Reviewerly/exHarmony huggingface dataset:
Visual Concept Reasoning Networks

A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. It approximates sparsely connected networks by explicitly defining multiple branches to simultaneously learn representations with different visual concepts or properties. Dependencies or interactions between these representations are typically defined by dense and local operations, however, without any adaptiveness or high-level reasoning. In this work, we propose to exploit this strategy and combine it with our Visual Concept Reasoning Networks (VCRNet) to enable reasoning between high-level visual concepts. We associate each branch with a visual concept and derive a compact concept state by selecting a few local descriptors through an attention module. These concept states are then updated by graph-based interaction and used to adaptively modulate the local descriptors. We describe our proposed model by split-transform-attend-interact-modulate-merge stages, which are implemented by opting for a highly modularized architecture. Extensive experiments on visual recognition tasks such as image classification, semantic segmentation, object detection, scene recognition, and action recognition show that our proposed model, VCRNet, consistently improves the performance by increasing the number of parameters by less than 1%.
Quantum Probability Oracles & Multidimensional Amplitude Estimation

We give a multidimensional version of amplitude estimation. Let p be an n-dimensional probability distribution which can be sampled from using a quantum circuit U_p. We show that all coordinates of p can be estimated up to error e per coordinate using O(1/(e)) applications of U_p and its inverse. This generalizes the normal amplitude estimation algorithm, which solves the problem for n = 2. Our results also imply a O(n/e) query algorithm for 𝓁₁-norm (the total variation distance) estimation and a O(√n/e) query algorithm for 𝓁₂-norm. We also show that these results are optimal up to logarithmic factors.
Cybercrime In The Philippines: A Case Study Of National Security

Transformations in technology that have impacted the global economy have ushered in an information and communication technology-led revolution. This global shift has paved the way for what many has described as “a borderless world” that has led to new ways of doing things in both the private and public sector of a nation in today’s modern times.The concept of E-service (short for electronic service) which is the hallmark of this technology represents an application that utilizes the use of information and communication mediated technologies for the facilitation of the delivery of a varied scope of services or aptly called “electronic services”. In the public sector, E-service enable public agencies who are the service providers achieve enhanced administration in delivering services for citizens or the public. This allows the flow and process of information for all stakeholders to be processed efficiently. In the private sector, applications of e-commerce are continually affecting trends and prospects for business over the internet. E-commerce in the country encourages shifts from traditional modes of doing business to electronic alternatives, thereby enhancing the processing of business transactions. Its benefits are limitless, from expanded geographic reach, easy access to information, marketing and advertising visibility and cost reduction which all bottom lines to increase revenues.Security is the challenge that faces the implementation of e-services in a country in the country where such implementation is crucial to the delivery of basic services to the populace.  Without a guarantee of privacy and security for information circulating in the internet, e-services will fail. The menace of the proliferation of cyber crime activities hampers the reach of e-government and compromises the achievement of its national goals in creating enhanced socio-economic environment for its citizens. In essence, it is tantamount to becoming a grave threat to national security.  This paper sought to present how cyber crime has impacted areas of both the public and private sector here in the Philippines that threaten national security of its citizens.  It presented among others, a profile of cyber criminals including their tools and motives, the modes and manner on how different cyber crimes are being committed and the implications to specific stakeholders most vulnerable.In view of the results, recognizing the national economic implications of cyber threats, this paper concludes that the implications of cybercrime in the Philippines are of an impending grave nature which threatens that national security of its people, its private sector and its government.
Multi-label Few-shot Learning with Semantic Inference (Student Abstract)

Few-shot learning can adapt the classification model to new labels with only a few labeled examples. Previous studies mainly focus on the scenario of a single category label per example but have not solved the more challenging multi-label scenario with exponential-sized output space and low-data effectively. In this paper, we propose a semantic-aware meta-learning model for multi-label few-shot learning. Our approach can learn and infer the semantic correlation between unseen labels and historical labels to quickly adapt multi-label tasks from only a few examples. Specifically, features can be mapped into the semantic embedding space via label word vectors to explore and exploit the label correlation, and thus cope with the challenge on the overwhelming size of the output space. Then a novel semantic inference mechanism is designed for leveraging prior knowledge learned from historical labels, which will produce good generalization performance on new labels to alleviate the low-data problem. Finally, extensive empirical results show that the proposed method significantly outperforms the existing state-of-the-art methods on the multi-label few-shot learning tasks.
Investigating Students’ Eyesights in the Utilization of Platforms in Learning ESP During the Covid-19 Pandemic

