Reviewerly/exHarmony huggingface dataset:
EPIC: Context Adaptive Lossless Light Field Compression using Epipolar Plane Images

This paper proposes extensions of CALIC for lossless compression of light field (LF) images. The overall prediction process is improved by exploiting the linear structure of Epipolar Plane Images (EPI) in a slope based prediction scheme. The prediction is improved further by averaging predictions made using horizontal and verticals EPIs. Besides this, the difference in these predictions is included in the error energy function, and the texture context is redefined to improve the overall compression ratio. The results using the proposed method shows significant bitrate-savings in comparison to standard lossless coding schemes and offers significant reduction in computational complexity in comparison to the state-of-the-art compression schemes.
Quaternion-Valued Twin-Multistate Hopfield Neural Networks With Dual Connections

Dual connections (DCs) utilize the noncommutativity of quaternions and improve the noise tolerance of quaternion Hopfield neural networks (QHNNs). In this article, we introduce DCs to twin-multistate QHNNs. We conduct computer simulations to investigate the noise tolerance. The QHNNs with DCs were weak against an increase in the number of training patterns, but they were robust against increased resolution factor. The simulation results can be explained from the standpoints of storage capacities and rotational invariance.
EMSGD: An Improved Learning Algorithm of Neural Networks With Imbalanced Data

In this paper, the influence of data imbalance on neural networks is discussed, and an improved learning algorithm to solve this problem is proposed.The experimental results show that in the case of imbalanced data, the training error of neural network converges slowly and the generalization ability is poor.Our theoretical analysis shows that in the process of training, the gradient descent direction of the weights is dominated by the major-classes, which accounts for the slow convergence of the training error.Based on these results, we propose the Equilibration Mini-batch Stochastic Gradient Descent (EMSGD) method to ensure the equilibrium of the data in the mini-batch.The advantage of this technique is that it makes full use of the existing random sampling step of MSGD without increasing the computational complexity.In addition, by over-sampling of minor-classes in the mini-batch, duplicated instances would be greatly reduced, thus preventing the model from overfitting.The experimental results show that under the condition of the imbalanced training data, EMSGD can make the neural network training error converge rapidly.
Novel data‐placement scheme for improving the data locality of Hadoop in heterogeneous environments

Summary To address the challenging needs of high‐performance big data processing, parallel‐distributed frameworks such as Hadoop are being utilized extensively. However, in heterogeneous environments, the performance of Hadoop clusters is below par. This is primarily because the blocks of the clusters are allocated equally to all nodes without regard to differences in the capability of individual nodes. This results in reduced data locality. Thus, a new data‐placement scheme that enhances data locality is required for Hadoop in heterogeneous environments. This article proposes a new data placement scheme that preserves the same degree of data locality in heterogeneous environments as that of the standard Hadoop, with only a small amount of replicated data. In the proposed scheme, only those blocks with the highest probability of being accessed remotely are selected and replicated. The results of experiments conducted indicate that the proposed scheme incurs only a 20% disk space overhead and has virtually the same data locality ratio as the standard Hadoop, which has a replication factor of three and 200% disk space overhead.
Leveraging Machine Learning for Software Redocumentation

Source code comments contain key information about the underlying software system. Many redocumentation approaches, however, cannot exploit this valuable source of information. This is mainly due to the fact that not all comments have the same goals and target audience and can therefore only be used selectively for redocumentation. Performing a required classification manually, e.g. in the form of heuristic rules, is usually time-consuming and error-prone and strongly dependent on programming languages and guidelines of concrete software systems. By leveraging machine learning, it should be possible to classify comments and thus transfer valuable information from the source code into documentation with less effort but the same quality. We applied different machine learning techniques to a COBOL legacy system and compared the results with industry-strength heuristic classification. As a result, we found that machine learning outperforms the heuristics in number of errors and less effort.
A Method of Navigational Information Display Using Augmented Virtuality

The preliminary research experiments described herein were aimed to choose an appropriate mixed reality technology for the construction of navigational information display method to be used onboard ships in restricted waters. The method assumes a possibly faithful representation of the environment and the actual navigational situation on a spatial decision support system (SDSS) interface during ship navigation and maneuvering in restricted waters. The paper also presents the architecture and process of building a SDSS, where the method of navigational information display using augmented virtuality was applied.
Indonesian Part of Speech Tagging Using Hidden Markov Model – Ngram &amp; Viterbi

