Reviewerly/exHarmony huggingface dataset:
Recognition of Arabic Handwritten Literal Amounts Using Deep Convolutional Neural Networks

In this paper, we propose using a convolutional neural network (CNN) in the recognition of Arabic handwritten literal amounts. Deep convolutional neural networks have achieved an excellent performance in various computer vision and document recognition tasks, and have received increased attention in the few last years. The domain of handwriting in the Arabic script specially poses a different type of technical challenges. In this work we focus on the recognition of handwritten Arabic literal amount with a limited lexicon. Our experimental results demonstrate the high performance of the proposed CNN recognition system compared to traditional methods.
Interchange-Based Privacy Protection for Publishing Trajectories

Information extracted from trajectory data is very useful in many practical application scenarios. Before trajectories for data mining are published, they need to be processed to protect the privacy of the trajectories' bodies. In this paper, a method for such privacy protection is proposed. Our method guarantees that the generated trajectory points satisfy the k-anonymity by interchanging the positions of the trajectory points on the k-core subnet of the relation network. The method treats the trajectory points as the privacy protection object. It overcomes the curse of dimensionality resulting from the K-anonymity of trajectories, and reduces the distortion of the generated trajectories significantly. Moreover, our proposed strategy can preserve the original positions of the trajectory points. Experiments on both real-life and synthetic data sets are carried out with different methods for comparison. The results show that our method has greater efficiency and lower distortion of the processed trajectories.
Facial Expression Recognition Using Improved Adaptive Local Ternary Pattern

Recently, there has been a huge demand for assistive technology for industrial, commercial, automobile, and societal applications. In some of these applications, there is a requirement of an efficient and accurate system for automatic facial expression recognition (FER). Therefore, FER has gained enormous interest among computer vision researchers. Although there has been a plethora of work available in the literature, an automatic FER system has not yet reached the desired level of robustness and performance. In most of these works, there has been the dominance of appearance-based methods primarily consisting of local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP). Keeping in view the popularity of appearance-based methods, in this paper, we have proposed an appearance-based descriptor called Improved Adaptive Local Ternary Pattern (IALTP) for automatic FER. This new descriptor is an improved version of ALTP, which has been proved to be effective in face recognition. We have investigated ALTP in more details and have proposed some improvements like the use of uniform patterns and dimensionality reduction via principal component analysis (PCA). The reduced features are then classified using kernel extreme learning machine (K-ELM) classifier. In order to validate the performance of the proposed method, experiments have been conducted on three different FER datasets using well-known evaluation measures such as accuracy, precision, recall, and F1-Score. The proposed approach has also been compared with some of the state-of-the-art works in literature and found to be more accurate and efficient.
No-Reference Image Quality Assessment Based on Multi-Task Generative Adversarial Network

Since human observers are the ultimate receivers of an image, most of the image quality assessment (IQA) methods are based on analysis of the properties and mechanism of the human visual system. However, due to the lack of undistorted images for a reference, the accuracy of the no-reference IQA (NR-IQA) cannot compete with that of the full-reference IQA (FR-IQA). To bridge the performance gap between the FR-IQA and NR-IQA methods, we propose a NR-IQA method based on multi-task generative adversarial network, which attempts to restore dependable hallucinated images to compensate for the missing corresponding reference images. Two tasks, hallucination images and the quality maps are outputted by the generator and are combined with the specific loss to improve the reliability of hallucination images. Besides, two discriminator networks are used to respectively distinguish the undistorted images and hallucination images pairs, quality maps and structural similarity index measurement maps pairs. Finally, the hallucination images and distorted images are input into the IQA network, and quality scores are evaluated based on the differences between them. The superiority of our proposed method is verified by several different experiments on the LIVE datasets, TID2008 datasets, and TID2013 datasets.
Parking information perception based on automotive millimeter wave SAR

Autonomous vehicle (Auto-v) has received wide attention for its possible advantages that can provide much safer driving than human drivers. Autonomous parking becomes a tricky issue because the parking environment is often too complex to be fully perceived. The information acquired with the millimeter wave synthetic aperture radar (SAR) could be helpful to solve this problem. In this paper, an information perception method for parking is presented. It adopts the visual saliency detection method based on spectral residual to obtain the locations of the vehicles and empty parking sites, and use the morphology filter to judge the postures of the vehicles. Then, the vehicle types are classified based on principal component analysis (PCA) and support vector machine (SVM). Finally, the suitable parking sites are obtained according to the parking information perception. This study can be used to search the available parking sites, and confirm whether the obstacles of the empty parking site exist, which can assist the safe parking of autonomous vehicle. Experimental results based on measured automotive millimeter wave SAR images show the effectiveness and accuracy of the proposed parking information perception method.
Hulls of linear codes revisited with applications

