Reviewerly/exHarmony huggingface dataset:
Exploring the Possibility of USB based Fork Bomb Attack on Windows Environment

The need for data exchange and storage is currently increasing. The increased need for data exchange and storage also increases the need for data exchange devices and media. One of the most commonly used media exchanges and data storage is the USB Flash Drive. USB Flash Drive are widely used because they are easy to carry and have a fairly large storage. Unfortunately, this increased need is not directly proportional to an increase in awareness of device security, both for USB flash drive devices and computer devices that are used as primary storage devices. This research shows the threats that can arise from the use of USB Flash Drive devices. The threat that is used in this research is the fork bomb implemented on an Arduino Pro Micro device that is converted to a USB Flash drive. The purpose of the Fork Bomb is to damage the memory performance of the affected devices. As a result, memory performance to execute the process will slow down. The use of a USB Flash drive as an attack vector with the fork bomb method causes users to not be able to access the operating system that was attacked. The results obtained indicate that the USB Flash Drive can be used as a medium of Fork Bomb attack on the Windows operating system.
Enhanced Text Matching Based on Semantic Transformation

Text matching is the core of natural language processing (NLP) system. It's considered as a touchstone of the NLP, and it aims to find whether text pairs are equal in semantics. However, the semantic gap in text matching is still an open problem to solve. Inspired by successes of cycle-consistent adversarial network (CycleGAN) in image domain transformation, we propose an enhanced text matching method based on the CycleGAN combined with the Transformer network. Based on the proposed method, the text semantics in a source domain is transferred to a similar or different target domain, and the semantic distance between text pairs is decreased. Meanwhile, we demonstrate our method in paraphrase identification and question answer matching. The matching degree is computed by a standard text matching model to evaluate the transforming influence on narrowing the text semantic gap. The experiments show that our method achieves text domain adaptation, and the effects on different matching models are remarkable.
Fast discrete cross-modal hashing with semantic consistency

Supervised cross-modal hashing has attracted widespread concentrations for large-scale retrieval task due to its promising retrieval performance. However, most existing works suffer from some of following issues. Firstly, most of them only leverage the pair-wise similarity matrix to learn hash codes, which may result in class information loss. Secondly, the pair-wise similarity matrix generally lead to high computing complexity and memory cost. Thirdly, most of them relax the discrete constraints during optimization, which generally results in large cumulative quantization error and consequent inferior hash codes. To address above problems, we present a Fast Discrete Cross-modal Hashing method in this paper, FDCH for short. Specifically, it firstly leverages both class labels and the pair-wise similarity matrix to learn a sharing Hamming space where the semantic consistency can be better preserved. Then we propose an asymmetric hash codes learning model to avoid the challenging issue of symmetric matrix factorization. Finally, an effective and efficient discrete optimal scheme is designed to generate discrete hash codes directly, and the computing complexity and memory cost caused by the pair-wise similarity matrix are reduced from O(n2) to O(n), where n denotes the size of training set. Extensive experiments conducted on three real world datasets highlight the superiority of FDCH compared with several cross-modal hashing methods and demonstrate its effectiveness and efficiency.
AUTOMATIC ANNOTATION OF DANCE VIDEOS BASED ON FOOT POSTURES

Along with the advancements in the field of artificial intelligence, machine learning, and deep learning video annotation has become a more exciting and crucial problem, especially the video from medical, arts performance games and so on.In India, there is an increased demand for digitizing and archiving of arts and culture .This paper focuses on annotating dance videos based on foot postures (stanas) in an automatic manner.Using transfer-learning, the features from the images are extracted and a deep neural network is used for image classification.A trained model of Deep Stana Classifier is used for identifying stanas from the frames of a video.A complete annotation system comprises of a coupled architecture, one Deep Stana Classifier module, and an annotation module.The findings on the accuracy obtained for the Deep Stana Classifier produced positive results.In the second phase, the result of the annotations made on the video is kept as an index structure in JSON object format.The scope and opportunity of this work in the future is consistent and useful for annotating dance videos as well as videos from another domain.
Verification of the UR5 robot’s properties after a crash caused by a fall of a transferred load from a crane

