Reviewerly/exHarmony huggingface dataset:
Reconstructing Training Data from Diverse ML Models by Ensemble Inversion

Model Inversion (MI), in which an adversary abuses access to a trained Machine Learning (ML) model attempting to infer sensitive information about its original training data, has attracted increasing research attention. During MI, the trained model under attack (MUA) is usually frozen and used to guide the training of a generator, such as a Generative Adversarial Network (GAN), to reconstruct the distribution of the original training data of that model. This might cause leakage of original training samples, and if successful, the privacy of dataset subjects will be at risk if the training data contains Personally Identifiable Information (PII). Therefore, an in-depth investigation of the potentials of MI techniques is crucial for the development of corresponding defense techniques. High-quality reconstruction of training data based on a single model is challenging. However, existing MI literature does not explore targeting multiple models jointly, which may provide additional information and diverse perspectives to the adversary.We propose the ensemble inversion technique that estimates the distribution of original training data by training a generator constrained by an ensemble (or set) of trained models with shared subjects or entities. This technique leads to noticeable improvements of the quality of the generated samples with distinguishable features of the dataset entities compared to MI of a single ML model. We achieve high quality results without any dataset and show how utilizing an auxiliary dataset that’s similar to the presumed training data improves the results. The impact of model diversity in the ensemble is thoroughly investigated and additional constraints are utilized to encourage sharp predictions and high activations for the reconstructed samples, leading to more accurate reconstruction of training images.
Non-malleable Vector Commitments via Local Equivocability

Vector commitments (VCs), enabling to commit to a vector and locally reveal any of its entries, play a key role in a variety of both classic and recently-evolving applications. However, security notions for VCs have so far focused on passive attacks, and non-malleability notions considering active attacks have not been explored. Moreover, existing frameworks that may enable to capture the non-malleability of VCs seem either too weak (non-malleable non-interactive commitments that do not account for the security implications of local openings) or too strong (non-malleable zero-knowledge sets that support both membership and non-membership proofs).
Clean Code and Design Educational Tool

Many different code snippets can implement the same software feature. However, a significant subset of these possible solutions contains difficult-to-understand code that harms the software's maintainability and evolution. Such low-quality code snippets directly harm profit, as frequent and fast code change enables businesses to seize new opportunities. Unfortunately, they are also prevalent in an industry that consists mostly of junior programmers. We developed a platform called Clean CaDET to tackle the prevalence of low-quality code from two angles. The Smell Detector module presents a framework for integrating AI-based code quality assessment algorithms to identify low-quality code as the programmer is writing it. The Smart Tutor module hosts a catalog of educational content that helps the programmer understand the identified issue and suggests possible solutions. By combining the quality assessment with the educational aspect, our integrated solution presents a novel approach for increasing the quality of code produced by our industry.
Automatic Tuning of Tensorflow’s CPU Backend Using Gradient-Free Optimization Algorithms

Modern deep learning (DL) applications are built using DL libraries and frameworks such as TensorFlow and PyTorch. These frameworks have complex parameters and tuning them to obtain good training and inference performance is challenging for typical users, such as DL developers and data scientists. Manual tuning requires deep knowledge of the user-controllable parameters of DL frameworks as well as the underlying hardware. It is a slow and tedious process, and it typically delivers sub-optimal solutions. In this paper, we treat the problem of tuning parameters of DL frameworks to improve training and inference performance as a black-box optimization problem. We then investigate applicability and effectiveness of Bayesian optimization, genetic algorithm, and Nelder-Mead simplex to tune the parameters of TensorFlow's CPU backend. While prior work has already investigated the use of Nelder-Mead simplex for a similar problem, it does not provide insights into the applicability of other more popular algorithms. Towards that end, we provide a systematic comparative analysis of all three algorithms in tuning TensorFlow's CPU backend on a variety of DL models. Our findings reveal that Bayesian optimization performs the best on the majority of models. There are, however, cases where it does not deliver the best results.
HOPE-Graph: A Hypothesis Evaluation Service considering News and Causality Knowledge

