Reviewerly/exHarmony huggingface dataset:
Real-time Object Detection using Deep Learning for helping People with Visual Impairments

Object detection plays a crucial role in the development of Electronic Travel Aids (ETAs), capable to guide a person with visual impairments towards a target object in an unknown indoor environment.In such a scenario, the object detector runs on a mobile device (e.g.smartphone) and needs to be fast, accurate, and, most importantly, lightweight.Nowadays, Deep Neural Networks (DNN) have become the state-of-the-art solution for object detection tasks, with many works improving speed and accuracy by proposing new architectures or extending existing ones.A common strategy is to use deeper networks to get higher performance, but that leads to a higher computational cost which makes it impractical to integrate them on mobile devices with limited computational power.In this work we compare different object detectors to find a suitable candidate to be implemented on ETAs, focusing on lightweight models capable of working in real-time on mobile devices with a good accuracy.In particular, we select two models: SSD Lite with Mobilenet V2 and Tiny-DSOD.Both models have been tested on the popular OpenImage dataset and a new dataset, named L-CAS Office dataset, collected to further test models' performance and robustness in a real scenario inspired by the actual perception challenges of a user with visual impairments.
Texture Feature Local Binary Pattern for Handwritten Character Recognition

Handwriting is still widely used as a research topic in computer vision topics. Two important parameters to consider are classification performance and classification time. Local Binary Pattern (LBP) is a method of extracting shape features in handwriting, but the number of features generated is quite large, resulting in increased classification time. This research proposes a method to speed up the classification time by dividing the LBP features into several grids. It is found that the proposed method can provide good performance at 90.99% accuracy and sufficient speed, on LBP parameter [33], 5x5 grid and KNN parameter k=3.
Single image deraining using Context Aggregation Recurrent Network

Single image deraining is a challenging problem due to the presence of non-uniform rain densities and the ill-posedness of the problem. Moreover, over-/under-deraining can directly impact the performance of vision systems. To address these issues, we propose an end-to-end Context Aggregation Recurrent Network, called CARNet, to remove rain streaks from single images. In this paper, we assume that a rainy image is the linear combination of a clean background image with rain streaks and propose to take advantage of the context information and feature reuse to learn the rain streaks. In our proposed network, we first use the dilation technique to effectively aggregate context information without sacrificing the spatial resolution, and then leverage a gated subnetwork to fuse the intermediate features from different levels. To better learn and reuse rain streaks, we integrate a LSTM module to connect different recurrences for passing the information learned from the previous stages about the rain streaks to the following stage. Finally, to further refine the coarsely derained image, we introduce a refinement module to better preserve image details. As for the loss function, the L1-norm perceptual loss and SSIM loss are adopted to reduce the gridding artifacts caused by the dilated convolution. Experiments conducted on synthetic and real rainy images show that our CARNet achieves superior deraining performance both qualitatively and quantitatively over the state-of-the-art approaches.
Sybil Detection as Graph Filtering

Sybils are users created for carrying out nefarious actions in online social networks (OSNs) and threaten the security of OSNs. Therefore, Sybil detection is an urgent security task, and various detection methods have been proposed. Existing Sybil detection methods are based on the relationship (i.e., graph structure) of users in OSNs. Structure-based methods can be classified into two categories: Random Walk (RW)-based and Belief Propagation (BP)-based. However, although almost all methods have been experimentally evaluated in terms of their performance and robustness to noise, the theoretical understanding of them is insufficient. In this paper, we interpret the Sybil detection problem from the viewpoint of graph signal processing and provide a framework to formulate RW- and BPbased methods as low-pass filtering. This framework enables us to theoretically compare RW- and BP-based methods and explain why BP-based methods perform well for scale-free graphs, unlike RW-based methods. Furthermore, by this framework, we relate RW- and BP-based methods and Graph Neural Networks (GNNs) and discuss the difference among these methods. Finally, we evaluate the validity of this framework through numerical experiments.
Cloud Architecture for Remote Medical Monitoring

The need to create a cloud architecture is due to the fact that most people do not pay proper attention to their health, often putting off an appointment with a doctor indefinitely. This circumstance in some cases can lead to grave consequences. Using the remote cloud monitoring model will allow patients registered in a medical institution to receive medical care in a timely manner due to remote automated health monitoring. The provision of services and emergency decisions with various levels of complexity is realized through the interaction of the web interface, the layer of intelligent data processing, the agent unit, the accumulation and analysis of experience, as well as by adapting the neural network data processing for high-performance computing systems.
Implementation and Analysis of Intelligent Inquiry Teaching Model in Primary School English Teaching

