Reviewerly/exHarmony huggingface dataset:
Adaptive Digital Image Compression

The article is devoted to the transformation of digital images based on their adaptive compression during processing and transmission in real time. To meet the requirements for the efficiency of processing and transmission of digital images when implementing compression methods, it is proposed to carry out an orthogonal transformation of the original image. A technique is proposed for compressing digital images in real time based on their adaptive compression, based on taking into account the sensitivity of the basic functions of the Haar transform. The development and application of the method of adaptive compression of digital images will ensure the fulfillment of the requirements for high-quality processing and transmission of digital video information in real time.
ITL: An Isolation-Tree-Based Learning of Features for Anomaly Detection in Networked Systems

With the advances in monitoring techniques and storage capability in the cloud, a high volume of valuable monitoring data is available. The collected data can be used for profiling applications behavior and detecting anomalous events that identify unexpected problems in the normal functioning of the system. However, the fast-changing environment of the cloud brings a need for fast and efficient analytic solutions to monitor the cloud system for its correct operational behavior. The isolation-based method is an effective approach for detecting anomalies. This method randomly samples the data and builds several isolation-trees (iTrees) data structures to find anomalous records. However, a common challenge of iTrees as well as other anomaly detection algorithms is dealing with high-dimensional data that can impact the accuracy and execution time of the process. This is an important issue for cloud-hosted applications where a variety of problems are constantly changing the normal pattern of features from low-level network data to high-level application performance. Therefore, refining the feature space for the removal of irrelevant attributes is a critical issue. In this chapter, we introduce an iterative iTree based Learning (ITL) algorithm to handle high-dimensional data. ITL takes the advantage of iTree structure to learn relevant features for detecting anomalies. Initially, it builds iTrees making use of all features of the data. Then, in the iterative steps, it refines the set of the features to find the most relevant ones by selecting highly ranked anomalies discovered in the previous iteration. Experiments are conducted to validate the performance of our proposed ITL method on several benchmark datasets. The results show that ITL can achieve significant speedups with the appropriate choice of the number of iTrees while achieving or exceeding the area under the curve (AUC) values of other state-of-the-art isolation-based anomaly detection methods.
Epistemic Proceduralism Stated

This chapter formulates the conceptual background of the rest of the book by introducing some of the basic formal machinery, providing a first iteration of the articulation of the core ideas of epistemic proceduralism, and outlining the overall argumentative plan that the rest of the book follows. A basic model of epistemic dynamics is spelled out, using the familiar labeled transition systems of the logical dynamics framework. From there, the content of epistemic rationality norms is regimented by two constructions. The first construction is the familiar “norm-kernel”, introduced by von Wright and adopted widely since. This construction brings in its train some logical tools, including the deontic operators. The second construction is a novel one. The content of epistemic rationality norms is regimented by means of elementary set-theoretic constructions called “restrictions on epistemic dynamics”. This construction harnesses some of the existing formal machinery for deontic logic and logical dynamics in order to provide an adaptable structure for analyzing epistemic norms and developing the protocol-theoretic logic of epistemic norms. From there, a number of interesting terminological disputes is raised and set aside as orthogonal to the project in the rest of the book.
Epistemic Proceduralism Defended I

Epistemic proceduralism is spelled out by clusters of principles characterizing the structure, function, and logic of procedural epistemic norms, norms that define restrictions on epistemic actions, processes, and procedures. With the content of epistemic norms regimented by means of norm-kernels and restrictions on epistemic dynamics, this chapter articulates and defends clusters of principles, D1 and D2. Roughly, these clusters of principles state that the content of procedural norms is program-like, dictating complex procedural operations on epistemic action, and that this program-like structure entails their irreducibility to static and dynamic norms. One of the core lines of argument in this chapter links the regulative function of epistemic norms to the existence and irreducibility of procedural norms. In the context of the book, the clusters of principles articulating these results stand as theoretical criteria or tests applicable to formal frameworks for representing and reasoning about epistemic norms.
The FASA model

Abstract People usually have the ability to ‘read the room’ and strike the ‘right tone’ in interaction situations. In most cases, they know what is considered (in)appropriate in specific situations. Moreover, even though the understanding of what is and is not socially appropriate in a certain situation varies among interaction participants, human beings have an outstanding ability to coordinate with one another very quickly and effectively repair any ‘breaks’ in communication that may have arisen. Within the FASA model we propose five factors and corresponding criteria that may influence what will be deemed socially appropriate or inappropriate.
An Algorithm for 3D Object Detection Based on Frustum-PSPNet

