Reviewerly/exHarmony huggingface dataset:
Video-Based Gender Profiling on Challenging Camera Viewpoint for Restaurant Data Analytics

The customer gender composition is an important consideration for certain business decisions. This study highlights the feasibility of implementing a vision-based gender classification feature on top of a pre-existing people counting system that makes use of a CCTV camera in a retail establishment. The main problem lies in doing so using the fixed angled viewpoint of the restaurant's camera, which represents a practical use case as CCTV systems are often geared for security purposes instead of data analytics. Thus, these existing camera configurations are not ideal for existing computer vision models. A two-step neural networks-based approach, namely head detection via YOLOv2 and gender classification via Inception V3, is used to integrate this feature into the pre-existing system. The researchers partnered with a restaurant in a business district to obtain footage and test the system. The proponents were able to attain the target accuracy of 80%, albeit the feature's speed and accuracy can be further improved through the exploration of other object classification algorithms and through the use of downsizing and down sampling.
COMMUNICATIVE SPACE OF DISTANCE LEARNING FOR UNIVERSITY STUDENTS

The article considers the actual problem of organizing the communication space of distance learning for first-year students of the university. The development of information technologies, the need to introduce innovative approaches, the emergence of global extreme social situations, such as the COVID-19 pandemic of 2019–2021years, – intensified the use of distance approach in educational activities. The purpose of this article is to study the experience of organizing the communicative space of distance learning, because communicative space is one of the key factors for effective learning. As a result of the empirical research, the issues of the formation of the communicative process, social connections and attitudes to educational activities among first-year students have been analysed. The paper concludes, that the effectiveness of the organization of the communicative space of distance learning depends largely on the communicative competencies of the teacher. The study reveals the most important competencies of teachers for organizing the communication space of online learning – the focus on innovation, the development of empathic abilities of teachers, business and Internet-communication skills.
Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation

We study the problem of unsupervised domain adaptation that aims at obtaining a prediction model for the target domain using labeled data from the source domain and unlabeled data from the target domain. There exists an array of recent research based on the idea of extracting features that are not only invariant for both domains but also provide high discriminative power for the target domain. In this paper, we propose an idea of improving the discriminativeness: Adding an extra artificial class and training the model on the given data together with the GAN-generated samples of the new class. The trained model based on the new class samples is capable of extracting the features that are more discriminative by repositioning data of current classes in the target domain and therefore increasing the distances among the target clusters in the feature space. Our idea is highly generic so that it is compatible with many existing methods such as DANN, VADA, and DIRT-T. We conduct various experiments for the standard data commonly used for the evaluation of unsupervised domain adaptations and demonstrate that our algorithm achieves the SOTA performance for many scenarios.
Video semantic segmentation using deep multi-view representation learning

In this paper, we propose a deep learning model based on deep multi-view representation learning, to address the video object segmentation task. The proposed model emphasizes the importance of the inherent correlation between video frames and incorporates a multi-view representation learning based on deep canonically correlated autoencoders. The multi-view representation learning in our model provides an efficient mechanism for capturing inherent correlations by jointly extracting useful features and learning better representation into a joint feature space, i.e., shared representation. To increase the training data and the learning capacity, we train the proposed model with pairs of video frames, i.e., Fa and Fb. During the segmentation phase, the deep canonically correlated auto encoders model encodes useful features by processing multiple reference frames together, which is used to detect the frequently reappearing. Our model enhances the state-of-the-art deep learning-based methods that mainly focus on learning discriminative foreground representations over appearance and motion. Experimental results over two large benchmarks demonstrate the ability of the proposed method to outperform competitive approaches and to reach good performances, in terms of semantic segmentation.
Evaluating Machine Learning Performance for Safe, Intelligent Robots

The rapid advancement of Machine Learning techniques has been the primary driver of improvements in the performance of Intelligent Robots. Performance evaluation is a vital part of specifying requirements and evaluating capabilities for such systems. However, the performance evaluation techniques commonly used for machine learning systems must be augmented for use with robotic systems that interact with the real world. This paper presents a survey and discussion of factors that must be considered, beyond traditional measures such as cross-validation accuracy, when developing evaluation criteria for machine learned intelligent robotic systems.
G-FAN: Graph-Based Feature Aggregation Network for Video Face Recognition

