Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Bionic Manipulator System with Wireless Control

Design a long-distance wireless control bionic robot arm control system built on the mobile platform. The Arduino Mega 2560 is used as the main control chip to control the whole system, and a potentiometer is used to build a bionic bracket with the same structure as the mechanical arm. The wireless transmission module transmits the potential change of the potentiometer to the control board of the robot arm in real time. The control board controls the robot arm by calling related instructions to realize the bionic control. The mechanical arm control board can also add some sensors according to different requirements, which makes it more intelligent. The mechanical arm is built on a mobile platform that can transmit video and wirelessly control in real time, which can realize various task requirements. The experimental results show that the mechanical bionic arm system has flexible control, good synchronization performance and wide application prospects.
Embedding Hierarchical Signal to Siamese Network for Fast Name Rectification

EDA tools are necessary to assist complicated flow of advanced IC design and verification in nowadays industry. After synthesis or simulation, the same signal could be viewed as different hierarchical names, especially for mixed-language designs. This name mismatching problem blocks automation and needs experienced users to rectify manually with domain knowledge. Even rule-based rectification helps the process but still fails when encountering unseen mismatching types. In this paper, hierarchical name rectification is transformed into the similarity search problem where the most similar name becomes the rectified name. However, naive full search in design with string comparison costs unacceptable time. Our proposed framework embeds name strings into vectors for representing distance relation in a latent space using character n-gram and locality-sensitive hashing (LSH), and then finds the most similar signal using nearest neighbor search (NNS) and detailed search. Learning similarity using Siamese network provides general name rectification regardless of mismatching types, while string-to-vector embedding for proximity search accelerates the recti-fication process. Our approach is capable of achieving 93.43% rectification rate with only 0.052s per signal, which outperforms the naive string search with 2.3% higher accuracy and 4,500 times speed-up.
Class Attention Map Distillation for Efficient Semantic Segmentation

In this paper, a novel method for capturing the information of a powerful and trained deep convolutional neural network and distilling it into a training smaller network is proposed. This is the first time that a saliency map method is employed to extract useful knowledge from a convolutional neural network for distillation. This method, despite of many others which work on final layers, can successfully extract suitable information for distillation from intermediate layers of a network by making class specific attention maps and then forcing the student network to mimic producing those attentions. This novel knowledge distillation training is implemented using state-of-the-art DeepLab and PSPNet segmentation networks and its effectiveness is shown by experiments on the standard Pascal Voc 2012 dataset.
Multi-stage Replica Consistency Algorithm Based on Activity

A good replica consistency algorithm can complete the overall management of replicas. An effective solution to the problem of replica consistency can significantly improve the performance and scalability of cloud computing environment, and reduce job execution time. This paper proposes an activity-based multi-phase replica consistency algorithm (AMRCA). Firstly, the algorithm divides the replicas into two categories according to their active degree, one is “active” replica and the other is “inactive” replica. For those “active” replicas, we use a strong consistency algorithm, requiring all replicas in the system to be consistent, and any updates must be propagated to all replicas. For those “inactive” replicas, we use the weak consistency algorithm. The advantage of this method is that it avoids the increase of system overhead caused by using strong consistency algorithm for all replicas, and ensures that all replicas with high “activity” in the system are up-to-date and consistent. Secondly, the algorithm divides the process of data updating into several stages, each stage works together to ensure data consistency among all valid replicas, and at the same time, it minimizes the failure interval of replicas in the updating process, thus providing more reliable data access services for upper application.
An Efficient Paillier Cryptographic Technique for Secure Data Storage on the Cloud

Homomorphic secret writing is one such public key science technique helps in maintaining the privacy and confidentiality of information within the cloud, by permitting the user to perform operations directly on the encrypted information while not decrypting them. Now, within the existing science technique i.e. when secret writing, the file size becomes Brobdingnagian and consumes high information measure throughout the transmission. In this paper, the study of the Paillier homomorphic secret writing theme is conducted. From the additive homomorphic property of the Paillier Cryptosystem, developed an approach that will facilitate in reducing the information measure throughout the transmission of the file and can create the system additional economical.
Not All Swear Words Are Used Equal: Attention over Word n-grams for Abusive Language Identification

The increasing propagation of abusive language in social media is a major concern for supplier companies and governments because of its negative social impact. A large number of methods have been developed for its automatic identification, ranging from dictionary-based methods to sophisticated deep learning approaches. A common problem in all these methods is to distinguish the offensive use of swear words from their everyday and humorous usage. To tackle this particular issue we propose an attention-based neural network architecture that captures the word n-grams importance according to their context. The obtained results in four standard collections from Twitter and Facebook are encouraging, they outperform the $$F_1$$ scores from state-of-the-art methods and allow identifying a set of inherently offensive swear words, and others in which its interpretation depends on its context.
Radar Seeker Anti-jamming Performance Prediction and Evaluation Method Based on the Improved Grey Wolf Optimizer Algorithm and Support Vector Machine

