Reviewerly/exHarmony huggingface dataset:
A method on Face Recognition of Contaminated Small Sample

In recent years, face recognition technology has been rapidly promoted and used because of its advantages of convenience, non-contact and good recognition performance, and achieved good results. But for the polluted situation, such as expression change, illumination influence, posture change, occlusion, etc., because the collected image lost the original intrinsic characteristics, seriously affect the effect of face recognition. In recent years, the SRC method and its variants have good robustness to shaded and contaminated samples. However, when certain training samples and test samples are contaminated, the performance will degrade. The method based on low rank matrix recovery (LRMR) can effectively deal with the simultaneous contamination of training samples and test samples. However, they either ignore relationships between similar samples or fail to learn a compact dictionary from contaminated training data. To solve these problems, a locally constrained low-rank representation and dictionary learning algorithm (LCLRRDL) is developed and applied to robust face recognition. In this paper, the theoretical and experimental research of locally constrained low-rank representation and dictionary learning algorithm (LCLRRDL) is carried out. A low-rank representation is introduced to deal with possible contamination of training and test data, and local constraints are introduced to identify the inherent manifold structure of training data. Under local constraints, similar samples often have similar representations, and the learned representations can be directly used for classification. At the same time, this method can learn a compact dictionary with better refactoring and recognition ability. The superiority of LCLRRDL algorithm is verified by comparing it with support vector machine (SVM) and Sparse representation (SRC) for contaminated small sample face recognition. By comparing the best recognition effect and classification time of the three algorithms, it is found that the three algorithms are robust to contaminated face recognition for two different face databases. LCLRRDL algorithm has the highest recognition rate, but its disadvantage is that the classification recognition time is long. Therefore, when the actual application does not require the recognition speed, the recognition rate of LCLRRDL algorithm is more suitable for high-precision face recognition.
REMOTE AUTOMATIC SYSTEMS FOR MEASUREMENT OF PATIENTS’ TEMPERATURES AND ARTERIAL PRESSURE IN MEDICAL INSTITUTIONS

This paper presents systems for remote automatic measurement of temperature and blood pressure in patients of medical organizations based on classical methods and devices used in medical institutions.
Multi-domain Abdomen Image Alignment Based on Joint Network of Registration and Synthesis

Multi-domain abdominal image alignment is a valuable and challenging task for clinical research. Normally, with the assistance of the image synthesizer, the register can perform well. However, the deviation of the synthesizer is likely to be the bottleneck of the register performance. In this case, using the registration training information to guide the synthesizer optimization is meaningful. Therefore, we propose the Joint Network of Registration and Synthesis (RSNet). The network calculates the loss caused only by the synthetic deviation according to the registration training, which effectively improves the performance of the synthesizer. Meanwhile, the network constructs an adaptive weighting factor based on the similarity measure of the synthetic image, which significantly generalizes the performance of the register. The real-world datasets are collected and processed with the help of a cooperative hospital. Our experiments demonstrate that RSNet can achieve state-of-the-art performance.
Blind Deconvolution for Restoring Blurred and Distorted Images Based on the Pyramid Approach and Genetic Algorithms

Automatic restoration of blurred and distorted images is one of the most urgent tasks of image processing. There are two main classes of methods for solving this problem – deep learning and optimization methods. Optimization methods are a classic way to solve this problem and are aimed at selecting an unknown distorting function. This task is called blind deconvolution. The advantage of these methods is their accuracy and quality of the result obtained. However, a significant disadvantage of these methods is their operation time, which can reach more than a dozen minutes. In most methods of this class, classical gradient optimization methods and their modifications are used. However, this approach has a number of disadvantages. One of them is that the objective function must be differentiable. Another significant disadvantage of these methods is the probability of stopping at a local extremum and not finding the optimal solution. Thus, the disadvantages of the step-by-step optimization methods used are added to the long operation time of these methods. Optimization models devoid of these disadvantages (except for optimization time) are genetic algorithms. In this paper, we propose a method of blind deconvolution based on a pyramid approach and the use of a genetic algorithm with a modified mutation operator as an optimization model.
Customer Support Chat Intent Classification using Weak Supervision and Data Augmentation

