Reviewerly/exHarmony huggingface dataset:
Design and Evaluation of a Collaborative POE-based Learning Model for Mathematics Learning

Many research indicates that the integration of mobile learning environment can effectively enhance students' motivation for learning. However, instructors adopt appropriate learning strategies is the main factor to help students improve their learning performance. The study expected the Predict-Observe-Explain (POE) teaching strategies in mathematics teaching has significant effects on learning effectiveness and retention. Few studies have explicitly incorporated the POE strategy and collaborative learning approach with mobile learning environment in elementary students' mathematics learning. The 41 fifth graders who participate in this research are assigned to an experimental group and a control group. The students in the experimental group will learn through the POE teaching strategy combined with the collaborative learning approach; while those who participant in control group will learn mathematics through the POE teaching strategy only. The aim of this research focuses on the effects of the POE strategy and collaborative learning approach with mobile learning environment on students' learning achievements, learning attitude, and self-efficacy of mathematics.
Learning directed acyclic graph SPNs in sub-quadratic time

In this paper, we present Prometheus, a graph partitioning based algorithm that creates multiple variable decompositions efficiently for learning Sum-Product Network structures across both continuous and discrete domains. Prometheus proceeds by creating multiple candidate decompositions that are represented compactly with an acyclic directed graph in which common parts of different decompositions are shared. It eliminates the correlation threshold hyperparameter often used in other structure learning techniques, allowing Prometheus to learn structures that are robust in low data regimes. Prometheus outperforms other structure learning techniques in 30 discrete and continuous domains. We also extend Prometheus to exploit sparsity in correlations between features in order to obtain an efficient sub-quadratic algorithm (w.r.t. the number of features) that scales better to high dimensional datasets.
Bottom-up Estimation of Geometric Layout for Indoor Images

In this paper, we propose a bottom-up approach to estimate the geometric layout of indoor images using latent variables. By utilizing latent variables to model subregions, the estimation accuracy of scene layout is implicitly improved. The proposed method consists of three sub-tasks: feature extraction, subregion classification and geometric layout classification. Firstly, the location features are extracted to roughly estimate the basic indoor structure. The influence of illumination, rich color, and foreground occlusion can be eliminated. Secondly, N-slack SSVM is applied to efficiently classify the location features extracted in the previous step. Finally, the bag-of-words model is combined with cosine similarity and information divergence filtering to improve the fault tolerance of the geometric layout classification task. The classification accuracy can reach 0.982, which well demonstrate the effectiveness of the proposed approach.
Advances and Challenges of Rank Metric Cryptography Implementations

Recent works on reducing the size of Error Correcting Codes have investigated the usage of rank metric instead of Hamming metric. Numerous proposals for the NIST Post-Quantum Cryptography competition, including four second round candidates, rely on these codes. In this paper, we discuss several non-trivial issues when porting these schemes into real-world systems on different platforms, such as Intel x86, Armv6 and Armv8. We provide insights on how to implement the underlying finite field and polynomial arithmetic, or the generation of errors of a given rank in constant-time, and report execution time of several rank-based cryptosystems, showing that the achieved performance is similar to those of some of the most popular lattice-based cryptosystems.
Solving Six-Player Games via Online Situation Estimation

While the artificial intelligence theory for solving the perfect-information games has been well developed in recent years, great challenges are still posed in dealing with the imperfect-information game due to the huge state space and hidden information involved in it. In this paper, we design an online strategy solving framework for six-player no-limit Texas hold'em poker. Based on hand isomorphism and hand strength evalution, the framework provides an efficient situation estimation method for six-player poker. Such method could greatly reduce the the state space in six-player poker as well as effectively evaluate the current hands. The poker agent based on our method won the third place in the 2018 AAAI-ACPC.
Face Detection Using Boosted Cascade of Simple Feature

Face detection from picture or capture is admired focus in biometrics study. A lot of public spaces typically include observation cameras for videotape capture and these cameras have their considerable value for safety intention. It is widely recognized that the face discovery includes a significant role in observation arrangement as it doesnt need the objects assistance. The actual compensation of face based recognition over other biometrics is individuality and acceptance. As a persons face is a dynamic thing that bears high degree of changeability in its look, it makes a computer vision to count face discovery as a tough problem. The main challenge in this field is the speed of detection and accuracy. This paper deals with estimating variety of face detection methods and provide a total solution for image based face detection with higher accurateness, an improved response rate as an initial step. The explanation is projected based on performed tests on various face rich databases. Initially, the segmentation of non-skin color pixels from the image is done by building a skin color model in chrominance space i.e. YCbCr. Later, in order to extract human face region, the mathematical morphological operators are used that removes noisy regions and fills the holes in skin-color region. Ultimately, to achieve face detection more accurately, the cascade classifier based on an AdaBoost algorithm is used to scan these face candidates.
Deep Learning for Customs Classification of Goods Based on Their Textual Descriptions Analysis

