Reviewerly/exHarmony huggingface dataset:
Splicing Localization in Tampered Blurred Images

Digital images are the fastest means of transferring information in the present era, but with the ubiquitousness of advanced photo-editing tools, image forgery has become easier and more frequent. A common class of image forgery techniques is called “splicing”, in which, the forger crops a region of the first image and places it in the second image. Doing so, the difference in the blur type of different regions can leave a trace for tracking the forgery, that is, the blur type of different regions are going to be inconsistent if the source and spliced images are different in their class of blurriness. An approach to detect splicing in images is to evaluate the inconsistency of statistical features which results when splicing occurs. Considering the importance of splicing detection, various methods have been proposed. Nevertheless, the lack of public benchmark dataset for fairly evaluating the splicing detection methods is a major problem. Consequently, we were motivated to prepare a dataset for exploiting the inconsistency of blur types with the purpose of localizing splicing in forged images. We called the first version of our dataset SBU-TIDED1 (SBU Tampered Image Detection Evaluation Dataset). Furthermore, we have explored the features used in blur type detection. A new set of features is proposed which leads to accuracy enhancement. It is apparent form the experimental results that the proposed features are efficient for detecting and classifying two blur types, namely, out-of-focus and motion blur.
FastProxy: Hardware and Software Acceleration of Stratum Mining Proxy

The Stratum protocol is the de facto protocol for mining proxies in proof-of-work (PoW) based cryptocurrencies such as Bitcoin. A Stratum mining proxy connects to an upstream mining pool server and to downstream miners through TCP/IP connections. The proxy receives periodic jobs from the pool and broadcasts them to the miners. The broadcast operation becomes a performance and scalability bottleneck when the number of miners increases significantly. In this paper, we propose a hardware/software co-designed architecture for the proxy to accelerate the broadcast of periodic jobs. We customize the Stratum protocol with a layer 2 broadcast mechanism instead of using TCP/IP connections. The proposed architecture is implemented on a Xilinx Zynq SoC (ARM processor and FPGA) board where the layer 2 broadcast mechanism is offloaded on the FPGA. Our experiments demonstrated a speedup of 2079x in transmission time with 225 miners connected to the proxy, compared to an implementation on an Intel i7 server.
Training a RoboCup Striker Agent via Transferred Reinforcement Learning

Recent developments in reinforcement learning algorithms have made it possible to train agents in highly complex state and action spaces, including action spaces with continuous parameters. Advancements such as the Deep-Q Network and the Deep Deterministic Policy Gradient were a critical step in making reinforcement learning a feasible option for training agents in real world scenarios. The viability of these technologies has previously been demonstrated in training a RoboCup Soccer agent with no prior domain knowledge to successfully score goals; however, this work required an engineered intermediate reward system to direct the agent in its exploration of the environment. We introduce the use of transfer learning rather than engineered rewards. Our results are positive, showing that it is possible to train an agent through a series of increasingly difficult tasks with fewer training iterations than with an engineered reward. However, when the agent’s likelihood of success in a task is low, it may be necessary to reintroduce an engineered reward or to provide extended training and exploration using simpler tasks.
Splaying Preorders and Postorders

Let T be a binary search tree of n nodes with root r, left subtree $$L=\text {left}(r)$$ , and right subtree $$R=\text {right}(r)$$ . The preorder and postorder of T are defined as follows: the preorder and postorder of the empty tree is the empty sequence, and $$\begin{aligned} \text {preorder}(T)&= (r)\oplus \text {preorder}(L)\oplus \text {preorder}(R)\\ \text {postorder}(T)&= \text {postorder}(L)\oplus \text {postorder}(R)\oplus (r), \end{aligned}$$ where $$\oplus $$ denotes sequence concatenation. (We will refer to any such sequence as a preorder or a postorder). We prove the following results about the behavior of splaying [21] preorders and postorders: For 1 and 2, we use the fact that preorders and postorders are pattern-avoiding: i.e. they contain no subsequences that are order-isomorphic to (2, 3, 1) and (3, 1, 2), respectively. Pattern-avoidance implies certain constraints on the manner in which items are inserted. We exploit this structure with a simple potential function that counts inserted nodes lying on access paths to uninserted nodes. Our methods can likely be extended to permutations that avoid more general patterns. The proof of 3 uses the fact that preorders and postorders of balanced search trees do not contain many large “jumps” in symmetric order, and exploits this fact using the dynamic finger theorem [5, 6]. Items 2 and 3 are both novel. Item 1 was originally proved by Chaudhuri and Höft [4]; our proof simplifies theirs. These results provide further evidence in favor of the elusive dynamic optimality conjecture [21].
Deep Learning Based Optical Camera Communications

