Reviewerly/exHarmony huggingface dataset:
A Fine Tuned Model of Grasshopper Optimization Algorithm with Classifiers for Optimal Text Classification

Text classification is widely used application of natural language processing and machine learning classifier. Tuning hyper-parameters of the classifier in machine learning is difficult and important step. The tuning of hyper-parameters means selection of the optimal hyper-parameters of the algorithm. Many approaches for tuning of the parameters have been tested and applied in the literature for the improvisation of the classification performance. Further, the feature selection using machine learning is a major task, dealing with high-dimensional dataset. In this paper, we proposed a hybrid model of tuned Grasshopper optimization algorithm with classifiers. Grasshopper optimization algorithm works on the mimic behavior of grasshoppers. The aim of this meta-heuristic approach is to determine the minimal feature subset from all features to improve the classification performance. For tuning of the classifiers, we have used random search technique. The classifiers chosen for classification are K-Nearest Neighbor and Support Vector Machine. Five multi-class datasets are used to evaluate the performance of the model in terms of Accuracy and AUC curve. All results are computed with 10-fold-cross validation method. The evaluated results of the proposed model is compared with other algorithms, which verifies the performance of our technique. The proposed model outperformed among all the compared state-of-the-art techniques.
Translation between High- and Low-frequency SAR Images using Cycle-Consistent Conditional Adversarial Network

Abstract The bi-frequency (high- and low) synthetic aperture radar (SAR) images cannot be directly compared due to their distinct statistical properties. To diminish their statistical difference, we manage to translate the bi-frequency SAR images into one another. Therefore, we propose a cycle-consistent conditional adversarial network to achieve the goal. The cycle-consistency criteria in the Cycle GAN and the conditional generation adversarial networks in the Pix2Pix are integrated to construct the cycle-consistent conditional adversarial network. Experiments on Ku-band and P-band SAR images validate that our method outperforms Cycle GAN and Pix2Pix.
Weight-Dependent Gates for Differentiable Neural Network Pruning

In this paper, we propose a simple and effective network pruning framework, which introduces novel weight-dependent gates to prune filter adaptively. We argue that the pruning decision should depend on the convolutional weights, in other words, it should be a learnable function of filter weights. We thus construct the weight-dependent gates (W-Gates) to learn the information from filter weights and obtain binary filter gates to prune or keep the filters automatically. To prune the network under hardware constraint, we train a Latency Predict Net (LPNet) to estimate the hardware latency of candidate pruned networks. Based on the proposed LPNet, we can optimize W-Gates and the pruning ratio of each layer under latency constraint. The whole framework is differentiable and can be optimized by gradient-based method to achieve a compact network with better trade-off between accuracy and efficiency. We have demonstrated the effectiveness of our method on Resnet34 and Resnet50, achieving up to 1.33/1.28 higher Top-1 accuracy with lower hardware latency on ImageNet. Compared with state-of-the-art pruning methods, our method achieves superior performance(This work is done when Yun Li, Weiqun Wu and Zechun Liu are interns at Megvii Inc (Face++)).
The Impact of Digital Text

Abstract This chapter discusses reading on screen and in print, as the emergence of digital age has transformed our reading and attention. Digital reading reshapes the concept of reading with the use of various forms of social media that are full of acronyms and emoticons or emoji. Advantages and disadvantages of reading on screen and in print are reviewed. The effects of digitally-mediated text on information processing and reading comprehension are also discussed. Although reading online has merits, such as convenience, low cost, and easy accessibility, readers are likely to scan through an F-shaped gaze pattern. The use of digital media may have a significant influence on brain networks due to the brain’s adaptability and accommodating abilities. Digital text that includes more images and visual aids than hardcopy text may lead to more balanced brain functions. This may have implications for reduced script relativity in the future.
Universal Bounds for Size and Energy of Codes of Given Minimum and Maximum Distances

We employ signed measures that are positive definite up to certain degrees to establish Levenshtein-type upper bounds on the cardinality of codes with given minimum and maximum distances, and universal lower bounds on the potential energy (for absolutely monotone interactions) for codes with given maximum distance and cardinality. The distance distributions of codes that attain the bounds are found in terms of the parameters of Levenshtein-type quadrature formulas. Necessary and sufficient conditions for the optimality of our bounds are derived. Further, we obtain upper bounds on the energy of codes of fixed minimum and maximum distances and cardinality.
TRETA-A Novel Heuristic Based Workflow Scheduling Algorithm in Cloud Environment