Installing robots in small businesses is primarily dependent on the ratio of money spent to achieve profit. In addition, from consumer pressure on the production, quality and low cost, the increasing cost of human labour leads to robot deployment. With falling prices of robots, their use is becoming increasingly common, which leads to small firms trying to implement robots that are not new. They try to buy a cheaper older robot or a robot that was damaged during its previous operation. It is necessary to eliminate the risk that arises from more serious damage to the robot, therefore, the requirements for verification of the robot’s resultant parameters after its repair performed in own interest. Based on such a requirement, the solution was to verify the parameters of the UR5 robot to confirm the possibility of its further use. After replacing the damaged parts, the robot was subjected to its parameter verification according to standard ISO 9283:2012. For measurement purposes, a measuring cube was designed and manufactured, as well as a measuring nest for Heidenhain sensors.
Comparison of distance measurement on k-nearest neighbour in textual data classification

One algorithm to classify textual data in automatic organizing of documents application is KNN, by changing word representations into vectors. The distance calculation in the KNN algorithm becomes essential in measuring the closeness between data elements. This study compares four distance calculations commonly used in KNN, namely Euclidean, Chebyshev, Manhattan, and Minkowski. The dataset used data from Youtube Eminem’s comments which contain 448 data. This study showed that Euclidian or Minkowski on the KNN algorithm achieved the best result compared to Chebycev and Manhattan. The best results on KNN are obtained when the K value is 3.
Visualize Different: Towards Researching the Fit Between Taxonomy Visualizations and Taxonomy Tasks

Yet despite the great interest in taxonomies, there is virtually no guidance on how to purposefully visualize them. Interestingly, taxonomies are visualized in ways as diverse as morphological boxes, hierarchies and mathematical sets, to name three typical examples. As a result, taxonomy builders face the following question: Which type of taxonomy task is best supported by which type of taxonomy visualization? This short paper raises the awareness of the problem and lays ground for conducting controlled experiments that have the potential to purposefully leverage taxonomy visualizations. We present an experimental design that allows to investigate the cognitive fit between the different types of taxonomy visualizations and taxonomy tasks. Thus, we contribute towards researching whether taxonomy visualizations make a difference when performing certain tasks by using taxonomies.
A Usability Model for Mobile Applications Generated with a Model-Driven Approach

Usability evaluation of mobile applications (referred to as apps) is an emerging research area in the field of Software Engineering.Several research studies have focused their interest on the challenge of usability evaluation in mobile context.Typically, the usability is measured once the mobile apps is implemented.At this stage of the development process, it is costly to go back and makes the required changes in the design in order to overcome usability problems.Model-driven Engineering (MDE) was proven as a promising solution for this problem.In such approach, a model can be build and analyzed early in the design cycle to identify key characteristics like usability.The traceability established between this model and the final application by means of model transformation plays a key role to preserve its usability or even improve it.This paper attempts to review existing usability studies and subsequently propose a usability model for conducting early usability evaluation for mobile apps generated with an MDE tool.
Salient Explanation for Fine-Grained Classification

Explaining the prediction of deep models has gained increasing attention to increase its applicability, even spreading it to life-affecting decisions. However there has been no attempt to pinpoint only the most discriminative features contributing specifically to separating different classes in a fine-grained classification task. This paper introduces a novel notion of salient explanation and proposes a simple yet effective salient explanation method called Gaussian light and shadow (GLAS), which estimates the spatial impact of deep models by the feature perturbation inspired by light and shadow in nature. GLAS provides a useful coarse-to-fine control benefiting from scalability of Gaussian mask. We also devised the ability to identify multiple instances through recursive GLAS. We prove the effectiveness of GLAS for fine-grained classification using the fine-grained classification dataset. To show the general applicability, we also illustrate that GLAS has state-of-the-art performance at high speed (about 0.5 sec per 224 × 224 image) via the ImageNet Large Scale Visual Recognition Challenge.
Diagonal Symmetric Pattern-Based Illumination Invariant Measure for Severe Illumination Variation Face Recognition

