Reviewerly/exHarmony huggingface dataset:
DATNet: Dense Auxiliary Tasks for Object Detection

Beginning with R-CNN, there has been a rapid advancement in two-stage object detection approaches. While two-stage approaches remain the state-of-the-art in object detection, anchor-free single-stage methods have been gaining momentum. We believe that the strength of the former is in their region of interest (ROI) pooling stage, while the latter simplifies the learning problem by converting object detection into dense per-pixel prediction tasks. In this paper, we propose to combine the strengths of each approach in a new architecture. In particular, we first define several auxiliary tasks related to object detection and generate dense per-pixel predictions using a shared feature extraction backbone. As a consequence of this architecture, the shared backbone is trained using both the standard object detection losses and these per-pixel ones. Moreover, by combining the features from dense predictions with those from the backbone, we realize a more discriminative representation for subsequent downstream processing. In addition, we feed the fused features into a novel multi-scale ROI pooling layer, followed by per-ROI predictions. We refer to our architecture as the Dense Auxiliary Tasks Network (DATNet). We present an extensive set of evaluations of our method on the Pascal VOC and COCO datasets and show considerable accuracy improvements over comparable baselines.
Implementing Security-Enhanced PHR System in the Cloud Using FAME

Personal Health Record (PHR) plays an important role in realizing the exchange and interaction of medical information as well as in managing chronic diseases. In the recent decade, plenty of researches have been carried out to resolve the secure sharing issue of PHR in the cloud via access control schemes. Attribute-based Encryption (ABE) is a promising technique, which implements access control by introducing the access policy. When ABE is used to construct access control scheme in the cloud, hiding and updating access policy have always been two research hotspots. However, all of the existing solutions only consider one of these two issues separately. How to combine policy hiding and policy updating to achieve secure and efficient access control has always been avoided. Furthermore, the practical deployments of traditional ABE in the medical environment are limited by its small attribute universe. In this paper, we propose a policy hiding and updating ABE (PHU-ABE) based on the Fast Attribute-based Encryption(FAME) to tackle these challenges. We argue the security of PHU- ABE. The experimental results demonstrate that our scheme not only protects users' privacy but also realizes efficient policy updating.
CapExec: Towards Transparently-Sandboxed Services

Network services are among the riskiest programs executed by production systems. Such services execute large quantities of complex code and process data from arbitrary - and untrusted - network sources, often with high levels of system privilege. It is desirable to confine system services to a least-privileged environment so that the potential damage from a malicious attacker can be limited, but existing mechanisms for sandboxing services require invasive and system-specific code changes and are insufficient to confine broad classes of network services. Rather than sandboxing one service at a time, we propose that the best place to add sandboxing to network services is in the service manager that starts those services. As a first step towards this vision, we propose CapExec, a process supervisor that can execute a single service within a sandbox based on a service declaration file in which, required resources whose limited access to are supported by Caper services, are specified. Using the Capsicum compartmentalization framework and its Casper service framework, CapExec provides robust application sandboxing without requiring any modifications to the application itself. We believe that this is the first step towards ubiquitous sandboxing of network services without the costs of virtualization.
Optimizing Energy Consumption for Cloud Computing

The increased use of IT technologies and number of IT users have triggered cloud computing resource demand including the need for more data centers. Each data center consumes electricity for its un-interrupted operations and maintenance, therefore responsible for the emissions of carbon dioxide, a potent greenhouse gas causing climate change. Hence, there is a necessity to provide a solution through which energy consumption for cloud data centers can be reduced. As virtual machine located in data center are run under loaded to maintain higher performance but it causes wastage of resources and power. While, task overloading severally reduce the performance of data center. To address this issue, we propose CMBA (Cluster and Migration Based Approach) for cloud resource allocation that maps groups of tasks to customized virtual machine types based on processing, memory and network requirements. Proper placement of workload with specific VMs and dynamic migration concept reduce energy consumption for running physical machine and its respective host or data centers. Taking altogether, intelligent customization of virtual machines by adopting CMBA approach will maintain high efficiency of datacenters with reduced energy consumption.
Deeply fusing multi-model quality-aware features for sophisticated human activity understanding

