Reviewerly/exHarmony huggingface dataset:
Development of wireless arm switch EMG control system for rehabilitation

This paper presents a proposed prototype model on the development of the wireless EMG control system application in the medical telemetry world. The area of the acquiring signal that has been focused is at the muscle activity of the arm. The current problems and motivations in relation to the development of proposed model described as to eliminate of the presently implemented complex wired control system. The objective of this model development is to design low cost portable wireless EMG control system by developing simple prototype using a miniaturized wheelchair model. Hence, by promoting this model, this model can be implemented in the real wheelchair for penetrating the market of rehabilitation aids device and equipment. The present study delineates the development of a wireless EMG control system conducted in separated parts. First, it deals with acquiring EMG signal, secondly, the classification of EMG signal and thirdly, the application of EMG signal. This proposed model suggested as a good alternative assistive technique for those suffering from extremely limited peripheral mobility and individuals suffered from Parkinson’s disease and Arthritis who claimed the current joystick on electric wheelchairs can be difficult to use. Moreover, the users learn to maneuver themselves independently with minimal need for assistance can be implemented.
Shared knowledge attributes’ impact on understanding strategic priorities

Purpose The purpose of this paper is to empirically explore the relationship between individuals’ shared core knowledge within a firm and a collective understanding of management’s strategic priorities. Design/methodology/approach The study develops three sets of competing hypotheses to predict how three different aspects of individuals’ shared core knowledge – extent, diversity and interpretation – are related to their understanding of the organization’s strategic priorities. The hypotheses are tested using a cognitive mapping approach within the context of a manufacturing plant in the USA. Findings Organizational members with a lower proportion of shared core knowledge exhibit a greater appreciation of the firm’s strategic priorities. More diversity in this shared knowledge is associated with a greater appreciation of strategic priorities and when members agree on the relative importance of different types of knowledge, whether they actually share this knowledge, they have a better understanding of the firm’s strategic priorities. Research limitations/implications The study uses data from a single firm in one industry. Originality/value This research helps to highlight and empirically isolate different aspects of shared knowledge that influence individuals’ understanding of organizational priorities. It also demonstrates the varying importance of different aspects of shared knowledge (e.g. extent, diversity and interpretation in explaining individuals’ understanding of the firm’s strategic priorities.
Improvement of Apriori-Pro Algorithm Based on MapReduce

The improved Apriori-Pro algorithm solves the problem that the traditional Apriori algorithm generates a large number of candidate sets, but in the case of a large amount of data, the time complexity is significantly improved. In order to solve the problem of time consumption of Apriori-Pro algorithm under big data, the Apriori-Pro algorithm based on MapReduce is proposed. Based on the Apriori-Pro algorithm to solve the candidate set, this method introduces MapReduce parallelization technology to reduce the time consumed by a large number of comparison TID (Transaction ID) columns. Through time complexity analysis between different methods, the improved algorithm reduces the time consumption of comparisons and connections when processing large-scale data. Experiments on the Hadoop platform show that the proposed method has higher efficiency under big data.
A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features': Learning from Incorrectly Labeled Data

Section 3.2 of Ilyas et al. (2019) shows that training a model on only adversarial errors leads to non-trivial generalization on the original test set. We show that these experiments are a specific case of learning from errors.
Cooperative Learning for Multi-perspective Image Classification

Data gathered from dense sensor networks is often highly correlated across collocated sensors. For example, in video surveillance networks, multiple cameras can observe the same object from multiple angles. Despite the spatial and temporal dependencies between video frames from different cameras, the deep learning algorithms used in today's video analytics problems treat all frames as independent inputs to image classifiers and object detectors. The outputs of these classifiers and detectors on multiple frames are then fused to extract information about the underlying sensor region. We present a cooperative learning framework that allows sensors to train deep learning systems on their own local data and compressed insights from neighboring sensors' input data. This system fuses sensor data before classification to allow learning agents to more naturally handle correlated inputs and cooperate with neighboring sensors with minimal communication costs.
Comparing SVM and SSD for classification of vehicles and pedestrians for edge computing

In the context of video processing, transmission to a remote server is not always possible nor suitable. Video processing on the edge could offer a solution. However, lower processing capacities constraint the number of techniques available for devices, in this work we report the performance of two techniques for classification from video on a minicomputer. The implementation of a real-time vehicle counting and classification system is evaluated through Support Vector Machine (SVM) and the Single Shot Detector Framework (SSD) in a minicomputer. The obtained results show that with a video resolution of 1280×720 pixels and using SVM, precision and recognition rates of 86 % and 94 % are obtained respectively, while with SSD 93 % and 67 % rates are reached with times of processing higher than SVM.
Activities Prediction Using Structured Data Base