A deep convolutional autoencoder (C-AE) for end-to-end adoption of orthogo-nal frequency division multiplexing (OFDM) based optical camera communications (OCC) is proposed and the feasibility of the neural network (NN) is verified by simulations.
Diurnal Rainfall Propagation Relate to Cold Surge-Cold Tongue Interaction over The Northern Coast of West Java

Abstract Interaction of the diurnal cycle of rainfall systems between land and sea were investigated by hourly rainfall of 3B41RT TRMM dataset, daily SST dataset from OISST, 6- hourly wind and temperature data from NCEP-NCAR Reanalysis, during period of NDJFM 2000-2016. The results showed that the diurnal cycle of rainfall from sea to land and from land to sea randomly occurred on four combinations of CENS-CT, NCENS-CT, CENS-NCT, NCENS-NCT. Diurnal rainfall in northern coast of West Java for normal conditions without CENS-CT has two maximas, namely late afternoon and early morning rainfall. CENS-CT interaction changes the normal diurnal rainfall pattern into three diurnal rainfall systems: diurnal rainfall with propagate B pattern which is identified by the afternoon continous to morning rainfall and there is an offshore propagation from sea to land that occurred in the morning, diurnal rainfall with a nonpropagate pattern, namely morning rainfall which has increased intensity and duration while afternoon rainfall is minimum, diurnal rainfall with propagate A pattern which experienced rainfall from land to sea and there was a merger between the afternoon rainfall system over the land and the morning rainfall system over the sea.
Subset-Saturated Cost Partitioning for Optimal Classical Planning

Cost partitioning is a method for admissibly adding multiple heuristics for state-space search. Saturated cost partitioning considers the given heuristics in sequence, assigning to each heuristic the minimum fraction of remaining costs that it needs to preserve its estimates for all states. We generalize saturated cost partitioning by allowing to preserve the heuristic values of only a subset of states and show that this often leads to stronger heuristics.
A simple entanglement criterion of two-qubit system

We present a novel method to judge the entanglement of two-qubit system, the density matrix of a two-qubit system can be constituted by the principle density matrix [Formula: see text] and the separable density matrices [Formula: see text]–[Formula: see text]. The necessary and sufficient conditions for the two-qubit system, the three involved coefficients p [Formula: see text] 0, p [Formula: see text] 0, p [Formula: see text] 0, and the principal density matrix [Formula: see text] being separable, then the two-qubit system is separable, otherwise the two-qubit system is entangled. Finally, the criterion for several known density matrices have been verified in this way.
Oversubscription Planning as Classical Planning with Multiple Cost Functions

The aim of classical planning is to minimize the summed cost of operators among those plans that achieve a fixed set of goals. Oversubscription planning (OSP), on the other hand, seeks to maximize the utility of the set of facts achieved by a plan, while keeping the cost of the plan at or below some specified bound. Here, we investigate the use of reformulations that yield planning problems with two separate cost functions, but no utilities, for solving OSP tasks. Such reformulations have also been proposed in the context of netbenefit planning, where the planner tries to maximize the difference between the utility achieved and the cost of the plan. One of our reformulations is adapted directly from that setting, while the other is novel. In both cases, they allow for easy adaptation of existing classical planning heuristics to the OSP problem within a simple branch and bound search. We validate our approach using state of the art admissible heuristics in this framework, and report our results.
Unifying Cyber Threat Intelligence

The threat landscape and the associated number of IT security incidents are constantly increasing. In order to address this problem, a trend towards cooperative approaches and the exchange of information on security incidents has been developing over recent years. Today, several different data formats with varying properties are available that allow to structure and describe incidents as well as cyber threat intelligence (CTI) information. Observed differences in data formats implicate problems in regard to consistent understanding and compatibility. This ultimately builds a barrier for efficient information exchange. Moreover, a common definition for the components of CTI formats is missing. In order to improve this situation, this work presents an approach for the description and unification of these formats. Therefore, we propose a model that describes the elementary properties as well as a common notation for entities within CTI formats. In addition, we develop a unified model to show the results of our work, to improve the understanding of CTI data formats and to discuss possible future research directions.
Robust Optimization Over Time with Differential Evolution using an Average Time Approach

This paper presents an extension of a preliminary study about differential evolution in the solution of robust optimization over time problems. A set of test instances with four dynamics with three different time window values are solved by six differential evolution variants, whose parameters were set by means of a tool based on statistical methods so as to promote a fair comparison. The results are compared against one particle swarm optimization algorithm found as very competitive when solving robust optimization over time problems. The results obtained suggest that the most popular differential evolution variant, DE/rand/1/bin, is the most competitive when compared with the particle swarm optimization algorithm.
Unmanned Aerial Vehicle Surveillance Under Visibility and Dwell-Time Constraints

