Reviewerly/exHarmony huggingface dataset:
Best Feature Selection using Correlation Analysis for Prediction of Bitcoin Transaction Count

Cryptocurrency made on the basis of block-chain technology Bitcoin is drawing the attention of individuals, corporations, governments and financial institutions today. As the number of Bitcoin transactions increases over the past years, the scale of the Bitcoin market has been increasing day by day. Predicting the number of transactions contained in a Bitcoin block is important in a Bitcoin network. The aim of this paper is to propose a learning feature selection method for designing a machine learning model that predicts the number of transactions contained in the Bitcoin block by applying the machine learning algorithm. Selecting the appropriate feature to design a machine learning model is crucial things to the performance of the model. We apply correlation analysis to select the appropriate learning feature of the transaction count prediction model in the Bitcoin block and verify the validity of the proposed method through experiments.
An ensemble image quality assessment algorithm based on deep feature clustering

To effective handle image quality assessment (IQA) where the images might be with sophisticated characteristics, we proposed a deep clustering-based ensemble approach for image quality assessment toward diverse images. Our approach is based on a convolutional DAE-aware deep architecture. By leveraging a layer-by-layer pre-training, our proposed deep feature clustering architecture extracted a fixed number of high-level features at first. Then, it optimally splits image samples into different clusters by using the fuzzy C-means algorithm based on the engineered deep features. For each cluster, we simulated a particular fitting function of differential mean opinion scores with each assessed image’s PSNR, SIMM, and VIF scores. Comprehensive experimental results on TID2008, TID2013 and LIVE databases have demonstrated that compared to the state-of-the-art counterparts, our proposed IQA method can reflect the subjective quality of images more accurately by seamlessly integrating the advantages of three existed IQA methods.
Improved Low Rank plus Structured Sparsity and Unstructured Sparsity Decomposition for Moving Object Detection in Satellite Videos

Detecting moving objects from satellite videos decomposes video frames into low rank background with additive sparse foreground, and moving objects are commonly rec-ognized by pixel-wise sparsity in the foreground. In prac-tice, moving objects are sets of spatially related pixels and tend to present structured sparsity rather than pixel-wise sparsity. Based on this spatial prior, Structured Sparsity Inducing Norm models moving objects as sparse sets of neighboring pixels, however, the isolated sparsity, which is unsuited to the spatial prior, is left to corrupt the low rank background. In order to address the corruption of unstructured sparsity to background, we proposed a new decomposition formulation named Improved Low Rank plus Structured Sparsity and Unstructured Sparsity Decomposition (ILRSUSD). An inexact Alternating Direction Method is then proposed to solve the improved decomposition formulation efficiently. Experimental result on a satellite video dataset demonstrates our improvement in background modeling with boosted moving object detection precision against state-of-art approaches.
On the Hypercube Subset Partitioning Varieties

In this paper, the problem of a quantitative description of partitions (QDP) of arbitrary m-subsets of the n-dimensional unit cube is considered for a given m, 0 ≤ m ≤ 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> . A necessary condition for the existence of a given QDP-subset is achieved in terms of minimal and maximal layers that are known by earlier publications. It is shown that QDP are in a correspondence to the upper homogeneous area elements of the n-cube and to the monotone Boolean functions. The NP-hardness of the QDP problem is proved. QDP singular points on different layers of the cube are described.
Dangerous behaviors detection based on deep learning

Deep learning has a high degree of popularity in recent years. It is widely used in computer vision, artificial intelligence and other fields. Sites with high safety needs, such as gas stations, have a high demand for monitoring of dangerous behaviors such as smoking. Under normal circumstances, gas stations will employ corresponding personnel to inspect and supervise, but such labor costs are higher, and the monitoring effect is not good. This article is to use an object detection system based on deep learning technology to detect the dangerous behavior of gas stations. This article mainly solves several problems for gas stations to detect dangerous behaviors: first, what technology is used to achieve object detection; secondly, how to increase the speed of detection as much as possible; and thirdly, how to improve the accuracy of detecting dangerous behavior. To solve the above problems, this article will introduce how to implement an object detection system based on deep learning technology. First, a data set containing dangerous goods is established, then the convolutional neural network is trained, and finally the test results of the training results are checked and transplanted. The results prove that the gas station dangerous behavior detection system based on deep learning technology realized can accurately and quickly detect dangerous objects (cigarettes, etc.) in the image.
Human motion quality assessment toward sophisticated sports scenes based on deeply-learned 3D CNN model