Understanding the actual intent of customers is an essential step in automating the conversational experience on a chat platform. Typically, chatbots are powered by machine learning algorithms that rely on the acquisition of a large amount of high quality labeled training data which can be prohibitively expensive. To overcome this dependence on labeled training data, weaker forms of supervision have been recently exploited to generate samples in a more cost effective manner though the samples may be noisy. In this paper, we analyse a use-case specific to food delivery services where customer-agent conversations in incoherent English and code-mixed language (Hindi mixed with English, commonly referred to as Hinglish) are associated with a single customer chosen noisy label (referred to as "Conversation Level Intent" in the current paper) for the entire conversation. However, in reality, a conversation can have several messages and can be comprised of multiple labels. Moreover, each label may be associated with one or more messages in the conversation. In this paper, we demonstrate how simple light-weight word embeddings based weak supervision techniques can be used to tag individual customer messages with the most relevant label. We also show that simple augmentation techniques can significantly improve performance on code-mixed messages. On an internal benchmark dataset, we show that our sampling approach achieves an absolute performance gain of 33% in F1 score on random sampling strategy, 19% in F1 score over an approach using entire raw samples and 4% over a Snorkel (a state-of-the-art weak supervision framework) based approach.
BioAR: Bio-Laboratory Automation Meets Augmented Reality

Modern laboratory is benefiting from science and engineering by adopting automated systems into labor-intensive lab routines. In this paper, a computer-aided system that involves augmented reality (AR) to promote the automatic levels of the bio-laboratory is introduced. The motivation of this work lies in the idea of offering visual guidance for users to pick a tube manually from the rack after the retrieval. Because it is difficult for people to tell the target tube from the others since they look almost the same. Design issues and implementation details are given after a careful analysis of the system requirements. The BioAR system is tested out and successfully applied to a bio-laboratory with tube related operations, whose performance is approved by the technicians. A video demo of the proposed system can be found at https://www.bilibili.com/video/BV1FX4y157WB/ .
A Novel Underwater Single Target Tracking Algorithm based on Siamese Convolutional Neural Network

Aiming at the problem of low accuracy of underwater target tracking due to poor contrast and sharpness of underwater visual data and complex background, a novel underwater single target tracking algorithm based on Siamese Convolutional Neural Network was proposed. Through the use of deep convolutional network, the target features could be better extracted to improve the algorithm’s ability to deal with the above problems. First, convolutional neural network is used to extract the features of the template frame image and the detection frame image. Then the convolution features of the two are cross-related by generating the region proposal network. Finally, the classification branch and regression branch are output. The output classification branch is used to classify the background and target, and the output regression branch is used to predict the location of the tracking target boundary box. The experiment shows that the above tracking algorithm can better adapt to underwater environment and improve the accuracy and success rate of underwater target tracking compared with the traditional underwater target tracking algorithm.
Comparison of Head and Body Movement Patterns in Naturalistic Human-Machine Interaction

This paper aims on the investigation and recognition of upper-body movements during a naturalistic Human-Machine Interaction, in which humans interact with a technical system while sitting in front of it. Therefore, we focus on the Last Minute Corpus, that provides such a naturalistic scenario in combination with multimodal recordings. For feature extraction an approach called Probabilistic Breadth Features was used, allowing a condensed investigation of movement patterns. Finally, the classification was based on Extreme Learning Machines, comparing features obtained in three different conditions: the Kinect's spine point, head point, and a combination of both. In context of this naturalistic interaction setting, a mean accuracy of 86.1% was achieved.
Spatial Variability and Data Analysis in Urban Soils

Urban soils are likely to be even more variable than soils in other environments, due to the inherent heterogeneity of urban environments which intensifies natural soil variability. This chapter examines soil variability related to urbanisation at several different spatial scales, from regional phenomena to differences observed on the scale of individual soil profilesProfile (soil profile). Soil sampling strategies and designs are described, with discussion of the issues related to sampling density, sample numbers, the geometric arrangement of sampling locations, and ‘hotspot’ detection. We present methods for qualitative and quantitative analysis of soil spatial data using maps, spatial autocorrelation analysis, and variograms and kriging. Basic but rigorous statistical methods are described in the context of soils and compositional data, including comparisons, relationships, and multivariate techniques.
Heuristic Approach to Sustainable Agile Development with Secure Indicators

Sustainable agile development is the act of cohesively adding sustainable elements to a dynamic agile development cycle. While agile development in general talks about an iterative approach to software delivery that builds software incrementally by taking modular steps, sustainability adds in few characteristics to the cycle making it environment friendly. This is also integrated with a few security indicators to ensure that the cycle is not prone to major attacks in a heuristic approach.
Passive Smartphone Contact Tracing and Continuous COVID-19 Infection Risk Assessment

