Reviewerly/exHarmony huggingface dataset:
Simultaneous Identification of Two-Independent Viscoelastic Characteristics with the Virtual Fields Method

In this study, a method for determining the viscoelastic material properties from displacement fields is proposed. Stress-strain relationship represented by the superposition integral is employed as the viscoelastic constitutive equation. Thus, the unknown properties are relaxation bulk and shear moduli. The virtual fields method based on the principle of virtual work is used as a method for the inverse analysis. The unknown material properties are determined by solving nonlinear simultaneous equations of the virtual work containing the constitutive equations. The validity of the proposed method is demonstrated by identifying the viscoelastic properties of a soft epoxy resin. Results show that the two independent viscoelastic material properties can be identified by proposed method.
Influenced Factors of B40 Students’ Academic Achievement

This paper aimed to discuss the influenced factors on the students' academic achievement among the B40 family. Identified factors are the findings after collecting views and findings from research studies. The student's level of academic achievement is influenced by a variety of factors such as parents, teachers and peers. This paper focusing on factors affecting the academic achievement of students from B40 family at the MARA Science College Mukah (MRSM). The establishment of the MARA Science College (MRSM) by the People's Trust Council (MARA) is to provide advanced education in science and technology to intelligent, highly educated Bumiputera students in line with national goals and requirements. However, the flexibility given to MARA gives priority to students from the B40 community living in rural and urban areas. In Sarawak and Sabah, MARA gives opportunities to the students to apply using the Primary School Assessment Exam (UPSR) qualification. It is hoped that this paper will be able to identify and understand the factors affecting the students' academic achievement of the B40 family. This is because, these students will be the human capital that will contribute to a competitive workforce and thus set a high level of economic achievement compared to other developed countries.
Grading Chinese Answers on Specialty Subjective Questions

It is an important task to grade answers on specialty subjective questions, which is helpful for the supervision of human review and improving the efficiency and quality of review process. Since this grading process should be performed at the same time with human review, there are only a few samples available for each question that can be provided by specialty experts before review process. We investigate the problem of grading Chinese answers on specialty subjective questions with a reference answer in this paper by proposing a grading model that combines two Bi-LSTM networks with attention mechanism. The first part is a sequence to sequence Bi-LSTM network that adopts the pre-trained word embeddings as input. Since there is no embedding for some specialty words, we instead use the fine-grained word embeddings. After the max-pooling on each sentence, we adopt the mutual attention mechanism to learn the matching degree on specialty knowledge between each pair of sentences of answer and reference. Then we adopt another Bi-LSTM with max-pooling to have an overall vector. By concatenating these two vectors from answer and reference, a multilayer perceptron is adopted to predicate the scores. We adopt the real datasets on a national specialty examination to thoroughly verify the model performance against different amount of training data, network structures, pooling strategies and attention mechanisms. The experimental results show the effectiveness of our method.
I2P Anonymous Communication Network Measurement and Analysis

The I2P (Invisible Internet Project) network is a low-latency anonymous network composed of I2P routers based on garlic routing, which is mainly to protect privacy and prevent tracking, such as evading censorship and hiding whistle-blowers. As opposed to well-known and well-research Tor network, I2P aims to organize itself and distribute its anonymity. To our best knowledge, the study of I2P measurements is still insufficient. Thus, this paper proposed a novel method to measure I2P anonymous network nodes, including passive measurement and active measurement, and designed a local I2P node analysis system. Through experiments, we collected 16040 I2P nodes and analyzed properties including country distribution, bandwidth distribution and FloodingFill node attributes.
Discrete Wavelet Transform Based Multiple Watermarking for Digital Images Using Back-Propagation Neural Network

A Discrete Wavelet Transform (DWT) based multiple watermarking technique for images using Back-Propagation neural networks (BPNN) are proposed. The successive/re- watermarking is one of the best method in multiple watermarking techniques. In successive/re-watermarking method, the various watermarks are embedded and extracted one by one. The wavelet coefficient is selected based on the weight factor by using Human Visual System (HVS). The BPNN is incredibly well-liked in neural networks and its variety of supervised learning neural networks. The two watermarks are embedding into the original image using improved BPNN, which can advance the speed of the erudition, reduce error and the trained neural networks be capable of extracting the two watermarks from the embedded images. The simulation results show that the proposed work achieves good quality on the embedded images and more robustness on extracted two watermarks.
Boundary Detector Encoder and Decoder with Soft Attention for Video Captioning