In this paper, an improved 3D objective detection algorithm called Frustum PSPNet (F-PSPNet) is proposed combing RGB algorithm and point cloud data. First, the 2D region proposals in the RGB image are generated by using the 2D CNN object detector, and each obtained 2D region proposal is lifted to 3D frustum through the camera projection matrix. Next, the points in the frustums are grouped by sliding along the frustum axis with stride. To improve the utilization of feature information, each set of points is sent to the PointNet added attention mechanism to extract point-wise features. Then, F-PSPNet arrays these features as a feature map for use of its subsequent component of the Pyramid Scene Parsing Network (PSPNet). Finally, 3D object detection and class confidence are done in the proposed F-PSPNet. Experiments on KITTI dataset show that the detection accuracy of F-PSPNet has improved compared with other models, especially in pedestrian detection.
Machine Learning-Assisted Entanglement Measurement in Quantum Many-Body Systems

Measuring entanglement in quantum many-body systems is a challenging task, especially for mixed states. All known computable entanglement measures depend non-linearly on the state, so the standard approach to quantify entanglement would require a complete state tomography, a highly demanding and not scalable experimental procedure, followed by complex manipulations of matrices whose dimension increases exponentially in the number of qubits. Here, we review a different method, first proposed in Gray et al. (Phys Rev Lett 121(15):150503, 2018) and then expanded in Elben et al. (Phys Rev Lett 125(20):200501, 2020); Zhou et al. (Phys Rev Lett 125(20):200502, 2020), based on the reconstruction of the partially transposed moments of a quantum many-body mixed state. The experimental procedure for such reconstructions scales linearly in the number of qubits and has already been demonstrated in different physical setups, such as quantum dot arrays and cold atoms in optical lattice. The method first proposed in Gray et al. (Phys Rev Lett 121(15):150503, 2018) is based on the observation that the mapping between partially transposed moments to the logarithmic negativity is basically smooth, so that it can be learnt by machine learning interpolation methods, e.g. based on neural networks. More precisely, a neural network model trained using just measurement outcomes from random quantum states, with no knowledge of the underlying physical systems, is able to accurately estimate the logarithmic negativity for different states, e.g. ground states of physical Hamiltonians or states appearing in the quench dynamics of quantum many-body systems.
A Cascade Matching Algorithm for Multi-object Tracking Systems Based on Detection Box Scores

Tracking by detection has become the mainstream paradigm of current multi-object tracking. A threshold is set to filter out low-score detection boxes in order to reduce the impact of background false detections. However, these low-score detection boxes may contain supplementary information useful for similarity computation and target matching. This paper proposes a cascade matching algorithm based on detection box scores, which makes full use of the position information, motion information and appearance information of detection boxes. Experimental results show that the proposed matching algorithm can effectively maintain identities and achieve continuous tracking in the case of ambient light interference, motion blur and partial occlusion.
Identification of Subtype Blood Cells Using Deep Learning Techniques

The deep learning mechanism has indicated power in numerous applications and is recognized as a superior technique by an ever growing number of people than the conventional models of machine learning. In particular, the use of deep learning algorithms, particularly convolutional neural networks (CNN), brings immense benefits to the clinical sector, where an immense amount of images must be prepared and analyzed. A CNN-based framework is generated to automatically classify the images of blood cells into subtypes of cells. This chapter suggested the deep learning models, which are the convolutional neural network, the deep convolutional neural network, and a CNN-based model built in combination with the recurrent neural network (RNN), which is called RCNN, to identify the monocytes, lymphocytes, and types of WBCs. These are monocytes, eosinophils, lymphocytes, basophils, and neutrophils.
Unmasking the Masked

Today, COVID-19 is one of the most severe issues that people are grappling with. Half of the faces are hidden by the mask in this instance. The region around the eyes is usually the sole apparent attribute that can be used as a biometric in these circumstances. In the event of a pandemic, the three primary biometric modalities (facial, fingerprint, and iris), which commonly enable these tasks, confront particular obstacles. One option that can improve accuracy, ease-of-use, and safety is periocular recognition. Several periocular biometric detection methods have been developed previously. As a result, periocular recognition remains a difficult task. To overcome the problem, several algorithms based on CNN have been implemented. This chapter investigated the periocular region recognitions algorithms, datasets, and texture descriptors. This chapter also discuss the current COVID-19 situation to unmask the masked faces in particular.
AI vs. Human