COVID-19 has infected millions since November 2019. The virus spreads through close contact with those who are infected. People are often unaware of or lose track of their behaviors, which increase their risk of infection. Passive methods to continuously monitor and track dangerous user behaviors, maintain and update a COVID risk score can enable high risk users to take preventive measures early. At the organization or institution level, such systems can provide insights on organization-wide patterns that exacerbate disease spread. This paper presents our vision of pervasive, continuous infectious disease contact tracing, risky behavior tracking and continuous risk score calculation from contact, place visit information and reported behaviors. As a specific example, we describe the research, design and development of the android app Goatvid Trace that continuously gathers smartphone sensor data, using it to calculcate smartphone users’ risk of exposure to COVID-19. Machine Learning methods for proximity detection from smart- phone Bluetooth RSSI signals are also described. GoatVid trace was deployed and evaluated on a small university community. Our evaluation study found that the mean COVID-19 risk score of college students in our study was 25.6%. Our risk score model correlated well with subject questionnaire responses with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.6166. The machine learning models for proximity detection estimated distances between two phones with a Cross Validation RMSE of 1.58766.
Mad Mixologist: Exploring How Object Placement in Tangible Play Spaces Affects Collaborative Interaction Strategies

Tangible games afford an engaging and often unique form of hybrid play (i.e., physical-digital elements), but there is currently limited work explicitly exploring how these games can be designed to provide spatial affordances that implicitly encourage collaboration. In this paper, we present a novel collaborative tangible game, titled Mad Mixologist, and investigate how making a simple change in the location of game objects within the tangible play space can lead to significantly different collaborative interaction strategies. The results from our group comparison study indicate that 1) players with exclusively direct access to multiple relevant resources (i.e., a digital instruction and a physical object) were more likely to assume responsibility for completing tasks in a shared play space and 2) distributing these same task-relevant resources across multiple players created ambiguity over whether the player with the digital or physical resource should engage with the shared play space. This study demonstrates one possible way in which the physical design of a tangible game can be arranged to implicitly encourage players to develop more collaborative interaction strategies, specifically by distributing exclusive resources across players. Overall, this study highlights and reinforces the connection between spatial affordances and social interactions via embodied facilitation within the context of collaborative tangible games.
Missile Trajectory Tracking Using IMM and GNN

The approach to track multiple missile trajectories from launch to impact on the surface of the earth uses various track and update algorithms, one of such is an Interactive multiple model (IMM) filter. IMM is capable of tracking highly maneuverable targets due to its ability to switch between different tracking filters to track the target at different phases of its trajectory. Global Nearest Neighbour (GNN), a single hypothesis tracker, provides an optimal solution for multi-target tracking. GNN is compatible with various tracking filters which includes Interactive Multiple Model (IMM) filters. Mean square errors of the respective filters are also discussed. Integration of IMM and GNN provides an adequately robust missile tracking system which is discussed in this paper.
Aspect Level Sentiment Analysis using Stacked Auto Encoder with Random Forest

Sentiment analysis is termed as recognition of emotions extracted from textual features and termed as one of the prominent part of opinion mining. The existing deep learning approach had showed good performance. But, to improve this performance level, a hybrid framework is proposed by combining lexicon features as well as deep aspect features with application of autoencoders in order to solve the limitations of earlier methods. Evaluating and testing of the designed framework flexibility for multiple domains. For the performance evaluation of proposed model, IMDB dataset is used. The simulation is performed on MATLAB platform and this proposed hybrid lexicon and deep aspect level feature extraction model represents better results as compared to other existing works.
A Simple Clustering Algorithm Based on Weighted Expected Distances

This paper contains a proposal to assign points to clusters, represented by their centers, based on weighted expected distances in a cluster analysis context. The proposed clustering algorithm has mechanisms to create new clusters, to merge two nearby clusters and remove very small clusters, and to identify points ‘noise’ when they are beyond a reasonable neighborhood of a center or belong to a cluster with very few points. The presented clustering algorithm is evaluated using four randomly generated and two well-known data sets. The obtained clustering is compared to other clustering algorithms through the visualization of the clustering, the value of the DB validity measure and the value of the sum of within-cluster distances. The preliminary comparison of results shows that the proposed clustering algorithm is very efficient and effective.
Fast Monotonicity Preserving Text Sorting On GPU