Cloud computing is a new computing paradigm that lets users access services over the internet. Cloud provides scalable, on-demand resources and highly accessible. Cloud charges its customers for only the usage. Workflow applications are used in many business processing, scientific fields and in many other domains. Cloud has become one of the optimum solutions for executing workflows as for the computing power and the benefits it offers. Workflow scheduling can reduce the overall cost of execution and optimize resource utilization in the cloud for both the cloud consumer and the service provider. In this paper, We compare our novel algorithm Total Resource Execution Time Aware Scheduling Algorithm (TRETA) with existing heuristics which considers the total execution time of the computing resource as a factor for finding an optimal schedule. To the best of our knowledge none of the previous work has not considered the above metric for finding a better schedule. We compare the proposed algorithm with state-of-art heuristics First Come First (FCFS), Maximum Completion Time (MCT), Maximum Execution Time (MET), MaxMin, MinMin and Distributed Heterogeneous Earliest Finish Time (DHEFT) in the heterogeneous computing environment. The proposed algorithm is compared with other heuristics for the Makespan, Throughput and Degree of Imbalance. The experimentation is done for real workload traces of the CyberShake workflow of different task sizes generated from the Pegasus workflow management system using WorkflowSim. The proposed algorithm gives a better makespan and a better throughput like the other heuristics and results in a better Degree of Imbalance than the other heuristics.
An Adaptive Cubature Kalman Filter Algorithm for Passive Location

Modern stealth fighters usually keep the radar silent during the flight mission. In order to ensure the stealth performance, the integrated electronic warfare system generally uses the electronic support measures for passive detection. It locks the target's position by receiving radiation from the target sensor. In this paper, the aircraft flight maneuver model is established. The interactive multiple model is used to describe the motion state of aircraft. Residual and innovation is used improve cubature kalman filter to adaptive cubature kalman filter. Cubature kalman filter and adaptive cubature kalman filter were used for filtering respectively, and compared the effect. Then analyzed their root mean square error.
UFOD: An AutoML framework for the construction, comparison, and combination of object detection models

Object detection models based on deep learning techniques have been successfully applied in several contexts; however, non-expert users might find challenging the use of these techniques due to several reasons, including the necessity of trying different algorithms implemented in heterogeneous libraries, the configuration of hyperparameters, the lack of support of many state-of-the-art algorithms for training them on custom datasets, or the variety of metrics employed to evaluate detection algorithms. These challenges have been tackled by the development of UFOD, an automated machine learning framework that trains several object detection algorithms (using different underlying frameworks and libraries), compares them, and finally selects the best model or ensembles them. Currently, the most well-known object detection algorithms have been included in our system, and new methods can be easily incorporated thanks to a high-level API. UFOD is available at https://github.com/ManuGar/UFOD/
Camera Latency Review and Parameters Testing for Real-Time Object Detection Implementation

In recent years, as computer vision and machine learning technologies continues to improve and become more available to general public, implementation of machine learning and computer vision based object detection also grown in popularity. Even more recently, advancement in computational power had enabled such systems to be implemented to process real time data and output real time decisions, such as surveillance, driving assistance, or pedestrian tracking. Computer vision-based object detection, in general, allows these implementations to be implemented on various kinds of cameras - USB Webcam, IP Camera, etc. While such implementations could do real-time processing and real-time decision making, but computer vision includes capturing process, which sometimes is caused by cameras itself. This paper reviews latencies caused by IP Camera and USB Webcam, by comparing captured stopwatch to real value of the stopwatch, and the data is used to compare those latencies to theoretical standard of real-time, imperceptible latency, to further compiled into a optimized parameters to be used in other implementations. Finally, this paper is used to test whether those latencies could be reduced by optimizing capture parameters so that computer vision implementations are true real-time from capture to output. These optimized settings could yield 176,67 milliseconds latency on USB Camera and 128,4 milliseconds on specific IP Camera.
Motion Planning of Redundant Dual-arm Based on Improved RRTstar

In view of the lack of search orientation and slow convergence speed of RRT * algorithm, this paper proposes an improved RRT * path planning algorithm. Firstly, after sampling random points, the attractive function is introduced to guide the expansion direction of the random tree. Then, the variable multiple step size method is used. Every time the random tree is expanded, if no collision is detected, the step size will be doubled in the next expansion, and so on; if collision is detected, the step size will be adjusted back to single step size for expansion, so as to speed up the algorithm and improve the efficiency. Then, after analyzing the workspace of the redundant double manipulator, the possibility of collision between the two arms is confirmed, and the loose coordination method of master-slave programming is used for the experiment. Simulation and experimental results show that compared with the original algorithm, the improved algorithm saves about 23% in time and 11% in path cost, which proves the effectiveness and efficiency of the proposed algorithm.
Forensic analysis in social networking applications

