Reviewerly/exHarmony huggingface dataset:
Collection and Analysis of Digital Forensic Data from Devices in the Internet of Things

Despite the abundance of articles that have been written about the Internet of Things (IoT), little attention has been given to how digital forensic approaches can be utilized to direct advanced investigations in IoT-based frameworks. Current digital forensic tools and functions are not generally prepared to tackle the complexity of IoT frameworks for the purpose of collecting, analyzing, and testing potential evidence from IoT environments that might be utilized as permissible evidence in a courtroom. Hence, the issue to be addressed is that; currently, there is no generally-accepted digital forensic framework that can be used to conduct digital forensic investigations in IoT-based environments. In addition, at the time of this writing, there has been little focus on how to gather and save network and server logs from IoT-based environments for investigative purposes. Based on this premise, we propose a digital forensic framework called Radlen, a lightweight digital forensic investigation model to enhance and support future IoT investigative capabilities. Radlen is also able to coordinate and manage IoT devices within a smart home environment using a smart watch or smart phone to satisfy the user’s needs, preserve security, and make decisions automatically. The authors simulate the Radlen system using a Java application that learns user’s needs and security preferences during installation and uses a MySQL server to log all data communications.
CNN-based System to Identify Bicycle Riders and Pedestrians: Toward Minor Collision Prevention on Sidewalks

In recent years, minor bicycle collisions on sidewalks have become a critical issue. To prevent them, various researches on person and bicycle detection have been conducted. However, existing methods cannot distinguish between bicycle riders and bicycle pushers because they rely on either the bicycle's shape or people's shape for detection. Therefore, we propose a CNN-based system that uses video frame images to identify not only walkers and bicycle riders, but also bicycle pushers, who have a shape similar to that of bicycle riders. Our system learned the features of 15,000 images of bicycle riders, bicycle pushers, and walkers and can automatically detect and identify the bicycle riders, bicycle pushers, and walkers in each frame of the input video. In an evaluation experiment, we recorded a video of pedestrians and bicycle riders on public roads to assess the bicycle rider identification rate of the proposed system. The bicycle rider identification rate achieved using the proposed method was 80.7%, which illustrates the effectiveness of our CNN-based identification approach.
Bootstrap Bias Corrected Cross Validation Applied to Super Learning

Super learner algorithm can be applied to combine results of multiple base learners to improve quality of predictions. The default method for verification of super learner results is by nested cross validation; however, this technique is very expensive computationally. It has been proposed by Tsamardinos et al., that nested cross validation can be replaced by resampling for tuning hyper-parameters of the learning algorithms. The main contribution of this study is to apply this idea to verification of super learner. We compare the new method with other verification methods, including nested cross validation. Tests were performed on artificial data sets of diverse size and on seven real, biomedical data sets. The resampling method, called Bootstrap Bias Correction, proved to be a reasonably precise and very cost-efficient alternative for nested cross validation.
Design and implementation of a mobile health electronic data capture platform that functions in fully-disconnected settings: a pilot study in rural Liberia

Mobile phones and personal digital assistants have been used for data collection in developing world settings for over three decades, and have become increasingly common. However, the use of electronic data capture (EDC) through mobile phones is limited in many areas by inconsistent network connectivity and poor access to electricity, which thwart data transmission and device usage. This is the case in rural Liberia, where many health workers live and work in areas without any access to cellular connectivity or reliable power. Many existing EDC mobile software tools are built for occasionally-disconnected settings, allowing a user to collect data while out of range of a cell tower and transmit data to a central server when he/she regains a network connection. However, few tools exist that can be used indefinitely in fully-disconnected settings, where a user will never have access to the internet or a cell network. This led us to create and implement an EDC software tool that allows for completely offline data transfer and application updating.We designed, pilot-tested, and scaled an open-source fork of Open Data Kit Collect (an Android application that can be used to create EDC systems) that allows for offline Bluetooth-based bidirectional data transfer, enabling a system in which permanently-offline users can collect data and receive application updates. We implemented this platform among a cohort of 317 community health workers and 28 supervisors in a remote area of rural Liberia with incomplete cellular connectivity and low access to power sources.Running a fully-offline EDC program that completely bypasses the cellular network was found to be feasible; the system is still running, over 4 years after the initial pilot program. The users of this program can theoretically collect data offline for months or years, assuming they receive hardware support when needed. Fully-offline EDC has applications in settings where cellular network coverage is poor, as well as in disaster relief settings in which portions of the communications infrastructure may be temporarily nonfunctional.
Easy-Assist: An Intelligent Haptic-based Affective framework for Assisted Living