&#x0D; The Covid-19 pandemic suddenly attacked the world and led every sector of life affected, including the education process, which is forced to shift into online mode. All educators are forced to adapt and find a learning platform to utilize in this emergency remote learning. This research uncovers the students' preference of digital platforms in learning ESP during the Covid-19 pandemic, the factors accepting their preference, and their perception of those learning platforms. The design of this study is qualitative. The participants of this study were 112 freshmen of a state university in Makassar who learn English for Specific Purposes (ESP), comprising 95 females and 17 males. The participants were consisted of 3 majors and involved in remote learning for one semester (16 meetings) through WhatsApp, Google Classroom, University LMS, YouTube, and Video Conference (Zoom and Google Meet). In order to collect the data, a questionnaire that consists of close-ended and open-ended questions was distributed to the participants. It is found that the students' preference of digital platform in learning ESP during the Covid-19 pandemic is WhatsApp. The factors affecting students' preference of the platform are demographic and economic constraints. The criteria of the application that is highly preferred by the students were the one which does not drain the internet quota, can still work correctly in which the internet connection is not strong, and can facilitate the interaction between lecturers and students. Finally, this study suggests that no one perfect digital platform can replace face-to-face learning since many things are missing during this online learning. So, to keep it running effectively, the combination of several platforms is required to be utilized.&#x0D;
Research on Innovative Teaching Method of University Physical Education Courses Based on Virtual Reality Technology — Taking Table Tennis Course Teaching as an Example

As the demand for talents in the society is increasing day by day, the all-round development of university students has become a key research topic in university education nowadays. Physical quality, as the most fundamental quality of university students, has also received much attention from scholars in the field of education. With the continuous progress of information technology, the information-based teaching technology represented by virtual reality technology is widely used in the teaching field of many disciplines, and certain research results have been achieved. This paper discusses the feasibility of introducing virtual reality technology into university physical education courses, and designs a set of innovative teaching method for university physical education courses, taking university table tennis courses as an example. The virtual teaching is combined with the actual teaching, so that students can learn the course through online training and offline practice, which enhances the fun of teaching and allows students to better master the skills of sports. Through the design of comparative experiments, the effectiveness of the teaching method proposed in this paper is verified, which helps to provide reference for the reform of university physical education courses.
Deep 3D Shape Reconstruction from Single-View Sketch Image

In this paper, we introduce a novel 3D shape reconstruction method from single-view sketch image based on deep neural network. The proposed pipeline is mainly composed of three modules. The first module is sketch component segmentation based on multi-modal DNN fusion and is used to segment a given sketch into a series of basic units and build transformation template by the knots between them. The second module is a non-linear transformation network for multifarious sketch generation with the obtained transformation template. It creates the transformation representation of a sketch by extracting the shape features of an input sketch and transformation template samples. The third module is deep 3D shape reconstruction using multifarious sketches, which takes the obtianed sketches as input to reconstruct 3D shapes with a generative model. It fuses and optimizes features of multiple views and thus is more likely to generate high-quality 3D shapes. To evaluate the effectiveness of the proposed method, we conduct extensive experiments on a public 3D reconstruction dataset. The results demonstrate that our model can achieve better reconstruction performance than peer methods.
A CNN Accelerator with Embedded Risc-V Controllers

Convolutional Neural Network is a promising technology in machine learning. Due to its vast computing and data requirements, it needs to be run with a specific accelerator to achieve reasonable energy efficiency. Improving the performance of accelerators has become the research hotspot. A mixed-precision structure can be used to improve hardware utilization, thus reducing area and power. However, the mixed-precision flow control is so complicated that it costs too much hardware resources. In this paper, a CNN accelerator with embedded RISC-V controllers is introduced to achieve flexible control at a very low cost. The ASIC synthesized results show that the proposed design area with two embedded cores is 5% less than the basic design.
An Efficient Method for Video Shot Transition Detection Using Probability Binary Weight Approach