As a linguistic discipline, the critical point of teaching English is to cultivate students’ comprehensive language application ability in listening, speaking, reading, and writing. Moreover, the cultivation of ability requires authentic linguistic context. The intelligent learning environment can provide a more authentic English situation in the intelligent inquiry teaching model and help students’ better familiar with communication methods. What is more, it attaches great importance to students’ learning process, such as discovery, induction, and evaluation, as a result, to promote and develop students’ intelligence. Based on intelligent inquiry teaching mode research, this article chose the E-book Bag as the learning environment. It focused on its improvement and application in primary school English teaching.
Fighting Terrorism in Africa: Complementarity between Inclusive Development, Military Expenditure and Political Stability

This study examines complementarities between inclusive development, military expenditure and political stability in the fight against terrorism in 53 African countries for the period 1998-2012. Hence the policy variables employed in the study are inclusive development, military expenditure and political stability. The empirical evidence is based on Generalised Method of Moments (GMM) with forward orthogonal deviations. The paper reports three main findings. Firstly, military expenditure and inclusive development are substitutes and not complements. Secondly, it is more relevant to use political stability as a complement of inclusive development than to use inclusive development as a complement of political stability. Thirdly, it can be broadly established that military expenditure and political stability are complementary. In the light of the sequencing, complementarity and substitutability, when the three policy variables are viewed within the same framework, it is more feasible to first pursue political stability and then complement it with military expenditure and inclusive development.
Hybrid Secure Data Transfer Scheme Using Cryptography and Steganography

This paper offers a naïve data security method using cryptography and image steganography schemes. The ciphertext message image is treated as secret image of steganographic encoding. In this proposed steganographic encoding scheme, both the secret image and cover image are blended together using the mixing operation that results as stego-image. Use of cryptography scheme embeds excellent security in the steganography scheme and also offers two level of security, whenever required. The strength of the proposed hybrid data hiding scheme is tested on different parameters that are shown in result part of the paper.
Gaussian Local Ternary Co-occurrence Pattern for Image Retrieval

Digital media is expanding day by day. It is a humongous effort to search for a relevant image manually from a large digital repository. This paper focuses on the problem of image browsing through a large image database by using content-based image retrieval. A novel feature, Gaussian local ternary co-occurrence pattern (GLTCoP), is developed to represent images, and comparison among huge sets of images is performed based on this pattern. The proposed strategy yields relevant images in a much shorter time. In GLTCoP, three smoother versions of the image are used to essentially capture coarse to fine details of image, by comparing it with the original image. Hence, GLTCoP captures highly distinct information to represent and differentiate similar kind of images. Image retrieval performance is tested on benchmark image databases, i.e., STex and Corel 1000. Also, results are compared with the existing state-of-the-art techniques, and better performance of the proposed feature is observed in terms of average precision and average recall.
Image-based Visual Servoing Control of a Quadcopter Air Vehicle

This article proposes two optimal controllers that stabilize the altitude, attitude, heading, and position of the quadcopter in space. The first optimal control method consists of computing the proportional controller gain by minimizing the error between measured and the desired feature vector values, as opposed to the classical visual servoing (VS) scheme that requires a known value of the proportional controller gain which is often chosen empirically. The second control method relies on the optimal tuning of PD controller gains applied on the altitude, attitude, and position loops. This, however, may lead to a convergence problem, which is overcome by the use of an optimization technique, the bat algorithm (BA). This latter is an efficient metaheuristic algorithm that has been successfully used in many optimization problems. Several simulations are run in MATLAB, in which visual servo control of the Vertical Takeof and Landing (VTOL) type of Unmanned Aerial Vehicles (UAVs), known as the quadcopter, is implemented. The obtained results show that the Bat algorithm renders the classical PD controller adaptive and provides a high flexibility in tracking the UAV trajectory even in the presence of disturbances. It also guarantees the stability and accuracy of the optimal, minimal time, vision-based controller.
Approximating Percentage of Academic Traffic in the World Wide Web and Rankings of Countries Based on Academic Traffic

The paper introduces a novel mechanism for approximating traffic of the academic sites (universities and research institutes) in the World Wide Web based on Alexa’s rankings. Firstly we introduce and discuss new method for calculating score (weight) of each site based on its Alexa’s rank. Secondly we calculate percentage of academic traffic in the World Wide Web. Thirdly we introduce and discuss two new rankings of countries based on academic traffic. Finally we discuss about five indicators and effects of them in traffic of the academic sites. Results indicate that the methodology can be useful for approximating traffic of the academic sites and producing rankings of countries in practice.
Levels of Detail in Visual Augmentation for Novice and Expert Audiences