In this paper, we propose a graph-based feature aggregation network (G-FAN) for video face recognition. Compared with the still image, video face recognition exhibits great challenges due to huge intra-class variability and high interclass ambiguity. To address this problem, our G-FAN first uses a Convolutional Neural Network to extract deep features for every input face of a subject. Then, we build an affinity graph based on the relationship between facial features and apply Graph Convolutional Network to generate fine-grained quality vectors for each frame. Finally, the features among multiple frames are adaptively aggregated into a discriminative vector to represent a video face. Different from previous works that take a single image as input, our G-FAN could utilize the correlation information between image pairs and aggregate a template of face images simultaneously. The experiments on video face recognition benchmarks, including YTF, IJB-A, and IJB-C show that: (i) G-FAN automatically learns to advocate high-quality frames while repelling low-quality ones. (ii) G-FAN significantly boosts recognition accuracy and outperforms other state-of-the-art aggregation methods.
Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants

As a large number of small-scale PV plants have been deployed in distribution systems, generation forecasting of such plants has recently been gaining interest. Because the PV power mainly depends on weather conditions, it is important to accurately collect weather data for relevant PV sites to enhance PV forecasting accuracy. However, small-scale PV plants do not often have their own measuring apparatus to get historical weather data, so they have used weather datasets from relatively nearby weather data centers (WDCs). Therefore, these small-scale PV plants have difficulty delivering robust and reliable forecasting accuracy because of inappropriate predicted weather data from a distance. In this paper, two weather data mixing models are proposed: (a) inverse distance weighting (IDW), and (b) inverse correlation weighting (ICW). These models acquire adequate mixed weather data for the day-ahead generation forecasting for small-scale PV plants. Furthermore, the mixed weather data are collected using the geographic distance between the PV site and WDCs, or correlation between the PV generation and weather variables from nearby WDCs. Interestingly, the proposed ICW model outperforms when WDCs are located distant from the PV plants, whereas IDW performs well with the closer WDCs. The forecasting performance of the proposed mixing models was compared with those of the existing weather data collection methods.
Authenticated logarithmic-order supersingular isogeny group key exchange

We present the first constant-round, tree-based, group key exchange protocol based on SIDH with logarithmic-order communication and memory complexity, where the only previous isogeny-based group key exchange, SIBD, has linear-order communication and memory complexity. We call our protocol the supersingular isogeny tree-based group key exchange (SIT). We show that our protocol satisfies post-quantum security through a reduction to the supersingular decisional Diffie–Hellman (SSDDH) problem in the security model of Manulis, Suzuki, and Ustaoglu. We also construct a peer-to-peer (sequential) version of SIT. Finally, we present a compiler that turns SIT into an authenticated group key exchange while maintaining the same complexity and security as SIT, resulting in the authenticated SIT group key exchange (A-SIT).
Learning dictionaries of kinematic primitives for action classification

This paper proposes a method based on visual motion primitives to address the problem of action understanding. The approach builds in an unsupervised way a dictionary of kinematic primitives from a set of sub-movements obtained by segmenting the velocity profile of an action on the basis of local minima derived directly from the optical flow. The dictionary is then used to describe each sub-movement as a linear combination of atoms using sparse coding. The descriptive capability of the proposed motion representation is experimentally validated on the MoCA dataset, a collection of synchronized multi-view videos and motion capture data of cooking activities. The results show that the approach, despite its simplicity, has a good performance in action classification, especially when the motion primitives are combined over time. Also, the method is proved to be tolerant to view point changes, and can thus support cross-view action recognition. Overall, the method may be seen as a backbone of a general approach to action understanding, with potential applications in robotics.
A Load Balancing with Fault Tolerance Algorithm for Cloud Computing

Cloud computing technology is evolving as an upcoming technology that acts as a platform for different services. However, there are major challenges that must be addressed to achieve security in cloud computing such as confidentiality, integrity, and availability. Techniques such as load balancing and fault tolerance have been used to provide a sustainable and high availability service. Many load balancing and fault tolerance methods that work individually or in cooperation have been proposed. In this paper, we introduce a new method that combines a load balancing algorithm with a fault tolerance technique to enhance availability in a cloud environment. In comparison with some of the previously proposed methods, the new method achieves a better performance in terms of downtime as a result of reducing the response time in case of failure. The proposed method is implemented using java and tested and evaluated using simulation under different scenarios.
TANTP: Conversational Emotion Recognition Using Tree-Based Attention Networks with Transformer Pre-training

