Reviewerly/exHarmony huggingface dataset:
Brain MRI Segmentation Using a Fast Fuzzy c-means Algorithm

In this paper, we introduced a fast version of Fuzzy C-Means (FCM) algorithm for the task of brain MRI segmentation. In the proposed algorithm FaFCM (Fast Fuzzy c means), the calculation of the membership matrix and the cluster centers depend on histogram bins instead of individual pixels values to decrease execution time of standard FCM. To evaluate the performance of the proposed algorithm, FaFCM has been tested over a set of MR brain images corrupted by different levels of noise and intensity non-uniformity. The experimental results proved that the proposed algorithm FaFCM converges much faster than the conventional FCM.
A Competent Intelligent Key Cryptography (IKC) Architecture

The human race is becoming too intelligent to create a robotic system to mimic the human actions. On the other hand, the same innovation has led to various negative aspects like theft, cybercrime and plenty of unseen malfunctions. In this digital world, it has become mandatory that everyone in some way or other communicate sensitive information via Internet. Though many security mechanisms are available, there is always a threat in its implementation. The development of fast performing quantum computers even though a boon to robotics and computing systems it has become a nightmare in the field of cryptography. The mathematical toughness in the modern cryptosystems has become very simple to solve by using quantum computers. This made NIST to solicit proposals for post-quantum cryptography, which should include standards to be used as quantum resistant counterparts to the existing standards. In this paper, a competent Intelligent Key Cryptography (IKC) system is proposed, highlighting that, there is no need to exchange the keys either by public or private means among the users but it resides in the transmitting message itself. IKC is a tamil based random key generator, which is a promising challenge to change the current key based crypto system. Furthermore, a performance analysis could be done as a future task.
Study of professional and emotional burnout in foreign psychological-pedagogical theory and practice

One of the acute problems in modern psychological and pedagogical theory and practice is the problem of emotional burnout of pedagogical workers In the process of studying emotional burnout, we first of all encounter with such general methodological problems, such as: the need to take into account all the main factors that are important for the emergence and formation of emotional burnout in a specialist, with the fact of variability of the main symptoms of emotional burnout at different stages of its formation (changes in thinking, behavior, feelings and health); taking into account probable moments in the development and formation of emotional burnout, etc. In our opinion, the following approaches can be solved by solving common methodological problems: interdisciplinary, systemic, empirical, personal-social-activity, situational. The article uses a complex of theoretical and empirical methods of analysis, systemization and generalization. Scientific understanding of foreign experience in studying the phenomenon of burnout, allowed to determine the degree of negativity of long-term professional stress, emotionally charged conditions of concert and stage activities and a large number of unforeseen situations of artistic and pedagogical interaction as a determinant of psychophysical burnout, emotional and intellectual I am a specialist. Thus, the approaches analysed in this article to the study of “emotional burnout” show that burnout manifests itself in various spheres of personality (cognitive, motivational, human rights to work), and there is a connection between burnout and exacerbation in all these areas, it seems to us important. Further research requires the development of technologies to overcome the syndrome identified by Ukrainian scientists.
“I want more than ” User-generated Icons for Better Video-mediated Communications on the Collaborative Design Process

The COVID-19 pandemic has led to the proliferation of non-face-to-face video-mediated communication such as through ZOOM or Google Meet. However, video-mediated communication has several limitations related to exchanging vocal reactions and non-verbal expressions. Consequently, although current video conferencing platforms provide visual support through icons, it remains challenging for users to express various intentions because of the limited number of icons, their uniform size, and their fixed location. In particular, these limitations challenge designers who require collaborative design processes such as brainstorming. To investigate user needs related to icons that better support video-mediated communication, we conducted a participatory design methodology. Based on the analyses of participants' brainstorming experiences with various icons through participatory paper prototyping, we found that icons that accurately reflect diverse user needs facilitated turn-taking during the design process, and allowed participants to exchange more opinions and emotions. Thus, they created a positive atmosphere in the online meeting environment.
High school math text similarity studies based on CNN and BiLSTM

Text similarity has been widely studied and applied in information retrieval, question answering system and so on. However, in the specific field of mathematics, there is little research at present. In this paper, aiming at the characteristics of high school mathematics text, such as strong logic, formula doping, including letters and numbers, a symmetrical twin network structure model combining CNN and BiLSTM is proposed. The local mathematical text feature matrix extracted by CNN and the global text feature matrix extracted by BiLSTM are modeled at different levels to obtain the deep level language features of sentences. Then the two similarity matrixes are fused and spliced to maximize the mathematical text features. Finally, the text similarity value is calculated by the matching layer, and then the answer score is obtained. The accuracy of the model is 84.3% when tested on real mathematics test questions. The experimental results show the effectiveness of the model.
Error-Driven Pruning of Language Models for Virtual Assistants