Abstract Digital musical instruments offer countless opportunities for musical expression by allowing artists to produce sound without the physical constraints of analog instruments. By breaking the intuitive link between gestures and sound they may hinder the audience experience, however, making the musician's contribution and expressiveness difficult to discern. To cope with this issue without altering the instruments, researchers and artists have designed techniques to augment their performances with additional information, through audio, haptic, or visual modalities. These techniques have, however, only been designed to offer a fixed level of information, without taking into account the variety of spectators' expertise and preferences. In this article, we introduce the concept of controllable levels of details (LODs) for visual augmentation. We investigate their design, implementation, and effect on objective and subjective aspects of audience experience. We conduct a controlled experiment with 18 participants, including novices and experts in electronic music. Our results expose the subjective nature of expertise and its biases. We analyze quantitative and qualitative data to reveal contrasts in the impact of LODs on experience and comprehension for experts and novices. Finally, we highlight the diversity of usage of LODs in visual augmentation by spectators and propose a new role on stage, the augmenter.
Binarized Neural Network Based On FPGA To Realize Handwritten Digit Recognition

We trained a binarized neural network (BNN) which can be used for handwritten digit recognition on software, and implemented this BNN on the FPGA using software training parameters without the help of high-level synthesis (HLS). This BNN contains an input layer, two convolutional layers, two pooling layers, a fully connected layer and an output layer. The first convolution layer contains 10 convolution kernels, each of which has a size of 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> 5. Each convolution kernel uses 25 arithmetic units to calculate in parallel, the calculation of 10 convolution kernels is also parallel. The size of the convolution kernels in the second convolutional layer is 38 * 3, the input and output channels are 10, we use 900 XNOR gates for calculation here. Compared with the traditional CNN, it does not need to consume DSP resources and requires less storage space for the parameters. And the power consumption obtained by Quartus ii software of this BNN is 0.136 W. The time to identify a picture is 18 μs, and the accuracy rate is 85% without the batch normalize (BN) layer.
Inferring spatial relations from textual descriptions of images

Generating an image from its textual description requires both a certain level of language understanding and common sense knowledge about the spatial relations of the physical entities being described. In this work, we focus on inferring the spatial relation between entities, a key step in the process of composing scenes based on text. More specifically, given a caption containing a mention to a subject and the location and size of the bounding box of that subject, our goal is to predict the location and size of an object mentioned in the caption. Previous work did not use the caption text information, but a manually provided relation holding between the subject and the object. In fact, the used evaluation datasets contain manually annotated ontological triplets but no captions, making the exercise unrealistic: a manual step was required; and systems did not leverage the richer information in captions. Here we present a system that uses the full caption, and Relations in Captions (REC-COCO), a dataset derived from MS-COCO which allows to evaluate spatial relation inference from captions directly. Our experiments show that: (1) it is possible to infer the size and location of an object with respect to a given subject directly from the caption; (2) the use of full text allows to place the object better than using a manually annotated relation. Our work paves the way for systems that, given a caption, decide which entities need to be depicted and their respective location and sizes, in order to then generate the final image.
Utilising LiDAR for fall detection

Autonomous driving generates several low-cost technologies, such as light detection and ranging (LiDAR). Due to this, the LiDAR technology has experienced impressive performance improvements. Our ambition is to capitalise on this development, where LiDAR is considered as the enabling technology for a non-invasive monitoring system for securing elder persons in their home. A motivation for technology-based securing of elder persons is that many countries experience a demographic change. Traditional personal care by care worker or re-location to special homes of elder persons does not scale due to the shrinking fraction of the working population. Technology can reduce some of the burden. This article proposes and assesses technology for securing a person's home. However, securing a person, based on monitoring, requires careful design because the technology should be non-invasive, reliable and low cost. LiDAR technology offers several crucial qualities that meet these system requirements. This article provides a proof of concept for a low-cost, non-invasive LiDAR-based monitoring system. Our proposed system can detect if a person has fallen, and it can trigger an alarm to the care services when required. We emphasise especially that our monitoring solution can operate in the bathroom and even in the shower.
Locality-Aware Scheduling for Scalable Heterogeneous Environments