Conversational emotion recognition has gained significant attention in data mining and text mining recently. Most existing methods only consider the utterance in conversations as a temporal sequence and ignore the fine-grained emotional clues in the compositional structure, where the non-ignorable semantic transitions and tone enhancement are implied. Consequently, such models hardly capture accurate semantic features of the utterance, which results in the accumulation of incorrect emotional features in the memory bank. To address this problem, we propose a novel framework, Tree-based Attention Networks with Transformer Pre-training (TANTP), which incorporates contextual representations and recursive constituency tree structure into the model architecture. Different from merely modeling the utterance in light of the time order, TANTP could effectively capture compositional emotion semantics of utterance features for the memory bank, where complex semantic transitions and emotional progression are difficult to be revealed by previous conventional sequential methods. Experimental results conducted on two public benchmark datasets demonstrate that TANTP could achieve superior performance compared with other state-of-the-art models.
Placement Effect of Motion Sensors for Human Activity Recognition using LSTM Network

There are various possibilities for wearable sensors to determine the recognition of human condition. In order to theoretically drive with wearable sensor technology, several difficult issues have been studied and solved. We currently succeeded in computing the efficient high performance deep learning model for the sensor-based human activity recognition (HAR). There is, however, an influence study challenge regarding the impact of the body-mounted sensor position on the Long Short-Term Memory (LSTM) network. In this work, we propose to conduct several experiments in order to discover the optimal placement for each physical activity. The experimental results show that by using heterogeneous sensor data fusion, the chest position is ideal for physical activity identification with a maximum accuracy of 94.18%.
MINT: Deep Network Compression via Mutual Information-based Neuron Trimming

Most approaches to deep neural network compression via pruning either directly evaluate a filter's importance using its weights or optimize an alternative objective function with sparsity constraints. While these methods offer a useful way to approximate contributions from similar filters, they often either ignore the dependency between layers or solve a more difficult optimization objective than standard cross-entropy. Our method, Mutual Information-based Neuron Trimming (MINT), approaches deep compression via pruning by enforcing sparsity based on the strength of the dependency between filters of adjacent layers, across every pair of layers in the network. The dependency is calculated using conditional geometric mutual information which evaluates the amount of similar information exchanged between filters using a graph-based criterion. When pruning a network, we ensure that retained filters contribute the majority of the information towards succeeding layers which ensures high performance. Our novel approach is highly competitive with existing state-of-the-art compression-via-pruning methods on standard benchmarks for this task: MNIST, CIFAR-10, and ILSVRC2012, across a variety of network architectures despite using only a single retraining pass. Also, we discuss our observations of a common denominator between our pruning methodology's response to adversarial attacks and calibration statistics when compared to the original network.
BP-net: deep learning-based superpixel segmentation for RGB-D image

In this paper, we propose a deep learning-based su-perpixel segmentation algorithm for RGB-D image. The proposed deep neural network called BP-net is composed of boundary detection network (B-net) that exploits multiscale information from depth image to extract the geometry edge of objects, and pixel labeling network (P-net) that extracts pixel features and generates superpixels. A boundary pass filter is proposed to combine the edge information and pixel features and ensures superpixels adhere better to geometry edges. To generate regular superpixels, we design a loss function which takes the shape regularity error and superpixel accuracy into account. In addition, for providing reasonable initial seeds, a new seeds initialization strategy is proposed, in which the density of seeds is investigated from a 2-manifolds space to reduce the number of superpixels that cover multiple objects in the region of rich texture. Experimental results demonstrate that our algorithm outperforms the existing state-of-the-art algorithms in terms of accuracy and shape regularity on the RGB-D dataset.
Synergic Feature Attention for Image Restoration

Local and non-local attentions are both effective methods in the domain of image restoration (IR). However, most existing image restoration methods use these two strategies indiscriminately, and how to make a trade-off between local and non-local attention operations has hardly been studied. Furthermore, the commonly used pixel-wise non-local operation tends to be biased during image restoration due to the image degeneration. To overcome these problems, in this paper, we propose a novel Synergic Attention Network (SAT-Net) for image restoration as an inventive attempt to combine local and non-local attention mechanisms to restore complex textures and highly repetitive details distinguishingly. We also propose an effective patch-wise non-local attention method to establish more reliable long-range dependences based on 3D patches. Experimental results on synthetic image denoising, real image denoising, and compression artifact reduction tasks show that our proposed model can achieve state-of-the-art performance under objective and subjective evaluations.
Active Learning for Classification With Abstention