The article is devoted to the customs classification of goods based on their textual descriptions. In our work we propose different machine and deep learning models for solving this task using two variants of datasets: with or without duplicates. Finally we discuss and compare the obtained results.
Improvised method of five modulus method embedded JPEG image compression with algebraic operation

In this paper color image is compressed using improvised five modulus method (IFFM). FMM method consists of transforming each pixel value in an 8×8 block into a multiple of 5 for each of the color elements R, G and B arrays. After that, the new values are divided by 5 and hence transformed values so obtained are 6-bits in length for each pixel and it is physically less in storage than the original value of input image which occupies 8-bits. This method enhances arithmetic calculation and embedded into JPEG format to improve the compression ratio. First RGB image converted into YCBCR then IFFM and DCT applied respectively. After that quantization of DCT coefficient being performed. Thus, quantized DCT image is become ready to perform zigzag scanning. Then the Huffman encoding and decoding has been applied. Proposed algorithm is tested on standard data set images of different classes. It exhibits much better compression ratio than the FJPEG (five modulus method embedded JPEG).
Hands on Wheel Classification Based on Depth Images and Neural Networks

This paper describes a system to automatically observe if the driver has his hands on the wheel, which is important to know that he can intervene if necessary. To accomplish this an artificial neural network is used, which utilizes depth information captured by a camera in the roof module of the car. This means that the driver and the steering wheel are viewed from above. The created classification system is described. It is designed to require as little computational effort as possible, since the target application is on an embedded system in the car. A dataset is presented and the effect of a class imbalance that is incorporated in it is studied. Furthermore, it is examined which part, i.e. the depth or the intensity image, of the available data is important to achieve the best possible performance. Finally, by examining a learning curve, an experiment is made to find out whether the recording of further training data would be reasonable.
Formalization and Verification of TESAC Using CSP

Cloud computing is an emerging computing paradigm in IT industries. The wide adoption of cloud computing is raising concerns about management of data in the cloud. Access control and data security are two critical issues of cloud computing. Time-efficient secure access control (TESAC) model is a new data access control scheme which can minimize many significant problems. This scheme has better performance than other existing models in a cloud computing environment. TESAC is attracting more and more attentions from industries. Hence, the reliability of TESAC becomes extremely important. In this paper, we apply Communication Sequential Processes (CSP) to model TESAC, as well as their security properties. We mainly focus on its data access mechanism part and formalize it in detail. Moreover, using the model checker Process Analysis Toolkit (PAT), we have verified that the TESAC model cannot assure the security of data with malicious users. For the purpose of solving this problem, we introduce a new method similar to digital signature. Our study can improve the security and robustness of the TESAC model.
Movie Frame Prediction Using Convolutional Long Short Term Memory

Video frame prediction involving predicting the future frames given some input frames has been in the limelight in the recent years. However, robust models have not been developed and tested which could handle the videos involving sequence of frames having characters and objects having high temporal variations. And more significantly there are no models which embrace the sequence of frames which are extracted from movies (Example: Cartoon movies).The focus of this paper is to explore a model which could learn the spatio-temporal consistency amongst the frames which are sampled from a cartoon movie. To this end, we propose a Convolutional Long Short Term Memory based architecture wherein the model harnesses the ability of conventional Long Short Term Memory (LSTM) and convolutional neural network (CNN) to learn the time series and spatial features of the given video frame sequences.
Absorptive protective padding with electrospinned polyester fibers with hydrogel matrix

