Reviewerly/exHarmony huggingface dataset:
Detection of structure query language injection vulnerability in web driven database application

Summary Structure Query Language Injection Attack is among the top 10‐security threats that can be used on the web application to cause severe damage or gain unauthorized data access to the application server. Many reports have indicated an average of 64% of global websites are at risk of being attack by SQL injection, and many of the top companies have experienced thousands of attacks attempts through SQL injection. The current trend shows the increasing number of attacks factor as a result of the daily deployment of these applications without security detection and prevention mechanism is placed. To overcome this challenge, researches in academia and industry presented a proposal that automates SQL injection vulnerabilities assessment on the tested application. Current studies show the need to enhance techniques of these proposals to reduce the false alarms. In this study, we propose a component‐based technique to minimize the incidence of inaccurate results, as well as enable the ease of improving the proposed solution. The study uses three costumed applications as tested to evaluate the accuracy of the proposed solution. Each of these testbed consists of several vulnerabilities where the experimental evaluation performs to test the proposed tool. An empirical evaluation is carried out on three vulnerable custom websites to evaluate the effectiveness of the proposed study. The experiment results indicated significant results in terms of high accuracy. On the other hand, the proposed solution also has better capabilities to analyze page response based on four different techniques. Moreover, the proposed solution is the only solution that performs stored procedure attacks SQL and bypass login authentication even if the returned records are limited restriction is applied.
Tiny Transfer Learning: Towards Memory-Efficient On-Device Learning

We present Tiny-Transfer-Learning (TinyTL), an efficient on-device learning method to adapt pre-trained models to newly collected data on edge devices. Different from conventional transfer learning methods that fine-tune the full network or the last layer, TinyTL freezes the weights of the feature extractor while only learning the biases, thus doesn't require storing the intermediate activations, which is the major memory bottleneck for on-device learning. To maintain the adaptation capacity without updating the weights, TinyTL introduces memory-efficient lite residual modules to refine the feature extractor by learning small residual feature maps in the middle. Besides, instead of using the same feature extractor, TinyTL adapts the architecture of the feature extractor to fit different target datasets while fixing the weights: TinyTL pre-trains a large super-net that contains many weight-shared sub-nets that can individually operate; different target dataset selects the sub-net that best match the dataset. This backpropagation-free discrete sub-net selection incurs no memory overhead. Extensive experiments show that TinyTL can reduce the training memory cost by order of magnitude (up to 13.3x) without sacrificing accuracy compared to fine-tuning the full network.
Knowledge Organization Systems for the Representation of Multimedia Resources on the Web: A Comparative Analysis

The lack of standardization in the production, organization and dissemination of information in documentation centers and institutions alike, as a result from the digitization of collections and their availability on the internet has called for integration efforts. The sheer availability of multimedia content has fostered the development of many distinct and, most of the time, independent metadata standards for its description. This study aims at presenting and comparing the existing standards of metadata, vocabularies and ontologies for multimedia annotation and also tries to offer a synthetic overview of its main strengths and weaknesses, aiding efforts for semantic integration and enhancing the findability of available multimedia resources on the web. We also aim at unveiling the characteristics that could, should and are perhaps not being highlighted in the characterization of multimedia resources.
Learning Semantic Correspondence Exploiting an Object-Level Prior

We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We propose to use images annotated with binary foreground masks and subjected to synthetic geometric deformations to train a convolutional neural network (CNN) for this task. Using these masks as part of the supervisory signal provides an object-level prior for the semantic correspondence task and offers a good compromise between semantic flow methods, where the amount of training data is limited by the cost of manually selecting point correspondences, and semantic alignment ones, where the regression of a single global geometric transformation between images may be sensitive to image-specific details such as background clutter. We propose a new CNN architecture, dubbed SFNet, which implements this idea. It leverages a new and differentiable version of the argmax function for end-to-end training, with a loss that combines mask and flow consistency with smoothness terms. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms the state of the art on standard benchmarks.
A Survey on Cloud Computing Security Issues, Attacks and Countermeasures

Cloud computing is an emerging technology which has laid the foundation of a new approach to computation for individuals as well as enterprises. There is an increasing trend of cloud adoption in recent years. Security has always been a major concern in cloud. Cloud platforms and computations are constantly under threats from attackers. In this paper, we will make a survey on major security concerns, attacks as well as solutions to limit the possibilities of attack in a cloud computing environment.
Physical properties of service robots which is referring to Joruri puppets