Heterogeneous computing promise boost performance of scientific applications by allowing massively parallel execution of computational tasks. However, manually managing extremely heterogeneous, multi-device systems is complicated and may result in sub-optimal performance. Specifically, data management is an extremely challenging problem on multi-device systems. In this work, we introduce two locality-aware schedulers for the Minos Computing Library (MCL), an asynchronous, task-based programming model and runtime for extremely heterogeneous systems. The first scheduler implements a pure locality-aware algorithm to maximize data reuse, though it might incur in “hot-spots” that limit system utilization. The second scheduler mitigates this drawback by dynamically targeting between locality-awareness and system utilization based on the current workload and available computing devices. Our results show that locality-awareness greatly benefit applications that exhibit data reuse, providing up to 6.9x and 7.9x over the original MCL scheduler and equivalent OpenCL implementations, respectively. Moreover, our schedulers introduce negligible overhead compared with the original MCL scheduler and achieve similar performance for applications that don't benefit from data locality.
Real-Time Numerical Gesture Recognition Using MPU9250 Motion Sensor

Human–computer interaction is one of the most exciting areas of research. Hand gesture recognition stands vital for developing better human–computer interaction systems. Most of the existing approaches using camera-based or 3D depth sensors for hand gesture recognition are rather expensive and sensitive to environmental changes. In this paper, we propose a low-cost data glove embedded with MPU9250 motion sensor which overcomes the drawbacks of existing systems. In our work, the primary focus is to develop a numerical gesture recognition system deployable in any real-time application. An extensive comparison of the performance of different machine learning and neural network models is presented. An optimal network model is chosen, and details of deploying the trained model in a real-time unity game application are presented. In our experiment, the highest accuracy achieved is 98.41% with an average real-time inference delay of 2 ms.
Similarity Measure Approaches Applied in Text Document Clustering for Information Retrieval

In today's world with ever increasing amount of text assets overloaded on web with digitized libraries, sorting out these documents got developed into a feasible need. Document clustering is an important procedure which consequently sorts out huge number of articles into a modest number of balanced gatherings. Document clustering is making groups of similar documents into number of clusters such that documents within the same group with high similarity values among one another and dissimilar to documents from other clusters. Common applications of document Clustering includes grouping similar news articles, analysis of customer feedback, text mining, duplicate content detection, finding similar documents, search optimization and many more. This lead to utilization of these documents for finding required information in a competent and efficient manner. Document clustering required a measurement for evaluating how surprising two given information are. This dissimilarity is often estimated by using some distance measures, for example, Cosine Similarity, Euclidean distance, etc. In our work, we evaluated and analyzed how effective these measures are in partitioned clustering for text document datasets. In our experiments we have used standard K-means algorithm and our results details on six text documents datasets and five most commonly used distance or similarity measures in text clustering.
Cricket Activity Detection Using Computer Vision

Nowadays the most trending and bookmark game is cricket in the whole world in which various types of activities occur like a No-ball, Wide Ball, Boundaries, etc. Here we detect a composite feature combining computer vision Algorithm along with camera view analysis. Many human errors occur in cricket matches because a wide ball or no ball creates very crucial situations and these decisions create very contradictorily during a match. Today technology is playing the most important role in the present world. So we decided that detect the various activities using computer vision techniques that occur during a cricket match like crucial catches, LBW, No ball, wide ball, etc. Here we will discuss activity detection using computer vision. Technology has various dimensions. Today the technology available is not computed the data. The technology has many different applications and magnitudes/aspect at which the software is achieving higher accuracy and greater results when the software is precisely performed. Implementation in any sport is much beneficial. Then Games such as Tennis, Baseball, Rugby, Soccer, Hockey, Cricket, Football, Kabaddi, etc. and single-player games like Chess, Badminton, Shooting, etc. are also being considered well thought out as honor to their countries.
Active Learning for Inference and Regeneration of Applications that Access Databases

We present K onure , a new system that uses active learning to infer models of applications that retrieve data from relational databases. K onure comprises a domain-specific language (each model is a program in this language) and associated inference algorithm that infers models of applications whose behavior can be expressed in this language. The inference algorithm generates inputs and database contents, runs the application, then observes the resulting database traffic and outputs to progressively refine its current model hypothesis. Because the technique works with only externally observable inputs, outputs, and database contents, it can infer the behavior of applications written in arbitrary languages using arbitrary coding styles (as long as the behavior of the application is expressible in the domain-specific language). K onure also implements a regenerator that produces a translated Python implementation of the application that systematically includes relevant security and error checks.