Reviewerly/exHarmony huggingface dataset:
Splicing detection in color image based on deep learning of wavelet decomposed image

Image splicing is done by duplicate part(s) of an image and pasted in a different image. This basic technique is very common in image forgery therefore it reduced the user confidence in the digital image. The need for a reliable and effective method to detect this type of counterfeiting has been increased. Splicing detection in colour image based on Deep Learning of Haar wavelet decomposed image is developed in this work. The colour image is converted to grayscale and decomposed into 1st level (LL1, LH1, HL1, and HH1). Then to detect splicing, we used the semantic segmentation of the decomposed image. Consider that the Convolutional Neural Network model was used to segment the decomposed LL1 image. The SegNet is applied to boost the semantic segmentation and utilize the classification in our proposed approach. The experimental work confirmed the efficiency of the suggested method where the forgery (splicing) detected in 89% of the tested images with a very high percentage of localization.
Revelio: A Lightweight Captcha Solver Using a Dictionary Based Approach

Captcha is an important security measure used by many websites to defend against malicious bot programs. However, with the advancement in the field of computer vision, seemingly complex Captcha schemes have been broken. Although Captcha solving techniques have improved significantly, we observed that many major banking and government websites are still relying on a relatively simple class of text Captchas to counter bot attacks. In this paper, we demonstrate that Captcha schemes deployed on State Bank of India (SBI), Axis bank and Indian Railways (IRCTC) websites can be easily broken using a repertoire of standard image processing techniques. We develop a Captcha solver tool called Revelio which is lightweight, automatic, efficient, and requires minimal labeled data and works in real-time. We evaluate the performance of our tool with the state-of-the-art CNN model on diverse Captcha schemes from 14 major Indian websites. The proposed solver achieves at least 90% accuracy on 10/14 Captcha schemes. Further, we found that for the targeted class of Captcha schemes and a given amount of labeled data, our solver outperforms the CNN based solver.
Research on items Recommendation Algorithm Based on Knowledge Graph

Traditional recommendation systems mostly use collaborative filtering algorithm, which has problems with cold start and data sparseness. A common idea to solve these problems is to introduce some auxiliary information as input in the recommendation algorithm. The knowledge graph contains rich semantic information, which can provide potential assistance for the recommendation system. The research on the existing recommendation methods based on knowledge graphs found that these methods lacked the consideration of entity attributes information. Therefore, this paper considers attribute factors and proposes the interest modeling method the entity attributes-based in the knowledge graph, and fusions with traditional collaborative filtering algorithm to improve the recommended effect. The results show that the proposed recommended algorithm has better property than other commonly used benchmark algorithms.
Implementation of a Fashion Virtual Assistant with the Use of a Kinect v2 Camera and Image Processing

This article is about the problem and development of a fashion virtual assistant proposed by using a Kinect v2 camera and image processing, for fashion retail stores. It comes up mainly as a response to the inability of providing unique experiences during the shopping process through the use of diverse devices. Because of this, similar virtual assistant solutions, oriented to provide clothing recommendations, were analyzed to be able to provide software that could give a more personalized suggestion for the users based on their physical characteristics.
An effective coded exposure photography framework using optimal fluttering pattern generation

Motion blurring often occurs during camera imaging process due to the relative motion between the scene and imaging sensor, which would degrade the quality of captured image. In this paper, an effective coded exposure photography framework is proposed to help obtain better motion deblurring results. An integrated optimal fluttering pattern generation criterion with several factors is designed to guarantee the limited frequency information loss using coded exposure photography. Furthermore, an effective fluttering code sequence search algorithm is put forward to obtain the optimal code sequence quickly with perfect balance consideration of global search and local search. The differential evolution algorithm is utilized to carry out effective global searching. Meanwhile, simulate annealing algorithm is introduce to implement supplementary local optimal search, which could help avoid falling into local optimizations and reduce the iteration numbers significantly. Various groups of synthetic and real-world deblurring experiments are carried out to verify the effectiveness of the designed imaging framework. Experimental results demonstrate that the optimal fluttering pattern could be calculated quickly by the presented approach and the deblurring images have superior subjective visual perception and objective image quality assessment performance.
IoT based Image Defogging System for road accident control during winters

