Reviewerly/exHarmony huggingface dataset:
Efficient Particle Scale Space for Robust Tracking

Both siamese network and correlation filter (CF) based trackers have recently achieved superior performance in tracking scenarios with various challenging factors. For the challenging scale variations, most of these state-of-the-art trackers usually employ multiple patches with different bounding boxes to estimate the target size. However, these patches are fixedly generated by the hand-crafted bounding boxes in spatial domains, which may be suboptimal to cope with scale changes due to the lack of temporal scale information. In this letter, we tackle the problem of efficient scale estimation by presenting a generic scheme that allows the adaptive generation of bounding boxes in temporal domains and improves the tracking accuracy. Specifically, we introduce the novel particle scale space by refining the conventional particle filter and extend this space to many siamese and CF trackers for robust tracking. Extensive experiments are performed on the OTB2013, OTB50, OTB100 and UAVDT datasets. The proposed variants maintain at least almost identical frame-rates with baseline trackers and perform favorably against them, as well as other state-of-the-art trackers.
Web Server Integrity Protection Using Blockchain

Most web applications use a remote web server to store a wide range of client data e.g., financial records, breaking news, stock prices, weather forecast, and medical record. In this model, a web application is completely dependent on integrity of remote server. A hostile or compromised web server may lead to security breach of client data or compromise query results. Due to explicit trust on the remote server, if an attacker gets control over the web server, one cannot guarantee the data security even if client-side is secure. One can imagine that if an attacker tampers critical data like stock prices or diagnostic medical records of patient; present on a web server that is to be used in decision making, it can cause some severe monetary and health damages. Even if we cannot prevent the data from getting tampered, but we can detect if someone has illegally tampered it. Instead of being tamper resistant we have provided an efficient and secure tamper evident solution to this problem using permissioned block-chains. Proposed solution provides a strong evidence to data user (decision maker) that if data provided by server is tampered or not, even if server is complete control of an attacker. Using blockchain technology as a trust base, our practical solution guarantees the correctness and freshness of data with minimum overhead.
Investigation on the high-order approximation of the entropy bias

The estimation of entropy from experimental data has a considerable bias when the discretization of the variable domain is comparable to the sample size. In this case, the source of the bias is the difference between the a priori distribution and the observed distribution from sampled data. In this paper, we estimate the entropy bias considering an infinite sum of central moments of the binomial distribution using two probability mass functions. We analyze the bias in the light of the ratio between the number of the partition of the domain and the sample size. The main motivation of this study is improving statistical hypothesis testing in which probabilities are conceived beforehand. We examine the adequacy of high-order approximation according to the ratio between the sample size and the number of domain partitions. Finally, we expand the analysis to the entropy-derived mutual information and present an application for network reconstruction.
Parallel Stream Processing with MPI for Video Analytics and Data Visualization

The amount of data generated is increasing exponentially. However, processing data and producing fast results is a technological challenge. Parallel stream processing can be implemented for handling high frequency and big data flows. The MPI parallel programming model offers low-level and flexible mechanisms for dealing with distributed architectures such as clusters. This paper aims to use it to accelerate video analytics and data visualization applications so that insight can be obtained as soon as the data arrives. Experiments were conducted with a Domain-Specific Language for Geospatial Data Visualization and a Person Recognizer video application. We applied the same stream parallelism strategy and two task distribution strategies. The dynamic task distribution achieved better performance than the static distribution in the HPC cluster. The data visualization achieved lower throughput with respect to the video analytics due to the I/O intensive operations. Also, the MPI programming model shows promising performance outcomes for stream processing applications.
Real-Coded Genetic Algorithm Realizing Fast Convergence by Reducing Its Population Size

Just generation gap (JGG) is a generational alternation model of real-coded genetic algorithms (GAs), and is excellent at finding the global optimum solution of a function optimization problem. However, its population size is large, and therefore its convergence speed is low. A method to accelerate the convergence speed is to reduce the population size. However, if it is reduced throughout the search by JGG, the population diversity is lost, which may cause the failure to find the global optimum solution. The population size should be reduced during only a part of the search period during which the population diversity is not lost. In this paper, we propose a real-coded GA realizing fast convergence by introducing the reduction of the population size into JGG. In the proposed method, the population size is reduced during only an early or late search period. The performance of the proposed method is empirically evaluated by comparing it with only JGG and an existing GA.
Research on Text Classification Method Based on PTF-IDF and Cosine Similarity

