Reviewerly/exHarmony huggingface dataset:
Analyzing User Digital Emotions from a Holy versus Non-Pilgrimage City in Saudi Arabia on Twitter Platform

Humans are the product of what society and their environment conditions them into being. People living in metropolitan cities have a very fast-paced life and are constantly exposed to different situations. A social media platform enables individuals to express their emotions and sentiments and thus acts as a reservoir for the digital emotion footprints of its users. This study proposes that the user data available on Twitter has the potential to showcase the contrasting emotions of people residing in a pilgrimage city versus those residing in other, non-pilgrimage areas. We collected the Arabic geolocated tweets of users living in Mecca (holy city) and Riyadh (non-pilgrimage city). The user emotions were classified on the basis of Plutchik’s eight basic emotion categories, Fear, Anger, Sadness, Joy, Surprise, Disgust, Trust, and Anticipation. A new bilingual dictionary, AEELex (Arabic English Emotion Lexicon), was designed to determine emotions derived from user tweets. AEELex has been validated on commonly known and popular lexicons. An emotion analysis revealed that people living in Mecca had more positivity than those residing in Riyadh. Anticipation was the emotion that was dominant or most expressed in both places. However, a larger proportion of users living in Mecca fell under this category. The proposed analysis was an initial attempt toward studying the emotional and behavioral differences between users living in different cities of Saudi Arabia. This study has several other important applications. First, the emotion-based study could contribute to the development of a machine learning-based model for predicting depression in netizens. Second, behavioral appearances mined from the text could benefit efforts to identify the regional location of a particular user.
Restoration and quality improvement of distorted tribal artworks using Particle Swarm Optimization (PSO) technique along with nonlinear filtering

Cultural artworks that are not properly cared for, degrades over time. However modern image processing techniques proves to be a boon for such artworks. Various techniques have been developed time to time for different aspects of image processing like noise removal, improving brightness and image quality, etc. One of such techniques is PSO (Particle Swarm Optimization) that can be used for image restoration. In this paper a combination of median filter and PSO has been used for the quality improvement of the distorted tribal artworks and the results are compared with few HE (Histogram Equalization) techniques like the CLAHE, BBHE and DSIHE. The comparison is done on the basis of various image quality assessment parameters, namely PSNR, AMBE, NMSE, CPP and MSE.
Emotion AI: Integrating Emotional Intelligence with Artificial Intelligence in the Digital Workplace

The recent advancements in artificial intelligence (AI) and digital transformation have led to a paradigm shift in the interact pattern of people with technology. With the adoption of artificial intelligence (AI) in all business processes, the biggest challenge being faced by organizations is the integration of artificial intelligence (AI) with emotional intelligence (EI). There is no denying the fact that as more and more technology is being involved, and it has become a two-way process. Human beings are trained to work with technology and the technology is taught to relate to people. An extensive secondary research is conducted to comprehend the digital transformations at workplace and explore the evolving concepts of AI and how will it connect with the emotions of the 'Analog' being applications. Through this paper, we will try to bring forth how artificial intelligence is being used as a support system to emotional intelligence and the emerging concept of emotion AI.
Fast median filters using separable sorting networks

Median filters are a widely-used tool in graphics, imaging, machine learning, visual effects, and even audio processing. Currently, very-small-support median filters are performed using sorting networks, and large-support median filters are handled by O (1) histogram-based methods. However, the constant factor on these O (1) algorithms is large, and they scale poorly to data types above 8-bit integers. On the other hand, good sorting networks have not been described above the 7 X 7 case, leaving us with no fast way to compute integer median filters of modest size, and no fast way to compute floating point median filters for any size above 7 X 7. This paper describes new sorting networks that efficiently compute median filters of arbitrary size. The key idea is that these networks can be factored to exploit the separability of the sorting problem - they share common work across scanlines, and within small tiles of output. We also describe new ways to run sorting networks efficiently, using a sorting-specific instruction set, compiler, and interpreter. The speed-up over prior work is more than an order of magnitude for a wide range of data types and filter sizes. For 8-bit integers, we describe the fastest median filters for all sizes up to 25 X 25 on CPU, and up to 33 X 33 on GPU. For higher-precision types, we describe the fastest median filters at all sizes tested on both CPU and GPU.
Design of a 2-Bit Neural Network Quantizer for Laplacian Source