Predicting human activities is very important for human-aware robotic applications. The goal of this work is to forecast human activities that may require robot assistance. The proposed method applies the depth and visual information and the database. The activity is parsed into consecutive actions, some attributes of the actions are described by the probability functions. The method delivers the motion trajectories to nominally possible motion goals. The reasoning process is described by the graphs. The approach was evaluated using four data sets: CAD 60, CAD-120, WUT-17, and WUT-18. The solution efficiency comparing to the other state-of-art was investigated.
Research on Fault Diagnosis for TCT Photovoltaic Array Based on BA-KELM Model

As photovoltaic arrays are mostly installed in harsh outdoor environments, faults occur frequently during operation, resulting in reduced power generation efficiency and service life of photovoltaic arrays. In this paper, aiming at four common types of PV array faults (open circuit, short circuit, degradation, partial shadow), a bat algorithm-kernel extreme learning machine (BA-KELM) model is proposed for fault diagnosis of TCT-connected PV arrays. On the basis of analyzing the output characteristics of the PV array under typical faults, the appropriate feature quantities are selected to form the sample data. The RBF radial basis kernel function is selected to optimize the KELM model structure, and the bat algorithm is used to optimize the kernel parameters and penalty factors in the KELM model to improve the diagnostic accuracy of the model. Through the comparative analysis with PSO-KELM and PSO-ELM models, it is found that the BA-KELM model converges faster in the process of parameter optimization, and the training accuracy and testing accuracy of the optimized model are also higher.
End-to-End Blind Image Quality Assessment with Cascaded Deep Features

The convolutional neural network (CNN) has achieved great success in many visual tasks. However, it has limited progress on image quality assessment (IQA) due to the lacking of IQA-oriented CNN framework which can efficiently represent the hierarchical quality degradation. In this paper, inspired by the hierarchical perception mechanism (from local structure to global semantics) in the human visual system, we design an end-to-end cascaded CNN framework for blind IQA (BIQA), in which multilevel features are extracted and concatenated to represent the hierarchical quality degradation. By jointly optimizing the feature extraction, hierarchical degradation integration, and quality prediction in an end-to-end manner, the novel cascaded CNN with hierarchical feature integration (CaHFI) for BIQA is designed. Experimental results on five benchmark IQA databases demonstrate that the proposed CaHFI achieves the state-of-the-art. And experiments on cross-database evaluation further prove the high generalization ability of the proposed CaHFI.
Soft Computing for Industrial Heat Exchanger

Heat Exchangers (HX) are used in most of the industries like thermal power plant, refineries and food processing industries. There are several types of heat exchangers available, mostly Shell and Tube type Heat Exchanger (STHX) is preferred widely, due to its robustness and more efficient during transient condition. The efficiency of HX is reduced due to deposition of extraneous material upon the heat transfer surface, called fouling, which introduces resistance to heat transfer. This deposition reduces lifetime of the HX. Measurement of fouling factor in real time operation is a tedious task. In this paper, state and parameter estimation is carried out to estimate oil/water outlet temperature and Overall Heat Transfer Coefficient (OHTC) is estimated using random walk model approach with Ensemble Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The simulation results show the effectiveness and reliability of both the filters.
Learning rotation-invariant binary codes for efficient object detection from remote sensing images

Object detection is one of the most important research topics for remote sensing image (RSI) analysis. Because of the rapid development of satellite imaging technology, the resolution of RSI has increased dramatically, which brings a great challenge to the efficiency of RSI-based object detection. We present an object detection model, which greatly improves the detection speed by using learning-based hashing techniques. Specifically, first, a selective search method is used to generate many high quality object proposals that may contain objects. Then for each proposal, we learn rotation-invariant binary codes, which can handle the objects with arbitrary orientations in RSI, to quickly eliminate most nonobject proposals in Hamming space. And finally, the object detection task can be achieved by classifying the left (very limited amount of) proposals with more discriminating classification model. Experimental evaluations on a public very high-resolution remote sensing dataset show the superiority of the proposed method. Specifically, the rotation-invariant learning metric has been combined with three popular hashing methods, and performance improvements have been obtained for all the cases. Furthermore, comparisons with deep learning-based detectors show that hash-based methods fail to achieve state-of-the-art detection performance, but when the graphics processing unit is inaccessible, it achieves a good compromise between performance and runtime.
Machine Learning Based Attack on Certain Encryption Schemes

