Reviewerly/exHarmony huggingface dataset:
Unsupervised Anomaly Event Detection in Video Based on Autoencoders and Motion Features

In this article, we propose an unsupervised learning method for anomaly detection in videos based on autoencoders to learn the distribution of normal events. In our model, if the behavior deviates from normal distribution, it is considered as abnormal behavior. We use 2D convolution to extract the spatial feature of the video data. In order to compensate for the temporal information of video data, we incorporate optical flow features which include two consecutive frames of motion features. To improve the expressive ability of our model and fit more complex distribution functions, a `micro network' is constructed and added into the model. Moreover, we add the convolutional long Short-Term Memory (ConvLSTM) in the middle section between encoder and decoder of our model to learn temporal evolution of spatial feature. The effectiveness of the proposed unsupervised anomaly event detection method is validated through experiments that are conducted on four publicly available data sets.
Confidence Belief Function Weighted Parallel Rank-level Fusion for Face recognition

In this paper, proficient feature extraction techniques using efficient neural networks (NN) with evidence theory for face recognition are presented. This approach is established to reduce the computation periods required by these NN. Evidence theory based single or multi biometric fusion methods have established promising performance, but they cannot handle the uncertainty appropriately, suggesting that further improvement of the performance of single biometric authentication systems. Conventional ranking is upgraded, using some associations among the outputs (belief confidence factors) of a classifier. Then, the final result is achieved by fusing results from the combined classifier output (belief confidence factors) with evidence theory. The face database usually severely affected by various degradations such as, illumination, noise and pose variations etc. which affects the overall recognition accuracy. The outcome establishes that the proposed rank-level fusion method attains superior recognition accuracy than other feature extraction and other related rank level fusion approaches.
A Game-theoretic Approach to Data Interaction

As most users do not precisely know the structure and/or the content of databases, their queries do not exactly reflect their information needs. The database management system (DBMS) may interact with users and use their feedback on the returned results to learn the information needs behind their queries. Current query interfaces assume that users do not learn and modify the way they express their information needs in the form of queries during their interaction with the DBMS. Using a real-world interaction workload, we show that users learn and modify how to express their information needs during their interactions with the DBMS and their learning is accurately modeled by a well-known reinforcement learning mechanism. As current data interaction systems assume that users do not modify their strategies, they cannot discover the information needs behind users’ queries effectively. We model the interaction between the user and the DBMS as a game with identical interest between two rational agents whose goal is to establish a common language for representing information needs in the form of queries. We propose a reinforcement learning method that learns and answers the information needs behind queries and adapts to the changes in users’ strategies and proves that it improves the effectiveness of answering queries, stochastically speaking. We propose two efficient implementations of this method over large relational databases. Our extensive empirical studies over real-world query workloads indicate that our algorithms are efficient and effective.
Generalized Probabilistic Bisection for Stochastic Root Finding

We consider numerical schemes for root finding of noisy responses through generalizing the Probabilistic Bisection Algorithm (PBA) to the more practical context where the sampling distribution is unknown and location dependent. As in standard PBA, we rely on a knowledge state for the approximate posterior of the root location. To implement the corresponding Bayesian updating, we also carry out inference of oracle accuracy, namely learning the probability of the correct response. To this end we utilize batched querying in combination with a variety of frequentist and Bayesian estimators based on majority vote, as well as the underlying functional responses, if available. For guiding sampling selection we investigate both entropy-directed sampling and quantile sampling. Our numerical experiments show that these strategies perform quite differently; in particular, we demonstrate the efficiency of randomized quantile sampling, which is reminiscent of Thompson sampling. Our work is motivated by the root-finding subroutine in pricing of Bermudan financial derivatives, illustrated in the last section of the article.
Image Steganography Based on Local Variance

In this paper, a simple and robust watermarking algorithm is presented by using the first, second, third and the fourth Least Significant Bits (LSBs). We embed two bits in two places out of four LSBs according to the local variance value. Compared to the simple LSB algorithm where we use bits 7 and 8 to embed information, the proposed algorithm is more robust to white noise and JPEG compression. Experimental results show that the quality of the watermarked image is high in terms of Peak Signal-to-Noise (PSNR) and Structural Similarity Index (SSIM).
Flexible multivariate regression density estimation

