Reviewerly/exHarmony huggingface dataset:
Lightning-Fast Dual-Layer Lossless Coding for Radiance Format High Dynamic Range Images

This paper proposes a fast dual-layer lossless coding for high dynamic range images (HDRIs) in the Radiance format. The coding, which consists of a base layer and a lossless enhancement layer, provides a standard dynamic range image (SDRI) without requiring an additional algorithm at the decoder and can losslessly decode the HDRI by adding the residual signals (residuals) between the HDRI and SDRI to the SDRI, if desired. To suppress the dynamic range of the residuals in the enhancement layer, the coding directly uses the mantissa and exponent information from the Radiance format. To further reduce the residual energy, each mantissa is modeled (estimated) as a linear function, i.e., a simple linear regression, of the encoded-decoded SDRI in each region with the same exponent. This is called simple linear regressive mantissa estimator. Experimental results show that, compared with existing methods, our coding reduces the average bitrate by approximately 1.57-6.68 % and significantly reduces the average encoder implementation time by approximately 87.13-98.96 %.
Is Ethereum Proof of Stake Sustainable? — Considering from the Perspective of Competition Among Smart Contract Platforms

Since the Merge update upon which Ethereum transitioned to Proof of Stake, it has been touted that it resulted in lower power consumption and increased security. However, even if that is the case, can this state be sustained?In this paper, we focus on the potential impact of competition with other smart contract platforms on the price of Ethereum’s native currency, Ether (ETH), thereby raising questions about the safety and sustainability purportedly brought about by the design of Proof of Stake.
Teach-DETR: Better Training DETR With Teachers

In this paper, we present a novel training scheme, namely Teach-DETR, to better train DETR-based detectors from versatile types of teacher detectors. We show that the predicted boxes from teacher detectors are effective medium to transfer knowledge of teacher detectors, which could be either RCNN-based or DETR-based detectors, to train a more accurate and robust DETR model. This new training scheme can easily incorporate the predicted boxes from multiple teacher detectors, each of which provides parallel supervisions to the student DETR. Our strategy introduces no additional parameters and adds negligible computational cost to the original detector during training. During inference, Teach-DETR brings zero additional overhead and maintains the merit of requiring no non-maximum suppression. Extensive experiments show that our method leads to consistent improvement for various DETR-based detectors. Specifically, we improve the state-of-the-art detector DINO Zhang et al. 2022 with Swin-Large Liu et al. 2021 backbone, 4-scale feature pyramid and 36-epoch training schedule, from 57.8% to 58.9% in terms of mean average precision on COCO 2017 <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">val</monospace> set.
Structure Learning of Bayesian Network from the Data

Learning the structure of Bayesian network is a two-step process, one is parameter learning, and other is finding the best structure among search space using uncertain and incomplete data. Structure learning is the most important and complex task (NP hard problem) in estimation theory. However, existing techniques such as K3 algorithm require topological order of the nodes or a constraint on the maximum number of parents for a node, and also, most of them are generating all possible graphs even for small number of random variables and consume large amount of space and time complexity (Buntine in IEEE Trans Knowl Data Eng 8:195–210 [1]; Chickering in Learning from data. Springer, pp 121–130 [2]) to verify each of the structure. In this work, we propose an algorithm that generates comparatively small number of graphs as a heuristic search technique, and Bayesian score is calculated for each candidate structure to find the best network structure. The proposed algorithm consumes comparatively less time complexity to discover network structure using the data.
Preparing Software Engineers for Management Roles

As software engineers progress in their careers, they often move from being a technical contributor to being a leader of a team to more senior roles in an organization, often overseeing the work of many people. However, computer science and software engineering curricula emphasize technical aspects, rather than the soft skills that are important for being a successful manager. This paper describes a graduate level course that introduces software engineers to many of the key issues they will encounter as they take on management responsibilities. These issues include project management, recruiting and hiring, working with other departments in a company, and addressing typical management problems that come with the job.
Design of a Virtual Mouse for Hand Gesture Recognition and Characterization