Text classification is a foundational task in many NLP applications. The text classification task in the era of big data faces new challenges. We propose a Promoted TF-IDF (Promoted-TF-IDF) and cosine similarity method for text classification. In our model, with the pre-trained word segmentation tool, we apply PTF-IDF method to judge which words play key roles in text classification to capture the key components in category. We also apply Cosine Similarity algorithm to judge similarity between text and category. We conduct experiments on commonly used datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets.
Fusion of the Color Channels Using Densitometry for an Acute Lymphoblastic Leukemia Detection

Leukemia, especially Acute Lymphoblastic Leukemia (ALL) is a dangerous disease which attacks the bone marrow. One of the challenges of detecting the ALL quickly is the limitation of the methods to segment and find the main features. This paper aims to develop a model for the ALL detection using the color Densitometry features selection. Our proposed method purposes to improve the green channel original image through the contrast limited adaptive Histogram equalization and extract the main features based on the Densitometry. However, we utilized all of the image channels to find the main features. i.e., the red energy, the entropy, the red shanon entropy, the red log entropy, the green energy, the green entropy, the green shanon entropy, the green log entropy, the blue energy, the blue entropy, the blue shanon entropy, and the blue log entropy. We employed the acute lymphoblastic leukemia image database 2 (ALL-IDB2) to assess the performance of our proposed method. The proposed method has produced a good accuracy at 93.08% and standard deviation at 0.020. The novelties of this research are a new model to improve an image through the green channel, enhance the green channel images using the contrast limited adaptive Histogram equalization (CLAHE) method, and utilize unsharp mask filtering to obtain the best image segmentation. Furthermore, this paper contributes by developing a new model to detect the ALL using color Densitometry for other researchers in computer vision development.
A Uniform Payment System for Hyperledger Fabric Blockchain

The payment scheme of Fabric 2.0 alpha is threatened by counterfeit tokens and its users are client apps, not the end consumers. In this paper, a uniform payment system is designed with the following advantages. (1). By limiting the token-minting right to a designated token-minting bank and the design of label, our system prevents the counterfeit token problem. (2). A complete transaction is divided into Bitcoin-level part and Fabric-level part. The Bitcoin-level part constructs inputs and outputs and signs these inputs, outputs and a nonce with a private key. This makes a micro enterprises or an end consumer can enjoy the payment service as the Bitcoin-level operation can be implemented on a mobile phone. The double-spending problem is prevented by the Fabric-level mechanism. (3). The system is easy to expand. When there is a new payment application, only one sub-function is added to the payment contract to implement the payment application, and a call to the sub-function is added. (4). Compared with Bitcoin, the unlockcode of UTXO only needs to be stored once, instead of being stored with each UTXO, thus reducing the sizes of transactions. As an application, based on the uniform payment system, we design an application of cash-on-delivery without any trusted third party. It provides technology support for any two persons to trade without any trusted third party. Similar to the payment system, a system for asset confirmation, asset transfer and redemption is designed.
Text Entry System for Smartphone Operation with User's Dominant Hand

Smartphone is an electronic device which everyone uses every day. To enter text on smartphone, people use the text input methods which have been used for electronic devices so far. On the other hand, text input methods designed for smartphone have also been developed. As to the major input method for smartphones, one can hardly say that individual handedness is taken into account in the current interface design. For example, the button arrangement is quite easy for right-handed users to press any of the buttons. However, it is rather difficult for left-handed users to use the buttons. Everybody enters text on smartphone every day. Therefore, it is important to provide a user interface equally usable for all. Accordingly, it is considered necessary to have a text input system for smartphones which everyone can use easily. Therefore, in this research, we propose a smartphone text entry system designed for operation with user's dominant hand.
Attention Analysis in Caption Generation

Caption Generation is one of the fundamental tasks combining computer vision and natural language processing. To achieve this goal, neural networks are employed to implement a caption generation system. In this paper, we proposed a caption generation system combining a CNN-based object detection system and a language model with a recurrent neural network. Especially, a vector which is sent from the object detection system to the language model is generated using an attention mechanism. Attention visualization can help us to understand the system focuses on a part of the input image in generating a caption. In the experiments, we evaluate the performance of the proposed system and discuss the effects of the attention mechanism in the image caption. Especially, the attention contributes to the improvement of caption generation but the attention is uncorrelated to system interpretation.
EYE SPY Face Detection and Identification using YOLO

