Reviewerly/exHarmony huggingface dataset:
Evaluating Excellence Webometric’s Criteria Weight using Consistent Fuzzy Preference Relation Method

Some methods in the world university ranking institutions still have weaknesses. Among the university rankings, Webometrics focused on quantitative studies related to website and content phenomena and is considered easy to measure because it bases ranking criteria on university activities on the internet. On the webometrics, the percentage of weight on the criteria of excellence which is quite high (40 %), makes the university pay attention to achieving that score. The existing method in weighting the criteria using the analytical hierarchy process (AHP) needs to include =n(n-1)/2 questions for each group of n-criteria paired comparisons. In this study, the Consistent Fuzzy Preference Relation (CFPR) method is used to study the factors/criteria to determine what strategy universities will take to improve the webometrics ranking in terms of excellence. The CFPR techniques are used to reduce expert assessment steps to only as much as n-1 to ensure consistency at the level with n criteria. Based on the calculation results, it can be concluded that the strategy for improving the excellence score is prioritized on three main criteria in sequence, namely improving scholarly rank (A), measuring the number of scientific papers (B), recognizing scholarly ranks (C). The weight of each criterion in the sequence is 0.51, 0.32, and 0.17. Some strategies to increase the excellence score based on the main priority of sub-criteria and sub-sub criteria are explained in this study.
An Adversarial Attack Algorithm based on Edge-Sketched Feature from Latent Space

Adversarial examples are specially designed examples which mislead deep learning models but could be correctly classified by human. They are usually generated by multiple iterations on the input according to the aim of increasing the classification loss. However, such approaches show slow training speed and unstable transferability. In this paper, we propose a latent space feature based adversarial attack algorithm, named as LSFAA, to address these technical challenges. LSFAA uses an Adversarial Feature Extraction Network to extract from inputs' latent space and utilizes these maps as adversarial perturbations. Thus the iterations are conducted on the parameters of the extraction network instead of the input. Therefore, the trained network is able to be used as a generator and mass generate adversarial examples without consuming too much time. Furthermore, an edge-sketched perturbation additional method is proposed to achieve imperceptible visualization. Inspired by convolutional neural network's characteristic of extracting edge information for further identification, we add the generated perturbation only on the chosen edge area of the input. Such method reaches a balance between attack ability and imperceptible visualization. Experimental results demonstrate that LSFAA achieves an average attack success rate of 99.21 % on four commonly used deep learning models.
Save-Points Detection for Machine Unlearning in Multiple Linear Regression Models

Good machine learning models are evidently available in the literature. More research towards even improved versions of these machine learning models is still on going. However, the machine unlearning side of the coin has been ignored. The little that has been done focused on erasing all the data previously used by a machine learning model as a means of making the machine learning model forget the learnt trends. However, erasing all prior learning experiences in order to then re-learn from scratch is time consuming and expensive. In addition, there are high risk of datasets pollution in the process. This study investigated the detection of save points in multiple linear regression models. Results show successful detection of save points where major trend changes in the data are visually noted. This is a plausible model that will potentially improve the quality of machine learning, as well as its speed.
Extracting Vignetting and Grain Filter Effects from Photos

Most smartphones support the use of real-time camera filters to impart visual effects to captured images. Currently, such filters come preinstalled on-device or need to be downloaded and installed before use (e.g., Instagram filters). Recent work [24] proposed a method to extract a camera filter directly from an example photo that has already had a filter applied. The work in [24] focused only on the color and tonal aspects of the underlying filter. In this paper, we introduce a method to extract two spatially varying effects commonly used by on-device camera filters—namely, image vignetting and image grain. Specifically, we show how to extract the parameters for vignetting and image grain present in an example image and replicate these effects as an on-device filter. We use lightweight CNNs to estimate the filter parameters and employ efficient techniques—isotropic Gaussian filters and simplex noise—for regenerating the filters. Our design achieves a reasonable trade-off between efficiency and realism. We show that our method can extract vignetting and image grain filters from stylized photos and replicate the filters on captured images more faithfully, as compared to color and style transfer methods. Our method is significantly efficient and has been already deployed to millions of flagship smartphones.
An Effective Integrity Verification Scheme for Ensuring Data Integrity in Cloud Computing

