Reviewerly/exHarmony huggingface dataset:
Multi-motion and Appearance Self-Supervised Moving Object Detection

In this work, we consider the problem of self-supervised Moving Object Detection (MOD) in video, where no ground truth is involved in both training and inference phases. Recently, an adversarial learning framework is proposed [32] to leverage inherent temporal information for MOD. While showing great promising results, it uses single scale temporal information and may meet problems when dealing with a deformable object under multi-scale motion in different parts. Additional challenges can arise from the moving camera, which results in the failure of the motion independence hypothesis and locally independent background motion. To deal with these problems, we propose a Multimotion and Appearance Self-supervised Network (MASNet) to introduce multi-scale motion information and appearance information of scene for MOD. In particular, a moving object, especially the deformable, usually consists of moving regions at various temporal scales. Introducing multiscale motion can aggregate these regions to form a more complete detection. Appearance information can serve as another cue for MOD when the motion independence is not reliable and for removing false detection in background caused by locally independent background motion. To encode multi-scale motion and appearance, in MASNet we respectively design a multi-branch flow encoding module and an image inpainter module. The proposed modules and MASNet are extensively evaluated on the DAVIS dataset to demonstrate the effectiveness and superiority to state-of-the-art self-supervised methods.
Promoting Student Self-Regulation and Motivation Through Active Learning

In the current context of social distancing due to the pandemic of COVID-19, teachers are willing to find how to animate their classes without being physically present. Active learning can guide cognitive, motivational, and emotional learning processes while giving learners control over their learning. Research has shown that active learning offers better outcomes than passive learning. Indeed, this learning technique can enhance motivation, self-regulation, as well as soft skills. However, there is a need to support students' self-regulation during active learning. Therefore, teachers can use techniques from technology-enhanced learning tools and online tools to design an active learning class. This chapter aims to study how digital learning tools can promote active e-learning and foster students' self-regulation and motivation. Further research directions are given, regarding neurolearning, the role of the body during a learning process, and neuroadaptive personalized learning environments.
Application of CLIP on Advanced GAN of Zero-Shot Learning

In recent years, deep learning models have achieved world-renowned achievements in the fields of image, speech and text recognition. However, the insufficient amount of labeled data has brought serious problems, and it is also difficult to identify unseen classes well. Therefore, if we want to achieve perfect recognition of unseen classes, we need to perform zero-shot learning. In order to solve the zero-shot learning problem, a better solution can be obtained by using the semantic space method. Zero-shot learning attempts to classify unseen data after learning the seen data. In this case, it is one of the most difficult learning methods to achieve perfect recognition. CLIP uses a data set of 400 million data pairs, resulting in higher efficiency and better robustness. Using the features obtained by traditional RESNET neural network and CLIP, two advanced methods, F-CLSWGAN and TF-VAEGAN, were tested. Through ZSL and GZSL experiments, excellent results have been achieved and the effectiveness of the combined method has been verified. This paper has tested the good effect of the application of CLIP on ZSL and GZSL. The experimental results show that CLIP has excellent performance on the AWA2 data set, whether it is using F-CLSWGAN or TF-VAEGAN. Among them, the effect of TF-VAEGAN is better.
Spoken word recognition

Abstract This chapter considers the challenges of recognizing spoken words from the speech signal. We focus initially on theoretical and computational challenges that remain even under a set of common simplifying assumptions (most notably that much work on spoken word recognition sidesteps signal-level challenges by assuming abstract inputs of some sort, e.g., phonemes, phonetic features, or human confusion probabilities). We review key theories and debates, as well as findings and challenges that will require theories to abandon common simplifying assumptions. We conclude that progress will require models that process the actual speech signal and leverage constraints from lower (speech perception) and higher levels (semantics, sentence processing, and beyond) of language processing, as well as constraints from neurobiology, rather than avoiding the challenges those levels present.
‘I keep my brain on my iPhone’ – being and becoming an emergency physician in a technological age

The use of mobile information and communication technologies within health and medical education contexts is widespread and generally viewed positively. Such technologies are primarily used for information seeking and are often undertaken informally. This qualitative, phenomenological study offers not only a deep and contextually sensitive discussion of how emergency physicians use mobile technologies in their day-to-day work, but also extends this discussion to the practice of ongoing professional learning in the workplace. We find that the most common uses for mobile technology include (re)checking medicine orders and/or diagnoses and for communicating with other physicians for advice, support, and on occasion, discussion. Moreover, physicians often perceive mobile technology as an extension of self and represent an externalisation of expertise. There is skepticism, however, about the use of these technologies particularly among more experienced and established Attending Physicians (or Consultants in other jurisdictions). In all, these findings provide new perspectives on and further directions for both research and practice in health and medical education contexts given that the use of mobile technology in the emergency department is ubiquitous and increasingly normalised, particularly within the clinical interaction with patients.
Object Detection and Face Recognition Using Yolo and Inception Model