One of the marvels of human–computer interaction (HCI) technology is the mouse. Since a wireless mouse or Bluetooth mouse still need a battery for power and a dongle to connect it to the PC, they are not entirely device free at this time. This restriction can be removed by using computer vision to capture hand movements and hand tip detection using a webcam or built-in camera. Without using a real mouse, the computer can be virtually controlled using hand gestures to accomplish left click, right click, scrolling, and computer cursor tasks. As a result, the suggested method will stop the spread of COVID-19 by doing away with human interaction and the need for external devices to operate the computer. A replacement of conventional mouse system is done by this proposed system and to perform various mouse functions as well as provide control. The web camera records the hand gestures, hand tips, and later processes the frames to perform the specific functions of mouse such as drag and drop, double click, scrolling, multiple item selection, volume control, brightness, left click, and right click. To do this, we’ll use PYTHON libraries like MediaPipe and comtypes, as well as OpenCV and CNN for hand aspect ratios and hand detection, respectively.
Efficient Model for Predicting Cyber Hacking Breaches

The cyber occurrence informational indexes as a means of expanding our understanding of the current threat situation is essential. More research is needed on this topic before finishing some research projects. This paper compares a break-in information collection to 12 years (2005–2017) of cyber hack exercises required malware invasion, and the results are quantifiable. Since autocorrelations are shown by both hackers break occurrence and penetration size, it shows that stochastic processes rather than conveyances should be used to depict these data. Stochastic method models are proposed to fit the break sizes and between appearance times. The informational index is being subjected to both subjective and quantitative pattern inspections in order to gather more information about the progression of hacker penetration occurrences. Despite the fact that cyberattacks are becoming more frequent, they are not getting much worse in terms of the damage they can cause.
Face Detections of a Person Wearing COVID Mask Using Multi-task Cascaded Convolutional Neural Networks

According to a report, over one percent of the world population is infected with COVID-19 in the last year. To avoid the spread of the infection, World Health Organization (WHO) suggested individuals to wear a face mask and maintain social distance. The use of face masks raised concerns about the accuracy of the facial recognition system used for office attendance, unlocking phones, etc. Masked faces make it difficult to recognize the individual. The objective of this study is to figure out who is wearing a face mask. There are two steps in recognizing a person’s identity; they are face detection and face recognition. The faces are detected using multi-task cascaded convolutional neural networks (MTCNN) trained on the WIDER FACE dataset. Face descriptors were obtained from MTCNN’s suggested region of interest using the model VGGFACE2. The MS-Celeb-1M dataset was used to train the VGGFACE2. Using facial descriptors, it is possible to identify a person. A cosine distance criterion is utilized to verify individuals, and the K-nearest neighboring classifier is used to determine a person’s identity. This research will provide better results in identifying a person wearing a face mask.
Hacks and the Price Synchronicity of Bitcoin and Ether

We use intraday trading data from the Kraken exchange to calculate the daily price synchronicity of Bitcoin and Ether from February 2018 to December 2022. We then use a comprehensive report provided by christalblockchain.com to investigate the impact of hacks on price synchronicity between the top two cryptocurrencies. Our results show that price synchronicity, as measured by the realized correlation, is consistently positive throughout the sample period, with only one (negative) exception. We further uncover a positive relationship between hacking events and the future price synchronicity of Bitcoin and Ether. This result is robust to an alternative price synchronicity measure.
SLRNetwork: Utilizing Skip Connections for Better Feature Extraction in Recognition of Sign Languages to Help Mutism

Through the use of sign language, persons with special needs who have oral and aural impairments have discovered a way to communicate and express their thoughts to society. However, simply knowing that sign language originated is insufficient for good dialogue and communication. We introduce SLRNetworks, a novel implementation of SLR (Sign Language Recognition) that makes use of CNNs customized along with improved feature map extraction by using the skip connections present in residual networks (ResNet). SLRNetworks will help efficiently classify the “signs” and predict their meanings, hence enabling a larger segment of society to use sign language. ResNet-18 network is used in the project's implementation as the feature extractor, while FCNs with Swiss (Softmax) activation function are used for multiclass classification. The sign language gesture images data set has been used for training and validation using the Tensorflow framework in order to ensure highly accurate results.
Detection of Cyberbullying in Social Media Using Machine Learning Techniques

Cyberbullying is the act of threatening someone while using technology. Social networking networks provide a thriving platform for threats who use them to harass or harm defenseless young adults. We can identify language patterns used by threats through machine learning, and we can develop criteria to recognize online harassment as a result. The majority of research on machine learning-based cyberbullying detection has been conducted on languages including English, Chinese, and Arabic. Regional Indian languages have not been the subject of many studies. We have created a methodology in this research that can identify cyberbullying content in a rare or rather regional Indian language.
High-Resolution Image Inpainting Using Generative Adversarial Networks