Cloud computing is growing in quality thanks to its ability to supply dynamically ascendible resources provisioned as services no matter user or location device. However, moving knowledge to the cloud means the management of the info is a lot of within the hands of the cloud supplier instead of the info owner. Despite the advantages that the cloud presents, cloud computing technology is moon-faced with a range of legal and technological challenges. Security and privacy are amongst the most important challenges as known. There should be certain type of system to guarantee the integrity of the records. The current Cloud safety model is sited on the interpretation that the client should trust the source. Guaranteeing documents integrity needs a promise of trust among the customer and the supplier. Numerous cryptographic methods have been developed and used to deliver improved safety for the sake of keeping integrity issues. We assume that our file (F) is divided into n shares. Poly1305 could be a cryptographically message authentication code (MAC). It is often wont to verify the info integrity and therefore the legitimacy of a message. Shamir Secret Sharing is used to split client file into n shares and these shares are further encrypted using poly1305. An Effective Integrity Verification Scheme (EIVS) based SSS and Poly-1305 is implemented and compared with SSS-AES. Entropy and Timing analysis of EIVS is done for short, long and larger text files.
Dynamic Attribute-Based Encryption Scheme Based on Hierarchical Time Tree

Aiming at the problem of authorization access and authorization revocation caused by user changes in attribute-based encryption. This paper proposes an attribute-based encryption scheme that can authorize new temporary users and effectively revocation temporary user access rights. This scheme first uses proxy re-encryption technology to update the ciphertext access policy, so that new users can be added to the system at any time without rebuilding the system. By introducing the hierarchical time tree, the time validity is added to the decryptable period in the user key and ciphertext. Only temporary users whose attributes meet the access policy and the validity period of attribute key completely covers the decipherable period of re-encrypted ciphertext can be decrypted. Achieve the purpose of expiring temporary user access. The scheme not only improves the scalability of attribute encryption scheme, but also reduces the computational cost of users.
Data enhancement method for object detection

Abstract A data enhancement method for object detection is proposed to address the problem that there are few data enhancement means for target detection. This method has obvious advantages for small targets and scenarios where the target background is lacking. It is also a method to solve the problem of small and unbalanced samples. In this paper, the object is subtracted from the bounding box, and after processing, it is fused into a new image using various image classification improvement means such as random brightness, random contrast, random rotation, random cropping, etc. Using mixup-like fusion methods, and XIOU is proposed to guarantee the t intersection ratio of the target synthesis during synthesis. An improvement of 2% is obtained on yolov3 over the VOC dataset.
Suspicious Behavior Detection Using Man Machine Model with Integration of Virtual Reality

Video or CCTV surveillance plays an important role in the security of any place whether it is residential areas, industries, public spaces like shopping malls, museums and other monuments, banks, offices, building sites, warehouses, airports, railway stations, etc. It will help in preventing theft and damage to manufactured goods and products as well as manufacturing equipment, having complete and recorded production accident data, having complete and recorded production accident data, monitoring every stage of the manufacturing process, and prevention and analysis of any type of crime. But the current systems rely too much on humans monitoring the feeds from these videos which are prone to some problems like reduced attention and fatigue during long stretches of monitoring. So, there is a need for a system where these humans are aided by the machines in the monitoring process. The system proposed and implemented in this study would help to overcome this problem by aiding the man with smart machines and neural networks. Also, the system works on video camera feed instead of static camera shots which would help in capturing the sequential information that may be missed when using static images. And along with this, a model has been proposed using neural network technology that can automatically identify the individuals exhibiting the suspicious behavior from live camera feed input. At last research has been done on whether to use virtual reality on live video or CCTV surveillance or not based on various research papers published in similar domain.
Understanding Metrics Team-Stakeholder Communication in Agile Metrics Service Delivery

In this paper, we explore challenges in communication between metrics teams and stakeholders in metrics service delivery. Drawing on interviews and interactive workshops with team members and stakeholders at two different Swedish agile software development organizations, we identify interrelated challenges such as aligning expectations, prioritizing demands, providing regular feedback, and maintaining continuous dialogue, which influence team-stakeholder interaction, relationships and performance. Our study shows the importance of understanding communicative hurdles and provides suggestions for their mitigation, therefore meriting further empirical research.
Mechanism Design with Narratives

Individuals use narratives as rationales or justifications to make their claims more convincing. I provide a general framework for partial verifiability based on narratives. Narratives give many reasons and arguments. The receiver derives the message’s meaning by aggregating these reasons; her private information tells her whether some potential reasons support the sender’s claims. Therefore, the receiver detects misreports with positive probability. Narratives flexibly allow for different degrees of partial verifiability and allow using the revelation principle. Considering mechanism design as an example, I prove that narratives are so powerful to implement efficient trade in the canonical bilateral-trade setting.
A New Method on Service Description Supporting Customization in Structured Networks