In digital forensics, social media is promptly evolving as a frontier technology. The digital information may explore the magazine contents on social media to offer incredible sustenance in the process of criminal investigations. Conversely, discovering the potential evidence using social media is not presented as a proof in any court of law. The forensic process involves social media to collect the evidence of appropriate legal, scientific, and discretion rights of the individuals. The legal process challenges practitioners and investigators to deal with the vibrant and dissimilar nature of social media. The forensic investigators engage in the service of effective investigation to collect, conduct, and manage the legal social contents provided with sophisticated tools. This chapter conducts the current state of existence, namely, acquisition of legal evidence, case admissibility, jurisdiction, and services in social network forensics. It also provides a part of direct challenges to collect, analyze, present, and validate the evidences of social media in lawful actions. This work studies on the current state-of-the-art approaches to highlight the open issues that deal with the mandatory challenges of social networks.
Production Prediction Model Of Complex Industrial Processes Based On GRU Neural Network

Raw materials consumption and many physical and chemical reactions in the industrial process are not conducive to analyze the production process. Therefore, the ethylene production prediction model based on gated recurrent unit (GRU) is brought forth in this paper, which can be used to accurately predict ethylene production capacity in complex industrial processes. Using the total consumption of raw materials, fuel, water, steam and electricity as the input of the network architecture and the ethylene production as the output, GRU network is set as the kernel to build the production prediction model. Form experiment results comparing with the back propagation (BP) neural network and the long short-term memory (LSTM), GRU can better predict ethylene production, thus reducing production energy consumption and improving production efficiency.
The need for a prediction model assessment framework – Authors' reply

We appreciate the feedback provided by Islam and Khosravi on our assessment.1 We agree that journals play an important role in prioritising transparency and reproducibility in publication decisions. Transparency guidelines have been developed. More than 1100 journal publishers have adopted the Transparency and Openness Promotion guidelines,2 including several journals that published models evaluated in our analysis. Despite these important initiatives and ongoing improvements, journals' peer review processes do not fully assess and control for model transparency.
Database Forensics and Security Measures to Defend from Cyber Threats

With the present technology, users are generating huge data by using various applications based on the usage data is transferred to database. Database technology provides an efficient, easy and secured procedures for managing the data and information systematically. All the operations of data manipulation and maintenance are done by using Database Management System (DBMS) with Computer Networks which has become a powerful medium for fast information exchange and access. Ensuring security of the database is very a critical issue for any organization. Mainly security threats include any action to deny, exploit and corrupt or destroy the information by attackers. The systems have to be defended from various security threats by adopting proper security measures and techniques.The need of Database Forensics is increasing for investigating the cyber threats and database level attacks. Database forensics is becoming an important field now a days, which can be used to identify, detect, acquire, analyze cyber-attacks in the databases and its related systems. It is also required to make suitable readiness for cybercrime investigation team at organizational level to make at least preliminary level investigations. This paper briefly discusses about the database forensics and its security measures.
User Authentication Through Pen Tablet Data Using Imputation and Flatten Function

Identifying a user or a person through handwriting-data is a popular technique. Many researches have been done in this area most of which are image or pattern-based analysis. The accuracy level depends on the quality of the image or pattern. In this paper, we proposed user authentication system using an individual's pen tablet handwriting data. The proposed system is concerned with the numerical value of a person handwriting data getting from the digital pen and tablet device. Hence, user authentication through pen tablet data ensures more accuracy by working with user's real time handwritten data. In the proposed system, 24 persons writing samples(1262 .csv files and 23 class)are used for extracting features to identify a user based on their handwriting attributes. Six completely separated features are extracted after data analysis and pre-processing. The extracted features are mainly concerned with the vital attributes of a user's handwriting. The extracted features are used for classification. With this concern, we utilized different classification algorithms such as Support Vector Machine (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA) and Random Forest (RF) classifier. From the implementation, different algorithms show different accuracy level. The testing accuracy rate of SVM, LR, LDA and RF is 87%, 85%, 76% and 77% respectively. The experimental analysis shows that we got more robust and satisfactory results which ensure the practicality of our system.
Region-dependent Scale Proposals for Super-Resolution in Object Detection

This paper presents a method for estimating object-scale proposals applied to super resolution (SR) for scale-optimized object detection. With the region-dependent scale proposals, we achieve scale-independent object detection. This object detection scheme consists of three functions; region-dependent scale proposals, SR, and object detection. While SR and object detection have been fused in deep end-to-end networks in previous works, region-dependent scale proposals are not provided or are performed independently of SR and object detection processes. The proposed region-dependent scale-proposal network is designed to explicitly estimate appropriate SR scales depending on the image region in accordance with scene contexts. Qualitative and quantitative experimental results show that our method can provide appropriate SR scales for improving detection accuracy. Our proposed method gains 2.7 points in AP with Centernet used as the base detector.
Block-Circulant Neural Network Accelerator Featuring Fine-Grained Frequency-Domain Quantization and Reconfigurable FFT Modules

