Reviewerly/exHarmony huggingface dataset:
Report When Malicious: Deniable and Accountable Searchable Message-Moderation System

Encrypted retrieval ensures the secure retrieval over the encrypted data without sacrificing the confidentiality. Its applications in the database systems have brought this primitive under the spotlight. Once the malicious sender sends the wrong message, conventional encrypted retrieval is arduous to provide message-moderation and even abuse reporting arises when any receiver is allowed to fabricate and convince a malicious message. We introduce the idea of searchable message-moderation, a newly exquisite framework named CleanSE that enables the system to simultaneously provide encrypted retrieval and fallacious reporting resistance. In a nutshell, CleanSE is achieved via a securely technical combination of asymmetric message franking and searchable encryption. We design two CleanSE schemes. The first, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Recon</i> , leverages designated verifier signatures to generate a report proof such as to prevent fallacious report and undeniability, and assists with designated-server searchable encryption to protect confidentiality and privacy. Our improved scheme is called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reclean</i> . Compared with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Recon</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Reclean</i> adds an additional algorithm, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {Clean}$ </tex-math></inline-formula> , to filter forged message and employs asymmetric message franking to provide stronger deniability and accountability. We implement and evaluate prototype of our CleanSE system to highlight its feasibility and practicality.
ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems

In recent years, the usage of ensemble learning in applications has grown significantly due to increasing computational power allowing the training of large ensembles in reasonable time frames. Many applications, e.g., malware detection, face recognition, or financial decision-making, use a finite set of learning algorithms and do aggregate them in a way that a better predictive performance is obtained than any other of the individual learning algorithms. In the field of Post-Silicon Validation for semiconductor devices (PSV), data sets are typically provided that consist of various devices like, e.g., chips of different manufacturing lines. In PSV, the task is to approximate the underlying function of the data with multiple learning algorithms, each trained on a device-specific subset, instead of improving the performance of arbitrary classifiers on the entire data set. Furthermore, the expectation is that an unknown number of subsets describe functions showing very different characteristics. Corresponding ensemble members, which are called outliers, can heavily influence the approximation. Our method aims to find a suitable approximation that is robust to outliers and represents the best or worst case in a way that will apply to as many types as possible. A ‘softmax’ or ‘soft-min’ function is used in place of a maximum or minimum operator. A Neural Network (NN) is trained to learn this ‘soft-function’ in a two-stage process. First, we select a subset of ensemble members that is representative of the best or worst case. Second, we combine these members and define a weighting that uses the properties of the Local Outlier Factor (LOF) to increase the influence of non-outliers and to decrease outliers. The weighting ensures robustness to outliers and makes sure that approximations are suitable for most types.
Multi-objective Genetic Programming for Explainable Reinforcement Learning

Deep reinforcement learning has met noticeable successes recently for a wide range of control problems. However, this is typically based on thousands of weights and non-linearities, making solutions complex, not easily reproducible, uninterpretable and heavy. The present paper presents genetic programming approaches for building symbolic controllers. Results are competitive, in particular in the case of delayed rewards, and the solutions are lighter by orders of magnitude and much more understandable.
Direct Policy Optimization with Differentiable Physical Consistency for Dexterous Manipulation

We present a gradient-based direct policy optimization method for efficiently learning dexterous manipulation tasks in simulation. To overcome local minima and discontinuities, we use a physical consistency loss with additional control variables for contact points and forces. During training, the weights of a neural network are optimized to simultaneously fulfill task goals and to minimize physical inconsistencies. Our method is able to learn policies for manipulating objects with a humanoid hand-arm system within a few minutes on a single CPU.
Device-Type Profiling for Network Access Control Systems using Clustering-Based Multivariate Gaussian Outlier Score

Behaviour profiling is used in organisations to identify the working patterns of agents: humans or devices. It can be used to detect abnormal patterns of devices in an organisation's BYOD network to help control network access. Although BYOD offers great benefits of improving productivity and job satisfaction while reducing cost, it bears some security issues around access control with limitations in addressing insider threat scenarios. This means that motivated and determined attackers with valid access credentials can exploit the weaknesses to compromise the system. The limitation of mobile devices can mean that traditional network access control mechanism are ineffective in addressing insider threats, and can impact on device capacity and functionality. Thus, it is crucial to explore other ways of identifying insider threats from anomalous device behaviours. We propose a device-type profiling for threat detection which uses packet inter-arrival time patterns of devices for automatic identification of abnormal device-types. Experimental tests using clustering-based multivariate gaussian outlier score (CMGOS) to clearly distinguish and label normal and abnormal inter-arrival time patterns yielded promising results. This affirms the effectiveness of the proposed approach to support system administrators in monitoring and detecting insider threats for timely and effective access control response.
Enhancement Of Cloud User Data Access Security Entrusted to AI Face Recognition Techniques

