Reviewerly/exHarmony huggingface dataset:
Cut-The-Rope: A Game of Stealthy Intrusion

A major characteristic of Advanced Persistent Threats (APTs) is their stealthiness over a possibly long period, during which the victim system is being penetrated and prepared for the finishing blow. We model an APT as a game played on an attack graph G, and consider the following interaction pattern: the attacker chooses an attack path in G towards its target $$v_0$$ , and step-by-step works its way towards the goal by repeated penetrations. In each step, it leaves a backdoor for an easy return to learn how to accomplish the next step. We call this return path the “rope”. The defender’s aim is “cutting” this rope by cleaning the system from (even unknown) backdoors, e.g., by patching systems or changing configurations. While the defender is doing so in fixed intervals governed by working hours/shifts, the attacker is allowed to take any number of moves at any point in time. The game is thus repeated, i.e., in discrete time, only for the defender, while the second player (adversary) moves in continuous time. It also has asymmetric information, since the adversary is stealthy at all times, until the damage causing phase of the APT. The payoff in the game is the attacker’s chance to reach this final stage, while the defender’s goal is minimizing this likelihood (risk). We illustrate the model by a numerical example and open access implementation in R.
Research on Improved Pedestrian Detection Algorithm Based on Convolutional Neural Network

As we all know, The feature obtained by the convolutional neural network (CNN) is the expression of higher level for the input image, which can improve the discrimination of the input data. In this paper, we use an excellent convolutional neural network framework to design the pedestrian detection algorithm. Aiming at the background interference between the descendant and the occlusion between the pedestrians, the head of pedestrian is considered as the detection object, so the features of the object to be tested are reduced. To obtain a better detection effect and accelerate the calculation speed, the regional proposal network (RPN) in fast RCNN can be used as a better pedestrian detector, but the latter classifier reduces the performance. The object detection network is improved based on fast RCNN, and the features of the first layer are merged, so that the obtained features are richer. Experiments show that the new method can separate the objects from the background accurately.
Distribution of “Invisible” Noble Metals between Pyrite and Arsenopyrite Exemplified by Minerals Coexisting in Orogenic Au Deposits of North-Eastern Russia

The study focused on the forms of occurrence and distribution of hidden (“invisible”) noble metals (NMs = Au, Ag, Pt, Pd, Ru) in the coexisting pyrites and arsenopyrites of four samples of mineral associations from three Au deposits in the north-east of Russia. The unique nature of our approach was the combination of methods of local analysis and statistics of the compositions of individual single crystals of different sizes. This allowed us to take into account the contribution of the surface component to the total NM content and to distinguish the structurally bound form of the elements. The following estimates of the distribution coefficients of the structural (str) and surficial (sur) forms of elements were obtained: D ¯ P y / A s p s t r = 2.7 (Au), 2.5 (Pd), 1.6 (Pt), 1.7 (Ru) and D ¯ P y / A s p s u r = 1.6 (Au), 1.1 (Pd), 1.5 (Pt and Ru). The data on Ag in most cases indicated its fractionation into pyrite ( D ¯ P y / A s p s t r = 17). Surface enrichment was considered as a universal factor in “invisible” NM distribution. A number of elements (i.e., Pt, Ru, Ag) tended to increase their content with a decrease in the crystallite size in pyrite and arsenopyrite. This may be due to both the phase size effect and the intracrystalline adsorption of these elements at the interblock boundaries of a dislocation nature. The excess of metal (or the presence of S vacancies) in pyrite increased Ag and Pt content in its structure and, to a lesser extent, the content of Ru, Pd and Au. Arsenopyrite exhibited a clear tendency to increase the content of Pt, Ru and Pd in samples with excess As over S. Sulphur deficiency was a favourable factor for the incorporation of Ag and platinoids into the structures of the mineral associations studied. Perhaps this was due to the lower sulphur fugacity. Pyrite with excess Fe was associated with higher contents of some NMs. The presence of other impurity elements was not an independent factor in NM concentration.
Dual-NMS: A Method for Autonomously Removing False Detection Boxes from Aerial Image Object Detection Results

