Reviewerly/exHarmony huggingface dataset:
An ASP-based framework for operating room scheduling

The Operating Room Scheduling (ORS) problem is the task of assigning patients to operating rooms, taking into account different specialties, the surgery and operating room session durations, and different priorities. Given that Answer Set Programming (ASP) has been recently employed for solving rea l-life scheduling and planning problems, in this paper we first present an off-line solution based on ASP for solving the ORS problem. Then, we present techniques for re-scheduling on-line in case the off-line schedule can not be fully applied. Results of an experimental analysis conducted on benchmarks with realistic sizes and parameters show that ASP is a suitable solving methodology also for the ORS problem. This analysis has been performed with a web framework for managing ORS problems via ASP that allows a user to insert the main parameters of the problem, solve a specific instance, and show results graphically in real-time.
Image Classification with a 3-Layer SOA-Based Photonic Integrated Neural Network

We demonstrate for the first time Iris flowers classification by implementing a trained 3-layer neural network with an SOA-based InP cross-connect chip. Classification accuracy of 85.8% is achieved, 9.2% lower than what obtained via a computer.
Mandrake: Implementing Durability for Edge Clouds

In this paper we present Mandrake, a software infrastructure for edge clouds (private clouds located at the network edge), designed to provide reliable, "lights out" unattended operation and application hosting in IoT deployments. Mandrake both implements reliable private cloud operation in restricted resource environments and data durability features that hosted applications can leverage. We describe leveraging Mandrake for hosting Hadoop applications at the edge. Our empirical evaluation shows that Mandrake is able to ensure Hadoop's data durability guarantees efficiently in the presence of relatively frequent failures even when resources are scarce.
Deep Learning-Based Classification of Illumination Maps for Exposing Face Splicing Forgeries in Images

This paper proposes a novel image forensics method which can detect splicing forgeries in human group portraits. The method converts an input image to an illumination map (IM), and the facial regions of the IM are compared in a pairwise manner using machine learning techniques to check the presence of splicing forgery. A siamese convolutional neural network (CNN) is first trained on an external training set to differentiate between face-IM pairs coming from similar and different illumination environments (IEs). Once trained, one of the twin CNNs of the siamese network is used as a feature extractor for each face present in a test image. The pairwise features are concatenated and classified using a support vector machine classifier for forgery detection. The advantage of the proposed method is its ability to learn features capable of differentiating faces coming from different IEs. The experimental results on multiple public datasets show the efficacy of the proposed method with respect to the state-of-the-art.
SIMGAN: Photo-Realistic Semantic Image Manipulation Using Generative Adversarial Networks

Semantic image manipulation (SIM) aims to generate realistic images from an input source image and a target text description, such that the generated images not only match the content of the description, but also maintain text-irrelevant features of the source image. It requires to learn a good mapping between visual features and linguistic features. Previous works on SIM can only generate images of limited resolution that typically lack of fine and clear details. In this work, we aim to generate high-resolution photo-realistic images for SIM. Specifically, we propose SIMGAN, a generative adversarial networks (GAN) based architecture that is capable of generating images of size 256 × 256 for SIM. We demonstrate the effectiveness of SIMGAN and its superiority over existing methods via qualitative and quantitative evaluation on Caltech-200 and Oxford-102 datasets.
Key Features Recommendation to Improve Bug Reporting

Bug reports are the primary means through which developers triage and fix bugs. To achieve this effectively, bug reports need to clearly describe those features that are important for the developers. However, previous studies have found that reporters do not always provide such features. Therefore, we first perform an exploratory study to identify the key features that reporters frequently miss in their initial bug report submissions. Then, we plan to propose an automatic approach for supporting reporters to make a good bug report. For our initial studies, we manually examine bug reports of five large-scale projects from two ecosystems such as Apache (Camel, Derby, and Wicket) and Mozilla (Firefox and Thunderbird). As initial results, we identify five key features that reporters often miss in their initial bug reports and developers require them for fixing bugs. We build and evaluate classification models using four different text-classification techniques. The evaluation results show that our models can effectively predict the key features. Our ongoing research focuses on developing an automatic features recommendation model to improve the contents of bug reports.
Shot Boundary Detection for Automatic Video Analysis of Historical Films