The use of Recurrent Neural Networks and Convolutional Neural Networks for video captioning has received widespread attention, since the deep learning has developed rapidly. Based on classical encoder-decoder approach, we modify the encoding networks and decoding networks to improve the performance of the entire networks. In this paper, we introduce an encoding scheme that can detect the hierarchical structure of the input video. What’s more, we use soft attention mechanism which can learn to automatically select the relevant input frames from the input video to generate the description of the input video. Extensive experiments are conducted on two datasets: the Microsoft Video Description Corpus and the MSR-Video To Text. Three metrics, BLEU@4, METEOR and CIDEr are used to evaluate our approach. Experimental results demonstrate the effectiveness of our approach.
Visual Servo Application Using Model Predictive Control (MPC) Method on Pan-tilt Camera Platform

Visual servoing is a technique which use feedback information extracted from a visual sensor to control the motion of a robot. Combined with an image processing algorithm, visual information from a camera becomes a visual sensor which provides feedback or measurement value i.e. center point coordinate value from detected face. The face tracking system with pan-tilt camera can be utilized for many applications. The 2 DOF (degree of freedom) camera platform being used consists of 2 servo motors which moves according to control signal calculated using a control method such that the system will be able to keep the tracked face at the center of the camera frame. In this research, Model Predictive Control (MPC) method is implemented to build a controller, in which each movement of pan and tilt servo camera is controlled by control signals calculated with MPC method. MPC application is chosen so the system can produce an optimal control signal while considering input signal saturation aspect. Optimal control signal provides by the MPC controller is produced through optimizing the system performance index. The result showed that MPC controller successfully implemented in pan-tilt camera platform. The MPC control signals deliver a good control pan and tilt system response with rise time <; 1 second and overshoot <; 15%.
Lower and Upper Bounds on the Randomness Complexity of Private Computations of AND

We consider multiparty information-theoretic private protocols, and specifically their randomness complexity. The randomness complexity of private protocols is of interest both because random bits are considered a scarce resource and because of the relation between that complexity measure and other complexity measures of boolean functions such as the circuit size or the sensitivity of the function being computed [Kushilevitz, Ostrovsky, and Rosén, J. Comput. Syst. Sci., 58 (1999), pp. 129--136] and [Gál and Rosén, SIAM J. Comput., 31 (2002), pp. 1424--1437]. More concretely, we consider the randomness complexity of the basic Boolean function \tt and, that serves as a building block in the design of many private protocols. We show that \tt and cannot be privately computed using a single random bit, thus giving the first nontrivial lower bound on the 1-private randomness complexity of an explicit Boolean function, $f: \{0,1\}^n \rightarrow \{0,1\}$. We further show that and, on any number of inputs $n$ (one input bit per player), can be privately computed using 8 random bits (and 7 random bits in the special case of $n=3$ players), improving the upper bound of 73 random bits implicit in [Kushilevitz, Ostrovsky, and Rosén, J. Comput. Syst. Sci., 58 (1999), pp. 129--136]. Together with our lower bound, we thus approach the exact determination of the randomness complexity of \tt and. To the best of our knowledge, the exact randomness complexity of private computation is not known for any explicit function (except for \tt xor, which is 1-random, and for several degenerate functions).
An Optimized Radio Frequency Fingerprint Extraction Method Applied to Low-End Receivers

This paper focuses on the method for classification and identification of radio transmitters in low-end receivers, to provide a new radio frequency fingerprint. The proposed algorithm based on the Empirical Mode Decomposition (EMD) is an optimized method for the actual radio frequency signal decomposition which will cause the problem of mode aliasing. Firstly, a signal superposition denoising process is applied before EMD to reduce the influence of channel noise. And then, an autocorrelation function discrimination method is performed to distinguish different modal components after EMD. Thirdly, box dimension is used to extract features to form feature vectors as the radio frequency fingerprint which is classified by the neural network classifier, defining the transmitting device of the signal. The experiment results show that the algorithm has a good recognition effect in the low-end signal receiving equipment USRP, and it can effectively recognize the signals from different interphone individuals, which verifies the effectiveness of the algorithm.
Named Entity Recognition in Prehospital Trauma Care.

