Reviewerly/exHarmony huggingface dataset:
Toward Invisible Adversarial Examples Against DNN-Based Privacy Leakage for Internet of Things

Deep neural networks (DNNs) can be utilized maliciously for compromising the privacy stored in electronic devices, e.g., identifying the images stored in a mobile phone connected to the Internet of Things (IoT). However, recent studies demonstrated that DNNs are vulnerable to adversarial examples, which are artificially designed perturbations in the original samples for misleading DNNs. Adversarial examples can be used to protect the DNN-based privacy leakage in mobile phones by replacing the photos with adversarial examples. To avoid affecting the normal use of photos, the adversarial examples need to be highly similar to original images. To handle a large number of photos stored in the devices at a proper time, the time efficiency of a method needs to be high enough. Previous methods cannot do well on both sides. In this article, we propose a broad class of selective gradient sign iterative algorithms to make adversarial examples useful in protecting the privacy of photos in IoT devices. By neglecting the unimportant image pixels in the iterative process of attacks according to the sort of first-order partial derivative, we control the optimization direction meticulously to reduce image distortions of adversarial examples without leveraging high time-consuming tricks. Extensive experimental results show that the proposed methods successfully fool the neural network classifiers for the image classification task with a small change in the visual effects and consume little calculating time simultaneously.
Game-Theoretical Analysis of Mining Strategy for Bitcoin-NG Blockchain Protocol

Bitcoin-NG (next generation), a scalable blockchain protocol, divides each block into a key block and many microblocks to effectively improve the transaction processing capacity. Bitcoin-NG has a special incentive mechanism (i.e., splitting transaction fees to the current and the next leader) to maintain its security. However, this incentive mechanism ignores the joint effect of transaction fees, mint coins, and mining duration lengths on the expected mining reward. In this article, we identify the advanced mining attack that deliberately ignores microblocks to enlarge the mining-duration length to increase the likelihood of winning the mining race. We first show that an advanced mining attacker can maximize its expected reward by optimizing its mining-duration length. We then formulate a game-theoretical model in which multiple mining players perform advanced mining to compete with each other. We analyze the Nash equilibrium for the mining game. Our analytical and simulation results indicate that all mining players in the mining game converge to having advanced mining at the equilibrium and have no incentives for deviating from the equilibrium; the transaction processing capability of Bitcoin-NG at the equilibrium is decreased by advanced mining. Therefore, we conclude that the Bitcoin-NG blockchain protocol is vulnerable to advanced mining.
Medical Treatment Migration Prediction Based on GCN via Medical Insurance Data

Nowadays, prediction for medical treatment migration has become one of the interesting issues in the field of health informatics. This is because the medical treatment migration behavior is closely related to the evaluation of regional medical level, the rational use of medical resources, and the distribution of medical insurance. Therefore, a prediction model for medical treatment migration based on medical insurance data is introduced in this paper. First, a medical treatment graph is constructed based on medical insurance data. The medical treatment graph is a heterogeneous graph, which contains entities such as patients, diseases, hospitals, medicines, hospitalization events, and the relations between these entities. However, existing graph neural networks are unable to capture the time-series relationships between event-type entities. To this end, a prediction model based on Graph Convolutional Network (GCN) is proposed in this paper, namely, Event-involved GCN (EGCN). The proposed model aggregates conventional entities based on attention mechanism, and aggregates event-type entities based on a gating mechanism similar to LSTM. In addition, jumping connection is deployed to obtain the final node representation. In order to obtain embedded representations of medicines based on external information (medicine descriptions), an automatic encoder capable of embedding medicine descriptions is deployed in the proposed model. Finally, extensive experiments are conducted on a real medical insurance data set. Experimental results show that our model's predictive ability is better than the best models available.
Improving Model Repair through Experience Sharing.