Hulls of linear codes have been of interest and extensively studied due to their rich algebraic structures and wide applications. In this paper, alternative characterizations of hulls of linear codes are given as well as their applications. Properties of hulls of linear codes are given in terms of their Gramians of their generator and parity-check matrices. Moreover, it is show that the Gramian of a generator matrix of every linear code over a finite field of odd characteristic is diagonalizable. Subsequently, it is shown that a linear code over a finite field of odd characteristic is complementary dual if and only if it has an orthogonal basis. Based on this characterization, constructions of good entanglement-assisted quantum error-correcting codes are provided.
BiRe: A client-side Bi-directional SYN Reflection mechanism against multi-model evil twin attacks

The evil twin attack (ETA) has been a persistent security threat for decades in wireless local area networks (WLANs). An ETA refers to a rogue access point (RAP) impersonating a legal access point (LAP) to allure wireless users' connection. Such attacks give rise to serious privacy leakage and property damages, motivating intensive research on ETA detection in both academic and manufacturing communities. Among existing ETA detection methods, those deployed at client side are superior to the typical admin-side ones because of the particular requirements on dedicated equipments at admin side and the lack of real-time protection. Unfortunately, available client-side ETA detection mechanisms are simply targeted to specific evil twin model and fail to provide adequate detection rate. In this paper, we propose a multi-model ETA detection mechanism at client side, called BiRe. Inspired by the request-response reflection stated in TCP handshake process, BiRe employs a novel Bi-directional TCP SYN Reflection to determine the existence of an ETA and differentiate among various attack models. A pair of wireless adapters are employed to cooperatively initiate TCP handshakes and monitor the absence of the expected TCP SYN-ACK packets. The remarkable feature of BiRe is to make the number of such absences as a feasible indicator for the ETA model identification. The results from extensive real-world experiments demonstrate the distinguishing performance of BiRe, achieving as high as 100% detection rate in multi-model ETA scenarios. Moreover, a free lightweight Linux tool has been developed based on BiRe to automate client-side ETA detection.
Vulnerability Selection for Remediation: An Empirical Analysis

Vulnerabilities are security flaws in software and network systems that criminal hackers can exploit to gain an asymmetric advantage. Cyber-Security Operations Centers must routinely triage and patch vulnerabilities in their system(s) to minimize external exposure to attackers. The personnel resources required to address vulnerability remediation tasks are limited and constrained, thus motivating the need for optimization approaches to improve the efficiency of the vulnerability selection process. This paper investigates two different approaches to vulnerability selection for mitigation through (a) Individual Attribute Value Optimization and (b) Multiple Attribute Value Optimization. The former approach presents a methodology that optimizes the selection of vulnerabilities for mitigation with respect to an individual attribute, while the latter approach considers multiple attributes in the vulnerability selection decision-making. Real scan data from a Cyber-Security Operations Center are used to compare the results between the two mathematical approaches. Furthermore, comparisons are made with the results obtained from (a) the actual (baseline) Cyber-Security Operations Center performance, and (b) a vulnerability prioritization algorithm called VULCON that appeared in recent literature.
Analyzing multiple types of behaviors from traffic videos via nonparametric topic model

Intelligent video surveillance systems have garnered substantial research attention in recent years within the transportation surveillance field. The systems can assist in identifying activities, interactions, and abnormal behaviors of individuals in traffic. We propose a novel unsupervised learning framework based on a two-layer BNBP-PFA topic model to simultaneously model multiple types of behaviors in crowded and complicated traffic videos. We provide the model's structure, its inference algorithm, and design a corresponding likelihood function based on an abnormality detection algorithm. Compared to similar existing algorithms, ours readily reveals both the local topic-motion pattern and the global topic-traffic pattern. Comparative experiments on two public traffic video datasets show that our model outperforms the state-of-art algorithms in regards to effective topic discovery and abnormality detection.
Patterson Algorithm for Decoding Separable Binary Goppa Codes

In this paper, we consider the Goppa codes, their decoding algorithms, their application in practice, and also the special properties of using the Patterson algorithm in the case of its use for decoding separable binary Goppa codes.
Playsound.space: Improvising in the browser with semantic sound objects