Part of Speech (POS) Tagging is a process of labelling word classes on sentences. One of the POS Tagging problems is some words that spelt the same but have a different POS Tag depending on the context of the sentence (ambiguity). The approach to solving this problem is using the Hidden Markov Model (HMM) Ngram Algorithm and the Viterbi Algorithm. This study discusses the development of a system for Indonesian POS Tagging using the HMM N-gram algorithm (Bigram and Trigram) and the Viterbi algorithm and compares the result between the HMM Bigram and HMM trigram. An Indonesian language corpus that has been manually labeled called Indonesian Manually Tagged Corpus is used as the knowledge for the system. Then the corpus is processed using the HMM N-gram algorithm to get the rules. Furthermore, process the data with Viterbi algorithm using the previous formed rules to determine the POS tag with the highest probability. The highest accuracy results is 77.56% using the HMM Bigram - Viterbi Algorithm. While the HMM Trigram- Viterbi algorithm has the highest accuracy of 61.67%. The result shows that the system can solve the problem of tag ambiguity with HMM Ngram - Viterbi algorithm and the accuracy of HMM Bigram is better than the HMM Trigram.
Using natural language processing to detect privacy violations in online contracts

As information systems deal with contracts and documents in essential services, there is a lack of mechanisms to help organizations in protecting the involved data subjects. In this paper, we evaluate the use of named entity recognition as a way to identify, monitor and validate personally identifiable information. In our experiments, we use three of the most well-known Natural Language Processing tools (NLTK, Stanford CoreNLP, and spaCy). First, the effectiveness of the tools is evaluated in a generic dataset. Then, the tools are applied in datasets built based on contracts that contain personally identifiable information. The results show that models' performance was highly positive in accurately classifying both the generic and the contracts' data. Furthermore, we discuss how our proposal can effectively act as a Privacy Enhancing Technology.
Amrita-CEN-SentiDB: Twitter Dataset for Sentimental Analysis and Application of Classical Machine Learning and Deep Learning

Social media is a platform in which the data is generated each and every day in an abundance manner. The data is so large that cannot be easily understood, so this has paved a path to a new field in the information technology which is natural language processing. In this paper, we use the text data for classification of tweets that determines the state of the person according of the sentiments which is positive, negative and neutral. Emotions are common between humans which has a way to express it that decides the person’s feelings which has a high influence on the decision making tasks. Here we have proposed the text representation, Term Frequency Inverse Document Frequency (tfidf), Keras embedding along with the machine learning and deep learning algorithms for classification of the sentiments, out of which Logistics Regression machine learning based methods out performs well when the features is taken in the limited amount as the features increases Support Vector Machine (SVM) that belongs to machine learning algorithm out performs well making a benchmark accuracy for this dataset as the 75.8%. The dataset is made publically available for research purpose.
Image forgery detection using deep textural features from local binary pattern map

Nowadays the manipulations of digital images are common due to easy access of many online photo editing applications and image editing softwares. Forged images are widely used in social media for creating deceitful propaganda of an individual or a particular event and for cooking up fake evidences even in court proceedings. Hence ensuring the integrity of digital images is of prime significance and it has become a hot research area. In this paper, a novel technique for image forgery detection is proposed. The method utilizes the layer activation of inception-ResNet-v2, a pretrained Convolutional Neural Network(CNN)to extract the deep textural features from Rotation Invariant – Local Binary Pattern (RI-LBP) map of the chrominance image. Non-negative Matrix Factorization (NMF) technique is used to reduce the dimensionality of the extracted features. The dimensionality reduced features are used to train a quadratic Support Vector Machine(SVM) classifier to classify images into forged or authentic. The method is assessed on four benchmark datasets (CASIA ITDE v1.0, CASIA ITDE v2.0, CUISDE and IFS-TC). Extensive experimental analysis is done and the results show an improved detection accuracy compared to the state-of-the-art methods.
Modeling user exposure with recommendation influence

Recommender systems learn users' preferences from their explicit feedback, such as ratings, and implicit feedback, such as purchases. Learning a user's preference from implicit feedback is difficult since inaction in such data can be because of either dislike for an item or unawareness. Exposure modeling has been proposed recently to address this ambiguity by decomposing inaction into dislike and unawareness. A user's awareness of items can be affected by several factors, and exposure modeling has been extended to include social and temporal influences on exposure to provide better recommendations. However, recommendation itself should also affect a user's awareness of items. Recommendations boost awareness differently for different users; while some users are attentive to and trust recommendations, others neglect or distrust recommendations. Furthermore, if a user purchases an item from a recommendation list, the user would have likely noticed other items on the same list. In this paper, we extend exposure modeling by considering the influence of recommendations on exposure. We accomplish this by incorporating: (1) user-dependent increase in awareness of recommended items and (2) indirect increase in awareness of an item by the purchase of other items from the same recommendation list. Through experiments using public datasets with recommendation logs, we demonstrate that considering recommendation influence improves purchase prediction.
Research on Cross-Chain Technology Architecture System Based on Blockchain