A framework is introduced for planning unmanned aerial vehicle flight paths for visual surveillance of ground targets, each having particular viewing requirements. Specifically, each target is associated with a set of imaging parameters, including a desired (i) tilt angle, (ii) azimuth, with the option of a 360-degree view, and (iii) dwell-time. Tours are sought to image the targets, while minimizing both the total mission time and the time required to reach the initial target. An epsilon-constraint scalarization is used to pose the multi-objective problem as a constrained optimization, which, through careful discretization, can be approximated as a discrete graph-search. It is shown that, in many cases, this approximation is equivalent to a generalized traveling salesperson problem. A heuristic procedure for solving the discrete approximation and recovering solutions to the full routing problem is presented, and is shown to have resolution completeness properties. Algorithms are illustrated through numerical studies.
Hierarchical Clustering for Discrimination Discovery: A Top-Down Approach

Today, data is an essential part of many decision-making processes in businesses and social life through the use of various machine learning techniques. These methods can easily perpetuate human bias in the data and result in discrimination. Despite a growing interest in data discrimination discovery and removal, to date there is a lack of a general and robust framework to distinguish discriminatory decision-making processes from non-discriminatory ones. In this work, we present a generic framework that helps detect possible discrimination by analyzing historical data and associated decisions using a top-down unsupervised approach, which we refer to as hierarchical clustering. Our approach is highly adaptive as it gradually "learns" users' inherent groups, and clusters their records using cohesiveness and density of points in the dataset. Moreover, we propose a progressive attribute-selection method to choose statistically relevant attributes, thus reducing the effect of noise. Finally, we adopt a recursive notion of cluster profile that is homogeneous w.r.t. decision labels. This allows for deeper insights on the data and on the decision-making underlying the final user classification. Our framework is able to identify both positive and negative bias resulting in discrimination. We also highlight patterns of discrimination revealed by the homogeneous cluster centroids, which otherwise could not be captured.
Adaptive Hardware Reconfiguration for Performance Tradeoffs in CNNs

in this work we propose an adaptive and scalable hardware implementation of convolutional Neural Networks. The adaptive hardware model is the result of a design loop that starts with a software implementation relying on standard scanning window and MAC operations. This design is developed into a deterministic, hardware-friendly model which introduces timing, fixed-point representation and a pixel streaming interface. Then finally HDL code is generated and an RTL of the system is created. Each step is analyzed and validated against pre-set objectives using a golden reference from the last step. The proposed system is capable of selective output execution of different data-paths. It allows for real time trade-offs between accuracy for execution time and power. This is achieved by implementing a CNN network through a number of sequential layer blocks. Layer-blocks can effectively be considered standalone networks with differing complexities. Each layer blocks branches off into an output that is independent of the block that follows it. This allows the system to execute partially or fully according to performance requirements. This reconfigurable model trades off accuracy for speed and power, results show a tradeoff in accuracy for a 50% and 70% gain in both speed and power respectfully.
Three-dimensional measurement of the thoracoabdominal body surface during respiration using three-dimensional wavelet transform

We present a measurement method for obtaining three-dimensional (3-D) images of the thoracic and abdominal body surface during respiration. This method generates a color pattern composed of R, G, and B cosine stripe patterns to obtain a single image-based 3-D measurement. The 3-D wavelet transform is adopted to extract wrapped phases from a stripe image sequence to improve the accuracy of the wrapped phase extraction. Then, a two-frequency temporal phase unwrapping formula is proposed and extended to a multifrequency formula according to the geometric relationship between the wrapped and the unwrapped phase curves, which enhances its anti-interference performance and calculation speed. Simulations and body measurements reveal that the proposed method can effectively measure the 3-D body surface during respiration. By using multiple characteristic parameters, this method overcomes the limitation of only using feature points to represent respiratory movement on the thoracic and abdominal body surface. This method has important applications for tracking respiratory motion during clinical radiotherapy.
A self-adaptive artificial bee colony algorithm with local search for TSK-type neuro-fuzzy system training

