Reviewerly/exHarmony huggingface dataset:
A prototype model of unmanned automatic level crossing system using piezoelectric sensor

Indian Railways is the monopoly Organization under the control of the Central Government of India. During 2015–-16, the Indian Railways carried 8.107 billion passengers and has 8500 stations. There are about 24,313 unmanned level crossing exists in India. It has resulted in so many mishaps, and several fatalities reported. Hence there is an urgent need to design a system to implement automatic closing of unmanned level crossing to avoid accidents. This paper has developed a prototype model of automatic gate closing using a piezoelectric vibration sensor. In this model, the sensor has fixed before level crossing at feet distance to detect the arrival of a train. As soon as the train crosses the sensor, the signal is transmitted to the gate using the RF module. The receiver at the gate receives the signal, and it closes the gate within 30 s. The gate gets released automatically after 180 s, thus provide the safe passage of people and vehicles. The model is a cost-effective method and very easy to design and implement.
Image Tampering Issues in Social Media with Proper Detection

In recent days due to obsolescence of conventional photograph, digital images are used vastly, but tampering of those digital images has become a common practices due to availability of low cost image manipulation software. Detection of photograph whether it has been genuinely taken by a camera or manipulated can't be executed only by seeing it. As a consequence, the image can't be reliable so far to be placed as proof in any places. Different social media websites like facebook, twitter, myspace etc allows the users to use a platform where they can post various pictures. Hence tamper detection of digital image has been a trend in research area to establish the authenticity of images or photographs due to its great societal impact. This current work offers to provide tamper detection technique for the images used in social media.
CORDIC-SNN: On-FPGA STDP Learning with Izhikevich Neurons

Summary form only given. This paper proposes a neuromorphic platform for on-FPGA online Spike Timing Dependant Plasticity (STDP) learning, based on the COordinate Rotation DIgital Computer (CORDIC) algorithms. The implemented platform comprises two main components. First, the Izhikevich neuron model is modified for implementation using the CORDIC algorithm, simulated to ensure the model accuracy, described as hardware, and implemented on FPGA. Second, the STDP learning algorithm is adapted and optimized using the CORDIC method, synthesized for hardware, and implemented to perform on-FPGA online learning on a network of CORDIC Izhikevich neurons to demonstrate competitive Hebbian learning. The implementation results are compared with the original model and state-of-the-art to verify accuracy, effectiveness, and higher speed of the system. These comparisons confirm that the proposed neuromorphic system offers better performance and higher accuracy while being straightforward to implement and suitable to scale.
Multimodal deep features fusion for video memorability prediction

This paper describes a multimodal feature fusion approach for predicting the short and long term video memorability where the goal to design a system that automatically predicts scores reflecting the probability of a video being remembered. The approach performs early fusion of text, image, and video features. Text features are extracted using a Convolutional Neural Network (CNN), an FBResNet152 pre-trained on ImageNet is used to extract image features and video features are extracted using 3DResNet152 pre-trained on Kinetics 400. We use Fisher Vectors to obtain a single vector associated with each video that overcomes the need for using a non-fixed global vector representation for handling temporal information. The fusion approach demonstrates good predictive performance and regression superiority in terms of correlation over standard features.
Speaking Problems of EFL Students at English Education Study Program in Muhammadiyah Luwuk University

This research emphasized the problems of speaking ability in English among students of English Education Study Program in Muhammadiyah Luwuk University. This type of research was descriptive qualitative. In this research, the researcher used interview as instrument to find out the problems faced by the students in speaking English. From the results of the research, the researcher found the main problems faced by the students were limitations on vocabulary, errors in pronunciation, and incorrect grammar. In addition, the students also got anxiety when they speak in English and they embarrassed when they speak incorrectly, and they are still affected by their native language until they have problems in speaking English.Keywords: Speaking; Speaking Problems.
Learning Image Aesthetics by Learning Inpainting

Due to the high capability of learning robust features, convolutional neural networks (CNN) are becoming a mainstay solution for many computer vision problems, including aesthetic quality assessment (AQA). However, there remains the issue that learning with CNN requires time-consuming and expensive data annotations especially for a task like AQA. In this paper, we present a novel approach to AQA that incorporates self-supervised learning (SSL) by learning how to inpaint images according to photographic rules such as rules-of-thirds and visual saliency. We conduct extensive quantitative experiments on a variety of pretext tasks and also different ways of masking patches for inpainting, reporting fairer distribution-based metrics. We also show the suitability and practicality of the inpainting task which yielded comparably good benchmark results with much lighter model complexity.
Keywords Extraction Algorithm of Financial Review Based on Dirichlet Multinomial Model