The task of facial recognition involves recognising faces in images, while object detection entails determining the location of objects in images. To accomplish this goal, we have developed a model capable of detecting objects as well as recognizing faces. The YOLO (You Only Look Once) model was used to detect objects in the image. If a person is detected by the model, then a cropped image of the person's face is passed to the pre-trained Inception model with additional custom hidden layers. The Inception model is trained on a facial dataset of size 1821 which consists of 5 classes. The Siamese network identifies the person by referring to the database of known people. By adding Siamese network, the framework becomes more scalable and adaptable. The testing accuracy of person recognition is 93.75%.
HACScale: Hardware-Aware Compound Scaling for Resource-Efficient DNNs

Model scaling is an effective way to improve the accuracy of deep neural networks (DNNs) by increasing the model capacity. However, existing approaches seldom consider the underlying hardware, causing inefficient utilization of hardware resources and consequently high inference latency. In this paper, we propose HACScale, a hardware-aware model scaling strategy to fully exploit hardware resources for higher accuracy. In HACScale, different dimensions of DNNs are jointly scaled with consideration of their contributions to hardware utilization and accuracy. To improve the efficiency of width scaling, we introduce importance-aware width scaling in HACScale, which computes the importance of each layer to the accuracy and scales each layer accordingly to optimize the trade-off between accuracy and model parameters. Experiments show that HACScale improves the hardware utilization by 1.92× on ImageNet, as a result, it achieves 2.41% accuracy improvement with a negligible latency increase of 0.6%. On CIFAR-10, HACScale improves the accuracy by 2.23% with only 6.5% latency growth.
PredStereo: An Accurate Real-time Stereo Vision System

Stereo vision algorithms are important building blocks of self-driving applications. The two primary requirements of a self-driving vehicle are real-time operation and nearly 100% accuracy in constructing the 3D scene regardless of the weather conditions and the degree of ambient light. Sadly, most real-time systems as of today provide a level of accuracy that is inadequate and this endangers the life of the passengers; consequently, it is necessary to supplement such systems with expensive LiDAR-based sensors. We observe that for a given scene, different stereo matching algorithms can have vastly different accuracies, and among these algorithms there is no clear winner. This makes the case for a hybrid stereo vision system where the best stereo vision algorithm for a stereo image pair is chosen by a predictor dynamically, in real-time.We implement such a system called PredStereo in ASIC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> that combines two diametrically different stereo vision algorithms, CNN-based and traditional, and chooses the best one at runtime. In addition, it associates a confidence with the chosen algorithm, such that the higher-level control system can be switched on in case of a low confidence value. We show that designing a predictor that is explainable and a system that respects soft real-time constraints is non-trivial. Hence, we propose a variety of hardware optimizations that enable our system to work in real-time. Overall, PredStereo improves the disparity estimation error over a state-of-the-art CNN-based stereo vision system by up to 18% (on average 6.25%) with a negligible area overhead (0.003mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) while respecting real-time constraints.
SYNTHNET: A High-throughput yet Energy-efficient Combinational Logic Neural Network

In combinational logic neural networks (CLNNs), neurons are realized as combinational logic circuits or look-up tables (LUTs). They make make extremely low-latency inference possible by performing the computation with pure hardware without loading weights from the memory. The high throughput, however, is powered by massively parallel logic circuits or LUTs and hence comes with high area occupancy and high energy consumption. We present SYNTHNET, a novel CLNN design method that effectively identifies and keeps only the sublogics that play a critical role in the accuracy and remove those which do not contribute to improving the accuracy. It captures the abundant redundancy in NNs that can be exploited only in CLNNs, and thereby dramatically reduces the energy consumption of CLNNs with minimal accuracy degradation. We prove the efficacy of SYNTHNET on the CIFAR-10 dataset, maintaining a competitive accuracy while successfully replacing layers of a VGG-style network which traditionally uses memory-based floating point operations with combinational logic. Experimental results suggest our design can reduce energy-consumption of CLNNs more than 90% compared to the state-of-the-art design.
Internet of Medical Things: A CT Image Denoising in Tetrolet Domain

