Reviewerly/exHarmony huggingface dataset:
Power Metering Wiring Evaluation System Based on Machine Vision Technology

Based on machine vision technology, the power metering wiring process evaluation system realizes the objective evaluation of the wiring process quality. Through the high-precision machine vision acquisition, the accuracy, horizontality level, verticality level and line spacing of the process wiring are objectively digitized, and the system gathers real-time machine vision data, automatic distortion correction through an adaptive correction algorithm to achieve image data cleaning and storage, and through the machine vision algorithm, the process wiring parameters such as horizontality and verticality, and score are calculated according to the scoring standard. The evaluation system also interprets deductions, finds problems, and realizes iterative improvement of the wiring process level driven by objective data.
UNDERLYING FACTORS AFFECTING UTILIZATION OF COMPUTER – BASED INTERNET TECHNOLOGIES BY AGRICULTURAL EXTENSION PERSONNEL IN AKWA IBOM, NIGER DELTA, NIGERIA

The study sought to examine factors affecting utilization of Computer Based Internet Technologies (CBIT) by extension personnel in Akwa Ibom State, Nigeria. Relevant data were obtained from 180 respondents, while data analyses were achieved with the aid of descriptive and inferential statistical tools. Findings showed that 57.8 % of the respondents were male, 91.7% were married, with a mean age of 43years, while 62.8% respondents had no formal computer training. It was also revealed that, of the nine surveyed CBIT facilities and applications, only mobile phones were highly utilized, while I-pads/laptops and YouTube were the least utilized. It was also revealed that 62.8% respondents indicated that they had a low level of CBIT utilization, while level of CBIT utilisation was found to be influenced by financial, training, policy/personal barriers, social, technical and management factors. Results of tested hypotheses revealed that CBIT utilisation was not influenced by respondents’ personal characteristics, and no significant difference was observed in terms of level of CBIT utilization between field extension personnel (FEP) and supervisory extension personnel (SEP). It was concluded that extension personnel are constrained from using CBIT due to the prohibitive cost of CBIT software and maintenance, inappropriate infrastructure and lack of access to training opportunities. It is recommended, among others, that policy makers should facilitate the evolution of appropriate CBIT policies to enhance access to appropriate tools by extension personnel.
Improving the Performance of 3D Shape Measurement of Moving Objects by Fringe Projection and Data Fusion

Fringe projection profilometry (FPP) has been widely studied for measurements of three-dimensional (3D) shapes.However, the measurement of moving objects is still a challenging problem associated with commonly used methods.Phase-shifting profilometry (PSP) suffers from motion-induced errors when applied to moving objects, while Fourier transform profilometry (FTP) is sensitive to background light and noise.This paper proposes a novel data fusion approach to improve the measurement of moving objects by alleviating noise-induced errors.Firstly, a set of coarse measurements on the shape of the object are obtained at different time instants by FTP.Then the parameters associated with the motion of the object over these time instants are retrieved from these coarse estimates by applying the iterative closest point (ICP) method.Finally, the parameters are utilized to adaptively fuse the coarse measurements based on their quality.Simulations and experiments verify that the proposed approach can significantly improve the measurement accuracy.
Research and Implementation of Open Domain Question Answering System Based on DuReader Dataset and BIDAF Model

Abstract At present, major search engines emerge in endlessly, and the pop-up result list of search keywords makes people dizzy. In order to improve this problem and make the question query more concise and convenient, this article is based on the DuReader data set and reproduces BiDAF (Bi-Directional Attention Flow) The model builds an intelligent question answering system in the open field. The final model can extract answers to questions more accurately after training. This article embeds deep learning technology into the system and uses intelligent chat to show them.
Design of the Convolution Layer Using HDL and Evaluation of Latency Using a Camera Signal

The Convolutional Neural Network (CNN) is applied to several applications and is expected to the embedded systems such as IoT devices. However, these systems need high calculation costs and high power consumption. Because in general, those system requires the using GPUs and its hard to implement on the small embedded system. In recent years, FPGAs have been applied to the auto-control systems, defect inspection systems, and the security systems. Especially applying for the image processing technologies for industrial products were adapted. Hardware acceleration is one of the techniques to improve processing speed and is often used for image processing. Our work has designed the hardware acceleration for CNN and compared it to software processing. The hardware-based modules of CNN were implemented on FPGA and tested. This paper shows the details architecture, was designed in our research, and the verification results of the real-time processing.
Unsupervised Learning of High Dimensional Environmental Data Using Local Fractality Concept