In the twenty-first century, deep learning and artificial intelligence are being used to solve multiple problems in the real world. When coming to images, removing certain unwanted objects from images as per our interest has been one of the most famous intriguing problem statements in photo-editing. Image inpainting is a technique which is used to fill the missing regions present in images is a promising yet difficult activity. Previous approaches have accomplished critical advancement using GANs. However, these methodologies might experience the ill effects of reproducing distorted images and blurred textures in high-resolution pictures. The difficulties primarily come from content understanding from fine-grained and distant contexts image content understanding in a missing region in an image. Being an avid researcher and enthusiast in the field of artificial intelligence, working in such a problem statement would be a good way to hone the skills in the field. We propose an enhanced model using GANs for image inpainting of high-resolution images. The enhanced block helps in capturing both rich patterns as well as context from distant regions. The discriminator is also tweaked to make sure the robustness of the model.
Care A Pet—Android Pet Care Application

These days, having a pet is a fun addition to everyone’s life. Keeping pets has become a habit, and caring for them has become part of our human culture. To take care of your pets when you are out on a vacation, we proposed a project called “CARE A PET”. The developed Android application will show the nearby pet care centers so that they can book slots according to their availability by chat. In case of an emergency time, we can call the pet specialist by taking an appointment by booking a vet on-call service using this app. The findings were divided into two sections: developing a mobile application for user advice and evaluating the application’s performance for research objectives. This project also has commercial potential, compared to other methods of caring for and monitoring pets through the use of pet resources, especially in an urban way of life.
Social Harassments Cyberbullying Detection Using Machine Learning

Because of the widespread use of online social networks and the high quality of different online platforms, we now have more opportunities than ever before. Unfortunately, most teenagers are victims of cyberbullying. Most of students in online are embarrassed, bullied, and harassed by unknown users and strangers through the use of posting of negative words, harmful, false, or mean content regarding. This can result in stress, unhappiness, depression, and even suicidal ideation. The main purpose of this research is to pinpoint and categorize offensive and non-offensive words used in social media posts as either harassment words/speech or not. Machine learning classifiers were used to determine whether tweets were offensive or not.
Dynamic Text Generation from Sign Language—Review

Recognizing the sign language is very important in research fields because that is the only way for the people with hearing problems to communicate with other people. By recognizing the gestures, people with the hearing problems can communicate with one they wanted to without the need of mediators. The system which we wanted to build will capture the gestures made by the people specially the hand movements and based upon the symbols they framed a grammatically correct sentence and displayed in the form of captions. Due to the modern world and technology, everything needs to be upgraded so our project is a real-time live sign language detector, which is a step forward to communicate with the people who have hearing and speaking problems. We are trying to develop a live sign language detector which identifies the signs through the convolutional neural networks (CNN) and also converts it into meaningful sentences. It is basically a series of image processing techniques with human hand movement classification and recognition. This paper presents a recommendation model to identify and translate the hand gestures of sign language into meaningful sentences in English. The search is mostly based on the CNN models, TensorFlow and Keras libraries. No facial or body movements were used in our model and our project is dynamic in nature as it processes the gestures through the videos from a HD cam. Our study is conducting various gestures feed to the model and the prediction of the sign language. The project which we are aiming to build uses a machine learning technologies built using the RNN and CNN models. The paper is completely user-independent, and the classification machine learning algorithms are trained using very large datasets. The user whoever wants to communicate can just open our application and record the video of the person, and the corresponding text will be displayed on the screen which will have a particular meaning.
Detecting Anomalies in the Virtual Machine Using Machine Learning Techniques

There is an increase in demand for cloud computing services, and optimizing the resources is crucial. Conventional algorithms are not adequate to address issues with the cloud, including uneven task distribution in virtual machines or inefficient resource delivery to cloud customers. To significantly enhance the outcome of cloud apps and address the aforementioned difficulties, it is necessary to investigate further sophisticated ways. This study looks into the most recent machine learning techniques that can handle the difficulties in a cloud context. A paradigm monitoring and identification of errors in Virtual Machine (VM) resource utilization is put forth in this study. The suggested methodology called IF-SVM (Isolation Forest-Support Vector Machine) is able to identify the errors. The VM workload trace from PlanetLab is used to test and train the samples. During this process, the concepts of ML techniques such as SVM and IF and the VM resource matrix is used. F1-score of 0.87 is able to achieve by using the SVM for the time series of 1 h and 0.91 F1-score for Isolation Forest for the 50 VMs. This result demonstrates the effectiveness of both methods for the model, but SVM outperforms well in terms of classification in comparison with Isolation Forest.
Case study on evolution performance of agile scrum software development life cycle for shopping cart applications