This paper describes the development and evaluation of the online music making tool Playsound.space, an open platform leveraging Freesound Creative Commons audio content. After discussing the interface which relies on semantic queries and spectrogram representations, we introduce new features aiming to enhance participation and creativity support. This includes a multi-user chat system providing language translation, and a Web Audio player enabling real-time sound editing and time-stretching. We present evaluations for live performance and soundscape composition sketching. Throughout our reflections, we provide directions for future artistic and pedagogical applications that can benefit the design of other ubiquitous music systems.
Real-Time Object Coordinate Detection and Manipulator Control Using Rigidly Trained Convolutional Neural Networks

Objects embedded in the environment, such as switches, control buttons, sockets, et al., are devices that need frequent operations. To devise manipulators to operate such devices automatically, we propose a visual-position control scheme that directly converts the visual coordinate detections to motor commands. We train ConvNets with rigid 3D coordinate information, which is obtained from a single basis image of the target object. Our proposed training data preparation frameworks automatically generate and organize the required structure of the training images for the network. The ConvNet's superior image recognition capability results in high success rate in object detection and high precision in coordinate estimation. In our static experiments, in-range plane coordinate detection achieves an average success rate of 91% from various view-point directions; the depth coordinate detection achieves an average success rate of 86% based on an extended success range. In our dynamic experiments, a low-precision manipulator was used to press a down elevator call button and achieved an overall success rate of 98%. A high-precision manipulator was used for an object localization task and achieved a precision of ± 0.3 mm using a low-resolution camera.
Learning with mobile devices: A comparison of four mobile learning pilots in Africa

This paper compares the mixed-methods evaluation findings of the ukuFUNda Virtual School (UVS) with evaluations of three different mobile learning (m-learning) programmes in Africa: the information and communication technologies for rural education (ICT4RED); the Kenya Primary Math and Reading (PRIMR) study and the Nokia Mobile Mathematics (MoMath) evaluation. The comparison applies a conceptual model based on m-learning affordances and configurations (Strigel & Pouezevara, 2012), as well as on uptake, use, and responses by program beneficiaries; and on stakeholder learning. The findings show varied successes across all four programs and highlight important lessons for stakeholders with particular reference to scaling up of m-learning interventions in an African context. Keywords: information and communication technologies (ICT); learning configurations; m-learning; m-learning affordances; mobile phones; mobile platforms; stakeholder learning; tablets
Space targets adaptive optics images blind restoration by convolutional neural network

To obtain sharp images of space targets, high-accuracy restoration of degraded images corrected by an adaptive optics (AO) system is necessary. Existing algorithms are mainly based on the physical constraints of both image and point-spread function (PSF), which are usually continuously estimated in an alternately iterative manner and take a long time to restore blurred images. We propose an end-to-end blind restoration method for ground-based space target images based on conditional generative adversarial network without estimating PSF. The whole network consists of two parts, generator network and discriminator network, which are used for learning the atmospheric degradation process and achieving the purpose of generating restored images. To train the network, a simulated AO image dataset containing 4800 sharp–blur image pairs is constructed by 80 three-dimensional models of space targets combined with degradation of atmospheric turbulence. Experimental results demonstrate that the proposed method not only enhances the restoration accuracy but also improves the restoration efficiency of single-frame object images.
A comparative study of multi-objective machine reassignment algorithms for data centres

At a high level, data centres are large IT facilities hosting physical machines (servers) that often run a large number of virtual machines (VMs)—but at a lower level, data centres are an intricate collection of interconnected and virtualised computers, connected services, complex service-level agreements. While data centre managers know that reassigning VMs to the servers that would best serve them and also minimise some cost for the company can potentially save a lot of money—the search space is large and constrained, and the decision complicated as they involve different dimensions. This paper consists of a comparative study of heuristics and exact algorithms for the multi-objective machine reassignment problem. Given the common intuition that the problem is too complicated for exact resolutions, all previous works have focused on various (meta)heuristics such as First-Fit, GRASP, NSGA-II or PLS. In this paper, we show that the state-of-art solution to the single objective formulation of the problem (CBLNS) and the classical multi-objective solutions fail to bridge the gap between the number, quality and variety of solutions. Hybrid metaheuristics, on the other hand, have proven to be more effective and efficient to address the problem—but as there has never been any study of an exact resolution, it was difficult to qualify their results. In this paper, we present the most relevant techniques used to address the problem, and we compare them to an exact resolution ($$\epsilon $$-Constraints). We show that the problem is indeed large and constrained (we ran our algorithm for 30 days on a powerful node of a supercomputer and did not get the final solution for most instances of our problem) but that a metaheuristic (GeNePi) obtains acceptable results: more (+ 188%) solutions than the exact resolution and a little more than half (52%) the hypervolume (measure of quality of the solution set).
Multi-stage Contracts in the UTXO Model