Sorting text is a commonly encountered computing problem in science and consumer applications. In particular, many of those applications, such as social media or data intelligence types, need to sort huge amount of text data. This motivate the development of parallel text sorting algorithms on modern computer platforms such as GPU. Text sorting and the commonly tackled sorting problems are very different, in that text sorting need to deal with variable key length and highly skewed data distribution, both of which introduce significant challenges to the parallelization of text sorting. Furthermore, GPU’s architecture characteristics is not a native good match of text sorting’s computation pattern. Existing text sorting algorithms are mostly comparison-based, which will lead to thread divergence on GPU, a performance. There are a small number of "radix" sorting algorithms for text. However, they all treat text as character based data, and lead to significant waste on memory space.In this paper, we propose a monotonicity preserving text transformation technique that can map text strings into fixed-length keys. The main innovation is that we carefully keep the original order of the strings during the transformation, i.e., monotonicity preserving, so that transformed keys can be safely radix sorted, in lieu of sorting the original much longer text strings. Our technical can efficiently parallelize the transformed sorting on GPU, and deal with the typical skewed text data distribution in real world applications. Compared with Thrust, a widely used library on GPU, our algorithm outperforms Thrust’s string sorting method with substantial margins. We achieve the average speed up of 2.68 and the max speedup 8.5.
A Bibliometric and Co-occurrence Analysis of Speech Processing Literature Published from the Year 2015 to mid of June 2021

Speech processing technology will continue to be a fast-increasing area of signal processing for many years to come, based on present trends. Research in this field is useful in a variety of fields, including voice identification, interactive voice systems, emotion recognition, call center automation, virtual assistants, and robotics. Artificial intelligence, machine learning, and signal processing have all made significant strides recently, making this viable. The goal was for authors/co-authors of documents to evaluate speech processing in Scopus in terms of research trends and leading countries. The search syntax was based on the title of the article. Our article summarizes the research that has been done on this topic over the last five years. This study looked at 5,082 "speech processing" records from January 1, 2015, to June 14, 2021, which were collected from the Scopus database using the keywords "Speech Processing" and "keyword in the title (item)" and then evaluated with VOSviewer software. Several countries are ranked substantially higher in Scopus in terms of journal coverage, according to this analysis. The United States, China, Japan, India, and the United Kingdom are the major contributors. The interpretation of the results is determined by the search phrase used and the database or information system under investigation. According to this survey, in the area of speech processing, 73 percent overall research papers published in the last five years are conference papers. The studied co-occurrence relationship between keyword in the title clearly demonstrates that this topic of research is steadily being explored. This analysis may inspire and motivates the research community who are working in this discipline.
Image Captioning: Generating Textual Description Using Modified Beam Search

In Artificial Intelligence, generating textual descriptions of images by describing them in words is a fundamental problem that connects computer vision and natural language processing. A single image may include several entities, their orientations, appearance, and position in a scene as well as their complex spatial interactions, thus leading to a lot of possible captions for an image. In this paper, we will be implementing a famous encoder-decoder model and will be looking into sentence generation strategies with a greater focus, proposing one strategy of our own. The present sentence generation strategy of Beam Search chooses the best B caption (B denotes beam-width) at a time and further expands these sentences and again chooses the best B captions. It has come into an observation that this returns around the same caption with minor changes of wordings. We came across another search strategy, Diverse M Best which uses M (M denotes the number of independent, diverse beam searches) beam searches from diverse starting statements and keeps the best output from each beam search, and removes the rest of (B-1) captions. This method would mostly lead us to many possible diverse generated sequences, but running Beam Search M several times would be computationally expensive. We thus propose an amalgamation of the two searches, calling it Modified Beam Search and compare its results with greedy and beam search. With modified beam search we expect a small caveat in time as compared to Beam Search, but in contrast, we get diverse and better results.
Constructing a Model of the Main Highway of Cybersecurity Policy in a Digital Society Based on Multi-level Analysis

The article deals with the problem of formation of information culture of cyberspace by methods of system analysis. An analysis of modern approaches to the formation of the concept of “information culture” taking into account trends in the development of information and communication technologies is conducted. A methodology for forming information culture levels on the basis of the systematic approach is presented. The peculiarities of informational culture formation in conditions of uncertainty are specified.