Reviewerly/exHarmony huggingface dataset:
Pattern Recognition of Effective Online Classified Advertisement

Classified advertising is one of the most popular online advertising especially for second-hand goods. One of the main functions of online classifieds is to help sellers sell their items quickly and easily. Optimized advertising is then essential to maintain user engagement in the platform. In this research, the factors and characteristics of online classified ads including structured data and free text that may affect the effectiveness of the advertising in terms of the ability to close the sales are investigated. Initially, we collected 148K classified ads data in an automotive category that have the published date between January 1, 2018, and July 31, 2018, provided by Kaidee, the top Thai online marketplace. The data is then preprocessed by data cleaning and features extraction techniques to find the important features for using in the analysis. We divide the focusing features into 4 main groups: ad components, car attributes, price, and text features. We apply k-means clustering integrated with decision tree classification in modeling to classify the ability to close the sales on Kaidee platform within 30 days. The predictive efficiency of the models will be measured and compared using accuracy, precision, recall, and F-scores by using the 3-fold Cross Validation method. Finally, we analyze the important features that are related to closing the sales of each feature group.
Appearance and Shape from Water Reflection

This paper introduces single-image geometric and appearance reconstruction from water reflection photography, i.e., images capturing direct and water-reflected real-world scenes. Water reflection offers an additional viewpoint to the direct sight, collectively forming a stereo pair. The water-reflected scene, however, includes internally scattered and reflected environmental illumination in addition to the scene radiance, which precludes direct stereo matching. We derive a principled iterative method that disentangles this scene radiometry and geometry for reconstructing 3D scene structure as well as its high-dynamic range appearance. In the presence of waves, we simultaneously recover the wave geometry as surface normal perturbations of the water surface. Most important, we show that the water reflection enables calibration of the camera. In other words, for the first time, we show that capturing a direct and water-reflected scene in a single exposure forms a self-calibrating HDR catadioptric stereo camera. We demonstrate our method on a number of images taken in the wild. The results demonstrate a new means for leveraging this accidental catadioptric camera.
iGPU Leak: An Information Leakage Vulnerability on Intel Integrated GPU

Hardware accelerators such as integrated graphics processing units (iGPUs) are increasingly prevalent in modern systems. They typically provide multiplexing support where several user applications can share the iGPU acceleration resources. However, security in this setting has not received sufficient consideration. In this work, we disclose a critical information leakage vulnerability due to defective GPU context management. In essence, residual register values and shared local memory in the iGPU are not cleared during a context switch. As a result, adversaries can recover the secret key of a cryptographic algorithm running on an iGPU from a single snapshot of the leaking channel. User privacy is also under threat due to browser activity eavesdropping through website-fingerprinting attack with high accuracy and resolution. Moreover, this vulnerability can constitute a covert channel with a bandwidth of up to 8 Gbps.
Can an algorithmic system be a 'friend' to a police officer's discretion?

This tutorial aims to increase understanding of the importance of discretion in police decision-making. It will guide computer scientists, policy-makers, lawyers and others in considering practical and technical issues crucial to avoiding the prejudicial and instead develop algorithms that are supportive - a 'friend'- to legitimate discretionary decision-making. It combines explanation of the relevant law and related literature with discussion based upon deep operational experience in the area of preventative and protective policing work.
Simulation-Based Assessment of Performance Indicator for Data Center Cooling Optimization

Abstract Data center power consumption has grown substantially in the past 20 years. According to the United States Data Center Energy Usage Report, the data center consumption in 2014 was estimated at 70 billion kWh, which accounted for 1.8% of the total U.S. electricity. The present effort investigates the effects of various data center parameters on a new set of metrics called the performance indicator to help assess and optimize the cooling performance of data centers. The three metrics in the performance indicator include power usage effectiveness ratio (PUEr), thermal conformance, and thermal resilience. The data center parameters investigated include computer room air handler (CRAH) setpoints, room configuration layouts, and containment strategies. The results show that the CRAH setpoint significantly influences the PUEr with higher setpoint values resulting in lower PUEr values. Room configuration layout changes and containment strategies showed substantial effects on thermal conformance and thermal resilience. The thermal conformance was increased approximately 10% with room configuration changes without changing the PUEr. Full hot aisle containment also improved the thermal conformance by 7.4%.
Automatic Recognition of Driving Events Based on Dashcam Videos

The recognition of driving events from dashcam videos is an important task in the intelligent transportation field. In this study, we compute the feature values of the dashcam videos using the optical flow method. We also propose a seven-region segmentation method to separate regions of different nature in a video frame. Then we use the random forest classifier to classify the output feature vectors into five driving event categories. We have tried a variety of different feature combinations for experiment. According to the experimental results, the recognition accuracy is up to 88%, and the average accuracy is 83.6%.
Classification of Beijing Metro Stations Based on Multi-source Data and Gaussian Mixture Model