In order to accurately evaluate the anti-jamming performance of radar seeker, an Improved Grey Wolf Optimizer (IGWO) algorithm is proposed to optimize the estimation and prediction of Support Vector Machine (SVM) parameters. Firstly, according to the characteristics of radar seeker, this paper constructs the index system of anti-jamming performance of radar seeker, and then, this paper introduces chaotic search mechanism, convergence and nonlinear adaptive weight to improve the traditional Grey Wolf Optimizer algorithm for a better global optimization ability. Finally, by using the IGWO algorithm to optimize the related parameters of Support Vector Machines (SVM), a comprehensive evaluation method is proposed for simulation experiment. Simulation results show that the proposed method has higher prediction accuracy and better generalization ability than SVM model and BP neural network.
Synergy of Organizational Patterns and Aspect-Oriented Programming

By observing Conway's law, which explains how organizing people directly affects the code they produce, and by taking into account the specifics of different programming paradigms, a lot can be done to improve the software development process. We analyzed organizational patterns of agile software development from the perspective of aspect-oriented programming, whose aim is to improve the separation of concerns in code, which, in turn, allows for better separation of tasks performed by people. We find aspect-oriented programming to be highly related to at least nine out of more than a hundred of organizational patterns in Coplien and Harrison's catalog, namely: Work Split, Team per Task, Sacrifice One Person, Divide and Conquer, Conway's Law, Form Follows Function, Shaping Circulation Realms, and Hallway Chatter. Aspect- oriented programming can support organizational patterns in division of labor, treating distractions, and increasing decoupling between the software modules developed by different parts of the organization. On the other hand, organizational patterns can help mitigate pointcut fragility and aspect obliviousness in general.
Reform of Higher Vocational English Teaching Based on Mobile Moodle Platform

Abstract Teaching reform is a new curriculum resource based on the Internet and information technology (IT). In higher vocational English teaching, a new model of “Teaching after learning, teaching based on learning” has been implemented in teaching reform. In particular, supported by the mobile Moodle platform, the reform of higher vocational English teaching has played a positive role in facilitating the improvement of listening, speaking, reading, writing, and translation levels in students. In this paper, based on the current higher vocational English teaching practice, the development of English teaching reform supported by the mobile Moodle platform is explored. In addition, the contents of English speaking, reading, writing, translation, etc. are combined to discuss how to use the Moodle teaching reform to improve the English teaching effectiveness.
Identification and Extraction of Text in Non- Blurred and Blurred Image Sequences

The images embedded with text contain information and therefore it is important to identify and extract text. Extracting text in a blurred image is a challenging task due to movement and defocus, whereas it is an easy task in the nonblurred image. In this paper, text identification from non-blur and blur images is done using an Otsu method and region of interest (ROI) and text are extracted using a convolution neural network (CNN). The evaluation results show 98.1% accuracy for non-blurred images and 96% accuracy for blurred images. The text is extracted and identified with efficient accuracies than the existing methods.
Singular spectrum analysis-based image sub-band decomposition filter banks

Abstract This paper presents a novel type of a filter bank for image decomposition that incorporates “singular spectrum analysis” (SSA). SSA is based on “singular value decomposition” (SVD).The presented filter banks provide good directional selectivity. Sub-band images highlight exposed directional features such as 0 ∘ ,90 ∘ ,+ 45 ∘ , and − 45 ∘ of the input image. The proposed method can be seen as a tool to separate an image into layers or to present an image as a sum of its high-frequency directional components and a low-frequency approximation.
Video Tampering Detection for Decentralized Video Transcoding Networks

This paper introduces a complete methodology based on Machine Learning and Computer Vision techniques for the verification of video transcoding computations in decentralized networks, particularly the Open Source project Livepeer. A base video dataset is presented, with over 180k samples transcoded using the x264 codec. As a novelty, we propose a set of four features computed as a full reference comparison between the source and the rendered videos. Using these features, a One Class Support Vector Machine is trained to identify good encodings with a high accuracy. Experimental results are presented and the particular constraints of this use case are explained.
Age-Structured Biogeography-based Optimization

