Reviewerly/exHarmony huggingface dataset:
Discriminative Transfer Learning for Driving Pattern Recognition in Unlabeled Scenes

Driving pattern recognition based on features, such as GPS, gear, and speed information, is essential to develop intelligent transportation systems. However, it is usually expensive and labor intensive to collect a large amount of labeled driving data from real-world driving scenes. The lack of a labeled data problem in a driving scene substantially hinders the driving pattern recognition accuracy. To handle the scarcity of labeled data, we have developed a novel discriminative transfer learning method for driving pattern recognition to leverage knowledge from related scenes with labeled data to improve recognition performance in unlabeled scenes. Note that data from different scenes may have different distributions, which is a major bottleneck limiting the performance of transfer learning. To address this issue, the proposed method adopts a discriminative distribution matching scheme with the aid of pseudolabels in unlabeled scenes. It is able to reduce the intraclass distribution disagreement for the same driving pattern among labeled and unlabeled scenes while increasing the interclass distance among different patterns. Pseudolabels in unlabeled scenes are updated iteratively via an ensemble strategy that preserves the data structure while enhancing the model robustness. To evaluate the performance of the proposed method, we conducted comprehensive experiments on real-world parking lot datasets. The results show that the proposed method can substantially outperform state-of-the-art methods in driving pattern recognition.
A Brief Survey of Deep Learning Techniques for Person Re-identification

Over the past decade’s person re-identification is being a hot research area and an active participant in the automated video surveillance. Besides monitoring of person of interest and human-machine interaction, person re-identification is also used in broadcast media and in forensic examination. Images and videos captured from different cameras gets suffers from low quality and resolution issues which generates difficulty in extraction of useful information. Deep learning plays a vital role in person re-identification in retrieval and determining similarity among the features from data. With the advancement in the deep learning techniques, favorable performances are obtained while handling challenges obtained from diverse viewpoints, dazedness, resolutions in image, settings of camera, blocking of an object of interest and irregular background across camera views. This paper presentes the current challenges that are overcome by the usage of the deep learning architectures.
SimuNN: A Pre-RTL Inference, Simulation and Evaluation Framework for Neural Networks

Neural networks have been widely deployed in a number of applications due to their strong learning and feature extraction ability. To meet the ever increasing accuracy requirements from various applications, neural network models have become more complicated and diversified by exploiting more layers, larger model size, and more diverse functions. As a result, the design of application specified hardware accelerators for neural networks is also becoming more difficult, especially for real-time embedded applications. To efficiently bridge the gap between algorithm and hardware design phases, a pre-RTL neural network simulator (SimuNN) is proposed in this paper to enable early phase verification and fast prototyping. SimuNN can be used for inference, simulation and also evaluation, results of which can guide the design of both neural network models and hardware accelerators. SimuNN supports inference in various data precision, and is compatible with TensorFlow, which makes it easy for platform migration. It provides multi-level trace results that can be taken as the gold model of RTL designs. Moreover, the hardware performance under various quantizations, dataflow organizations and hardware configurations can be evaluated by SimuNN based on a generalized hardware model. Based on that, two dataflow organization schemes are concluded for determining the optimal configurations of hardware architecture under various hardware and performance constraints.
Machine Learning Approach to Predict Computer Operating Systems Vulnerabilities

Information security is everyone’s concern. Computer systems are used to store sensitive data. Any weakness in their reliability and security makes them vulnerable. The Common Vulnerability Scoring System (CVSS) is a commonly used scoring system, which helps in knowing the severity of a software vulnerability. In this research, we show the effectiveness of common machine learning algorithms in predicting the computer operating systems security using the published vulnerability data in Common Vulnerabilities and Exposures and National Vulnerability Database repositories. The Random Forest algorithm has the best performance, compared to other algorithms, in predicting the computer operating system vulnerability severity levels based on precision, recall, and F-measure evaluation metrics. In addition, a predictive model was developed to predict whether a newly discovered computer operating system vulnerability would allow attackers to cause denial of service to the subject system.
The Effect of Different Deep Network Architectures upon CNN-Based Gaze Tracking