Machine learning provides a very promising approach to attack cryptographic implementations. In this paper, a machine learning based attack on text encrypted with public key encryption schemes like RSA and Elliptic Curve Cryptography (ECC) is shown. Decision Trees, a popular approach to perform classification are utilized as multi-class classifiers to learn the structure of the text from training examples so that an unknown similar text can be decrypted successfully. The attack is performed on a section of the Enron email dataset. Three different feature sets are created and then their individual performance is evaluated. Finally, their results are combined together to find out the total percentage of correct partial decrypted text in the test set.
GPU-Accelerated Learning of Neuro-Fuzzy System Based on Fuzzy Truth Value

The article is devoted to the problem of the computational complexity of fuzzy inference and neuro-fuzzy system learning in the case of fuzzy inputs. We resort to parallel computations to reduce computation time. In the article, we suggest an algorithm using GPU to efficiently perform fuzzy inference based on fuzzy truth values and the extension of this algorithm to neuro-fuzzy system learning by evolution strategy. We demonstrate the importance of the algorithm and include a benchmark to compare the computation time on CPU against GPU.
A Hybrid Path Planning Method for Mobile Robot Based on Artificial Potential Field Method

This paper proposes a hybrid path planning method based on artificial potential field method (APF) for mobile robot, which combines wall following method (WFM) and obstacles connecting method (OCM) for dealing with local minimum. The environment information is took into consideration to decide the escape direction of WFM. To ensure the success of escaping from local minimum, more reliable switching conditions are designed. OCM is applied to reduce the difficulty of path planning for complex workspace with concave obstacles. Simulation studies have been carried out to verify the validity of the proposed method.
Preserving Secrecy in Mobile Social Networks

Location-based services are one of the most important services offered by mobile social networks. Offering this kind of service requires accessing the physical position of users together with the access authorizations, i.e., who is authorized to access what information. However, these physical positions and authorizations are sensitive information that have to be kept secret from any adversary, including the service providers. As far as we know, the problem of offering location-based services in mobile social networks with a revocation feature under collusion assumption, i.e., an adversary colludes with the service provider, has not been studied. In this article, we show how to solve this problem in the example of range queries. Specifically, we guarantee any adversary, including the service provider, is not able to learn (1) the physical position of the users, (2) the distance between his position and that of the users, and (3) whether two users are allowed to learn the distance between them. We propose two approaches, namely, two-layer symmetric encryption and two-layer attribute-based encryption. The main difference between them is that they use, among other encryption schemes, symmetric and attribute-based encryption, respectively. Next, we prove the secrecy guarantees of both approaches, analyze their complexity, and provide experiments to evaluate their performance in practice.
The Case for Learning-and-System Co-design

While decision-makings in systems are commonly solved with explicit rules and heuristics, machine learning (ML) and deep learning (DL) have been driving a paradigm shift in modern system design. Based on our decade of experience in operationalizing a large production cloud system, Web Search, learning fills the gap in comprehending and taming the system design and operation complexity. However, rather than just improving specific ML/DL algorithms or system features, we posit that the key to unlocking the full potential of learning-augmented systems is a principled methodology promoting learning-and-system co-design. On this basis, we present the AutoSys, a common framework for the development of learning-augmented systems.
Research on Sensitive Content Recognition Method Based on Multi-Filter Fusion

This paper focuses on the strict control over the transfer of sensitive content in the intranet of enterprises and institutions as well as the relatively clear characteristics of sensitive information within enterprises and institutions. A method to identify sensitive contents based on multiple filtering strategies is proposed, and a rule-based counting voting strategy is adopted. With the Naïve Bayes content recognition model combined with a rule-based hierarchical priority strategy, the document type of files can be classified and recognized, And based on user feedback for incremental learning to achieve recognition model optimization. In this paper, the design details of the content recognition method, the functional architecture, the conceptual model and the data processing logic are introduced in detail. Finally, the experimental results show that the content recognition method can make the recognition faster and more accurate. The content recognition method based on multiple filtering strategies is proved to be effective.
Gold distribution and lithogeochemical discrimination of residual regolith and transported overburden, Minotaur gold deposit, Lake Lefroy, Western Australia