Recently, there have been a lot of researches on Internet of Things (IoT). We have proposed the active sensing system model using service robots. On this model, the robots collect and analyze user's behavior information while providing robot services. Based on this model, we implemented a questionnaire service using robots, and verified the effectiveness at large and small events for years. In this service, it is one of the important points to attract the target persons (customers) of sensing with the attractiveness of the robots. Therefore, we developed unique robots referring puppets of Ningyo-Joruri, which is the Japanese traditional puppet show registered as a UNESCO Intangible Cultural Heritage. Ningyo-Joruri has accumulation of techniques that have been inherited for hundreds of years. In this paper, we attempt to systematize a gesture generation model for utilizing the various gestures and manipulations of Joruri puppets on service robots. In addition, we extract and analyze the gestures of Joruri puppets, propose a method to generate the high-quality gestures on the service robots, and verify the effectiveness.
Data fusion using Tabu crossover genetic algorithm in information retrieval

This article explores the odd and even point crossover based Tabu Genetic Algorithm. The search optimization tools equipped with exploration and exploitation operators. Those operators assist the optimization tools for finding the optimal solution. Few problems demand vigorous exploration and minimal exploitation. The vigorous exploration needs some specialized operators, which is capable of carrying out the task. In this article, we explore one such possible operator using odd and even point (OEP) crossover. The resultant hybrid GA namely OEP crossover based Tabu GA has two tuning factors namely tenure period and OEP crossover probability (Podd). The tenure period may be a single entity or a group of entities. However, Podd is single, as the tenure period is involved with group of entities, it demands some fine tuning. The fine tuning may alter the proportion of exploration and exploitation. Hence, we proposed a method for selecting the tenure period. The proposed exploration operator and the method for fixing the tenure period has been tested over the data fusion problem in information retrieval. The results look promising.
A secure and size efficient algorithm to enhance data hiding capacity and security of cover text by using unicode

With the advancement of technology, the maximum data hiding capacity and security of cover objects have become a very challenging task for researchers, particularly in text carrier. Text carrier depicts low hiding capacity but more secure for the detection of confidential information. It demands novelty in data hiding algorithms. In this regard, a novel algorithm is proposed by using steganography and cryptography together for the enhancement of capacity and security of confidential data. The recommended algorithm uses a linguistic steganography method to conceal data into the Arabic text carrier. In the described algorithm, the identification of secret information from text files is hard due to less redundant bits in the text as compared to the image, audio, and video steganographic mediums. The current solution uses Unicode characters such as Zero-Width-Character (ZWC) and Zero-Width-Joiner (ZWJ) to hide the secret information. Before hiding confidential information, secret data is encrypted by using bit inversion due to which algorithm achieved high security. It is observed from the simulation results that the proposed algorithm successfully achieved high cover medium capacity, security, and robustness.
Stemming javanese affix words using nazief and adriani modifications

Stemming is the process of finding a basic word with several stages of affix removal. The main reason for stemming is to check spelling and machine translation and to support the effectiveness of the retrieval process. This study uses the Nazief and Adriani algorithm for stemming Javanese-influenced words. The first step taken is data collection and making a basic word dictionary. Then do the stemming process. Before stemming, modifications are made to the rules. The rules of the Nazief and Adriani algorithm, which are based on the morphology rules of the Indonesian language, are modified to suit the morphological rules of the Javanese language. Of the 366 words that were tested, it produced 351 correct basic words and 15 basic words that experienced errors. The results show that this algorithm can be used for stemming Javanese with an accuracy value of 95.9%.
Epione: Lightweight Contact Tracing with Strong Privacy

Contact tracing is an essential tool in containing infectious diseases such as COVID-19. Many countries and research groups have launched or announced mobile apps to facilitate contact tracing by recording contacts between users with some privacy considerations. Most of the focus has been on using random tokens, which are exchanged during encounters and stored locally on users' phones. Prior systems allow users to search over released tokens in order to learn if they have recently been in the proximity of a user that has since been diagnosed with the disease. However, prior approaches do not provide end-to-end privacy in the collection and querying of tokens. In particular, these approaches are vulnerable to either linkage attacks by users using token metadata, linkage attacks by the server, or false reporting by users. In this work, we introduce Epione, a lightweight system for contact tracing with strong privacy protections. Epione alerts users directly if any of their contacts have been diagnosed with the disease, while protecting the privacy of users' contacts from both central services and other users, and provides protection against false reporting. As a key building block, we present a new cryptographic tool for secure two-party private set intersection cardinality (PSI-CA), which allows two parties, each holding a set of items, to learn the intersection size of two private sets without revealing intersection items. We specifically tailor it to the case of large-scale contact tracing where clients have small input sets and the server's database of tokens is much larger.
Web Pages Credibility Scores for Improving Accuracy of Answers in Web-Based Question Answering Systems

