate normal cells. The tumor of the brain can be broadly categorized into two categories: benign tumor and malignant tumor. As the presence of a tumor is harmful and may lead to the loss of life of the person, an accurate prediction of the tumor is a must. In this chapter, a novel technique that increases the detection accuracy of the brain tumor from magnetic resonance imaging images is presented. To increase the detection accuracy, the thresholded image is overlapped with the canny edge–detected output image. The resulted overlapped image is then inverted followed by watershed segmentation of the image. After this, the processed image is fed to the convolutional neural network model for predictions. In this way, the model's prediction accuracy has increased.
Quest: Privacy-Preserving Monitoring of Network Data: A System for Organizational Response to Pandemics

Most modern organizations today support network infrastructure to provide ubiquitous network coverage at their premises. Such a network infrastructure consisting of a set of access points deployed at different locations in buildings can be used to support coarse-level localization of individuals, who connect to the infrastructure using their mobile devices. This paper describes a system, entitled <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quest</small> that supports a variety of applications ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</i> ., identifying hotspot regions, finding people who are potentially exposed to a condition such as COVID-19, occupancy count of a region/floor/building) based on network data to empower organizations to maintain safety at their workplace/premises. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quest</small> builds the above functionalities while fully protecting the privacy of individuals. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quest</small> incorporates computationally- and information-theoretically-secure protocols that prevent adversaries from gaining knowledge of an individual's location history (based on WiFi data). We describe the architecture, design choices, and implementation of the proposed security/privacy techniques in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quest</small> . We, also, validate the practicality of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quest</small> and evaluate it thoroughly via an actual campus-scale deployment at our organization over a very large dataset of over 50M rows.
SCRM in the Automotive Industry: AutoSCRM

Transportation, health, and economy are integral parts of a whole for sustainable development. The environment of high uncertainty hinders the development and implementation of strategies by vision and risk management systems for transportation sectors and their supply chains, as applied in other sectors. Thus, optimized management of risk factors that impact operational, environmental—especially health, nowadays—and economy areas in the transportation sector is pivotal to evaluate corporate performances in a wider perspective. Furthermore, it is a key requirement for the creation of both added value and sustainable development. This much sought-after managerial optimization can be approached by developing risk management systems and applying them in supply chain management, by reshaping them for industries and/or businesses.
Compile of the Questionnaire of College Students’ Attitude towards Music Creativity Based on Artificial Intelligence

In recent years, with the application of artificial intelligence technology in all fields of society, it is different from the current common display of technology in the above cases of performing arts. And technology is not only a tool, but also an object of speculation. Technology and human beings share a common destiny, between which the complicated entanglement faced with, the classical perspective of humanism is afraid to be unable to cope with it. Purpose: The reliability of the questionnaire about music creativity attitude of college students was verified. Methods: Based on the existing research and domestic and foreign questionnaires on creativity attitude, a questionnaire containing 34 questions was compiled according to the actual situation of college students in China and relevant research reference, and a test was conducted on 214 formal subjects. Conclusion: The reliability of the questionnaire of college students’ attitude towards music creativity and of each dimension are ideal and meet the requirements of surveying.
A Selection-Based Framework for Building and Validating Regression Model for COVID-19 Information Management

The world is facing pandemic situation, i.e., COVID-19, all the researchers and scientist are working hard to overcome this situation. Being human it is everyone’s duty to take care of family and the society. In this case study, an attempt has been made to find the relation between various variables by dividing them into the independent and dependent variables. A dataset is selected for analysis purpose which consists of variables like location (countries across the globe, date, new cases, new deaths, total deaths, smoking habits washing habits, diabetic prevalence, etc. Approach is to identify the impact of independent variable on the dependent variable by applying the regression modeling. Hence, proposed case study is based on selection-based framework for validating the regression modeling for COVID-19 data analysis. Regression modeling is applied, and few representations are shown to understand the current pandemic situation across the world. In the end, using regression modeling interceptor and coefficient values for different approaches (using different variables) is computed.
Low Processing Power Algorithm to Segment Tumors in Mammograms