With the growing diversity of new technologies and network services, the limitations of current layered networks have become more obvious. The layered structure increases the difficulty of designing a protocol for each new service. A better alternative is to construct a structured and modular architecture for the design and implementation of new standards and protocols. Structured networks provide a service through the dynamic composition of generic protocol function blocks with respect to user requirements. In order to make such mechanism work properly, how to describe network services becomes one of the most important issues. Therefore, this paper presents a new method on service description supporting customization. Firstly this paper presents several new kinds of structured networks which can be divided into two types, and then the categories of atomic services and service instances are summarized. The second part introduces a new network service description method for formal description and analysis of network behavior. This method contains formal definitions and a processing model based on process algebra. Furthermore, this paper discusses a study of description rules based on the XML schema. Finally, the simulation results have proved its feasibility and effectivity.
Innovation evaluation framework using state transition probability of the product

In the age of digital disruption, coming up with continuous innovation has become a major challenge. The first step to overcoming this challenge is to understand the current state of the innovation activities with reference to customer value. We propose an innovation evaluation framework using the state transition probability of a product. This framework not only provides cost and delivery efficiency in terms of customer contribution, but also provides the innovation characteristics of the enterprise. We applied the proposed framework to software products of FY 2018 and found that the average product had a 14% probability of being added to a service within one year of starting development and that 28% of the development costs contributed to customer value. We also uncovered various structural issues in the innovation process. These findings are consistent with our intuitive impressions, thus demonstrating the effectiveness of the proposed framework as a means of grasping the status quo.
Multi-Relational Hierarchical Attention for Top-k Recommendation

As one of the critical application directions in the Recommendation Systems domain, the top-k recommendation model is to rank all candidate items through non-explicit feedback (e.g., some implicit interact behavior, like clicking, collecting, or viewing) from users. In this ranking, the rank shows the users’ satisfaction with recommended items or the relevance of the target item. Although previous methods all improve the performance of the final recommended ranking, they suffer from several limitations. To overcome these limitations, we propose a Multi-Relational Hierarchical Attention within Graph Neural Network (GNN)-attention-Deep Neural Network (DNN) architecture for the top-k recommendation, named MRHA for brevity. In our proposed method, we combine the GNN’s ability to learn the local item representation of graph-structure data and attention-DNN architecture’s ability to learn the user’s preference. For processing the multi-relational data that occurs in the real application scenarios, we propose a novel hierarchical attention mechanism based on the GNN-attention-DNN architecture. The comparative experiments conducted on two real-world representative datasets show the effectiveness of the proposed method.
Ilustremos: UX+XR and Parametric Design for MUVE

In the midst of a global pandemic crisis, this proposal seeks to solve a need for a synchronous and multi-user interactive virtual environment for the exhibition of artworks as part of an international illustrators encounter.This research shows a UX+XR design strategy based on UX methodology and the inclusion of parametric design for MUVEs that is framed in a UX+XR prototype for the exhibition, virtual-meeting place, and pedagogical experiential approach directed to defined users.The result, a virtual space named "Ilustremos" (let's illustrate) is created to promote creativity, celebrate art, create community and multidisciplinary collaboration.
Design of Face Detection Algorithm Accelerator Based on Vitis

With the development of artificial intelligence, Machine learning based FPGA (Field Programmable Gate Array) is becoming more and more important, Compared with CPU and GPU, FPGA has the advantages of reconfigurability, low power consumption and high performance of parallel computing. Due to the complexity of FPGA development process. This can be said to be the biggest obstacle for FPGA to be widely used in the field of artificial intelligence. In this paper, the FPGA accelerator is designed by a concise computation framework. This development method shortens the development time. And the accelerator built in this paper detection speed is 9 times that of CPU. The detection power consumption is about 0.1 times that of GPU.
Intersection of Technology and Online Gender Violence in India