The Internet of Medical Things (IoMT) is a huge community of linked medical equipment and technologies that communicate with a large number of Servers over the internet to provide a variety of services such as medical image denoising. Projection-based reconstructed images in computed tomography (CT) are noisy due to thermal noise and electrical noise, both of which are approximately AWGN in magnitude. Because of the variety of clinical features and textures included in CT reconstructed images, denoising is a difficult process. It is possible to estimate the true noise of a single image, and then to minimize both the real noise and the additional noise by reconstructing CT images. A unique modified approach is proposed in this work, in which the noise will be calculated actual as well as added, followed by a proposed method for denoising, which is based on thresholding in tetrolet domain. Different denoising schemes are used in the experiments, and the results are compared to one another. Experimentation has revealed that both in terms of PSNR and visual quality, the suggested technique yields promising results that are favorable to both.
Generative Adversarial Attack on Ensemble Clustering

Adversarial attack on learning tasks has attracted substantial attention in recent years; however, most existing works focus on supervised learning. Recently, research has shown that unsupervised learning, such as clustering, tends to be vulnerable due to adversarial attack. In this paper, we focus on a clustering algorithm widely used in the real-world environment, namely, ensemble clustering (EC). EC algorithms usually leverage basic partition (BP) and ensemble techniques to improve the clustering performance collaboratively. Each BP may stem from one trial of clustering, feature segment, or part of data stored on the cloud. We have observed that the attack tends to be less perceivable when only a few BPs are compromised. To explore plausible attack strategies, we propose a novel generative adversarial attack (GA2) model for EC, titled GA2EC. First, we show that not all BPs are equally important, and some of them are more vulnerable under adversarial attack. Second, we develop a generative adversarial model to mimic the attack on EC. In particular, the generative model will simulate behaviors of both clean BPs and perturbed key BPs, and their derived graphs, and thus can launch effective attacks with less attention. We have conducted extensive experiments on eleven clustering benchmarks and have demonstrated that our approach is effective in attacking EC under both transductive and inductive settings.
The Golden Ratio of Area Method Based on Fuzzy Number Area as a Defuzzyfier

This article presents a new method of focusing, which may be implemented in fuzzy controllers. The peculiarity of the proposed method is the use of an universal solution in the form of Golden Ratio and the widely applied method of the centre of gravity. The traditional methods of defuzzying, such as: MOM, FOM, LOM, COG, BADD and many others are used in many fields, however they are not universal solutions. This conclusion enables the author of this paper to present a new solution that we refer to as the Golden Ratio of Area, in abbreviation: GRoA. To verify the efficiency of the GRoA method, the results are compared to existing defuzzying methods. An example of use is the controller process solution of the combustion process, for which only the resulting membership function is presented. To determine the fitness of this method, the measure of dispersion was used as standard deviation. The analysis of methods leads to the conclusion that there are cases that significantly deviate from central values and should be eliminated in the control. Another conclusion is that the application of a pattern other than central value will enable a surer selection of defuzzying methods. The analysis shows that the GRoA method works properly and may be used in fuzzy controller. In the last section, the author presents further propositions of research, for example ordered fuzzy numbers (OFN).
Multiple Suboptimal Policies Integrated Reinforcement Learning Algorithm for Path Planning

Reinforcement learning performs well in path planning tasks with unknown environments, where the agent relies on its own exploration capabilities to gather environment information and find the optimal path. However, in sparse reward tasks, the difficulty rises exponentially in obtaining external rewards and collecting available state-action pairs. The agent's policy may not converge by random exploration. In this paper, we propose a multiple suboptimal policies integrated reinforcement learning algorithm for path planning, which uses a reward shaping algorithm to integrate the best parts of different suboptimal policies into intrinsic reward and to assist the agent in finding the near-optimal path. On a sparse reward path planning task in the discrete action space, our method outperforms both the imitation learning method and deep Q-learning from demonstration in terms of convergence speed and policy stability.
Relationship Between Personality Patterns and Harmfulness

