Reviewerly/exHarmony huggingface dataset:
Combination of Acoustic and Vibration Sensor Data Using Support Vector Machines and One-Versus-All Technique Data Fusion for Detecting Objects

This paper aims to create a device that will be able to detect the presence of an object and classify the object into human, animal, or vehicle by using the information obtained from acoustic and seismic signals. The specific objectives are to develop a hardware device based from Raspberry Pi Minicomputer with seismic and acoustic sensors and transmit sensor signals to a computer for feature extraction and data fusion, to develop a software using Python, MATLAB, and use Data Fusion with the use of Support Vector Machine with One-Versus-All technique, to accurately classify the object into human, animal (canine), or vehicle, to use statistical treatment using multi-class confusion matrix to determine the F-score or accuracy of the classifiers, as an aid for answering the formulated hypotheses. In the testing phase, blind test was performed for the classifiers, using different gathered samples. The F-score of the human, animal, and vehicle classifiers were, respectively, 93.549%, 98.305%, and 100%. The researchers recommend a ground-mounted seismic sensor for comparison of its F-score contribution with the used seismic sensor. Training the SVM models with different parameters could also lead to potential increase in accuracy, such as the number of k-fold cross validations. SVM can as well be compared to other classifier models.
Trust-Aware Sequence Recommendation Based on Attention Mechanism

Existing trust-aware recommendation methods has problems such as inability to optimally allocate attention and ignore time sensitivity. Therefore, in order to improve the accuracy of the Top-N recommendation, we fuse the trust-based method with the sequence-based method. An attention-based trust recommendation sequence recommendation model (ATRec) was proposed, ATRec uses a self-attention block to learn short-term user preferences based on user interaction sequences, assigns users' attention to different trusted users through an attention mechanism, and finally balances the two with parameter. Our model has been tested on real data sets, and the experimental results are better than the baseline. Owing to the parallelism of self-attention, our model is also superior to the deep learning-based method in the baseline in terms of training speed.
Content-based Re-ranking Method for Recommendation

Abstract The recommendation system is one of the effective tools to solve information overload. Most of the current deep learning recommendation algorithms only focus on the accuracy of the recommendation results by learning users’ preferences. However, the diversity of the recommendation results is neglected, resulting in homogeneous recommendation results, which reduces users’ satisfaction. Diversity can not only effectively avoid over-fitting, but also comprehensively consider multiple dimensions to improve the quality of recommendation results. Therefore, we propose a content-based re-ranking method (CBR) for recommendation systems. The proposed method can make full use of the data set, and effectively supplement and re-rank the recommended results produced by some deep learning algorithms based on the edge information such as tags to be recommended from different perspectives, thus can effectively improve the diversity while preserving the accuracy of recommendation results. Experimental results demonstrate the significant improvements of the proposed re-ranking method.
A Novel Method for Ghost Removal in High-Dynamic Range Images

High-dynamic range images can be created by combining multiple images of a scene with varying exposures, and the resulting images may contain ghosting artifacts due to the presence of movement in the scene at the time of capture. To address this problem, most of the existing algorithms conduct ghost detection techniques followed by a ghost region filling with the probable radiance of the scene. In this paper, we propose a novel ghost-removal procedure that does not require precise ghost detection and does not suffer from color artifacts. Results show that the newly devised algorithm performs better than the ones described in the previous research.
Analyzing the Understandability of Data Warehouse Conceptual Model Using Fuzzy Logic Techniques

Data warehouse plays an inevitable role for decision making in any organization. Therefore, high quality of data warehouse must be maintained using appropriate models, techniques and tools. Researchers and experimenters in past, have proposed various structural metrics to compute the understandability of data warehouse conceptual model. The main focus of our research work in this paper, is to generate heuristic rules using data mining classification algorithms and validate them using fuzzy logic technique to predict the understandability of data warehouse conceptual model. The results are compared with previously conducted controlled experiments to prove and validate the effectiveness of fuzzy prediction system.
The Workload Assessment of National Grid Big Data Projects Based on Content Recommendations and Text Classification

The evaluation of workload in big data project is an important prerequisite for improving the management of the National Grid big data projects. The lack of relevant models and specifications of the evaluation is caused by the exploratory, uncertainty and characteristics of big data projects. Based on the information of typical projects, this paper proposes to combine with Natural Language Processing (NLP)and machine learning to solve the problem. Firstly, this method obtains the reference value of project workload impact factors through content recommendation, neural network and other algorithms. Then the workload estimation model is built based on the measurement of each impact factor. The results show that the method proposed in this paper can effectively identify and predict the attribute class impact factors of the new project, and can also reasonably complete the workload estimation of the project, which is greatly significant to realize the rational allocation of resources.
A Speculative Parallel Optimization Method for Industrial Big Data Algorithms