Video may be subject to various distortions during acquisition, processing, compression, storage, transmission, and reproduction, and it results in reduced visual quality. In complex sports scenes under big data environment, the human body's movements are even more so. The quality of human motion can intuitively affect the human visual experience. Therefore, it is necessary to determine an intelligent quality assessment model to evaluate human motion in complex motion scenarios under big data environment. It can be used to dynamically monitor and adjust video quality, and it can be used for algorithms and parameter settings in motion image processing systems. With the popularity of deep learning, convolutional neural networks have become a very important method in the field of computer vision research. Based on the 2D-CNN algorithm, we propose a 3D convolutional neural network model for human motion quality assessment in complex motion scenarios. The model captures the pose characteristics, motion trajectory, video brightness and contrast in time and space. The model feeds back the reference and distorted video pairs into the network, with each output layer acting as a feature map. The local similarity between the feature maps obtained from the reference video and the distorted video is then calculated and combined to obtain a global image quality score. Experiments show that the model can achieve competitive performance in big data environment for video quality assessment.
A steganographic method based on optimized audio embedding technique for secure data communication in the internet of things

Abstract The rapid growth of the internet and the internet of things (IoT) refers to the next phase of information revolution whose context involves billions of smart devices and sensors interconnected to facilitate speedy information and data exchange under soft real‐time constraints. Digital information revolution has caused significant changes in the data communication. This data communication may require private, secure, and sometimes malicious communication. Competent secrecy can be accomplished by applying novel and inventive audio steganography. This article focuses on the secret message followed by shuffled embedded bit substitution in original audio stream by adopting optimized audio embedding technique (OAET) from the technological observation. To hide the information in the deeper layer of the audio stream, this method uses a new elevated bit range least significant bit (LSB) audio steganography technique that decreases distortion and improves the robustness of the embedded audio stream. The proposed technique proves that the perceptual quality of audio steganography is better than the previous standard LSB technique. Experiment results proved that the cladding of the OAET provides high‐level security to the universal cyber data. The interpretation of results shows that embedding data in audio enhances the level of security when used as IoT smart speakers, where the attackers could not distinguish between the original audio and the embedded audio streams.
Hardware-assisted Trusted Execution Environments

Over the last two decades, hardware-based isolated execution environments, commonly known as "trusted execution environments" or TEEs, have become widely deployed [1,2,3,4]. However, concerns about vulnerabilities (like the Foreshadow attacks [5]), and potential for abuse have been persistent and have recently become increasingly pronounced. In this talk, I will discuss the history of (mobile) TEEs [6], what motivated their design and large-scale deployment, and how they have evolved during the last two decades. I will then discuss some of their shortcomings and potential approaches for overcoming them. I will also briefly touch on other types of hardware security primitives that are being rolled out by processor manufacturers and the opportunities they offer for securing computing
Representing and planning with interacting actions and privacy

Interacting actions – actions whose joint effect differs from the union of their individual effects – are challenging both to represent and to plan with due to their combinatorial nature. So far, there have been few attempts to provide a succinct language for representing them that can also support efficient centralized planning and distributed privacy preserving planning. In this paper we suggest an approach for representing interacting actions succinctly and show how such a domain model can be compiled into a standard single-agent planning problem as well as to privacy preserving multi-agent planning. We test the performance of our method on a number of novel domains involving interacting actions and privacy.
A Web-based Augmented Reality Plat-form using Pictorial QR Code for Educational Purposes and Beyond