Achieving real-time inference is one of the major issues in contemporary neural network applications, as complex algorithms are frequently being deployed to mobile devices that have constrained storage and computing power. Moving from a full-precision neural network model to a lower representation by applying quantization techniques is a popular approach to facilitate this issue. Here, we analyze in detail and design a 2-bit uniform quantization model for Laplacian source due to its significance in terms of implementation simplicity, which further leads to a shorter processing time and faster inference. The results show that it is possible to achieve high classification accuracy (more than 96% in the case of MLP and more than 98% in the case of CNN) by implementing the proposed model, which is competitive to the performance of the other quantization solutions with almost optimal precision.
Matrix completion on learnt graphs: Application to collaborative filtering

There are two broad frameworks for collaborative filtering. Chronologically, the neighborhood based models came first – they were based on linear interpolation where the interpolation weights were proportional to the similarities between users’ and items’. Latent factor models were introduced later; they were based on the underlying assumption that the data matrix is low-rank. Both the approaches essentially completed the partially observed rating matrix. These two approaches were later combined by regularizing the latent factor model with graph Laplacian computed from the similarities defined in the neighborhood based models. However, this graph was computed from partially observed data and may have failed to represent the true similarities. This work addresses this issue; instead of computing the graph and assuming it to be fixed during the matrix completion process, we will alternate between updating (refining) the graph assuming the matrix to be fully observed, and completing the matrix, assuming the graph to be fixed. Benchmarking with fixed graph techniques as well as state-of-the-art collaborative filtering algorithms on popular Movielens datasets have shown that our proposed approach indeed improves over the rest.
Pixel-Attention CNN With Color Correlation Loss for Color Image Denoising

Convolutional neural networks (CNNs) have been applied to many image processing tasks and achieve great successes. In order to extract common features, every pixel in an image shares the same filters. However, pixels in different regions of an image varies dramatically and shared filters may lose some important local information. Rather than shared filters, smart filters which can be adapted to image context should be designed to better remove noise which occurs randomly in noisy image. Meanwhile, current CNN architectures compute the loss of each color channel independently, regardless of the potential color information. In this letter, we proposed a pixel-attention convolutional neural network (PACNN) with color correlation loss for the color image denoising task. The pixel-attention mechanism could generate pixel-wise attention maps which help remove random noise. The color correlation loss exploits color correlation to further improve denoising performance on color noisy images. The experimental results on several standard datasets demonstrate the state-of-the-art (SOTA) performance and the superiority of the proposed method.
Cognitive Impairments in Schizophrenia: A Study in a Large Clinical Sample Using Natural Language Processing

Background: Cognitive impairments are a neglected aspect of schizophrenia despite being a major factor of poor functional outcome. They are usually measured using various rating scales, however, these necessitate trained practitioners and are rarely routinely applied in clinical settings. Recent advances in natural language processing techniques allow us to extract such information from unstructured portions of text at a large scale and in a cost effective manner. We aimed to identify cognitive problems in the clinical records of a large sample of patients with schizophrenia, and assess their association with clinical outcomes. Methods: We developed a natural language processing based application identifying cognitive dysfunctions from the free text of medical records, and assessed its performance against a rating scale widely used in the United Kingdom, the cognitive component of the Health of the Nation Outcome Scales (HoNOS). Furthermore, we analyzed cognitive trajectories over the course of patient treatment, and evaluated their relationship with various socio-demographic factors and clinical outcomes. Results: We found a high prevalence of cognitive impairments in patients with schizophrenia, and a strong correlation with several socio-demographic factors (gender, education, ethnicity, marital status, and employment) as well as adverse clinical outcomes. Results obtained from the free text were broadly in line with those obtained using the HoNOS subscale, and shed light on additional associations, notably related to attention and social impairments for patients with higher education. Conclusions: Our findings demonstrate that cognitive problems are common in patients with schizophrenia, can be reliably extracted from clinical records using natural language processing, and are associated with adverse clinical outcomes. Harvesting the free text from medical records provides a larger coverage in contrast to neurocognitive batteries or rating scales, and access to additional socio-demographic and clinical variables. Text mining tools can therefore facilitate large scale patient screening and early symptoms detection, and ultimately help inform clinical decisions.
Blockchain-based IoT: An Overview