In this paper, we explore the effect of using different convolutional layers, batch normalization and the global average pooling layer upon a convolutional neural network (CNN) based gaze tracking system. A novel method is proposed to label the participant’s face images as gaze points retrieved from eye tracker while watching videos for building a training dataset that is closer to human visual behavior. The participants can swing their head freely; therefore, the most real and natural images can be obtained without too many restrictions. The labeled data are classified according to the coordinate of gaze and area of interest on the screen. Therefore, varied network architectures are applied to estimate and compare the effects including the number of convolutional layers, batch normalization (BN) and the global average pooling (GAP) layer instead of the fully connected layer. Three schemes, including the single eye image, double eyes image and facial image, with data augmentation are used to feed into neural network to train and evaluate the efficiency. The input image of the eye or face for an eye tracking system is mostly a small-sized image with relatively few features. The results show that BN and GAP are helpful in overcoming the problem to train models and in reducing the amount of network parameters. It is shown that the accuracy is significantly improved when using GAP and BN at the mean time. Overall, the face scheme has a highest accuracy of 0.883 when BN and GAP are used at the mean time. Additionally, comparing to the fully connected layer set to 512 cases, the number of parameters is reduced by less than 50% and the accuracy is improved by about 2%. A detection accuracy comparison of our model with the existing George and Routray methods shows that our proposed method achieves better prediction accuracy of more than 6%.
Detection of Data Matrix Encoded Landmarks in Unstructured Environments using Deep Learning

Visual based navigation in industrial environments has been a challenge because of the complex and unstructured nature of the surroundings that Automated Guided Vehicles (AGV) and autonomous robots have to traverse in a continuous routine in many production facilities. Since natural landmarks are still a huge challenge due to the highly dynamic configuration of the environment and the varying nature of these landmarks, simple and low cost artificial landmarks appear as a potential solution. The one exploited in this work uses simple sheets of paper with Data Matrix encoded markers spread in the environment trying to create a constellation of landmarks in such a way that several of them are always visible to a set of cameras on board the robot or AGV. All codes are unique, which makes robot continuous localization a much simpler challenge whenever at least two or three landmarks are visible. When using traditional vision techniques in large images of the scene, one of the most demanding parts is to detect the landmarks to further process them. For that purpose, this paper proposes a technique based on deep learning that efficiently detects these special landmarks in images. A dedicated dataset was created and a Faster R-CNN architecture was adapted and trained for that purpose. The results show that almost all markers was detected in the images with a processing speed larger more than one order of magnitude than traditional techniques, including in demanding situations of poorer illumination or partial occlusions.
A Security Verification Template to Assess Cache Architecture Vulnerabilities

In the recent years, cache based side-channel attacks have become a serious threat for computers. To face this issue, researches have been looking at verifying the security policies. However, these approaches are limited to manual security verification and they typically work for a small subset of the attacks. Hence, an effective verification environment to automatically verify the cache security for all side-channel attacks is still missing. To address this shortcoming, we propose a security verification methodology that formally verifies cache designs against cache side-channel vulnerabilities. Results show that this verification template is a straightforward, automated method in verifying cache invulnerability.
Exploring Factors Affecting User's Cybersecurity Behaviour by Using Mobile Augmented Reality App (CybAR)

In the field of cybersecurity, human behaviour is considered as the weakest link. Augmented Reality (AR) is one of top technologies that enhances users' experience by overlaying computational information into their reality. In spite of wide range of AR applications in different fields, there exists no AR application that helps to educate users and raise their awareness in cybersecurity aspects.
Multiscale-Wavelet Regularization Method for the Inverse Problem of Electrocardiography

Purpose: The objective of the inverse problem in electrocardiography (ECG) is noninvasively to recover regional information about intra-cardiac electrical events for diagnosis by reconstructing the heart surface potential distribution from body surface potentials. However, ECG inverse problem is ill-posed, new regularization methods are needed to obtain a more accurate solution. Methods: In this paper, we propose a new approach which utilize different regularization methods in wavelet domain to solve the ECG inverse problem. Due to the different distribution of ECG signal in different wavelet coefficients which effect on heart surface potential reconstruction, our method applies different regularization methods for different wavelet coefficients. Conclusion: We use the weighted underestimate indicator(WUI) and the weighted overestimate indicator(WOI) of lesion to evaluate our methods on the basis of public simulation dataset. By comparing results of the reconstruction of heart surface potentials of our new approach with the other two traditional approaches, Tikhnonv regularization method in time domain and Tikhnonv regularization method in wavelet domain, it is proved that our approach can improve the accuracy of the solution of ECG inverse problem.
The Information-Geometric Perspective of Compositional Data Analysis