Language models (LMs) for virtual assistants (VAs) are typically trained on large amounts of data, resulting in prohibitively large models which require excessive memory and/or cannot be used to serve user requests in real-time. Entropy pruning results in smaller models but with significant degradation of effectiveness in the tail of the user request distribution. We customize entropy pruning by allowing for a keep list of infrequent n-grams that require a more relaxed pruning threshold, and propose three methods to construct the keep list. Each method has its own advantages and disadvantages with respect to LM size, ASR accuracy and cost of constructing the keep list. Our best LM gives 8% average Word Error Rate (WER) reduction on a targeted test set, but is 3 times larger than the baseline. We also propose discriminative methods to reduce the size of the LM while retaining the majority of the WER gains achieved by the largest LM.
Dual-Mode iterative denoiser: Tackling the weak label for anomaly detection

Crowd anomaly detection suffers from limited training data under weak supervision. In this paper, we propose a dual-mode iterative denoiser to tackle the weak label challenge for anomaly detection. First, we use a convolution autoencoder (CAE) in image space to act as a cluster for grouping similar video clips, where the spatial-temporal similarity helps the cluster metric to represent the reconstruction error. Then we use the graph convolution neural network (GCN) to explore the temporal correlation and the feature similarity between video clips within different rough labels, where the classifier can be constantly updated in the label denoising process. Without specific image-level labels, our model can predict the clip-level anomaly probabilities for videos. Extensive experiment results on two public datasets show that our approach performs favorably against the state-of-the-art methods.
Artifact: Reducing DNN Properties to Enable Falsification with Adversarial Attacks

We present an artifact to accompany Reducing DNN Properties to Enable Falsification with Adversarial Attacks which includes the DNNF tool, data and scripts to facilitate the replication of its study. The artifact is both reusable and available. DNNF is available on Github, and we provide an artifact to reproduce our study as a VirtualBox virtual machine image. Full replication of the study requires 64GB of memory and 8 CPU cores. Users should know how to use VirtualBox, as well as have basic knowledge of the bash shell.
Dependency parsing with bottom-up Hierarchical Pointer Networks

Dependency parsing is a crucial step towards deep language understanding and, therefore, widely demanded by numerous Natural Language Processing applications. In particular, left-to-right and top-down transition-based algorithms that rely on Pointer Networks are among the most accurate approaches for performing dependency parsing. Additionally, it has been observed for the top-down algorithm that Pointer Networks’ sequential decoding can be improved by implementing a hierarchical variant, more adequate to model dependency structures. Considering all this, we develop a bottom-up oriented Hierarchical Pointer Network for the left-to-right parser and propose two novel transition-based alternatives: an approach that parses a sentence in right-to-left order and a variant that does so from the outside in. We empirically test the proposed neural architecture with the different algorithms on a wide variety of languages, outperforming the original approach in practically all of them and setting new state-of-the-art results on the English and Chinese Penn Treebanks for non-contextualized and BERT-based embeddings.
Traditional Indian Education Values and New National Education Policy Adopted by India

Ancient Indian education was value-based consisting of Gurukul system and world-famous “Nalanda and Takshashila Universities.” The value-based education policy reforms were the basis for utilizing the knowledge for benefits of mankind. In ancient Bharat, there were knowledgeable personalities like Sushrut, Aryabhatta, Panini, and Chanakya who set examples in front of the world. The national long-term impact is its shift from 10+2 pattern to 5+3+3+3+4. This decision will bring a major change in Indian education system after three decades. The new National Education Policy is promoting Indian value-based education with all-inclusive ecodevelopment of Holistic Education, Bharat-centric Education, Development of Knowledge-based Society, and Emphasis on Knowledge-based Education.
Crypto-Oriented Neural Architecture Design

Sending private data to Neural Network applications raises many privacy concerns. The cryptography community developed a variety of secure computation methods to address such privacy issues. As generic techniques for secure computation are typically prohibitively expensive, efforts focus on optimizing these cryptographic tools. Differently, we propose to optimize the design of crypto-oriented neural architectures, introducing a novel Partial Activation layer. The proposed layer is much faster for secure computation as it contains fewer non linear computations. Evaluating our method on three state-of-the-art architectures (SqueezeNet, ShuffleNetV2, and MobileNetV2) demonstrates significant improvement to the efficiency of secure inference on common evaluation metrics.
DEEPTALK: Vocal Style Encoding for Speaker Recognition and Speech Synthesis