With the scattering and absorption of light by atmospheric particles in the fog, outdoor images have bad visibility under poor weather conditions. Images captured outside depend upon various factors for having reduced in contrast and colors which may be due to the presence of haze, fog, rain, and so on. Because of foggy weather conditions during winters, it has become very difficult for drivers to identify the obstacles and other vehicles, consequently led them to have road accidents. To avoid this, the defogging system has been introduced to enhance the image visibility and improves the performance of computer vision at a significant rate. Authors have studied the specific area of a subject related to haze in which is adopted from different image dehazing methods for effective haze removal from the foggy image. They have developed an IoT-based dehazing system that can reduce the number of accidents that happen on the roads due to the problem of poor visibility that happens because of haze particles suspended in the atmosphere especially during winters.
DaDu series

Research on accelerators for robotics is increasing. This article introduces the kinematics, motion planning and collision detection algorithms and our accelerators in robotics, and then analyzes their advantages, disadvantages and bottlenecks. In view of the shortcomings of the existing accelerators, this paper will show a series accelerators named "DaDu" that we have proposed. For kinematics, we have proposed Dadu [1] to accelerate the inverse kinematics algorithm, which achieves 1700x speedup than the CPU implementation, 30x speedup than the GPU implementation, and 776x higher energy efficiency than the GPU implementation. For motion planning, we have proposed Dadu-P [2] to accelerate the PRM algorithm. It can get 26.5x speedup than an existing CPU-based approach for collision detection. Furthermore, with an incremental approach, the performance of motion planning can further be improved by 10x while the solution quality is degraded by 10% only. For the collision detection algorithm in motion planning, the proposed accelerator Dadu-CD [3] elaborates the in-memory processing architecture, achieving at least 5x speedup than Dadu-P in the total planning time and 9.55x lower energy consumption than Dadu-P.
Cubature Kalman Filter Based Multi-sensor Data Fusion Algorithm for Maneuvering Target Tracking

To improve the accuracy of ballistic target tracking, a multi-sensor data fusion algorithm based on Cubature Kalman filter is introduced in this paper. Firstly, the dynamic model of a midcourse ballistic target is established via force analysis of the flight in the Earth Centered Inertial (ECI) frame. The model is then transformed to the local East-North-Up (ENU) frame via coordinate transformation. Secondly, one certain sensor is selected as the information fusion center, and a centralized measurement model is constructed through multi-sensor measurement augment in the ENU frame. Finally, based on the state-space equation and observation equation established previously, a Cubature Kalman filter algorithm is designed to obtain the real-time state estimation of the target, and the tracking effects as well as the algorithm performance are evaluated through Monte Carlo simulations. The results of simulation show that the proposed algorithm can achieve high precision and stable tracking performance of the target.
A new signal denoising method based on double dictionary learning and moving ruler strategy

Abstract The denoising of vibration signals is useful with regards to various applications such as equipment monitoring. This study proposes a double dictionary learning method with a moving ruler strategy. The moving ruler strategy is used to construct a multi-layer and multi-position dictionary to reduce the gap between adjacent columns. However, the drawbacks of this strategy are high dictionary redundancy and reduced computation efficiency. Considering this issue, a double dictionary model is proposed combining the fixed dictionaries with learning dictionaries. The sparse coding and dictionary updating are improved correspondingly. Simulations demonstrate that the proposed method can outperform the existing denoising approaches. Moreover, an application of gearbox is presented to further validate the proposed method.
A research on security problems with data storage in cloud computing

Abstract Cloud computing is a transformative mechanism that changes the way hardware and software design and procurement are built for businesses. Everybody pushes data and application applications to cloud data centers due to cloud simplicity. The Cloud Service Provider (CSP) can maintain integrity, accessibility, privacy, and confidentiality, but CSP does not provide client and stored customer data with secure data services. This research discusses problems related to the storage of cloud data, such as data breaches, data theft, and cloud data unavailability. Finally, we are offering potential solutions to related cloud problems.
Control power of quantum channels is not multiplicative

Abstract We construct a family of parallel channels for controlled quantum communication, and show that the control power of a quantum channel can become larger when it is combined with a zero control power channel. As a consequence, we demonstrate for the first time that control power is non-multiplicative. In contrast to previous results which indicated that increasing control power requires the consumption of channel capacity or the consumption of additional non-local three-qudit entangled resources, in our construction the quantum capacity of the tensor product of two channels remain unchanged and it does not consume non-local three-qudit entangled resources. We show that this lossless channel capacity method is quite generically applicable: every channel can be embedded into our construction, and control power is increased whenever the given channel configuration matches the effect of local operations. Our protocol not only provides a feasible method for achieving higher control power for information transmission with less entangled resources, but also provides new understanding of the resource theory of quantum channels.
Energy Minimization for Cloud Services with Stochastic Requests