In automatic video content analysis and film preservation, Shot Boundary Detection (SBD) is a fundamental pre-processing step. While previous research focuses on detecting Abrupt Transitions (AT) as well as Gradual Transitions (GT) in different video genres such as sports movies or news clips only few studies investigate in the detection of shot transitions in historical footage. The main aim of this paper is to create an SBD mechanism inspired by state-of-the-art algorithms which is applied and evaluated on a self-generated historical dataset as well as a publicly available dataset called Clipshots. Therefore, a three-stage pipeline is introduced consisting of a Candidate Frame Range Selection based on the network DeepSBD, Extraction of Convolutional Neural Network (CNN) Features and Similarity Calculation. A combination of pre-trained backbone CNNs such as ResNet, VGG19 and SqueezeNet with different similarity metrics like Cosine Similarity and Euclidean Distance are used and evaluated. The outcome of this paper displays that the proposed algorithm reaches promising results on detecting ATs in historical videos without the need of complex optimization and re-training processes. Furthermore, it points out the main challenges concerning historical footage such as damaged film reels, scratches or splices. The results of this paper contribute a significant base for future research on automatic video analysis of historical videos.
Revisiting Multi-Level Feature Fusion: A Simple Yet Effective Network for Salient Object Detection

It is widely accepted that the top sides of neural networks convey high-level semantic features and the bottom sides contain low-level details. Therefore, most of recent salient object detection models aim at designing effective fusion strategies for the side-output features of convolutional neural networks (CNNs). Although significant progress has been achieved in this direction, the network architectures become more and more complex, which will make the future improvement difficult and heavily engineered. Moreover, the manually designed fusion strategies would be sub-optimal due to the large search space of possible solutions. To address above problems, we propose an Automatic Top-Down Fusion (ATDF) model, in which the global information at the top sides are flowed into bottom sides to guide the learning of low layers. We design a novel module at each side to control the information flowed into a specific side, called valve module, by which each side is expected to receive the necessary top information. We perform extensive experiments to demonstrate that ATDF is simple yet effective and thus opens a new path for saliency detection. Code is available at https://github.com/yun-liu/ATDF.
A personalized range‐sensitive privacy‐preserving scheme in LBSs

Summary Mobile social network has become a necessary part in our daily life, and location‐based services (LBSs) provide unprecedented convenience to mobile users. However, these attracting services are accompanied with privacy disclosures, including location privacy and query privacy. Mobile users have to expose their personal information to untrusted location‐based service provider (LSP) in order to obtain relevant service data. To address these privacy issues, we proposed a personalized range‐sensitive privacy‐preserving scheme, called PRPS, which considers the relationship between locations, query ranges, and query contents. Moreover, PRPS employs map storing algorithm (MSA) to facilitate the storage of two‐dimensional local map, reducing the cost of storage. Furthermore, range estimating algorithm (REA) adopts binary quad‐tree to decide the query radius of each submitted location, avoiding inference attacks by adversary. The requirements generating algorithm (RGA) selects relevant query content for each dummy location, guaranteeing mobile user's location privacy and query privacy. Finally, we illustrate the privacy analysis to proof PRPS's privacy degree; then, the performance and privacy evaluation results indicate that the proposed PRPS is effective and efficient.
A divide-and-conquer approach to geometric sampling for active learning

Active learning (AL) repeatedly trains the classifier with the minimum labeling budget to improve the current classification model. The training process is usually supervised by an uncertainty evaluation strategy. However, the uncertainty evaluation always suffers from performance degeneration when the initial labeled set has insufficient labels. To completely eliminate the dependence on the uncertainty evaluation sampling in AL, this paper proposes a divide-and-conquer idea that directly transfers the AL sampling as the geometric sampling over the clusters. By dividing the points of the clusters into cluster boundary and core points, we theoretically discuss their margin distance and hypothesis relationship. With the advantages of cluster boundary points in the above two properties, we propose a Geometric Active Learning (GAL) algorithm by knight’s tour. Experimental studies of the two reported experimental tasks including cluster boundary detection and AL classification show that the proposed GAL method significantly outperforms the state-of-the-art baselines.
A Fuzzy Based Trust Evaluation Model for Service Selection in Cloud Environment

Cloud computing is a popular computing paradigm among several computing environments, but a deficit in trust among the users and the service providers prevents the large adoption of the cloud in most of the businesses. Cloud service providers should give assurances for providing the reliable services to the cloud consumers. The proposed work explains about the architecture of the trust evaluation model and considered four service measurement indexes (SMI) namely: availability, success rate, turnaround efficiency and feedback about a resource. The trust value for each resource is estimated by the fuzzy evaluation engine in which a fuzzy input set is derived from the SMI parameters. By applying a fuzzy inference rule on fuzzy input sets will yield a fuzzy output set and finally, the most trusted resource value is calculated by defuzzification process called center of gravity. The proposed work is done the implementation by using cloudsim with jfuzzycloud.
CloudDBGuard: A framework for encrypted data storage in NoSQL wide column stores