With the development of blockchain technology, blockchain technology has strong vitality in various industries due to its characteristics of dispersion, anti-mite modification and traceability. Blockchain technology allows trusted parties to trust transactions and realize value streams. Since there are different values in different chains, will the values of different chains also flow? This is a problem that needs to be solved in the cross-chain interaction. Under the scenario for cross-chain interaction between private chain or alliance chain between enterprises, this paper proposes an alliance chain model that can realize cross-chain interaction between enterprises. On this basis, it analyzes the difficulties of cross-chain interaction technology and analyzes the model. At last, we summarize the paper.
Unleashing the Potentials of Immersive Augmented Reality for Software Engineering

In immersive augmented reality (IAR), users can wear a head-mounted display to see computer-generated images superimposed to their view of the world. IAR was shown to be beneficial across several domains, e.g., automotive, medicine, gaming and engineering, with positive impacts on, e.g., collaboration and communication. We think that IAR bears a great potential for software engineering but, as of yet, this research area has been neglected. In this vision paper, we elicit potentials and obstacles for the use of IAR in software engineering. We identify possible areas that can be supported with IAR technology by relating commonly discussed IAR improvements to typical software engineering tasks. We further demonstrate how innovative use of IAR technology may fundamentally improve typical activities of a software engineer through a comprehensive series of usage scenarios outlining practical application. Finally, we reflect on current limitations of IAR technology based on our scenarios and sketch research activities necessary to make our vision a reality. We consider this paper to be relevant to academia and industry alike in guiding the steps to innovative research and applications for IAR in software engineering.
Multi-scale Supervised Attentive Encoder-Decoder Network for Crowd Counting

Crowd counting is a popular topic with widespread applications. Currently, the biggest challenge to crowd counting is large-scale variation in objects. In this article, we focus on overcoming this challenge by proposing a novel Attentive Encoder-Decoder Network (AEDN), which is supervised on multiple feature scales to conduct crowd counting via density estimation. This work has three main contributions. First, we augment the traditional encoder-decoder architecture with our proposed residual attention blocks, which, beyond skip-connected encoded features, further extend the decoded features with attentive features. AEDN is better at establishing long-range dependencies between the encoder and decoder, therefore promoting more effective fusion of multi-scale features for handling scale-variations. Second, we design a new KL-divergence-based distribution loss to supervise the scale-aware structural differences between two density maps, which complements the pixel-isolated MSE loss and better optimizes AEDN to generate high-quality density maps. Third, we adopt a multi-scale supervision scheme, such that multiple KL divergences and MSE losses are deployed at all decoding stages, providing more thorough supervisions for different feature scales. Extensive experimental results on four public datasets, including ShanghaiTech Part A, ShanghaiTech Part B, UCF-CC-50, and UCF-QNRF, reveal the superiority and efficacy of the proposed method, which outperforms most state-of-the-art competitors.
k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources

Analysis of Electromyographic signals (EMG) allows obtaining useful information to develop gesture recognition applications. In this paper we propose an FPGA-based wearable EMG gesture recognition system to support the communication of customized phrases for people with speech impairments. Machine learning algorithms can accurately identify gestures, usually relying in a large number of features or large training datasets. In this work, we focus on using a small number of features in the gesture classification process in order to reduce the need for computational power. Namely, we propose using only one feature, the Root Mean Square (RMS) signal value, and the KNN supervised classification algorithm. The system is evaluated in an DE10-Standard FPGA to demonstrate portability to wearable devices with limited hardware resources. Tests show that subjects only need three seconds per gesture to train the system, which avoids processing large amounts of data and improves user experience during the equipment setup. Furthermore, the accuracy of the system reaches 95% using only 2 seconds of data effectively maintaining both human and hardware resources low.
A Generalization of Principal Component Analysis

Conventional principal component analysis (PCA) finds a principal vector that maximizes the sum of second powers of principal components. We consider a generalized PCA that aims at maximizing the sum of an arbitrary convex function of principal components. We present a gradient ascent algorithm to solve the problem. For the kernel version of generalized PCA, we show that the solutions can be obtained as fixed points of a simple single-layer recurrent neural network. We also evaluate our algorithms on different datasets.
Improving Cross-Dataset Performance of Face Presentation Attack Detection Systems Using Face Recognition Datasets