Apache Spark is one of the most advanced distributed computing platforms at present, more and more industrial big data algorithms are implemented on this platform to achieve better performance. However, some of the algorithms cannot be executed on Apache Spark in parallel because of their complex inner control dependencies. In order to solve the problem, by introducing Software Thread-Level Speculation technique, this paper proposes a method to optimize industrial big data algorithms, and makes them run on Apache Spark in parallel. Specifically, the proposed method analyzes how complex inner dependencies affect algorithm's parallelism, then speculatively partitions the algorithm into subtasks to conquer these dependencies, and predicts inputs for the subtasks. After executing the subtasks in parallel, the results are collected and validated, the correct results are kept and committed while the incorrect ones are abandoned. By this way the optimal parallelism for industrial big data algorithms can be achieved. The experiments show that by the proposed method, the particle swarm optimization algorithm can achieve speedup by 150%-230% comparing with the unspeculative one. Therefore, the execution efficiency of low parallelized algorithm on Apache Spark can be markedly enhanced by the proposed optimization method.
DUST-MASK: A Framework for Preventing Bitcoin’s Dust Attacks

Bitcoin Cryptocurrency is an advanced decentralized electronic payment system which stores all the transaction records in a public ledger connected to the blockchain. Many cyber-attacks including Double-Spending, Distributed denial-of-service (DDOS), Sybil attack and Dust attack, have been targeting Blockchain-based application Cryptocurrency, mainly Bitcoin cryptocurrency. In this paper, the proposed DUST-MASK, a secure Bitcoin system against dust attack, protects Bitcoin's availability and pseudo-anonymity from attackers sending dust transactions in order to analyze the data and link the transactions to a specific user. The proposed system will inform the user of those kinds of malicious transactions and guarantee the user a choice whether to accept or reject them.
Safety improvement of Ant Colony Algorithm for UAV Conflict Resolution

To improve the reliability of flight conflict resolution for UAV for safety operation in airspace, ant colony algorithm is introduced to improve the safety for UAV collision advoidance. Firstly, the airspace environment of UAV operation is described, and then path planning of UAV is elaborated, finally safety improvement of ant colony algorithm is given for UAV conflict resolution. Simulation vallidates the effectiveness and feasibility.
Reward Value-Based Goal Selection for Agents’ Cooperative Route Learning Without Communication in Reward and Goal Dynamism

Abstract This paper proposes a goal selection method to operate agents get maximum reward values per time by noncommunicative learning. In particular, that method aims to enable agents to cooperate along to dynamism of reward values and goal locations. Adaptation against to these dynamisms can enable agents to learn cooperative actions along to changing transportation tasks and changing incomes/rewards because of transporting tasks for heavy/valuable and light/valueless items in a storehouse. Concretely, this paper extends the previous noncommunicative cooperative action learning method (Profit minimizing reinforcement learning with oblivion of memory: PMRL-OM) and sets the two unified conditions combined of the number of time steps and the rewards. One of the unified conditions is calculated the approximated number of time steps if the expected reward values are the same each other for all purposes, and the other is the minimum number of time steps divided by the reward value. The proposed method makes all agents learn to achieve the purposes in the order in which they have the minimum number of the condition values. After that, each agent learns cooperative policy by PMRL-OM as the previous method. This paper analyzes the unified conditions and derives that the condition calculating the approximated time steps can be combined both evaluations with almost same weight unlike the value the other condition, that is, the condition can help the agents to select the appropriate purposes among them with the small difference in terms of the two evaluations. This paper tests empirically the performances of PMRL-OM with the two conditions by comparing with the PMRL-OM in three cases of grid world problems whose goal locations and reward values are changed dynamically. The results of this derive that the unified conditions perform better than PMRL-OM without some conditions in grid world problems. In particular, it is clear that the condition calculating the approximated time step can direct the appropriate goals for the agents.
TRAINING. THEORY AND METHODOLOGY OF THE USE OF TRAINING FORMS

&#x0D; &#x0D; &#x0D; The article deals with the use of training forms while teaching students by means of interactive technologies. Every graduate of pedagogical educational establishments, who has certain knowledge of general, age psychology and personality psychology, should be familiar with training as a form of class conduction. It is desirable to conduct classes in the form of training at higher education establishments, since it is a means of influence aimed at developing knowledge, social competence, skills and experience in the sphere of interpersonal communication; development of communicational competence, which is a factor of proficiency.&#x0D; &#x0D; &#x0D;
Internal Abnormalities’ Detection of Human Body Analyzing Skin Images Using Convolutional Neural Network

