Reviewerly/exHarmony huggingface dataset:
Security and Forensics in the Internet of Things: Research Advances and Challenges

Considering the billions of Internet of Things (IoT) devices around the world, the IoT has brought convenience to people's lives, but also created a larger attack surface. Therefore, specific attention should be paid to IoT, especially from two aspects, namely, security and forensics. Security properties, especially authentication, ensure the integrity of large amounts of data processed in IoT networks, while forensic investigations can identify, collect, and retain evidence when abuse in IoT systems occurs. Regarding these two critical aspects, this article introduces and analyzes the latest biometric-based authentication research effort in IoT, as well as some IoT forensic investigation models/frameworks. In addition, we also investigate some unresolved challenges that IoT research still faces and provide a direction for future work.
Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANS

Ultrasound (US) is widely accepted in clinic for anatomical structure inspection. However, lacking in resources to practice US scan, novices often struggle to learn the operation skills. Also, in the deep learning era, automated US image analysis is limited by the lack of annotated samples. Efficiently synthesizing realistic, editable and high resolution US images can solve the problems. The task is challenging and previous methods can only partially complete it. In this paper, we devise a new framework for US image synthesis. Particularly, we firstly adopt a sketch generative adversarial networks (Sgan) to introduce background sketch upon object mask in a conditioned generative adversarial network. With enriched sketch cues, Sgan can generate realistic US images with editable and fine-grained structure details. Although effective, Sgan is hard to generate high resolution US images. To achieve this, we further implant the Sgan into a progressive growing scheme (PGSgan). By smoothly growing both generator and discriminator, PGSgan can gradually synthesize US images from low to high resolution. By synthesizing ovary and follicle US images, our extensive perceptual evaluation, user study and segmentation results prove the promising efficacy and efficiency of the proposed PGSgan.
Wavelet Based Multivariate Signal Denoising Using Mahalanobis Distance and EDF Statistics

A multivariate signal denoising method is proposed which employs a novel multivariate goodness of fit (GoF) test that is applied at multiple data scales obtained from discrete wavelet transform (DWT). In the proposed multivariate GoF test, we first utilize squared Mahalanobis distance (MD) measure to transform input multivariate data residing in M-dimensional space R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sup> to a single-dimensional space of positive real numbers R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sub> , i.e., R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sup> → R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sub> , where M > 1. Owing to the properties of the MD measure, the transformed data in R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sub> follows a distinct distribution. That enables us to apply the GoF test using statistic based on empirical distribution function (EDF) on the resulting data in order to define a test for multivariate normality. We further propose to apply the above test locally on multiple input data scales obtained from discrete wavelet transform, resulting in a multivariate signal denoising framework. Within the proposed method, the reference cumulative distribution function (CDF) is defined as a quadratic transformation of multivariate Gaussian random process. Consequently, the proposed method checks whether a set of DWT coefficients belong to multivariate reference distribution or not; the coefficients belonging to the reference distribution are discarded. The effectiveness of our proposed method is demonstrated by performing extensive simulations on both synthetic and real world datasets.
Predicting the loan risk towards new customer applying data mining using nearest neighbor algorithm

Abstract Unstable economic conditions require Bank must be careful in deciding towards lending customers. Banks should not take the risk of giving loans to customers who cannot afford to pay. This study aims to assist bank in predicting lending. The study was conducted at the Bank Perkreditan Rakyat in Medan. The study was conducted applying data mining using the nearest neighbor algorithm. This algorithm was chosen because the nearest neighbor can calculate the closeness between new cases and old cases based on matching weights from a number of existing features. This algorithm will calculate the closeness with predetermined criteria. Hoped bank will be helped in making predictions.
Pseudo distribution on unseen classes for generalized zero shot learning