In model-driven software engineering, models are used in all phases of the development process.These models may get broken due to various editions throughout their life-cycle.There are already approaches that provide an automatic repair of models, however, the same issues might not have the same solutions in all contexts due to different user preferences and business policies.Personalization would enhance the usability of automatic repairs in different contexts, and by reusing the experience from previous repairs we would avoid duplicated calculations when facing similar issues.By using reinforcement learning we have achieved the repair of broken models allowing both automation and personalization of results.In this paper, we propose transfer learning to reuse the experience learned from each model repair.We have validated our approach by repairing models using different sets of personalization preferences and studying how the repair time improved when reusing the experience from each repair.
Accurate Real‐time 3D Gaze Tracking Using a Lightweight Eyeball Calibration

Abstract 3D gaze tracking from a single RGB camera is very challenging due to the lack of information in determining the accurate gaze target from a monocular RGB sequence. The eyes tend to occupy only a small portion of the video, and even small errors in estimated eye orientations can lead to very large errors in the triangulated gaze target. We overcome these difficulties with a novel lightweight eyeball calibration scheme that determines the user‐specific visual axis, eyeball size and position in the head. Unlike the previous calibration techniques, we do not need the ground truth positions of the gaze points. In the online stage, gaze is tracked by a new gaze fitting algorithm, and refined by a 3D gaze regression method to correct for bias errors. Our regression is pre‐trained on several individuals and works well for novel users. After the lightweight one‐time user calibration, our method operates in real time. Experiments show that our technique achieves state‐of‐the‐art accuracy in gaze angle estimation, and we demonstrate applications of 3D gaze target tracking and gaze retargeting to an animated 3D character.
A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty

This letter proposes a novel Nonlinear Model Predictive Control (NMPC) framework for Micro Aerial Vehicle (MAV) autonomous navigation in indoor enclosed environments. The introduced framework allows us to consider the nonlinear dynamics of MAVs, nonlinear geometric constraints, while guarantees real-time performance. Our first contribution is to reveal underlying planes within a 3D point cloud, obtained from a 3D lidar scanner, by designing an efficient subspace clustering method. The second contribution is to incorporate the extracted information into the nonlinear constraints of NMPC for avoiding collisions. Our third contribution focuses on making the controller robust by considering the uncertainty of localization in NMPC using Shannon's entropy to define the weights involved in the optimization process. This strategy enables us to track position or velocity references or none in the event of losing track of position or velocity estimations. As a result, the collision avoidance constraints are defined in the local coordinates of the MAV and it remains active and guarantees collision avoidance, despite localization uncertainties, e.g., position estimation drifts. The efficacy of the suggested framework has been evaluated using various simulations in the Gazebo environment.
An Edge 3D CNN Accelerator for Low-Power Activity Recognition

3D convolutional neural networks (CNNs) are gaining increasing popularity in the area of video-based action/activity analysis. Compared to 2D convolutions that share the filters in a 2D spatial domain, 3D convolutions further reuse filters in the temporal dimension to capture temporal-domain features in the video. How to exploit the data locality in the temporal dimension directly impacts the energy efficiency of specialized architectures for 3D CNN inference. Prior works on specialized 3D-CNN accelerators employ additional on-chip memories and multicluster architecture to reuse data among the process element (PE) arrays, which is very expensive for low-power chip implementation. Instead of harvesting in-memory data locality, we propose the architecture of systolic cube to exploit the spatial and temporal localities in 3D CNNs, which moves the reusable data in-between PEs connected via a 3D-cube network-on-chip. Furthermore, due to the existence of visual feature reappearance in the temporal domain, there exists a considerable portion of repetitive pixels and activations among the feature maps captured at adjacent time slots. To eliminate such temporal redundancy in 3D CNNs, the proposed accelerator architecture is equipped with a redundancy detection and elimination mechanism, capable of skipping the computations with the same activations and parameters when reusing the convolutional filters along the temporal dimension. In our evaluation, the experimental results show that the systolic-cube architecture contributes to a considerable energy-efficiency boost for state-of-the-art activity-recognition benchmarks and datasets.
Low Illumination Video Image Enhancement

Due to weather conditions, brightness conditions, capture equipment and other factors, leads to video unclear or even abnormally confused, which is not conducive to monitoring, and can not meet the needs of applications. Based on the actual data of night video surveillance, this paper proposes a new low illumination video image enhancement algorithm, which overcomes the existing problems. We analyze the characteristics of low illumination video image, and use HSV color space instead of traditional RGB space to enhance the robustness of video contrast and color distortion. At the same time, we use wavelet image fusion to highlight the details of video image, so the enhanced video has higher clarity and visual effect. Compared with other four algorithms, the proposed algorithm outperforms the above algorithms in subjective evaluation and objective evaluation. At the same time, compared with other algorithms, the proposed algorithm has faster processing time for each frame. Experiments show that the algorithm can effectively improve the overall brightness and contrast of video images, and avoid the over-enhancement of bright areas near the light source, which can meet the practical application requirements of video surveillance.
A Fuzzy Virtual Actuator for Automated Guided Vehicles

