Reviewerly/exHarmony huggingface dataset:
Markov Decision Process for Video Generation

We identify two pathological cases of temporal inconsistencies in video generation: video freezing and video looping. To better quantify the temporal diversity, we propose a class of complementary metrics that are effective, easy to implement, data agnostic, and interpretable. Further, we observe that current state-of-the-art models are trained on video samples of fixed length thereby inhibiting long-term modeling. To address this, we reformulate the problem of video generation as a Markov Decision Process (MDP). The underlying idea is to represent motion as a stochastic process with an infinite forecast horizon to overcome the fixed length limitation and to mitigate the presence of temporal artifacts. We show that our formulation is easy to integrate into the state-of-the-art MoCoGAN framework. Our experiments on the Human Actions and UCF-101 datasets demonstrate that our MDP-based model is more memory efficient and improves the video quality both in terms of the new and established metrics.
Exploiting Workflow Languages and Semantics for Validation of Security Policies in IoT Composite Services

Internet of Things (IoT) ecosystems are recently experiencing a significant growth in complexity. Most IoT applications in domains like healthcare, industry, automotive, and smart energy are composed of several interconnected subsystems that produce, collect, process, and exchange a huge amount of data, and that offer composite services to the end users based on these data. This scenario is exacerbated by the dynamism of the IoT device layer, which may be subject to structural or technological changes over time, to cope for example with the need for new sensing/actuation capabilities requirements or with technical issues. Due to the inherent sensitive nature of the data that is typically processed by IoT applications, security represents one of the primary issues to address. It is worth noting that each subsystem integrated within a composite IoT application may have different requirements and enforce different local security policies, and the policies that result globally enforced at the system level may not comply with the existing global requirements. In general, the analysis and validation of security properties in a composite IoT system represents a very complex task, made even more complex by the introduction of new laws and regulations during system life. To cope with the above issues, in this article, we propose a methodology that leverages both workflow languages and semantics in order to enable the validation of the security features offered by a composite IoT system, with the goal of verifying whether they match with global end-user policies and even with national and international laws and rules.
Hybrid cryptography and steganography method to embed encrypted text message within image

Abstract The businesses in various fields use the online communication application to gather their data and information with local and global sources. The gathered data may sensitive such as the financial and businesses development information. The hackers or online thief try to stole the valuable data i.e. credit card numbers. The organizations looking for secure online channels in order to transfer their data efficiently and avoid the data thieving. One of the most applicable methods that developed to secure the online transferred data is the cryptography which transfers the original data or information to encrypted formulation. Cryptography still has many drawbacks such as stole and decrypts the original texts using automatic decryption counter. The main aim of this research is to improve the cryptography securing level using supportive method which is Steganography. The Steganography is the processes of hide the data or information in media files such as video, images and audio files. There are four stages represent the methodology of this paper; (1) encrypt the original texts using RSA algorithm, (2) hide the encrypted texts in Image files, (3) extract the encrypted texts from Image files, and (4) decrypt the original texts using decryption key of RSA algorithm. It is expected to improve the security level of the online transferred textual data. The performance of the final results will be evaluated through compare the Image files quality before and after hide the data in these files. The quality of the original and stego Image files need to be same or near in order to maximize the difficulty of detect that there data hide in these files.
Hybrid binary Sine Cosine Algorithm and Ant Lion Optimization (SCALO) approaches for feature selection problem

These days, a massive quantity of data is produced online and is incorporated into a variety of datasets in the form of features, however there are lot of features in these datasets that may not be relevant to the problem. In this perspective, feature selection aids to improve the classification accuracy with lesser number of features, which can be well thought-out as an optimization problem. In this paper, Sine Cosine Algorithm (SCA) hybridized with Ant Lion Optimizer (ALO) to form a hybrid Sine Cosine Ant Lion Optimizer (SCALO) is proposed. The proposed algorithm is mapped to its binary versions by using the concept of transfer functions, with the objective to eliminate the inappropriate features and to enhance the accuracy of the classification algorithm (or in any case remains the same). For the purpose of experimentation, this research considers 18 diverse datasets and moreover, the performance of the binary versions of SCALO is compared with some of the latest metaheuristic algorithms, on the basis of various criterions. It can be observed that the binary versions of SCALO perform better than the other algorithms on various evaluation criterions for solving feature selection problem.
Perceptual QoE-Optimal Resource Allocation for Adaptive Video Streaming