Automatic speaker recognition algorithms typically characterize speech audio using short-term spectral features that encode the physiological and anatomical aspects of speech production. Such algorithms do not fully capitalize on speaker-dependent characteristics present in behavioral speech features. In this work, we propose a prosody encoding network called DeepTalk for extracting vocal style features directly from raw audio data. The DeepTalk method outperforms several state-of-the-art speaker recognition systems across multiple challenging datasets. The speaker recognition performance is further improved by combining DeepTalk with a state-of-the-art physiological speech feature-based speaker recognition system. We also integrate DeepTalk into a current state-of-the-art speech synthesizer to generate synthetic speech. A detailed analysis of the synthetic speech shows that the DeepTalk captures F0 contours essential for vocal style modeling. Furthermore, DeepTalk-based synthetic speech is shown to be almost indistinguishable from real speech in the context of speaker recognition.
How Statically-Typed Functional Programmers Author Code

How working statically-typed functional programmers author code is largely understudied. And yet, a better understanding of developer practices could pave the way for the design of more useful and usable tooling, more ergonomic languages, and more effective on-ramps into programming communities. The goal of this work is to address this knowledge gap: to better understand the high-level authoring patterns that statically-typed functional programmers engage in. I did a grounded theory analysis of thirteen programming sessions of practicing functional programmers, eight of which also included a semi-structured interview. The theory I developed gives insight into how the specific affordances of statically-typed functional programming affect domain modeling, type construction, focusing techniques, exploratory strategies, mental models, and expressions of intent. The success of this approach in revealing program authorship patterns suggests that the same methodology could be used to study other understudied programmer audiences.
Stabilized Calculation of Gaussian Smoothing and Its Differentials Using Attenuated Sliding Fourier Transform

Gaussian smoothing and its first and second differentials are very important for image processing and computer vision. We already have methods based on the sliding Fourier transform (SFT) in order to calculate Gaussian smoothing efficiently. However, errors in floating point calculation accumulate quickly when we use the single-precision floating-point format. Also, the resultant discontinuities at the edges of SFT intervals generate false extreme points easily. To resolve the abovementioned problems, we propose the attenuated SFT (ASFT) by introducing a decay factor to the original SFT together with a new criterion for determining coefficients to effectively approximate Gaussian function and its differentials. Extensive experiments demonstrate a decisive superiority of the proposed ASFT over the original SFT.
Is Agile Not Agile Enough? A Study on How Agile is Applied and Misapplied in the Video Game Development Industry

Video games have become the most dominant and successful entertainment industry worldwide; however, many video game development (VGD) projects and studios struggle to succeed. At present, there are no commonly accepted VGD best practices or frameworks that can bring together the complex and competing needs of software engineering and creative production. Although studios are reportedly using agile frameworks, the actual extent of application and effectiveness of agile practices in the VGD context is unclear. Therefore, the aim of this study is to empirically determine how and why agile frameworks are applied in VGD. Semi-structured interviews were conducted with eight New Zealand VGD studios. It was found that the agile frameworks, Scrum and Kanban, must often be adapted from their conventional use to meet the needs of different pipelines and delivery milestones within the phases of VGD. However, it seems that not all the needs of VGD can be met by current agile frameworks. Furthermore, inexperience with agile practices often leads to misunderstanding and misimplementing them in ways that seem to contribute to commonly experienced collaboration challenges.
TGCRBNW: A Dataset for Runner Bib Number Detection (and Recognition) in the Wild

Racing bib number (RBN) detection and recognition is a specific problem related to text recognition in natural scenes. In this paper, we present a novel dataset created after registering participants in a real ultrarunning competition which comprises a wide range of acquisition conditions in five different recording points, including nightlight and daylight. The dataset contains more than 3K samples of over 400 different individuals. The aim is to provide an “in the wild” benchmark for both RBN detection and recognition problems. To illustrate the present difficulties, the dataset is evaluated for RBN detection using different Faster R-CNN specific detection models, filtering its output with heuristics based on body detection to improve the overall detection performance. Initial results are promising, but there is still significant room for improvement. And detection is just the first step to accomplish “in the wild” RBN recognition.
Stereo Rectification Based on Epipolar Constrained Neural Network