Shot boundary detection in videos is one of the most fundamental tasks towards content-based video retrieval and analysis. In this aspect, an efficient approach to detect abrupt and gradual transition in videos is presented. The proposed method detects the shot boundaries in videos by extracting block-based mean probability binary weight (MPBW) histogram from the normalized Kirsch magnitude frames as an amalgamation of local and global features. Abrupt transitions in videos are detected by utilizing the distance measure between consecutive MPBW histograms and employing an adaptive threshold. In the subsequent step, co-efficient of mean deviation and variance statistical measure is applied on MPBW histograms to detect gradual transitions in the video. Experiments were conducted on TRECVID 2001 and 2007 datasets to analyse and validate the proposed method. Experimental result shows significant improvement of the proposed SBD approach over some of the state-of-the-art algorithms in terms of recall, precision, and F1-score.
An Algorithm for Constructing the Optimal Code Trees for Binary Alphabetic AIFV-m Codes

We call the alphabetic version of the AIFV-m code the alphabetic AIFV-m codes. This paper defines binary alphabetic AIFV-m codes and proposes an algorithm to design the optimal binary alphabetic AIFV-m codes in terms of the minimum average codeword length for stationary memoryless sources. The proposed method is based on an iterative optimization algorithm and a dynamic programming algorithm.
Function Composition from Sine Function and Skew Tent Map and Its Application to Pseudorandom Number Generators

In cryptography, the pseudorandom number sequences must have random appearance to be used in secure information systems. The skew tent map (STM) is an attractive map to produce pseudorandom sequences due to its easy implementation and the absence of stability islands when it is in chaotic behavior. Using the STM and sine function, we propose and analyze a function composition to propose a pseudorandom number generator (PRNG). In the analysis of the function composition, we use the bifurcation diagram and the Lyapunov exponent to perform a behavioral comparison against the STM. We show that the proposed function composition is more sensitive to initial conditions than the STM, and then it is a better option than the STM for cryptography applications. For the proposed function we determine and avoid the chaos annulling traps. The proposed PRNG can be configured to generate pseudorandom numbers of 8, 16 or 32 bits and it can be implemented on microcontrollers with different architectures. We evaluate the pseudorandomness of the proposed PRNG using the NIST SP 800-22 and TestU01 suites. Additionally, to evaluate its quality, we apply tests such as correlation coefficient, key sensitivity, statistical and entropy analysis, key space, linear complexity, and speed. Finally, we performed a comparison with similar PRNGs that produce pseudorandom sequences considering numbers of 8 and 32 bits. The results show that the proposed PRNG maintains its security regardless of the selected configuration. The proposed PRNG has five important features: easy implementation, configurable to produce number with 8, 16 or 32 bits, high processing speed, high linear complexity, and wide key space. These features are necessary for cryptographic systems.
Significance and Impact of Blockchain Technology in Education System

Blockchain is an emerging technology which implementation is not only limited to the crypto currencies and finance but playing an essential role in each part of our daily lives. Now, it has emerged as a technology that has vast potential to change the way of storing and sharing of critical information on the internet. Blockchain-based applications have the properties of decentralization, immutability, transparency, and consensus. Blockchain technology has a great potential for storing, accessing, and maintaining immutable records. Educational organizations, particularly tertiary institutions, are currently looking at to utilize a blockchain application to improve educating and learning exercises and advance joint effort among the partners like students, teachers, and guardians. Blockchain is the new way to provide a novel perspective about how we compose and use student and staff information online, giving them a feeling of proprietorship, straightforward entry, and immutable nature. In this chapter, we have discussed the various aspects and concept of blockchain technology for the educational record repository, which enables us to store records securely and access it easily from anywhere and anytime. Blockchain-based repository manages and distributes the educational assets like e-certificates, identification documents among the academic professionals, and allows these educational assets to be securely transferred, shared, and distributed by the various parties.
An Electromagnetic Situation Calculation Method Based on Edge Computing and Cloud Computing

Cloud computing model is very powerful in massive data processing and comprehensive analysis, but there are still limitations in the new application scenarios. The proposed edge computing is a good supplement to the shortcomings of cloud computing, but it cannot replace cloud computing. This paper proposes a computing structure based on fusion of cloud computing and edge computing, which organically combines cloud computing technology and edge computing technology, complements each other's advantages, and provides a software and hardware support architecture for the electromagnetic information processing. At the same time, a distributed deep learning algorithm based on edge computing is proposed with considering the combination of edge computing technology and deep learning method. With depth feature extraction and compression coding technology, the communication cost between sensing device and cloud center is reduced. And the computing cooperation between edge device and fusion center is used to speed up the response speed of the system. Finally the delay and energy consumption of the system is reduced and the accuracy of target recognition is guaranteed.
Towards Reusable Network Components by Learning Compatible Representations

