Reviewerly/exHarmony huggingface dataset:
Accurate Design Pattern Detection Based on Idiomatic Implementation Matching in Java Language Context

Design patterns (DPs) are widely accepted as solutions to recurring problems in software design. While numerous approaches and tools have been proposed for DP detection over the years, the neglect of language-specific mechanism that underlies the implementation idioms of DPs leads to false or missing DP instances since language-specific features are not captured and similar characteristics are not distinguished. However, there is still a lack of research that emphasizes the idiomatic implementation in the context of a specific language. A vital challenge is the representation of software systems and language mechanism. In this work, we propose a practical approach for DP detection from source code, which exploits idiomatic implementation in the context of Java language. DPs are formally defined under the blueprint of the layered knowledge graph (LKG) that models both language-independent concepts of DPs and Java language mechanism. Based on static analysis and inference techniques, the approach enables flexible search strategies integrating structural, behavioral and semantic aspects of DPs for the detection. Concerning emerging patterns and pattern variants, the core methodology supports pluggable pattern templates. A prototype implementation has been evaluated on five open source software systems and compared with three other approaches. The evaluation results show that the proposed approach improves the accuracy with higher precision (85.7%) and recall (93.8%). The runtime performance also supports its practical applicability.
k-DDD Measure and MapReduce Based Anonymity Model for Secured Privacy-Preserving Big Data Publishing

Nowadays, big data publishing is the emerging trend since they have good potential for the decision support in the applications, such as a hospital, government, industries, etc. Existing algorithms have many problems in preserving the privacy of the data when the data is in large size. To avoid these problems, this paper introduces a novel anonymity model for the data publishing based on K-DDD measure and MapReduce. This paper presents the Duplicate-Divergence-Different properties enabled dragon Genetic (DDDG) algorithm based on the k-DDD anonymization and the dragon operator based genetic algorithm. The proposed DDDG algorithm allows the privacy preservation in the big data by modifying the MapReduce techniques with the proposed DDDG algorithm. The performance of the proposed anonymity model is analyzed with the metrics such as information loss (IL) and the classification accuracy (CA). The adult database from the UC Irvine dataset is used for the simulation. The simulation results show that the proposed DDDG algorithm achieved the lowest IL of 0.0191 and the highest CA with the value of 0.8977 than the existing algorithms for k value of 2.
Augmenting Dialogue Response Generation With Unstructured Textual Knowledge

The dialogue response generation is a challenging task in chatbot applications. Recently neural-network-based dialogue models, including the sequence-to-sequence model and the RNN language models, are able to generate fluent and grammatically compliant responses, while there is a major limitation that most of the responses generated by these models are of chit-chat style instead of being informative. After investigating the currently used models, we found that one primary challenge is to model and generate informative words, such as named entities, especially when the entities have sparsely existed in training corpus. To address this problem, we propose to augment neural network-based generative architecture with knowledge embedding and knowledge attentive reader to incorporate external textual knowledge into the dialogue model to facilitate the dialogue modeling and generation. We evaluate the model with the Ubuntu dataset through automatic evaluation metrics and human evaluation. The experimental study has shown our methods outperform strong baselines in multiple metrics. We also visualize how the attention works in the dialogue context to verify the effectiveness of knowledge attentive reader mechanism.
A New Method for Adaptive Median Filtering of Images

A new method of adaptive median filtering of impulse noise in images is proposed in this paper. The method is based on the combined using of iterative image processing and post-processing of the median filtering result. The results of a comparison of the quality of the proposed method with known methods are shown in the experimental part of the article. The simulation results showed that the proposed method performs better then known methods in all cases. The results obtained in the paper can find wide practical application in the processing of satellite and medical images, geophysical data and other applications of digital image processing.
Convolutional Neural Network Based Side-Channel Attacks in Time-Frequency Representations

Profiled attacks play a fundamental role in the evaluation of cryptographic implementation worst-case security. For the past sixteen years, great efforts have been paid to develop profiled attacks from Template Attacks to deep learning based attacks. However, most attacks are performed in time domain – may lose frequency domain information. In this paper, to utilize leakage information more effectively, we propose a novel deep learning based side-channel attack in time-frequency representations. By exploiting time-frequency patterns and extracting high level key-related features in spectrograms simultaneously, we aim to maximize the potential of convolutional neural networks in profiled attacks. Firstly, an effective network architecture is deployed to perform successful attacks. Secondly, some critical parameters in spectrogram are studied for better training the network. Moreover, we compare Template Attacks and CNN-based attacks in both time and time-frequency domain with public datasets. The heuristic results in these experiments provide a new perspective that CNN-based attacks in spectrograms give a very feasible option to the state-of-the-art profiled attacks.
An integrated approach to emotion recognition and gender classification