Natural language processing (NLP) methods would improve outcomes in the area of prehospital Emergency Medical Services (EMS) data collection and abstraction. This study evaluated off-the-shelf solutions for automating labelling of clinically relevant data from EMS reports. A qualitative approach for choosing the best possible ensemble of pretrained NLP systems was developed and validated along with a feature using word embeddings to test phrase synonymy. The ensemble showed increased performance over individual systems.
Sharing Ghost Variables in a Collection of Abstract Domains

We propose a framework in which we share ghost variables across a collection of abstract domains allowing precise proofs of complex properties. In abstract interpretation, it is often necessary to be able to express complex properties while doing a precise analysis. A way to achieve that is to combine a collection of domains, each handling some kind of properties, using a reduced product. Separating domains allows an easier and more modular implementation, and eases soundness and termination proofs. This way, we can add a domain for any kind of property that is interesting. The reduced product, or an approximation of it, is in charge of refining abstract states, making the analysis precise. In program verification, ghost variables can be used to ease proofs of properties by storing intermediate values that do not appear directly in the execution. We propose a reduced product of abstract domains that allows domains to use ghost variables to ease the representation of their internal state. Domains must be totally agnostic with respect to other existing domains. In particular the handling of ghost variables must be entirely decentralized while still ensuring soundness and termination of the analysis.
News Readers' Sentiment Analysis Based on Fused-KNN Algorithm

News readers' sentiment analysis means speculating emotions that readers may express after reading the news according to the comments. When traditional KNN algorithm is applied to the emotional prediction task, there are some deficiencies in definiting distance and judging category. These issues ultimately lead to poor evaluation of the classification's results. Therefore, this paper proposed the Fused-KNN algorithm which adopted the improved neighbor sample selection strategy and the improved category judgment strategy. Firstly, with considering the specific application area of news readers' sentiment analysis, emotional amount is structured combining the Emotion Word Ontology of Dalian University of Technology and semantic rules. Then, four attributes are proposed which include emotional amount and the number of commendatory terms, neutral terms and derogatory terms. Distance between samples is measured by the attribute-correlated distance. Based on this, the k nearest neighbors are selected. Meanwhile, emotional polarity is predicted by the sum of neighbor samples' polarity influence factors, instead of the most neighbor samples' category in traditional method. The experiment results show that the algorithm achieves superior performance and can better complete the sentiment classification task for news comment samples.
Recurrent Deep Network Models for Clinical NLP Tasks: Use Case with Sentence Boundary Disambiguation.

Although a number of foundational natural language processing (NLP) tasks like text segmentation are considered a simple problem in the general English domain dominated by well-formed text, complexities of clinical documentation lead to poor performance of existing solutions designed for the general English domain. We present an alternative solution that relies on a convolutional neural network layer followed by a bidirectional long short-term memory layer (CNN-Bi-LSTM) for the task of sentence boundary disambiguation and describe an ensemble approach for domain adaptation using two training corpora. Implementations using the Keras neural-networks API are available at https://github.com/NLPIE/clinical-sentences.
Research and Practice of Image Processing Based on Python

Abstract Image is an important means to acquire, express and transmit information. Digital image processing means computer processing of images. In this paper, firstly mainly analyse the application characteristics of Python, focus on the typical functions of image processing PIL library, and then consult the relevant application cases of image processing. Finally, the image processing functions of PIL library are put into practice, which has certain reference and guiding significance for the image processing functions of Python and other platforms. Image processing and graphics processing have always been important fields in the development of computer technology.
Review-Based Topic Distribution Profile for Recommender Systems

Using social media and e-commerce sites, users convey their preferences and interests via reviews, feedback, and comments. These comments and reviews consist of details about a given product or an item and also users’ thoughts. Different features of user-generated content include various features such as emotions, sentiments, review usefulness, and so forth that exhibit a promising exploration in the domain of recommendation frameworks. This paper harness reviews as the content generated from user to exploit topics based on topic modeling using latent Dirichlet allocation for generating topic distribution profile of users. Examination of topic distribution profile of users gives us a new prospect for recommendation of products which is based on hidden thematic framework of user preferences. Assessment on books and movie dataset confirms the adequacy of the suggested topic distribution profile for recommendation system framework.
Comparative Study of LASSO, Ridge Regression, Preliminary Test and Stein-type Estimators for the Sparse Gaussian Regression Model

This paper compares the performance characteristics of penalty estimators, namely, LASSO and ridge regression (RR), with the least squares estimator (LSE), restricted estimator (RE), preliminary test estimator (PTE) and the Stein-type estimators. Under the assumption of orthonormal design matrix of a given regression model, we find that the RR estimator dominates the LSE, RE, PTE, Stein-type estimators and LASSO estimator uniformly, while, similar to Hansen (2013), neither LASSO nor LSE, PTE and Stein-Type estimators dominates the other. Our conclusions are based on the analysis of L_2-risks and relative risk efficiencies (RRE) together with the RRE related tables and graphs.
A Mechanism to Support Agile Frameworks Enhancing Reliability Assessment for SCS Development: A Case Study of Medical Surgery Departments