Video streaming in mobile environments has always been challenging due to various factors. The time-varying wireless channel, limited and shared transmission resources, fluctuating network conditions between the video server and the end user etc. greatly affect the timely delivery of videos. Given these factors, it is important that the wireless networks perform optimal allocation of resources and cater to the demands of the video streaming users without degrading their quality-of-experience (QoE). Modeling streaming QoE as perceived subjectively by the users is non-trivial, and in general a complex task, as it is continuous, dynamic, and time-varying in nature. The continuous perceptual QoE degradation due to network induced artifacts such as time-varying video quality and rebuffering events has not been considered in the literature for resource allocation (RA). In this paper, we propose Video Quality Aware Resource Allocation (ViQARA), a perceptual QoE based RA algorithm for video streaming in cellular networks. ViQARA leverages the strength of the latest continuous QoE models and integrates it with the generalized $\alpha $ -fair strategy for RA. Through extensive simulations, we demonstrate that ViQARA can provide significant improvement in the users perceptual QoE as well as a remarkable reduction in the number of rebufferings when compared to existing throughput based RA methods. The proposed algorithm is also shown to provide better QoE optimization of the available resources in general, and especially so when the cellular network is resource constrained and/or experiences large packet delays.
Few-Shot Breast Cancer Metastases Classification via Unsupervised Cell Ranking

Tumor metastases detection is of great importance for the treatment of breast cancer patients. Various CNN (convolutional neural network) based methods get excellent performance in object detection/segmentation. However, the detection of metastases in hematoxylin and eosin (H&E) stained whole-slide images (WSI) is still challenging mainly due to two aspects. (1) The resolution of the image is too large. (2) lacking labeled training data. Whole-slide images generally stored in a multi-resolution structure with multiple downsampled tiles. It is difficult to feed the whole image into memory without compression. Moreover, labeling images for the pathologists are time-consuming and expensive. In this paper, we study the problem of detecting breast cancer metastases in the pathological image on patch level. To address the abovementioned challenges, we propose a few-shot learning method to classify whether an image patch contains tumor cells. Specifically, we propose a patch-level unsupervised cell ranking approach, which only relies on images with limited labels. The main idea of the proposed method is that when cropping a patch A from the WSI and further cropping a sub-patch B from A, the cell number of A is always larger than that of B. Based on this observation, we make use of the unlabeled images to learn the ranking information of cell counting to extract the abstract features. Experimental results show that our method is effective to improve the patch-level classification accuracy, compared to the traditional supervised method. The source code is publicly available at https://github.com/fewshot-camelyon.
Characterization of metal-bearing particles in groundwater from the Weilasituo Zn-Cu-Ag deposit, Inner Mongolia, China: Implications for mineral exploration

Nanoparticles in groundwater have important implications for facilitating the transportation of metals in groundwater and, to a certain extent, can reveal the geochemical conditions of the groundwater. Therefore, there is an increasing interest in analytical techniques that allow for in-depth characterization of nanoparticles in the groundwater and utilize these characteristics for geochemical prospecting. Metal-bearing particles in groundwater samples from deep-lying orebodies (referred to as “deep groundwater”) of the Weilasituo Zn-Cu-Ag Deposit, Inner Mongolia, China and the groundwater from motor-pumped wells of a catchment basin around Weilasituo were chosen to study. Using high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and transmission electron microscopy (TEM) coupled with energy dispersive X-ray spectrometer (EDS), individual particles were characterized. Based on the TEM compositional and structural investigation, Ag-, Pb-, Cu-, Zn-, Sn-, As- and Mo-bearing particles were observed in the groundwater samples collected in the deep ore and the catchment basin. These metal-bearing particles are compositionally and structurally similar to the ore mineral/metal-bearing particles observed in the ore bodies and can directly reflect the ore-forming elemental characteristics, e.g., metallogenic element assemblage and mineralization type, of the Weilasituo deposit. The result indicates that the metal-bearing particles originating from the concealed ore bodies migrate via the groundwater flow and discharge to the catchment basin. This finding implies that the metal-bearing particle phases have great significances in facilitating the transportation of ore-related species and forming the geochemical anomalies in the groundwater. This field-scale study demonstrates that, by applying the analytical transmission electron microscopy, the nanocharacteristics of metal-particles can be utilized to improve the accuracy and sensitivity for geochemical mineral exploration.
A new cast shadow detection method for traffic surveillance video analysis using color and statistical modeling