At present, the main method of keyword extraction is usually based on topical discovery model, and the most popular model is LDA (Latent Dirichlet Allocation) topical model. However, LDA model has poor performance in dealing with short texts, which is the imperfection of the current topical discovery model. This paper propose a keywords extraction model SDMM (Statistical Dirichlet Multinomial model) to extract the keywords of short financial review. This keywords extraction model use DMM (Dirichlet Multinomial Model) for topic discovery, and calculate the similarity between the words and keywords, thus improve the performance of keyword extraction for short texts. Experimental results on several financial review datasets show that the proposed model is better than the existing models in evaluation indicator Precision, Recall and F-measure.
Orchestrating Medical Image Compression and Remote Segmentation Networks

Deep learning-based medical image segmentation on the cloud offers superb performance by harnessing the recent model innovation and hardware advancement. However, one major factor that limits its overall service speed is the long data transmission latency, which could far exceed the segmentation computation time. Existing image compression techniques are unable to achieve an efficient compression to dramatically reduce the data offloading overhead, while maintaining a high segmentation accuracy. The underlying reason is that they are all developed upon human visual system, whose image perception pattern could be fundamentally different from that of deep learning-based image segmentation. Motivated by this observation, in this paper, we propose a generative segmentation architecture consisting of a compression network, a segmentation network and a discriminator network. Our design orchestrates and coordinates segmentation and compression for simultaneous improvements of segmentation accuracy and compression efficiency, through a dedicated GAN architecture with novel loss functions. Experimental results on 2D and 3D medical images demonstrate that our design can reduce the bandwidth requirement by 2 orders-of-magnitude comparing with that of uncompressed images, and increase the accuracy of remote segmentation remarkably over the state-of-the-art solutions, truly accelerating the cloud-based medical imaging service.
Extreme Learning Machines on Cayley-Dickson Algebra Applied for Color Image Auto-Encoding

This paper aims to provide a useful framework for extreme learning machines (ELMs) on Cayley-Dickson algebras. Cayley-Dickson algebras, which include complex numbers, quaternions, and octonions as particular instances, are hyper-complex algebras defined using a recursive procedure. Firstly, we review some basic concepts on Cayley-Dickson algebras and formulate Cayley-Dickson matrix product using real-valued linear algebra. Then, we propose the Cayley-Dickson ELMs and derive their learning using Cayley-Dickson least squares problem. Lastly, we compare the performance of real-valued and four-dimensional Cayley-Dickson ELM models, including quaternion-valued ELM, in an experiment on color image auto-encoding using the well-known CIFAR dataset.
Multi-Agent Task Allocation Based on the Learning of Managers and Local Preference Selections

This paper discusses an adaptive distributed allocation method in which agents individually learn strategies for preferences to decide on the rank of tasks which they want to be allocated by a manager. In a distributed edge-computing environment, multiple managers that control the provision of a variety of services requested from different locations have to allocate the corresponding tasks to appropriate agents, which are usually programs developed by different companies. In our proposed method, each agent learns which manager will allocate tasks it performs well and how to declare its preferred tasks. We experimentally evaluated the proposed learning method and showed that agents using the proposed method could effectively execute requested tasks and could adapt to changes in patterns of the requested tasks.
Virtual Machine Migration in Heterogeneous Clouds - A Practical Approach

In modern times, Cloud Computing is viewed as more promising technology than any other traditional Information Technology Computing paradigms. It basically serves as an on-demand resource provisioning platform without any active intervention by its user. The resource provisioning strategies require appropriate load distribution management across the cloud network, without which the cloud would face biased workload performance. Virtualization is the backbone of Cloud Computing, which enables the distribution and management of data by initiating the Virtual Machines (VMs). Furthermore, a Cloud Service Provider(CSP) has to monitor, analyze, and manage the workload distribution for servers when VMs are migrated. It presents the need to consider VM migration as an important activity that would unload the cloud server that is overloaded to migrate it to the server that can handle the workload. This paper proposes a technique that initiates the migration of VMs between heterogeneous cloud environments that would lead to a stable and well-balanced cloud network. The process of VM migration is very intensive in terms of resources, and hence intelligent approaches are required. It should effectively reduce the utilization of network bandwidth by minimizing the downtime of the server. However, the migration of VMs between the heterogeneous cloud would be challenging, but the right solution would benefit the cloud network managers on a large scale. Our proposed technique demonstrates heterogeneous VM migration between various cloud platforms built on different architectures. Various parameters have to be technically tuned for the conversion of VM images according to the Cloud Architecture. The performance of the proposed technique is evaluated based on the time taken for migration.
Conceptual Explanations of Neural Network Prediction for Time Series