Information geometry uses the formal tools of differential geometry to describe the space of probability distributions as a Riemannian manifold with an additional dual structure. The formal equivalence of compositional data with discrete probability distributions makes it possible to apply the same description to the sample space of Compositional Data Analysis (CoDA). The latter has been formally described as a Euclidean space with an orthonormal basis featuring components that are suitable combinations of the original parts. In contrast to the Euclidean metric, the information-geometric description singles out the Fisher information metric as the only one keeping the manifold’s geometric structure invariant under equivalent representations of the underlying random variables. Well-known concepts that are valid in Euclidean coordinates, e.g., the Pythagorean theorem, are generalized by information geometry to corresponding notions that hold for more general coordinates. In briefly reviewing Euclidean CoDA and, in more detail, the information-geometric approach, we show how the latter justifies the use of distance measures and divergences that so far have received little attention in CoDA as they do not fit the Euclidean geometry favored by current thinking. We also show how Shannon entropy and relative entropy can describe amalgamations in a simple way, while Aitchison distance requires the use of geometric means to obtain more succinct relationships. We proceed to prove the information monotonicity property for Aitchison distance. We close with some thoughts about new directions in CoDA where the rich structure that is provided by information geometry could be exploited.
Correlance and discordance: computable measures of nonlocal correlation

We present six new measures of nonlocal correlation for discrete multipartite quantum systems; correlance, statance, probablance, strong discordance, discordance, and diagonal discordance. The correlance measures all nonlocal correlations (even bound entanglement) and is exactly computable for all pure and mixed states. Statance and probablance are not yet computable, but motivate the strong discordance (for nonlocal correlation beyond that achievable by a strictly classical state), discordance (a measure of all nonlocal correlation in distinguishably quantum states), and diagonal discordance (for nonlocal correlation in diagonal states), all of which are exactly computable for all states. We discuss types of correlation and notions of classicality and compare correlance, strong discordance, and discordance to quantum discord. We also define diagonal correlance to handle strictly classical probability distributions, providing a powerful tool with wide-ranging applications.
Design of Conceptual Model for RaisingAwareness of Digital Threats

In today’s information-communication environment and the connected world awareness of and preparedness for digital threats is of utmost importance for organizational systems. It is not possible to fully guard against or eliminate all digital threats but with an educated and trained awareness and recognizing, acknowledgment and management of the risks along with appropriate policies, tactics, techniques, procedures and processes in place organizational systems are competent to become digital resilient. In this paper our aim is to investigate how digital threat awareness can improve digital resilience. Related to this the goal is to identify digital threat awareness components within cybersecurity and digital security. In this regard we used modelbased approach and methodology to create model scheme so the novel Cyber Resilience Model within digital resilience concept is created. Digital resilience modelling approach takes account components of digital threats within digital security and engagement.
Enhancing the Capabilities of Solr Information Retrieval System: Arabic Language

Arabic language is one of the most complex languages in Natural Language Processing (NLP). Solr is an Information Retrieval System (IRS) that is widely known for its accurate results and high performance in English. However, Arabic stemmer that is currently used by Solr is called Light-10 which has some deficiencies. In this approach, we evaluated two light stemmers (Assem, Tashaphyne) and two root stemmers (Khoja, ISRI) and chose the two stemmers that the experiments show the best; in addition to Light-10 stemmer. The highest two stemmers are Assem and Khoja. So, we used these two stemmers and Light-10 to evaluate the search retrieval accuracy of Solr in Arabic, then evaluated them again with synonyms. The evaluation is based on using two metrics Precision and Normalized Discounted Cumulative Gain (NDCG). Assem stemmer has the highest accuracy which is 86%, Light-10 is 83% and Khoja is 81%. Finally, Assem stemmer has been used as the stemmer for Almufed search engine that we developed in this approach based on Solr for more than 6000 Arabic books from Alshamela Library.
Development of augmented reality in the basic physics practicum module

Abstract This research aims to design and develop a media lab in the form of Augmented Reality applications that are used in basic physics lab module. AR applications work with a tracking system and the reconstruction of the 2D marker contained in the experimental module to display 3D video animation. The method used in this research is the development of software includes the stages of collecting and analyzing the needs, do the design and manufacturing applications, validation and testing by the user, and revision. Results of the assessment by the AR application validator media and materials are respectively 80% and 85% in the predicate is valid and may be used after minor revisions. The results of trials by students as the user indicates that the 2D marker on lab module was initially less obvious and less sensitive at the time of scanning. However, after the revision of the 2D marker on lab module, 2D AR marker was sufficiently clear and sensitive at the time of scanning so that AR can help the general practical implementation and the AR content to support the practical implementation. The results showed that the development of AR applications had been successfully carried out and the AR application can function well with the scan time 1-1.5 second at a distance of 10-20 cm and successfully used in the experimental module at an angle of &lt;90 0 , with the result that, AR applications in basic physics practicum module as the media are supporting practicum implementation
Quantitative Evaluation of Dense Skeletons for Image Compression