YOLO (You Only Look Once) is a state of the art object detection system. The object detection algorithms of YOLO is taken and is used on a custom made dataset to identify the person on camera. This method will revolutionize surveillance and security methods. This paper addresses fundamental challenges faced in making the dataset. It also compares conventional methods with YOLO. YOLO is the fastest and most accurate object detection technique. This paper also states the applications of this technology along with its advantages and various disadvantages. It uses a multi-scale training method that can run at various sizes offering an excellent relation speed and accuracy. We have based our model on yolo v2 all the while bringing our own changes for shifting from object detection to Face identification.
Speeding up SimRank computations by polynomial preconditioners

SimRank is popularly used for evaluating similarities between nodes in a graph. Though some recent work has transformed the SimRank into a linear system which can be solved by using conjugate gradient (CG) algorithm. However, the diagonal preconditioner used in the algorithm still left some room for improvement. There is no work showing what should be an optimal preconditioner for the linear system. For such a sake, in this paper, we theoretically study the preconditioner problem and propose a polynomial preconditioner which can improve the computation speed significantly. In our investigation, we also modify the original linear system to adapt to all situations of the graph. We further prove that the condition number of a polynomial preconditioned matrix is bounded in a narrow interval. It implies that the new preconditioner can effectively accelerate the convergence rate of the CG algorithm. The experimental results show the proposed algorithm outperforms the state-of-the-art algorithms for the all-pairs SimRank computation.
Toward Optimum Resiliency: Practical Alignment of BCM and ERM Processes

Nowadays, enterprise business processes significantly relay on sophisticated and complex IT technologies to enhance business productivity which in turn increase operations' risks of failures and interruptions. Enterprises strive to continuously improve their business continuity program by testing business continuity plans to ensure optimum productivity and resiliency. However, usually observations recorded from drills' activities are not addressed effectively and in timely manner. Business Continuity Management (BCM) and Enterprise Risk Management (ERM) processes should be aligned together to reduce the time to address recorded observations. This paper illustrates a new implemented approach by aligning BCM and ERM processes. The alignment is based on extended collaboration. For example, risk register should be designed and shared collaboratively with business continuity practitioners to ensure defining accurate risks mitigation plans and controls for all drills' scenarios as well. Both processes practitioners shall work closely in conducting Business Impact Analysis and Risk Assessments (BIA/RA) and measure the impact of IT services' disruptions. Moreover, ERM practitioners should be involved in all BCM activities to detect areas of improvement and measure the effectiveness of developed plans and controls. Once observations are recorded from business continuity and disaster recovery (BC/DR) drills and compliances, they should be presented to a joint steering committee, prioritized based on severity and then transferred to the enterprise risk register. Both processes should operate side by side toward robust IT operations. This practice has been proven to be very practical and effective to reduce business overall risks and overdue observations.
Study on Optimal Design of Floating Body and Regulating Mechanism of Water Purification Equipment

In order to meet the requirements of the water purification equipment with floating body and improve its operation stability and comprehensive performance, the design and research of floating body and height self-adjusting mechanism are carried out, and the comprehensive operation characteristics of spherical and regular tetrahedral floating body are compared and analyzed. Based on the simulation study, an analysis model of floating body regulating mechanism is established, and its dynamic performance optimization analysis based on parametric adjustment is carried out. The position of key hinge points of the mechanism and the theoretical basis of self-adjusting cylinder selection are determined. The results show that the mass of spherical floating body is about 30% less than that of tetrahedron with the same buoyancy.
Evolutionary Approach based Scheduler for Speculative Task Execution

Hadoop is an open source framework to implement MapReduce. It stores and processes the data in distributed, highly scalable, parallel and fault tolerant environment. Job scheduling shows a significant role in optimizing the functioning of Hadoop. Hadoop default scheduler is not suitable for heterogeneous environment and not robust to identify the stragglers task which prolongs total execution time. Evolutionary approach based scheduler for speculative task execution is proposed in this paper. In this work we are proposing a new method to select the best nodes to run the speculative copy of the slow task. Two parameters such as network information and resource utilization are used to select the optimal nodes to execute the speculative copy of the stragglers task. Experiments have been conducted on web log file of academic website for obtaining the click count. Experimental results show that the execution time is reduced by 31% for 1 GB input data and 23% for 2 GB input data. On an average, the execution time is improved by 21% compared to conventional scheduler.
Projection of a Renewable Energy System for the Observatory of Extraterrestrial Life in Ecuador and Peru