Although Zero Shot Learning (ZSL) has attracted more and more attention due to its powerful ability of recognizing new objects without retraining, it has a serious drawback that it only focuses on unseen classes during prediction. To solve this issue, Generalized ZSL (GZSL) extends the search range to both seen and unseen classes, which makes it a more realistic and challenging task. Conventional methods on GZSL often suffer from the domain shift problem on seen classes because they have only seen data for training. Deep Calibration Network (DCN) tries to minimize the entropy of assigning seen data to unseen classes to balance the training on both seen and unseen classes. However, there are still two problems for DCN, one is the hubness problem and another is the lack of training guidance. In this paper, to solve the two problems, we propose a novel method called PSeudo Distribution (PSD), which exploits the attribute similarity between seen classes and unseen classes as the training guidance to assign the seen data to unseen classes. In addition, the attribute similarity is also compressed to one-hot vector to further encourage the certainty of the model. Besides, the visual space is utilized as the embedding space, which can well settle the hubness problem. Extensive experiments are conducted on four popular datasets, and the results show the superiority of the proposed method.
Nuclei Segmentation Using Mixed Points and Masks Selected From Uncertainty

Weakly supervised learning has drawn much attention to mitigate the manual effort of annotating pixel-level labels for segmentation tasks. In nuclei segmentation, point annotation has been successfully used for training. However, points lack the shape information. Thus the segmentation of nuclei with nonuniform color is unsatisfactory. In this paper, we propose a framework of weakly supervised nuclei segmentation using mixed points and masks annotation. To save the extra annotation effort, we select typical nuclei to annotate masks from uncertainty map. Using Bayesian deep learning tools, we first train a model with points annotation to predict the uncertainty. Then we utilize the uncertainty map to select the representative hard nuclei for mask annotation automatically. The selected nuclear masks are combined with points to train a better segmentation model. Experimental results on two nuclei segmentation datasets prove the effectiveness of our method. The code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://github.com/huiqu18/WeaklySegMixedAnno.
Detection of Breast Cancer Based on Fuzzy Frequent Itemsets Mining

Background: Breast cancer, a type of malignant tumor, affects women more than men. About one third of women with breast cancer die of this disease. Hence, it is imperative to find a tool for the proper identification and early treatment of breast cancer. Unlike the conventional data mining algorithms, fuzzy logic based approaches help in the mining of association rules from quantitative transactions. Methods: In this study a novel fuzzy methodology IFFP (Improved Fuzzy Frequent Pattern Mining), based on a fuzzy association rule mining for biological knowledge extraction, is introduced to analyze the dataset in order to find the core factors that cause breast cancer. This method consists of two phases. During the first phase, fuzzy frequent itemsets are mined using the proposed algorithm IFFP. Fuzzy association rules are formed during the second phase, indicating whether a person belongs to benign or malignant. This algorithm is applied on WBCD (Wisconsin Breast Cancer Database) to detect the presence of breast cancer. Results: It is determined that the factor, Mitoses has low range of values on both malignant and benign and hence it does not contribute to the detection of breast cancer. On the other hand, the high range of Bare Nuclei shows more chances for the presence of breast cancer. Conclusion: Experimental evaluations on real datasets show that our proposed method outperforms recently proposed state-of-the-art algorithms in terms of runtime and memory usage.
Innovation and Economic Growth in Sub-Saharan Africa: Why Institutions Matter? An Empirical Study Aross 37 Countries

Based on empirical panel data for a sample of 37 sub-Saharan African economies for 1996–2016, this inquiry examines the extent to which institutional quality explains the existing cross-country difference in economic performance in sub-Saharan Africa. While most of the existing studies focus only on the direct effect of institutional quality, this article investigates the direct and indirect effects of institutions. It also reflects on impact of the interaction between institutional quality and innovation on economic growth in developing countries. The evidence provides very strong support for the direct effect of institutional quality development on economic performance as well as for its indirect effect via its impact on innovation. However, the results do not support theories that argue in favour of interaction between institutional quality such as democracy, governance quality and innovation, thereby pointing to the need for better calibration of the numerous existing theoretical postulations and related empirical measures. JEL: D70, D72, O15, O30, O31, O55
The optimalization of backpropagation neural networks to simplify decision making

Abstract Private or public companies need tools to make easy decision making process. This research conducted at PT. FIF Medan. Focus in decision making to provision motor vehicle loans. The study do because PT. FIF Medan has problems to determining whether consumers are given financing. Research using Back Propagation Neural Network. Back Propagation Neural Network is a method that simplifies complex problems. Simplifying model by taking the most essential core issues. Research will be carried out with the stages of identifying problems, determining needs, analyzing, and choosing alternatives. 8 (eight) criteria will be parameters, including the amount of dependents, length of work, home status, history of finance, employee workplace, vehicle type, financing tenure, and monthly income.
Intent-based resource matching strategy in cloud