Web-based question answering (QA) systems are effective in corroborating answers from multiple Web sources. However, Web also contains false, fabricated, and biased information that can have adverse effects on the accuracy of answers in Web-based QA systems. Existing, solutions focus primarily on finding relevant Web pages but either do not evaluate Web pages’ credibility or evaluate two to three out of seven credibility categories. This research proposed a credibility assessment algorithm that uses seven categories, including correctness, authority, currency, professionalism, popularity, impartiality, quality, for scoring credibility, where each credibility category consists of multiple factors. The credibility assessment module is added on top of an existing QA system to score answers based on the credibility of Web pages. The system ranks answers based on the Web pages’ credibility from where answers have been taken. The research conducted extensive quantitative tests on 211 factoid questions, taken from TREC QA data from 1999-2001. Our research findings show that credibility categories including correctness, professionalism, impartiality, and quality significantly improved the accuracy of answers. On the other hand, categories such as authority, currency, popularity played a minor role instead. This research hopes to allow researchers and experts in using the Web credibility assessment model to improve the accuracy of information systems. Credibility scores should assist Web users in selecting credible information, while also forcing content creators to focus more on publishing credible content.
Hierarchical Clustering: a 0.585 Revenue Approximation

Hierarchical Clustering trees have been widely accepted as a useful form \nof clustering data, resulting in a prevalence of adopting fields\nincluding phylogenetics, image analysis, bioinformatics and more. \nRecently, Dasgupta (STOC 16') initiated the analysis of \nthese types of algorithms through the lenses of approximation. \nLater, the dual problem was considered by Moseley and Wang (NIPS 17') dubbing it the Revenue goal function. \nIn this problem, given a nonnegative weight $w_{ij}$ for each pair \n$i,j \in [n]=\{1,2, \ldots ,n\}$, the objective is to find a tree $T$ whose\nset of leaves is $[n]$ that maximizes the function \n$\sum_{i<j \in [n]} w_{ij} (n -|T_{ij}|)$, where $|T_{ij}|$ is the number\nof leaves in the subtree rooted at the least common ancestor of $i$ and $j$.\n\n \nIn our work we consider the revenue goal function and prove the following \nresults. First, we prove the existence of a bisection \n(i.e., a tree of depth $2$ in which the root has two children, each being\na parent of $n/2$ leaves) \nwhich approximates the general optimal tree solution up to a factor of \n$\frac{1}{2}$ (which is tight). Second, we apply \nthis result in order to prove a $\frac{2}{3}p$ approximation for the general revenue problem, where $p$ is defined as the approximation ratio of the \textsc{Max-Uncut Bisection} problem. \nSince $p$ is known to be at least $0.8776$ \citep{Better_Balance_by_Being_Biased_A_08776_Approximation_for_Max_Bisection} \citep{An_improved_semidefinite_programming_hierarchies_rounding_approximation_algorithm_for_maximum_graph_bisection_problems}, \nwe get a $0.585$ approximation algorithm for the revenue problem. This \nimproves a sequence of earlier results which culminated in an\n$0.4246$-approximation guarantee \citep{Bisect_and_Conquer:_Hierarchical_Clustering_via_Max-Uncut_Bisection}.
Longevity of Smart Homes

Progress in the field of IoT has enabled home automation and the interconnection of devices to a smart home. These smart homes are composed from various systems and physical devices and are interconnected according to an architecture. The system architectures are not optimized for the aspect of longevity of their installations. In our research, we focus on threats to the longevity of such systems and additionally apply these insights to the field of smart cities.
THE CAPABILITY ENHANCEMENT TRAINING READING COMPREHENSION: First and Second Grade in Enrekang Regency through Pictorial Story Media and SQ3R Method