For this research work, a system composed of solar panels was considered as a renewable energy source for a future Extraterrestrial Life Observatory in the Galapagos-Ecuador and San Lorenzo-Peru islands, since they are strategic points where nature and tranquility they are an alloy for these scientific tasks in favor of knowing signs of the real existence of life in other places outside the earth. In this article we consider useful the energy produced by solar radiation to transform into electric energy that will serve to supply the different facilities of the Observatory projected in each locality. The simulations were carried out in two parts, one in MATLAB based on a mathematical model that was transformed into a design tool based on different income data for different radiation conditions and the other in DIALUX to represent the base structure of the equipment and the conditions of luminosity of the observatory without interfering with the microanalysis and the signals that can be received from outer space.
RecordFlux: Formal Message Specification and Generation of Verifiable Binary Parsers

Various vulnerabilities have been found in message parsers of protocol implementations in the past. Even highly sensitive software components like TLS libraries are affected regularly. Resulting issues range from denial-of-service attacks to the extraction of sensitive information. The complexity of protocols and imprecise specifications in natural language are the core reasons for subtle bugs in implementations, which are hard to find. The lack of precise specifications impedes formal verification. In this paper, we propose a model and a corresponding domain-specific language to formally specify message formats of existing real-world binary protocols. A unique feature of the model is the capability to define invariants, which specify relations and dependencies between message fields. Furthermore, the model allows defining the relation of messages between different protocol layers and thus ensures correct interpretation of payload data. We present a technique to derive verifiable parsers based on the model, generate efficient code for their implementation, and automatically prove the absence of runtime errors. Examples of parser specifications for Ethernet and TLS demonstrate the applicability of our approach.
Interpretation of Scimago Ranking in Terms of Success Probabilities

We derive various probabilistic expressions about the capabilities of certain social groups to complete a scientific article with prospective indexation in the database Scopus. Guided by the latest Scimago ranking, we carried out estimations about the produced scientific papers by taking into account different factors that might be crucial for a optimal production of papers. We illustrate the case of Peru where the proposed model is applied resulting in that 1 of 10 trained professionals might be erroneously accounted in the Scimago ranking of the total number of published Scopus documents.
iDocChip - A Configurable Hardware Architecture for Historical Document Image Processing: Text Line Extraction

Digitizing historical archives poses a great challenge due to the quality degradation existing in these documents. Hence, even well-established Optical Character Recognition (OCR) systems, such as Abby, OCRopus, Tesseract, etc., fail to give sufficient recognition accuracy for historical archives, since they are optimized for transcribing contemporary documents. In contrast, the open-source anyOCR system is designed specifically for digitizing historical documents with state-of-the-art image processing techniques, to achieve high accuracy. Nowadays, the retrieval of historical document images for further OCR requires special scanning devices that are bulky and stationary. As a result, a portable device that combines scanning and OCR capabilities is beneficial to transcribe documents without the need to remove them from where they are archived. For example, smart goggles equipped with embedded OCR device can be used for instant word spotting. However, the available anyOCR software implementation has long runtime and high power consumption. As a solution, we propose a low power, energy-efficient accelerator with real-time capabilities called iDocChip, which is a hybrid hardware-software programmable System-on-Chip (SoC) for digitizing historical documents. This chip can be easily integrated in a portable device. This paper focuses on one of the most crucial processing steps in anyOCR: text line extraction. We propose, to the best of our knowledge, the first hybrid hardware-software architecture of the text line extraction technique implemented on an FPGA based programmable SoC. The resulting custom hardware accelerator outperforms the existing anyOCR software implementation by 120×, while achieving 1700× higher energy efficiency without affecting the high accuracy of the system.
Convolutional Neural Networks on Time Series for Smartphone Application Activations Using Wavelet Transform

This research focuses on time series of smartphone application activation (SAA) analyses. The inputs are big data of three categories of time series automatically collected from a single source panel of individuals from smartphones. The types of consumer activation outputs are precategorized. This study extracts effective features to identify categories of individual website access activities. This is an important but challenging task in the age of digital marketing. Most existing studies rely on conventional simple statistical analyses, time-series analyses, or shallow neural networks that cannot identify features to accurately classify plural categories of website access activities. This study proposes an automatic feature learning model from the raw inputs for the SAA problem by using a deep convolutional neural network (CNN) and Bayes optimization. Data are non-converted or converted with two levels by wavelet transformation for possible application in digital marketing.