Reviewerly/exHarmony huggingface dataset:
A Statistical Learning Model with Deep Learning Characteristics

Although machine learning has achieved great success in many fields, the lack of interpretability and excessive computational amount and poor robustness severely limits its wide application in real-world tasks, especially security-sensitive tasks. But the current deep learning research is still far from truly solving these problems. In order to overcome these problems encountered by the deep learning model, this article does not intend to modify the deep learning model itself, but design a new machine learning model to avoid various problems of deep learning. We proposes a new statistical learning model that learn from the characteristics of deep learning. Then we evaluate these models on two datasets and found that the new model is significantly better than the deep learning model in terms of computational complexity, robustness and interpretability.
Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies

People worldwide communicate online and create a great amount of data on social media. The understanding of such large-scale data generated on social media and uncovering patterns from social relationship has received much attention from academics and practitioners. However, it still faces challenges to represent and manage the large-scale social relationship data in a formal manner. Therefore, this study proposes a social relationship representation model, which addresses both conceptual graph and domain ontology. Such a formal representation of a social relationship graph can provide a flexible and adaptive way to complete social relationship discovery. Using the term-define capability of ontologies and the graphical structure of the conceptual graph, this paper presents a social relationship description with formal syntax and semantics. The reasoning procedure working on this formal representation can exploit the capability of ontology reasoning and graph homomorphism-based reasoning. A social relationship graph constructed from the Lehigh University Benchmark (LUBM) is used to test the efficiency of the relationship discovery method.
Civil infrastructure data acquisition in urban environments based on multi-UAV mission

Increased capabilities of modern unmanned aerial vehicles (UAVs) in terms of autonomy and payload operations lead towards development of multi-agent UAV systems. While the multi-UAV systems have a great potential in many different scenarios, increasing complexity of missions gave raise to a new set of problems. Some of them include high workload periods of a single operator supervising multiple agents, loss of the situation awareness, increased amount of the data generated by the system and problems in communication channels between the operator and UAVs. All of them could lead to performance degradation in the mission execution or even critical failures of system elements. In general, there are two aspects that help to avoid or at least to decrease mentioned problems - mission planning and human-machine interface (HMI) design. In this paper, we mainly address the mission planning aspect and only briefly present an HMI design that allows simultaneous handling of three UAVs in civil infrastructure data acquisition missions. The proposed interface, among the standard telemetry data, includes data download/upload from/to external services, a trajectory planner and the acquired data representation. The proposed architecture has been tested in a simulation and in a real environment.
Analytical Study of Handwritten Character Recognition: A Deep Learning Way

In this current tech-savvy world, there is a rising challenge for software systems to be able to recognize characters via computing systems, a lot of crucial and sensitive data is scanned through documents that are solely paper-based and are accessible to us only in the form of newspapers, books, thesis, articles, documents etc. which are in printed format only. Nowadays, there is an ever-increasing demand for storing this crucial data that is apparently present only in these paper-based documents into a storage disk of digital nature and then reutilizing the same whenever deemed necessary simply by a predefined search process. A simple way to transfer data from these paper documents into digital storage systems is to first scan those documents and then store them as images. But the challenge is introduced when we feel the need to reutilize this data as it gets quite challenging to read a specific data from these documents. A major cause for this challenge is that the font properties of these characters that appear in paper documents are different when compared to the fonts of the characters in computing systems. Hence, a computer is ceases to recognize these characters while reading them. This concept of processing data from hard paper documents in digital storage spaces and then reading it is called Document Processing. In Document Processing, we make use of a system called Optical Character Recognition to achieve the needful. To further expand our understanding of how these systems work, this paper analyzes and compares several neural networks viz: Simple (Artificial) Neural Network, Convolutional Neural Network and Recurrent Neural Network, that use Deep Learning to implement Handwritten Character Recognition.
CloudSkulk: A Nested Virtual Machine Based Rootkit and Its Detection

When attackers compromise a computer system and obtain root control over the victim system, retaining that control and avoiding detection become their top priority. To achieve this goal, various rootkits have been developed. However, existing rootkits are still easy to detect as long as defenders can gain control at a lower level, such as the operating system level, the hypervisor level, or the hardware level. In this paper, we present a new type of rootkit called CloudSkulk, which is a nested virtual machine (VM) based rootkit. While nested virtualization has attracted sufficient attention from the security and cloud community, to the best of our knowledge, we are the first to reveal and demonstrate how nested virtualization can be used by attackers to develop rootkits. We then, from defenders' perspective, present a novel approach to detecting CloudSkulk rootkits at the host level. Our experimental results show that the proposed approach is effective in detecting CloudSkulk rootkits.
MRS-Net+ for Enhancing Face Quality of Compressed Videos