Skeletons are well-known descriptors used for analysis and processing of 2D binary images. Recently, dense skeletons have been proposed as an extension of classical skeletons as a dual encoding for 2D grayscale and color images. Yet, their encoding power, measured by the quality and size of the encoded image, and how these metrics depend on selected encoding parameters, has not been formally evaluated. In this paper, we fill this gap with two main contributions. First, we improve the encoding power of dense skeletons by effective layer selection heuristics, a refined skeleton pixel-chain encoding, and a postprocessing compression scheme. Secondly, we propose a benchmark to assess the encoding power of dense skeletons for a wide set of natural and synthetic color and grayscale images. We use this benchmark to derive optimal parameters for dense skeletons. Our method, called Compressing Dense Medial Descriptors (CDMD), achieves higher-compression ratios at similar quality to the well-known JPEG technique and, thereby, shows that skeletons can be an interesting option for lossy image encoding.
A Hybrid RSA Algorithm in Support of IoT Greenhouse Applications

Internet of Things (IoT) is being utilized in a plethora of applications, many of which aim to improve system performance. IoT nodes suffer from several limitations, such as power supply, computational capability, and information security. The current state of IoT information and the potential for security breaches represent a significant untoward condition, especially regarding the organizational necessity for confidentiality and privacy. In this paper, we propose a strong, simple and energy conserving three-stage data encryption algorithm with a focus on securing IoT data in support of greenhouse applications. The stages include: (1) a novel implementation of the K-Map substitution functions; (2) the utilization of a chaotic equation to generate a sequence of random numbers, which are added to the result of the first stage; (3) the third stage incorporates the Rivest, Shamir, and Adelman (RSA) algorithm, performed on feeds from the output of the second stage, resulting in encrypted data, requiring private key decryption. The proposed algorithm eliminates the handshaking of the traditional RSA to exchange the keys (private and public) between IoT nodes and the cloud (server), then reduce the transmission time by 30%. The proposed cryptography algorithm is implemented and tested using two evaluation methods: a single micro-controller (standalone) and on a server (cloud). The algorithm is tested in both directions up/down link, and provides an acceptable and stable performance with 1.3 faster than the original RSA.
Blockchain Based Storage and Verification Scheme of Credible Degree Certificate

Blockchain is a distributed shared ledger with the advantage of distributed multi-center, collective maintenance and not being easy to tamper. The data is redundantly stored by all peer nodes in the network. A storage and verification scheme of degree certificate is proposed which combines with the characteristics of blockchain. The scheme is feasible and correct and can effectively avoid the problem of tampering and the single point of failure. Kafka is used as the message queue for transactions to achieve high throughput in transaction processing. The results of tests show that the scheme has higher transaction processing speed than other blockchain-based schemes.
Recovery-Conscious Adaptive Watermark Generation for Time-Order Event Stream Processing

Achieving low latency in time-order real-time event stream processing of data produced by a large number of IoT devices is difficult due to the buffering time required for sorting event messages arrived. Despite the existence of real-time IoT applications that highly depend on the order of event occurrences, event messages arriving from the devices to the event processing system are not time-ordered in many cases. Therefore, such an event processing system needs to buffer arriving event messages, sort, and then process them. This buffering time tends to be decided as a safe large period to guarantee correctness in terms of time-ordering, and leads to an increase of latency. Furthermore, failure recovery mechanism of a stateful stream processing deployed into distributed processing nodes makes this problem more difficult. It frequently causes large time-order reversal among event messages in the distributed stream processing system, that cannot be handled with a buffering time designed for real-time processing during normal periods. In this paper, we introduce a novel technique to adaptively determine the minimum necessary amount of buffering time to guarantee time-order sorting depending on the event-time progress in arrival event messages. To be more precise, instead of specifying the actual buffering time, the method adaptively controls the minimum time margin to determine the current "watermark" that shows the event time upon which a processing unit in a distributed stream processing system believes to have received all the messages. Further, we developed a complete function (called "time alignment") to sort the time-order of messages while keeping low latency in our developed distributed event stream processing platform. Through the experimental evaluations, it is shown that the developed method has the capability to determine a proper watermark to achieve both a strong guarantee on time-order correctness and low latency in real-time event stream processing.