Augmented Reality (AR) provides the capability to overlay virtual 3D information onto a 2D printed flat surface; for example, displaying a 3D model on a single flat card that accompanies with the diagram shown in a learning text-book. The student can zoom in and out, rotate, and perceive the animation of the figure in real-time. This will make the educational theory more attractive; hence, motivates students to learn. AR is a great tool; however, the setup and display are not straight-forward (there are many different AR markers with different encryption, decryption methods, and displaying flat-forms). In this paper, we proposed a portable browser-based platform which uses the advantages of AR along with scan-able QR Code on mobile phones to enhance instant 3D visualisation. The user only needs a smart-phone (Apple iPhone or Android) with Internet-enabled; no specific Apps are needed to install. The user scans the QR Code embedded in a colour image, the code will link to a public website, and the website will produce AR Experience right on top of the browser. As a result, it provides a stress-free, low-cost, portable, and promising solution for not only educational purposes but also many other fields such as gaming, property selling, e-commerce, reporting. The set up is convenient: the user uploads a picture (e.g. a racing car), and what actions to be related to it (a 3D model to display, or a movie to play). The system will add on the picture one small colour QR code (to redirect to an online URL) and a thin black border. The user also uploads the 3D model (GLTF files) that he wants to display on top of the card to finish the set-up. At the display, the user can print the AR card, point their smart-phone towards the card, and pre-setup AR models or actions will appear on it. To students, these 3D graphics or animations will allow them to learn and understand the lessons in a much more intuitive way.
DataBlinder

Business application owners want to outsource data storage, including sensitive data, to the public cloud for economical reasons. This is often challenging since these businesses are and remain responsible for regulatory compliance and data protection, even though cloud providers may do their best to offer (data) protection. Meanwhile, data protection techniques evolve and get better because of continuous research and improvement of advanced encryption. Numerous cryptographic tactics have been proposed, e.g., searchable symmetric encryption (SSE) and homomorphic encryption (HE), that support search and aggregation functions on encrypted data. Each of these tactics has a trade-off between security, performance and functionality, but there is no one-size-fits-all solution. For the application developer, the underpinning concepts of these tactics are complex to comprehend, complex to integrate in a distributed application, and prone to implementation mistakes.
LiteEmo: Lightweight Deep Neural Networks for Image Emotion Recognition

Psychology studies have shown that an image can invoke various emotions, depending on the visual features as well as semantic content of the image. Ability to identify image emotion can be very useful for many applications, including image retrieval and aesthetics prediction. Notably, most of the existing deep learning-based emotion recognition models do not capitalize on additional semantics or contextual information and are computational expensive. Inspired to overcome these limitations, we proposed a lightweight multi-stream deep network that concatenates several MobileNet networks for performing image emotion analysis. Each stream in the multi-stream deep network represents the core emotion recognition, object recognition and image category recognition models respectively. Experimental results demonstrate the effectiveness of the additional contextual information in producing comparable performance as the state-of-the-art emotion models, but with lesser parameters, thus improving its practicality.
Implementation and calibration of an odometry system for mobile robots, based on optical computer mouse sensors

Although the application of computer mouse sensors as optical flow recorders in mobile robot odometry holds great promise, it is still hampered by inconsistent performance of the sensors in measuring displacements of the robot on different floor surfaces. This issue can be overcome by a re-calibration of the sensitivity of the sensor measurement when the robot moves on a new type of surface. With the calibrated figure for the sensitivity of each sensor, the positions and orientations of the sensors on the robot frame can be derived from the sensor readings of a set of constrained displacements of the robot. Sensitivity, position and orientation of each sensor are the key parameters in the conversion from the optical flow measured by the sensors, into an estimated displacement of the robot. To minimize the occurrence of systematic errors in this conversion, it is essential to calibrate these parameters as accurately as possible. This paper proposes a novel procedure to perform their calibration based on a micro-controller setup that implements strict synchronization of the acquisition of optical flow data from all mouse sensors. Execution of the procedure is simple and requires no more than one measurement with a yardstick to obtain the calibrated figures for all key parameters. The collected data set can also be used to verify the calibration with a position calculation. The observed inaccuracy of the calculated location represents an excellent benchmark to compare the performance of different robot localization systems. The efficacy of the calibration method is experimentally tested and validated in a number of calibration scenarios.
Retro-Actions: Learning 'Close' by Time-Reversing 'Open' Videos

We investigate video transforms that result in class-homogeneous label-transforms. These are video transforms that consistently maintain or modify the labels of all videos in each class. We propose a general approach to discover invariant classes, whose transformed examples maintain their label; pairs of equivariant classes, whose transformed examples exchange their labels; and novel-generating classes, whose transformed examples belong to a new class outside the dataset. Label transforms offer additional supervision previously unexplored in video recognition benefiting data augmentation and enabling zero-shot learning opportunities by learning a class from transformed videos of its counterpart. Amongst such video transforms, we study horizontal-flipping, time-reversal, and their composition. We highlight errors in naively using horizontal-flipping as a form of data augmentation in video. Next, we validate the realism of time-reversed videos through a human perception study where people exhibit equal preference for forward and time-reversed videos. Finally, we test our approach on two datasets, Jester and Something-Something, evaluating the three video transforms for zero-shot learning and data augmentation. Our results show that gestures such as 'zooming in' can be learnt from 'zooming out' in a zero-shot setting, as well as more complex actions with state transitions such as 'digging something out of something' from 'burying something in something'.
Android-based teaching material for straight-sides solid