Daily released news and analyst reports can affect economic activities such as product marketing and stock trading. At the same time, we often interpret news by using our domain knowledge to see what influence an event might have. Therefore, it is an important analytical task to evaluate hypotheses or opinions by considering simultaneously the public opinion formed by these news sources and one’s own causality knowledge. In this paper, we present HOPE-Graph: a hypothesis evaluation service that considers news and the knowledge of causal relationships between hypotheses. This service is implemented by integrating news analysis obtaining pro/con rates for hypotheses into a hypothesis evaluation framework that propagates pro/con rates based on positive/negative causal relationships. The framework is conceived as an abstract argumentation framework that evaluates arguments on the basis of the support/attack relationships between them. By using the causality knowledge, it is possible to obtain a score that preemptively incorporates the score of the causal hypothesis. To investigate the effectiveness of our service, we checked the Granger causality from the score of the hypothesis of a company’s performance to the stock price and found that proper consideration of causality knowledge can lead to scores that are more relevant to stock prices in more cases.
gGuard: Enabling Leakage-Resilient Memory Isolation in GPU-accelerated Autonomous Embedded Systems

Graphics processing units (GPUs) are being widely used as co-processors for performance acceleration in many autonomous embedded systems such as robotics and autonomous vehicles. However, current GPU hardware and systems software, including GPU device drivers, compilers, and operating systems, do not implement proper memory protection mechanisms due to performance and proprietary reasons, causing severe vulnerabilities such as information leakage. In this paper, we present gGuard, a leakage-resilient GPU memory management system with strong isolation. Based on the intrinsic characteristics of information leakage vulnerabilities on GPUs, gGuard develops a set of efficient and accurate data shredding techniques implemented at the compiler, library, and operating system levels, with the core idea of exploring the data access patterns and dependencies for efficient application-aware data shredding. Our implementation and evaluation show that gGuard can provide effective mitigation on GPU data leakage issues through efficient GPU data shredding while introducing less than 6% overhead in all tested scenarios.
A Survey on Visual Question Answering

Visual Question Answering (VQA) is an inspiring task that includes two major fields of AI namely Natural Language Processing and Computer Vision in which an image is given with a question in natural language and the task is to find the answer to that question. The Visual Question Answering (VQA) task involves challenges in which we have to process the data with visual as well as linguistic processing to find the answer of basic common sense questions related to a given image. This task requires reasoning capabilities on visual features and objects of the image along with that general knowledge to predict the correct answer to the given question. In this survey paper, we will discuss various state-of-the-art methodologies, algorithms, and datasets for VQA and breakthrough timeline in Visual Question Answering in detail. Some common techniques of combining recurrent and convolutional neural networks with LSTM or GRU in order to map questions as well as the image to a common interface are also explored. Each dataset contains questions of a distinct level of complexity. Different types of reasoning and capabilities are required to solve various complex images. In this survey, we will also cover current VQA datasets released in this field along with various types of question patterns and machine learning (ML)models. Finally, DL models which shown excellent performance on various benchmark VQA datasets are discussed.
Main Path Analysis to Filter Unbiased Literature

Citations are references used by researchers to recognize the contributions of researchers in their articles. Citations can be used to discover hidden patterns in the research domain, and can also be used to perform various analyses in data mining. Citation analysis is a quantitative method to identify knowledge dissemination and influence papers in any research area. Citation analysis involves multiple techniques. One of the most commonly used techniques is Main Path Analysis (MPA). According to the specific use of MPA, it has evolved into various variants. Currently, MPA is carried out in different domains, but deep learning in the field of remote sensing has not yet been considered. In this paper, we have used three centrality attributes which are Degree, Betweenness and Closeness centrality to automatically identify important papers by applying clustering method based on machine learning (i.e., K-means). In addition, the main path is drawn from important papers and compared with existing manual methods. In order to conduct experiments, a data set from Web of Science (WOS) has been established, which contains 538 papers in the field of deep learning. Compared with existing works, our method provides the most relevant papers on the main path.
Accidents Analysis and Severity Prediction Using Machine Learning Algorithms

Traffic congestion and road accidents have been important public challenges that impose a big burden on society. It is important to understand the factors that contribute to traffic congestions and road accidents so that effective strategies can be implemented to improve the road condition. The analysis on traffic congestion and road accidents is very complex as they not only affect each other but are also affected by many other factors. In this research, we use the US Accidents data from Kaggle that consists of 4.2 million accident records from February 2016 to December 2020 with 49 variables for the study. We propose to use statistical techniques and machine learning algorithms that include Logistic Regression, Tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train a large amount of data to obtain predictive models for traffic congestion and road accidents. The proposed predictive models are expected to be more accurate by incorporating the impact of multiple environmental parameters. The proposed models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
Consensus-Based Unbiased Converted Measurement Information Filter for Space Target Tracking