Companies are using automated technologies customer support to elevate customer experiences. However, the question arises as to whether this strategic move is beneficial for companies. Though several studies have demonstrated the use of anthropomorphic chatbots could positively impact customers, studies still lack comparing this benefit with real-time human interaction. Thus, this study aims to examine user experience between AI and human-based chat support and understand whether AI-based chatbots necessitate replacing human interaction in customer support. The results of the study indicated that perceived benefit and relevance among human-based chats are relatively high, and perceived risk was only limited to delayed response and negative attitude. However, chatbots evidenced their perceived risk was high in anthropomorphic chatbots. Risk factors such as ethical concerns, lack of human touch were commonly seen in anthropomorphic chatbots. On the other hand. their perceived benefit and relevance were seen in chatbots acting as substitute of search queries, web navigation. or product demonstration.
A Study and Analysis of Deep Neural Networks for Cancer Using Histopathology Images

Due to the complexity of histopathology tissues, an accurate classification and segmentation of cancer diagnosis is a challenging task in computer vision. The nuclei segmentation of microscopic images is a key prerequisite for cancerous pathological image analysis. However, an accurate nuclei segmentation is a long running major challenge due to the enormous color variability of staining, nuclei shapes, sizes, and clustering of overlapping cells. To address these challenges and early diagnosis as well as reduce the bias decisions of expert lab technician of cancer in clinical practice, the authors study the classification of computer-aided frameworks and automatic nuclei segmentation frameworks based on histopathology images by convolutional deep learning. The authors have used a publicly available PatchCamelyon and 2018 Data Science Bowl histology image dataset for this study. The results are compared and expected to be useful clinically for technician experts in the analysis of cancer diagnosis and the survival chances of patients.
Survey and Review of Parallel Glossary of Indian Languages

The research paper highlights the survey review of the parallel vocabulary of Indian languages, which are useful for today's education world. The purpose of this research paper is to broaden the natural language resource by storing words of different languages ??and strengthen the three-lingual formula in the education world, preserve extinct languages ??and facilitate machine translation, etc.
Learning aspect-level complementarity for intent-aware complementary recommendation

Complementary recommendation aims to recommend items that are dissimilar but relevant to, and likely purchased together with, the items a user has purchased. Although many efforts have been made, the existing works for complementary recommendation still suffer from two shortcomings. First, the existing works often model the complementarity between items in terms of their co-occurrence patterns, which overlooks the influence of user intent. In fact, the intents of the users even with similar historical behaviors might be different and consequently need different complements. Second, the existing works often encode the complementary relationship at item level. In real world, however, different aspects of an item might contribute different complementarities to the same item, and the complementary information at aspect-level tends to be related with the intents of users. To overcome the two shortcomings, in this paper we propose a novel model called Aspect-level Complementarity Learning for Intent-aware Complementary Recommendation (AICRec). In particular, we propose a User Intent Perceiving (UIP) module, which enables AICRec to differentiate users’ separate intents even though they are in similar scenarios. Meanwhile, we also devise an Aspect-level Complementarity Learning (ACL) module to infer an item’s finer-grained complementarities to a user’s intent at aspect-level, which helps AICRec personalize the recommended complementary items with respect to the user’s intent. At last, extensive experiments conducted on real datasets verify the superiority of AICRec over the state-of-the-art methods for complementary recommendation.
Bounded memory in a changing world: Biases in behaviour and belief

A decision-maker faces a decision problem to choose an action, at a randomly determined time, to match an unknown state of nature. She has access to a sequence of signals partially informative of the current state of nature. The state of nature evolves according to a Markov chain. The decision-maker is subject to constraints on information-processing capacity, modelled here by a finite set of memory states. We characterize when optimal inference is possible with these constraints and, when it is not, what the optimal constrained inference is in two broad classes of environments. In the first class where the signals have similar strengths, optimal inference can be represented by simple rules corresponding to heuristics, like the “recency bias”, which have been studied by experimental researchers. In the second class where one signal is very informative, the constrained optimal rule ignores the possibility of regime changes.
BEHAVIORAL IMPACT OF IMITATING ONLINE GAME SAKURA SCHOOL SIMULATOR

