Reviewerly/exHarmony huggingface dataset:
Natural Statistics Of Network Activations And Implications For Knowledge Distillation

In a matter that is analogous to the study of natural image statistics, we study the natural statistics of the deep neural network activations at various layers. As we show, these statistics, similar to image statistics, follow a power law. We also show, both analytically and empirically, that with depth the exponent of this power law increases at a linear rate.As a direct implication of our discoveries, we present a method for performing Knowledge Distillation (KD). While classical KD methods consider the logits of the teacher network, more recent methods obtain a leap in performance by considering the activation maps. This, however, uses metrics that are suitable for comparing images. We propose to employ two additional loss terms that are based on the spectral properties of the intermediate activation maps. The proposed method obtains state of the art results on multiple image recognition KD benchmarks.
The Bibliometric Analysis of Economics and Business Research Trends in Vietnam

The study applies the bibliometrics method to analyze 5,389 publications of research institutions in Vietnam for the economic and business field in the Scopus database. The results of the study provide an overview of publishing trends in the business and economic field in Vietnam such as the most cited articles, the network of these publications, the most productive authors, the most influential journals, the keywords co-occurrence network and the research cooperation between Vietnam and other countries. The purpose of this study is to provide a comprehensive picture of the current state in the field of economics and business research in order to suggest future research directions.
Goal-driven active learning

Abstract Deep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.
Learning Task Constraints in Visual-Action Planning from Demonstrations

Visual planning approaches have shown great success for decision making tasks with no explicit model of the state space. Learning a suitable representation and constructing a latent space where planning can be performed allows non-experts to setup and plan motions by just providing images. However, learned latent spaces are usually not semantically-interpretable, and thus it is difficult to integrate task constraints. We propose a novel framework to determine whether plans satisfy constraints given demonstrations of policies that satisfy or violate the constraints. The demonstrations are realizations of Linear Temporal Logic formulas which are employed to train Long Short-Term Memory (LSTM) networks directly in the latent space representation. We demonstrate that our architecture enables designers to easily specify, compose and integrate task constraints and achieves high performance in terms of accuracy. Furthermore, this visual planning framework enables human interaction, coping the environment changes that a human worker may involve. We show the flexibility of the method on a box pushing task in a simulated warehouse setting with different task constraints.
Expert System For Diagnosing Diseases In Children Under Five Uses Certainty Factor Based Websites

Immunity in children under five is not as good and perfect as adult immunity. Therefore, children under five are susceptible to disease and easily contracted by adults. Lack of knowledge about the symptoms of a disease that attacks children under five can aggravate the situation and result in delays in treatment by medical personnel. Therefore, this expert system is made to make it easier for parents to recognize the symptoms of diseases that attack their toddlers. With this expert system, parents can find out the early symptoms of the disease that attacks their child. In making this expert system using a certainty factor method or a method that defines the measure of certainty of facts or rules to describe an expert's belief in the problem at hand. This expert system will diagnose the symptoms that the patient has previously selected. Then from the choice of these symptoms will be obtained using the value of expert confidence that has been stored in the knowledge. The results of this study were to build an expert system for diagnosing diseases in children under five using a certainty factor method based on a website. With this website, we can take advantage of today's technology to diagnose diseases in children under five. This website can make it easier for parents to consult because they can use it anywhere, effectively and quickly.
Extended Reality technologies as a tool for managing crises and shaping tourism safety perceptions

New technologies are considered by different industries as a useful tool for having efficient emergency and crisis management. For tourism industry in particular (that involves and interfaces with multiple other industries), it is critically important to act proactively in a risk situation, to effectively face a disaster, and to reduce the impact of a crisis. This chapter provides an overview of the Extended Reality (XR) technologies (Augmented Reality [AR]; Virtual Reality [VR]; Mixed Reality [MR]). It discusses opportunities of using XR in tourism, and it provides contemporary examples of XR applications. It also focuses on emergency management via XR in tourism. Finally, it provides specific recommendations for XR use before, during, and after a crisis in order to better prepare for, manage and recover from emergencies and crisis.
Prevent Cybercrime By Using Data Analytics Model