This paper proposes a novel deep neural network-based method for stereo image rectification. The neural network is mainly based on the theoretical basis of epipolar constraints from multi-view geometry and intensity constraints of images, which separately describes the relationship of the corresponding epipolar lines between a pair of image, including the epipolar-line slope and y-intercept consistency of the epipolar lines and the consistency of the corresponding intensity values between two images. Benefiting from the designed rectification framework together with a feature matching module to extract accurate corresponding key-points between views, our method is able to realize a stable and accurate stereo rectification process. Compared with classic feature-based rectification methods, our proposed method can rectify small errors, and achieve a much more accurate rectification performance. Experiments conducted on synthetic face dataset and real-world KITTI dataset demonstrate the effectiveness and robustness of the proposed method.
Messaging and Video Calling Application for Specially Abled people using Hand Gesture Recognition

According to the World Health Organization (WHO) over 5% of the world's population have impaired hearing and speaking loss. It is very difficult for people with disabilities to communicate. This paper proposes a system to overcome these barriers and allow everyone to interact with each other irrespective of their disabilities and facilitate everyone to communicate with each other through messaging and video calling irrespective of their disabilities. This paper proposes a vision based application which can be used for the communication of such people using text and video calling. For better accuracy various object detection and image classification algorithms are implemented. The application uses Indian Sign Language as the dataset.
Sine Cosine Algorithm with Centroid Opposition-Based Computation

Sine cosine algorithm (SCA), a population-based optimization algorithm, is recently developed to solve optimization problems. In SCA, mathematical functions sine and cosine are utilized to fluctuate the candidate solutions towards or outwards the best solution. SCA gets trapped in local optima and suffers from premature convergence for some problems due to a lack of exploration of the search space. In this paper, SCA is improved by incorporating an opposition-based learning (OBL) scheme called centroid opposition-based computing (COBC) in it to enhance its exploration ability. The proposed algorithm is termed as COBSCA in this paper. It is applied to solve 28 CEC2013 benchmark problems. The results of COBSCA are compared with SCA and opposition-based SCA (OBSCA). The experimental results demonstrate that the COBSCA statistically outperforms others in solving most of the problems in the CEC2013 benchmark suite.
Multipartite entanglement dynamics and transfer in double and triple Tavis–Cummings models

In this paper, we have investigated the dynamics and transfer of multipartite entanglement via concurrence and three-tangle in a double Tavis-Cummings Model (TCM) and a triple TCM. The results are compared with those of the double Jaynes-Cummings Model (JCM) and the triple JCM. We find that the dynamics and transfer of multipartite entanglement are dependent only on the initial entanglement type itself but not on model type for the double TCM and the triple TCM. For the triple TCM and the triple JCM we find that an entanglement of different type from the initial entanglement is created. Our work will further enrich the previous results for the JCM and the TCM.
Recommender Systems for E-Learning

The recommender systems are really important in this phase because the users want to be concentrated and to be focused on the domain in which they are interested. There should be minimal deviation in the topics suggested by the recommendation engines. Some of the famous e-learning platforms suggest recommendations based on tags such as highest rated, bestsellers, and so on in various domains. This ultimately makes the users deviate from the domain in which they have to master, and it results in not satisfying the user needs. So, to address this problem, effective recommendation engines will help provide recommendations according to the users by implementing the machine learning techniques such as collaborative filtering and content-based techniques. In this chapter, the authors discuss the recommendation systems, types of recommendation systems, and challenges.
Ensemble Distillation Approaches for Grammatical Error Correction

Ensemble approaches are commonly used techniques to improving a system by combining multiple model predictions. Additionally these schemes allow the uncertainty, as well as the source of the uncertainty, to be derived for the prediction. Unfortunately these benefits come at a computational and memory cost. To address this problem ensemble distillation (EnD) and more recently ensemble distribution distillation (EnDD) have been proposed that compress the ensemble into a single model, representing either the ensemble average prediction or prediction distribution respectively. This paper examines the application of both these distillation approaches to a sequence prediction task, grammatical error correction (GEC). This is an important application area for language learning tasks as it can yield highly useful feedback to the learner. It is, however, more challenging than the standard tasks investigated for distillation as the prediction of any grammatical correction to a word will be highly dependent on both the input sequence and the generated output history for the word. The performance of both EnD and EnDD are evaluated on both publicly available GEC tasks as well as a spoken language task.
Show and Speak: Directly Synthesize Spoken Description of Images

This paper proposes a new model, referred to as the show and speak (SAS) model that, for the first time, is able to directly synthesize spoken descriptions of images, bypassing the need for any text or phonemes. The basic structure of SAS is an encoder-decoder architecture that takes an image as input and predicts the spectrogram of speech that describes this image. The final speech audio is obtained from the predicted spectrogram via WaveNet. Extensive experiments on the public benchmark database Flickr8k demonstrate that the proposed SAS is able to synthesize natural spoken descriptions for images, indicating that synthesizing spoken descriptions for images while bypassing text and phonemes is feasible.