Reviewerly/exHarmony huggingface dataset:
Signal Demodulation without Channel Equalizer Using Machine Learning Techniques

With the unprecedented availability of computing and data resources, there has been a widespread trend in using machine learning techniques in the field of communication as well. In the existing communication system, signal demodulation in multipath channel works with the aid of equalizers to mitigate the effect of inter-code cross talk and inter-symbol interference. Owing to the ability other machine learning models to learn the complex structure in feature space, deep learning architectures such as Deep Belief Network(DBN) and Stacked Auto Encoders(SAE) are used for demodulation in this paper. The experiments were conducted in a multipath Rayleigh channel. Simulation results shows the feasibility and hegemony of the deep learning model over the conventional demodulation techniques using equalization.
X-ray Image Enhancement: A Technique Combination Approach

Medical X-ray images are an important and valuable source of studies and diagnoses for diseases with low cost besides its high availability. However, radiological images are subject to degradations related to low contrast and presence of noise. Based on this finding, this article presents a simple but efficient enhancement method for these images with the objective of contrast gain and noise removal. The proposed method (MP) consists of a sequence of interactive steps. Start from the step of double precision conversion and end with removing impulsive noises. An evaluation with the PSNR, Entropy, AMBE, and IQR indicators was performed, besides gain check on the thresholding process and the histogram characterization. The evaluation was conducted on three different datasets in a total of 1409 images chest X-rays. The results compared to others known in the literature proved to be promising and put it as an interesting alternative in the process of enhancement medical X-ray images.
Deep Ensemble Learning Network for Kidney Lesion Detection

Computed tomography (CT) plays an increasingly important role in the diagnosis of kidney diseases. According to CT, doctors can make high-precision diagnosis of renal cell carcinoma. How to use massive CT image data to design a Computer-aided detection/diagnosis (CADe/CADx) system for the kidney has great significance. In this paper, we propose a deep ensemble learning network for kidney lesion detection(EN-KLD). This system can automatically identify the kidney lesions and obtain the specific location of the lesions. The system consists of two branch networks. Both branch networks are used the Faster Region-Convolutional Neural Networks (Faster R-CNN). One uses Visual Geometry Group(VGG-16) as the backbone and another is replacing the single-scale feature map with feature pyramid network(FPN) in region proposal network(RPN). The data used in the experiment came from the Deep Lesion dataset. The experimental results indicate that the integration of the two networks can improve the detection performance of the model. Proposed method has a lower rate of missed diagnosis and higher positioning accuracy than a single network.
A Method of Bank Card Number Recognition and End-to-End Integration Based on Deep Learning

Accurate identification of bank card number has a wide range of application scenarios in banking business. Because of the complexity of data samples, the lack of data sets and the confidentiality of information, the identification of bank card numbers is still a difficult task. In this paper, we present a bank card Network (BCNet) to identify bank card numbers and integrate an end-to-end system which can be used conveniently in banking business. It includes two core architectures: text detection and recognition. In order to adapt to various bank card number recognition scenarios, we optimize the data enhancement and proofread the recognition results. Experiments show that the recognition accuracy of our model reaches 99%, and the system takes 58ms to run. The system shows excellent performance in both accuracy and running time.
Collusion Attack from Hubs in The Blockchain Offline Channel Network

Offline channels can improve the scalability of blockchains by reducing the number of transactions in the blockchain. Offline channels provide Path-Based fund Transfer (PBT) service which allows a pair of peers without a mutual channel to transfer fund between them using paths in the channel network. In PBTs, peers allow a 3rd party to use their channel for fund transfer in exchange for a transfer fee. There are channels in the Bitcoin Lightning network which are designed to collect such PBT transfer fees. An analysis of Bitcoin's Lightning network revealed the existence of hubs or nodes with very high degree in the channel network. There are only 10 nodes who own more than 50% funds in the Lightning network. These nodes are designed to facilitate PBTs among peers with a low degree (number of channels) in exchange for transfer fees. The emergence of hubs in channel network created the possibility of collusion attack on the channel network where a group of hubs deliberately make few channels non-operational to prevent PBTs involving a selected set of hubs (victims of the collusion attack). In this paper, we model such collusion attack using cooperative game theory and using Banzhaf index we classify the vulnerability of the hubs from the collusion attacks. We propose a design principle of the channel network that can decrease the possibility of collusion attacks.
Design of Paper Counting Algorithm based on Texture Image