In the field of aerial image object detection based on deep learning, it's difficult to extract features because the images are obtained from a top-down perspective. Therefore, there are numerous false detection boxes. The existing post-processing methods mainly remove overlapped detection boxes, but it's hard to eliminate false detection boxes. The proposed dual non-maximum suppression (dual-NMS) combines the density of detection boxes that are generated for each detected object with the corresponding classification confidence to autonomously remove the false detection boxes. With the dual-NMS as a post-processing method, the precision is greatly improved under the premise of keeping recall unchanged. In vehicle detection in aerial imagery (VEDAI) and dataset for object detection in aerial images (DOTA) datasets, the removal rate of false detection boxes is over 50%. Additionally, according to the characteristics of aerial images, the correlation calculation layer for feature channel separation and the dilated convolution guidance structure are proposed to enhance the feature extraction ability of the network, and these structures constitute the correlation network (CorrNet). Compared with you only look once (YOLOv3), the mean average precision (mAP) of the CorrNet for DOTA increased by 9.78%. Commingled with dual-NMS, the detection effect in aerial images is significantly improved.
Chaos Theory on Generative Adversarial Networks for Encryption and Decryption of Data

Today's world involves sharing a tremendous amount of vital information and data over the web and cloud for almost everything. Any hacker or cyber-terrorist can get access to the data and hence the security of the data becomes extremely essential. Through this research, the possibilities of improving the cryptosystem has been explored by making use of generative adversarial networks in which our own shared key, which is generated with the help of chaotic generator has been incorporated. The key formed leads to the increase in randomness which in turn makes it even more difficult to crack it, thus making the system even more secure.
Qubit Fault Detection in SoC Logic

A memory-driven technology for diagnosing and testing digital systems-on-chips is proposed, which allows increasing the performance of design and test processes by an order of magnitude. The technology differs from the classical theory in the absence of traditional logic for the synthesis and analysis of computing devices and the use of qubit data structures of quantum computing. All the functionalities of computer architectures are designed on the basis of memory components, which implement sequential primitives and combinational circuits. The model of a universal digital primitive excludes a truth table and is represented by a qubit binary vector that adequately describes the behavior of any functionality of combinational or sequential type. Methods for the synthesis of functionalities based on qubit vectors in memory are considered, which compactly describe the behavior of computer components. Methods for analyzing digital architectures based on qubit vectors are proposed, which make it possible to effectively simulate the behavior of computing devices by using only read-write transactions in memory. Methods for testing digital systems defined by qubit vector structures are considered, which make it possible to effectively generate tests and simulate faults for computing devices by using parallel vector logic operations. The effectiveness of the proposed models and methods for the synthesis and analysis of digital systems is metrically evaluated by performance parameters, memory hardware costs, and also the quality of the designed processes and products.
Transfer Learning for Classifying Single Hand Gestures on Comprehensive Bharatanatyam Mudra Dataset

For any dance form, either classical or folk, visual expressions - facial expressions and hand gestures play a key role in conveying the storyline of the accompanied music to the audience. Bharatanatyam - a classical dance form which has origins from the southern states of India, is on the verge of being completely automated partly due to an acute dearth of qualified and dedicated teachers/gurus. In an honest effort to speed up this automation process and at the same time preserve the cultural heritage, we have chosen to identify and classify the single hand gestures/mudras/hastas against their true labels by using two variations of the convolutional neural networks (CNNs) that demonstrates the exceeding effectiveness of transfer learning irrespective of the domain difference between the pre-training and the training dataset. This work is primarily aimed at 1) building a novel dataset of 2D single hand gestures belonging to 27 classes that were collected from Google search engine (Google images), YouTube videos (dynamic and with background considered) and professional artists under staged environment constraints (plain backgrounds), 2) exploring the effectiveness of Convolutional Neural Networks in identifying and classifying the single hand gestures by optimizing the hyperparameters, and 3) evaluating the impacts of transfer learning and double transfer learning, which is a novel concept explored in this paper for achieving higher classification accuracy.
Merged Tree-CAT: A fast method for building precise computerized adaptive tests based on decision trees

Over the last few years, there has been an increasing interest in the creation of Computerized Adaptive Tests (CATs) based on Decision Trees (DTs). Among the available methods, the Tree-CAT method has been able to demonstrate a mathematical equivalence between both techniques. However, this method has the inconvenience of requiring a high performance cluster while taking a few days to perform its computations. This article presents the Merged Tree-CAT method, which extends the Tree-CAT technique, to create CATs based on DTs in just a few seconds in a personal computer. In order to do so, the Merged Tree-CAT method controls the growth of the tree by merging those branches in which both the distribution and the estimation of the latent level are similar. The performed experiments show that the proposed method obtains estimations of the latent level which are comparable to the obtained by the state-of-the-art techniques, while drastically reducing the computational time.
Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important effort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the different outputs for the different techniques.
A Secure Private Charging Pile Sharing Scheme with Electric Vehicles in Energy Blockchain