Nowadays, cloud storage providers are widely used for outsourcing data. These remote cloud servers are not trustworthy when storing sensitive data. In this article we focus on the use case of storing data in a cloud database using a particular sub-category of NoSQL databases — so-called wide column stores. Unfortunately security was not a primary concern of the NoSQL systems designers. Using encryption before outsourcing the data can provide security. Conventional encryption however limits the options for interaction because the encrypted data lacks properties of the plaintext data that the database systems rely on. Various schemes have been proposed for property-preserving encryption in order to overcome these issues, allowing a database to process queries over encrypted data. In this article we comprehensively present details of our framework CloudDBGuard that allows using property-preserving encryption in unmodified wide column stores. It hides the complexity of the encryption and decryption process and allows various adjustments on specific use cases in order to achieve a maximum of security, functionality and performance.
Named Entity Extraction from Semi-structured Data Using Machine Learning Algorithms

The modern society have been witnessed that intensive development of Internet technologies had followed to information explosion during last decades. This explosion had been expressing by an exponential growth of data volume among the low-quality information. This paper is designed to provide detailed information about some intellectual tools which are support decision taking by automatic knowledge extraction. In the first part of paper, we considered a preprocessing contains morphological analysis of texts. Then we had considered the model of text documents in the form of a hypergraph and implementation of the random walk method to extract semantically close word’s pairs, in other words, pairs that often appears together. Result of calculations is matrix with word affinity coefficients corresponding to each other component of vocabulary vector. In the second part we describe training of neural network for linguistic constructions extraction. These ones include possible values of text named entities descriptors. The neural network enables to retrieve information on one preselected descriptor, for example, location, in the form of the final result of the name of geographical objects. In a general case, the neural network can retrieve information on several descriptors simultaneously.
Hand Gesture Recognition System Based on LBP and SVM for Mobile Devices

With the advent of smart devices and the massive use of smartphones, hand gesture recognition by mobile devices is being a major difficulty due to their technical specificities. To find a balance between speed and accuracy, we propose a new approach to recognize hand gestures by a smart device. This topic has some current interest and future applicability. In this paper, we present a new gesture detection framework for mobile devices based on LBP and SVM. LBP provides good texture representation properties. First, the proposed LBP on each non-overlapping blocks of a hand pose image is computed and a histogram of these LBPs is obtained. Those histograms are used as feature vectors for gesture classification as they demonstrate their robustness against compression and uniform intensity variations. The classification has been done by using Support Vector Machine (SVM). Since SVM is commonly used for pattern recognition, it is good for the explicit classification of form-dependent data, such as hand gestures. A recognition rate of approximately 93% is obtained based the enhanced NUS database I. In addition, the impact of using such a hand pose estimation task in an embedded device is studied. We conduct experiments on the speed of detection on different mobile devices. The impact of using SVM as a classifier for a gesture recognition task in an embedded device like smartphone is studied.
AUTO-G: Gesture Recognition in the Crowd for Autonomous Vehicl

Autonomous driving is an active area of research. An important aspect of this problem is recognizing the gestures made by humans, both inside and outside the vehicle. In this paper, we present the Auto-G database that comprises different hand gestures for autonomous driving. The database encompasses several challenges such as occlusion, low resolution, motion blur, illumination variation, extreme pose variations, along with the presence of multiple gestures within a frame. We also propose an end-to-end pipeline for hand detection and gesture recognition. The proposed pipeline achieves a frame gesture recognition accuracy of 90.23% on the proposed Auto-G database.
Exploiting Serverless Runtimes for Large-Scale Optimization

Serverless runtimes provide efficient and cost-effective environments for scalable computations, thanks to their event-driven and elastic nature. So far, they have mostly been used for stateless, data parallel and sporadic computations. In this work, we propose exploiting serverless runtimes to solve generic, large-scale optimization problems. To this end, we implement a parallel optimization algorithm for solving a regularized logistic regression problem, and use AWS Lambda for the compute-intensive work. We show that relative speedups up to 256 workers and efficiencies above 70% up to 64 workers can be expected.
Investigation Internet of Things (IoT) Device using Integrated Digital Forensics Investigation Framework (IDFIF)