During the past few years, face videos, e.g., video conference, interviews and variety shows, have grown explosively with millions of users over social media networks. Unfortunately, the existing compression algorithms are applied to these videos for reducing bandwidth, which also bring annoying artifacts to face regions. This paper addresses the problem of face quality enhancement in compressed videos by reducing the artifacts of face regions. Specifically, we establish a compressed face video (CFV) database, which includes 196,337 faces in 214 high-quality video sequences and their corresponding 1,712 compressed sequences. We find that the faces of compressed videos exhibit tremendous scale variation and quality fluctuation. Motivated by scalable video coding, we propose a multi-scale recurrent scalable network (MRS-Net+) to enhance the quality of multi-scale faces in compressed videos. The MRS-Net+ is comprised by one base and two refined enhancement levels, corresponding to the quality enhancement of small-, medium- and large-scale faces, respectively. In the multi-level architecture of our MRS-Net+, small-/medium-scale face quality enhancement serves as the basis for facilitating the quality enhancement of medium-/large-scale faces. We further develop a landmark-assisted pyramid alignment (LPA) subnet to align faces across consecutive frames, and then apply the mask-guided quality enhancement (QE) subnet for enhancing multi-scale faces. Finally, experimental results show that our MRS-Net+ method achieves averagely 1.196 dB improvement of peak signal-to-noise ratio (PSNR) and 23.54% saving of Bjøntegaard distortion-rate (BD-rate), significantly outperforming other state-of-the-art methods.
Recognition of maximal speed limit traffic signs for use in advanced ADAS algorithms

Advanced Driver Assistance Systems (ADAS) have been increasingly developing, specifically in the last decade. One of such ADAS is that intended for traffic signs recognition. This paper deals with the recognition of a specific subset of traffic signs, i.e. speed limit traffic signs. The complete solution is based on the usage of machine learning and finally implemented in the C programming language. After the optimization process, the final solution is implemented on the real ADAS board, to check its performance in a real operational environment. Due to the limited resources of the ADAS board itself, a simple Convolutional Neural Network (CNN) was created to recognize speed limit traffic signs. For CNN training a large database of 6891 training images is used. When testing the solution, 731 test images from the real traffic are used, as well as 123 real video sequences. The test results show that in certain situations the proposed solution is capable of achieving high performance in terms of precision, while in some cases additional improvements of the solution should be investigated. It is capable of processing 12 frames per second when operating with state-of-the-art automotive camera resolution, i.e. 1280x720 pixels.
Enhanced Deep Type-2 Fuzzy Logic System For Global Interpretability

The recent advances in the field of Artificial Intelligence (AI) have led to the rapid deployment of AI systems in a variety of fields such as healthcare, financial, education etc. However, many of the AI systems are black boxes which restricts the use of these AI in applications that are highly regulated (such as financial, justice, medical, autonomous vehicles etc.) where it is necessary to provide satisfactory explanations for the decisions taken. A variety of approaches that have been proposed to tackle this problem, but these approaches generally emphasize providing satisfactory explanations for individual predictions at the cost of providing explanations at the global level. Hence, to solve these problems, in this paper, we present a hybrid deep learning type-2 fuzzy logic system which addresses these challenges by providing a highly interpretable model that can be trained using both labelled and unlabeled data. We also present a method to extract global and local explanations for this model. We also show that the presented model has reasonable performance when compared to stacked autoencoders deep neural networks.
Multilevel Latent Transition Mixture Modeling: Variance Decomposition and Application

Person-centered methodologies generally refer to those that take unobserved heterogeneity of populations into account. The use of person-centered methodologies has proliferated, which is likely due to a number of factors, such as methodological advances coupled with increased personal computing power and ease of software use. Using latent class analysis and its extension for longitudinal data, [latent transition analysis (LTA)], multiple underlying, homogeneous subgroups can be inferred from a set of categorical and/or continuous observed variables within a large heterogeneous data set. Such analyses allow researchers to statistically treat members of different subgroups separately, which may provide researchers with more power to detect effects of interest and closer alignment between statistical modeling and one’s guiding theory. For many educational and psychological settings, the hierarchical structure of organizational data must also be taken into account; for example, students (i.e., level-1 units) are nested within teacher/schools (i.e., level-2 units). Finally, multilevel LTA can be used to estimate the number of latent classes in each structured unit and the potential movement, or transitions, participants make between latent classes across time. The transitions/stability between latent classes across time can be treated as the outcome in and of itself, or the transitions/stability can be used as a correlate or predictor of some other, distal outcome. The purpose of the paper is to discuss multilevel LTA, provide considerations for its use, and demonstrate variance decomposition, which requires numerous steps. The variance decomposition steps are presented didactically along with a worked example based on analysis from the Social Rating Scale of ECLS-K.
Efficient Super Resolution by Recursive Aggregation