For the stacked paper placed parallel to each other, the characteristics of the side texture image are analyzed, and an algorithm for counting compensation combined with the overall information is proposed. Considering that the most algorithms only deal with one-dimensional information, it is easy to generate the blind area of counting, resulting in false detection and missing detection. On this basis, this paper counts the edges of the paper in combination with the overall image information. This method detects the binarized side image of the paper at the suspected position by using a rectangular template to eliminate the false detection position and recover the missed detection position. The experimental results show that the accuracy of the algorithm is significantly improved, and the counting error is controlled within the required range.
Bitcoin’s Blockchain Data Analytics: A Graph Theoretic Perspective

Bitcoin is the first and most widely used cryptocurrency in the world. It provides a pseudonym identity to its users that is established using the user’s public key, which leads to preserving the user’s privacy. Each transfer of bitcoin cryptocurrency among the users makes a transaction. The pseudonym identities are considered as transaction end-points. These transactions are recorded on an immutable public ledger called Blockchain which is an append-only data structure. The popularity of Bitcoin has increased unreasonably. The general trend shows a positive response from the common masses indicating an increase in trust and privacy concerns which makes an interesting use case from the analysis point of view. Moreover, since the blockchain is publicly available and up-to-date, any analysis would provide a live insight into the usage patterns which ultimately would be useful for making a number of inferences by law-enforcement agencies, economists, tech-enthusiasts, etc. In this paper, we study various applications and techniques of performing data analytics over Bitcoin blockchain from a graph theoretic perspective. We also propose a framework for performing such data analytics and explored a couple of use cases using the proposed framework.
Covert Data Exfiltration Using Light and Power Channels

As the Internet of Things (IoT) continues to expand into every facet of our daily lives, security researchers have warned of its myriad security risks. While denial-of-service attacks and privacy violations have been at the forefront of research, covert channel communications remain an important concern. Utilizing a Bluetooth controlled light bulb, we demonstrate three separate covert channels, consisting of current utilization, luminosity and hue. To study the effectiveness of these channels, we implement exfiltration attacks using standard off-the-shelf smart bulbs and RGB LEDs at ranges of up to 160 feet. We analyze the identified channels for throughput, generality and stealthiness, and report transmission speeds of up to 832 bps.
A feature extraction technique in stereo matching network

Feature extraction is the basic and vital part of stereo matching. Accurate and effective feature extraction is the precondition of correct disparity estimation. Large-scale pooling is widely used to extract high-level features in the stereo matching network, but a lot of semantics is discarded. Meanwhile, frank bilinear interpolation with rare high-level information causes error propagation in the matching cost calculating. We apply Feature Pyramid Net to extract the multi-scale features with more context information and restore resolution layer by layer. The approach is validated on KITTI and Scene Flow.
Constrained State-Preserved Extreme Learning Machine

Reducing the training time for neural networks is a primary focus of research in the field of machine learning. Currently, the Levenberg-Marquardt (LM) method is one of the fastest backpropagation methods. A recently popular alternative to LM backpropagation is the Extreme Learning Machine (ELM), which produces a closed form optimization of a Single Layer Feed Forward Network for an initially randomized input weight matrix. In this study, we further extend the performance of an ELM by incrementally building on the state of the art, the State-Preserved ELM (SPELM), to produce a Constrained SPELM (CSPELM). To do so, we introduce a constraint, ", which randomly perturbs the input weight matrix after each training cycle, providing a honing mechanism during the search for a better local optimum. We evaluated CSPELM against 13 benchmark datasets, both categorical and continuous. For 8 of the 13 benchmark datasets, CSPELM outperformed, with respect to average accuracy and RMSE, the ELM, SPELM, and LM methods. Further, the results show that in 8 of the 13 benchmark datasets used, CSPELM was the best performing model and only reached a maximum of 195.10 seconds total training time in one example. The results show a more consistent and higher accuracy than the ELM and SPELM and competitive or better results with LM with training time being only approximately 10% of traditional LM backpropagation training time.
Circle Hough Transform (CHT) method applied to coin identifier and recognition based on computer vision and android developer

Abstract The coin recognition system is a very important role in currency processing systems which is aims to minimize human miscalculation and time to calculate coin values. The coin identifier and recognition to calculate the value of Indonesian coins based on android developer presented in this paper. The two coin parameters are used for coin recognition which are radius and colour of the coins. The Circle Hough Transform (CHT) method with gray scaling and erosion process as pre-processing image used to calculate radius parameters, based on android developer and computer vision. After the radius value is obtained then the average RGB value of the coins is detected to determine the colour parameters. By calculating the diameter and determining the colour, the coins can be recognized and the total value of the coins in the input image can be known. The experimental results show that the system has been obtained indicate that the system successfully recognized coins based on determined algorithm. That is mean the application using computer vision can recognition coin and count the value of coins.
Local Path Planning for USV Based on Improved Quantum Particle Swarm optimization