We consider the problem of flexibly modeling the conditional density of a multivariate response given covariates. We model the regression density function as a mixture of multivariate normals density with the mean vectors and mixing probabilities varying smoothly as functions of the covariates. A fast Variational Bayes fitting algorithm is developed with variable selection and the number of components selection conveniently and efficiently embedded within the variational Bayes update. The proposed method is applicable to high-dimensional settings where the number of potential covariates can be larger than the sample size. The methodology is demonstrated through simulated and real data examples. The R code is available upon request.
A New Feature Extraction Approach Using Contourlet Transform and T-Test Statistics for Mammogram Classification

In this study, a CAD system is recommended for the classification of mammography images as normal-abnormal and benign malignant. The proposed system consists of the feature extraction, determination of the distinguishing capabilities of the features and selection of the features using by dynamic thresholding according to the determined distinguishing capabilities. It uses the contourlet transform to extract features. The distinguishing capabilities of the features are determined by using t-test statistics, and the thresholds are applied to those values to select effective ones. Classification is performed using support vector machine algorithm for every iteration with each thresholding step. Among the results of the iteration performed, the optimum data that have the best performance, which is they have maximum accuracy result with the minimum number of features, is selected as the optimum value. To evaluate the optimal feature set, classification carries out using the feature set applying 5-fold cross-validation. According to the results, the proposed method can be accepted as a successful CAD system.
Discussion on the Construction of Computer Software Specialty in Private Institutions

Under the background of the transformation and development of private colleges and universities, the software majors should be employment-oriented, capability-oriented, and build curriculum system and professional construction. This paper discusses this and puts forward some suggestions.
Design of a Prototype for IT Security Architectures in a Public Organization for Latin America

Public organizations in Latin America on security issues at the level of information architecture are delayed a few years, there was legislation to protect information, while in Latin America only few countries defined their strategy. We had principles of information security such as confidentiality, integrity and availability. As an objective, an analysis of the sectors of public organizations was carried out, in order to support the strategy, an information technology security architecture prototype was designed where to achieve this, architectures models and design principles aligned to good industry practices. It was used method, models were reviewed and analyzed. It was a prototype architecture that managed to protect information at different levels of organizations. It was concluded that the prototype of IT security architecture for organizations allows everyone involved in the organization to participate in it because technology is currently present at all levels of public companies.
Research on 3D CAD Model Retrieval Algorithm Based on Global and Local Similarity

Content-based 3D CAD model retrieval takes a 3D CAD model as input and finds other models with the same or similar structure. This paper proposes a two-stage retrieval method that can take into account the global and local similarity of CAD models. In the first stage, the CAD model formation candidate modles with high global matching degree with the query model is selected, and the TF-IMF (Term Frequency-Inverse Model Frequency) vector method is proposed to describe the global surface line distribution of the 3D CAD model. In the second stage, on the basis of the global similarity, the models which are locally similar with the query models are further filtered, and the attribute adjacency graphs between models are calculated by ACO (ant colony optimization) algorithm. Experimental results show that the proposed method achieves better retrieval performance than the maximum clique method based on the attribute adjacency graph (NDCG), which is 90.68%, and has higher retrieval efficiency.
Guided Stereo to Improve Depth Resolution of a Small Baseline Stereo Camera Using an Image Sequence

Using calibrated synchronised stereo cameras significantly simplifies multi-image 3D reconstruction. This is because they produce point clouds for each frame pair, which reduces multi-image 3D reconstruction to a relatively simple process of pose estimation followed by point cloud merging. There are several synchronized stereo cameras available on the market for this purpose, however a key problem is that they often come as fixed baseline units. This is a problem since the baseline that determines the range and resolution of the acquired 3D. This work deals with the fairly common scenario of trying to acquire a 3D reconstruction from a sequence of images, when the baseline of our camera is too small. Given such a sequence, in many cases it is possible to match each image with another in the sequence that has a more appropriate baseline. However is there still value in having calibrated stereo pairs then? Clearly not using the calibrated stereo pairs reduces the problem to a monocular 3D reconstruction problem, which is more complex with known issues such as scale ambiguity. This work attempts to solve the problem by proposing a guided stereo strategy that refines the coarse depth estimates from calibrated narrow stereo pairs with frames that are further away. Our experimental results are promising, since they show that this problem is solvable provided there are appropriate frames in the sequence to supplement the depth estimates from the original narrow stereo pairs.
지진 관련 SNS 텍스트에 나타난 비명시적 감성표현 연구