The Internet of Things (IoT) has revolutionized the human world by transforming ordinary everyday objects into smart devices. These autonomous devices have reshaped our lives. The emerging technology is expanding day-by-day with the increasing need for smart devices as so the issues are also increasing w.r.t security, data reliability, maintenance and authentication. On the other hand, another innovative technology- Blockchain- has transformed our financial world by introducing sophisticated security. An integrated Blockchain-IoT system can resolve the problems they face individually and serve the technological world better. The paper provides a comprehensive study of both technologies by highlighting their features and challenges. The article further critically analyses existing approaches that discussed various issue about IoT and Blockchain.
On Soft-Biometric Information Stored in Biometric Face Embeddings

The success of modern face recognition systems is based on the advances of deeply-learned features. These embeddings aim to encode the identity of an individual such that these can be used for recognition. However, recent works have shown that more information beyond the user’s identity is stored in these embeddings, such as demographics, image characteristics, and social traits. This raises privacy and bias concerns in face recognition. We investigate the predictability of 73 different soft-biometric attributes on three popular face embeddings with different learning principles. The experiments were conducted on two publicly available databases. For the evaluation, we trained a massive attribute classifier such that can accurately state the confidence of its predictions. This enables us to derive more sophisticated statements about the attribute predictability. The results demonstrate that the majority of the investigated attributes are encoded in face embeddings. For instance, a strong encoding was found for demographics, haircolors, hairstyles, beards, and accessories. Although face recognition embeddings are trained to be robust against non-permanent factors, we found that specifically these attributes are easily-predictable from face embeddings. We hope our findings will guide future works to develop more privacy-preserving and bias-mitigating face recognition technologies.
Multiscale Content-Independent Feature Fusion Network for Source Camera Identification

In recent years, source camera identification has become a research hotspot in the field of image forensics and has received increasing attention. It has high application value in combating the spread of pornographic photos, copyright authentication of art photos, image tampering forensics, and so on. Although the existing algorithms greatly promote the research progress of source camera identification, they still cannot effectively reduce the interference of image content with image forensics. To suppress the influence of image content on source camera identification, a multiscale content-independent feature fusion network (MCIFFN) is proposed to solve the problem of source camera identification. MCIFFN is composed of three parallel branch networks. Before the image is sent to the first two branch networks, an adaptive filtering module is needed to filter the image content and extract the noise features, and then the noise features are sent to the corresponding convolutional neural networks (CNN), respectively. In order to retain the information related to the image color, this paper does not preprocess the third branch network, but directly sends the image data to CNN. Finally, the content-independent features of different scales extracted from the three branch networks are fused, and the fused features are used for image source identification. The CNN feature extraction network in MCIFFN is a shallow network embedded with a squeeze and exception (SE) structure called SE-SCINet. The experimental results show that the proposed MCIFFN is effective and robust, and the classification accuracy is improved by approximately 2% compared with the SE-SCINet network.
Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters

In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation. There is a significant drop in performance of an existing segmentation model when training (source domain) and testing (target domain) data originate from different LiDAR sensors. To overcome this shortcoming, we propose an unsupervised domain adaptation framework that leverages unlabeled target domain data for self-supervision, coupled with an unpaired mask transfer strategy to mitigate the impact of domain shifts. Furthermore, we introduce the gated adapter module with a small number of parameters into the network to account for target domain-specific information. Experiments adapting from both real-to-real and synthetic-to-real LiDAR semantic segmentation benchmarks demonstrate the significant improvement over prior arts.
Employing scrambled alpha-numeric randomization and RSA algorithm to ensure enhanced encryption in electronic medical records

Diverse business sectors want to ensure that their data is secure and remains confidential. A major solution to the problem of data insecurity is the concept of cryptography. Various cryptographic algorithms have been developed over the years in order to ensure maximum level of message security during transmission over insecure mediums. There is no doubt that these algorithms have been able to remarkably curb the issue of data insecurity and reduce cyber-attacks. Both asymmetric cryptographic algorithms and symmetric cryptographic algorithms are secure. However for the purpose of this paper emphasis will be laid on asymmetric cryptography. Hence, this paper emphasizes on popular asymmetric algorithms like the RSA Cryptographic algorithm. In this paper we propose a secure means of data encryption and decryption by applying the concept of scrambled alpha-numeric randomization to provide a clear understanding of the operational mechanism of the RSA algorithm during the encryption and decryption process. The scrambled alpha-numeric randomization technique employs a unique numbering and or lettering sequence to every letter of the alphabet in the case of every new message receiver. This approach also implements the cryptographic message syntax as well as the use of American standard code for information interchange (ASCII) encoding. Furthermore, the understanding of this approach will encourage new ideas for further advancements and improvement of the existing RSA cryptographic algorithm. However, the major advantage will be to increase the security of data and ensure proper authentication of data.
Autonomous Car Driving Using Deep Learning