The Yilgarn Craton of Western Australia hosts a number of Cenozoic paleochannels, which have been incised into the underlying Paleozoic and Precambrian rocks, filled with sediments and subsequently weathered. The paleochannels are of particular interest in mineral exploration as they may not only host placer-type deposits but also overlie significant supergene and primary mineralisation. Paleochannels also pose particular challenges during exploration as they mask underlying geochemical anomalies, including gold and pathfinder elements. This study investigates a method of distinguishing transported overburden from residual regolith utilising a combination of field and laboratory-based techniques. At the Minotaur deposit, the residual regolith and transported overburden are mineralogically similar, although the presence of biotite, chlorite and muscovite is more characteristic of the residual regolith. Geochemically, Zr, Ti, Co and Sc ratios form distinct groupings allowing more confident discrimination of transported overburden from the underlying residual regolith units. The presence of a distinct gold depletion zone at the redox front was also identified to be a feature of the Minotaur deposit, with Au enrichment occurring above (within transported overburden) and below (within saprolite) the redox front, similar to other gold deposits on Lake Lefroy. The lithological, stratigraphic and Au-enrichment characteristics at Minotaur have also been compared with regional studies and suggest the long-lived impact of paleovalleys on the depositional systems and Au distribution of Lake Lefroy. This work provides a basis for future studies of the region in particular the variation of depositional sequences within regional paleochannel networks.
A New Syntactic Approach for Masses Classification in Digital Mammograms

Breast cancer is one of the most common cancers that affect women worldwide being responsible for about 15% of all deaths related to cancer in the world. Mammography is one of the main techniques to help early detection of breast cancer. Although there are some characteristics that should be considered to discriminate benign and malignant masses, only about 15 to 30% of the cases sent to biopsies are malignant. To aid in the diagnosis of this disease, several CAD systems were proposed and developed to make a second opinion to the physicians, but the theory of formal languages is underexplored in this field. This paper presents a new syntactic approach to discriminate benign and malignant masses in digital mammography. Preliminary results showed that this approach is very promising, since our classifier achieved accuracies from 80% to 100% depending on the model and features used, applied on two different databases.
Dual reference age synthesis

Age synthesis methods typically take a single image as input and use a specific number to control the age of the generated image. In this paper, we propose a novel framework taking two images as inputs, named dual-reference age synthesis (DRAS), which approaches the task differently; instead of using “hard” age information, i.e. a fixed number, our model determines the target age in a “soft” way, by employing a second reference image. Specifically, the proposed framework consists of an identity agent, an age agent and a generative adversarial network. It takes two images as input – an identity reference and an age reference – and outputs a new image that shares corresponding features with each. Experimental results on two benchmark datasets (UTKFace and CACD) demonstrate the appealing performance and flexibility of the proposed framework.
Automatic Construction of Coal Mine Accident Ontology

Due to the rapid growth of text data resources in coal mine accidents, the construction of coal mine accidents by means of manual construction has been unable to meet current needs. Taking the coal mine accident case text as the data source, the BP neural network based method is used to automatically extract the ontology concept. Then automatically extract the relationship between ontology concepts through hierarchical clustering and association rules. Finally, the Protégé ontology editor is used to add the concepts, relationships and examples of the ontology to obtain the coal mine accident ontology. By constructing the coal mine accident ontology by means of automatic construction, not only can a large amount of manpower and material resources be saved, but also the constructed body can be more comprehensive and effectively applied to coal mine safety warning.
Functorial Model Management

Abstract In this paper we use formal tools from category theory to develop a foundation for creating and managing models in systems where knowledge is distributed across multiple representations and formats. We define a class of models which incorporate three different representations---computations, logical semantics, and data--as well as model mappings (functors) to establish relationships between them. We prove that our models support model merge operations called colimits and use these to define a methodology for model integration.
Least square support vector machine technique for short term solar irradiance forecasting

Application of support vector machine (SVM) has been widely used in regression and prediction. Accurate prediction of daily solar irradiance is important in photovoltaic power system since rapid changes in solar irradiance will easily affect the performance of the whole system. This paper presents the short term future prediction of the solar irradiance using least square support vector machine (LSSVM). Historical solar data set including daily solar irradiance over a period of three years (1 January 2014 to 31 December 2016) has been collected at Green Energy Research Centre, Universiti Teknologi Mara (UiTM) Shah Alam, Selangor. This related information shall be used in prediction of the future solar irradiance which useful for predicting electrical parameters of a PV system especially large scale solar (LSS) farm. The simulation was carried out using SVM Toolbox in MATLAB software. The results show good agreement between the predicted and the measured values.