Distributed state estimation algorithm plays a key role in target tracking via sensor networks. This paper proposes a consensus-based unbiased converted measurement information filter for target tracking in a space-based radar network. The proposed filter can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can linearizing the measurement equation by employing the unbiased measurement conversion method. The performance of the proposed filter is investigated by considering a low Earth orbit target tracking problem. The simulation results show that the proposed algorithm outperforms the traditional consensus-based distributed state estimation method in aspect of convergence rate. Also, the impact of measurement errors on the accuracy of the proposed method is researched.
Classification of Cognitive Load based on Oculometric Features

Cognitive load is related to the amount of working memory resources used in the execution of various mental tasks. Different multimodal features extracted from peripheral physiology, brain activity, and oculometric reactions have been used as non-intrusive, reliable, and objective measures of cognitive load. In this paper, we use data from 38 participants performing a four-level difficulty n-back task (0-, 1-, 2-, and 3-back task), with their oculometric reactions simultaneously recorded. Based on the neuroanatomic structure and function of the visual system, 26 oculometric features are extracted and organized into 3 groups related to: pupil dilation (PD), blinking, and fixation. The discriminative power of each group of features was evaluated in four-level cognitive load classification using a support vector machine (SVM) model and feature selection, and the achieved classification accuracies were: 33.33% using only pupil dilation features, 30.90% using only blink-related features, 30.21% using only fixations-related features. Finally, a 36.11% classification accuracy was achieved using a combination of all extracted oculometric features. The presented results show that various groups of oculometric features provide complementary information about the subject's cognitive load. The comparison of the extracted groups of features is given, and the most important features in terms of classification performance are discussed.
Data Mining Classification Techniques to Assign Individual Personality Type and Predict Job Profile

This paper proposes an advanced personality classification and professional profile prediction system. The system grasps from the personality patterns by using advanced data mining to mine user characteristics data. This E based on its past classifications from the data mining algorithms, and it classifies or predicts the user's personality. Several user characteristics and behaviors are analyzed by this system and based on the patterns observed, it stores its own user characteristics patterns which then are stored in a database. On the basis of personality data which was stored by classification of previous user data, the system predicts user personality. This can further be used for job prediction, subject selection, etc. This system is useful for hiring purposes of companies as well as for characterization of team members already working in a professional environment.
The value of conscious paternity as a priority direction in the vocational training of social workers

The objective of the research consisted in establishing and experimentally testing the methodology of formation of the value of conscious parenting in future specialists of social work, for its transformation into professional and evaluative orientation. Methods of analysis, systematization and generalization of ideas and scientific approaches, specification of practical tasks, the pedagogical experiment and pedagogical diagnostic methods were used to study the real state of the problem and achieve the objective of the research. As a result, based on the authors' interpretations of the main concepts investigated, a model for transforming the personal value of conscious parenting into a professional and value orientation has been suggested. A workshop has been suggested as an effective way of working. The authors established that the optimization of the process of formation of the value of conscious parenthood in social workers and its transformation into professional and value orientation, depends on the modification of the educational process in the institution of higher education through innovative processes, content and use of interactive forms and methods.
Active Human Pose Estimation for Assisted Living

Active and Assisted Living has found itself in one of the application areas of technological advancement the world is witnessing. The objective is to provide the elderly people with facilitated living environment so as to assist them in carrying out daily activities without them being prone to injury or any other undesirable event. These residential facilities prove to be even more beneficial when equipped with technology to prevent any fatalities or aid immediately in case fatality occurs. One such harmful event is falling. Falling especially in the case of elderly can have serious impacts on their health. Hence, several attempts have been made to provide aid immediately whenever such event occurs. This includes usage of different techniques like Wearable sensors, Computer vision or Ambient sensors. This paper aims at exploring Computer vision technique to determine fall. For this, key points of human body are located which are then used to identify if the fall has occurred or not. The proposed algorithm uses publicly available dataset to train on detecting fall. Several classifiers like SVM, AdaBoost, Logistic Regression has been used for classification with SVM reporting 82.07% accuracy, AdaBoost with 99.64% accuracy and Logistic Regression with 98.92% accuracy.
Improving the Role Model of Community Auxiliary Robots Testing Using Robot Operating System 2