We construct and analyze active learning algorithms for the problem of binary classification with abstention, in which the learner has an additional option to withhold its decision on certain points in the input space. We consider this problem in the fixed-cost setting, where the learner incurs a cost λ ∈ (0, 1/2) every time the abstain option is invoked. Our proposed algorithm can work with the three most commonly used active learning query models, namely, membership-query, pool-based, and stream-based models. We obtain a high probability upper-bound on the excess risk of our algorithm, and establish its minimax near-optimality by deriving matching lower-bound (modulo polylogarithmic factors). Since our algorithm relies on the knowledge of the smoothness parameters of the regression function, we also describe a new strategy to adapt to these unknown parameters in a data-driven manner under an additional quality assumption. We show that using this strategy our algorithm achieves the same performance in terms of excess risk as their counterparts with the knowledge of the smoothness parameters. We end the paper with a discussion about the extension of our results to the setting of bounded rate of abstention.
Hand Gesture Recognition Model using Standard Deviation-based Dynamic Time Warping Technique

Gestures are common ways of human communication that represent language and emotion using signs and symbols drawn by the movements of the hands or fingers of a person. In this study, the main focus on air-written English Capital Alphabets. However, most of the systems that operate recognition of these gestures like air-drawn letters often misclassify some of them, especially those having similar slope sequence. This study focuses on the use of the Leap Motion Controller, Air-writing of English Capital Alphabets, and Dynamic Time Warping to recognize air-drawn letters. Using Dynamic Time Warping, the system can recognize letters in two-dimensional values despite the input set and the reference template having different number of points, and can also classify letters by finding the lowest difference of the standard deviation between the input and templates. The existing systems depend on the slopes or angles of the letters formed, either time-based or geometrically-based, and rely on zero difference or null set as signal of identification of a letter. The Leap Motion Controller is used to detect the position of the finger of the user, which acts as the "pen" in air-writing English Capital Alphabets. The proponents evaluated the overall reliability of the system by letting the users test the system by air-writing each of the 26 letters numerous times.
Dynamic Curriculum Learning via Data Parameters for Noise Robust Keyword Spotting

We propose dynamic curriculum learning via data parameters for noise robust keyword spotting. Data parameter learning has recently been introduced for image processing, where weight parameters, so-called data parameters, for target classes and instances are introduced and optimized along with model parameters. The data parameters scale logits and control importance over classes and instances during training, which enables automatic curriculum learning without additional annotations for training data. Similarly, in this paper, we propose using this curriculum learning approach for acoustic modeling, and train an acoustic model on clean and noisy utterances with the data parameters. The proposed approach automatically learns the difficulty of the classes and instances, e.g. due to low speech to noise ratio (SNR), in the gradient descent optimization and performs curriculum learning. This curriculum learning leads to overall improvement of the accuracy of the acoustic model. We evaluate the effectiveness of the proposed approach on a keyword spotting task. Experimental results show 7.7% relative reduction in false reject ratio with the data parameters compared to a base-line model which is simply trained on the multiconditioned dataset.
Opinionated Text Classification For Hindi Tweets Using Deep Learning

The recent years have witnessed a significant growth in the data collected from the reviews posted on various websites. Reviews are a direct way of getting the response of the customers and clients of any business, making it a convenient way for getting feedback for marketing, performance and other such characteristics in association with any product or service. The opinions mined from these collections of data can provide strategies to improve the sales based on how well a product is received. This is done in two steps, first being the Subjectivity Detection followed by Sentiment Analysis. For this process, various methods have been already introduced in this field. These vary from SVMs, Naive-Bayesian, deep learning etc. Since, English is the most commonly used language in the world, it is not surprising that most work done in this field focuses on the same. But it is already known that there are roughly around 6500 languages used around the world. India alone has 447 languages which ranks it fourth on the list of countries with the greatest number of languages. The proposed research work focuses on sentiment classification in Hindi language text. The proposed research work has attempted to experiment with a method that does not rely on availability language dictionaries. This is done by creating a completely numerical data corresponding to the text. The model proposed in this paper will use a combination of Recurrent Neural Network and Convolutional Neural Network model to extract the subjective data form the given dataset of movie reviews.
Group Recommendation Techniques for Feature Modeling and Configuration

In large-scale feature models, feature modeling and configuration processes are highly expected to be done by a group of stakeholders. In this context, recommendation techniques can increase the efficiency of feature-model design and find optimal configurations for groups of stakeholders. Existing studies show plenty of issues concerning feature model navigation support, group members' satisfaction, and conflict resolution. This study proposes group recommendation techniques for feature modeling and configuration on the basis of addressing the mentioned issues.