The diagnosis based on mammography depends on relevantly upon the radiologist’s experience and accuracy in identifying shapes and tenuous contrast in the images. A possible improvement in the mammography consists of image analysis algorithms suitable for detecting and segmenting tumors. However, such an approach usually demands high computational processing. In this context, the present work objective was to develop an algorithm to accurately search and segment tumors in mammography images without requiring high processing power. Thus, efficient, low computational demanding, and automatic image segmentation occurred by performing traditional image processing, including uniform equalization, adaptive enhancement (CLAHE), simple thresholding, Otsu multiple thresholds, and morphology operators. The development of the algorithm divides into two steps. The first step was the isolation of the breast and removing the pectoral muscle. The breast isolation occurred by removing artifacts outside of the breast image. The pectoral muscle removes as this region presents gray levels similar to the tumor. Finally, the second step segmented the tumor mass. The algorithm validation occurred by determining the accuracy and the Dice similarity coefficient, which values (mean ± standard deviation) were, respectively, 0.75 ± 0.09 and 0.71 ± 0.11. These average values resulted after processing 150 images of the CBIS-DDSM database, signifying a good segmentation outcome. As for the processing speed, the algorithm spent 14,5 s to segment a tumor in an image sizing 5383 × 3190 pixels (16 bits), using a conventional computer. The tool was able to segment both tiny and large tumors, and it may represent a convenient approach to assist in the analysis of mammograms without requiring high computational resources.
The Application of Computer Network Technology in Business English Teaching

In recent years, cross-border economic trade has become more frequent. In order to communicate well, English can be used as a bridge of communication. Therefore, the study of business English (BE) is particularly important. To this end, the purpose of this article is to use Internet multimedia technology to study BE teaching (BET) methods in order to improve students' learning ability and flexibly use the language they learn to create value. This article uses the data analysis method and the survey method to carry out relevant research on the application of computer network technology in BE. The survey results show that 17.8% of people agree with multimedia teaching methods, and 21.1 % support the integrated application of traditional teaching and multimedia teaching in BE.
Repairing Adversarial Texts Through Perturbation

It is known that neural networks are subject to attacks through adversarial perturbations. Worse yet, such attacks are impossible to eliminate, i.e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training. Multiple approaches have been developed to detect and reject such adversarial inputs. Rejecting suspicious inputs however may not be always feasible or ideal. First, normal inputs may be rejected due to false alarms generated by the detection algorithm. Second, denial-of-service attacks may be conducted by feeding such systems with adversarial inputs. To address this, in this work, we focus on the text domain and propose an approach to automatically repair adversarial texts at runtime. Given a text which is suspected to be adversarial, we novelly apply multiple adversarial perturbation methods in a positive way to identify a repair, i.e., a slightly mutated but semantically equivalent text that the neural network correctly classifies. Experimental results show that our approach effectively repairs about 80% of adversarial texts. Furthermore, depending on the applied perturbation method, an adversarial text could be repaired about one second on average.
A Simulation Device Model for Managing Access to Confidential Information Stored in a Cloud

The article offers a look at a model to be used for information resource protection in clouds, which allows managing authorized access to segments of the resources based on employees’ biometric features. Ensuring a higher level of confidential data protection takes convenient access, which implies using biometric features. This process is impossible to be studied to the fullest with no model building. By using research methods like measurement and comparison subject to selected features, which characterize the management system operation, data storage and processing, ratios were obtained to be used for analyzing the outcomes of external effects caused on the recognition system, as well as for comparing incoming data. Such a study will result in developing a simulation device model, which is proposed to be employed for creating an access control system for data resources. The reliability of the model, as well as the calculation convergence between the impact and the system response were evaluated based on Fechner’s mathematics. The integrated model can be incorporated into the data security employees’ peripheral devices at automated workplaces, which will enhance significantly the durability of the protected data.
Finding All Shortest Meaningful Meta-Paths Between Two Vertices of a Secured Large Heterogeneous Information Network Using Distributed Algorithm