The research deals with an exploration of high dimensional environmental data using unsupervised learning algorithms and the concept of local fractality. The proposed methodology is applied to geospatial data used for the wind speed prediction in a complex mountainous region. It is shown, that the approach provides important additional information on data manifold useful in data analysis, data visualisation and predictive modelling.
Accelerated supervised learning to detect spam using feature selection and Apache Spark architecture

Spam can have fake links and trick users into redirecting to fake pages and stealing users' information on these pages. Machine learning methods are typically used to detect spam to distinguish the spam pattern from normal text. A good way to detect spam is to use machine learning techniques that can detect the pattern of spam. While large volumes of text or email need to be considered, these methods take a long time to make using these methods impractical. A good way to accurately and quickly detect large volumes of spam is to use big data processing platforms such as Apache Spark. In this research, an attempt has been made to use feature selection by meta-heuristic methods to increase the speed and efficiency in spam detection. In this method, the data is entered into Apache Spark for final processing to be detected by spam with high-speed distributed learning. Experiments show that using the feature selection mechanism can increase learning accuracy by 98.66%. The processing speed in Apache Spark is about 5.453 times higher than non-Spark systems.
FB2vec: A Novel Representation Learning Model for Forwarding Behaviors on Online Social Networks

Representation learning in online social networks has been an important research task for better service, which targets at learning the low-dimensional vector representation for nodes in a network. There exists a kind of social network, which not only includes the topological structure and node attributes, but other information, such as the user behaviors. It is necessary to use these behaviors to learn the node representations. In this paper, we propose FB2vec to analyze forwarding behaviors and achieve better node representations. Moreover, an information intensity function based on the utility function is proposed to measure the possibility of forwarding behaviors. However, the intensity function can not reflect exact possibility value and only provide a relative intensity order. Therefore, we sample the intensity order pairs from datasets and train the intensity function to adapt original orders by an attribute-reserved siamese network. Extensive experiments demonstrate the effectiveness of FB2vec and the visualization of information intensity function indicates the rationality of FB2vec.
STM32 Single Chip Microcomputer Signal Recognition Method Based on Sparse Filtering Neural Network

Due to the high dimension of STM32 signal characteristic graph, the signal recognition process will produce signal overlap, resulting in the decline of recognition rate. In order to solve this problem, a signal recognition method based on sparse filter neural network is designed. The signal type of STM32 single-chip microcomputer is defined, the characteristic parameters of single-chip microcomputer signal are determined, and the interference caused by overlapping signal is eliminated by sparse filtering of overlapping signal. The identification process is controlled by neural network of full connection layer, and finally the recognition of STM32 single-chip microcomputer signal is realized. The experimental results show that the proposed method has higher recognition rate and reliability.
ADA-SR: Activity detection and analysis using security robots for reliable workplace safety

BACKGROUND: Human-Robot Interaction (HRI) has become a prominent solution to improve the robustness of real-time service provisioning through assisted functions for day-to-day activities. The application of the robotic system in security services helps to improve the precision of event detection and environmental monitoring with ease. OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process. RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset. CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.
A Question Answering System of Ethnic Minorities Based on Knowledge Graph

In recent years, Question Answering System has become a main focus of human machine interaction. Using the question answering system for information retrieval is convenient and efficient. Traditional question answering systems mostly use template matching. The question and answer data sets usually rely on manual design. The question and answer system implemented by this method has a quick query response and can answer relatively complex questions. But manually defining templates and rules is time-consuming and laborious. Therefore, this paper introduces the knowledge graph in the process of constructing the question answering system. The proposed method combines knowledge graph with question answering systems to realize an intelligent question answering system in the field of ethnic minorities, so as to spread ethnic minority knowledge and promote ethnic minority culture. The construction of a question answering system is mainly divided into two modules: question analysis and answer generation. The question analysis module includes named entity recognition, similarity calculation and question classification. The answer generation module includes entity mapping and graph retrieval. The experimental results show that our approach by combined with natural language processing technology and domain knowledge graph can well accurately feedback the answers to user queries. And the answer accuracy of the question answering system can reach about 81%.
Prevention and Maintenance Technology for Equipment Family Defects Based on Account Data Map