Abstract Hip fractures has been prevalent especially among aged elderly. This can lead to permanent disability of the elderly as they are wheel-chaired bound after suffering from hip fractures. In this work, we explore the design of a protective hip pad which encapsulate the hydrogel and fibres composite in a laminated sheet. The hydrogel was fabricated using Polyvinyl alcohol (PVA) with distilled water. Two types of electrospinned fibres, Nylon-6 fibres and polyester fibres were used as filler. The fabrication of hydrogel matrix composite was varied by the weight percent of nylon 6,6 pellets dissolving in formic acid as compared to Hydrolysed Poly(Vinyl alcohol) to form crosslinked PVA with water. The impact absorption concept was to allow the gel-like hydrogel to flow through the fibres pores and during this process, part of the energy would be dissipated in the matrix composite. This lower the impact force being transferred to the user’s hip and prevent the facture of the elderly’s hips during fall. The protective padding with 20 layers of polyester fibres with 2.5wt % of PVA had the lowest impact force of 0.8 kN as compared to conventional protective padding with measured 1.07kN impact force for impact study.
Hybrid DWT- DCT image steganography for encrypted secret image

Steganography is the science of hiding secret information in any cover medium like image or video, for the purpose of secure transmission. However, steganography ensures protection of data by hiding it in a cover object. Moreover, cryptography along with steganography, ensures security in depth. Cryptography is the technique of encrypting the original information using a key. Challenging aspects in Image steganography are to increase the payload capacity of secret information and robustness against visual attacks and statistical attacks. DWT and DCT techniques when applied simultaneously provide advantages of both the techniques. The proposed technique of image steganography provides higher robustness against statistical attacks, higher imperceptibility. This technique is more secure as the encrypted secret image is hidden in two cover images, with prior application of DWT and DCT on both cover images. The simulation results illustrate the high embedding capacity and reasonable PSNR values.
Detection of Dangerous Objects By Pan-tilt Camera

Security cameras have increased in public facilities. The number of crimes has decreased by security cameras, but we will have too much data of cameras. In this paper, we have aim of security improvement. First, we search whether there are humans in images by OpenPose. We then obtain position of human's hands. Finally, we detect dangerous objects around the hands by image classification.
Comparative Analysis and Implementation of Different Human Detection Techniques

Object Detection is used in making several smart surveillance applications which are used in detecting and tracking suspicious activities. Object Detection also plays a very crucial role in several latest inventions like the Google self-driving car. Over the past decade several image processing algorithms have evolved which have been used for human detection after preprocessing of the images. But in several cases these algorithms were found to be less accurate and more time taking. With the advent of the era of Machine Learning, the focus has gradually shifted towards computer vision and deep learning methodologies for human detection. Deep learning algorithms are far more accurate than the traditional methodologies as they employ feature extraction from the images followed by classification according to the dataset provided to them, thus enabling more accurate detection than their ancestors. Nevertheless its also true that some deep learning algorithms like the different versions of R-CNN and YOLO take much more processing time than the traditional methodologies. In our paper we have compared the performance of some traditional and some deep learning algorithms in different scenarios.
Touch Screen: An interactive platform to learn arithmetic concepts for hearing impaired children attending preschool: Case Study

Technology is playing an important role in the field of education. The present study made an effort, to develop a mobile application and to explore the usefulness of technology in learning arithmetic skills on children with hearing impairment. Study included 10 participants in ages ranging from 2-6years, who were divided into 5 groups. Each group included 2 participants, one as experimental and another as control. The study was conducted across the groups using mobile application tool. Level 1 of the application tool included basic arithmetic concepts. It was designed, developed and validated before applying it on the children for field testing. The study revealed the positive impact of learning arithmetic concepts using touch screen mobile on children with bilateral severe hearing impairment. The activities attempted over a period of time kept on increasing, which was depicted by the scores obtained. Customer satisfaction was high and the caregivers of these children were happy to try this innovative technological intervention using touch screen mobile application.
Detasyr: Decentralized Ticket-based Authorization with Sybil Resistance

A frequent problem of Internet services are Sybil attacks, i.e., malicious users create numerous fake identities for themselves. To avoid this, many services employ obstacles like Captchas to force (potentially malicious) users to invest human attention in creating new identities for the service. However, this only makes it more difficult but not impossible to create fake identities. Sybil attacks are especially encountered as a problem in decentralized systems since no single trust anchor is available to judge new users as honest or malicious. The avoidance of a single centralized trust-anchor, however, is desirable in many cases. As a consequence, various decentralized Sybil detection approaches have been proposed. The most promising ones are based on leveraging the trust relationships embedded within social graphs. While most of these approaches are focusing on detecting large existing groups of Sybil identities, our approach Detasyr instead restricts the creation of numerous Sybil identities. For that, tickets are distributed through the social graph and have to be collected, allowing for decentralized and privacy preserving authorization. Additionally, it offers a proof of authorization to users that are considered to be honest, allowing them to display their authorization towards others.
Exact Distance Query in Large Graphs through Fast Graph Simplification