Unlike the younger population that uses wearables such as smartwatches for monitoring health on daily basis, elderly people need assistance in the use of technology and interpreting the data obtained through these smart connected frameworks. The current monitoring systems are primarily designed to monitor the physiological signals on daily basis. The aim of this proposed research, Easy-Assist, is to help older people to maintain their emotional well-being. This research is focused on developing a wearable affective framework, which can help in detecting the emotions of the user in addition to monitoring their physiological signals. The proposed framework can be used in an automated assisted living environment, where the user's emotional state can be balanced using a haptic-based emotional elicitation system after the user's emotion is recognized, detected and interpreted in real-time. The proposed framework is validated using a fall detection algorithm deployed in a custom-built watch wearable, built using off-the-shelf components and an emotion detection framework built using a single board computer. A dataset of 21700 samples acquired using the proposed framework yielded a maximum efficiency of 97.25%, 96 %, and 94 %, in classifying the state and emotion classes into Alert, Active and Normal classes respectively, using multi-class SVM model. The overall latency of the proposed research was in few orders of milli-seconds.
Extracting boolean and probabilistic rules from trained neural networks

This paper presents two approaches to extracting rules from a trained neural network consisting of linear threshold functions. The first one leads to an algorithm that extracts rules in the form of Boolean functions. Compared with an existing one, this algorithm outputs much more concise rules if the threshold functions correspond to 1-decision lists, majority functions, or certain combinations of these. The second one extracts probabilistic rules representing relations between some of the input variables and the output using a dynamic programming algorithm. The algorithm runs in pseudo-polynomial time if each hidden layer has a constant number of neurons. We demonstrate the effectiveness of these two approaches by computational experiments.
Institutional Transmission in Economic Development

The main objective of this study is to provide a theoretical and empirical framework for analyzing the relationship between economic growth and the property rights institute in different countries. The key idea is that property rights are multifarious and can be classified according to their role in economic development. These days it is the intellectual property rights that impact the economic development through propensity to innovate. However, the protection of intellectual property rights does matter for economic growth only in a well-developed political and legal environment. We find that economic performance in highly developed countries is to a greater extent contingent upon a quality of the protection of intellectual property rights than in less developed economies. This finding raises an important question about the credibility of the preponderant approach based on a simple unification of countries with a different institutional framework under one umbrella when their effects on growth. Such an approach may provide inconsistent and misleading results and lead to false conclusions and wrong policies.
Overview of Some Intelligent Control Structures and Dedicated Algorithms

Automatic control refers to the use of a control device to make the controlled object automatically run or keep the state unchanged without the participation of people. The guiding ideology of intelligent control is based on people’s way of thinking and ability to solve problems, in order to solve the current methods that require human intelligence. We already know that the complexity of the controlled object includes model uncertainty, high nonlinearity, distributed sensors/actuators, dynamic mutations, multiple time scales, complex information patterns, big data process, and strict characteristic indicators, etc. In addition, the complexity of the environment manifests itself in uncertainty and uncertainty of change. Based on this, various researches continue to suggest that the main methods of intelligent control can include expert control, fuzzy control, neural network control, hierarchical intelligent control, anthropomorphic intelligent control, integrated intelligent control, combined intelligent control, chaos control, wavelet theory, etc. However, it is difficult to want all the intelligent control methods in a chapter, so this chapter focuses on intelligent control based on fuzzy logic, intelligent control based on neural network, expert control and human-like intelligent control, and hierarchical intelligent control and learning control, and provide relevant and useful programming for readers to practice.
Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation

New research is being published at a rate, at which it is infeasible for many scholars to read and assess everything possibly relevant to their work. In pursuit of a remedy, efforts towards automated processing of publications, like semantic modelling of papers to facilitate their digital handling, and the development of information filtering systems, are an active area of research. In this paper, we investigate the benefits of semantically modelling citation contexts for the purpose of citation recommendation. For this, we develop semantic models of citation contexts based on entities and claim structures. To assess the effectiveness and conceptual soundness of our models, we perform a large offline evaluation on several data sets and furthermore conduct a user study. Our findings show that the models can outperform a non-semantic baseline model and do, indeed, capture the kind of information they're conceptualized for.
Utterance-Level Sequential Modeling for Deep Gaussian Process Based Speech Synthesis Using Simple Recurrent Unit