Detecting and understanding human action under sophisticated lighting condition and backgrounds, also known as human action recognition in real-world context, is an indispensable component in modern intelligent systems and has becoming a hot research topic currently. Nowadays, human action recognition is still a tough challenge due to intra-class and inter-class, environment and temporal-level differences of the same action. Algorithms based on the single visual channel cannot achieve satisfactory performance. Thus, in this paper, we propose a novel action recognition framework towards sophisticated activity understanding, focusing on intelligently combining multimodel quality-related action features. Specifically, we first design a multi-channel feature fusion (MCFF) algorithm to capture visual appearance, motion and acoustic patterns from each video frame, where image-level labels are characterized by choosing high quality multimodel features. Subsequently, we design an adaptive key frame selection algorithm that can be applied to characterize human action from human action video stream. Thereafter, we engineer a multimodel feature based on an auxiliary human action retrieval system to achieve sophisticated activity understanding. Extensive experimental evaluations have demonstrated that the effectiveness and robustness of our proposed method.
AGR-FCN: Adversarial Generated Region based on Fully Convolutional Networks for Single- and Multiple-Instance Object Detection

Addressing the problem that object instance detection has poor detection effect on occluded objects in unstructured environment when using deep learning network, we explore the use of the strategy of adversarial learning in this paper. A three-step pipeline is carried to build a novel learning framework denoted as Adversarial Generated Region-based Fully Convolutional Networks (AGR-FCN). Our method first training the noted deep model Region-based Fully Convolutional Networks (R-FCN), and then an Adversarial Mask Dropout Network (AMDN), which can generate occlusion features for training samples, is designed based on the trained R-FCN. Through the training strategy of adversarial learning between network R-FCN and network AMDN, the ability of network R-FCN to learn the features of occluded objects as well as its instance-level object detection performance is improved. Numerical experiments are conducted for instance detection to compare our proposed AGR-FCN with the original R-FCN on the self-made BHGI Database and the public database GMU Kitchen Dataset, which demonstrate that our proposed AGR-FCN outperforms original R-FCN and can achieve an average detection accuracy of nearly 90%.
Data Siphoning Across Borders: The Role of Internet Tracking

We investigate the role of Internet tracking in siphoning users' personal data from one country to another. We use the term "siphon" to indicate a one-way channel that once set up will result in a continuous flow of personal information from the source to the destination. We conduct a web privacy measurement study using a Germany-Russia scenario; we collect and analyze tracker data from 12 mainstream news sites in Germany, 1000 top sites in Germany and Russia, and 1000000 top sites in the world. We identify five tracking patterns that can siphon data from users in Germany to Russia; two key parameters of the tracking patterns, distance-to-data and type-of-control, determine timeliness, accuracy and granularity of siphoned data. Results show that Russian trackers are widespread on German news sites. Lastly, we discuss the impact of data siphoning on General Data Protection Regulation (GDPR). Analyses show that tracking patterns can facilitate siphoning of personal data across borders while subverting requirements of GDPR.
Diagnostic Increasing The Water Content of The Transformers in PLN UPT Karawang

The power transformer is a primary part of the transmission line, PT. PLN (Persero) UPT Karawang as a maintenance unit must ensure the power transformer can operate sustainably. One of the most important from power transformer is the oil insulation because they function not only as a coolant but also as insulation. Water content is the main enemy of insulation because of the water content impact on the actual operating conditions and the lifetime of the transformer. To control that, PLN already implemented characteristics testing for decades as a method to assess the condition of power transformers. The increasing the value of water content in the transformer can be caused Ingress of moisture from the atmosphere, degradation of insulation and error during sampling and testing.
Examining Hierarchy and Granularity of Classifiers in Compatibility-based Classifier Personalization

We have been investigating a method of personalizing activity recognition system based on compatibility with pooled classifiers. In this paper, we examine two factors that may affect the effectiveness of the compatibility-based method over traditional “one classifier fits all (OFA)” approach: The hierarchy of classifiers and the granularity of classifiers in compatibility calculation. We confirmed that formations of activity group in the hierarchical model exist that drastically improve the performance over OFA approach and that a classifier trained by dataset from larger number of people does not always perform better.
Deep-Learning Based Pedestrian Direction Detection for Anti-collision of Intelligent Self-propelled Vehicles

In this work, an effective deep learning based object detection and recognition method by the modified YOLO (You only look once) based model is developed for pedestrian direction detections. The proposed YOLO based detection model has lightweight network structure, needs less computing dependence, and performs high efficiency for detections. The proposed pedestrian direction detection technology provides the direction and bounding box information of pedestrians, and the proposed design enables the anti-collision function of intelligent self-propelled vehicles when it is moving in crowds. Compared with the direct YOLOv2 design, the performance of precision is better, and the frames per second in software implementation are also larger by the modified YOLO based detector.
Comparative Analysis of Global Solar Radiation Estimation Models - A case study: Algeria