Deep neural networks have achieved remarkable results on image super resolution (SR), but the efficiency problem of deep SR networks is rarely studied. We experimentally find that many sequentially stacked convolutional blocks in nowadays SR networks are far from being fully optimized, which largely damages their overall efficiency. It indicates that comparable or even better results could be achieved with less but sufficiently optimized blocks. In this paper, we try to construct more efficient SR model via the proposed recursive aggregation network (RAN). It recursively aggregates convolutional blocks in different orders, and avoids too many sequentially stacked blocks. In this way, multiple shortcuts are introduced in RAN, and help gradients easier flow to all inner layers, even for very deep SR networks. As a result, all blocks in RAN can be better optimized, thus RAN can achieve better performance with smaller model size than existing methods.
Applying Augmented Reality Capabilities to Enhance General Aviation Weather Training

Augmented reality (AR) learning experiences were designed for general aviation (GA) weather training to enhance students understanding of aviation weather information. Students inside and outside the classroom lack opportunities to experience different types of weather patterns and practice correlating weather knowledge with weather-related decision-making. AR experiences can immerse students in weather content that is three-dimensional (3D), animated, and interactive. Student interaction with this content may help close gaps in how students learn about weather patterns and help students apply knowledge to correlate weather theory in weather-related situations and decision-making. In this study, AR learning experiences were designed and sequenced to enhance how students learn about thunderstorm weather processes, thunderstorm hazards to flight, and thunderstorm avoidance procedures. A preliminary user evaluation was conducted to assess students’ learning experiences and outcomes. The preliminary results of the evaluation found that the experiences resulted in a significant improvement in weather and spatial knowledge with high student satisfaction and motivation. AR learning experiences may be designed for various weather phenomenon to enhance learning inside and outside of the classroom.
A Blockchain Platform for the Decentralized Operation of Active Distribution Networks

Blockchain technologies and smart contracts are getting more attention for potential smart grids applications as they are able to decentralize the data management in a secure and transparent way. In this work, a smart contract deployed in a simulated Ethereum blockchain is used to coordinate the decentralized solution of the Economic Dispatch (ED) problem of Distributed Generation (DG) units. The distributed optimization problem is first formulated and solved using the Alternating Directional Method of Multipliers (ADMM) and then a smart contract plays the role of the decentralized coordinator and data aggregator. Results from the combined simulation of the method are provided through a variety of scenarios, that investigate the efficacy and practical applicability of the method.
Fractionalization, Polarization and Banking Stability in Africa

In this study, I examine the impact of diversity (ethnic & religious fractionalization and polarization) on banking stability in Sub-Saharan Africa (SSA). Using data from 1996 to 2014, I employ the system Generalized Method of Moments (sys-GMM) approach to examine this relationship. I find that countries in SSA are more polarized religiously than they are ethnically implying that, the continent is more likely to experience conflicts and other associated adverse effects of polarization through religion rather than ethnicity. The region is, however, more ethnically fractionalized than they are religiously. Further, I conjecture that banks in more heterogeneous societies will experience poor asset quality and lower stability. I however postulate that banks offset the risks from diversity at certain levels of net interest margin (NIM). I provide empirical evidence to support these conjectures. I find varying threshold NIM values for each diversity indicator depending on the stability measure used. I also find that opening up the banking system to foreign entry can help offset the negative impact of ethnic and religious fractionalization and polarization on banking stability. Policy implications are discussed.
A State of Art of Machine Learning Algorithms Applied Over Language Identification and Speech Recognition Models

Over the years, we have observed several applications of machine learning. One of the applications is in speech recognition. One of the sub-topics in speech recognition is language identification. A typical language identification system involves a feature extraction and classification stage. Machine learning classifiers have been extensively used in the classification stage of the LID system. The current paper examines the chronological involvement of machine learning in the LID system. The paper also details the chronological development and refinement of the LID system.
Correlation-based Contactless Hand-Gesture Detection for Capacitive Sensing

