Reviewerly/exHarmony huggingface dataset:
CVShield

The emerging Connected Vehicle (CV) technology enables vehicles to wirelessly exchange safety and mobility information (e.g., location and speed) with traffic infrastructure and other vehicles. Existing CV applications heavily rely on sensor inputs (e.g., GPS). However, previous work has shown that the attacker can cause severe congestion or increased safety risks by compromising vehicles and broadcasting falsified sensor data. Thus, it is highly desirable to ensure the integrity of sensor data. In this paper, to prevent compromised vehicles from sending falsified sensor data, we propose a system CVShield, which utilizes the recent advances in hardware-assisted security (e.g., ARM TrustZone). CVShield can ensure the integrity of the sensor data from their reading to their transmission at the vehicle side. In general, we relocate all codes that are related to sensor data reading, processing, encapsulation, and transmission from the rich execution environment (REE) into the trusted execution environment (TEE). However, manually extracting code sections is laborious and error-prone. Also, we should minimize the size of the trusted computing base (TCB) in TEE to reduce the attack surface. To achieve these goals, we propose to leverage program slicing to automatically extract code sections and eliminating irrelevant codes in large codebases. Our initial results demonstrate that CVShield can support GPS data reading, and our optimization can eliminate the time overhead introduced by context switches of TrustZone.
Personality Dimensions of Intelligent Personal Assistants

An earlier study of intelligent personal assistants (IPAs) identified a large number of user utterances aimed at understanding an IPA's personality by testing its responses to questions about itself and about users. Based on primary and secondary sources, we developed a dataset of IPA-directed personality utterances and analyzed IPA responses to such utterances from four major IPAs-Amazon Alexa, Google Assistant, Apple Siri and Microsoft Cortana-using Fiske's stereotype content model. The model supports classification of user perceptions of other people or objects on warmth and competence dimensions. The findings suggest that while all four IPAs were frequently perceived as competent, Alexa and Siri were more frequently perceived as low-warmth compared to the higher-warmth Google Assistant and Cortana. Future work aims to verify the initial classification of IPA personality responses using warmth-competence dimensions, and test user demographic and situational preferences for personality manifestations in IPA responses.
Multi-Swarm Cuckoo Search Algorithm with <i>Q-</i>Learning Model

Abstract As a novel swarm intelligence optimization algorithm, cuckoo search (CS) has been successfully applied to solve diverse problems in the real world. Despite its efficiency and wide use, CS has some disadvantages, such as premature convergence, easy to fall into local optimum and poor balance between exploitation and exploration. In order to improve the optimization performance of the CS algorithm, a new CS extension with multi-swarms and Q-Learning namely MP-QL-CS is proposed. The step size strategy of the CS algorithm is that an individual fitness value is examined based on a one-step evolution effect of an individual instead of evaluating the step size from the multi-step evolution effect. In the MP-QL-CS algorithm, a step size control strategy is considered as action, which is used to examine the individual multi-stepping evolution effect and learn the individual optimal step size by calculating the Q function value. In this way, the MP-QL-CS algorithm can increase the adaptability of individual evolution, and a good balance between diversity and intensification can be achieved. Comparing the MP-QL-CS algorithm with various CS algorithms, variants of differential evolution (DE) and improved particle swarm optimization (PSO) algorithms, the results demonstrate that the MP-QL-CS algorithm is a competitive swarm algorithm.
Hybrid Collaborative Recommendation via Dual-Autoencoder

With the rapid increase of internet information, personalized recommendation systems are an effective way to alleviate the information overload problem, which has attracted extensive attention in recent years. The traditional collaborative filtering utilizes matrix factorization methods to learn hidden feature representations of users and/or items. With deep learning achieved good performance in representation learning, the autoencoder model is widely applied in recommendation systems for the advantages of fast convergence and no label requirement. However, the previous recommendation systems may take the reconstruction output of an autoencoder as the prediction of missing values directly, which may deteriorate their performance and cause unsatisfactory results of recommendation. In addition, the parameters of an autoencoder need to be pre-trained ahead, which greatly increases the time complexity. To address these problems, in this paper, we propose a Hybrid Collaborative Recommendation method via Dual-Autoencoder (HCRDa). More specifically, firstly, a novel dual-autoencoder is utilized to simultaneously learn the feature representations of users and items in our HCRDa, which obviously reduces time complexity. Secondly, embedding matrix factorization into the training process of the autoencoder further improves the quality of hidden features for users and items. Finally, additional attributes of users and items are utilized to alleviate the cold start problem and to make hybrid recommendations. Comprehensive experiments on several real-world data sets demonstrate the effectiveness of our proposed method in comparison with several state-of-the-art methods.
Silhouette-Assisted 3D Object Instance Reconstruction from a Cluttered Scene