Skin is considered to be the largest organ of the human body. By a numerous number of recent studies, dermatologists of renowned schools of medicine as well as American Academy of Dermatology have discovered that being the largest external organ, skin could be considered as a window in one’s health and through which signs of internal diseases could be discerned way before they are developed. Presently, various kinds of diseases are well known for causing high death rates, such as several heart diseases and strokes, persistent irritant lung disease, and respiratory disorders, and predominantly, diverse cancers are the widespread dominant cause of death worldwide. Early detection of any disease could increase the chances of wellness. Despite being a great phenomenon in modern health science, no remarkable work has been executed on this so far. From that point, in this paper, a universal methodology of detecting abnormalities on skin from images which is not visible by human eye compared with normal skin images applying a convolutional neural network (CNN) is proposed. We have collected 100 skin images from different people by standard camera, and by using various image augmentation techniques, an increased number of datasets are obtained. We have achieved a 70% of accuracy rate. We aim to attain a higher accuracy rate in future by enhancing the dataset and implement our model for small devices.
METHODS OF EFFECTIVE ORGANIZATION OF TEACHING OF MATHEMATICS BY USING THE PROBLEM-BASED TEACHING

&#x0D; &#x0D; &#x0D; &#x0D; In this article: strengthening the material-technical and informational base of higher education institutions, further improving the quality of teaching and learning processes in mathematics and independent learning by providing high- quality educational literature, modern teaching methods and educational technologies; the stages of changing of the student's perceptual activity related to solve problematic situations in the effective organization of math classes are analyzed.&#x0D; &#x0D; &#x0D; &#x0D;
Comparison between Naïve Bayes and certainty factor to predict big five personality

Abstract The Big Five is one of the methods known in the world of psychology to interpret persons personality, especially to find a relationship between personality and work environment. The Big Five consists of openness (O), conscientiousness (C), extraversion (E), agreeableness (A), and neuroticism (N). This study aims to predict the level of agreeableness in the classification of The Big Five. The method used in detecting this level of agreeableness is using the Certainty Factor and Naive Bayes algorithms. The results obtained in a comparison of the level of accuracy in personality prediction, the Certainty Factor algorithm get results of 80% and Naive Bayes get 90%. From these results it can be concluded that the Naive Bayes algorithm is better at predicting personality.
Choosing Exploration Process Path in Data Mining Processes for Complex Internet Objects

We present an experimental case study of a novel and original framework for classifying aggregate objects, i.e. objects that consist of other objects. The features of the aggregated objects are converted into the features of aggregate ones, by use of aggregate functions. The choice of the functions, along with the specific method of classification can be automated by choosing of one of several process paths, and different paths can be picked for different parts of the domain. The results are encouraging and show that our approach allowing for automated choice, can be beneficial for the data mining results.
Study on the Effect of Appearance of the Primary Standard on Standardizing the Standard Solution: Borax as an Example

Borax in different appearance is compared and studied in standardization of the standard hydrochloric acid as primary standard.The obtained concentrations of hydrochloric acid are 0.1049 mol•L -1 and 0.1052 mol•L -1 , respectively.Then based on the statistical test of the data, it was revealed that there are significant differences between the two.Additionally, appearance differences of borax, which may be caused by the change of its properties, will give rise to the error of subsequent measurements.
Analysis Of The Impact Narrative Algorithm Method, Pseudocode And Flowchart Towards Students Understanding Of The Programming Algorithm Courses

Abstract The definition of an algorithm is to describe a problem into a sequence of systematic steps in solving problems. Arranging the algorithm in sequence from beginning to end so that it can solve the problem. The results of the implementation of the algorithm are programs. So the purpose of making an algorithm is to facilitate the making of the program. There are 2 types of algorithms, 1) Text (narration and pseudocode), 2) Visual / image (flowchart). Comparison between the two, students understand it which is higher between the text or visual model. This study aims to see how much infuence the value of students understands the algorithm in text or in an image. In conclusion, students are easier to understand the types of algorithms in an image. The test uses the Paired Sample T test, with a 5% significance level obtained value = 0.05. This means that the t count is greater than t table of 2.06390 and P Value (0,06211 &gt; 0,05) or T-stat smaller than T-critical two tile, it means H1 can be accepted or T-count &lt; T-table (-1,95597&lt;2,06390), so H0 can be accepted.
Research on Speaker Verification Based on Cloud Computing Platform