Safety-critical systems (SCSs) are distributed in many areas such as flight, railway, medical, nuclear and defense, and failure of these systems can harm human life or damage the environment. There are several ways to reduce or possibly eliminate failure, according to researchers improving the development processes of SCSs reduces failure by 40%. Therefore, developers have invented many development methods and measurement techniques to reduce failures during development processes. Agile Software Development (ASD) has entered the field of safety-critical system development (SCSD) in the past few years. Agile has produced reliable and high-quality products by expanding its principles and adapting new measurement techniques. However, there is still a gap in improving measurement techniques and stopping failures. This paper proposed a new measurement mechanism called Package Metrics for Improving Software Development (PM-ISD). The proposed measurement enhances reliability assessment and decision-making during agile development processes. PM-ISD assists the Agile management team throughout the Medical Surgery Department (MSD) project phases to track the completion of tasks.
Quality Characteristics of Mobile Applications

Mobile devices and their applications became popular since the creation of the app stores' distribution model. There is a great variety of apps available and we are inclined to use the best ones, what makes it essential to develop apps of distinctive quality. This paper described the execution and the results of a survey on the quality characteristics of mobile applications. The questions in the survey were based on the results of a previously conducted systematic mapping study, which identified occurrences of quality characteristics in the context of mobile applications. At all 500 answers were obtained. The analysis of these answers led to several results upon the opinion and preferences of users when using mobile applications. Usefulness and Functional Correctness were the most pertinent quality characteristics in the respondent's opinion. We expect that the outcomes of this study will help the development and the quality evaluation of mobile applications.
A Secured Framework to Protect Association Rules in the Big Data Environment Using Fuzzy Logic

Data security is an important issue in the age of big data.The existing data security approaches should be improved to cover inactive databases, i.e. the databases with existing information only, and suit the requirements of big data mining.Therefore, this paper proposes framework to protect the data anonymity in big data environment.The framework is mainly implemented in three steps: mining the association rules, computing the confidence of each rule, and determining the sensitivity of each rule using fuzzy logic.To process massive data, the authors paid attention to enhance the parallelism and scalability of the proposed framework.The proposed framework was verified through experiments on two datasets.Judging by metrics like lost, ghost and false rules, it is confirmed that our framework can protect the association rules efficiently in the big data environment.
AMRL: Aggregated Memory For Reinforcement Learning

In many partially observable scenarios, Reinforcement Learning (RL) agents must rely on long-term memory in order to learn an optimal policy. We demonstrate that using techniques from NLP and supervised learning fails at RL tasks due to stochasticity from the environment and from exploration. Utilizing our insights on the limitations of traditional memory methods in RL, we propose AMRL, a class of models that can learn better policies with greater sample efficiency and are resilient to noisy inputs. Specifically, our models use a standard memory module to summarize short-term context, and then aggregate all prior states from the standard model without respect to order. We show that this provides advantages both in terms of gradient decay and signal-to-noise ratio over time. Evaluating in Minecraft and maze environments that test long-term memory, we find that our model improves average return by 19% over a baseline that has the same number of parameters and by 9% over a stronger baseline that has far more parameters.
Image-based visual servoing using a set for multiple pin-in-hole assembly

Purpose This paper aims to present an image-based visual servoing algorithm for a multiple pin-in-hole assembly. This paper also aims to avoid the matching and tracking of image features and the remaining robust against image defects. Design/methodology/approach The authors derive a novel model in the set space and design three image errors to control the 3 degrees of freedom (DOF) of a single-lug workpiece in the alignment task. Analytic computations of the interaction matrix that link the time variations of the image errors to the single-lug workpiece motions are performed. The authors introduce two approximate hypotheses so that the interaction matrix has a decoupled form, and an auto-adaptive algorithm is designed to estimate the interaction matrix. Findings Image-based visual servoing in the set space avoids the matching and tracking of image features, and these methods are not sensitive to image effects. The control law using the auto-adaptive algorithm is more efficient than that using a static interaction matrix. Simulations and real-world experiments are performed to demonstrate the effectiveness of the proposed algorithm. Originality/value This paper proposes a new visual servoing method to achieve pin-in-hole assembly tasks. The main advantage of this new approach is that it does not require tracking or matching of the image features, and its supplementary advantage is that it is not sensitive to image defects.