The human-computer communication adds to worldwide access to training, information upgrade and the conveyance of value learning and instructions. Furthermore, the changing paradigm of machine perceiving the human emotions effectively is a technological advancement for the learning community. The automatic recognition of emotions reflected from speech and facial expressions for making the machine to understand the human verbal and non-verbal emotions is coined as “Emotion Recognition”. Emotion recognition systems, regardless, are not totally seen as subject independent dynamic features, so they are not sufficiently vigorous for constant acknowledgment assignments with subject assortment (human face), head movement, illumination change, speech, noise and so on when thought about exclusively. Hence, it is proposed to fuse the features of facial expressions and speech. The present framework utilizes the Speech (Mel Frequency Cepstral Coefficients) features and Facial (Maximally Stable Extremal Regions) features to predict the emotions of a person through a systematic and scientific study. Specifically, when combining MSER with MFCC, the recognition rates can be further improved by 2 to 3% on Indian Face Database and Berlin Speech Database.
An efficient resource prediction–based scheduling technique for scientific applications in cloud environment

Cloud computing makes scientists to run complex scientific applications. The research community is able to access on-demand compute resources within a short span of time instead of experiencing peak demand bottlenecks. As the demand for cloud resources is dynamic and volatile in nature, this in turn affects the availability of resources during scheduling. In order to allocate sufficient resources for scientific applications with different execution requirements, it is necessary to predict the appropriate set of resources. To attain this objective, a resource prediction–based scheduling technique has been introduced which automates the resource allocation for scientific application in virtualized cloud environment. First, the proposed prediction model is trained on the dataset generated by concurrently deploying tasks of a scientific application on cloud. Then, the resources are scheduled based on the output of proposed prediction technique. The main objective of resource prediction–based scheduling technique is to efficiently handle the resources for virtual machines in order to reduce the execution time, error rate, and improve the accuracy.
Designing High Accuracy Statistical Machine Translation for Sign Language Using Parallel Corpus

In this article, the authors deal with the machine translation of written English text to sign language. They study the existing systems and issues in order to propose an implantation of a statistical machine translation from written English text to American Sign Language (English/ASL) taking care of several features of sign language. The work proposes a novel approach to build artificial corpus using grammatical dependencies rules owing to the lack of resources for sign language. The parallel corpus was the input of the statistical machine translation, which was used for creating statistical memory translation based on IBM alignment algorithms. These algorithms were enhanced and optimized by integrating the Jaro–Winkler distances in order to decrease training process. Subsequently, based on the constructed translation memory, a decoder was implemented for translating English text to the ASL using a novel proposed transcription system based on gloss annotation. The results were evaluated using the BLEU evaluation metric.
Corporate Capture of Blockchain Governance

We develop a theory of blockchain governance. In our model, the proof-of-work system, which is the most common set of rules for validating transactions in blockchains, creates an industrial ecosystem with specialized suppliers of goods and services. We analyze the interactions between blockchain governance and the market structure of the industries in the blockchain ecosystem. We show that the proof-of-work system may lead to a situation where some large firms in the blockchain industrial ecosystem -- blockchain conglomerates -- capture the governance of the blockchain.
Mapping the wavelength position of mineral features in hyperspectral thermal infrared data

The Wavelength Mapper is an algorithm that searches for the deepest absorption feature in each pixel of a hyperspectral image. On a per pixel basis, it extracts the wavelength position, which serves as a proxy of the mineralogy and the feature depth as a proxy for the relative abundance. This algorithm has been used with near and shortwave infrared data, but has not yet been tested on hyperspectral thermal infrared images. It is unclear what results are expected when the Wavelength Mapper algorithm is applied to hyperspectral thermal infrared data since reststrahlen features characteristically overlap in emissivity spectra. In this paper, the Wavelength Mapper is tested on a multi-flightline airborne hyperspectral TIR dataset acquired over the Yerington Batholith, Nevada. Observations were made in the 8.05–11.65 μm wavelength range to include thermal spectral features of major rock-forming minerals, and a new color ramp is created to separate quartz-rich rocks from plagioclase-rich rocks. Our results indicate that the Wavelength Mapper creates coherent spatial patterns across flightlines. The results displayed represent different types of igneous and sedimentary rocks, as well as the products of hydrothermal alteration via different colors, mainly based on the relative abundance of quartz, feldspar and garnet, as well as mica and epidote. Comparison with published maps indicate that the Wavelength Mapper represents for each pixel a parameter value that can be linked to the spectrally dominate rock-forming mineral of that area, as mapped with traditional fieldwork methods. In conclusion, the Wavelength Mapper can be applied to airborne hyperspectral TIR data to achieve a simple, repeatable, per-pixel overview map of the dominating rock-forming mineral occurrences.
Object Tracking Over Distributed WSNs With Consensus on Estimates and Missing Data