Unmanned surface vehicle (USV) plans the global path before the mission starts. Since there are some unexpected threats during the voyage, the global path that the USV sailing along needs to be adjusted locally. In this study, a local path planning algorithm combines velocity obstacle method (VOM) with improved quantum particle swarm optimization (IQPSO) algorithm is proposed. By VOM, the USV local path planning is transformed into a multi-objective and multi-constrained optimization problem, and the IQPSO algorithm is used for optimization. IQPSO is an improved algorithm which benefits from quantum computing and quantum-behaved particle swarm optimization. The results of simulations verify that the optimal velocity and course of the USV can be obtained by IQPSO to avoid the obstacle effectively.
Wavelet Feature Family for Image Compression

The article contains the results of experimental studies on compression of high and ultra-high definition images by families of wavelet functions. The dependences of the compression coefficients for the Daubechi, Haar, Coifleti, Meyer and biorthogonal wavelets are obtained. It is shown that wavelet functions allow more flexible control of the bit stream compared to a discrete cosine transform taking into account the detail of the images.
Security Test

Cybercriminals can break into any connected system. Traditionally, IT systems with their many open interfaces had been in the focus of attackers, while embedded systems were perceived to be too difficult to hack and not worth the time and energy required. But as systems have added Ethernet, WLAN, USB, Bluetooth, GPS, and other connectivity features, the number of attack surfaces has increased. The most popular hacking method involves attacking a diagnosis port, or otherwise open interface, which can give a malevolent party access to functions or, at least, the ability to corrupt data and prohibit performance such as denial-of-service attacks.
FPGA implementation of ECDSA for Blockchain

In this paper, we address Field Programmable Gate Array (FPGA) implementation of the Elliptic Curve Digital Signature Algorithm(ECDSA), which is suitable for cryptocurrencies in blockchain. Although the ECDSA requires high computational efforts, utilization of a specific logic circuit allows us quick and easy computation. In this paper, we give implementation results of the ECDSA on an FPGA, and mention the superiority of FPGA implementation by comparing its simulation result with that of software based computation using a CPU.
A Comparative Evaluation of Preprocessing Techniques for Short Texts in Spanish

Natural Language Processing (NLP) is used to identify key information, generating predictive models, and explaining global events or trends. Also, NLP is supported during the process to create knowledge. Therefore, it is important to apply refinement techniques in major stages such as preprocessing, when data is frequently produced and processed with poor results. This document analyzes and measures the impact of combinations of preprocessing techniques and libraries for short texts that have been written in Spanish. These techniques were applied in tweets for analysis of sentiments considering evaluation parameters in its analysis, the processing time and characteristics of the techniques for each library. The performed experimentation provides readers insights for choosing the appropriate combination of techniques during preprocessing. The results show improvement of up to 5% to 9% in the performance of the classification.
DiscreteZOO: A Fingerprint Database of Discrete Objects

Abstract In this paper, we present DiscreteZOO, a project which illustrates some of the possibilities for computer-supported management of collections of finite combinatorial (discrete) objects, in particular graphs with a high degree of symmetry. DiscreteZOO encompasses a data repository, a website and a SageMath Package.
Image Style Transformation Based on structure GAN

A large amount of datasets are required for the off-line test of unmanned vehicles. Since the redundancy of different image datasets exists, we apply image style transformation to generate new images, which simplifies the data collection process. In this paper, an image-to-image transformation network is proposed based on the structural information. The proposed network consists of two branches, one for style transfer and the other for semantic segmentation. We use the structural information extracted by semantic segmentation to constrain the transformed image, so that the transformed image can preserve the integrity and structural consistency. We conduct the experiments and comparisons with the baseline methods. The experimental results prove that the proposed method can generate more visually pleasant images for the tasks of day-night transformation, sunny-foggy transformation, and summer-winter transformation.
Saliency Detection With a Three-Stage Hierarchical Network

Deep learning approaches for saliency detection have attracted much attention and have been exploited widely in recent years. In this article, we propose a three-stage hierarchical neural network for modeling the detection. Initially, fast R-CNN is used to extract features for each superpixel, and the high-level prior information of traditional models is incorporated to weight the deep learning features. Next, in the regional stage, a self-attention mechanism is used to expand the receptive field from one superpixel to its surrounding and relevant regions. And last, saliency scores are sampled by the Gumbel–Softmax trick in a global regression model. In the experiments, we compare our models including two variations (networks without self-attention or prior weights) with 12 previous methods and test them on several benchmark datasets. Different kinds of strategies are also adopted for evaluation and the results demonstrate that our method achieves excellent performance.
Activity Learning for Intelligent Buildings