This paper studies domain-dependent implicit sentiment expressions in earthquake-related social media texts. In sentiment analysis, domain-dependent implicit sentiment expressions are difficult to estimate and recognize, which often results in the depreciation of system performance. In this study, we analyzed two types of implicit sentiment expressions that may occur in the causal clause of the sentence conveying ‘fear’ sentiment: {simple predicate} type such as ‘collapse’ and {complex predicate} such as ‘blow up’. When these clauses appear without main clauses that express explicit sentiments such as ‘anxiety’ or ‘panic’, they imply the omitted sentiment and thus implicitly express certain sentiments by themselves. The DECO T-Crawler platform was used to collect earthquake-related data in social media texts and the DECO Korean dictionary and local-grammar graph formalism were used to the corpus. The performance of resources built in this study showed 84.5% accuracy in a test corpus, which is about 23% higher than the accuracy expected when using only sentiment-conveying words.
Automatic Counting Based On Scanned Election Form Using Feature Match and Convolutional Neural Network

In a democratic state, General Election (Pemilu) is a procedure for selecting regional heads regulated in Article 1 paragraph 3 of the 1945 Constitution. KPU (Komisi Pemilihan Umum) is a state institution that organizes by prioritize transparency and accountability in each stage of general elections in Indonesia. One form of openness that has always been in the media spotlight is the vote counting process. The manual calculation process carried out by the General Election Commissions (KPU) on form C1 is time-consuming and resourceful because it involves paid volunteers. In this study, the authors used the proposed method to build a numerical handwriting recognition system on the C1 KPU form. Method proposed is a recognition flow including table detection with candidate contour techniques, feature matching, number segmentation, and digit classification with the convolutional neural network (CNN). The datasets used are from the official KPU election websites in 2014 and 2019. We use capsnet to classify each segmented digit with 95.65% accuracy. The trained model was tested using validation form and reach 80.73% document accuracy using 2019 election form.
COOPERATIVE LEARNING DESIGN IN WEB MEDIA: CONCEPTUAL REVIEW

This paper proposes the conceptual feasibility of designing cooperative learning on web media. Web-based cooperative learning is done by incorporating elements of cooperative learning on the web. Various cooperative learning studies are used as a basis for building cooperative learning steps designed. Preparation before implementing cooperative learning on a website plays an important role. The initial process of preparing cooperative learning on the web: 1) Preparing data about students, 2) Developing an activity plan including objectives, 3) Determining teaching materials, 4) Organizing groups of students, 5) Planning weight values, 6) Determining action instruments (observation sheets, learning outcomes test, questionnaire about learning responses).&#x0D; The results of the study formulate cooperative learning steps on the web using forum chat facilities: 1) Present goals and instruction sets; 2) Arrange students to become learning teams; 3) Helping team work and learning; 4) Test material; 5) Give recognition.
PVW: An Efficient Dynamic Symmetric Cipher to Enable End-to-End Secure Communication

There is a global move towards the usage of mobile devices due to the tremendous growth in mobile technologies. These mobile devices are utilized by more than billions of users for commercial and social purposes. In many scenarios, sensitive information should be sent from our mobile phones through the Internet, which is considered as an open and insecure network. Therefore, we have to secure our sensitive data before sending from our mobile phones. These mobile phones have limitations in resources such as memory space, processing and battery capacity. In this paper, a secure, fast and efficient cryptographic algorithm for mobile and lightweight cryptographic application is proposed. PVW cipher is a block symmetric cipher, which follows a Feistel structure. PVW cipher works on two basic principles, confusion and diffusion. The basic operations used are permutation, substitution, mixing, addition and XOR (exclusive OR). In PVW cipher, the basic operations are dynamic in nature. The inputs to PVW cipher are 128-bit plain text and 160-bit key. In PVW cipher, there are 8–16 rounds. Finally, we show an implementation of PVW cipher in eight rounds. The performance of PVW cipher is measured in terms of time complexity, space complexity, security. The results show that PVW cipher is very efficient, fast and secure against brute force attack, differential attack and linear attack.
Design of Efficient Sign Detector for Moduli Set $\{2^{n}-1,2^{n}, 2^{n}+1\}$ in Residue Number System