At present, the accuracy of speaker verification is not high and the training speed is slow. In order to improve the accuracy of speaker verification, this paper uses transfer learning and score fusion technology. Experimental results show that with the knowledge learned from the VoxCelebl dataset, transfer learning reduced the equal error rate (ERR) by 0.67% on the aishell-l dataset. By fusing the convolutional neural network-based model CNN and the DenseNet-BiGRUbased model DR, the ERR is reduced above 0.63%. In the aspect of training speed improvement, this paper uses data parallel technology based on the cloud platform, and studies the impact of the communication window value of Downpour mechanism and starting multiple workers in each cloud node on training speed and accuracy. The experimental results show that starting multiple workers in each cloud node can effectively improve the training acceleration ratio. This method has a more significant effect on the model DR of lower communication overhead. At the same time, the Downpour mechanism can also improve the training acceleration ratio, and it has a better acceleration effect on the model CNN of higher communication overhead. In the case where the cloud platform has 3 nodes, the acceleration ratio of the DR model is 3.94, which exceeds the linear acceleration ratio. Meanwhile, the impact on the speaker verification accuracy is less than 1% compared with stand-alone training. The acceleration ratio of the CNN model is 2.69, which is close to the linear acceleration ratio.
Model Learning of Robot Inverse Dynamics based on Self-Organizing Map Gaussian Process Regression

An accurate inverse dynamics model of robotic systems is key in robotic applications. Analytic inverse dynamics models suffer from uncertainties of physical parameters estimation, some complex frictions and actuator dynamics. In such cases, developing machine learning algorithm to approximate the inverse dynamics model from collected data become a hot research field. In this paper, a novel approach based on Self-Organizing Map Gaussian Process Regression (SOM-GPR) is proposed for modeling inverse dynamics of a robotic arm, which is a combination of the SOM and GPR. In our method, we use the SOM neural network to divide the global GP model into multiple small-sized local GP models. The prediction for a test point is performed by the nearest local GP model. With our approach, we can achieve the accurate modeling of robot inverse dynamics models. The effectiveness of our proposed algorithm has been verified in the experiment. The result of the experiment shows that the combination of two techniques we propose has a better performance over other extensions of GPR applied to learn the inverse dynamics model.
Securing graph steganography by using Shamir’s secret sharing

Abstract Nowadays, information exchange becomes popular along with the development of information technology. To protect their confidential information, people usually use steganography because it hides the presence of the secret information in a media file called as cover . In 2008, Desoky et.al. proposed a method namely graphstega or graph steganography that conceals message as data points in a graph. However, this method is vulnerable since the value represented by the graph is not realistic and this will raise suspicion. To overcome the problem, this paper presents a novel approach by exploiting a graph built based on real data as the cover. To securing the position of the secret message, Shamir’s secret sharing scheme is used. The experiment showed that this method has better security than graphstega does.
The role of Wi-Fi network in producing social space in Mentri Supeno Park Semarang, Indonesia

Abstract Mentri Supeno Park is an open public space in the form of a park located in the middle of the city in which its position is a strategic location as a social space. This park has various facilities, one of them is the Wi-Fi Network. A combination of open space and Wi-Fi has become a necessity in almost all levels of society to form community activity points that spread to both individual and groups on each side of the park. The purpose of this study was to find out how the role of Wi-Fi in generating social space in open spaces in the park, so it could be known the types of created social spaces and management of spaces that were obtained. This study used a qualitative method with a behavioral mapping approach as its analysis technique. The results of this study showed five types of social space activities, with types of individual and collective activities. With the results obtained, it is expected to provide recommendations for the management of social space, so it does not lead to uncontrolled things. Moreover, it can help build the character of the community and align with the regional development agenda.
Suppressed MT static effect by two wavelet threshold criteria

In the Magnetotelluric sounding (MT), due to the electrical in-homogeneities of the small near-station conductor on surface, static effect are usually caused. In order to suppress this effect, the wavelet threshold is adopted. First, we decomposed the MT data with wavelet coefficients and then used threshold method to reconstruct. The reconstructed signal can effectively suppress the static effect of the MT data. The selection of threshold criterion is the key to suppress the static effect by wavelet. In this paper, two threshold criteria (DT and ST) of the wavelet processing the MT data are compared. The results show that the two threshold methods have certain effects on suppressing static effect.
Who Reads More?: Comparing Book Consumption Between Various Groups

Course: PSYC 541 Graduate Statistics for Behavioral Sciences. Per the MSU-B catalog, Provides students with proficiency in using the most common statistical techniques for analyzing research data, including analysis of variance, correlation, regression, and nonparametric statistics.Assignment: To choose a data set, analyze and present findings in a report format.