The objective of our work is to reconstruct 3D object instances from a single RGB image of a cluttered scene. 3D object instance reconstruction is an ill-posed problem due to the presence of heavily occluded and truncated objects, and self-occlusions that lead to substantial regions of unseen areas. Previous works for 3D reconstruction take clues from object silhouettes to carve reconstructed outputs. In this paper, we explore two ways to include silhouette learnable in the network for 3D instance reconstruction from a single cluttered scene image. To this end, in the first approach, we automatically generate instance-specific silhouettes that are compactly encoded within our network design and used to improve the reconstructed 3D shapes; in the second approach, we find an efficient design to regularize object reconstruction explicitly. Experimental results on the SUNCG dataset show that our methods have better performance than the state-of-the-art.
Micro-architectural analysis of OLAP

Understanding micro-architectural behavior is important for efficiently using hardware resources. Recent work has shown that in-memory online transaction processing (OLTP) systems severely underutilize their core micro-architecture resources [29]. Whereas, online analytical processing (OLAP) workloads exhibit a completely different computing pattern. OLAP workloads are read-only, bandwidth-intensive, and include various data access patterns. With the rise of column-stores, they run on high-performance engines that are tightly optimized for modern hardware. Consequently, micro-architectural behavior of modern OLAP systems remains unclear. This work presents a micro-architectural analysis of a set of OLAP systems. The results show that traditional commercial OLAP systems suffer from their long instruction footprint, which results in high response times. High-performance columnstores execute tight instruction streams; however, they spend 25 to 82% of their CPU cycles on stalls both for sequential- and random-access-heavy workloads. Concurrent query execution can improve the utilization, but it creates interference in the shared resources, which results in sub-optimal performance.
Intelligent User Assistance for Automated Data Mining Method Selection

Abstract In any data science and analytics project, the task of mapping a domain-specific problem to an adequate set of data mining methods by experts of the field is a crucial step. However, these experts are not always available and data mining novices may be required to perform the task. While there are several research efforts for automated method selection as a means of support, only a few approaches consider the particularities of problems expressed in the natural and domain-specific language of the novice. The study proposes the design of an intelligent assistance system that takes problem descriptions articulated in natural language as an input and offers advice regarding the most suitable class of data mining methods. Following a design science research approach, the paper (i) outlines the problem setting with an exemplary scenario from industrial practice, (ii) derives design requirements, (iii) develops design principles and proposes design features, (iv) develops and implements the IT artifact using several methods such as embeddings, keyword extractions, topic models, and text classifiers, (v) demonstrates and evaluates the implemented prototype based on different classification pipelines, and (vi) discusses the results’ practical and theoretical contributions. The best performing classification pipelines show high accuracies when applied to validation data and are capable of creating a suitable mapping that exceeds the performance of joint novice assessments and simpler means of text mining. The research provides a promising foundation for further enhancements, either as a stand-alone intelligent assistance system or as an add-on to already existing data science and analytics platforms.
When Autonomous Intelligent Goodware Will Fight Autonomous Intelligent Malware: A Possible Future of Cyber Defense

In the coming years, the future of military combat will include, on one hand, artificial intelligence-optimized complex command, control, communications, computers, intelligence, surveillance and reconnaissance (C4ISR) and networks and, on the other hand, autonomous intelligent Things fighting autonomous intelligent Things at a fast pace. Under this perspective, enemy forces will seek to disable or disturb our autonomous Things and our complex infrastructures and systems. Autonomy, scale and complexity in our defense systems will trigger new cyber-attack strategies, and autonomous intelligent malware (AIM) will be part of the picture. Should these cyber-attacks succeed while human operators remain unaware or unable to react fast enough due to the speed, scale or complexity of the mission, systems or attacks, missions would fail, our networks and C4ISR would be heavily disrupted, and command and control would be disabled. New cyber-defense doctrines and technologies are therefore required. Autonomous cyber defense (ACyD) is a new field of research and technology driven by the defense sector in anticipation of such threats to future military infrastructures, systems and operations. It will be implemented via swarms of autonomous intelligent cyber-defense agents (AICAs) that will fight AIM within our networks and systems. This paper presents this cyber-defense technology of the future, the current state of the art in this field and its main challenges. First, we review the rationale of the ACyD concept and its associated AICA technology. Then, we present the current research results from NATO's IST-152 Research Task Group on the AICA Reference Architecture. We then develop the 12 main technological challenges that must be resolved in the coming years, besides ethical and political issues.
Performance Analysis of Supervised Machine Learning Algorithms to Recognize Human Activity in Ambient Assisted Living Environment