Presentation attack detection (PAD) is now considered critically important for any face-recognition (FR) based access-control system. Current deep-learning based PAD systems show excellent performance when they are tested in intra-dataset scenarios. Under cross-dataset evaluation the performance of these PAD systems drops significantly. This lack of generalization is attributed to domain-shift. Here, we propose a novel PAD method that leverages the large variability present in FR datasets to induce invariance to factors that cause domain-shift. Evaluation of the proposed method on several datasets, including datasets collected using mobile devices, shows performance improvements in cross-dataset evaluations. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Acoustic Model Adaptation for Presentation Transcription and Intelligent Meeting Assistant Systems

We present our solution for unsupervised rapid speaker adaptation in a state-of-art presentation and intelligent meeting transcription system. We adopt the Kullback-Leibler (KL) divergence regularized model adaptation paradigm. For the adaptation architecture, we found that the linear projection layer adaptation yields competitive performance with the additional benefit in its simplicity and robustness to small amount of adaptation data. To address the imperfect supervision, we use a supervision committee formed by multiple systems or single-system n-best to mask possibly mislabeled frames. To relieve the data sparsity issue, we apply noise and speaking rate perturbation data augmentation techniques to create a richer adaptation data set. In summary, the proposed solution consists of the KL-divergence regularized linear projection layer adaptation with frame masking and data augmentation. On a presentation transcription and a meeting transcription task, our proposed methodology yields 7.3 % and 7.9 % relative word error rate (WER) reduction against a strong baseline model trained from tens of thousand hour speech. To the best of our knowledge, this is a first reported work on rapid speaker adaptation on a state-of-art production system.
Multicluster Class-Balanced Ensemble

Ensemble classifiers using clustering have significantly improved classification and prediction accuracies of many systems. These types of ensemble approaches create multiple clusters to train the base classifiers. However, the problem with this is that each class might have many clusters and each cluster might have different number of samples, so an ensemble decision based on large number of clusters and different number of samples per class within a cluster produces biased and inaccurate results. Therefore, in this article, we propose a novel methodology to create an appropriate number of strong data clusters for each class and then balance them. Furthermore, an ensemble framework is proposed with base classifiers trained on strong and balanced data clusters. The proposed approach is implemented and evaluated on 24 benchmark data sets from the University of California Irvine (UCI) machine learning repository. An analysis of results using the proposed approach and the existing state-of-the-art ensemble classifier approaches is conducted and presented. A significance test is conducted to further validate the efficacy of the results and a detailed analysis is presented.
Mixup Multi-Attention Multi-Tasking Model for Early-Stage Leukemia Identification

Recently, several image processing and deep learning techniques have been applied to automate the detection of Acute Lymphoblastic Leukemia cells (ALL). However, most of them have consistently focused on classification mature stage cell images into binary categories of ALL or normal cells. The real impetus of biomedical imaging lies in detecting early-stage cases since early-stage ALL cells have unintuitive global contextual and local spatial features, making their detection non-trivial. To this effect, we propose a novel architecture termed as Mixup Multi-Attention Multi-Task Learning Model (MMA-MTL), which introduces Pointwise Attention Convolution Layers and Local Spatial Attention blocks to capture global and local features simultaneously. We also introduce Rademacher Paired Sampling Mixup to prevent memorization of training data in cases of limited categorical shift. Our proposed method shows competitive performance on the ISBI-2019 CNMC dataset and benchmarks appropriate design choices for future biomedical imaging tasks.
Real Time Facial Expression and Emotion Recognition using Eigen faces, LBPH and Fisher Algorithms

Biometrics are used to characterize a person's DNA, hand geometry, confront, and so on or behavioral qualities, for example, hand signature, voice tone, keystrokes et cetera. For that reason, these organic attributes are remarkable for each person. Much of the time, confront acknowledgment related advancements are winding up more mainstream among biometric-based advances that measure a person's regular information. Hereditary biometrics has, for the most part, used to validate and distinguish people by examining their physical attributes, for example, unique finger impression, eye iris, vein and so forth. Rather than utilizing a bank card, a camera presented at the ATM's would get pictures of countenances of clients, and separate them and the photographs of record holders in the database of banks to confirm the client's character. The motivation for driving this paper is to exhibit a Windows-based advancing application framework utilizing face certification checks.