The center symmetric pattern (CSP) was widely used in the local binary pattern based facial feature, whereas never used to develop the illumination invariant measure in the literature.This paper proposes a novel diagonal symmetric pattern (DSP) to develop the illumination invariant measure for severe illumination variation face recognition.Firstly, the subtraction of two diagonal symmetric pixels is defined as the DSP unit in the face local region, which may be positive or negative.The DSP model is obtained by combining the positive and negative DSP units in the even × even block region.Then, the DSP model can be used to generate several DSP images based on the 2 × 2 block or the 4 × 4 block by controlling the proportions of positive and negative DSP units, which results in the DSP2 image or the DSP4 image.The single DSP2 or DSP4 image with the arctangent function can develop the DSP2-face or the DSP4-face.Multi DSP2 or DSP4 images employ the extended sparse representation classification (ESRC) as the classifier that can form the DSP2 images based classification (DSP2C) or the DSP4 images based classification (DSP4C).Further, the DSP model is integrated with the pre-trained deep learning (PDL) model to construct the DSP-PDL model.Finally, the experimental results on the Extended Yale B, CMU PIE, AR, and VGGFace2 face databases indicate that the proposed methods are efficient to tackle severe illumination variations.
An overview of the emerging JPEG Pleno standard, conformance testing and reference software

A new image compression standard, known as JPEG Pleno, is currently under its final standardization stage and has been developed by the ISO/IEC JTC 1/SC 29/WG 1 standardization committee. This paper reports the current status of the final draft of JPEG Pleno Part 1, Part 2 and a detailed description of the conformance tests and the reference software under standardization as Part 3 and 4.
Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices

It is challenging to build a real-time information retrieval system, especially for systems with high-dimensional big data. To structure big data, many hashing algorithms that map similar data items to the same bucket to advance the search have been proposed. Locality-Sensitive Hashing (LSH) is a common approach for reducing the number of dimensions of a data set, by using a family of hash functions and a hash table. The LSH hash table is an additional component that supports the indexing of hash values (keys) for the corresponding data/items. We previously proposed the Dynamic Locality-Sensitive Hashing (DLSH) algorithm with a dynamically structured hash table, optimized for storage in the main memory and General-Purpose computation on Graphics Processing Units (GPGPU) memory. This supports the handling of constantly updated data sets, such as songs, images, or text databases. The DLSH algorithm works effectively with data sets that are updated with high frequency and is compatible with parallel processing. However, the use of a single GPGPU device for processing big data is inadequate, due to the small memory capacity of GPGPU devices. When using multiple GPGPU devices for searching, we need an effective search algorithm to balance the jobs. In this paper, we propose an extension of DLSH for big data sets using multiple GPGPUs, in order to increase the capacity and performance of the information retrieval system. Different search strategies on multiple DLSH clusters are also proposed to adapt our parallelized system. With significant results in terms of performance and accuracy, we show that DLSH can be applied to real-life dynamic database systems.
On Obtaining Energy-Optimal Trajectories for Landing of UAVs

The optimization issues connected to a landing task of an unmanned aerial vehicle are discussed in the paper, based on a model of a mini-class drone. Three landing scenarios are considered, including minimum-time landing, landing with minimum energy consumption, and planned landing. With the use of classical dynamic programming techniques, including the minimum principle of Pontryagin, as well as the calculus of variations, the optimal altitude reference trajectories are found, to form the altitude control system in such a way as to mimic the profile of the reference trajectory by the actual altitude of the UAV. The simulation results conducted with the use of the Simulink Support Package for Parrot Minidrones verify the correctness and effectiveness of the method, and open the research directions for further analysis, especially to tune altitude controller in a way, as to track the reference profile. Up to this point, optimization tasks considered in the literature, with respect to the drones, were connected to swarm formation optimization, optimization of the take-off process or landing process limited to optimal path planning. This paper thus considers a new topic in the field.
A Robust Method for Nuclei Segmentation of H&amp;E Stained Histopathology Images

Segmentation of histopathology images is an initial and vital step for image understanding. To increase the throughput and to maintain high accuracy, we have to go for an automatic image segmentation method. Here, a robust method for segmentation of cell nuclei in Hematoxylin and Eosin (H&E) stained histopathology images is proposed. The proposed segmentation step consists of an initial pre-processing step containing adaptive colour de-convolution and a succession of morphological operations, followed by multilevel thresholding and post-processing steps. Minimum region size is the one parameter which is necessary for this method and set according to the resolution of histopathology image. The proposed nuclei segmentation method does not require any assumptions or prior information about cell morphology. Hence, proposed method applies to the analysis of a wide range of tissues such as liver, kidney, breast, gastric mucosa, and bone marrow and H&E stained liver histopathology images from the Hospital. Results yield that proposed nuclei segmentation provides better results in terms of quantitatively and qualitatively on two datasets.
Auxiliary Detection Head for One-Stage Object Detection