As technology progresses, in figuring and broadcast communications, electronic images and video are playing more important roles in today's data age. The Human Face is a key component of video and audio databases utilized by observation frameworks. Recognizing and locating human features and face highlights in a photograph or series of photographs is a difficult task in unusual scenarios, such as recordings, when acoustic conditions, illumination, subject regions, and posture can vary dramatically from edge to edge. A Human Face is an automated framework that a school uses to monitor the engagement of its employees and students. Using the Real-Time Face Recognition utility, distinct customer faces are identified and perceived with the information base to look after a company's representatives and their activities. Although the cloud offers numerous advantages, it does have one clear disadvantage: the level of protection required to access user data. The cloud poses the possibility of unauthorized access to user data. The user data was taken from the cloud application due to a security issue in the cloud. As a result, many people are concerned about their data. So, in this study, we'll use Face Recognition Technology to solve the problem. Using Artificial Intelligence Face Recognition The user is the only one who has access to the cloud. If someone else tries to access or steal data from the cloud, it will notify the user. User data may be safeguarded and only the verified user can access the Cloud utilizing real Face Recognition Technology.
Adversarially Reprogramming Pretrained Neural Networks for Data-limited and Cost-efficient Malware Detection

To mitigate evolving malware attacks, machine learning models have been successfully deployed to detect malware. However, these models are often challenged by data scarcity, design efforts and constrained resources. Inspired by the adversarial vulnerability of machine learning, in this paper, we design a novel model Adv4Mal to adversarially reprogram an ImageNet classification neural network for malware detection in both white-box and black-box settings. As such, a small or moderate amount of data are sufficient to train a promising malware detection model, the varying software features can be uniformly processed without extra efforts, and the majority of computation can be wisely shared and reused to save the resources. This, to the best of our knowledge, has not yet been explored. Specifically, Adv4Mal proceeds by embedding software features into a host image to construct new data, and learning a universal perturbation to be added to all inputs in an imperceptible manner, such that the outputs of the pretrained model can be accordingly mapped to the final detection decisions for all software. We evaluate Adv4Mal on three software datasets. The experimental results demonstrate that Adv4Mal can successfully exploit ImageNet model's learning capability and limited data to achieve high performance in malware detection, and also yield significant advantages of model flexibility to different features, and cost efficiency in computing resources.
On the evaluation of NLP-based models for software engineering

NLP-based models have been increasingly incorporated to address SE problems. These models are either employed in the SE domain with little to no change, or they are greatly tailored to source code and its unique characteristics. Many of these approaches are considered to be outperforming or complementing existing solutions. However, an important question arises here: Are these models evaluated fairly and consistently in the SE community?. To answer this question, we reviewed how NLP-based models for SE problems are being evaluated by researchers. The findings indicate that currently there is no consistent and widely-accepted protocol for the evaluation of these models. While different aspects of the same task are being assessed in different studies, metrics are defined based on custom choices, rather than a system, and finally, answers are collected and interpreted case by case. Consequently, there is a dire need to provide a methodological way of evaluating NLP-based models to have a consistent assessment and preserve the possibility of fair and efficient comparison.
Digital Forensics Investigation for Attacks on Artificial Intelligence

The new research approaches are needed to be adopted to deal with security threats in Artificial Intelligence (AI)-based systems. This research is aimed at investigating the AI attacks that are “malicious by design.” It also deals with conceptualization of the problem and strategies for attacks on AI using digital forensic tools. A specific class of problems in adversarial attacks are tampering of images for computational processing in applications of digital photography, computer vision, pattern recognition (facial capping algorithms). State-of-the-art developments in forensics, such as 1. Application of end-to-end Neural Network training pipeline for image rendering and provenance analysis. 2. Deep fake image analysis using frequency methods, wavelet analysis, and tools like Amped Authenticate. 3. Capsule networks for detecting forged images. 4. Information transformation for feature extraction via image forensic tools, such as EXIF-SC, Splice Radar, and Noiseprint. 5. Application of generative adversarial networks (GAN) based models as anti-image forensics [8], will be studied in great detail and a new research approach will be designed incorporating these advancements for utility of digital forensics.
Knowledge-enhanced Spherical Representation Learning for Text Classification