A significant challenge to provide services to the inhabitant in a smart environment resides in the effective implementation of models. Most of the proposed models are conceptual and lacks practical consideration. Human activity recognition is one of the most challenging tasks to offer the solution for ambient assisted living. In this work, we explore a time series classification problem - human activity recognition. Total of nine machine learning and deep learning algorithms implemented and evaluated using the same dataset. The results are analyzed using different parameters. This paper aims at providing help to select a practical machine learning approach for activity recognition process in ambient assisted living systems. The comparative analysis shows that in deep learning, Long Short-Term Memory (LSTM) network performed best with a classification accuracy of 92%. In machine learning, Logistic Regression and Gradient Boosting came out with a classification accuracy of greater than 90%, and others came out the worst with classification accuracy less than 90%.
University Chatbot using Artificial Intelligence Markup Language

Chatbots are conversational systems that can do chat interactions with human automatically. It is developed to be virtual assistant, making entertainment for people, helping for answering the questions, getting driving directions, serving as human partner in smart homes etc. Most of the chatbots utilize the algorithms of artificial intelligence (AI) in order to get the required responses. In this paper, we provide the design of a University Chatbot that provides an efficient and accurate answer for any user questions about university information. This is the first University Chatbot for inquiring about school information in Myanmar Language based on Artificial Intelligence Markup Language and uses Pandorabots as the interpreter.
3-D Deconvolutional Networks for the Unsupervised Representation Learning of Human Motions

Data representation learning is one of the most important problems in machine learning. Unsupervised representation learning becomes meritorious as it has no necessity of label information with observed data. Due to the highly time-consuming learning of deep-learning models, there are many machine-learning models directly adapting well-trained deep models that are obtained in a supervised and end-to-end manner as feature abstractors to distinct problems. However, it is obvious that different machine-learning tasks require disparate representation of original input data. Taking human action recognition as an example, it is well known that human actions in a video sequence are 3-D signals containing both visual appearance and motion dynamics of humans and objects. Therefore, the data representation approaches with the capabilities to capture both spatial and temporal correlations in videos are meaningful. Most of the existing human motion recognition models build classifiers based on deep-learning structures such as deep convolutional networks. These models require a large quantity of training videos with annotations. Meanwhile, these supervised models cannot recognize samples from the distinct dataset without retraining. In this article, we propose a new 3-D deconvolutional network (3DDN) for representation learning of high-dimensional video data, in which the high-level features are obtained through the optimization approach. The proposed 3DDN decomposes the video frames into spatiotemporal features under a sparse constraint in an unsupervised way. In addition, it also can be regarded as a building block to develop deep architectures by stacking. The high-level representation of input sequential data can be used in multiple downstream machine-learning tasks, we evaluate the proposed 3DDN and its deep models in human action recognition. The experimental results from three datasets: 1) KTH data; 2) HMDB-51; and 3) UCF-101, demonstrate that the proposed 3DDN is an alternative approach to feedforward convolutional neural networks (CNNs), that attains comparable results.
Poster: Preparation of Engineering University Students for Academic Mobility to French Universities

The paper focuses on the practice of preparing Kazan National Research Technological University students for academic mobility to universities of France and development of a language training program to facilitate the students’ academic mobility. Every year, several KNRTU students, after completing the undergraduate studies, leave to do the Masters at French universities. In order to better prepare students for academic mobility to French universities, French teachers have developed a special training program, aiming to make it easier for students to continue their studies at universities and everyday life in France. The paper shows the methodology of the educational program of preparation for academic mobility. Besides, KNRTU French teachers have developed a program to prepare students for international DELF (B1, B2) exam to gain an international diploma, as students have the right to study at French universities only with these international diplomas.
Blockchain-based Universal Loyalty Platform

Loyalty Programs are reward programs offered by a company to customers who frequently make purchases. However, there are major issues with traditional loyalty programs like low redemption rates, expiring of points, customer acquisition costs, and the nuisance of keeping track of multiple loyalty programs. Blockchain provides a solution to a number of these issues. This paper explores the issues of the current loyalty programs and how Blockchain technology can be used to tackle the underlying issues of traditional loyalty programs. Finally, we propose our own Blockchain-based universal loyalty platform, which aims at making loyalty programs more beneficial for customers, thereby increasing customer retention rates as well as more convenient for companies to have their own loyalty program. This system aims to integrate individual loyalty programs into a unified platform that will allow transfer and exchange of loyalty points between users and promote co-branding among various companies located worldwide.
Transforming the Know Your Customer (KYC) Process using Blockchain