Biogeography-based optimization (BBO) is a nature-inspired metaheuristic, motivated by the migration of species between habitats. It has been shown to provide good performance on several optimization tasks. However, BBO does not capture the effects of age structures of individuals. In the past, the concept of age has been incorporated by other nature- inspired algorithms, leading to a general improvement in their optimization capability. To reap similar benefits, a novel variant of BBO called age-structured BBO (ASBBO) is proposed, that introduces the effects of the age of individuals on the migration process. It is based on the observation that, in nature, young and old individuals are less suitable for migration in comparison to those of the middle-age. Individuals with birth rates and survival rates are introduced to capture age-related dynamics. Age structures allow information to be shared based not only on the current goodness of a solution, but also on its history, resulting in a more informed decision, and subsequently, an improved search capability. Computational experiments conducted on standard benchmark functions validate the superiority of ASBBO over BBO and three other have known nature-inspired optimization algorithms, namely, ant colony optimization (ACO), genetic algorithm (GA) and particle swarm optimization (PSO).
A Contemplator on Reversible Data Hiding in Encrypted Domain

Reversible Data Hiding (RDH) has proved itself to be the best technique for providing security to sensitive datasets. RDH provides confidentiality, preserves privacy of secret and personal data and ensures reversibility. Several working principles are adhered for different RDH techniques for encryption and data embedding, in the past two decades. RDH covers all the basics of a proper cryptographic algorithm, i.e. confidentiality, integrity and availability. Furthermore, it works better for sensitive images. Based on the working methodologies and implementation area RDH schemes can be categorically divided into 4 types: (i) RDH in compressed domain, (ii) RDH in spatial domain, (iii) RDH for contrast enhancement and (iv) RDH in encrypted domain. This work presents a survey on the some recent RDH mechanisms working on encrypted domain. RDH schemes achieve different outcomes based on the intension and implementation. This review, shows the different services that RDH provides and the best base-methodologies to achieve high security along with maximum reversibility.
Recommendation System Using Self Organizing Map (SOM) for Thesis Examiners and Preceptors of Universitas Sebelas Maret - Department of Informatics

Thesis is a scientific work prepared by students based on the results of laboratory research, field research, and can be accounted by the student as a requirement for obtaining a bachelor's degree. The roles of thesis preceptors and examiners are critical. The preceptor and examiner have to master the fields required for research to help create excellent and effective tutoring. The purpose of this study is to build recommendation system for thesis examiners and preceptors that considering lecturer's workload. Lecturer's publications and student thesis of Informatics Department Universitas Sebelas Maret are used as data training. This study proposes to apply Self Organizing Map (SOM) method to cluster data training to reduce the amount of data to make matching process more straightforward. This study also proposes to calculate the similarity between documents on selected clusters with thesis proposal, so the result of recommendation are lecturers who are competent in the field of the research. The result shows there are 97 clusters filled, with error value using Mean Absolute Error is 24.976 and average silhouette value 0.97. Validation using subset shows that the accuracy of recommendation result is 44% while validation using expert judgement shows the accuracy of recommendation result is 93%.
Designing Voice-Controllable APIs

The main purpose of a voice command system is to process a sentence in natural language and perform the corresponding action. Although there exist many approaches to map sentences to API (application programming interface) calls, this mapping is usually performed after the API is already implemented, possibly by other programmers. In this paper, we describe how the API developer can use patterns to map sentences to API calls by utilizing the similarities between names and types in the sentences and the API. In the cases when the mapping is not straightforward, we suggest the usage of suitable annotations (attribute-oriented programming).
Efficient Embedded Machine Learning applications using Echo State Networks

The increasing role of Artificial Intelligence (AI) and Machine Learning (ML) in our lives brought a paradigm shift on how and where the computation is performed. Stringent latency requirements and congested bandwidth moved AI inference from Cloud space towards end-devices. This change required a major simplification of Deep Neural Networks (DNN), with memory-wise libraries or co-processors that perform fast inference with minimal power. Unfortunately, many applications such as natural language processing, time-series analysis and audio interpretation are built on a different type of Artifical Neural Networks (ANN), the so-called Recurrent Neural Networks (RNN), which, due to their intrinsic architecture, remains too complex and heavy to run efficiently on embedded devices. To solve this issue, the Reservoir Computing paradigm proposes sparse untrained non-linear networks, the Reservoir, that can embed temporal relations without some of the hindrances of Recurrent Neural Networks training, and with a lower memory usage. Echo State Networks (ESN) and Liquid State Machines are the most notable examples. In this scenario, we propose a performance comparison of a ESN, designed and trained using Bayesian Optimization techniques, against current RNN solutions. We aim to demonstrate that ESN have comparable performance in terms of accuracy, require minimal training time, and they are more optimized in terms of memory usage and computational efficiency. Preliminary results show that ESN are competitive with RNN on a simple benchmark, and both training and inference time are faster, with a maximum speed-up of 2.35x and 6.60x, respectively.
Multi-criteria spatial screening and uncertainty analysis applied to direct-use geothermal projects