With the rapid advance of electric vehicles (EVs) and the sparse public charging infrastructure, the private charging pile sharing networks (PCPSNs) hold the potential to improve the quality of experience (QoE) of using EVs by leveraging private charging piles (PCPs) as shared charging points to charge a group of distributed EVs. However, due to the potential security and privacy issues for EVs and PCPs caused by untrusted participants in decentralized energy market, it becomes a crucial challenge to optimally schedule the charging and sharing strategies of EVs and PCPs in PCPSNs. In this paper, we propose a secure and permissioned blockchain-based PCP sharing scheme for EVs in PCPSNs. Firstly, we present an energy blockchain-based PCPSN framework to enhance the security of distributed energy trading. Secondly, we develop a reputation-based secure PCP sharing protocol to efficiently reach consensus in the blockchain with the implement of BLS multi-signature. Thirdly, we investigate a distributed reputation model to evaluate the trustworthiness of participants and identify malicious users. In addition, based on the many-to-one matching game, the optimal strategies of EVs and PCPs are analyzed by searching the stable matching pairs. Finally, simulation results show that the proposed scheme can not only improve the QoE of users, but also protect the network from adversaries.
Single Image Multi-Spectral Photometric Stereo Using a Split U-Shaped CNN

We present a system to extract surface orientation and albedos from a single shot image using three differently colored illumination sources. Photometric stereo allows one to extract local surface information such as normals or gradients. Traditionally, the local orientations and albedos are computed using serveral acquisitions of the same viewing angle and under varying illumination directions. In applications with moving objects, where the acquisition-as well as processing speed are essential, such setups are poorly suited. We propose a single shot decomposition using three differently colored light sources under defined illumination directions. To allow for a fast and regularized inference, we built a split U-shaped convolutional neural network, which takes a single shot input and estimates both the surface orientation and albedo simultaneously.
Security Considerations in 5G Networks: A Slice-Aware Trust Zone Approach

A security study of 5G networks and the use of security trust zones in network slicing architectures are presented in this paper. In such an approach, the security trust zone concept is analyzed in terms of a profiling methodology that takes into account the characteristics of the supported network slice. Moreover, the performance and isolation capabilities of the trust zone approach is assessed via a simulation framework, in terms of its ability to detect and mitigate simulated threats. Finally, the security study complements the simulated framework with a use-case analysis of a network testbed, carried out in an industrial environment. The conducted analysis shows that security trust zones can offer a security level which can cover the detection capabilities of critical parts of the network.
Optimal energy-efficient policies for data centers through sensitivity-based optimization

In this paper, we propose a novel dynamic decision method by applying the sensitivity-based optimization theory to find the optimal energy-efficient policy of a data center with two groups of heterogeneous servers. Servers in Group 1 always work at high energy consumption, while servers in Group 2 may either work at high energy consumption or sleep at low energy consumption. An energy-efficient control policy determines the switch between work and sleep states of servers in Group 2 in a dynamic way. Since servers in Group 1 are always working with high priority to jobs, a transfer rule is proposed to migrate the jobs in Group 2 to idle servers in Group 1. To find the optimal energy-efficient policy, we set up a policy-based Poisson equation, and provide explicit expressions for its unique solution of performance potentials by means of the RG-factorization. Based on this, we characterize monotonicity and optimality of the long-run average profit with respect to the policies under different service prices. We prove that the bang-bang control is always optimal for this optimization problem, i.e., we should either keep all servers sleep or turn on the servers such that the number of working servers equals that of waiting jobs in Group 2. As an easy adoption of policy forms, we further study the threshold-type policy and obtain a necessary condition of the optimal threshold policy. We hope the methodology and results derived in this paper can shed light to the study of more general energy-efficient data centers.
Path Planning for Autonomous Drones using 3D Rectangular Cuboid Decomposition