Discovering relationships between vertices in a secured information network is an important task in information network analysis. In HIN, meta-path, or a sequence of vertex types and edge types connecting two vertices. Path instance of a meta-path is path in HIN that satisfies the meta-path. The length of meta-path is the number of relations (edges) in this meta-path. Meaningful meta-path is a meta-path with at least one path instance. Recent works on meta-path discovery mainly focus on in-memory algorithms that fit in only one computer. In this chapter, we propose distributed algorithms to discover all shortest meaningful meta-paths between two vertices of a large HIN using Apache Spark. Shortest meaningful meta-path is a meaningful meta-path with shortest length. We employ a scalable implementation of the Distributed Breadth-First Search (D-BFS) algorithm as a baseline approach. Finding all possible shortest paths in a large HIN can be time consuming. Therefore, we propose a novel algorithm called shortest meaningful meta-path based search (S-MPS). S-MPS first searches all shortest meta-path candidates between vertices in the graph of the network schema of HIN. We conduct experiments on DBLP data set to prove the efficiency of our proposed S-MPS algorithm over D-BFS.
Path optimization for D-star algorithm modification

The main task of mobile robots is to follow the route quickly and without damage. For this reason, it is important to solve the problem of navigation for mobile robots. There are some methods. One of the most effective is the method of constructing a navigation system based on the simultaneous localization and mapping algorithm and obstacle avoidance algorithms. The D-star algorithm often used for obstacles avoidance, but despite its effectiveness, it has some drawbacks. Previously a modification was described that allows to eliminate some problems arising during the implementation of the navigation system. In this article we presented the optimization of the path for that modification which allows to avoid wasting time on stopping and turning in the right direction.
Designing a recommender system based on the application of decision tree algorithm in data mining with KNIME (for recommending the topic of undergraduate’s thesis)

Data mining has many algorithms; one of the most frequently used is the decision tree algorithm. This article described the result of the research of a data mining approach that used the decision tree algorithm; it aimed to create and provide a recommendation system for university students to choose and decide the topic for their final projects. The final project (so-called as thesis) is one of the graduation requirements for undergraduate students; many university students are confused in choosing and deciding the right topic for their thesis, and this results in not completing it in the required time. The impact of overdue thesis is the extension of their period of studies. In this study, data mining was carried out based on 9 attribute data sets which were reduced from 21 derived factors. The data processing of the research has used the free software so-called KNIME. It was started with attribute correlation testing; it was continued by formulating KNIME workflow and resulted in the decision tree. Then, the tree diagrams were translated into the 65 IF-THEN rules. Based on these 65 IF-THEN rules, this study developed a recommender system using C# language; the recommendation system was tested on 45 students which gave its accuracy more than 70%.
Design and Implementation of a Reaction Timer Device Based on Visual Stimulation and Motor Reaction

Reaction time (RT) is one of the most essential characteristics of people, which depends on the speed of the information processing in their brains. It is a crucial ability of the athletes, and they usually try to improve their RT by practice and mental concentration. In this study, a novel device is designed to measure the motor response of the subjects to the visual stimuli. First, the visual stimulation, through the halogen lamps placed in a unique circular surface applied. Then, the practitioners should touch the sensors connected to the lamps with their feet in the shortest possible time. Finally, the total time between the visual stimuli and the motor responses will be measured as the reaction time. The implemented device consists of an Arduino UNO controller, 8 halogen lamps, and TTP223 Touch sensors. To approve the performance of the device, 40 subjects were invited to participate in our experimental trials, which were categorized into two groups of athletes and non-athletes. The differences between the RTs of the groups were investigated based on the analysis of variance statistics. Our results showed that the reaction times associated with the athletes were significantly lower than those of non-athletes (p<0.001). Also, the implemented device can be an appropriate tool for quantifying the performance of the athletes and designing practical training.
Evaluating a sonic interaction design based on a historic theatre sound effect