Abstract Shortest path distance query is one of the most fundamental problems in graph theory and applications. Nowadays, the scale of graphs becomes so large that traditional algorithms for shortest path are not available to answer the exact distance query quickly. Many methods based on two-hop labeling have been proposed to solve this problem. However, they cost too much either in preprocessing or query phase to handle large networks containing as many as tens of millions of vertices. In this paper, we propose a novel $k$-hub labeling method to address this problem in large networks with less preprocessing cost while keeping the query time in the microsecond level on average. Technically, two types of labels are presented in our construction, one for distance queries when the actual distance is at most $k-2$, which we call local label, and the other for further distance queries, which we call hub label. Our approach of $k$-hub labeling is essentially different from previous widely used two-hop labeling framework since we construct labels by using hub network structure. We conduct extensive experiments on large real-world networks and the results demonstrate the higher efficiency of our method in preprocessing phase and the much smaller space size of constructed index compared to previous efficient two-hop labeling method, with a comparatively fast query speed.
Two-stage Photovoltaic Power Forecasting based on Extreme Learning Machine and Improved Pointwise Mutual Information

Photovoltaic power forecasting plays an important role in the power system planning and contributes to the development of renewable energy. This paper proposed a two-stage forecasting method based on Extreme Learning Machine (ELM) and Improved Pointwise Mutual Information (IPMI), which is responsible for short-term forecasting of the small-scale PV station. The method like a hybrid method requires past measured Numerical Weather Prediction (NWP) data and time series of PV power output as input of the system. During the first stage, PMI algorithm is applied to solve the coupling problem and to determine the weather features that contribute to the PV power output. Locally weighted regression (LOESS) smoothing is used as a nonparametric technique to fit a smooth curve to calculate regression error. Then, historical data will be classified into several groups by affinity propagation (AP) clustering method, combing with principal component analysis (PCA). For the second stage, training models and prediction models based on ELM network will be set up for each group obtained by AP clustering respectively. Numerical results for a small-scale PV plant in Beijing present improvements in forecasting accuracy and computation efficiency when compared to other forecasting methods.
Wireless Data Transmission Method of Earthquake Early Warning Information in High Speed Railway

The earthquake has a serious threat to the safety of high-speed railway operation. Sending earthquake early warnings to trains in time is an important means to avoid or mitigate the effects of disasters. According to the technical characteristics of China's railway mobile communication system, this paper proposes a wireless data transmission scheme for earthquake early warning, which uses cell-based positioning method to track the trains' position and the earthquake early warnings are redundantly transmitted by means of packet-switched data and cell broadcast short message. The test results show that the proposed method meets the requirements of earthquake early warning for high-speed railways in China, and can realize the rapid release of warning information.
Point Flow Edge Detection Method Based on Phase Congruency

This paper aims to detect image edges based on the Point Flow (PF) theory [14]. in the original PF model in [14], the vector field is built on image gradient. However, due to the noise sensitivity and weak edges confusion of local image gradients, the PF model might fail in detecting low-contrast edges. In this paper, we propose to build the vector field based on phase congruency (PC), which exploits image non-continuities (e.g., edges) in frequency domain instead of image domain. Since the phase of each frequency component at signal non-continuities is identical, e.g., after Fourier transform, the phase of all sinusoidal wave at rising edges of the square wave is 0 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">circ</sup> , phase congruency indicates the image edges in frequency domain and avoids the effect of signal local contrast. Natural and synthetic images are tested to investigate the effectiveness of the proposed phase congruency based point flow model (PCPF). Compared with the classical Canny operator, the gpb-ucm detector and the original point flow model [14], our method yields higher detection accuracy and more detail edges.
Smart Blockchain

A blockchain is a digitized, decentralized, open system of records. Of late, there is a phenomenal spurt in the research and application activities of the catch-all phrase analytics, which subsumes machine learning, text mining, classical optimization, artificial intelligence, evolutionary computing, visual analytics, big data analytics, etc. in many a diverse field. Consequently, even new technology like blockchain is not left behind. This chapter presents a comprehensive survey of 33 papers that appeared between 2016 and 2018 under the theme, ‘Analytics and Blockchain', which focuses on how analytical approaches and blockchain implementation are symbiotically related to improve their overall performance in solving various real-world problems. The core idea behind the survey is to facilitate the reader to appreciate the utility of analytical methods to the design, implementation, and application of blockchain and suggesting future directions for further research.