In this paper, we introduce a self-adaptive artificial bee colony (ABC) algorithm for learning the parameters of a Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy system (NFS). The proposed NFS learns fuzzy rules for the premise part of the fuzzy system using an adaptive clustering method according to the input-output data at hand for establishing the network structure. All the free parameters in the NFS, including the premise and the following TSK-type consequent parameters, are optimized by the modified ABC (MABC) algorithm. Experiments involve two parts, including numerical optimization problems and dynamic system identification problems. In the first part of investigations, the proposed MABC compares to the standard ABC on mathematical optimization problems. In the remaining experiments, the performance of the proposed method is verified with other metaheuristic methods, including differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and standard ABC, to evaluate the effectiveness and feasibility of the system. The simulation results show that the proposed method provides better approximation results than those obtained by competitors methods.
Health, safety, and well‐being through ethical behaviours in computer and information systems development and deployment

Abstract Literature on systems development has been progressively identifying the importance of social aspects in systems development. However, there is often a failure of participants in the recognition, and fulfilment, of ethical duties concerning the concepts of health, safety, and well‐being. A rational appeal can be made to normative defensible ethical rules in order to arrive at a judicious, morally justifiable judgement. In this paper, our first step is to report on the findings of a literature review, which presents the current health and safety issues concerning usage of computers in organisations and the workplace. Building on our earlier research on basic generic deontological and teleological moral principles and theories, we prescribe a set of moral rights and duties that must be exercised and fulfilled by stakeholders in systems development in order for them to exhibit moral behaviour. By identifying, and recommending a set of defensible moral obligations that must be fulfilled in the development and deployment of systems, protagonists—such as, project managers, software engineering teams, systems analysts, and clients—can fulfil their ethical duties, thus increasing the likelihood of deployed systems that are compliant with principles of health, safety, and well‐being of its users.
Restoration of the Blind Zone of the Image of the Underlying Surface for Radar Systems with Doppler Beam Sharpening

This article discusses the possibility of building a radar map in the mode of Doppler beam sharpening. Spatial autocorrelation is considered as an image preprocessing for subsequent analysis when restoring a part of the map in the direction course carrier of the radar. Present is method for restoring the blind zone of images of the underlying surface map based on the search for similar blocks and their combination using a neural network. The efficiency of this approach is shown on several examples for different areas with lost pixels.
Security Risk-Aware Resource Provisioning Scheme for Cloud Computing Infrastructures

The last decade has witnessed a growing interest in exploiting the advantages of Cloud Computing technology. However, the full migration of services and data to the Cloud is still cautious due to the lack of security assurance. Cloud Service Providers (CSPs)are urged to exert the necessary efforts to boost their reputation and improve their trustworthiness. Nevertheless, the uniform implementation of advanced security solutions across all their data centers is not the ideal solution, since customers' security requirements are usually not monolithic. In this paper, we aim at integrating the Cloud security risk into the process of resource provisioning to increase the security of Cloud data centers. First, we propose a quantitative security risk evaluation approach based on the definition of distinct security metrics and configurations adapted to the Cloud Computing environment. Then, the evaluated security risk levels are incorporated into a resource provisioning model in an InterCloud setting. Finally, we adopt two different metaheuristics approaches from the family of evolutionary computation to solve the security risk-aware resource provisioning problem. Simulations show that our model reduces the security risk within the Cloud infrastructure and demonstrate the efficiency and scalability of proposed solutions.
Benefits and Trade-Offs of Different Model Representations in Decision Support Systems for Non-expert Users

Researchers have reported a lack of experience and low graph literacy as significant problems when making visual analytics applications available to a general audience. Therefore, it is fundamental to understand the strengths and weaknesses of different visualizations in the decision-making process. This paper explores the benefits and challenges of an intuitive, a compact, and a detailed visualization for supporting non-expert users. Using objective and subjective means proposed by earlier work, we determine the benefits and trade-offs of these visualizations for different task complexity levels. We found that while an intuitive visualization can be a good choice for easy level and medium level tasks, hard level tasks are best supported with a richer, yet visually more demanding visualization.
Human Pose Estimation Using Deep Convolutional Densenet Hourglass Network with Intermediate Points Voting

Human pose estimation is a long-standing and challenging problem in computer vision. The problem involves high freedom of articulation of body limbs, different occlusions such as self-occlusion or occlusion by other objects or persons, various clothing, various background in the natural image and foreshortening due to different capturing angle of the camera. In this work we present 1) how the DenseNet module can be used to improve the original ResNet hourglass model, 2) how intermediate points derived from ground truth joint segments can be used as output augmentation of a convolutional neural network (ConvNet) to improve the prediction accuracy. Further improvement has also been made via intermediate points voting by optimizing the joint probability distribution of human joints and the intermediate points. Experimental results on the effects of intermediate point and optimization scheme are presented. We are able to achieve competitive results to the state-of-the-art methods by the proposed method.