Block-circulant based compression is a popular technique to accelerate neural network inference. Though storage and computing costs can be reduced by transforming weights into block-circulant matrices, this method incurs uneven data distribution in the frequency domain and imbalanced workload. In this paper, we propose RAB: a Reconfigurable Architecture Block-Circulant Neural Network Accelerator to solve the problems via two techniques. First, a fine-grained frequency-domain quantization is proposed to accelerate MAC operations. Second, a reconfigurable architecture is designed to transform FFT/IFFT modules into MAC modules, which alleviates the imbalanced workload and further improves efficiency. Experimental results show that RAB can achieve 1.9x/1.8x area/energy efficiency improvement compared with the state-of-the-art block-circulant compression based accelerator.
A Generalized-Momentum-Accelerated Hessian-Vector Algorithm for High-Dimensional and Sparse Data

Precisely understanding high-dimensional and sparse (HiDS) user-item interactions is the most important issue in a recommender system. A latent factor analysis (LFA)-based model has proven to be efficient in addressing it, while current models of this kind mostly rely on first-order optimizers. It is vital to implement an LFA-based model able to approach the second order stationary points efficiently for improving its representative learning ability. To do so, this work presents a Generalized-momentum-accelerated Hessian-vector Algorithm (GHA) for HiDS data. Its main idea includes a) adopting the principle of a Hessian-vector-product-based method to avoid operating a Hessian matrix directly, and b) incorporating a generalized momentum method into its parameter learning process for further enhancing its ability in approaching a stationary point. Experimental results on two industrial datasets demonstrate that when compared with state-of-the-art LFA-based models, a GHA-based LFA model achieves gains in accuracy and convergence rate. These positive outcomes also indicate that a generalized momentum method is compatible with algorithms implicitly relying on gradients like a second-order algorithm.
Perspectives of Educational Robotics in the Ongoing Technological Transformations

The paper discusses educational robotics’ potential to contribute to the process of societal transformation caused by the Fourth Industrial Revolution (4IR). An overview of educational robotics’ questions is presented based on the authors’ experience teaching supplementary education programs to K-12 students in digital fabrication environment as well as on several external review papers. A case study is presented to reveal and confirm some of the problems common for such project-oriented creative STEM related activities in supplementary education environment. One of which being inclusion in practical technical studies of different groups of students - a common challenge for the given educational robotics context.
Symptom and Pathology Report Generation for Ophthalmic Diseases in Fundus Images

While fundus image interpretation and report writing are routine procedures in the diagnosis of ophthalmic diseases, they are error-prone for inexperienced ophthalmologists and laborious and tedious for experienced ophthalmologists. Therefore, there is an urgent need for an automated ophthalmic report generation tool. Existing methods for captioning medical images are confined to the field of radiological images, and there is no such method for fundus images. To address this issue, we studied automated report generation for fundus images. Unlike the radiological field, this task presents several challenges. First, there are a wide variety of ophthalmic diseases, and identifying diseases in fundus images is sometimes difficult since the diseases can have different manifestations. Second, an ophthalmic disease may have multiple subtypes, which can be distinguished from one another only through accurate identification o f specific ophthalmic symptoms. To overcome these challenges, we propose a method with the following features: (1) a multitask learning framework for predicting both ophthalmic symptoms and pathologies and for generating paragraphs, (2) two-branch architecture for handling ophthalmic symptoms and pathologies separately, and (3) a causal linkage module for transferring the symptom information for accurately predicting ophthalmic pathologies and thereby facilitating the identification of subtypes. We demonstrate the effectiveness of the proposed method for an in-house fundus image data set.
Multinomial Logit Model Building via TreeNet and Association Rules Analysis: An Application via a Thyroid Dataset

A model-building framework is proposed that combines two data mining techniques, TreeNet and association rules analysis (ASA) with multinomial logit model building. TreeNet provides plots that play a key role in transforming quantitative variables into better forms for the model fit, whereas ASA is important in finding interactions (low- and high-order) among variables. With the implementation of TreeNet and ASA, new variables and interactions are generated, which serve as candidate predictors in building an optimal multinomial logit model. A real-life example in the context of health care is used to illustrate the major role of these newly generated variables and interactions in advancing multinomial logit modeling to a new level of performance. This method has an explanatory and predictive ability that cannot be achieved using existing methods.