Energy optimization for cloud computing services has gained a considerable momentum over the recent years. Unfortunately, minimizing energy consumption of cloud services has its own unique research problems and challenges. More specifically, it is difficult to select suitable servers for cloud service systems to minimize energy consumption due to the heterogeneity of servers in cloud centers. In this paper, the energy minimization problem is considered for cloud systems with stochastic service requests and system availability constraints where the stochastic cloud service requests are constrained by deadlines. An energy minimization algorithm is proposed to select the most suitable servers to achieve the energy efficiency of cloud services. Our intensive experimental studies based on both simulated and real cloud instances show the proposed algorithm is much more effective with acceptable CPU utilization, saving up to 61.95% energy consumption, than the existing algorithms.
Pre-processing of data on the behavior of users of the Moscow Electronic School service

Abstract The article presents the results of preliminary processing of data on the behavior of users of the Moscow Electronic School with a volume of about 50 thousand observations. The relevance of the study is due to the increase in the volume of distance and mixed education in the context of digitalization of space. The aim of this study was to identify groups of teachers (users) exhibiting differing behaviors when interacting with educational content on the Moscow Electronic School platform. According to the results of the study, the relationships between the behavioral parameters of users were determined and 5 behavioral types of the Moscow Electronic School users were identified, which will further allow using this approach in increasing the efficiency of the Moscow Electronic School platform as a recommendation system.
Shape measurement of a thin glass plate through analyzing dispersion effects in a white-light scanning interferometer

Utilization of a complex-valued interference signal (CVIS) in a white-light scanning interferometer is a powerful method to measure exactly surface positions of a glass plate. In this method a zero phase position nearest to a maximum amplitude position in the CVIS is a measured surface position. Proportional coefficients that relate the maximum amplitude position and the zero phase position of a rear surface to the thickness of a glass plate are required for measuring exactly the thickness. These coefficients and periodical position jump occurring on the zero phase position are investigated by simulations. The number of occurrences of the position jump is calculated from the maximum amplitude and zero phase positions. Shape measurement of a glass plate with about 20 μm-thickness is achieved by using the coefficients and the number of occurrences of the position jump.
Channel Pruning for Accelerating Convolutional Neural Networks via Wasserstein Metric

Channel pruning is an effective way to accelerate deep convolutional neural networks. However, it is still a challenge to reduce the computational complexity while preserving the performance of deep models. In this paper, we propose a novel channel pruning method via the Wasserstein metric. First, the output features of a channel are aggregated through the Wasserstein barycenter, which is called the basic response of the channel. Then the channel discrepancy based on the Wasserstein distance is introduced to measure channel importance, by considering both the channel’s feature representation ability and the substitutability of the basic responses. Finally, channels with the least discrepancies are removed directly, and the loss in accuracy of the pruned model is regained by fine-tuning. Extensive experiments on popular benchmarks and various network architectures demonstrate that the proposed approach outperforms the existing methods.
Deep Learning on the Web: State-of-the-art Object Detection using Web-based Client-side Frameworks

In the present paper we make a comparative study and evaluation of frameworks and libraries for deep learning purposes on the client-side, considering libraries such as TensorFlow.js, brain.js, Keras.js, ConvNet.js and others. It is examined how feasible and efficient it is to execute deep learning tasks, using client-side libraries and frameworks in contrast to the conventional approach. Moreover, we focus on the computer vision field of object detection and we examine the problem of object detection through different state-of-the-art approaches and object detectors. At the same time, we evaluate whether it is feasible and efficient to detect objects in the browser environment using a prototype implementation based on some of the libraries that are studied.
Developing a Model and a Tool for the Formation of Project Teams

Given the importance of team quality in the success of a project, this chapter presents a summary of the results obtained in 10 years of work, dedicated to the study and development of models and tools for the formation of project teams, from identifying the diversity of factors that can be taken into account in different contexts. The main characteristics of a model and versions of a configurable tool that support the model are described, which facilitates its application in different contexts and allows experimenting with different algorithms and solution methods to identify those that offer the best results.
Automatic fuzzy rules production based on clustering and implication selection