We introduce Knowledge-enhanced Spherical Representation Learning (K-SRL), a generative probabilistic model of text documents that combines word embeddings and knowledge graph embeddings to effectively encode the semantic information of text and the related background knowledge into a low-dimensional representation. More specifically, the proposed model represents each text document as a combination of both words and entities linked to an external large knowledge graph and models them as points on the unit hypersphere using the von Mises-Fisher distribution. Furthermore, we develop an efficient variational Bayesian inference algorithm to learn unsupervised text embeddings in the spherical space. Experimental results on multiple benchmark datasets demonstrate that our model outperforms existing probabilistic models on common text classification tasks, including text categorization and sentiment analysis.MSC codesRepresentation learningvon Mises-FisherKnowledge graph embeddingBayesian inference
Improving Security with Federated Learning

Data leakage is the intentional or unintended transmission of stable or personal data to outside recipient. Such leakage in mobile community increases the chance of compilation. Hence encryption and storage of the secure data must be accomplished by usage of a few techniques. Federated learning (FL), which falls under distributed machine learning, helps preserve clients’ private data on various device as the centralized model receives only weight updates. Sensitive private data is open for access by analyzing submitted attributes from clients using techniques like weights developed in deep neural networks. To effectively preserve statistics from leakage, this study analyzes a novel framework using differential privacy (DP), in which synthetic noises are provided to parameters on the customers' side prior to aggregation, FLAG <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">noise</inf> (FL with noise aggregated).The system analyses the dataset consisting of information about the client. Federated learning with Advanced Encryption Standard (AES) algorithm and Differential privacy is then applied. It is found that the Federated learning model have better privacy than the Differential privacy model and gives the accuracy of 97.3%.
Few-Shot Image and Sentence Matching via Aligned Cross-Modal Memory

Image and sentence matching has attracted much attention recently, and many effective methods have been proposed to deal with it. But even the current state-of-the-arts still cannot well associate those challenging pairs of images and sentences containing few-shot content in their regions and words. In fact, such a few-shot matching problem is seldom studied and has become a bottleneck for further performance improvement in real-world applications. In this work, we formulate this challenging problem as few-shot image and sentence matching, and accordingly propose an Aligned Cross-Modal Memory (ACMM) model to deal with it. The model can not only softly align few-shot regions and words in a weakly-supervised manner, but also persistently store and update cross-modal prototypical representations of few-shot classes as references, without using any groundtruth region-word correspondence. The model can also adaptively balance the relative importance between few-shot and common content in the image and sentence, which leads to better measurement of overall similarity. We perform extensive experiments in terms of both few-shot and conventional image and sentence matching, and demonstrate the effectiveness of the proposed model by achieving the state-of-the-art results on two public benchmark datasets.
Strategies for Bridging the Digital Divide by Enhancing Communication Competence

The digital divide reflects disparities in social status and well-being that depend on access to physical infrastructure, acquired skills in the use of hardware and software, ability to interact in cyberspace. The pandemic shifted all activities to a remote mode, prompting people to enhance their communicative competence. The survey shows a funnel of online-communication choices depending on the criteria of experience and education; higher the intensity of activity and education, narrower the scope and nature of the content for networking, less time spent online.
Spatial-temporal Transformer For Skeleton-based Action Recognition

In the area of skeleton-based human action recognition, GCN has achieved good results in previous research due to its excellent modeling ability on graph data. Recently, transformers have achieved extraordinary results in many computer vision fields. Comparing transformer and GCN, from a certain point of view, we can regard transformer as a kind of dynamic GCN, and the weight of each node is dynamically determined by data. In this work, a three-dimensional position encoding was proposed by us to solve the representation of node spatial information, in order to apply the transformer to the graph data. In addition, similar to Spatial-Temporal Graph Convolutional Networks (ST-GCN), we proposed a Space-Time Transformer (ST-TR), which applies transformers in space and time to extract spatiotemporal feature of skeleton data to complete action recognition.
The Use of Internet Technologies in Teaching Bachelors-Economists Mathematics as a Factor of Students’ Professional Growth