Community service activities (PKM) carried out by the implementing team provided a brief description of the procedures for implemented and used pictorial story media and the SQ3R method in the learning process and the process of enhanced students' understanding ability in reading sources or reading material they had. The PKM activity was carried out in Enrekang Regency south Sulawesi district. The school chosen as the object of this PKM activity that was SMA Negeri 2 Enrekang. Participants who were the targets of this PKM activity were first and second grade students of SMA Negeri 2 Enrekang. The organizing committee for community service activity (PKM) chosen first and second grade students of SMA Negeri 2 Enrekang because most of their reading learning materials were closely related to understood process fill in the reading material in detail. The main objectives or benefits of the community service activity (PKM) were: first, the community service activities (PKM) provided knowledge about pictorial stories media and SQ3R method. And the second was community service activities (PKM) that provided tips, strategies, and new methods for English teachers in locations that were the object of community service activities (PKM) so that they could help them increased their students' reading comprehension. Keywords: Pictorial Story Media, and SQ3R Method.
Image Captioning using Facial Expression and Attention

Benefiting from advances in machine vision and natural language processing techniques, current image captioning systems are able to generate detailed visual descriptions. For the most part, these descriptions represent an objective characterisation of the image, although some models do incorporate subjective aspects related to the observer’s view of the image, such as sentiment; current models, however, usually do not consider the emotional content of images during the caption generation process. This paper addresses this issue by proposing novel image captioning models which use facial expression features to generate image captions. The models generate image captions using long short-term memory networks applying facial features in addition to other visual features at different time steps. We compare a comprehensive collection of image captioning models with and without facial features using all standard evaluation metrics. The evaluation metrics indicate that applying facial features with an attention mechanism achieves the best performance, showing more expressive and more correlated image captions, on an image caption dataset extracted from the standard Flickr 30K dataset, consisting of around 11K images containing faces. An analysis of the generated captions finds that, perhaps unexpectedly, the improvement in caption quality appears to come not from the addition of adjectives linked to emotional aspects of the images, but from more variety in the actions described in the captions.
Alzheimer Disease Prediction Model Based on Decision Fusion of CNN-BiLSTM Deep Neural Networks

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder. Early prediction of Alzheimer’s progression is a crucial process for the patients and their families. As a chronic disease, AD data are multimodal and time series in nature. Building a deep learning model to optimize multi-objective cost function produces a more stable and accurate model. In this paper, we propose a multimodal multitask deep learning model for AD progression detection based five time series modalities and a collection of static data. The model predicts AD progression as a multi-class classification task and four critical cognitive scores as regression tasks. The experimental results show that our model is medically intuitive, more accurate, and more stable than the state-of-the-art studies.
Deep Learning Applications in Medical Imaging

Deep learning (DL) and artificial intelligence (AI) are emerging tools in the healthcare sector for medical diagnostics. This chapter elaborates on general reasons for the popularity of computational techniques such as deep learning and machine learning (ML) applications in the medical image processing domain. The initial part of this chapter focuses on reviewing the fundamental concepts of DL algorithms, competence with machine learning, need in healthcare, applications, and challenges in medical image processing. Doing so allows understanding the reasons for the construction of all of them and offers a different view on various domains in the medical sector. The tools and technology required for DL, selection, implementation, optimization, and testing are discussed with respect to an application of cancer detection. Thus, this chapter gives an overall vision of deep learning concepts related to biomedical research.
Towards Software-Defined Buffer Management

Buffering architectures and policies for their efficient management are core ingredients of a network architecture. However, despite strong incentives to experiment with and deploy new policies, opportunities for changing anything beyond minor elements are limited. We introduce a new specification language, OpenQueue, that allows to express virtual buffering architectures and management policies representing a wide variety of economic models. OpenQueue allows users to specify entire buffering architectures and policies conveniently through several comparators and simple functions. We show examples of buffer management policies in OpenQueue and empirically demonstrate its impact on performance in various settings.
Partial Gated Feedback Recurrent Neural Network for Data Compression Type Classification

Owing to the widespread use of digital devices such as mobile phones and tablet PCs that are capable of easily viewing contents, the number of digital crimes committed using these digital devices has increased. One of the most common digital crimes is to hide the header information of the compressed data, which makes the user's data unusable. It is difficult to restore original data without the header because header contains the compression type. In this paper, we propose a Partial Gated Feedback Recurrent Neural Network (PGF-RNN) for the identification of lossless compression algorithms. We modify the gated recurrent units to improve the correlation of layers by grouping the fully-connected layers to effectively determine the characteristics of the compressed data. We emphasize on the temporal features, which consider a wide range of data, and spatial features from fully-connected layers to extract the feature vectors of each compression type. To improve the performance of the proposed PGF-RNN, we apply post-processing that considers the frequency of bit sequences on some compression types with similar compressed data. The proposed method is evaluated on 31 well-known lossless compression algorithms of the Association for Computational Linguistics dataset. The average top 1 accuracy of the proposed method is 92.63%.