At present, the metro plays an important role in people’s daily travel. In order to clarify the function of the metro stations and to improve the service level of the metro, the reasonable classification of metro stations is particularly necessary. In this paper, multi-source data including Internet data and ridership data is obtained, and the data is analyzed to obtain 12 clustering initial variables. After that, 3 common factors are extracted from the 12 initial variables by factor analysis. According to the extracted common factors, 249 metro stations in Beijing are divided into 4 clusters by Gaussian mixture model, and the probability values that a station belongs to each cluster are obtained.
Discriminative Representation Learning for Face Recognition

A significant progress has been made on face recognition in recent years because of the rapid development of deep learning approaches. Deep learning approaches offer a powerful toolbox for tackling many aspects of face recognition, including the search for effective discriminative features. We compare several state-of-the-art face recognition methods and combine different modules from those methods to propose a special approach for discriminative representation learning. Our approach has a special architecture for representation learning combined with a latest design of classification loss function, making it a highly effective solution for uncontrolled face recognition. Experimented on the Labeled Faces in the Wild (LFW), the Celebrities in Frontal-Profile dataset (CFP), and the AgeDB datasets, our approach shows competitive performance to other state-of-the-art methods.
Hybrid Signature Scheme Integrating Elliptic Curve Cryptosystem with Ong, Schnorr, and Shamir Digital Signature Scheme

Abstract Transmitting data over unsecured Channels is one of the most indispensable issues which motivates researchers to study. In this paper, a new hybrid digital signature approach is going to be proposed which integrates the elliptic curve cryptosystem (ECC) into Ong, Schnorr, and Shamir signature (OSS). A self-invertible 8×8 key matrix will be utilized as a portion of the OSS digital signature. The strength of the proposed technique relies on combining couple of fundamental mathematical problems namely the discrete logarithm problem (DLP) and integer factorization problem (IFP). Elaborated results regarding security and time rendering analysis have been measured and confirmed its durability against diverse patterns of attacks. The submitted technique can be applied as a substitutional protocol if all the familiar systems are executed.
Experimental research on characteristics of error surface shape and reset of spatial attitude variations in phase measuring deflectometry system

We investigate the characteristics of the error surface shape (ESS) induced by spatial attitude variations (SAVs) of the tested surface in the phase measuring deflectometry (PMD) system. In a PMD system, the spatial attitude of the target mirror might deviate from its initial status due to the instability and the stress release of the assembly structure. According to the principle of the PMD, there are such three SAVs as displacement, pitch and yaw angle of the target mirror that will bring about the ESS. In this paper, the principle of the ESS induced by the three SAVs are measured and analyzed based on an established PMD system. Based on the ESS characteristics, a two-step reset method (TSRM) is proposed to reset the spatial attitude of the target mirror in high precision. Experimental results verify that the PMD system could be used to monitor the SAVs of the target mirror and accurately reset it by using the TSRM; the measurement and alignment performance of the PMD system would definitely be improved accordingly.
Thai Vowels Speech Recognition using Convolutional Neural Networks

The vowel is considered as the core of syllable in each word. This paper aims to present noisy Thai vowels speech recognition by using Convolutional Neural Network (CNN). The noisy Thai vowels dataset is the speech of Thai vowels in real-world situations. The sound is collected in a real environment from several areas which consist of many types of noise at 30-40dB SNR (Signal to Noise Ratio). It constrains 16 kHz speech data is recorded from a mobile phone. The vowel speeches are separated into 2 groups: male's voice and female's voice from 25 male and 25 female speakers. In this research, it constrains 18 classes (9 short vowels and 9 long vowels). Mel Frequency Cepstral Coefficients (MFCCs) are used for feature extraction. The most accuracy rate of the CNN-Thai Simple Vowel (CNN-TSV) model is 90.00% and SS.S9% on female and male voices respectively. The comparison results of CNN-TSV model with other models such as Multilayer Perceptron (MLP) and Support Vector Machines (SVM) show that the CNN-TSV model is the most effective for both female and male voices. This research can be used as one of an alternative model to apply for Computer-assisted language learning (CALL) in the future development direction.
A Deep Temporal Collaborative Filtering Recommendation Framework via Joint Learning from Long and Short-Term Effects