In the last decades, virtual sensors have found increasing attention in the research community. Virtual sensors employ mathematical models and different sources of information such as actuator states or sensors, which are already existing in a system, in order to generate virtual measurements. Additionally, in recent years, the concept of virtual actuators has been proposed by leading researchers. Virtual actuators are parts of a fault-tolerant control strategy and aim to accommodate faults and to achieve a safe operation of a faulty plant. This paper describes a novel concept for a fuzzy virtual actuator applied to an automated guided vehicle (AGV). The application of fuzzy logic rules allows integrating expert knowledge or experimental data into the decision making of the virtual actuator. The AGV under consideration disposes of an innovative steering concept, which leads to considerable advantages in terms of maneuverability, but requires an elaborate control system. The application of the virtual actuator allows the accommodation of several possible faults, such as a slippery surface under one of the drive modules of the AGV.
Dual-view catadioptric panoramic system based on even aspheric elements

We propose a compact catadioptric imaging system based on even aspheric elements to solve some major limitations of conventional panoramic structure, such as field of view, blind spot, resolution, illumination, and less structure is introduced. The design includes a catadioptric front unit that is capable of providing a compression image of a panoramic scene and of relaying high-performance aspheric lenses to a decompression image. It is arranged to project two uncompressed images from two channels on a single sensor. Their optical paths do not interfere with each other, and there is no blind-spot image.
Characteristics and optimization of core local network: Big data analysis of football matches

The current study constructed social network using player positions and passing process based on available literature. Multiple indicators were used to measure the importance of positions comprehensively. The results showed that in the football passing process, the attacking midfielder was the most important position, followed by the central defending midfielder. Based on two-sample difference tests, the results showed that the winning teams usually had better performance on positions of forward on left, central forward, defender on left and defender on right. To analyze the effects of playing positions on the whole network and test the sensitivity of passing networks, we deleted n (n = 1, 2, 3…, 10) positions of a team, and then tested the efficiency of the networks based on positions left.
High-accuracy detection of malaria mosquito habitats using drone-based multispectral imagery and Artificial Intelligence (AI) algorithms in an agro-village peri-urban pastureland intervention site (Akonyibedo) in Unyama SubCounty, Gulu District, Northern Uganda

The purpose of this research is to design, deploy and validate artificial intelligence (AI) algorithms operating on drone videos to enable a real time methodology for optimizing predictive mapping unknown, geographic locations (henceforth, geolocation) of potential, seasonal, Anopheles (gambiae, and funestus) larval habitats in an agro-village, epi-entomological, intervention site (Akonyibedo village) in Gulu District, Northern Uganda.Formulae are developed for classifying the drone swath, capture point, land cover in Akonyibedo agro-village.An AI algorithm is designed for constructing a smartphone application (app) in order to enable automatic detection of potential larval habitats from drone videos.The aim of this work is to enable scaling up to larger intervention sites (e.g., district level, sub-county) and then throughout entire Uganda.We demonstrate how capture point, stratifiable, drone swath coverage in Akonyibedo village can be accomplished employing temporal series of re-centered, real time, imaged, Anopheline, larval habitat, seasonal, map projections.We also define a remote methodology for detecting unknown, georeferenceable, capture point geolocations of potential, seasonal, breeding sites employing multispectral, wavelength, signature, reflux emissivities in a drone spectral library.Our results show that high-resolution drone imagery when processed employing state of the art AI algorithms can discriminate a profile of water bodies where Anopheles mosquitoes are most likely to breed (overall ground truth accuracy of 100%).Live, high definition, Anopheline larval habitat signature maps can be generated in real-time drone AI app on a smartphone or Apple device while the image is being captured or larvicidal application is taking place.
Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases

Knowledge-based question answering (KB-QA) has long focused on simple questions that can be answered from a single knowledge source, a manually curated or an automatically extracted KB.In this work, we look at answering complex questions which often require combining information from multiple sources.We present a novel KB-QA system, MULTIQUE, which can map a complex question to a complex query pattern using a sequence of simple queries each targeted at a specific KB.It finds simple queries using a neural-network based model capable of collective inference over textual relations in extracted KB and ontological relations in curated KB.Experiments show that our proposed system outperforms previous KB-QA systems on benchmark datasets, ComplexWebQuestions and WebQuestionsSP.* NB and XZ contributed equally to this work.2. What college did the author of 'The Hobbit' attend?nesting 3. Which Portuguese speaking countries import fish from Brazil? conjunction 1.Where was Rihanna born?simple
Random Probing Security: Verification, Composition, Expansion and New Constructions