In traffic surveillance video analysis systems, the cast shadows of vehicles often have a negative effect on video analysis results. A novel cast shadow detection framework, which consists of a new foreground detection method and a cast shadow detection method, is presented in this paper to detect and remove the cast shadows from the foreground. The new foreground detection method applies an innovative Global Foreground Modeling (GFM) method, a Gaussian mixture model or GMM, and the Bayes classifier for foreground and background classification. While the GFM method is for global foreground modeling, the GMM is for local background modeling, and the Bayes classifier applies both the foreground and the background models for foreground detection. The rationale of the GFM method stems from the observation that the foreground objects often appear in recent frames and their trajectories often lead them to different locations in these frames. As a result, the statistical models used to characterize the foreground objects should not be pixel based or locally defined. The cast shadow detection method contains four hierarchical steps. First, a set of new chromatic criteria is presented to detect the candidate shadow pixels in the HSV color space. Second, a new shadow region detection method is proposed to cluster the candidate shadow pixels into shadow regions. Third, a statistical shadow model, which uses a single Gaussian distribution to model the shadow class, is presented for classifying shadow pixels. Fourth, an aggregated shadow detection method is presented for final shadow detection. Experiments using the public video data ‘Highway-1’ and ‘Highway-3’, and the New Jersey Department of Transportation (NJDOT) real traffic video sequences show the feasibility of the proposed method. In particular, the proposed method achieves better shadow detection performance than the popular shadow detection methods, and is able to improve the traffic video analysis results.
Real Time Edge Detection via IP-Core based Sobel Filter on FPGA

Edge detection is one of the most important application in image/video processing. The aim of this paper is to extract edges of real time video stream. The platform consists of OV7670 CMOS-Camera module, Digilent Basys3 FPGA board and VGA monitor to display the processed video stream. This study is constructed from three parts: capturing RGB data from the camera module, converting RGB data to grayscale and then implementing Sobel filter to detect the edges of real time video. Whole design is executed by using custom IP-Cores coded with Very High-Speed Integrated Circuit Hardware Description Language (VHDL) on Vivado 2018.1 FPGA Design Suite. From the results, it is shown that for the sake of FPGA parallel processing ability the design is realized with high accuracy and low-level of resource utilization.
Using Theory of Mind to Assess Users’ Sense of Agency in Social Chatbots

The technological advancements in the field of chatbot research is booming. Despite this, it is still difficult to assess which social characteristics a chatbot needs to have for the user to interact with it as if it had a mind of its own. Review studies have highlighted that the main cause is the low number of research papers dedicated to this question, and the lack of a consistent protocol within the papers that do address it. In the current paper, we suggest the use of a Theory of Mind task to measure the implicit social behaviour users exhibit towards a text-based chatbot. We present preliminary findings suggesting that participants adapt towards this basic chatbot significantly more than when they conduct the task alone (p < .017). This task is quick to administer and does not require a second chatbot for comparison, making it an efficient universal task. With it, a database could be built with scores of all existing chatbots, allowing fast and efficient meta-analyses to discover which characteristics make the chatbot appear more 'human'.
An Interactive Web Tool for Classification Problems Based on Machine Learning Algorithms Using Java Programming Language: Data Classification Software

Classification analysis is a frequently used approach in fields such as biomedical, bioinformatics, medical and engineering. In the field of health, it has become common to classify diseases based on risk factors by machine learning methods and to determine the effect sizes of these risk factors on the disease. There are many analysis tools used to guide researchers in classification analysis. While some of these tools are commercial and provide basic methods for classification analysis, some offer advanced analysis techniques and are desktop applications such as the WEKA environment. The WEKA environment includes comprehensive tools for classification analysis. However, use of the WEKA environment can be difficult and time-consuming, especially when a quick assessment is essential for users who do not have WEKA tool on their computer (doctors, etc.). Therefore; fast, comprehensive, free and easy to use analysis tool is required. The purpose of this study is to develop a user-friendly web tool (Data Classification Software; DCS) based on the classification algorithms of WEKA tool in Java programming language. The data classification software can be used on any device with an internet connection, which is independent of the any operating systems. In the developed web-based tool, data preprocessing module consists of missing value assignment, variable type conversion and normalization-standardization methods. Classification module encapsulates random forest, Naive Bayes, Bayes Network, j48, sequential minimal optimization, a rule and attribute selected classifier algorithms. This web tool can be accessed free of charge at http://biostatapps.inonu.edu.tr/DCS/.
Toward Perception-Based Evaluation of Clustering Techniques for Visual Analytics