Smart contract platforms such as Bitcoin and Ethereum allow writing programs that run on a decentralized computer. Bitcoin uses short-lived immutable data structures called UTXOs for data manipulation. Ethereum, on the other hand uses, long-lived mutable data structures called accounts. UTXOs are easier to handle, less error prone and scale better because the only operation we can do with them is to create or destroy (i.e., spend) them. The code inside a UTXO is executed only once, when it is spent. Additionally, this code refers to only local context (i.e., it is stateless). In Ethereum's account based system, there is a shared global context which each account can access and modify, thereby causing side affects. However, the benefit of persistent storage offered by accounts makes up for these drawbacks. In this work, we describe how to emulate persistent storage in UTXO based systems using a technique called transaction trees. This allows us to emulate the functionality of account-based systems such as Ethereum without the overhead of accounts. We demonstrate this via several examples which include contracts for a Rock-Paper-Scissors game, crowdfunding and an initial coin offering (ICO). The contracts are created in a UTXO based smart contract platform called Ergo that supports transaction trees.
Chemomertic Risk Assessment of Soil Pollution

Abstract In this study, an interpretation and modeling of the soil quality by monitoring data using an intelligent data analysis is presented. On an annual average, values of 12 soil surface chemical parameters as input variables were determined at 35 sampling sites as objects of the study in the region of Burgas, Bulgaria are used as input data set. Cluster analysis (hierarchical and non hierarchical methods abbreviated as HCA and K-means, respectively) and the principal components analysis (PCA) are used as chemometric tools for data interpretation, classification and modeling. Additionally, principal components regression analysis (APCS approach) is introduced to determine the contribution of each identified by PCA latent factor to the total concentration of the chemical parameters. The formation of different patterns of similarity between the variables or the objects of the study by cluster analysis is interpreted with respect to the risk of pollution or spatial conditions. The input data set structure is analyzed by PCA in order to determine the most significant factors responsible for the data structure. Four major patterns of similarity between the chemical parameters measured are found to define soil quality in the region related to industrial and agricultural activity in the region since the objects are separated into two patterns corresponding to each geographical location of the sampling sites. Analogous results were obtained by the use of PCA where the level of explanation of the data set structure is quantitatively assessed by the total explained variance of the system. The apportionment model indicated that the contribution of latent factors (sources of pollution) to the total chemical concentration of the species tested – pH, soil nutrition components, total and organic carbon content and toxic metals.
Fusing Object Information and Inertial Data for Activity Recognition

In the field of pervasive computing, wearable devices have been widely used for recognizing human activities. One important area in this research is the recognition of activities of daily living where especially inertial sensors and interaction sensors (like RFID tags with scanners) are popular choices as data sources. Using interaction sensors, however, has one drawback: they may not differentiate between proper interaction and simple touching of an object. A positive signal from an interaction sensor is not necessarily caused by a performed activity e.g., when an object is only touched but no interaction occurred afterwards. There are, however, many scenarios like medicine intake that rely heavily on correctly recognized activities. In our work, we aim to address this limitation and present a multimodal egocentric-based activity recognition approach. Our solution relies on object detection that recognizes activity-critical objects in a frame. As it is infeasible to always expect a high quality camera view, we enrich the vision features with inertial sensor data that monitors the users' arm movement. This way we try to overcome the drawbacks of each respective sensor. We present our results of combining inertial and video features to recognize human activities on different types of scenarios where we achieve an F 1 -measure of up to 79.6%.
An Analysis of User Perception Regarding Body-Worn 360° Camera Placements and Heights for Telepresence

Our work investigates body-worn 360° camera placements for telepresence, to balance height and clarity of view. We conducted a user study in a Virtual Reality (VR) simulation, using a 3x3 within-subjects experimental design varying placement and height, with 26 participants. We found that shoulder mounted cameras were significantly less preferable than our other conditions due to the occlusions caused by the wearer’s head. Our results did not show a significant effect of camera height within a range of +/- 12 inches from the user’s natural height. As such, in the context of body-worn 360° cameras, there is leeway for camera height, whereas strategic bodily placements are more important. Based on these results, we provide design recommendations for content creators using wearable cameras for immersive telepresence.