A major yet trivial problem in the banking industry right now is how tedious and costly the traditional Know- Your-Customer(KYC) process is. The process is also tiresome for customers as they need to undergo the same process for each bank or financial institution with which they intend to work. Personal experiences of people dictate the cumbersome nature of the process, thereby demanding an efficacious alternative. Through this paper, we intend to do exactly that. We propose a new solution based on Distributed Ledger Technology or Blockchain technology, which will reduce the traditional KYC verification process cost for Institutions and cut short the general time line of the completion of the process while making it smoother for the customers. Major enhancement in our solution over the conventional methods is that the whole verification process is conducted only once for each customer, irrespective of number of institutions he or she wishes to be linked to. Also, since we are using the DLT, verification results can be securely shared with the customers thereby increasing transparency. Following this approach, we developed a Proof of Concept (POC) with the Ethereum API, websites as endpoints and an android app as front office; realising the feasibility and effectiveness of this approach. All in all, this approach improves customer experience, reduces cost overheads, and increases transparency in the process of onboarding a customer.
Lion Algorithm and Its Applications

Lion algorithm (LA) is specified as a novel nature-inspired optimization algorithm on the basis of unique social behavior of lions. It is one among the high-performance nature-inspired algorithms that are introduced in the 21st century. LA has two distinctive updating processes called territorial defense and territorial takeover. These processes act as a major role in searching for global optimal solution and evasion from local optimal solution. The processes are supported by important steps like nomad coalition, survival fight, cub growth, and mating. The algorithm is verified to be superior in solving optimization problems of diverse characteristics like multimodal optimization problem, large-scale optimization problems, huge search space problems, and so on. LA has been applied to system identification problem that is considered as a challenging and significant problem in the entire disciplines. As of its application, it has been deployed for the entire optimization problems in the diverse engineering disciplines. This chapter presents the biological motivation from the lion’s behavior and its interpretation to the LA. Since the algorithm was developed in two stages, the chapter briefly discusses the first version of the algorithm followed by its detailed description with illustration. Subsequently, the chapter discusses the performance accomplishments of the LA in solving different benchmark suites as well as notable applications with problem formulations.
Sign Language Gesture Recognition Using Doppler Radar and Deep Learning

In this paper, we study American sign language (ASL) hand gesture recognition using Doppler radar. A set of ASL hand gesture motions are captured as micro- Doppler signals using a microwave X-band Doppler radar transceiver. We apply joint time-frequency analysis and observe the presence of the micro- Doppler signatures in the spectrogram. The micro- Doppler signatures of different hand gestures are analyzed using Matlab. Each hand gesture is observed to contain unique spectral characteristics. Based on unique spectral characteristics, we investigate the classification of ASL essential short phrases including emergency signals. For recognizing and characterizing the presence of micro-Doppler signatures in spectrogram we explore deep convolution neural network (DCNN) algorithm. We show that the DCNN algorithm can classify different sign language gestures based on the presence of micro- Doppler signatures in the spectrogram with fairly high accuracy. Experimental results reveal that utilizing 80% of data for training, and the remaining 20% for validation purposes in DCNN algorithm a validation accuracy of 87.5% is achieved. To further improve the recognition system, we apply a very deep learning algorithm VGG-16 using transfer learning, which improves the validation accuracy to 95%.
Communications Aware Adversarial Residual Networks for over the Air Evasion Attacks

Recent work in adversarial radio frequency machine learning has demonstrated the use of untargeted adversarial machine learning techniques for over the air evasion of raw inphase and quadrature based Automatic Modulation Classification Deep Neural Networks. However, most of the proposed methodologies only consider the effect of adversarial machine learning on the underlying transmission as an evaluation metric or don't consider it at all. Furthermore, all of the proposed techniques require gradient computation for each example in order to craft an adversarial perturbation, which makes deployment of these adversarial methodologies to communications hardware difficult. The current work addresses both of these shortcomings. First, methodology is developed that directly accounts for the bit error rate of the underlying transmission in the adversarial optimization problem. Additionally, the learned model for perturbation creation is encapsulated in a fully convolutional adversarial residual network. Once the parameters of this network are learned, it can be easily deployed. The methodology is found to perform equivalently or better than a comparison adversarial evasion attack using the well known Fast Gradient Sign Method.