The term Cybercrime refers to claimed wrongdoing and federal offences involving computers, communications, aims, and instruments, as well as to correspond with the widespread use of latest technology. Pornography, cyber-bullying, identity theft, cyber fraud, credit-and debit-card theft, cyber crime, privacy violations, graphic violence, malware, and other cyber hacking may be the common types of cyber-crime. In order to achieve this we are using four phases: In the first phase, For the investigation of cybercrime, we offer a data-analysis approach.; the second phase, we determine the CaaS; the third phase, we use an appropriate classification model; and in forth phase, we will create a sample application to demonstrate how to use the proposed classification, the structure of the model can be applied in practice. This article contributes to the design of artifact, as well as the ideas and methodologies utilized in the field, by using a design science research approach. They also provide useful information on the practice, such as advice on how governments and businesses of all sizes should prepare for cybercrime assaults on the dark web.
Demonstrating Dothraki: Tracking Tangibles Atop Tabletops Through De-Bruijn Tori

We demonstrate usage examples and technical properties of Dothraki, an inside-out tracking technique for tangibles on flat surfaces. An optical mouse sensor embedded in the tangible captures a small (36×36 pixel / 1×1 mm), unique section of a black-and-white De-Bruijn dot pattern printed on the surface. Our system efficiently searches the pattern space in order to determine the precise location of the tangible with sub-millimeter accuracy. Our proof-of-concept implementation offers a recognition rate of up to 95%, robust error detection, an update rate of 14 Hz, and a low-latency relative tracking mode. The MuC demonstration encompasses four separate demos that showcase typical application scenarios and features: a magic lens, two tangibles that know each others relative position, a simple geometry application that measures distances and angles, and tangibles that know on which of multiple surfaces they have been placed.
Data Augmentation for Human Motion Prediction

Human motion prediction is seldom deployed to real-world tasks due to difficulty in collecting a huge amount of motion data. We propose two motion data augmentation approaches using Variational AutoEn-coder (VAE) and Inverse Kinematics (IK). Our VAE-based generative model with adversarial training and sampling near samples generates various motions even with insufficient original motion data. Our IK-based augmentation scheme allows us to semi-automatically generate a variety of motions. Furthermore, we correct unrealistic artifacts in the augmented motions. As a result, our method outperforms previous noise-based motion augmentation methods.
Blind Image Deblurring Based on Dual Attention Network and 2D Blur Kernel Estimation

In the problem of image deblurring, the restoration of details in severely blurred images has always been difficult. In this paper, we focus on effectively eliminating the ringing artifact and wrinkles that appear after deburring, and propose a novel blind debluring method based on dual attention deep image prior (DADIP) network and 2-dimensional (2D) blur kernel estimation with convolutional neural network (CNN). In the DADIP network, the dual attention mechanism is firstly combined with squeeze and excitation network (SENet), which greatly improves the restoration effect of image details. More importantly, the 2D blur kernel estimation approach via CNN is developed to suppress the ringing artifact of the image, which significantly outperforms previous fully connected network based methods. Experiments show that our deblurring approach achieves superior performance compared with most existing methods.
The Novel Efficient Transformer for NLP

Reducing the numerical precision of weights and activations of deep neural networks have proven to be a stunningly efficient way of deploying deep networks on edge devices with limited resources. With the advent of the Transformer model, several quantization techniques have been proposed to reduce the computation and model size. However, these existing quantization techniques use fixed bit-width assignments, which result in a significant degradation in the accuracy of the model. We present in this work an efficient Transformer based on our novel multi-layer quantization technique, which reduces the precision of data based on the characteristics of weights and activations in each layer of the Transformer architecture while at the same time preserving the model’s structure. The WMT2014 DE-EN and WMT2014 FR-EN datasets are used to evaluate. The results show that our efficient Transformer achieves 4x compression with improved accuracy and an overall reduction in the training time overhead. By comparing with existing state-of-the-art techniques, we further proved that with a minimum of 3-bit and a maximum of 8-bit quantization, comparable state-of-the-art BLEU scores can be obtained.
Unsupervised Learning to Heterogeneous Cross Software Projects Defect Prediction