This paper proposes to make a first step towards compatible and hence reusable network components. Rather than training networks for different tasks independently, we adapt the training process to produce network components that are compatible across tasks. In particular, we split a network into two components, a features extractor and a target task head, and propose various approaches to accomplish compatibility between them. We systematically analyse these approaches on the task of image classification on standard datasets. We demonstrate that we can produce components which are directly compatible without any fine-tuning or compromising accuracy on the original tasks. Afterwards, we demonstrate the use of compatible components on three applications: Unsupervised domain adaptation, transferring classifiers across feature extractors with different architectures, and increasing the computational efficiency of transfer learning.
Development of an educational tool in human anatomy with the use of QR codes

INTRODUCTION The anatomy's study has a fundamental role in the health area courses, but the approach towards it has been inappropriate at many times. The challenging terminology and the difficult handling of the anatomic pieces require compatibility between the teaching method and the student's preferences. As shown in many studies, a large number of students are not satisfied with anatomy's teaching methods and do not learn as much as they think they could. OBJECTIVE This study seeks to develop an effective tool for the learning of anatomy, using QR codes linked to information on determined muscles. MATERIALS AND METHODS The elected structures for this study were the gastrocnemius (GM) and the rectus femoris (RFM) muscles. We selected dissected anatomical specimens from the collection of the Department of Morphology, Santa Casa de São Paulo School of Medical Sciences which GM and RFM were intact and with well visible belly and attachments. To approach them, we photographed and produced digital content with information about their origin, insertion, and action, along with additional topics. The website used to produce this content was “ Wix.com ” each link was associated with a QR code. RESULTS The QR codes were printed and attached to the specimen in a way that students could use their cell phones to scan the codes and access the content corresponding to the muscle in question (Figure 1). The photo of the dissected muscle is then displayed on the screen accompanied by the basic function and fixation information (Figure 2). In this same screen, a link for those who want more information about this muscle, such as irrigation, innervation and clinical applications (Figure 3). This tool can be used by either IOS or Android phones. CONCLUSION The final result will be of value to increase the undergraduate's performance and will contribute to the development of different teaching methods, stimulating the student to use his own cell phone as a tool to aid his study alongside the dissected anatomical specimen. Support or Funding Information PiBiC, CNPq 2018, Brazil This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Self-other distinction modulates the social softness illusion

Abstract The social softness illusion (i.e., the tendency to perceive another person's skin as softer than our own) is thought to promote the sharing of social-emotional experiences because of the rewarding properties of receiving and giving social affective touch. Here we investigated whether the ability to distinguish someone else’s body from our own modulates the social softness illusion. In particular, we tested whether the spatial perspective taken by the participants and seeing or not the touched arms could alter this illusion. Pairs of female participants were assigned the roles of either the giver (i.e., delivering the touches) or the receiver (i.e., being touched). We manipulated the location of the touch (palm or forearm), the spatial perspective of the receiver’s body with respect to the giver’s body (egocentric or allocentric perspective), and the vision of the touched body part (the giver could either see both her own and the receiver’s body part, or she was blindfolded). Consistently with previous findings, the skin of another person was perceived as softer than the own one. Additionally, the illusion was present for both the forearm and the palm, and it was stronger in allocentric compared to the egocentric perspective (i.e., when the self-other distinction was clearer). These findings show that the mechanisms underpinning the ability to represent another person’s body as distinct from our own modulates the social softness illusion, and thus support the role of the social softness illusion in fostering social relationships.
The Use of Electronic Learning Aid (Elena); It’s Impact on Student Satisfaction

Abstract This research aims to describe the use of Electronic Learning Aid (ELENA) at the Universitas Negeri Semarang (UNNES) as an online learning platform during the Covid-19 pandemic. It also explains the effect of using ELENA on the students’ satisfaction. The research objects are the lecturers and the students of the Economic Education study program, Faculty of Economics of UNNES. The data are collected using documentation and questionnaire methods. The data are then analyzed using descriptive statistics. The results show that the lecturers have made good use of ELENA as an online learning platform. They can operate some features available in ELENA (attendance, files, assignments, quizzes, links, labels, discussions, video conferencing) well. The students also think that their lecturers have sufficient competence in utilizing ELENA. Their satisfaction level is in a good category. However, they also suggest some other learning activities that the lecturers can conduct by utilizing other applications besides ELENA.