This paper presents a deep Gaussian process (DGP) model with a recurrent architecture for speech sequence modeling. DGP is a Bayesian deep model that can be trained effectively with the consideration of model complexity and is a kernel regression model that can have high expressibility. In the previous studies, it was shown that the DGP-based speech synthesis outperformed neural network-based one, in which both models used a feed-forward architecture. To improve the naturalness of synthetic speech, in this paper, we show that DGP can be applied to utterance-level modeling using recurrent architecture models. We adopt a simple recurrent unit (SRU) for the proposed model to achieve a recurrent architecture, in which we can execute fast speech parameter generation by using the high parallelization nature of SRU. The objective and subjective evaluation results show that the proposed SRU-DGP-based speech synthesis outperforms not only feed-forward DGP but also automatically tuned SRU- and long short-term memory (LSTM)-based neural networks.
Classification of Breast Ultrasound Tomography by Using Textural Analysis

Background: Ultrasound imaging has become one of the most widely utilized adjunct tools in breast cancer screening due to its advantages. The computer-aided detection of breast ultrasound is rapid development via significant features extracted from images. Objectives: The main aim was to identify features of breast ultrasound image that can facilitate reasonable classification of ultrasound images between malignant and benign lesions. Patients and Methods: This research was a retrospective study in which 85 cases (35 malignant [positive group] and 50 benign [negative group] with diagnostic reports) with ultrasound images were collected. The B-mode ultrasound images have manually selected regions of interest (ROI) for estimated features of an image. Then, a fractal dimensional (FD) image was generated from the original ROI by using the box-counting method. Both FD and ROI images were extracted features, including mean, standard deviation, skewness, and kurtosis. These extracted features were tested as significant by t-test, receiver operating characteristic (ROC) analysis and Kappa coefficient. Results: The statistical analysis revealed that the mean texture of images performed the best in differentiating benign versus malignant tumors. As determined by the ROC analysis, the appropriate qualitative values for the mean and the LR model were 0.85 and 0.5, respectively. The sensitivity, specificity, accuracy, positive predicted value (PPV), negative predicted value (NPV), and Kappa for the mean was 0.77, 0.84, 0.81, 0.77, 0.84, and 0.61, respectively. Conclusion: The presented method was efficient in classifying malignant and benign tumors using image textures. Future studies on breast ultrasound texture analysis could focus on investigations of edge detection, texture estimation, classification models, and image features.
Design and Performance Comparison of CNN Accelerators Based on the Intel Movidius Myriad2 SoC and FPGA Embedded Prototype

Evolving Convolutional Neural Networks (CNNs) and their execution time performance are key factors for a wide range of applications that are based on deep learning. The need for meeting the applications' time constraints led to design AI accelerators and the current work contributes to this effort by presenting CNN accelerators based on two different design approaches: a) developing CNNs on a power efficient System on Chip (SoC), the Myriad2 and b) a VHDL application specific design and the corresponding FPGA architecture. Both systems target the optimization of time performance regarding the MNIST dataset application. The paper describes the two systems and compares the performance results.
International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2020)

Both search and recommendation algorithms provide results based on their relevance for the current user. In order to do so, such a relevance is usually computed by models trained on historical data, which is biased in most cases. Hence, the results produced by these algorithms naturally propagate, and frequently reinforce, biases hidden in the data, consequently strengthening inequalities. Being able to measure, characterize, and mitigate these biases while keeping high effectiveness is a topic of central interest for the information retrieval community. In this workshop, we aim to collect novel contributions in this emerging field and to provide a common ground for interested researchers and practitioners.
Publishing Anonymized Set-Valued Data via Disassociation towards Analysis

Data publishing is a challenging task for privacy preservation constraints. To ensure privacy, many anonymization techniques have been proposed. They differ in terms of the mathematical properties they verify and in terms of the functional objectives expected. Disassociation is one of the techniques that aim at anonymizing of set-valued datasets (e.g., discrete locations, search and shopping items) while guaranteeing the confidentiality property known as k m -anonymity. Disassociation separates the items of an itemset in vertical chunks to create ambiguity in the original associations. In a previous work, we defined a new ant-based clustering algorithm for the disassociation technique to preserve some items associated together, called utility rules, throughout the anonymization process, for accurate analysis. In this paper, we examine the disassociated dataset in terms of knowledge extraction. To make data analysis easy on top of the anonymized dataset, we define neighbor datasets or in other terms datasets that are the result of a probabilistic re-association process. To assess the neighborhood notion set-valued datasets are formalized into trees and a tree edit distance (TED) is directly applied between these neighbors. Finally, we prove the faithfulness of the neighbors to knowledge extraction for future analysis, in the experiments.
A New Weighted Relative Entropy Pre-Fitting for Active Contour based Image Segmentation