Accurate and efficient resource allocation based on user intent is an important issue in a large-scale, distributed environment, such as cloud computing. Although a large number of cloud resource matching models he been proposed, these models do not consider the interests of user and cloud service provider objectively and fairly. Thus, a novel resource matching strategy that regulates multiattribute matching between cloud resources and tasks is proposed in this study. Tasks and resources are initially clustered on the basis of the attribute characteristics to reduce the scope of resource retrieval. Then, the tasks are matched to the appropriate resources in terms of the strict bilateral matching algorithm to improve the satisfaction of both parties. Finally, a series of experiments are reported to show the effectiveness of the algorithm. Experimental results conclusively demonstrate that our proposed methods can availably decrease the scheduling overhead and improve the overall satisfaction of both parties simultaneously.
A Nonlinear Five-Term System: Symmetry, Chaos, and Prediction

Chaotic systems have attracted considerable attention and been applied in various applications. Investigating simple systems and counterexamples with chaotic behaviors is still an important topic. The purpose of this work was to study a simple symmetrical system including only five nonlinear terms. We discovered the system’s rich behavior such as chaos through phase portraits, bifurcation diagrams, Lyapunov exponents, and entropy. Interestingly, multi-stability was observed when changing system’s initial conditions. Chaos of such a system was predicted by applying a machine learning approach based on a neural network.
Emotion-Semantic-Enhanced Bidirectional LSTM with Multi-Head Attention Mechanism for Microblog Sentiment Analysis

The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment, as well as the utilization of emojis. To this end, we propose the emotion-semantic-enhanced bidirectional long short-term memory (BiLSTM) network with the multi-head attention mechanism model (EBILSTM-MH) for sentiment analysis. This model uses BiLSTM to learn feature representation of input texts, given the word embedding. Subsequently, the attention mechanism is used to assign the attentive weights of each words to the sentiment analysis based on the impact of emojis. The attentive weights can be combined with the output of the hidden layer to obtain the feature representation of posts. Finally, the sentiment polarity of microblog can be obtained through the dense connection layer. The experimental results show the feasibility of our proposed model on microblog sentiment analysis when compared with other baseline models.
Comparison of Lossless Data Compression Techniques in Low-Cost Low-Power (LCLP) IoT Systems

With the recent advances and proliferation of the Internet of Things (IoT) devices, there is a huge demand placed on its infrastructure requirements. The amount of data generated by these small low-cost, low-power (LCLP) IoT devices is phenomenal and at the same time due to the devices being low-powered, they cannot be used to perform complex computations and other algorithm implementations. There are also limitations in communication data rates at different stages in a Wireless Sensor Network (WSN), which mainly uses wireless technologies such as Bluetooth, Zigbee, LoRa, etc to achieve low power communication. These technologies come with limited bandwidth and are not very reliable at high data rates. Hence the challenge of handling high amounts of data with low bandwidth communication technologies is one of the main hurdles inefficient LCLP IoT system deployments. To address this problem, we propose a combination of data compression techniques, which will result in reduced data size, without compromising affecting the quality of the data. This paper describes the implementation of a combination of Delta and RLE compression techniques on specific sensor data, particularly those used in our deployment of the World's First Wireless Sensor Network-based System for Early warning and Monitoring of Rainfall induced Landslides in Southern India. The test results show a good compression ratio of 52.67% for 12bit ADC, without compromising on the quality of the data. This has been implemented on a Programmable System-on-a-Chip (PSoC) system and the results presented.
KuroNet: Regularized Residual U-Nets for End-to-End Kuzushiji Character Recognition

Abstract Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the eighth century. Over 3 million books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts. Thus there has been a great deal of interest in using machine learning to automatically recognize these historical texts and transcribe them into modern Japanese characters. Our proposed model KuroNet (which builds on Clanuwat et al. in International conference on document analysis and recognition (ICDAR), 2019) outperforms other model for Kuzushiji recognition. In this paper, KuroNet achieves higher accuracy while still recognizing entire pages of text using the residual U-Net architecture from adding more regularization. We also explore areas where our system is limited and suggests directions for future work.
Obstacle avoidance in dynamic environments based on velocity space optimization