Autonomous driving is a hot topic in the area of artificial intelligence and machine learning where numerous researches is being conducted to make driverless vehicles mainstream. A successful autonomous driving system should contain accurate results, interpretability (for safety and reliability), and cost-effective. The core of autonomous driving is a union of perception (conscious about surroundings- roads, obstacles, etc.) and decision making (making decisions to drive corresponding to the perceived environment). Problems with the current innovations, too complex model architecture to achieve the state-of-the-art results which makes it too expensive and hard to interpret. In this paper, we show that using simple models (vanilla UNet/FCN for perception and linear algebra techniques for decision making) which are easy to interpret and produce a cost-effective system, attains good results. Our idea serves for level 1 autonomous driving which can be further scaled up for higher levels.
Blockchain-Enabled Trust Management in Service-Oriented Internet of Things: Opportunities and Challenges

With the development of information and computer technology, internet of things (IoT) technology has brought tremendous changes to people's production and life. The emergence of IoT creates new opportunities to stimulate innovative services that better converge the information resources of IoT objects. However, due to the increasing scale of the network and the intelligence of devices, the development of the service-oriented IoT has also been accompanied by the issue of trust which is crucial to the security and stability of the system. Blockchain, as an emerging distributed ledger technology, is considered to be a driving force to improve the trust management of the service-oriented IoT. This research is a systematic analysis with the goal of illustrating and analyzing how the blockchain contributes to addressing the issue of trust management in service-oriented IoT. The objective of this paper is to understand the trust issue in the service-oriented IoT and analyze the potential blockchain-based solution, thereby motivating further research interest in this field.
Convolutional Neural Networks Hyperparameters Tuning

Digital images have revolutionized work in numerous scientific fields such as healthcare, astronomy, biology, agriculture as well as in every day life. One of the frequent tasks in applications with digital images is image classification which is a very challenging task. Major progress was made when convolution neural networks were introduced. The use of CNN has produced significant improvements in applications that require image classification. With today's technology, it is relatively simple to implement and use CNNs but in order to obtain the best possible results it is necessary to find the optimal architecture and hyperparameters for every single task. Due to a large number of hyperparameters, it is difficult to find the optimal configuration and there is no deterministic way to do it. In this early stage of CNN development, the commonConvolutional Neural Networks (CNN) method of tuning CNN is by guessing and estimating, known as the guestimating method. Since this is a hard optimization problem, there is a chance to apply an optimization metaheuristic. There are several studies that have applied different optimization methods for tuning CNN hyperparameters have applied. One promising approach is the application of swarm intelligence algorithms. In this paper, a brief review of the CNN hyperparameters tuning will be presented and discussed.
Development of VR educational instruments for school pre-professional education in a research university

The area of computer science puts a lot of effort into the development of virtual reality technologies (VR technologies), especially in pre-professional education – school etc. Technologies of VR opens a lot of new opportunities in training and education, it is widely used in education establishment, especially in computer sciences and mechatronics and robotics education in Russian schools and universities. VR technologies helps to save time, money and other resources in contrast to the traditional approach of teaching. At the National Research Nuclear University MEPhI we have an extensive experience of application of high technology in education. In this article we present the results of our practical research of development of VR educational instruments for school pre-professional education. The specificity of our research is that we have developed an educational program for the acquisition of skills in working with VR technologies in accordance with CDIO standards.
An Empirical Study on Application of Machine Learning and Neural Network in English Learning

With the continuous development of neural network theory itself and related theories and related technologies, neural network is one of the main branches of intelligent control technology. Artificial neural network is a nonlinear and adaptive information processing composed of a large number of processing units. In this paper, an adaptive fuzzy neural network (FNN) is used to construct an intelligent system architecture for English learning, and activation function is used to apply the knowledge of computer science and linguistics to English learning. The network neural structure diagram is presented. English machine learning model framework is established based on recursive neural network. On this basis, feature vector extraction and normalization algorithm are used to meet the needs of neural network model. After acquiring the feature vectors of users’ learning styles, the clustering algorithm is used to effectively form a variety of learning styles. The validity of the English learning model was verified by designing the functional flow based on tests. Accurate mastery can activate the corresponding brain regions not only to improve the efficiency of learning, but also to better facilitate language learning.