Abstract. Personal professional growth enhances the human psyche, introduces special meaning into human life activity. Mathematical education, as one of the crucial aspects in training a specialist, should reflect the nature of future professional activity and contribute to students’ professional growth. To a large extent, it can be related to mathematical training of economics students in which information and communication technologies, due to the uniqueness of the interaction of educational mathematics content and economic reality, can serve as an important means of expanding the professional orientation of education. Methods. One of the modern Internet-based interactive educational technologies which enable students’ professional growth is an educational webquest. Educational mathematical webquests contribute to the professional self-development and self-organisation of economics students. The authors have developed educational mathematics webquests for bachelors of economics. Results. The diagnostics of educational findings has proven the increase of the cognitive motivation to teaching mathematics and value attitude to the knowledge bound with professional interests; high level of fundamental and applied knowledge of mathematics necessary for the future professional activity; considerable improvement of research skills; intensification of creativity and reflection; capability to perform self-analysis and self-assessment of one’s personal activity. Conclusion. Serving as a key factor in training a perspective specialist, mathematical education should reflect the nature of future professional activity and stimulate a student’s professional growth. Educational mathematical webquests promote professional self-development of economics students.
Diagnosis and prognosis prediction of ovarian cancer with feedforward neural network by mining real-world laboratory tests

Objective: Ovarian cancer remains the most lethal gynecological cancer, mainly because of the lack of an effective screening strategy. We aimed to demonstrate the value of laboratory tests in ovarian cancer detection and prognosis prediction with deep learning in a real-world setting.
The Impact of Online Learning on the Learning Motivation of Junior High School Students

Online learning is a basic need of education in the 21st century. The benefits of online learning are felt in the continuity of education during the Covid-19 pandemic. This study aims to examine the impact of online learning on the learning motivation of Junior High School (SMP) students. The results of the research discussion indicate that online learning has advantages, namely 1) as a medium to encourage independent learning; 2) as an effort to modernize the concept of education in the 21st century; 3) increasing the intensity of the interaction between teachers and students; 3) allows for an interaction that is not limited to space and time, and 4) meaningful learning. The impact of online learning on student learning motivation tends to be negative due to the unpreparedness of various parties, including teachers, parents, students, and schools. Early adolescents who need direct guidance, direction, and motivation must lose control due to reduced direct interaction with the teacher. The monotonous implementation of online learning, lack of direction and supervision, pressure for students to study from home, and low teacher innovation are the main causes of decreased learning motivation due to online learning.
Local institutions on small firm investments: degrees of institutional persistence matter

This study investigates the influence of local institutions including corruption, administration transparency, and leadership proactivity on small firm investment. Drawing from the institutional theory, we suggest that the persistence (resistance to changes) of institutional forces is an important determinant of their effects on investment. Using a dataset of 945,725 firm observations in Vietnam from 2006 to 2015, we find that: 1) investment is a U-shaped function of corruption controls; 2) transparency has a positive effect on investment; 3) leadership proactivity can moderate the adverse effects of corruption and opaqueness in public services. This variety in the association patterns between investment and institutional forces is due to the degree of institutional persistence in local norms of doing business.
Environmental Monitoring using Embedded Systems on UAVs

Nowadays, Earth is being affected by the greenhouse effect. A first step to improve the current situation is to track the involved variables under temporal and spatial sampling. In this paper, we propose an integrated system for monitoring the related greenhouse effect gasses and several environmental variables. The system has been designed to be mounted on an unmanned aerial vehicle to overcome the spatial constraints and it also has been designed to work under the Internet of Things paradigm to overcome the temporal constraints. Unlike previous approaches, we have integrated an optimal filtering step with Kalman Filter, improving the reliability and precision of the measurements. Our experiments show that the proposed system can provide the information to the final user in near real-time. In addition, the use of the Kalman filter decreases the mean square error of our system with respect to a reference sensor.
An Efficient Email Spam Detection using Support Vector Machine

This research paper proposes the electronics mail is small known as E-mail is used for communication between the people to person. E mail is providing as an necessary contribution for messaging by internet. Spam e mails are the unwanted messages that arise in high volume and are used by spammers for revealing users personal credentials. These e mails are regularly some sort of company/control announcement or viruses that the user receive without any notification. So as to defeat it, there need aid exactly existing frameworks that still don't keep them from striking. Therefore, there is a require should manufacture and proficient framework that adequately detects and more keeps the spam messages In those server utilizing the Naïve bayes classifier. Naïve bayes classifier is a mainstream statistical classifier utilized fundamentally for content arrangement.