This paper reports on the procedure and results of a preliminary experiment to evaluate participants’ perceptual experiences of a mechanical theatre sound effect and its digital counterpart. The theatre sound effect chosen - an acoustic wind machine - affords a simple rotational gesture; turning its crank handle at varying speeds produces a convincing wind-like sound. A prototype digital model of a working acoustic wind machine was programmed. The mechanical interface of the acoustic wind machine drove both the digital model and its own acoustic sound in performance, therefore preserving the same tactile and kinaesthetic feedback across the two continuous sonic interactions. Participants were presented with two listening tests to examine the perceived similarity of these wind-like sounds and the perceived connection between the speed of the crank handle and the resulting sound. Participants’ performances of both the acoustic and digital systems were then elicited with sound stimuli produced from simple gestural performances of the wind-like sounds. The results of this study show that, while the sound of the prototype digital model requires further calibration to bring the experience of its performance closer to that of its acoustic counterpart, the acoustic wind machine is significantly easier to play, and the mechanism of its interface may play a role in perceptually guiding performance gestures.
The Impulse Output Control of Multiple Thrusters AUV Based on the S-plane algorithm

This paper considers the problem of excessive inertial deviation in the attitude adjustment process of multi-propulsion vehicle, which decreases the path tracking accuracy of the AUV in the complex underwater environment. In order to address this problem, the S-plane control algorithm is adopted to realize the tracking control of the vehicle by combining the multi-propulsion pulse output asymptotic control. The kinematic model and dynamics model of the navigator are verified by simulation, and the field experiments are carried out on the AUV "Haiwei 1" developed by the authors. The results show that the method can effectively reduce the inertial deviation in the process of attitude adjustment of the vehicle and improve the accuracy of path tracking control.
Spectral–Spatial Feature Completely Separated Extraction with Tensor CNN for Multispectral Image Compression

Considering the rich spectral and spatial information of multispectral image, a learned multispectral image compression method named spectral-spatial feature completely separated extraction with tensor CNN is proposed. In encoder, two parallel modules, spectral module and spatial module, are applied to extract spectral and spatial features respectively. Then in order to reduce the loss in the downsampling process, two tensor layers that integrate tucker decomposition and neural network are utilized to decompose the multiway features. The decomposed feature tensors are concatenated along the channel dimension, saving the spectral and spatial features separately. To recover the features, the concatenated tensor is split in symmetric decoder after quantizer and entropy codec. Finally, post-processing module is utilized to remove the compression artifacts of reconstructed image fused by recovered feature tensors. Besides, rate-distortion optimization is embedded to balance the trade-off between the rate loss and the distortion. Experiments on eight-band dataset from the WorldView-3 satellite demonstrate our proposed method outperforms JPEG2000 and 3D-SPIHT in PSNR.
Background and Motivation

Abstract We discuss how consciousness emerges in human brains and speculate whether it can also emerge in computational substrates. We discuss the relevance of emotional aspects for cognitive architectures, and we characterize the future role of artificial intelligence.
Optimal Cyclic Shift Transposition based Data Hiding for Audio Steganography

Audio steganography based on optimal compression–encryption image algorithm is demonstrated in this paper. Initially, Dual tree complex wavelet transform (DTCWT) is applied to input cover audio signal. To improve the data hiding process, secret image is encrypted using Cyclic Shift Transposition algorithm (CSTA). After the encryption, Modified Huffman Encoding (MHE) algorithm is used to compress the secret image. Then the proposed encryption-compression secret image is embedded in the audio signal. For embedding, the suggested method selects the optimal position with the help of improved salp swarm optimization (ISSO). Here the traditional SSO algorithm is modified based on the opposition-based learning (OBL). After selecting the optimal position, the encryption-compression secret image is embedded to the audio sub-band. In the extraction phase, from the stego audio the secret image is retrieved. The performance of the proposed is evaluated by peak signal to noise ratio (PSNR) and Signal to noise ratio (SNR). Experimental outcomes demonstrate that the implemented audio steganography method maintains embedding quality with high PSNR of 55.82dB with minimum MSE value. The implementation of the proposed method is done by MATLAB.