Abstract The Internet of Things (IoT) in the Industrial Revolution 4.0 refers to networks of objects, objects, or devices that are connected on a large scale connected to the Internet. These objects, become smart, feel the environment and collect and exchange data with other objects. Devices on the internet of things are interconnected with other devices for the purpose of securely exchanging data. This new technology appears in various fields of human life. Therefore there are many new challenges for forensic investigators in finding digital evidence on these IoT devices. A digital forensic researcher will face many challenges to gather evidence from infected components on an Internet of Things (IoT) device and also face difficulties in analyzing the evidence. In this study, there will be an investigation of network forensic attacks on the Internet of Things (IoT) devices using the IDFIF method.
Research on Complex Structures in Space Fault Network for Fault Data Mining in System Fault Evolution Process

To study the system fault evolution process, and mine the relationships among the fault data, fault factors and fault events, we propose the theory of space fault network. The paper is composed of three parts. In the first part, the shortcomings of the existing studies on unidirectional ring in space fault network are discussed, and three new network description forms of typical unidirectional rings are formulated and the corresponding space fault tree transformation mechanism. The concepts of ring structure, ordered relation, equivalence event and equivalence connection are defined according to the need. Three methods for calculating the target event probability are given. The second part studies the all event induction fault evolution process. All of its edge events and process events lead to the target event. The meaning of all event induction fault evolution process is discussed. Two methods, general fault evolution process and all event induction fault evolution process, are used to calculate the target event probability in the evolution process of a single route and network structure. In the third part, the impact of some event repeatability on the fault and the time characteristics of the evolution process are studied, and the definitions of event repeatability and timeliness are proposed. The occurrence time and duration of events and connections are given to characterize the time characteristics. Finally, according to repeatability and timeliness, the measures to prevent the target event are given. The paper provides a new theory and method for mining system fault evolution process from fault data, fault factors and fault events.
DCVGAN: Depth Conditional Video Generation

In the past few years, several generative adversarial networks (GANs) for video generation have been proposed although most of them only use color videos to train the generative model. However, to make the model understand scene dynamics more accurately, not only optical information but also three-dimensional geometrical information is important. In this paper, using depth video together with color video, we propose a GAN architecture for video generation. In the generator of our architecture, the depth video is generated in the first half and in the second half, the color video is generated by solving the domain translation from the depth to the color. By modeling the scene dynamics with a focus on the depth information, we were able to produce videos of higher quality than the conventional method. Furthermore, we show that our method produces better video samples than ones by conventional method in terms of both variety and quality when evaluating on facial expression and hand gesture datasets. The codes and generated sample videos are publicly available on Github.
An Investigation of LPI Radar Waveforms Classification in RoF Channels

Intensive research has been developed to either design or classify low probability of intercept (LPI) radar signals. These types of signals are used in different sensitive electronic warfare applications such as electronic support, electronic attack, and radar emitter identification. Linear frequency modulation, nonlinear frequency modulation, frequency shift keying, polyphase Barker, polyphase P1, P2, P3, P4 and Frank codes are examples of LPI waveforms. In this paper, we consider the modulation classification problem under the effect of transporting the captured radar signals through radio over fiber channels. Distortions and noise introduced by such channels are likely to affect the performance of LPI classification algorithms. Here, we investigate the accuracy of a recently proposed hierarchical decision-tree automatic modulation classification algorithm for additive white Gaussian noise channels and provide the necessary adjustments when the intercepted radar signals are transmitted over fiber optic channels. The investigation is conducted by simulations and experimental demonstration. The obtained results show that for an 80 km fiber link and noisy intercepted LPI signals, the average identification accuracy reaches more than 98%, at 16 dB optical signal-to-noise ratio.
Towards improved GADT reasoning in Scala

Generalized algebraic data types (GADT) have been notoriously difficult to implement correctly in Scala. Both major Scala compilers, Scalac and Dotty, are currently known to have type soundness holes related to them. In particular, covariant GADTs have exposed paradoxes due to Scala's inheritance model. We informally explore foundations for GADTs within Scala's core type system, to guide a principled understanding and implementation of GADTs in Scala.
Computer Geometry: Rep-Tiles with a Hole

A cube is an 8-rep-tile: it is the union of eight smaller copies of itself. Is there a set with a hole which has this property? The computer found an interesting and complicated solution, which then could be simplified. We discuss some problems of computer-assisted research in geometry.