Deep neural networks are black boxes by construction. Explanation and interpretation methods therefore are pivotal for a trustworthy application. Existing methods are mostly based on heatmapping and focus on locally determining the relevant input parts triggering the network prediction. However, these methods struggle to uncover global causes. While this is a rare case in the image or NLP modality, it is of high relevance in the time series domain.This paper presents a novel framework, i.e. Conceptual Explanation, designed to evaluate the effect of abstract (local or global) input features on the model behavior. The method is model-agnostic and allows utilizing expert knowledge. On three time series datasets Conceptual Explanation demonstrates its ability to pinpoint the causes inherent to the data to trigger the correct model prediction.
HCNet: Hierarchical Context Network for Semantic Segmentation

Global context information is vital in visual understanding problems, especially in pixel-level semantic segmentation. The mainstream methods adopt the self-attention mechanism to model global context information. However, pixels belonging to different classes usually have weak feature correlation. Modeling the global pixel-level correlation matrix indiscriminately is extremely redundant in the self-attention mechanism. In order to solve the above problem, we propose a hierarchical context network to differentially model homogeneous pixels with strong correlations and heterogeneous pixels with weak correlations. Specifically, we first propose a multi-scale guided pre-segmentation module to divide the entire feature map into different classed-based homogeneous regions. Within each homogeneous region, we design the pixel context module to capture pixel-level correlations. Subsequently, different from the self-attention mechanism that still models weak heterogeneous correlations in a dense pixel-level manner, the region context module is proposed to model sparse region-level dependencies using a unified representation of each region. Through aggregating fine-grained pixel context features and coarse-grained region context features, our proposed network can not only hierarchically model global context information but also harvest multi-granularity representations to more robustly identify multi-scale objects. We evaluate our approach on Cityscapes and the ISPRS Vaihingen dataset. Without Bells or Whistles, our approach realizes a mean IoU of 82.8% and overall accuracy of 91.4% on Cityscapes and ISPRS Vaihingen test set, achieving state-of-the-art results.
A Computational Approach to Identify Interfering Medications on Urine Drug Screening Assays without Data from Confirmatory Testing

Abstract Urine drug screening (UDS) assays can rapidly and sensitively detect drugs of abuse but can also produce spurious results due to interfering substances. We previously developed an approach to identify interfering medications using electronic health record (EHR) data, but the approach was limited to UDS assays for which presumptive positives were confirmed using more specific methods. Here we adapted the approach to search for medications that cause false positives on UDS assays lacking confirmation data. From our institution’s EHR data, we used our previous dataset of 698,651 UDS and confirmation results. We also collected 211,108 UDS results for acetaminophen, ethanol and salicylates. Both datasets included individuals’ prior medication exposures. We hypothesized that the odds of a presumptive positive would increase following exposure to an interfering medication independently of exposure to the assay’s target drug(s). For a given assay–medication pair, we quantified potential interference as an odds ratio from logistic regression. We evaluated interference of selected compounds in spiking experiments. Compared to the approach requiring confirmation data, our adapted approach showed only modestly diminished ability to detect interfering medications. Applying our approach to the new data, we discovered and validated multiple compounds that can cause presumptive positives on the UDS assay for acetaminophen. Our approach can reveal interfering medications using EHR data from institutions at which UDS results are not routinely confirmed.
Voice Controlled Wheelchair

This paper is a design of Voice Controlled Wheel Chair for people who has any physical illness. Here Arduino, microcontroller and geetech voice recognition module are used to support the motion of the wheelchair. In order to provide the battery level, a battery level indicator is also provided. Based upon the direction specified in the commands, the Arduino will drive the 2 motors. People those who has disabilities with their hands, foot and lower body are unable to perform tasks on regular basis. So, there are many applications which help handicapped person to perform their tasks. The aim of this system to help people who cannot move properly without help others due to any physical illness or disabilities. Speech recognition technology will provide a new way of human interaction with machine.
A Comparative Analysis of the Solar Energy Receiving Share Using Four Tracking System Types at Mid-Latitude Regions