This paper hypothesize that harmful utterances need to be judged in context of whole sentences, and extract features of harmful expressions using a general-purpose language model. Based on the extracted features, we propose a method to predict the presence or absence of harmful categories. In addition, the authors believe that it is possible to analyze users who incite others by combining this method with research on analyzing the personality of the speaker from statements on social networking sites. The results confirmed that the proposed method can judge the possibility of harmful comments with higher accuracy than simple dictionary-based models or models using a distributed representations of words. The relationship between personality patterns and harmful expressions was also confirmed by an analysis based on a harmful judgment model.
An IoT Based Intelligent Screening System for Preventing COVID 19 Spread in Premises

The outbreak of COVID-19 led to huge number of security issues and casualties. In order to effectively prevent the spread of COVID-19, wearing mask and contactless thermal scanning has become mandatory. The conventional methods for visitor screening and temperature measurement have become inappropriate in this situation to avoid any contact. The traditional face recognition techniques are also ineffective as wearing mask hides some parts of the face. This paper presents an Intelligent IoT based screening system capable of contactless thermal scanning, mask detection and masked face recognition. The person not following the Covid-19 guidelines will be denied the access inside the premise. The system uses deep-learning based techniques for masked face recognition by discarding masked region. The data collected has been sent to the cloud using custom protocol stack which is lightweight, reliable and efficient for data communication. Using web application and mobile application, the data corresponding to each user can be monitored remotely using internet. The data collected can be shared to healthcare agencies for further analysis.
Zeta revisited

Abstract It has been claimed that traditional interpretations of Zeta based on graphs with bisector lines (Craig and Kinney, 2009, Shakespeare, Computers, and the Mystery of Authorship. Cambridge: Cambridge University Press.) are unsound, that validation with a held-out segments is inadequate, that counting types rather than tokens is suspect, and that the relationship between stronger Zeta scores and shorter word n-grams is an artifact of the method rather than a research finding (Rizvi, 2019a, The interpretation of zeta test results. Digital Scholarship in the Humanities, 34(2): 401–18). All of these claims are unsound. The separation of base and counter segments in a Zeta analysis is in fact an important research finding, the traditional interpretation of Zeta results based on a bisector line remain sound, and validation with a held-out segments is an appropriate method. Zeta’s reliance on consistency rather than frequency is not a bug but valuable feature that provides an important complementary method to those based on frequency. The results of extensive testing on corpora of prose, poetry, and drama show that increasing word n-gram length (but not character n-gram length) is often negatively correlated with classification accuracy. Higher Zeta scores are often correlated with better results, though the results vary a great deal depending on the corpus, the classification method, and the type and length of n-gram. Most importantly, these results show that Zeta gives strong results that are competitive with methods like Cosine Delta and Support Vector Machine on these classification tasks.
Secure Online Examination System on Azure Cloud with Load Balancer

There are certain disadvantages of traditional web hosting. It is more expensive as in some cases the setup costs exceed $100. Also, while using standard shared hosting, there&#x0027;s a chance that the site may be taken down if another website on the server becomes infected with a virus or malware. Scalability is not that efficient. To solve these problems cloud hosting is the only solution. Hosting the applications on cloud has become widely popular these days. But with increased access to internet and being online, a large section of people need to work with the same application atthe same time. With many requests from end users, most of the times servers crash and create inconvenience to the users. Also, there is no security for the applications on shared cloud. The aim of our project is to resolve the problem of server crashing and ensuring proper security to the application hosted on the public cloud. This has been ensured by the creation of two virtualmachines which act as servers and the configuration of the load-balancer which reduces the load on these servers by diverting the requests equally to both of them after checking their health. Also, the load-balancer acts as a reverse proxy and hides the backend servers IP address from external hackers thus ensuring security.
New Level-Set-Based Shape Recovery Method and its application to sparse-view shape tomography

The recovery of shapes from a few numbers of their projections is very important in Computed tomography. In this paper, we propose a novel scheme based on a collocation set of Gaussian functions to represent any object by using a limited number of projections. This approach provides a continuous representation of both the implicit function and its zero level set. We show that the appropriate choice of a basis function to represent the parametric level-set leads to an optimization problem with a modest number of parameters, which exceeds many difficulties with traditional level set methods, such as regularization, re-initialization, and use of signed distance function. For the purposes of this paper, we used a dictionary of Gaussian function to provide flexibility in the representation of shapes with few terms as a basis function located at lattice points to parameterize the level set function. We propose a convex program to recover the dictionary coefficients successfully so it works stably with only four projections by overcoming the issue of local-minimum of the cost function. Finally, the performance of the proposed approach in three examples of inverse problems shows that our method compares favorably to Sparse Shape Composition (SSC), Total Variation, and Dual Problem.