Wireless sensor network (WSN) technologies are used to provide mobile object tracking due to advantages such as mobility, scalability, and flexibility. However, wireless interaction between the network nodes is often accompanied by missing data, which requires robustness from the estimator. This paper develops an iterative distributed unbiased finite impulse response (dUFIR) filtering algorithm for object tracking via WSNs with consensus on estimates and shows that it has higher robustness than the distributed Kalman filter (dKF). The tracking problem is viewed as a real-time position estimation of an unmanned ground vehicle (UGV). The extensive simulations are provided using real sensor parameters and measurements of the UGV position with missing data. Two different scenarios are considered when: 1) each sensor is capable of measuring the UGV position and 2) sensors have different time-varying noise variances, as in practical WSNs. The higher robustness of the dUFIR against the dKF is demonstrated under diverse operation conditions.
A Deep Learning Facial Expression Recognition based Scoring System for Restaurants

Recently, the popularity of automated and unmanned restaurants has increased. Due to the absence of staff, there is no direct perception of the customers' impressions in order to find out what their experiences with the restaurant concept are like. For this purpose, this paper presents a rating system based on facial expression recognition with pre-trained convolutional neural network (CNN) models. It is composed of an Android mobile application, a web server, and a pre-trained AI-server. Both the food and the environment are supposed to be rated. Currently, three expressions (satisfied, neutral and disappointed) are provided by the scoring system.
Effectiveness of a digital pen-based learning system with a reward mechanism to improve learners’ metacognitive strategies in listening

A number of prominent educators have confirmed that developing learners’ metacognitive strategies about listening is an important way of helping them learn how to listen to improve their listening. With the rapid development of information and communication technologies, many listening training systems provide various types of help options in support of listening comprehension. However, learners may be unaware of how effectively use these help options or they may rely too heavily on them, thus reducing the effectiveness of their listening practice. Therefore, this study develops a digital pen-based learning system with a reward mechanism that guides learners through the metacognitive processes effectively use available help options to develop listening skills. Two experiments were performed to evaluate the effects of the proposed system on learners’ listening achievement, motivation, and metacognitive awareness. The experimental results indicated that the proposed system improved learners’ listening comprehension, learning motivation, and metacognitive awareness. A lag-sequential analysis was conducted to infer learners’ behavioral patterns to explore how learners used the help options to perform listening tasks. Several interesting behavioral patterns were found and discussed.
Research on Microseismic Source Locations Based on Deep Reinforcement Learning

The technology used to locate the sources of shallow underground seismic activity is mainly used to assess underground damage, the positioning of bombs in ordnance test fields, and the positioning of charges in engineering blasting.Due to sensor acquisition clock errors and the lack of generalizability of vibration data, the current sourced location methods cannot easily meet the requirements of precise positioning and system reaction time in assessing near-field microseismic activity.Based on the seismic wave analysis technique and the deep reinforcement learning method, this paper proposes a dynamic model for locating shallow underground seismic sources.The model allows the generalization of low-dimensional vibration waves information to high dimensions through spatial scanning.The process of source detection is treated as a Markov process.The correspondence between the center of a source and a high-dimensional energy distribution is established, and training is then gradually performed using the deep reinforcement learning method.The optimization of the center of the source to accurately determine the position of the focal center is provisionally called the reinforcement learning source scanning algorithm (RL-SSA).In addition, a small site-based static explosion test shows that the positioning method can greatly improve the positioning accuracy (<1 m) in the location of near-field microseismic sources.INDEX TERMS Convolutional neural network (CNN), deep reinforcement learning (DRL), deep Q-learning (DQN), source-scanning algorithm (SSA).
Initial Results from Using Preference Ranking Organization Methods for Enrichment of Evaluations to Help Steer a Powered Wheelchair