Recommendations based on deep learning technologies are attracting increasingly attentions from academia and industry recently, due to it has the powerful ability of data representation learning, and can capture the complex and non-linear user-item interaction patterns. Previous studies on deep learning based recommenders heavily emphasize on learning the short-term or long-term temporal effects in separate ways. Furthermore, most of the existing recommenders studied on temporal dynamics hidden in user-item interactions by using ratings or review texts solely, without utilizing these heterogeneous side information in a comprehensive manner. As a result, they own a limited ability to exploit all of available user-item interaction data. To address the above issues, we propose a temporal collaborative filtering recommendation framework with utilizing deep learning technologies, which can capture the drift of users' preferences and items' attributes over time from available heterogeneous feedback. For studying the rich sentiment information about particular item features hidden in reviews and capturing the short-term temporal dynamics in user preference, a parallel recurrent neural network (RNN) coupled in the last layer is presented. With consideration of the life cycle of each item, we further propose an item-based time evolution model as a supplement to enrich the long-term temporal effects when recommendation. In addition, a modified cosine similarity function is incorporated to produce the personalized candidate list for each user. Finally, extensive experimental are conducted on Amazon's three datasets. They indicate that the proposed method outperforms the other state-of-the-art recommenders in terms of precision and recall when doing the Top-n recommendation task.
A Graph-Enhanced Convolution Network with Attention Gate for Skeleton Based Action Recognition

Action recognition based on skeleton data is a challenging field which attracted much attention in computer vision. Previous research mostly leverage the joints level graph to represent spatial features. In this paper, we propose a novel graph-enhanced convolution network with attention gate (GECN-AG) which uses both joints level graph and distilled body-part level graph to extract fine-grained and coarse-grained spatial features. Considering simultaneously use of the velocity and position features may improve the ability to focus on critical temporal frames, we propose a novel attention module for temporal feature extraction. Furthermore, a regularized cross-entropy loss is designed to optimize the training process. Our approach achieves competitive performance on two widely-used benchmark datasets.
More Flexible: A Free-Customization Technique Based on Log Visualization

This paper proposes a free-customization dashboard technology, which combines data visualization technology with drag-and-drop positioning technology and belongs to the category of user interface design. A minimalist interactive way is designed with data-driven thinking to form a complete log visualization framework finally. This technology not only supports the reposition and free-scaling of subgraphs through drag-and-drop but also uses anti-shake and lazy loading technology for performance optimization during the drag-and-drop process, to greatly improve the performance in the process of free-customization. Based on log data, this technique improves the visual effect of the log. Moreover, the mechanism of absolute positioning pixel value conversion to relative position in this technology guarantees the cross-end adaptive feature, and the customization of the log dashboard is extensible, typesetting flexibility and visual aesthetics.
MD5 Encryption Algorithm Enhanced Competitive Swarm Optimizer for Feature Selection

Feature selection plays a crucial role in machine learning. For evolutionary computation based feature selection, the same particles may be repeatedly generated many times during the population iteration process, and recalculating the fitness values of these particles will cost a large amount of computational resources. Therefore, it is necessary to find these particles fast and skip calculating their fitness to save computation resources. This paper proposes the combination of MD5 encryption algorithm and the competitive swarm optimizer (CSO) algorithm for feature selection. Every particle in the population generated by CSO is encoded with MD5 encryption algorithm. Then the HashMap is used to quickly search for the repetitive particles. Through MD5 coding and HashMap searching, the repetitive particles are found fast, and then the recalculation of the repeated particles are avoid. Experimental results show that our proposed algorithm can significantly reduce the running time of feature selection for low-dimensional data, medium-dimensional data and high-dimensional data. Moreover, our algorithm does not change the framework of the competitive population optimization algorithm, only avoids calculation of the repeatedly generated particles, and therefore has no effect on the accuracy and number of features. Our algorithm can be widely applied to other computational intelligence methods.
Ultra-Fast Saliency Detection Using Qr Factorization

In this work, we propose an ultra-fast unsupervised saliency detection algorithm based on QR matrix factorization. The algorithm works by dividing an image into blocks and computing the QR factors in each local neighborhood where salient patches correspond to columns in R with largest L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm. Experimental results on the MSRA10K salient object database show that the proposed algorithm is competitive with state-of-the-art real-time saliency detection algorithms in terms of AUC and execution time. Additionally, we study the various parameters that control the performance of the proposed algorithm such as threshold values and processing-block sizes and their effect on the overall performance.
Scenario-based Customer Service VR Training System with Honorific Exercise