Studying of solar radiation is essential to estimate its amount at ground level. In practice, there are two sources of solar data; the first consists of the measured ground data available in meteorological networks and radiometric solar stations. And, the other consists of extracting solar data from digital satellite images. However, only a few stations measured irregularly the amount solar radiation. Hence, in this paper, we will present a description and classification of some solar radiation estimation models based on ground measurements. Moreover, satellite image processing models are also discussed, an improvement of these models using fuzzy logic is also viewed. The validity of these models will be judged by comparing the obtained results by actually measured values of the available ground stations.
Automatically extracted parallel corpora enriched with highly useful metadata? A Wikipedia case study combining machine learning and social technology

Abstract The extraction of large amounts of multilingual parallel text from web resources is a widely used technique in natural language processing. However, automatically collected parallel corpora usually lack precise metadata, which are crucial to accurate data analysis and interpretation. The combination of automated extraction procedures and manual metadata enrichment may help address this issue. Wikipedia is a promising candidate for the exploration of the potential of said combination of methods because it is a rich source of translations in a large number of language pairs and because its open and collaborative nature makes it possible to identify and contact the users who produce translations. This article tests to what extent translated texts automatically extracted from Wikipedia by means of neural networks can be enriched with pertinent metadata through a self-submission-based user survey. Special emphasis is placed on data usefulness, defined in terms of a catalogue of previously established assessment criteria, most prominently metadata quality. The results suggest that from a quantitative perspective, the proposed methodology is capable of capturing metadata otherwise not available. At the same time, the crowd-based collection of data and metadata may face important technical and social limitations.
On the relative error between the binomial and the hypergeometric distribution

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Bond transaction link prediction based on dynamic network embedding and time series analysis

Trading behavior prediction is to estimate the possibility of the occurrence of links in a dynamic network of bond transactions. At present, most of the existing link prediction models are link predictions for static networks such as social networks that do not consider time dimension. Since the evolution of the network over time is not considered, it is difficult to meet the object of effective link prediction of bond transactions. In this paper, in order to meet the link forecasting demand of bond market risk warning, DNETSA's link prediction method is proposed to realize the link prediction task under dynamic network, which provides a basis for financial risk warning. The DNETSA method can effectively extract the advantage of the structural information of the network in each time period. Then combine it with the link number attribute information by means of the time series model, which realizes the prediction ability of the link in the dynamic network and overcomes the problem that the static network link prediction does not consider the shortcomings of the network evolution over time. The effective integration and utilization of the dynamic network structure information, time information and attribute information makes the DNETSA method increase the AUC value by 22% compared with the LMPF method, and the AUC value by 13% compared with the TS-sim method. Compared to the TS-occ method AUC, the value is increased by 12%, which is 9% higher than the AUC value of the SOTS method. In summary, the DNETSA method makes up for the shortcomings of other methods and can satisfy the prediction of bond trading behavior.
Review of Parallel Processing Methods for Big Image Data Applications

The coexistence of technologies, like big data application, cloud computing, and the numerous images in the Web has paved the need for new image processing algorithms that exploit the processed image for diverse applications. There arises a need for new image processing algorithms to utilize the processed image for diverse applications though many techniques with variations exist. Ultimately, the enduring issue is to enhance the effectiveness of huge image processing and to maintain the combination of the same with recent works. This paper presents a review of the newest progress in researches on parallel processing methods for the processing of big data. Initially, the reviews about the parallel processing methods were carried out by highlighting some promising parallel processing methods in recent studies, such as the representation of MapReduce (MR) framework, distributed, parallel methods, and Hadoop framework. Subsequently, focus on analysis and deliberations about the challenges and promising solutions of parallel computing methods on big data in various applications and on image processing applications were made and concluded with a summary of number of open problems and research areas.
G-Key: An Authentication Technique for Mobile Devices Based on Gravity Sensors

User authentication is an essential issue of information security and privacy protection in mobile device based applications. Traditional password authentication causes a heavy memory burden. Biometric authentication on mobile devices are confronted with privacy issues and risks of information disclosure. Based on the characteristics of the built-in sensors of mobile devices, we propose G-key, a novel authentication method. Primitive postures are defined, which can be identified by reading of gravity sensors of the device. A G-key recognition algorithm is designed, in which parameters can be adjusted to achieve balance between accuracy and efficiency. Users can arbitrarily define a private gesture key from posture combinations as their authentication basis. Compared with biometric authentication and password-based authentication, the authentication process of G-Key is more private. It does not require storing sensitive biological information of individuals, and no memory burden is involved. We implement G-Key in the Android platform, and experiments show that it works with high accuracy.
Novel distortion free and histogram based data hiding scheme

In the most data hiding methods, the cover media is entirely distorted and it cannot be recovered when the secretive data is extracted. However, in some applications, such as medical imaging, military imaging and law enforcement, even a slight modification in the cover image is impermissible. Therefore, reversible data hiding schemes have proposed as a solution of this issue, recently. Histogram based embedding schemes are among wide investigated reversible data hiding schemes owing to the high quality of the marked images. In histogram based embedding techniques, construction of histogram greatly affects the embedding capacity and the distortion performance of schemes. In this study, the authors propose a novel reversible data hiding scheme for grey scale images. In this method, a new histogram is generated according to the differences between pixels and their neighbouring pixels values. The proposed scheme can achieve higher embedding capacity than other existing reversible data hiding algorithms in the literature by considering the same value of peak signal-to-noise ratio. Experimental and analytical results show that this scheme is successfully employed for data hiding in a wide range of images.
Using Machine Learning Algorithms for the Prediction of Industrial Process Parameters Based on Product Design

In the present paper, a method of defining the industrial process parameters for a new product using machine learning algorithms will be presented. The study will describe how to go from a final product characteristics till the prediction of the suitable machine parameters to produce a good quality of this product, and this is based on an historical training data set of similar products with their respective process parameters. In the first part of our study, we will focus on the ultrasonic welding process definition, welding parameters and on how it operates. While in second part, we will present the design and the implementation of the prediction models such as multiple linear regression, support vector regression, and we will compare them to the artificial neural networks algorithm. In the following part, we will present a new application of Convolutionnal Neural Networks (CNN) algorithm to the industrial process parameters prediction. In addition, we will propose the generalization approach of our CNN to any prediction problem of the industrial process parameters. Finally, we will deploy our models into a physical device with an interactive graphical user interface. This prediction device allows the user to move freely on the manufacturing field and perform process development operations.
A New Method For Secret Image Transmission via Mosaic Fragments using ECC Key

The intrusion of networks has increased enormously and become major threat to both the public platform and for the classified platforms used by private/government bodies. In order to stay out from the sight of intruder a new mosaic image technique is introduced in this research work, which aims at bringing a high amount of security for the data that is being transmitted over the internet. A method is suggested in this paper where two images are merged to form a single image. The resulting image which needs to be transmitted is called as the secret image. This is then converted into tile format and is made to hide behind the image blocks. A target image is chosen which is similar to the image that is to be transmitted. While splitting the two images the different attributes like Colour conversion, generation of keys, obtaining similar blocks etc. needs to be considered carefully. For the process of encryption and decryption, a secret ECC key is used. The proposed results, is then compared with the other algorithms like RSA, AES etc. The solution obtained using ECC key method is encouraging. The use of ECC key increases system performance because of the reduced key size and thus, helps to encrypt and decrypt an image in an easier and quicker manner.
Threats and Vulnerabilities Affecting Fitness Wearables: Security and Privacy Theoretical Analysis

The introduction of fitness wearables has encouraged users to take control of their health and fitness habits. These wearables are capable of collecting real-time data through the sensors embedded within the devices. The collection of real-time data about the users is a concern, as exactly what data is collected by these wearables is not clear to the users. Security threats and vulnerabilities in the fitness wearable domain continue to increase due to the increasing use of these wearables. This study aims to investigate and analyse security vulnerabilities and threats that affect fitness wearables from a security and privacy perspective. The execution of this study involves two phases of methodology. The first phase employs a systematic literature review and qualitative content analysis to identify the threats and vulnerabilities affecting fitness wearables. The second phase employs the Microsoft STRIDE framework and CIA triad to conduct an analysis of the threats and vulnerabilities. The output of this study indicates that security is still a great concern, as these fitness wearables are exposed to various security threats. Furthermore, these security threats increase due to the many components that are part of the fitness wearable architecture creating multiple entry points for attackers.