Heterogeneous Cross-Project Defect Prediction (HCPDP) aims to predict defects with insufficient historical defect data in a target project through a defect prediction (DP) model trained using another source project. It doesn’t demand the same set of metrics between two applications, and it also builds DP model based on matched heterogeneous metrics showing analogous distribution in their values for a given pair of datasets. This paper proposes a novel HCPDP model consisting of four phases:data preprocessing phase, feature engineering phase, metric matching phase, and lastly, training and testing phase. One may employ supervised and unsupervised learning techniques to train the DP model. Supervised method of learning uses tagged data or well-defined instances to train the model. On the other hand, unsupervised learning techniques attempt to train the model by identifying specific hidden patterns in the distribution of unlabeled instance’s values. The advantage of using unlabeled data is that, it is easier to get from a machine than labeled data, which requires manual efforts. This paper empirically and theoretically assesses the impact of the training process on the efficiency of the HCPDP model using an unsupervised learning method. Beyond this, a comparative study has been done among HCPDP with supervised learning, HCPDP with unsupervised learning, and the standard DP approach, i.e., WithIn-Project Defect Prediction (WPDP). Logistic Regression and Km++ Clustering are used as supervised and unsupervised techniques, respectively. Results show that for both classes of DP, HCPDP, and WPDP, unsupervised learning method demonstrates comparable performance compared to supervised learning method.
Visualization of Multivariate Time Series pollutant variables in Malaysia

Abstract Visualization and exploratory analysis is a crucial preliminary part of any data analysis process. Several visualization approaches have been introduced to evaluate the behaviors of time-dependent data. However, the visualization technique tends to be challenging when the data are high-dimensional and voluminous. Environmental data such as pollutant variables are often collected in multi-variables form and over time, resulting in a form of multivariate time-series data. To deal with this issue, this study provides several graphical approaches and methods which include the plots of multiple individually on a time-series, correlation matrix visualization and smoothing multivariate time-series. A case study involving data on air-pollution variables in Klang, Malaysia have been analyzed. The results found the all the methods able to provide an informative visualization on the behavior of multivariable time series of pollutant data.
An Efficient Method for Stamps Verification Using Haar Wavelet Sub-bands with Histogram and Moment

Stamps have been used as visible certificates for documents since long time ago. At present, they are used for certifying official books, memos, agreements those government institutions issued in order to ensure that their contents are not changed. The stamp image suffers from various problems such as (different sizes, different shapes, irregular shapes, ink color difference, geometric deformations, and non-uniform background); which in turn make the recognition and verification tasks more difficult and challenging. In this paper, an efficient stamp recognition and verification system are presented. This paper proposed a method to verify stamps, by using Haar wavelet transform with histogram and moment. The devised method has four stages: (i) stamp extraction; (ii) preprocessing the image; (iii) feature extraction; and (iv) matching. Stamp extraction is done to isolate complex background and removing unwanted data or noise that surrounding the stamp area. The preprocessing stage is necessary to improve the stamp brightness and, also to eliminate the rotation that occurs during the stamping process. In feature extraction, the extracted information will be representing the desirable features vector in order to discriminate between stamps. Finally, each extracted feature vector will be saved in the dedicated system database for matching purpose. The performance of the proposed stamp recognition and verification system is affected by different factors which are the number of blocks, and overlapping ratio. The test results conducted on the public stamps dataset and indicate that the proposed system provides a high recognition rate (i.e., 93.35%), while the verification tests indicated the achieved minimum equal error rate (EER) is 0.106%, and accuracy is 99.98%.
An Effective Sharpness Assessment Method For Shallow Depth-Of-Field Images

No-reference (NR) image sharpness assessment is an important issue for image quality assessment and algorithm performance evaluation. Many objective NR sharpness assessment metrics have been proposed which are often intended to be strongly associated with the human visual system (HVS). However, recent studies show that common sharpness assessment indicators may misjudge the degree of blurring for images with shallow depth of field that are often used to highlight the main subject in the view. This paper proposes an efficient no-reference objective image sharpness assessment metric based on the product of bidirectional pixel intensity differences that is computed block-by-block (PBDB). This paper contributes the following: (1) the sharpness of shallow depth-of-field images can be accurately evaluated with the proposed algorithm when traditional methods do not work well. (2) Experimental results on three public datasets demonstrate competitiveness and effectiveness of the proposed algorithm when compared with several state-of-the-art methods.
On the number of solutions of systems of certain diagonal equations over finite fields