Customer service plays an important role in increasing customer satisfaction in service industries. In this paper, we propose a scenario-based customer service VR training system with an honorific exercise. First, the concept and general requirements of the service training system using voice conversation with a 3D customer agent is described. Trainees can participate in virtual customer service situations with various types of customers in restaurants, shops, and airports. We then focus on an honorific exercise during customer service. Japanese society places great importance on courtesy and, accordingly, honorifics are a frequent theme when training new employees. With this system, users receive training on how to use honorifics in speech, and a diagnostics report is automatically generated to aid improvement. Initial evaluation results of the accuracy of honorific misuse judgment are also described.
Population Distribution Dynamics in Genetic Algorithms with High-Probability Mutation

This paper contains an investigation into the GA population dynamics induced by very high mutation operator probabilities (≈ 0.95). Drawing inspiration from Consensus Sequence Plots and Estimation of Distribution Algorithms, population distribution natϊve changes are computed between successive generations. This metric is used to characterise multiple parameter variants for a Simple Genetic Algorithm, contrasting low-and high-probability mutation, and low-and high-entropy mutation.
Frame-Based Object Detection in Videos using the N-Modal Discrete Model

Detection of objects from a moving platform is a challenging problem. When detection of ships is attempted with data acquired from electro-optical (EO) and infrared (IR) sensors on an airborne platform, state-of-the-art algorithms fail. This paper provides a solution for detection of ships from EO/IR video dataset obtained from an airborne platform. A recently proposed statistical thresholding method called N-Modal Discrete (NMD) method that was successfully applied to synthetic aperture radar (SAR) datasets, is modified and applied to the EO/IR video datasets of interest. Results are presented to demonstrate the ability of the proposed method to detect objects in a single-frame.
An Online Face Clustering Algorithm for Face Monitoring and Retrieval in Real-Time Videos

Facial recognition in real-time videos will output a feature vector for each face tracking process, namely Euclidean embedding. The task of face retrieval is to find similar faces from the historical face database. Clustering historical face Euclidean embedding can improve retrieval performance. The data collected by the facial recognition system from real-time video is not all available. Since the traditional clustering algorithms operate under the condition that all face embedding data is available, it is categorized as an offline clustering method and offline clustering methods are not suitable for real-time scenarios. Based on real-time video surveillance scenarios, we propose an online clustering algorithm which can cluster the face embedding and achieve an accuracy of 84% on the benchmark video dataset. Our algorithm achieves better results than state-of-the-art online methods, and it is also comparable and even superior to the offline clustering algorithms.
Lightweight Evolving 360 VR Adaptive Video Delivery

Moving towards 4K and 360 Virtual Reality (VR) video streaming is becoming bandwidth prohibitive with users demanding a personalized streaming experience without increasing costs. Existing efforts to improve the streaming experience has centered largely around client-side adaptive bitrate (ABR) algorithms; however, these approaches continue to experience fundamental flaws resulting in subpar content delivery network (CDN) performance. In this paper, we propose a method that addresses long-tail content in CDNs through the implementation of a multipath-aware peer-to-peer mechanism to distribute a video-specific lightweight neural network model, optimize ABR algorithms through reinforcement learning, and actively manage multipath transport networks; all of which significantly increase the 360 VR video streaming experience. Experimental results revealed that our architecture was superior in comparison to many leading ABR algorithms in multipath networks with video bitrates doubling while simultaneously reducing disruptive field-of-view latency switches.
Cuckoo Search: From Continuous to Combinatorial

When solving combinatorial optimization problems with nature-inspired metaheuristics (which are mostly initiated in continuous spaces), the first constraint to manage is how to move in the combinatorial space of solutions without affecting the performance of these metaheuristics. The definition of the neighborhood of a solution in space represents a second constraint.
The Relevance of Mobile and Distance Education: Developing and Advanced Approach to Learning

In recent years’ education has transformed from its traditional arrangements to blending education, where traditional learning meets technologies. Learners recommend on what type of educational styles they prefer. They expect the incorporation of mobile technologies into educational learning. Higher educational institutions are challenged to innovate and give new educational opportunities. Distance learning has been on the scene for several years. It provides an amazing opportunity that allows teacher-student communication without a specific location to study. It enables interactions via digital platforms where learning information is exchanged. The purpose of the study is identifying how relevant is distance and M-learning to the modern, digital society. The study investigates details of distance and M-learning, analysing advantages and disadvantages. The study is intended to explore the reasons why some countries do and do not accept this type of learning. The study also provides an observational analysis of Georgia and the rest of the world. The result of the study is supposed to create awareness and promote distance and M-learning, especially in Georgia. The results of the observation studies indicate that distance and M-learning are gaining more and more interest globally. Developing countries are investing in having distance and M-learning, however, educators still must fight government policies. The results also that accreditation and credibility of the courses is the main challenge. Case on Georgia shows that there is a huge inter-est in distance education, however, quality assurance still does not recognize those courses.