Research is presented that uses PROMETHEE II to determine a direction for a powered wheelchair. This is the only time that PROMETHEE II has been employed for this sort of use. A wheelchair driver proposes a preferred speed and direction, and PROMETHEE II recommends a reliable and trustworthy bearing. The two directions are combined so that the wheelchair safely avoids obstacles. Ultrasonic sensors and joysticks provide the inputs and the final direction is a combination of the preferred bearing and a route that safely avoids obstacles. The systematic decision-making process assists the user with steering safely. Sensitivity analysis explores the potential directions and an appropriate direction is chosen. A driver can reject or cancel a decision suggestions by holding their joystick in a fixed place.
Overview of Image Denoising Based on Deep Learning

With the advent of the artificial intelligence era, deep learning technology is widely used in various fields, showing a good advantage in image noise reduction. In order to let more scholars understand the progress of machine learning in the field of image noise reduction, the research on machine learning in image denoising is reviewed. This paper mainly introduces three kinds of models, such as convolutional neural network, pulse coupled neural network and wavelet neural network, which are commonly used in image denoising. The nonlocal mean noise reduction method based on machine learning is described as a concrete case. The purpose of the article is to clearly understand the latest developments in deep learning in the field of image noise reduction.
Social Activity Recognition on Continuous RGB-D Video Sequences

Modern service robots are provided with one or more sensors, often including RGB-D cameras, to perceive objects and humans in the environment. This paper proposes a new system for the recognition of human social activities from a continuous stream of RGB-D data. Many of the works until now have succeeded in recognising activities from clipped videos in datasets, but for robotic applications it is important to be able to move to more realistic scenarios in which such activities are not manually selected. For this reason, it is useful to detect the time intervals when humans are performing social activities, the recognition of which can contribute to trigger human-robot interactions or to detect situations of potential danger. The main contributions of this research work include a novel system for the recognition of social activities from continuous RGB-D data, combining temporal segmentation and classification, as well as a model for learning the proximity-based priors of the social activities. A new public dataset with RGB-D videos of social and individual activities is also provided and used for evaluating the proposed solutions. The results show the good performance of the system in recognising social activities from continuous RGB-D data.
Multimodal Sparse Representation-Based Classification Scheme for RF Fingerprinting

In this letter, we propose a multimodal method for improving radio frequency (RF) fingerprinting performance that uses multiple features cultivated from RF signals.Combining multiple features, including a falling transient feature that has not previously been used in RF fingerprinting studies, we aim to demonstrate that the proposed method results in improved accuracy.We show that a sparse representation-based classification (SRC) scheme can be a good platform for combining multiple features.The experimental results on RF signals acquired from eight walkie-talkies show that the RF fingerprinting accuracy of the proposed method improves significantly as the number of features increases.
Machine Translation Evaluation: Unveiling the Role of Dense Sentence Vector Embedding for Morphologically Rich Language

Machine Translation (MT) evaluation metrics like BiLingual Evaluation Understudy (BLEU) and Metric for Evaluation of Translation with Explicit Ordering (METEOR) are known to have poor performance for word-order and morphologically rich languages. Application of linguistic knowledge to evaluate MTs for morphologically rich language like Hindi as a target language, is shown to be more effective and accurate [S. Tripathi and V. Kansal, Using linguistic knowledge for machine translation evaluation with Hindi as a target language, Comput. Sist.21(4) (2017) 717–724]. Leveraging the recent progress made in the domain of word vector and sentence vector embedding [T. Mikolov and J. Dean, Distributed representations of words and phrases and their compositionality, Adv. Neural Inf. Process. Syst. 2 (2013) 3111–3119], authors have trained a large corpus of pre-processed Hindi text ([Formula: see text] million tokens) for obtaining the word vectors and sentence vector embedding for Hindi. The training has been performed on high end system configuration utilizing Google Cloud platform resources. This sentence vector embedding is further used to corroborate the findings through linguistic knowledge in evaluation metric. For morphologically rich language as target, evaluation metric of MT systems is considered as an optimal solution. In this paper, authors have demonstrated that MT evaluation using sentence embedding-based approach closely mirrors linguistic evaluation technique. The relevant codes used to generate the vector embedding for Hindi have been uploaded on code sharing platform Github. a