The focus of this work is general methods for prioritization or screening of project sites based on the favorability of multiple spatial criteria. We present a threshold-based transformation of each underlying spatial favorability factor into a continuous scale with a common favorability interpretation across all criteria. We compare several methods of computing site favorability and propagating uncertainty from the data to the favorability metrics. Including uncertainty allows decision makers to determine if seeming differences among sites are significant. We address uncertainty using Taylor series approximations and analytical distributions, which are compared to computationally intensive Monte Carlo simulations. Our methods are applied to siting direct-use geothermal energy projects in the Appalachian Basin, where our knowledge about any particular site is limited, yet sufficient data exist to estimate favorability. We consider four factors that contribute to site favorability: the thermal resource described by the depth to 80°C rock, natural reservoir productivity described by rock permeability and thickness, potential for induced seismicity, and the estimated cost of surface infrastructure for heat distribution. Those factors are combined in three ways. We develop favorability uncertainty propagation and sensitivity analysis methods. All methods are general and can be applied to other multi-criteria spatial screening problems.
A Passive Detection Method of Divers Based on Deep Learning

This paper analyses the time-frequency spectrum characteristics of a diver's inspiration signals collected by a passive sonar and presents a passive detection method of divers by taking the diver's inspiration signals as the keywords of a detection model, which is built by data synthesis and convolutional neural network (CNN), gated recurrent unit (GRU), and other deep learning algorithms. Through combining with the periodic characteristics of the diver's respirations, the method provided in this paper can detect the inspiratory signals quickly and realize the hierarchical recognition of divers, which is helpful to the rapid response of a diver warning system. The feasibility of this method is verified by a sea experiment, and it can be used for reference in the study of passive diver detection.
A Proficient Video Compression using Block based Fragmented Cosine Transform (BFCT) in Wireless Multimedia Sensor Network (WMSN)

The main challenge of Wireless multimedia sensor network (WMSN) is lack of power of communication, bandwidth and storage. In this paper we propose a new technique of transmitting sensed video in reduced format with a newly proposed Block based Fragmented Cosine Transform (BFCT). The scheme works by modifying DCT based operation which is computationally intensive in WMSN. The correlation among the frames of the video is measured followed by applying BFCT over only the sorted frames. As the technique is applied over an array of sensor so a newly designed dedicated routing technique is also employed along with BFCT. This helps in reducing the amount of transmitted data and hence saving communication energy. The performance of the scheme is measured by energy analysis, peak signal to noise ratio (PSNR) followed by statistical analysis. The efficacy of the scheme is judged through simulation results based on energy and acceptable reconstruction quality.
Educational STEM Laboratory

In this paper, we describe the educational and technological design of a highly modularized STEM learning toolkit for students to learn the technologies behind IoT and Fintech through setting up an unmanned smart shop on their own. The modularized design allows students to work individually or collaborate as a team, which facilitates both the teaching needs required from the experimental class and also the self-study needs of researchers. This physical unmanned smart space laboratory is a project from a vocational institute in Hong Kong. We conducted a user study to evaluate the performance of the smart shop learning toolkit and had received positive feedback, in which 87% of the students prefer to learn the smart shop technologies and STEM through our toolkit. Our results demonstrate that our smart technology learning toolkit is an effective and fun way to teach students about STEM and the cutting-edge technologies.
Parallel Implementation of the DRLSE Algorithm

The Distance-Regularized Level Set Evolution (DRLSE) algorithm solves many problems that plague the class of Level Set algorithms, but has a significant computational cost and is sensitive to its many parameters. Configuring these parameters is a time-intensive trial-and-error task that limits the usability of the algorithm. This is especially true in the field of Medical Imaging, where it would be otherwise highly suitable. The aim of this work is to develop a parallel implementation of the algorithm using the Compute-Unified Device Architecture (CUDA) for Graphics Processing Units (GPU), which would reduce the computational cost of the algorithm, bringing it to the interactive regime. This would lessen the burden of configuring its parameters and broaden its application. Using consumer-grade, hardware, we observed performance gains between roughly 800% and 1700% when comparing against a purely serial C++ implementation we developed, and gains between roughly 180% and 500%, when comparing against the MATLAB reference implementation of DRLSE, both depending on input image resolution.