Abstract Straight-sides solid subject matter was one of difficult subject matter to be understood by junior high school students in Indonesia. Developing teaching material was one of alternatives to solve that problem. This research aims to develop the application of Android-based teaching material for straight-sides solid for supporting students of junior high school in comprehend of straight-sides solid subject matter. Define, design, develop, and disseminate were the steps in developing the application. The first step of developing was identify the problem and define the product which will be developed. The next step after product defined was design the storyboard as the guidance to develop the application. Teaching experts and media experts had validated the application. They judged that the application developed was valid. The application had been disseminated to mathematics teachers and students in junior high school. Teachers stated that this application was very useful to delivered concept of straight-sides solid in junior high school while the students stated that it helped them in understanding the concepts of straight-sides solid.
Modified Opposition Based Learning to Improve Harmony Search Variants Exploration

Harmony Search Algorithm (HS) is a well-known optimization algorithm with strong and robust exploitation process. HS such as many optimization algorithms suffers from a weak exploration and susceptible to fall in local optima. Owing to its weaknesses, many variants of HS were introduced in the last decade to improve its performance. The Opposition-based learning and its variants have been successfully employed to improve many optimization algorithms, including HS. Opposition-based learning variants enhanced the explorations and help optimization algorithms to avoid local optima falling. Thus, inspired by a new opposition-based learning variant named modified opposition-based learning (MOBL), this research employed the MOBL to improve five well-known variants of HS. The new improved variants are evaluated using nine classical benchmark function and compared with the original variants to evaluate the effectiveness of the proposed technique. The results show that MOBL improved the HS variants in term of exploration and convergence rate.
Orientation Analysis for Chinese News Based on Word Embedding and Syntax Rules

In this era of Internet and big data, there is billions of news generated every day, and the traditional manual methods are insufficient for public opinion orientation analysis. Especially for Chinese, which has more complicated syntax and semantic structure, and there is no space between words as separator. This greatly increases the difficulty of analyzing opinion orientation. In this paper, a novel approach is proposed aiming at solving the problem of public opinion orientation analysis based on Chinese news. The approach combines word2vec, sentiment dictionaries and syntax rules, where the word2vec can map words into different vectors with finite dimensions. Through it we can calculate the cosine similarity between the words and sentiment dictionaries to get the orientation value of target words, which is helpful for calculating the orientation value of key sentences and full text. Specifically, the process consists of three steps. First, word2vec is used to train word embedding, and every word in corpus is mapped into a given vector space. Then, key sentences are extracted from news content. Finally, pre-defined syntax rules with word vector similarity are used to analyze document orientation based on key sentences. Several experiments are conducted on both closed and open datasets, and the results validate the effectiveness of the proposed approach.
Filtering and Classifying Relevant Short Text with a Few Seed Words

Filtering out irrelevant documents and classifying the relevant ones into topical categories is a de facto task in many applications. However, supervised learning solutions require extravagant human efforts on document labeling. In this paper, we propose a novel seed-guided topic model for dataless short text classification and filtering, named SSCF. Without using any labeled documents, SSCF takes a few “seed words” for each category of interest, and conducts short text filtering and classification in a weakly supervised manner. To overcome the issues of data sparsity and imbalance, the short text collection is mapped to a collection of pseudodocuments, one for each word. SSCF infers two kinds of topics on pseudo-documents: category-topics and general-topics. Each category-topic is associated with one category of interest, covering the meaning of the latter. In SSCF, we devise a novel word relevance estimation process based on the seed words, for hidden topic inference. The dominating topic of a short text is identified through post inference and then used for filtering and classification. On two real-world datasets in two languages, experimental results show that our proposed SSCF consistently achieves better classification accuracy than state-of-the-art baselines. We also observe that SSCF can even achieve superior performance than the supervised classifiers supervised latent dirichlet allocation (sLDA) and support vector machine (SVM) on some testing tasks.