Robotic obstacle avoidance is an important issue in robotic navigation for unknown or partially known, dynamic environments. A good number of techniques have already been proposed to navigate obstacles in this kind of environment. They include a series of velocity space methods that have been successful implemented in several applications. They formulate the problem as one of constrained optimization in the velocity space of the robot. The constraints include the obstacles in the environment assuming they are static. In this paper, we present an efficient, real-time method (BCM-DO) to include the restrictions imposed by dynamic objects. The optimization function has also been adapted to include these new restrictions. The new function is evaluated in two sets of points. A first set is obtained from a coarse sampling in the reachable window of velocities and a second set is selected in the limits of each curvature interval to avoid missing small openings between static objects. The whole system has first been extensively tested in several simulated robots and finally applied to a hotel assistant robot (BellBot) resulting in an efficient, real-time obstacle avoidance method that produces smooth and reliable routes.
Residue Number System-Based Solution for Reducing the Hardware Cost of a Convolutional Neural Network

Convolutional neural networks (CNNs) represent deep learning architectures that are currently used in a wide range of applications, including computer vision, speech recognition, time series analysis in finance, and many others. At the same time, CNNs are very demanding in terms of the hardware and time cost of a computing system, which considerably restricts their practical use, e.g., in embedded systems, real-time systems, and mobile volatile devices. The goal of this paper is to reduce the resources required to build and operate CNNs. To achieve this goal, a CNN architecture based on Residue Number System (RNS) and the new Chinese Remainder Theorem with fractions is proposed. The new architecture gives an efficient solution to the main problem of RNSs associated with restoring the number from its residues, which determines the main contribution to the CNN structure. In accordance with the results of hardware simulation on Kintex7 xc7k70tfbg484-2 FPGA, the use of RNS in the convolutional layer of a neural network reduces hardware cost by 32.6% compared to the traditional approach based on the binary number system. In addition, the use of the proposed hardware-software architecture reduces the average image recognition time by 37.06% compared to the software implementation.
Monitoring of technogenic pollution of soil in the region

Abstract The purpose of the study is to conduct monitoring of technogenic pollution of soil in the Samara region. The objectives of the study are to identify the sources of technogenic pollution of soil, to analyze the minimum and maximum concentrations of heavy metal on long-term observation plots and background plots, to assess the average and the maximum values of total petroleum hydrocarbons (TPH) in soil of observed land plots. The allowable concentration levels of heavy metals (manganese, lead, cadmium, copper, nickel and zinc) in the soil of the Samara region are not exceeded. Mean heavy metal concentrations in soil did not exceed the maximum permissible concentration (MPC) and the approximate permissible concentration (APC) and was 0.1-0.6 MPC (APC). The maximum concentration of metals in soil was 0.1-0.9 MPC (APC). However, the obtained results were higher than background concentrations of metals. The mean concentrations of aluminium exceeded the background levels by a factor of 3.4-6.7. The maximum concentrations of aluminium in soil was observed in the area of the Samara metallurgic plant (ZAO ‘Arconik’) and was 9.2 of background.
H2BR

HTTP-based Adaptive Streaming (HAS) plays a key role in over-the-top video streaming. It contributes towards reducing the rebuffering duration of video playout by adapting the video quality to the current network conditions. However, it incurs variations of video quality in a streaming session because of the throughput fluctuation, which impacts the user's Quality of Experience (QoE). Besides, many adaptive bitrate (ABR) algorithms choose the lowest-quality segments at the beginning of the streaming session to ramp up the playout buffer as soon as possible. Although this strategy decreases the startup time, the users can be annoyed as they have to watch a low-quality video initially. In this paper, we propose an efficient retransmission technique, namely H2BR, to replace low-quality segments being stored in the playout buffer with higher-quality versions by using features of HTTP/2 including (i) stream priority, (ii) server push, and (iii) stream termination. The experimental results show that H2BR helps users avoid watching low video quality during video playback and improves the user's QoE. H2BR can decrease by up to more than 70% the time when the users suffer the lowest-quality video as well as benefits the QoE by up to 13%