Function location type, category, model, manufacturer, equipment parameter, defect information and operation/maintenance information are key fields of electrical power system equipment account data, which are organized with account data ID as the main keyword. In the past, there was a lack of mining methods and a large amount of account data was not analyzed. In this paper, a standardized technique for computer implementation is proposed, which transforms any account data table into sparse adjacency matrix, and then transforms adjacency matrix into map. With the same functional location type in the account data map, according to the equipment model, manufacturer and technical parameters associated, the information of potential spare parts and the quality of account data can be got. With the same category and model, according to the function position type, defect appearance, defect reason and treatment measure field, family defects of equipment is got. According to the treatment measure field, the prevention and maintenance method for family defect of equipment can be formed.
Hyperspectral Image Denoising Based on Non- convex Low-rank Regularization in Tensor Transform Domain

Hyperspectral image includes rich spectral and spatial information with strong correlations, which makes it granted that hyperspectral image is modeled as a low-rank third- order tensor. In terms of the low-rank representation of tensor, non-convex low-rank approximation is regarded as an effective method. In this paper, based on the tensor singular value decomposition in transformed domain, a non-convex low-rank representation of tensor is proposed. Firstly, the unitary transformation is carried out along each mode of the tensor, thus the corresponding transformed tensor nuclear norm is developed. Then, the newly defined tensor nuclear norm is relaxed by a non- convex Gamma function, which leads to a non-convex low-rank regularized hyperspectral denoising model. The alternating direction multiplier method (ADMM) is employed to solve the model. Compared with the existing methods, the proposed model can effectively remove Gaussian and impulse noises, and achieves better performance on spatial structure preservation and spectral fidelity.
Images to Signals, Signals to Highlights

In this paper, we propose a framework to generate cricket highlights from broadcasted cricket matches. Generating cricket highlights is a difficult problem, due to the duration and rules of the game. We formulate the highlight generation problem as a key-event initialization and key-event-closure identification problem. We propose an Inverse Hierarchical Framework, which is generic and capable of automatically generating highlights of a broadcasted cricket match. We introduce a novel context-aware approach for event-initialization and a Structural Similarity Index-based approach for event-closure detection. Despite the quality of highlights being a subjective measure we provide an evaluation of our framework by comparing it with official highlights on various metrics. We also perform a user-survey on the generated highlights. The approval of the users and overlap between the generated highlights and official highlights indicate the robustness of our framework.
Personalization Models for Human Activity Recognition with Distribution Matching-Based Metrics

Building activity recognition systems conventionally involves training a common model from all data of training users and utilizing this model to recognize activities of unseen subjects. However, participants come from diverse demographics, so that different users can perform the same actions in diverse ways. Each subject might exhibit user-specific signal patterns, yet a group of users may perform activities in similar manners and share analogous patterns. Leveraging this intuition, we explore Frechet Inception Distance (FID) as a distribution matching-based metric to measure the similarity between users. From that, we propose the nearest-FID-neighbors and the FID-graph clustering techniques to develop user-specific models that are trained with data from the community the testing user likely belongs to. Verified on a series of benchmark wearable datasets, the proposed techniques significantly outperform the model trained with all users.
Pelican: Continual Adaptation for Phishing Detection

An increasing number of people are using social media services and with it comes a more attractive outlet for phishing attacks. Our initial focus is to analyze Twitter as it is one of the most popular social media services. Phishers on Twitter curate tweets that lead users to websites that download malware. This is a major issue as phishers can then gain access to the user's digital identity and perform malicious acts. Phishing attacks have the potential to be similar in different regions, perhaps at different times. We have developed a novel semi-supervised machine learning algorithm, which we call Pelican, that detects potential phishing attacks in real-time on Twitter. Pelican can be used for early detection of potential phishing attacks and is able to detect potential new attacks without pre-existing assumptions about the type of data or understanding of the characteristics of the attacks. The technique uses ensembles and sampling methods to handle class imbalances in real-world applications. The technique continuously detects unusual behaviour or changes in Twitter. We have investigated changes in trends across Twitter to detect changes in online behaviour of potential phishing links. The technique uses a change detector that enables automatic retraining when there is unusual behaviour detected. Pelican is a novel technique that adapts to changes within phishing attacks in real-time. The technique detects 93.94% of the phishing tweets in real-world data that we collected over a 9 month period, which is higher than benchmark algorithms.
Research on the Integration of Chinese Education and Computer Literacy Education