Abstract :This research was conducted against the background of the impact on students playing the online game sakura school simulator on changes in social behavior imitating elementary school students. The purpose of this study was to determine the impact of students playing the online game sakura school simulator on the social behavior of imitating students at Madarasah Ibtidaiyah. This study uses qualitative methods with case study instruments, collecting data from online game players through direct observation, interviews and documentation. Based on the results of the study, it was concluded that playing the online game sakura school simulator had an impact on imitating social behavior in students. Children are very easy to imitate or imitate a model that is found and desired to be imitated. The results of imitating the online game sakura school simulator can be shown directly or in the future. The procedures carried out in the imitation process include (1) the process of paying attention, (2) the process of remembering, (3) motor reproduction, and (4) the reinforcement and motivational process.Keywords: online games, imitating social behavior
Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation

Abstract In order to solve the problem of Bragg equation, a research on retrieval and feature of multimedia testing machine is proposed. Firstly, this paper investigates the various stages of multimedia retrieval system and some mainstream multimedia retrieval algorithms, in order to cover all aspects of relevant algorithms. Secondly, on the basis of investigation, eight representative algorithms are selected. These algorithms cover three stages of multimedia retrieval system, including feature extraction stage, feature matching stage and geometric verification stage. Based on the above observed characteristics, finally, some relevant suggestions on architecture design and system optimization are put forward, including the design of single node processor, the design in parallel environment and the suggestions for simulation and evaluation using this test set.
Comparative Evaluation of Different following Mechanisms in VR Guided Tour: A Preliminary Study

Given virtual reality (VR)’s popularity, VR already impacts various applications such as education and tourism. In the above applications, users usually need to follow a specific target, such as a teacher or a tour guide, to explore and learn from the environment. However, because of the constraint with visual senses and unintuitive locomotion, following a target in VR is not as easy as doing it in real life. As a result, the user may not concentrate on the audio narration or the surroundings. Therefore, finding a following mechanism that can help the user concentrate and learn in VR is important. In this paper, we focus on the following techniques in VR. We propose four types of following mechanisms: limited (which restricts the user’s ability to move), semi-limited (which constrains the user’s range of activity), improved semi-limited (with visual assistance), and not limited (user’s movement is not constrained). This study consisted of 29 participants divided into four groups and aimed to evaluate how the following mechanisms affect user experience and make users more concentrated in the virtual world. The experiment shows that the semi-limited following mechanism with visual assistance is superior to the other three types in the performance of the touring experience and helping users pay attention to the tour content.
Machine Translation: Spanish-to-English Translation System using RNNs

Abstract: Machine translation has undergone a revolution in recent years. Older translation systems developed by linguists utilizing decades of statistical study have been superseded by new systems developed using deep learning. The core idea of a translation system will be a neural network that takes in input and generates the translation of a given sentence. This paper discusses the model which uses intermediate numerical encoding to learn the mapping from one human language to another. Two neural networks connected end-to-end provide the basis of a neural translation system. The first neural network encodes sequences of words in Spanish into an array of numbers with the same meaning. The second neural network learns to translate the numbers back into a string of words that all have the same meaning in English.
Data-Driven Thyroid Nodule Diagnosis Using Belief Rule Base

Doctors' diagnosis preferences are different, which makes them adopt different assumptions in medical decision making. Taking the diagnosis of thyroid nodules as an example, this study compares three assumptions, namely deletion, imputation based on the distribution (distribution), and benign by default (benign). For deletion, which is the most used assumption, the clinical reports with missing features would be deleted. For distribution, the missing features would be replaced with a distribution of features with respective probabilities. Besides the two assumptions, certain doctors have also stated that they leave benign features unrecorded because they think that such benign features are irrelevant to the final diagnosis. Under the benign assumption, the missing features would be replaced with benign features. The three assumptions are tested comparatively. Moreover, the belief rule base (BRB) is used to construct the diagnostic model under the three assumptions since it is essentially a white-box approach that can provide good interpretability and direct access to doctors and patients. A total of 3766 clinical reports on thyroid nodule diagnosis were collected from ten radiologists over a seven-year period. Case study results validate that the benign by default assumption has produced the optimal results, although different doctors could present varied tendencies towards different assumptions. Guidance and suggestions for doctors' practical work have been made based on the study results to improve work efficiency and diagnostic accuracy.