This paper presents a comparative study of the share of direct solar radiation received by flat solar converters (photovoltaic modules, solar thermal collectors) using four tracking systems of equatorial, azimuth, pseudo-equatorial, and pseudo-azimuth type. The study is developed for the Brașov, Romania location (45.65° N, 25.60° N), but its results can be extended for most mid-latitude regions. The effect of dual-axis vs. single-axis tracking is studied in the first part, highlighting high shares of received solar energy using single-axis diurnal tracking with an optimum fixed tilt angle. The four tracking systems with unequal steps tracking algorithms are compared with a fixed tilted system as reference. The paper outlines the ability of single-axis tracking in receiving high shares of direct solar radiation. This methodology allows the designers to rank the four tracking system types, for mid-latitudes, according to the criteria of solar energy receiving share and tracking angular strokes.
Neural Network-Based Side Channel Attacks and Countermeasures

This paper surveys results in the use of neural networks and deep learning in two areas of hardware security: power attacks and physically-unclonable functions (PUFs).
Evaluation of the quantization error in convergence stereo cameras

Computer vision techniques including image acquisition, image processing, and data analysis are currently popular and widespread. Stereo vision systems in the area of computer vision have been widely used in many applications, such as 3D depth reconstruction, 3D gaming, robot vision navigation, remote sensing, medical image processing, and so on. However, highly accurate 3D stereo vision scanners do not completely satisfy industry requirements due to the high price of these systems. In this paper, what we believe to be a novel method is proposed that increases the 3D measurement accuracy of stereo vision systems with low resolution and cheap cameras. This was achieved through discussing the quantization error of the 3D depth reconstruction of stereo cameras. Accordingly, a mathematical model was introduced to evaluate the quantization error of convergence stereo cameras. Meanwhile, a synthetic experiment was employed to validate our proposed mathematical model. Finally, simulation experiments were conducted to demonstrate how to apply the presented approach to increase the accuracy measurement of low resolution stereo vision cameras. The simulation results show a considerable measurement improvement by changing the convergence angle of stereo vision cameras.
Efficient verification of parallel matrix multiplication in public cloud: the MapReduce case

Abstract With the advent of cloud-based parallel processing techniques, services such as MapReduce have been considered by many businesses and researchers for different applications of big data computation including matrix multiplication, which has drawn much attention in recent years. However, securing the computation result integrity in such systems is an important challenge, since public clouds can be vulnerable against the misbehavior of their owners (especially for economic purposes) and external attackers. In this paper, we propose an efficient approach using Merkle tree structure to verify the computation results of matrix multiplication in MapReduce systems while enduring an acceptable overhead, which makes it suitable in terms of scalability. Using the Merkle tree structure, we record fine-grained computation results in the tree nodes to make strong commitments for workers; they submit a commitment value to the verifier which is then used to challenge their computation results’ integrity using elected input data as verification samples. Evaluation outcomes show significant improvements comparing with the state-of-the-art technique; in case of 300*300 matrices, 73% reduction in generated proof size, 61% reduction in the proof construction time, and 95% reduction in the verification time.
A feasibility study to estimate optimal rigid‐body registration using combinatorial rigid registration optimization (CORRO)

Abstract Purpose Clinical image pairs provide the most realistic test data for image registration evaluation. However, the optimal registration is unknown. Using combinatorial rigid registration optimization (CORRO) we demonstrate a method to estimate the optimal alignment for rigid‐registration of clinical image pairs. Methods Expert selected landmark pairs were selected for each CT/CBCT image pair for six cases representing head and neck, thoracic, and pelvic anatomic regions. Combination subsets of a k number of landmark pairs (k‐combination set) were generated without repeat to form a large set of k‐combination sets (k‐set) for k = 4,8,12. The rigid transformation between the image pairs was calculated for each k‐combination set. The mean and standard deviation of these transformations were used to derive final registration for each k‐set. Results The standard deviation of registration output decreased as the k‐size increased for all cases. The joint entropy evaluated for each k‐set of each case was smaller than those from two commercially available registration programs indicating a stronger correlation between the image pair after CORRO was used. A joint histogram plot of all three algorithms showed high correlation between them. As further proof of the efficacy of CORRO the joint entropy of each member of 30 000 k‐combination sets in k = 4 were calculated for one of the thoracic cases. The minimum joint entropy was found to exist at the estimated mean of registration indicating CORRO converges to the optimal rigid‐registration results. Conclusions We have developed a methodology called CORRO that allows us to estimate optimal alignment for rigid‐registration of clinical image pairs using a large set landmark point. The results for the rigid‐body registration have been shown to be comparable to results from commercially available algorithms for all six cases. CORRO can serve as an excellent tool that can be used to test and validate rigid registration algorithms.