Abstract From the point of view of history, historical materials include historical materials and physical materials, and the extension between historical materials and the three concepts of “corpus” and “text” in linguistic research is consistent. However, the focus of corpus research is grammatical phenomenon, and the focus of text research is literariness. Literary research can not be divorced from language research, but literary laws are not linguistic laws. Historical research can not be separated from “corpus” or “text”, but language law is obviously not historical law history is the subject of studying social and historical behavior, especially in middle school. It is not enough to pay attention to social consciousness [1] , but it seems that the attention to individual consciousness is not the field of history. The main expression of literature is to visualize social activities and consciousness, and his research must be characterized by the combination of reason and image thinking, which is different from history.
High Performance and Low Latency ECC Processor for Cryptography

Elliptic curve cryptography processor implemented for point multiplication on field programmable gate array. Segmented pipelined full-precision multiplier is used to reduce the latency and also data dependency can be avoided by modifying Lopez-Dahab Montgomery PM Algorithm, results in drastic reduction in the number of clock cycles required. The proposed ECC processor is implemented on Xilinx FPGA families i.e. virtex-4, vitrtex-5, virtex-7.single and three multiplier based designs show the fastest performance compared with reported work individually. Our three multiplier based ECC processor implementation is taking the lowest number of clock cycles on FPGA based design processor.
The performance of typical QKD scheme under the condition of quantum measurement noise

Abstract The performance of typical quantum key distribution protocol under the condition of quantum measurement noise has been explored in this paper. Additionally, the realization steps for quantum key distribution are presented. Based on the successful probability, the performance of quantum key distribution is studied. The results show that the total probability is not influenced by the phase factors, and this probability is relative with the amplitude parameters. The results about quantum key distribution protocol with measurement noise would be useful in the field of quantum communication.
Audio Feature Extraction on SIBI Dataset for Speech Recognition

Mel Frequency Cepstral Coefficients has been regarded as the standard method of feature extraction for Automatic Speech Recognition (ASR) systems for the last few years. Its performance may be affected by multiple variables, such as the number of features, audio channels, filter width, or the types of filter banks used. In this paper, several comparisons were made to find the best combination of variables that provides the best results on the SIBI (Indonesian Sign Language) dataset, which consists of utterances of sentences by both Deaf and Hard of Hearing (DHH) and non-DHH people. Based on this experiment, although generally the ASR on DHH dataset is lower than those of the non-DHH dataset, the results are still relatively high, around 4.71 % WER and 10.30 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> SER compared to 0.15% and 0.40% in WER and SER, respectively.
Kill Two Birds with One Stone: Auto-tuning RocksDB for High Bandwidth and Low Latency

Log-Structured Merge (LSM) tree based key-value stores are widely deployed in data centers. Due to its complex internal structures, appropriately configuring a modern key-value data store system, which can have more than 50 parameters with various hardware and system settings, is a highly challenging task. Currently, the industry still heavily relies on a traditional, experience-based, hand-tuning approach for performance tuning. Many simply adopt the default setting out of the box with no changes. Auto-tuning, as a self-adaptive solution, is thus highly appealing for achieving optimal or near-optimal performance in real-world deployment.In this paper, we quantitatively study and compare five optimization methods for auto-tuning the performance of LSM-tree based key-value stores. In order to evaluate the auto-tuning processes, we have conducted an exhaustive set of experiments over RocksDB, a representative LSM-tree data store. We have collected over 12,000 experimental records in 6 months, with about 2,000 software configurations of 6 parameters on different hardware setups. We have compared five representative algorithms, in terms of throughput, the 99th percentile tail latency, convergence time, real-time system throughput, and the iteration process, etc. We find that multi-objective optimization (MOO) methods can achieve a good balance among multiple targets, which satisfies the unique needs of key-value services. The more specific Quality of Service (QoS) requirements users can provide, the better performance these algorithms can achieve. We also find that the number of concurrent threads and the write buffer size are the two most impactful parameters determining the throughput and the 99th percentile tail latency across different hardware and workloads. Finally, we provide system-level explanations for the auto-tuning results and also discuss the associated implications for system designers and practitioners. We hope this work will pave the way towards a practical, high-speed auto-tuning solution for key-value data store systems.