To be considered intelligent, buildings need to operate automatically in a user-centric fashion. Intelligent buildings enable and ensure a healthy, comfortable and productive working environment for their occupants. The transition to intelligent buildings requires systems that can recognise the needs and anticipate the behaviour of the users. We foster such a transition by learning the activities that occupants perform in buildings. This is done by first recognising the activities and then predicting the time of their next occurrences. The idea rests upon an existing formalism for a building environment and applies machine learning. We implement and deploy a system in a living lab environment, showing that it can learn diverse occupant activities with high accuracy, provided simple and unobtrusive sensors.
A Natural inclusion of Motives inside BDI agents

The Belief-Desire-Intention (BDI) model is amongst the most popular approaches to design agents with flexible behaviors. It is based essentially on the processing of beliefs and goals. On the other hand, it is well accepted that the motive is another relevant concept for the agent's reasoning and its flexibility. As the BDI model does not include this important concept, several BDI agent' s extensions, with a new and a special component expressing the motive, were proposed in the literature. In this paper, we show that it is not necessary to add this new component to express a motive, as a motive can be expressed by a particular type of goal. This allows including and taking advantage from the reasoning about motives without additional components.
ORB-based Template Matching Through Convolutional Features Map

Template matching is an important part of computer vision, but most of common methods do not work well in some cases, such as complicated background clutter, deformation and partial occlusion. Therefore, we present a novel method for image matching, which is useful, robust and fast. Its essence is the ORB-based Convolutional Features Map (CFM), which is used to measure the similarity between template and target image. We study its properties and apply it to a real-world dataset in complex environment. The result of experiments demonstrates that our algorithm outperforms other commonly used algorithm.
Efficient and Secure Group Data Sharing Model based on Selection scheme in Cloud environment

Cloud computing is said to be the service oriented computing technology, which are affordable and flexible over the internet. In past few years the cloud has become more matured and provided many services, one of the primary service is data sharing in Group, where the data can be easily shared from one member to another. However, while sharing the data security is one of the primary concern. In past several methodology has been proposed. However, these methods lacked from the feasibility. Hence, in this paper we have propose methodology is based on the selection scheme. Here General Group Key is generated and moreover General Key agreement protocol is decentralized based model where the data are controlled by the owner within the same group. Moreover, the proposed methodology is evaluated by analyzing the comparative analysis based on the various number of parameter. Result Analysis suggest that our methodology simply outperforms the existing one.
Video Summarization using Submodular Convex Optimization with Dynamic Support Vector Machine for Forest Fire Sequence Classification

This paper presents a new video summarization (VS) model, which summarizes the original and generally captured videos. The intention lies in the creation of precise summary which will convey the entire information. The summary includes the attractive and representative of the original video series. The earlier techniques are mainly based on simple considerations and optimizations. At the same time, they have utilized a hand-oriented objective which undergo sequential optimization by taking hard decisions. It restricts the usage in wide applicability. In this paper, a Submodular Convex Optimization (SCX) and dynamic support vector machine (DSVM) based VS model called Submodular Dynamical Video Summarization (SDVS) model is introduced. SCX is used for subset selection and DSVM is applied to classify the video summary. At the initial level, video sequence is given as input to the SDVS model. The transformation of input videos takes place to a set of frames. Next, extraction of key frames is carried out form the entire frame count for the generation of the video summary. For measuring the goodness of the SDVS model, a set of 8 videos are gathered from the Internet sources. The simulation outcome pointed out that the presented model achieved a maximum precision of 88.54, recall of 89.32 and accuracy of 88.91 respectively.
Building a Digital Learning Culture by Rethinking Pedagogies for the 21st Century

The new digital learning culture requires teachers to evolve with a learner-centred mindset and to become digital teaching designers, mentors, facilitators, and evaluators who effectively integrate technology into the educational context. Moreover, the digital learning culture has also led to a change in the roles of teachers as digital classroom managers as well as computational-thinking designers. This chapter discusses the effective integration of technology in building a new digital learning culture by considering existing and emerging pedagogies, teaching methods, and learning activities. The discussion centres on how to effectively integrate technology and create a new digital learning culture framed according to the 2017 ISTE Standards for Educators, where teachers are expected to model learner, leader, citizen, collaborator, designer, facilitator, and analyst roles throughout their profession.