Sign detector is an important unit of the residue number system (RNS) for numerous digital signal processing applications. It has received considerably less attention than its reverse conversion and scaling problems. However, the sign detection is essentially required for residue number comparison, division and other difficult arithmetical operations in RNS. In this contribution, a sign detection algorithm for classical moduli-set {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -1,2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> +1} is proposed with less complex architecture by using scaling technique. The proposed sign detector circuit is implemented using a carry save adder (CSA), a carry-generation circuit and a simplified parallel prefix adder. The proposed algorithm is synthesized using design compiler of Synopsys tool and compared against the available literatures for the same moduli-set. The proposed design exhibits considerable reduction in time-delay, area and power as compared to others.
Pose-Based Multi-Target Tracking

Human multi-target tracking in video is an important issue in the field of computer vision. It is necessary to detect the target on each frame, and to connect the targets of all frames into a target sequence. For target matching among different frames, we propose a tracking algorithm for constructing object pose sequence(COPS) based on Openpose. The position status and the ORB feature of the target pose are dynamically weighted and fused into new features. Target pose is searched in the corresponding target pose sequence by comparing the new features between the target pose in the sequence and every pose in current frame. When the target pose is matched, the influence of the position feature on the pose similarity could be enhanced when the target motion is continuously detected. When the target scale changes too much, the method can expand the contribution of the ORB feature to the pose similarity comparison. The experiments of human multitarget tracking algorithm are carried out on the PoseTrack and MOT datasets, and the results show that the proposed tracking algorithm overcomes the problem of target matching between frames.
Elephants Herding Optimization to Solve the Multiple Travelling Salesman Problem

The Multiple travelling salesman problem (MTSP) is a typical computationally complex combinatorial optimization problem, which is an extension of the famous travelling salesman problem (TSP) classified as NP-Hard. The MTSP can be generalized to a wide variety of routing and scheduling problems. The paper proposes a novel metaheuristic called Elephant Herding Optimization (EHO) to solve the MTSP. The EHO algorithm is bio-inspired from the natural herding behavior of elephant groups, which has proved its efficiency in solving the classic TSP and continued optimization problems. So, to extend the application of this algorithm, The paper makes the attempt to show how the EHO algorithm can be applied to the MTSP by testing some random benchmark instances of TSPLIB. The obtained results shows the performance of the proposed method.
Design Idea of Professional Talent Evaluation System Based on Mobile Internet

With the development of whole society informatization application and popularization, the use of modern information technology, based on mobile internet,in the form of online learning and assessment, guiding students to reasonable use of learning time, using the Internet education thought and advanced online teaching platform system architecture, to realize of teaching and learning, exam, management and other multiple functions, which has become an important means of teaching of higher vocational colleges. Professional talents evaluation system based on mobile Internet is using big data and AI technology, through online teaching platform system realize quantitative statistics to the students’ learning situation, and help build professional talents evaluation system, to establish a multi-dimensional assessment report, so as to provide the recommended basis for students’ future employment. Through the analysis of the reliability and security of the professional talent evaluation system and introduce the comprehensive test method of the system performance, form the design conception of the professional talent evaluation system. In the near future the vocational talent evaluation system will be further developed and used, which will play a great role in the professional education of higher vocational schools.
Deep Reinforcement Learning Control of an Autonomous Wheeled Robot in a Challenge Task: Combined Visual and Dynamics Sensoring

This paper presents a Deep Reinforcement Learning agent for a 4-wheeled rover in a multi-goal competition task, under the influence of noisy GPS measurements. A previous related work has implemented a similar agent to the same task using only the raw dynamics measurements as observations. The Proximal Policy Optimization algorithm combined to Universal Value Function Approximators resulted in a system able to successfully overcome very noisy GPS observations and complete the challenge task. This work introduced a frontal camera to add visual input to the rover observations during the task execution. The main change on the algorithm is on the neural networks' architectures, in which a second input layer was added to deal with the image observations. In a few alternate versions of the networks, Long Short-Term Memory (LSTM) cells were included in the architecture as well. The addition of the camera did not present a significant increase in stability or performance of the network, and the computation time require increased.
The Use of Augmented Reality in the Marketing Mix of Physical Products

The chapter explores current practices regarding the use of AR in the marketing mix of physical products and the implications for the future. Authors identify common aspects and recommend future studies and practices. Twenty cases of physical products using AR as part of their marketing mix were identified and analysed. The EPI Cube framework and its contextualization in the customer experience was used as a theoretical model for guiding the analysis. Findings are presented and implications for theory and practice are discussed.