In this paper we obtain explicit estimates and existence results on the number of Fq-rational solutions of certain systems defined by families of diagonal equations over finite fields. Our approach relies on the study of the geometric properties of the varieties defined by the systems involved. We apply these results to a generalization of Waring's problem and the distribution of solutions of congruences modulo a prime number.
Airborne gamma-ray spectrometric technique for exploring potential hydrocarbon accumulations in Ar-Rassafeh Badyieh Area (Area-2), Syria

The airborne gamma-ray spectrometry technique is newly practiced in Syria as an indirect approach for petroleum prospecting to detect structural traps favorable for possible hydrocarbon accumulations in Ar-Rassafeh Badyieh Area (Area-2). The thorium (Th) normalization technique is used in this approach to suppress regional geological influences. Three anomalous microseepage zones that are suitable as hydrocarbon occurrences were detected in Area-2 and identified by low residual potassium values and high uranium residual values relative to potassium values. The location of the first zone coincides with a productive hydrocarbon field, confirming the efficacy of the Th normalization approach with the microseepage model for detecting new probable hydrocarbon occurrences in Area-2.
Automatic Face Tagging Using Image Processing

In today’s era, people upload their photographs on social media. Many applications like Facebook, Google, or in many fields like health care, advertising, criminal identification, etc., use facial recognition to identify the person or features of the object. Face tagging is an approach to find the person in the photograph automatically by using various techniques like LBP, regression analysis, eigenface, etc. This feature helps us to identify the person so that it becomes easier to get the details of every person tagged in the photograph. We can also find behavior or analyze their expression using the person’s face. Faces can be tagged without any contact with nature. This kind of image processing is better than image processing. It is more secure and cheap. In this paper, our objective is to find out efficiency in the future.
Blockchain and Green Networking Analytics in 5G Networks and Beyond

The objective of green networking is to minimize greenhouse gas emissions while maintaining the same level of performance. Green networking refers to all processes used to optimize networking and inter-networking functions to make it more energy efficient. Green networking concepts can be extended to cover any method that reduces latency, save bandwidth, and/or decrease computation time, as a reduction in these factors invariably leads to power savings. These savings can directly translate into lowering greenhouse gas emissions and reduce computing's carbon footprint and its impact on the environment. Energy-awareness is critical in the networking infrastructure, especially in wireless 5G networks and beyond. Research on blockchain for 5G wireless networks is still in its infancy. But it is obvious that blockchain will significantly uplift the shape and experience of future mobile applications and services. Identifying the green networking analytics will lead to sustainable energy policy planning for the future.
On the Materialist Basis of Teachers’ Holistic Professional Consciousness

Teachers’ holistic professional consciousness is the ideological embodiment of their full professional attributes and the core of their professional development. Organically composed of the teachers’ professional consciousness of learning, disciplines and teaching, it is characterized by criticalness, development, emancipation and otherness. The base of the teachers’ professional consciousness contains objective contradictions (between teachers and students, teachers and knowledge, and teachers and education environment), communicative praxis (inter-subject communication between teachers and students with “holistic person communication” as the main form and content knowledge as the main element or carrier) and historical development (teaching practice and teacher education).
Receiver Selective Opening Chosen Ciphertext Secure Identity-Based Encryption

In the situation where there are one sender and multiple receivers, a receiver selective opening (RSO) attack for an identity-based encryption (IBE) scheme considers adversaries that can corrupt some of the receivers and get their user secret keys and plaintexts. Security against RSO attacks for an IBE scheme ensures confidentiality of ciphertexts of uncorrupted receivers. In this paper, we formalize a definition of RSO security against chosen ciphertext attacks (RSO-CCA security) for IBE and propose the first RSO-CCA secure IBE schemes. More specifically, we construct an RSO-CCA secure IBE scheme based on an IND-ID-CPA secure IBE scheme and a non-interactive zero-knowledge proof system with unbounded simulation soundness and multi-theorem zero-knowledge. Through our generic construction, we obtain the first pairing-based and lattice-based RSO-CCA secure IBE schemes.