The Active Contour Model (ACM) has demonstrated its promises for image segmentation. For developing an ACM, a new construction of local fitting energy with a weighted relative entropy is reported in this paper. With a level set method based on local pre-fitted images and local-dispersion based scaling, a weighted energy functional is formulated. To improve the performance of segmentation, the new energy functional relies on the relative entropy to include local similarity estimations for better curve evolution. Locally pre-fitted images are constructed to provide initialization robustness, noise endurance and to reduce computational complexity. Besides, the energy is scaled with the local dispersion to infuse edge mapping in the energy functional for improving accuracy and reducing the effect of inhomogeneity. The proposed model has thereby demonstrated the improvement in initialization, curve evolution, time efficiency, computational complexity, and noise endurance.
POCL-R

Running complex applications on mobile devices stress their performance and power consumption limits. As new wireless networking technologies that promise lower communication latencies appear, it is interesting to study whether dynamic offloading from mobile devices across a network to a nearby compute cluster is feasible when quality and complexity can be traded off dynamically. This type of scheme calls for a light weight heterogeneous distributed programming layer.
Performance of Cryptographic Algorithms based on Time Complexity

Nowadays, a tremendous amount of data is produced every single day by computational systems and electronic instruments, whether its an online transaction or data manipulation over the internet, security has dependably remained the best concern in any correspondence communication system. The amount of information to be exchanged is not the problem. The essential variable is the Channel, through which the data is transferred, ought to be secured. So in terms of secure transmission of data, the best technique usually used these days is cryptography. Cryptography is a procedure of making data incomprehensible to an unapproved individual. Different cryptographic algorithms can be utilized in a very efficient manner. In a perfect world, a user needs the best cryptographic algorithm with low cost and high in performance measure. So for the critical needs of the user, the cryptographic algorithm chose the best algorithm amongst existing algorithms. This research paper presents a neat and clean study of various cryptographic algorithms (DES, 3DES, AES, RSA) based on the computational complexity and performance measure.
Transductive Nonnegative Matrix Tri-Factorization

Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two lower-rank nonnegative matrices.Since NMF learns parts-based representation, it has been widely used as a feature learning component in many fields.However, standard NMF algorithms ignore the training labels as well as unlabeled data in the test domain.In this paper, we propose a transductive nonnegative matrix tri-factorization method (T-NMTF) to simultaneously exploit the label information of training examples and the statistical structure of features in the test domain.Different from standard NMF, nonnegative matrix tri-factorization (NMTF) decomposes a nonnegative matrix into the product of three lower-rank nonnegative matrices, and thus provides a flexible framework to transduce discriminative information of training examples to test examples.In particular, the proposed T-NMTF projects both training examples and test examples into a unified subspace, and expects the coefficients of training examples close to their label vectors.Since training examples and test examples are assumed to identically distributed, it is reasonable to expect the learned coefficients of test examples approximate their label vectors well.To estimate the T-NMTF parameters, we develop an efficient multiplicative update rule and prove its convergence.In addition, we propose a manifold regularized T-NMTF (MT-NMTF) algorithm that exploits the local geometry structure of the dataset to boost discriminant power.Experimental results on face recognition demonstrate the effectiveness of T-NMTF and MT-NMTF.
Cyber Attacks for Sale

The infamous darknet hosts an underground economy for illegal goods and services, some of which can be purchased and used for cyber attacks. By analyzing the properties and popularity of such items, we can get indications about the type and capabilities of potential attackers, what assets they are targeting and which vulnerabilities they are likely to exploit. We have conducted an online study of eleven marketplaces residing within the darknet, addressing what kind of cyber attack items are available and where the profit lies. The results have been used to create a detailed categorization of items, showing a distribution based on item type and availability. This has been compared to the number of sold items and revenue from four of the marketplaces, and we discuss these different views. Aided by related studies, we have identified trending cyber threats such as phone hacking, information theft and bitcoin stealing.