Automatic clustering techniques play a central role in Visual Analytics by helping analysts to discover interesting patterns in high-dimensional data. Evaluating these clustering techniques, however, is difficult due to the lack of universal ground truth. Instead, clustering approaches are usually evaluated based on a subjective visual judgment of low-dimensional scatterplots of different datasets. As clustering is an inherent human-in-the-loop task, we propose a more systematic way of evaluating clustering algorithms based on quantification of human perception of clusters in 2D scatterplots. The core question we are asking is in how far existing clustering techniques align with clusters perceived by humans. To do so, we build on a dataset from a previous study [1], in which 34 human subjects la-beled 1000 synthetic scatterplots in terms of whether they could see one or more than one cluster. Here, we use this dataset to benchmark state-of-the-art clustering techniques in terms of how far they agree with these human judgments. More specifically, we assess 1437 variants of K-means, Gaussian Mixture Models, CLIQUE, DBSCAN, and Agglomerative Clustering techniques on these benchmarks data. We get unexpected results. For instance, CLIQUE and DBSCAN are at best in slight agreement on this basic cluster counting task, while model-agnostic Agglomerative clustering can be up to a substantial agreement with human subjects depending on the variants. We discuss how to extend this perception-based clustering benchmark approach, and how it could lead to the design of perception-based clustering techniques that would better support more trustworthy and explainable models of cluster patterns.
Activities of students in using worksheet based on Contextual Teaching and Learning

Abstract This study aims to describe the activities of students in using student worksheets based on Contextual Teaching and Learning (CTL). The method used is descriptive quantitative. Student worksheets based on Contextual Teaching and Learning that are used in learning consist of three sub-layers are atmosphere, lithosphere, and hydrosphere. In CTL-based student worksheets, there are 7 main components of CTL are constructivist, questioning, modelling, finding, community learning, reflection, and authentic assessment. Learning outcomes using CTL-based Student Worksheets are easier for students to understand learning material, this is shown by students in solving problems on student worksheets properly. Based on the results of the study it can be concluded that learning by using Student Worksheets based on Contextual Teaching and Learning helps students more easily understand the subject matter.
The Use of Wearable Sensors in Human Movement Analysis in Non-Swimming Aquatic Activities: A Systematic Review

The use of smart technology, specifically inertial sensors (accelerometers, gyroscopes, and magnetometers), to analyze swimming kinematics is being reported in the literature. However, little is known about the usage/application of such sensors in other human aquatic exercises. As the sensors are getting smaller, less expensive, and simple to deal with (regarding data acquisition), one might consider that its application to a broader range of exercises should be a reality. The aim of this systematic review was to update the state of the art about the framework related to the use of sensors assessing human movement in an aquatic environment, besides swimming. The following databases were used: IEEE Xplore, Pubmed, Science Direct, Scopus, and Web of Science. Five articles published in indexed journals, aiming to assess human exercises/movements in the aquatic environment were reviewed. The data from the five articles was categorized and summarized based on the aim, purpose, participants, sensor's specifications, body area and variables analyzed, and data analysis and statistics. The analyzed studies aimed to compare the movement/exercise kinematics between environments (i.e., dry land versus aquatic), and in some cases compared healthy to pathological participants. The use of sensors in a rehabilitation/hydrotherapy perspective may provide major advantages for therapists.
Identifying Unintended Harms of Cybersecurity Countermeasures

Well-meaning cybersecurity risk owners will deploy countermeasures (technologies or procedures) to manage risks to their services or systems. In some cases, those countermeasures will produce unintended consequences, which must then be addressed. Unintended consequences can potentially induce harm, adversely affecting user behaviour, user inclusion, or the infrastructure itself (including other services or countermeasures). Here we propose a framework for preemptively identifying unintended harms of risk countermeasures in cybersecurity. The framework identifies a series of unintended harms which go beyond technology alone, to consider the cyberphysical and sociotechnical space: displacement, insecure norms, additional costs, misuse, misclassification, amplification, and disruption. We demonstrate our framework through application to the complex, multi-stakeholder challenges associated with the prevention of cyberbullying as an applied example. Our framework aims to illuminate harmful consequences, not to paralyze decision-making, but so that potential unintended harms can be more thoroughly considered in risk management strategies. The framework can support identification and preemptive planning to identify vulnerable populations and preemptively insulate them from harm. There are opportunities to use the framework in coordinating risk management strategy across stakeholders in complex cyberphysical environments.