This paper deals with improving the approximation capability of fuzzy systems. Fuzzy negations produced via conical sections are a promising methodology towards better fuzzy implications in fuzzy rules. The linguistic variables and the fuzzy rules are induced automatically following a fuzzy equivalence relation. The uncertainty of linear or nonlinear systems is thus dealt with. In this study, the clustering is optimized without human intervention, but also the best inference mechanism for a particular dataset is prescribed. It has been found that clustering based on fuzzy equivalence relation and fuzzy inference via conical sections leads to remarkably accurate approximations. A fuzzy rule based system with fewer control parameters is proposed. An application on telecom data shows the use of the methodology, its applicability to a real problem and its performance compared to other alternatives in terms of quality.
A Markov Process-Based Anomaly Detection of Time Series Streaming Data

When monitoring highly dynamic time series streaming data, the traditional anomaly detection methods have many disadvantages, e.g., inadaptability to the dynamic streaming data and low detection accuracy. In order to distinguish abnormal data from streaming data, a Markov Process-Based anomaly detection method is proposed. Firstly, the dimensionality of raw data is reduced through Locality Preserving Projection algorithm to bring redundancy down. Then, an Elbow-Based K-Means clustering algorithm is proposed to determine the rational number of mode, and the dimensionally-reduced data are divided into normal and abnormal modes through the algorithm. Finally, a Markov Process-Based anomaly detection model is established to detect the data in abnormal mode, where the effect of concept drift is avoided and a loose mode containing the data in the future unknown mode is introduced into the model to enhance model integrity. Simulation results demonstrate that the proposed method can achieve quasi-optimal performance compared with other contrasting methods.
Blockchain-Based E-Voting Protocol

Today, each individual voter expects that his vote is counted correctly, and that the election results are not rigged. Every voting process affects voter’s trust in the system. In other words, it is very important that the government is able to successfully hold non-fraudulent, transparent, trustworthy and most importantly, secure elections to affirm the faith of public. In this paper, we propose a system that makes use of blockchain to make the elections secure, maintain anonymity of voter and is decentralized. We propose a new e-voting protocol which adheres to the fundamental e-voting properties and in addition offers to make the process decentralized.
Identification of related languages from spoken data: Moving from off-line to on-line scenario

The accelerating flow of information we encounter around the world today makes many companies deploy speech recognition systems that, to an ever-growing extent, process data on-line rather than off-line. These systems, e.g., for real-time 24/7 broadcast transcription, often work with input-stream data containing utterances in more than one language. This multilingual data can correctly be transcribed in real-time only if the language used is identified with just a small latency for each input frame. For this purpose, a novel approach to on-line spoken language identification is proposed in this work. Its development is documented within a series of consecutive experiments starting in the off-line mode for 11 Slavic languages, going through artificially prepared multilingual data for the on-line scenario, and ending with real bilingual TV programs containing utterances in mutually similar Czech and Slovak. The resulting scheme that we propose operates frame-by-frame; it takes in a multilingual stream of speech frames and outputs a stream of the corresponding language labels. It utilizes a weighted finite-state transducer as a decoder, which smooths the output from a language classifier fed by multilingual and augmented bottleneck features. An essential factor from the accuracy point of view is that these features, as well as the classifier itself, are based on deep neural network architectures that allow the modeling of long-term time dependencies. The obtained results show that our scheme allows us to determine the language spoken in real-world bilingual TV shows with an average latency of around 2.5 seconds and with an increase in word error rate by a mere 2.9% over the reference 18.1% value yielded by using manually prepared language labels.
Phone Calls Speech-to-Text: A Comparison Between APIs for the Portuguese Language

Automation and artificial intelligence technologies are bringing improvements in efficiency and cost savings to companies in different areas. These technological developments and software use, platforms and infrastructure as a service have grown significantly in recent years, especially in the field of speech-to-text conversion. The rapid scalability and abstraction associated with the democratization of technology may be the cause of this growth. Developers often make the latest technologies and solutions available through routines and programming standards, the Application Programming Interface—API. Therefore, this paper aims to compare the effectiveness of speech-to-speech conversion tools available by cloud solution providers based on Portuguese telephone call recordings. For the development of this paper, some audio recordings and text conversion were made in order to compare the efficiency of the conversion. The test shows that Word Error Rate (WER) has a direct impact on conversion quality.