Over the past few years, robots have undergone significant developments. One of this development is socially assistive robots (SARs) which are able to assist users in the form of social interaction. However, due to their nature which involves direct interaction with the user, testing SARs could be difficult and risky. For this reason, this study proposes a simulation environment for testing SARs created using the Gazebo simulator. In this simulation environment, the robot model will be tested virtually with a user model and other object models. In order for the test performed in the simulation could be applied to real robots, the controller system in the robot will be abstracted by separating each component into nodes using ROS 2. As a result, the system created can produce the same action in moving the robot model and the real robot with an error difference of 2.6\% in the simulation and 12.5\% in the real world. With the performance of ROS 2, images delivery with a resolution of up to 640 x 480 can produce delays below 50 ms and frequencies above 90\% on the same device and between devices. In the simulation, the user model proved capable of being used to simulate the user through the results of the pose detection experiment, while the simulation environment proved capable of being used to simulate the room through the results of the SLAM experiment.
Enhanced global and local face feature extraction for effective recognition of facial emotions

Abstract Emotion recognition is a challenging task in the field of human computer interaction. For a successful human emotion recognition system, a robust, discriminative, and sensitive feature extraction is an essential need. In this article, the extraction of global features is done by the proposed frequency decoded lifting wavelet pattern descriptor (FDLWP) and extraction of local features is done by the proposed local gradient difference zig‐zag pattern descriptor (LGDZP). The face parts are detected using viola jones algorithm and the selection of optimal face active regions is accomplished by the calculation of structural similarity index measure. Eventually the local spatial zig‐zag structure of the face region is utilized to attain LGDZP descriptor. The fusion of local and global features is accomplished using canonical correlation analysis. The classification made using bank of restricted Boltzmann machine (RBM) classifiers yields promising results for the proposed method. Furthermore, the proposed recognition method delivers a promising accuracy in varying illumination, occlusion, and noise. The accuracy of the method is analyzed by doing experiments with the databases such as JAFFE, CK+, MMI, Oulu‐CASIA, and SFEW. The obtained results of the proposed method yield better accuracy than the existing state of art methods in this field.
Trust Modeling in Recommendation

In social networks, trust is a fundamental notion affecting the nature and the strength of ties between individuals. It is also a piece of useful auxiliary information for improving the performance of recommendation systems. The number of ratings given by a user is minimal compared to all items in popular, widely-used e-commerce sites. Therefore, the user-item matrix that is used in collaborative filtering suffers from data sparsity, resulting in poor recommendation quality. Another issue is the cold start problem, which occurs for the inclusion of new users and new items to the system. Trust notion is helpful for alleviating the effect of these problems by providing additional relationships between the users and pointing out strong relationships. Information as to the trust between users can be explicitly available. However, such information is not widely available, and hence implicit trust models have been employed. This work analyzes two sub-problems under trust modeling for recommendation: (1) What is the relationship between explicit and implicit trust scores, are they replaceable? (2) Can we model explicit trust in a trust network? For the first problem, we present an implicit trust model and analyze the compatibility of implicit and explicit trust scores. For the second problem, we model explicit trust modeling as a link prediction problem and analyze the performance of the prediction models we generate on a set of benchmark data sets.
Security Testing for Blockchain Enabled IoT System

The IoT and blockchain were the biggest technology that was growing in day to day life. The testing as well as validating is the one that deals with all the errors. The testing and validating are heterogeneous, and it has its own challenges. The emphasis on formal confirmation procedures has implied that less consideration has been given toward testing systems. The scope of this work relies on security testing for blockchain-enabled IoT system. The blockchain is used to store the database and provides security from unauthorized user. Right now, we talk about plan, testing strategies, furthermore, instruments to permit security testing at scale of blockchain and IoT applications. A framework influences Ethereum for authentication, reputation, identity of IoT gadgets. Gadgets are enlisted in a keen agreement by means of a web interface and send cryptographically marked messages to a stage that validates them by blockchain. Then by using security testing, it determines the data and the resources are protected and also it will be preserved from vulnerabilities and checks if it provides better platform for authorized users.