This paper targets the problem of generating a trajectory free of collisions for autonomous drones evolving in 3D static environments. To solve this problem, the work is focused on extending the classical 2D rectangular cell decomposition towards a 3D rectangular cuboid representation of the free space. The new approach is compared to a rapidly-exploring random tree method for the same scenario. Testing and evaluation through numerical simulation prove the effectiveness of the proposed path planning approach.
A Signal Processing Perspective of Monitoring Active Volcanoes [Applications Corner]

A volcano is a rupture in Earth's crust that unleashes lava, volcanic ash, and gases from a cavity beneath the surface. Monitoring volcanoes is crucial for getting an insight into the dynamics of the different events taking place, modeling their behavior, and establishing mechanisms to efficiently deal with eruptions. The seismic signals coming from volcanoes are a great source of information about what happens inside their structure and thus are helpful in forecasting their activity. Their study is important to ensure the safety of people who live in the vicinity of different volcanoes around the world. This is why volcanicactivity monitoring is being supported by applying modern signal processing and machine-learning methods.
Improving Multi-head Attention with Capsule Networks

Multi-head attention advances neural machine translation by working out multiple versions of attention in different subspaces, but the neglect of semantic overlapping between subspaces increases the difficulty of translation and consequently hinders the further improvement of translation performance. In this paper, we employ capsule networks to comb the information from the multiple heads of the attention so that similar information can be clustered and unique information can be reserved. To this end, we adopt two routing mechanisms of Dynamic Routing and EM Routing, to fulfill the clustering and separating. We conducted experiments on Chinese-to-English and English-to-German translation tasks and got consistent improvements over the strong Transformer baseline.
Fine-grained quantum computational supremacy

(pp1089-1115) Tomoyuki Morimae and Suguru Tamaki doi: https://doi.org/10.26421/QIC19.13-14-2 Abstracts: Output probability distributions of several sub-universal quantum computing models cannot be classically efficiently sampled unless some unlikely consequences occur in classical complexity theory, such as the collapse of the polynomial-time hierarchy. These results, so called quantum supremacy, however, do not rule out possibilities of super-polynomial-time classical simulations. In this paper, we study ``fine-grained" version of quantum supremacy that excludes some exponential-time classical simulations. First, we focus on two sub-universal models, namely, the one-clean-qubit model (or the DQC1 model) and the HC1Q model. Assuming certain conjectures in fine-grained complexity theory, we show that for any a&gt;0 output probability distributions of these models cannot be classically sampled within a constant multiplicative error and in 2^{(1-a)N+o(N)} time, where N is the number of qubits. Next, we consider universal quantum computing. For example, we consider quantum computing over Clifford and T gates, and show that under another fine-grained complexity conjecture, output probability distributions of Clifford-T quantum computing cannot be classically sampled in 2^{o(t)} time within a constant multiplicative error, where t is the number of T gates.
Design of a 2D Median Filter with a High Throughput FPGA Implementation

In this paper, a hybrid technique for median filtering of images affected by impulse noise is proposed. Our technique combines impulse noise detection, histogram-based median calculation and bit-plane processing to obtain approximate median with the aim of optimizing the throughput at minimum cost of image quality. The proposed median filter is implemented on FPGA with pipelining and is significantly faster than existing FPGA based pipelined median filter architectures. Implementation of the proposed median filter hardware provides a throughput of 282 Full High Definition (FHD) frames per second on Zynq-7 FPGA; 48% higher than the throughput of low-latency median filter. Compared to FPGA implementation of a low complexity noise removal, the proposed median filter utilizes only 45% of FPGA slices and provides a speed-up of 2.2 on Zynq-7 FPGA.
High-speed Sparse Ising Model on FPGA

To implement the Ising model on hardware and create "things" able to process optimization problems for the future Internet of Things (IoT), a method is proposed for speeding up an Ising model, which is equivalent to being able to reduce power. By implementing a 6×6 spin Ising model in a 16 nm complementary metal-oxide-semiconductor field-programmable gate array (CMOS FPGA), we found that the optimal solutions can be given for the traveling salesman problem (TSP), which is the typical combinatorial optimization problem, and the support vector machine (SVM), which is a general optimization problem. By implementing parallel updates, higher processing speed is achieved. Furthermore, the Ising model in which the interactions are reduced on the basis of the sparsity is more than nine times faster than the fully connected Ising model according to measurement with an FPGA. As a result, an Ising model on an FPGA is demonstrated with high speed, equivalent to being able to reduce power for IoT applications.