This article considers gesture-based algorithms that allow us to control various devices with movements of our hands. Correlation coefficient is used to determine the sign of the derivative of the signal from the sensor to detect flick gesture (hovering over an area where sensors are located) and click gesture (approaching and distancing this area). The results of numerical experiments are presented hereinafter, which confirms the robustness of the proposed gesture identification algorithm for controlling various devices.
Matchmaker, matchmaker, make me a match: geometric, variational, and evolutionary implications of criteria for tag affinity

Abstract Genetic programming and artificial life systems commonly use tag matching to decide interactions between system components. However, the implications of criteria used to determine affinity between tags with respect evolutionary dynamics have not been directly studied. We investigate differences between tag-matching criteria with respect to geometric constraint and variation generated under mutation. In experiments, we find that tag-matching criteria can influence the rate of adaptive evolution and the quality of evolved solutions. Better understanding of the geometric, variational, and evolutionary properties of tag-matching criteria will facilitate more effective incorporation of tag matching into genetic programming and artificial life systems. By showing that tag-matching criteria influence connectivity patterns and evolutionary dynamics, our findings also raise fundamental questions about the properties of tag-matching systems in nature.
HLDNet: Abandoned Object Detection Using Hand Luggage Detection Network

Abandoned object detection has recently been studied to detect dangerous situations such as illegally dumping explosives. Existing abandoned object detection used background subtraction, which is sensitive to noise and clutters of a similar size in outdoor environments. Unlike existing approaches, this article presents a deep learning-based detection method that is suitable for use in outdoor environments because it is robust to ghost effects and illumination changes. The proposed method consists of three parts: 1) pedestrian detection and tracking; 2) hand luggage detection (HLD); and 3) abandoned object decision. Since the proposed method detects the abandoned object through HLD instead of directly detecting the abandoned object itself, we could successfully improve the detection accuracy and robustness. The proposed method can also detect dangerous weapons or restricted hand luggage through HLD. This article presents an intelligent abandoned object detection system that can be applied for consumer applications.
Lossless archiving view arrays from plenoptic cameras when camera sensor images are available

We propose two lossless archiving methods for the light field (LF) array of views created by plenoptic cameras, when the camera sensor images (also called lenslet images) are available. The two archiving methods are based on generative mechanisms, where we encode all information needed to run the processing pipeline that generates the LF array of views starting from source sensor images. The first archiving method starts by encoding the two sensor images needed as input in the processing pipeline: one scene sensor image, and one white image (available from the camera database of white images). The second archiver encodes a single lenslet-like image obtained by devignetting and debayering the scene sensor image and the white image. After encoding the input lenslet images both methods proceeds to encode all additional meta-information necessary for running the processing chain, starting from sensor image to the final LF array of views, and any needed corrections due to possible quantization along the processing chain, finally creating a lossless archive of the light field array of views. We exemplify the performance for a database of plenoptic camera images that was extensively used in the light field lossless compression literature, obtaining competitive archive file sizes.
Power Side Channel Attack of AES FPGA Implementation with Experimental Results using Full Keys

The ability to attack Advanced Encryption Standard (AES) algorithms in the last round has been shown to be possible by enumerating the key guesses one byte at a time. However, attacking the first round does not lend itself to such a technique because of the presence of the mix-column layer. We demonstrate an attack on the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> round of AES encryption by defining a leakage function based on the full key, and then we apply correlation power analysis to successfully uncover the encryption key. The success rate, defined by the Euclidian distance fluctuation is 0.788, which is higher than similar applications in the current literature. We also introduce the concept of Kullback-Leibler entropy as a distinguisher for discriminating between the power measurements and the estimated power of the key guesses. We demonstrate this as a new way of reducing the key search space via key ranking with applications for power side-channel attacks on the implementations of AES algorithms in an FPGA.
Belief Propagation based Deep Neural Networks for MIMO Detection: DNN-BP

The paper introduces principles of Belief Propagation (BP) algorithm and Damped BP algorithm firstly. Then, with the BP algorithm and machine learning, this paper proposes a detection algorithm based on BP based deep neural networks (DNN-BP). The DNN-BP algorithm selects the optimal damping factor through machine learning methods, which solves the problem that the damping factor is difficult to determine in traditional algorithms. Simulation results show that the DNN-BP detection algorithm can further improve the convergence of the BP algorithm and improve the Bit Error Rate performance.