This paper presents the novel agile scrum methodology for the shopping cart product. The existing software development life cycle, such as waterfall, iterative methods, and spiral life cycles, has a larger failure ratio and cost factors when a connected component fails during the execution of a product. In this paper, we devise a novel agile scrum methodology where sprint backlog and product backlog process are demonstrated with the cost and failure efficiency. The role of the scrum master, standard deviation, mean and completion and task progression are illustrated in the methodology as shown in the result discussion.
In-Sample and Out-of-Sample Predictability of Cryptocurrency Returns

This paper investigates whether the price of cryptocurrency is determined by the US dollar index, the price of investment assets such gold and oil, and the implied volatility of the KOSPI. Overall, the returns on cryptocurrencies are best predicted by the trading volume of the cryptocurrency both in-sample and out-of-sample. The estimates of gold and the dollar index are negative in the return prediction, though they are not significant. The dollar index, gold, and the cryptocurrencies seem to share characteristics which hedging instruments have in common. When investors take notice of the imminent market risks, they increase the demand for one of these assets and thereby increase the returns on the asset. The most notable result in the out-of-sample predictability is the predictability of the returns on value-weighted portfolio by gold. The empirical results show that the restricted model fails to encompass the unrestricted model. Therefore, the unrestricted model is significant in improving out-of-sample predictability of the portfolio returns using gold. From the empirical analyses, we can conclude that in-sample predictability cannot guarantee out-of-sample predictability and vice versa. This may shed light on the disparate results between in-sample and out-of-sample predictability in a large body of previous literature.
More than skin-deep: The influence of presence dimensions on purchase intentions in augmented reality shopping

Virtual try-on applications use augmented reality to virtually display products on consumers’ faces or bodies. That is, they simulate a believable try-on experience by means of psychological presence, whereby the virtual experience feels real to the consumer. Grounded in social cognitive theory, this study is the first to investigate spatial presence, social presence, and self-presence to clarify the impact of virtual try-ons on consumers’ purchase intentions. We demonstrate that the three dimensions of presence have varying effects on purchase intentions. Furthermore, the different outcomes of the presence dimensions are attributed to the symbolic significance ascribed to body modification consumption. We confirm this effect for two contexts that represent low and high levels of body modification. The findings reveal the roles of (1) spatial presence in the low-level body modification context, (2) self-presence in the high-level body modification context, and (3) social presence across different body modification contexts.
Searching natural neighbors in an accelerated way

Natural neighbor (NaN) is an adaptive neighbor concept for automatically determining k value for k-nearest neighbors, which has been proved effective in clustering analysis, classification, and outlier detection. However, the existing algorithms for searching NaN employ the global search strategy, which increases unnecessary consumption of time on non-critical points. To solve this problem, we propose a novel accelerated algorithm by utilizing a local search strategy, called ASNN. ASNN is based on the fact that if remote points have reverse nearest neighbors, others are likely to have reverse nearest neighbors. Firstly, it extracts the remote points by defining remote radius. Secondly, only the neighbors of remote points are searched, so that ASNN can quickly obtain the natural neighbor eigenvalue (NaNE). Compared to existing algorithms for searching NaNs, ASNN drastically reduces the number of query points and time consumption. The efficiency and effectiveness of ASNN are proved on synthetic and real datasets.
Self-supervised temporal autoencoder for egocentric action segmentation

Given an egocentric video, action temporal segmentation aims to temporally segment the video into basic units, each depicting an action. As the camera is constantly moving, some important objects may disappear in some consecutive frames and cause an abrupt change in the visual content. Recently works fail to deal with this condition in the absence of manually annotating abundant frames. In this study, we propose a temporal-aware clustering method for egocentric action temporal segmentation: a self-supervised temporal autoencoder (SSTAE). Instead of directly learning visual features, the SSTAE is implemented by encoding the preceding target frame and predicting subsequent frames in the temporal relationship domain, which takes into account the local temporal consistency. Our proposed algorithm is guided by the reconstruction and predicted losses. Consequently, local temporal contexts are naturally integrated into the feature representation, and a clustering step is performed. Experiments on three egocentric datasets demonstrate the our proposed approach outperforms the state-of-the-art methods by clustering Accuracy(ACC) 7.57%, Normalized Mutual Information(NMI) 8.17%, Adjusted Rand Index(ARI) 8.6%.