The auxiliary classifier can improve the performance of classification networks. However, the utility of the auxiliary detection head has not been explored in the object detection field. In this paper, we propose an auxiliary detection head to boost the performance of one-stage object detectors. Similar to other detection heads, the auxiliary detection head consists of a classification subnet and a regression subnet, which are essentially two convolution layers. Thus the auxiliary detection head is computationally efficient. Besides, the auxiliary detection head achieves implicit two-step cascaded regression. Specifically, the auxiliary detection head uses its output boxes as anchors for further regression. Within the auxiliary detection head, refinement of object localization corresponds to adjust the positions of its output boxes towards ground truth boxes, which helps the network learn more robust features. At inference, the auxiliary detection head can be removed without any adverse effect on the performance of the main detector head, which benefits from its independence and leads to two advantages: shrink the model size and shorten inference time. The proposed method is evaluated on Pascal VOC and COCO datasets. By incorporating the auxiliary detection head into a state-of-the-art object detector in parallel with the main detection head, we show consistent improvement over its performance on different benchmarks, whereas no extra parameters are introduced at inference time.
Prediction of deformations in the subsiding soils of Eastern Donbass’ undermined areas

Subsiding soils are structurally unstable and are common and Russia (Northern Caucasus, Western Siberia, etc.) as well as worldwide (China, Mongolia, Central Asia, North America, etc.). Construction and operation of buildings and structures on such soils are associated with the nonuniform subsiding of soil due to water saturation, which causes excessive deformations in basements and foundations. The geotechnical and technogenic conditions of Eastern Donbass feature the commonness of subsiding loess soils and undermining of industrial areas prone to waterlogging. The three mentioned factors have a negative impact on ground facilities due to nonuniform vertical and horizontal deformations of the Earth’s surface. Despite considerable scientific progress in studying the surficial deformations attributable to various factors, the concurrent effects of undermining, technogenic waterlogging, and manifestation of subsiding properties on the condition of buildings and structures are currently understudied. This paper proposes a method for predicting the deformation of subsiding massifs in the undermined areas of Eastern Donbass. The research team has developed an analytical method for assessing the damage to a typical urban structure simultaneously exposed to soil subsidence and undermining.
Adversarial training on SAR images

Recent studies have shown that machine learning networks trained on simulated synthetic aperture radar (SAR) images of vehicular targets do not generalize well to classification of measured imagery. This disconnect between these two domains is an interesting, yet-unsolved problem. We apply an adversarial training technique to try and provide more information to a classification network about a given target. By constructing adversarial examples against synthetic data to fool the classifier, we expect to extend the network decision boundaries to include a greater operational space. These adversarial examples, in conjunction with the original synthetic data, are jointly used to train the classifier. This technique has been shown in the literature to increase network generalization in the same domain, and our hypothesis is that this will also help to generalize to the measured domain. We present a comparison of this technique to off-the-shelf convolutional classifier methods and analyze any improvement.
Privacy-Preserving Deep Learning for the Detection of Protected Health Information in Real-World Data: Comparative Evaluation

Background Collaborative privacy-preserving training methods allow for the integration of locally stored private data sets into machine learning approaches while ensuring confidentiality and nondisclosure. Objective In this work we assess the performance of a state-of-the-art neural network approach for the detection of protected health information in texts trained in a collaborative privacy-preserving way. Methods The training adopts distributed selective stochastic gradient descent (ie, it works by exchanging local learning results achieved on private data sets). Five networks were trained on separated real-world clinical data sets by using the privacy-protecting protocol. In total, the data sets contain 1304 real longitudinal patient records for 296 patients. Results These networks reached a mean F1 value of 0.955. The gold standard centralized training that is based on the union of all sets and does not take data security into consideration reaches a final value of 0.962. Conclusions Using real-world clinical data, our study shows that detection of protected health information can be secured by collaborative privacy-preserving training. In general, the approach shows the feasibility of deep learning on distributed and confidential clinical data while ensuring data protection.
Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications

Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring.However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users.Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training.In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences.In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training.Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets.These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data.Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications.