Reviewerly/exHarmony huggingface dataset:
Secure Encrypted Data Deduplication for Cloud Storage against Compromised Key Servers

Message-locked encryption (MLE) is a special type of symmetric encryption enabling deduplication over ciphertexts. Since an MLE key is extracted from the message itself, it is vulnerable to brute-force attacks. Existing schemes employ an independent key server to help in generating MLE keys, where the MLE key is extracted from the message and a server-side secret to thwart brute-force attacks. Whereas, the security of these schemes depends on the reliability of the key server, which causes the single-point-of- failure problem. In this paper, we propose DECKS, an encrypted data \underline{de}duplication scheme against the \underline{c}ompromised \underline{k}ey \underline{s}erver. DECKS employs multiple key servers to assist users in generating MLE keys using an oblivious and threshold-based protocol, such that compromising any key server would not break the security. To free DECKS from trusting a specific group of key servers during the lifetime of protected data, the key servers are periodically replaced by new ones to renew the security protection. Provable security and high efficiency of DECKS are demonstrated by comprehensive analyses and experimental evaluations.
An Improved Onset Time Picking Method for Low SNR Acoustic Emission Signals

Automatic and accurate onset time picking of the acoustic emission (AE) signals is of great significance for the location of acoustic events. However, the onset times are difficult to be picked accurately due to the presence of noise in signal records. To overcome this problem, an improved method including three consecutive processes is proposed in this study. First, the windowing Lempel-Ziv (WLZ) complexity method is applied to determine an approximate onset time. Then, the time series selected from the start time to the vicinity of the approximate onset time is coarsened at different scales with the application of multi-scale (MS) theory. Finally, Akaike information criterion (AIC) method is utilized to pick up the accurate onset time. Feasibility and performance of the proposed improved method are tested and compared with the other automatic onset time picking methods, such as AIC, short and long time average ratio (STA/LTA), modified energy ratio (MER), AIC with low pass filter and STA/LTA with low pass filter, using the same broken load experiment and 100 rock fracture signals with various signal-to-noise ratios (SNRs). The results show that the accuracy of automatic picks provided by the improved method are far beyond the other methods mentioned. Meanwhile, both the accuracy stability and SNR adaptability of the improved method for rock fracture signals are better than other methods, which proves that the improved method has the excellent performance to pick up onset times of AE signals.
Scale-aware Conditional Generative Adversarial Network for Image Dehazing

Outdoor images are often deteriorated due to the presence of haze in the atmosphere. Conventionally, the single image dehazing problem aims to restore the haze-free image. Previous successful approaches have utilized various hand-crafted features/priors. However, such images suffer from color degradation and halo artifacts. By way of analysis, these artifacts, in general, prevail around the regions with high-intensity variation, such as edgy structures. This finding inspires us to consider the Laplacians of Gaussian (LoG) of the images which exceptionally retains this information, to solve the problem of single image haze removal. In this line of thought, we present an end-to-end model that learns to remove the haze based on the per-pixel difference between LoGs of the dehazed and original haze-free images. The optimization of the proposed network is further enhanced by using the adversarial training and perceptual loss function. The proposed method has been appraised on Synthetic Objective Testing Set (SOTS) and benchmark real-world hazy images using 16 image quality measures. Based on the Color Difference (CIEDE 2000), an improvement of ~ 15.89% has been observed over the state-of-the-art method, Yang et al. [50]. An ablation study has been presented at the end to illustrate the improvements achieved by various modules of the proposed network.
Cross-Domain Face Synthesis using a Controllable GAN

The performance of face recognition (FR) systems for video surveillance has been shown to improve when the design data is augmented through synthetic face generation. This is true, for instance, with pair-wise matchers (e.g., deep Siamese networks) that rely on a reference gallery, typically with one still image per individual. However, generating synthetic images based on stills (from the source domain) may not improve performance during operations due to the domain shift w.r.t. the target domain. Moreover, despite the emergence of Generative Adversarial Networks (GANs) for realistic synthetic generation, it is often difficult to control the conditions under which synthetic faces are generated. In this paper, a cross-domain face synthesis approach is proposed that integrates a new Controllable GAN (C-GAN). It employs an off-the-shelf 3D face model as a simulator to generate facial images under various poses. The simulated images and noise are input to the C-GAN for realism refinement. It relies on an additional adversarial game as a third player to preserve the identity and specific facial attributes of the refined images. This allows generating realistic synthetic face images that reflect capture conditions in the target domain, while controlling the GAN output such that faces may be generated under desired pose conditions. Experiments were performed using videos from the Chokepoint and COX-S2V datasets, and a deep Siamese network for FR with a single reference still per person. Results indicate that the proposed approach can provide a higher level of accuracy compared to state- of-the-art approaches for synthetic data augmentation <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A literature review of the current applications of machine learning and their practical implications

Customer Relationship Management (CRM) has been an important ally to companies willing to improve their customer experiences. Lately, Machine Learning (ML) and its techniques have been transforming the way companies interact with their customers through data analysis. This paper reviews the literat ure on the application of ML techniques to improve CRM processes and provides an overview of used techniques and their application to each CRM dimension and element. In addition, the practical implications are analyzed based on recent developments of CRM tools in the field of ML. The paper delivers insights for future studies as well as for CRM tools willing to improve their analytical and automated services and features.
A novel temporal and spatial panorama stream processing engine on IoT applications

Nowadays, more and more streaming data are generated with the development of Internet of Things. Although, streaming data show great application values in practical scenarios, raw streams from terminal sensors are quite massive, heterogeneous and complex, and those features make it difficult for applications to deal with them. In order to simplify streaming data processing for applications, a novel temporal and spatial panorama stream processing engine is proposed. This stream processing engine forms an effective link between bottom sensors and upper stream-based applications, and provides configurable, flexible, available and usable stream services for various upper applications. With support of Internet plus and domain data, raw streaming data are formatted thorough data fusion and are represented in temporal, spatial, logic and storage views. Fusion data are encapsulated with multiple strategies and methods of clustering models on every dimension. According to configurable strategies, encapsulated data are extracted, partitioned and distributed as the form of dynamical variable gratitude data blocks into stream channels. Our engine is applied to a practical application scenario. Case study of dynamic power distribution application based on people crowd streaming data proves the customized service capabilities of our engine. And the outstanding performance of the engine is shown further by experiments evaluation in this case.
Arabic sign language intelligent translator

Arabic sign language (ArSL) is method of communication between deaf communities in Arab countries; therefore, the development of systemsthat can recognize the gestures provides a means for the Deaf to easily integrate into society. In this research we implemented a computational structurefor an intelligent interpreter that automatically recognizes the isolated dynamic gestures. The proposed system recognizes and translates gesturesperformed with one or both hands. It comprises five subsystems, building dataset, video processing, feature extraction, mapping between ArSL and Arabictext, and text generation. To apply the system, 100-signs of ArSL was used, which was applied on 1500 video files. It's were divided into five classes:alphabet, numbers, "prepositions, pronouns and question words", Arabic life expressions, and "nouns and verbs". The evaluation indicated that thesystem automatically recognizes and translates isolated dynamic ArSL gestures by highly accurate manner. The results showed that the system accuracy is 95.8%.
Boosting Deep Face Recognition via Disentangling Appearance and Geometry

In this paper, we propose a framework for disentangling the appearance and geometry representations in the face recognition task. To provide supervision for this aim, we generate geometrically identical faces by incorporating spatial transformations. We demonstrate that the proposed approach enhances the performance of deep face recognition models by assisting the training process in two ways. First, it enforces the early and intermediate convolutional layers to learn more representative features that satisfy the properties of disentangled embeddings. Second, it augments the training set by altering faces geometrically. Through extensive experiments, we demonstrate that integrating the proposed approach into state-of-the-art face recognition methods effectively improves their performance on challenging datasets, such as LFW, YTF, and MegaFace. Both theoretical and practical aspects of the method are analyzed rigorously by concerning ablation studies and knowledge transfer tasks. Furthermore, we show that the knowledge leaned by the proposed method can favor other face-related tasks, such as attribute prediction.
Thermal Model and Countermeasures for Future Smart Glasses

The market for wearable devices such as smart watches and smart glasses continues to grow rapidly. Smart glasses are attracting particular attention because they offer convenient features such as hands-free augmented reality (AR). Since smart glasses directly touch the face and head, the device with high temperature has a detrimental effect on human physical health. This paper presents a thermal network model in a steady state condition and thermal countermeasure methods for thermal management of future smart glasses. It is accomplished by disassembling the state by wearing smart glasses into some parts, creating the equivalent thermal resistance circuit for each part, approximating heat-generating components such as integrated circuits (ICs) to simple physical structures, setting power consumption to the heat sources, and providing heat transfer coefficients of natural convection in air. The average temperature difference between the thermal network model and a commercial thermal solver is 0.9 °C when the maximum temperature is 62 °C. Results of an experiment using the model show that the temperature of the part near the ear that directly touches the skin can be reduced by 51.4% by distributing heat sources into both sides, 11.1% by placing higher heat-generating components farther from the ear, and 65.3% in comparison with all high conductivity materials by using a combination of low thermal conductivity materials for temples and temple tips and high conductivity materials for rims.
Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature Dimensions

We propose the Interactive Constrained MAP-Elites, a quality-diversity solution for game content generation, implemented as a new feature of the Evolutionary Dungeon Designer: a mixed-initiative co-creativity tool for designing dungeons. The feature uses the MAP-Elites algorithm, an illumination algorithm that segregates the population among several cells depending on their scores with respect to different behavioral dimensions. Users can flexibly and dynamically alternate between these dimensions anytime, thus guiding the evolutionary process in an intuitive way, and then incorporate suggestions produced by the algorithm in their room designs. At the same time, any modifications performed by the human user will feed back into MAP-Elites, closing a circular workflow of constant mutual inspiration. This paper presents the algorithm followed by an in-depth analysis of its behaviour, with the aims of evaluating the expressive range of all possible dimension combinations in several scenarios, as well as discussing their influence in the fitness landscape and in the overall performance of the mixed-initiative procedural content generation.
Guidelines for evaluating bug‐assignment research

Abstract Bug assignment is the task of ranking candidate developers in terms of their potential competence to fix a bug report. Numerous methods have been developed to address this task, relying on different methodological assumptions and demonstrating their effectiveness with a variety of empirical studies with numerous data sets and evaluation criteria. Despite the importance of the subject and the attention it has received from researchers, there is still no unanimity on how to validate and comparatively evaluate bug‐assignment methods and, often times, methods reported in the literature are not reproducible. In this paper, we first report on our systematic review of the broad bug‐assignment research field. Next, we focus on a few key empirical studies and review their choices with respect to three important experimental‐design parameters, namely, the evaluation metric(s) they report, their definition of who the real assignee is, and the community of developers they consider as candidate assignees. The substantial variability on these criteria led us to formulate a systematic experiment to explore the impact of these choices. We conducted our experiment on a comprehensive data set of bugs we collected from 13 long‐term open‐source projects, using a simple Tf‐IDf similarity metric. On the basis of our arguments and/or experiments, we provide useful guidelines for performing further bug‐assignment research. We conclude that mean average precision (MAP) is the most informative evaluation metric, the developer community should be defined as “all the project members,” and the real assignee should be defined as “any developer who worked toward fixing a bug.”
Short term prediction of sun coverage using optical flow with GoogLeNet

One of the challenges of PV power generation is solar intermittency from weather conditions. Solar irradiance prediction is therefore required to deal with this issue. Several prediction methods have been proposed based on whole sky image processing techniques. This paper presents a combination technique of image processing with a convolution neural network (CNN) based on GoogLeNet for raising trigger events before the sun cover happens 1 to 2 min in advance. The captured sky images are preprocessed and in the next step, we use Hough transform to find the sun position and use optical flow to track cloud movement. Finally, we use a CNN to generate trigger events in advance before the sun occlusion happens. The results of prediction stage show error percentage as low as 5.26% in a clear sky day.
The generative adversarial networks and its application in machine vision

In recent years, the model of improved GAN has been widely applied in the field of machine vision. It not only covers the traditional image processing, but also includes image conversion, image synthesis and so on.. Firstly, this paper describes the basic principles and existing problems of GAN, then introduces several improved GAN models, including Info-GAN, DC-GAN, f-GAN, Cat-GAN and others. Secondly, several improved GAN models for different applications in the field of machine vision are described. Finally, the future trend and development of GAN are prospected.
An Efficient Optimal Path Finding for Mobile Robot Based on Dijkstra Method

This paper introduces a smart mobile robot for tracking an optimal path road network. The development of smart mobile robots became more useful and an efficient in a wide range of areas. Therefore, the proposed system was designed by using a microcontroller type Raspberry Pi 3 with associate the Python language and implemented on the a known road networks based on Dijkstra's algorithm to choosing the best path to reach the target by the smart mobile robot. The Dijkstra's algorithm has putted a solution for shortest length between two nodes in the graphic search. The experimental results were taken from the gradient training by Dijkstra's algorithm for obtain path set out. These results appeared a high accuracy to select the shortest path for robot moving. The mobile robot closed path for reach to the target in the shortest possible time and minimum weight and less expensive.
Network-Aware Container Scheduling in Multi-Tenant Data Center

Network management on multi-tenant container-based data center has critical impact on performance. Tenants encapsulate applications in containers abstracting away details on hosting infrastructures, and entrust data center management framework with the provisioning of network Quality-of-Service requirements. In this paper, we propose a network-aware multi-criteria container scheduler to jointly process containers and network requirements. We introduce a new Mixed Integer Linear Programming formulation for network-aware scheduling encompassing both tenants and providers metrics. We describe two GPU-accelerated modules to address the complexity barrier of the problem and efficiently process scheduling requests. Our experiments show that our scheduling approach accounting for both network and containers outperforms traditional algorithms used by containers orchestrators.
An approach for semantic integration of heterogeneous data sources

Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semi-structured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (Data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view.
Terrorism and financial flows in Africa

African countries amidst low credit security, low-income level and sub-optimal growth outcomes are facing many challenges on the incidence of terrorism and how to combat it. The increased terrorist incident in Africa has structural implications for a continent that has relied heavily on foreign inflow to jump-start and augment her developmental process. This paper leaned empirical credence to the terrorism-financial flow literature by estimating cross-country data on indices of terrorism and financial flows. We employed the Pooled Mean Group (PMG) estimation procedure and found the long-run negative relationship among the increase in the number of terror incidents foreign direct investment, remittances inflow and portfolio investment. Short-run estimates show that military expenditure is positive and statistically significant for all indices of financial flow (foreign direct investment, remittances, official development assistance and portfolio investment) in Africa. Based on the findings, we recommend that counter-terrorism policymakers should employ in specific terms, targeted measures, to attack the socio-economic roots of terrorism, that improve the fiscal responsibility, control inflation and all policy components will help to reduce the threat of terrorism and aid improve financial flows to Africa.
A multi-label text classification model based on ELMo and attention

Text classification is a common application in natural language processing. We proposed a multi-label text classification model based on ELMo and attention mechanism which help solve the problem for the sentiment classification task that there is no grammar or writing convention in power supply related text and the sentiment related information disperses in the text. Firstly, we use pre-trained word embedding vector to extract the feature of text from the Internet. Secondly, the analyzed deep information features are weighted according to the attention mechanism. Finally, an improved ELMo model in which we replace the LSTM module with GRU module is used to characterize the text and information is classified. The experimental results on Kaggle’s toxic comment classification data set show that the accuracy of sentiment classification is as high as 98%.
Survey of Swarm Intelligence Algorithms

Swarm Intelligence (SI) is an AI technique that has the collective behavior of a decentralized, self-organized system. SI has more advantages such as scalability, adaptability, collective robustness and individual simplicity and also has the ability to solve complex problems. Besides, SI algorithms also have few issues in time-critical applications, parameter tuning, and stagnation. SI algorithms need to be studied more to overcome these kinds of issues. In this paper, we studied a few popular algorithms in detail to identify important control parameters and randomized distribution. We also studied and summarized the performance comparison of SI algorithms in different applications.