This study deals with online gender violence in India which is done with the aid of new advanced technology, its various forms and its causes. Online gender violence is the result of blend of digital revolution in the form of access of internet and social evil prevailing in society. Gender based violence is quite common in India. It is a harsh reality that the women in India have been ill-treated for ages in our male dominated society. She is not only robbed of her dignity and pride by way of seduction by the men outside, but also, has become a victim of cruelty by her saviors, within the four walls of her own house. The phenomenon of violence against women arises from patriarchal notions of Indian society. Gender violence is not a new concept in India it has been around in the Indian society for a long time only with the advancement of society it has changed its nature with its characteristics. Technological development has given rise to various new inventions in the virtual world especially in the field of Information technology in the form of digital communication which is considered to be one of the paramount gifts of modern day development of technology and science. But these gifts are being misused for harassing women in cyber space known as online violence against women. Online gender violence in India is another type of violence against women which is encouraged by internet and information technology which has given birth to new kind of crime against women called as cyber or online crime against women. Women who are often seen as at risk or who experience high volume of online harassment and are easily targeted via new avenues of harassment and violence such as cyber stalking, morphing, pornography, online defamation, blackmailing. Technology has integrated into pre-existing forms of criminal violence and harassment against women. This shows that women are not only subject to physical gender violence but are also subject to online gender violence which is increasing day by day with great extent. These online gender violence shows that there was a change to the background or scenery of crime and criminal justice system. In spite of several of legislations, efforts of government and judiciary the percentage of online gender violence is increasing day by day. So there is an urgent need to find the measure to cope up with the menace of online gender violence.
Extraction of centerline of large-format laser stripes against ambient light interference

Abstract In order to carry out real-time 3D reconstruction of large components in the outdoor environment, this paper proposes a laser stripe centerline extraction algorithm that is resistant to ambient light interference. The median filter is used to preprocess the laser stripe to be extracted, and the Rosenfeld thinning and least square fitting algorithm are organically combined to extract the centerline of the laser stripe. Experimental results show that this method can effectively avoid strong light interference and accurately obtain the centerline of the laser stripe to be extracted.
Multidisciplinary Real-Time Model for Krishi Seva

In India, Farmers contribute more than 17% to the Indian GDP but they are not getting what they deserve. Krishi - Seva is an online portal where farmers can check their soil rating and expected crop production, can directly communicate to consumers, can have a contract with third-party (exporters), and also can consult with the farming experts whoever they want. This platform is implemented using HTML, CSS, JavaScript, and used PHP for the backend. During this project, we used the Scrum framework of the Agile Development Model. This paper shows the requirement of such a platform so that farmers can get more attention based on Rating.
5 Minutes of Drawing: Instagram for Scientific, Pedagogical and Artistic Communications in the Covid-19 Era

In this article, we present the international event -5 Minutes of Drawing.Created by a researchers group at the Fine Arts Faculty of the University of Lisbon, the event transforms the model of conferences established within the scientific environment by using a massive social network combined with video, thus providing opportunities to communicate theoretical content about drawing developed not only by academics but also from artists outside the mainstream.This project emerges as a response to the Covid-19 context.The methods chosen make use of Instagram IGTV and Live tools.The event is programmed with a curatorial approach.In this article we highlight and reflect on four case studies selected from the 59 participants.We then conclude the paper with the outcomes (exponential impacts outside the university) and statistics after one successful year, completing 12 series with 117 videos published about drawing, totaling 75.200 views.
Data Transfer between Cloud Document Management Systems in Serverless Paradigm

Data and communication are at the heart of business and interactions. With the onset of economic and reliable Document Management System (DMS) providers, there has been a large-scale migration to these cloud-based services. When it comes to Data Transfer we find ourselves in the realm of mailing files, link sharing, FTP and the list goes on, however, all of these methods involve some level of local computing and storage resource consumption and do not support transfers across different DMS providers. This paper proposes a system that aims to facilitate cloud-agnostic data transfers built over a cloud system with Serverless computing paradigm.
Characterizing Global Hand-Crafted Feature Descriptors for Sketch-Based Image Retrie

Sketch-based Image Retrieval (SBIR) is an essential method of searching objects by drawing them in two-dimensional (2-D). The SBIR problem was that the image inside the dataset did not match the pair due to its modality difference. The main objective of this research is to compare which descriptor is better adapted to the SBIR. Histogram of Oriented Gradient (HOG), Gabor, and Local Binary Pattern (LBP) are selected for the study. Each sample will be divided into distance measures with features vector that corresponds to the training dataset will be compared the performance in order to study the best adapted to SBIR. Histogram of Oriented Gradient (HOG) is identified as the best descriptor for SBIR. HOG has shown a better performance than Gabor and LBP since the method shows the highest percent of distance matrices accuracy.