The masking countermeasure is among the most powerful countermeasures to counteract side-channel attacks. Leakage models have been exhibited to theoretically reason on the security of such masked implementations. So far, the most widely used leakage model is the probing model defined by Ishai, Sahai, and Wagner at (CRYPTO 2003). While it is advantageously convenient for security proofs, it does not capture an adversary exploiting full leakage traces as, e.g., in horizontal attacks. Those attacks target the multiple manipulations of the same share to reduce noise and recover the corresponding value. To capture a wider class of attacks another model was introduced and is referred to as the random probing model. From a leakage parameter p, each wire of the circuit leaks its value with probability p. While this model much better reflects the physical reality of side channels, it requires more complex security proofs and does not yet come with practical constructions. In this paper, we define the first framework dedicated to the random probing model. We provide an automatic tool, called VRAPS, to quantify the random probing security of a circuit from its leakage probability. We also formalize a composition property for secure random probing gadgets and exhibit its relation to the strong non-interference (SNI) notion used in the context of probing security. We then revisit the expansion idea proposed by Ananth, Ishai, and Sahai (CRYPTO 2018) and introduce a compiler that builds a random probing secure circuit from small base gadgets achieving a random probing expandability property. We instantiate this compiler with small gadgets for which we verify the expected properties directly from our automatic tool. Our construction can tolerate a leakage probability up to $$2^{-8}$$ , against $$2^{-25}$$ for the previous construction, with a better asymptotic complexity.
Bringing UMAP Closer to the Speed of Light with GPU Acceleration

The Uniform Manifold Approximation and Projection (UMAP) algorithm has become widely popular for its ease of use, quality of results, and support for exploratory, unsupervised, supervised, and semi-supervised learning. While many algorithms can be ported to a GPU in a simple and direct fashion, such efforts have resulted in inefficent and inaccurate versions of UMAP. We show a number of techniques that can be used to make a faster and more faithful GPU version of UMAP, and obtain speedups of up to 100x in practice. Many of these design choices/lessons are general purpose and may inform the conversion of other graph and manifold learning algorithms to use GPUs. Our implementation has been made publicly available as part of the open source RAPIDS cuML library (https://github.com/rapidsai/cuml).
Towards automated verification of smart contract fairness

Smart contracts are computer programs allowing users to define and execute transactions automatically on top of the blockchain platform. Many of such smart contracts can be viewed as games. A game-like contract accepts inputs from multiple participants, and upon ending, automatically derives an outcome while distributing assets according to some predefined rules. Without clear understanding of the game rules, participants may suffer from fraudulent advertisements and financial losses. In this paper, we present a framework to perform (semi-)automated verification of smart contract fairness, whose results can be used to refute false claims with concrete examples or certify contract implementations with respect to desired fairness properties. We implement FairCon, which is able to check fairness properties including truthfulness, efficiency, optimality, and collusion-freeness for Ethereum smart contracts. We evaluate FairCon on a set of real-world benchmarks and the experiment result indicates that FairCon is effective in detecting property violations and able to prove fairness for common types of contracts.
The effect of entrepreneurial orientation on innovation performance: The mediation role of learning orientation on Kuwait SMEs

SMEs play a critical role for the economic growth of nations by providing the opportunity for individuals to develop their skills and to be self-employed.SMEs are flexible and are able to quickly acclimatize to the factors affecting supply and demand.In todays' rapidly changing business environment, SMEs are faced with difficult challenges to effectively implement the practices that are frequently adopted by larger business rivals.This study will discuss the concept of entrepreneurial orientation, innovation performance, and learning orientation in the context of SMEs in Kuwait.This study assumes that Entrepreneurial Orientation has both direct and indirect impacts on Innovation Performance (IP) through the mediating role of learning orientation.This study used quantitative methods to investigate and test the hypothesis that the association of entrepreneurial orientation with innovation performance was mediated by learning orientation.The target population in the present study is the owners and CEOs of SMEs in Kuwait, and the stratified random sampling method was used to select 221 participants.This study used a structured questionnaire containing close ended questions to gather the data.The data was analyzed using the partial least square structural equation modelling (PLS-SEM).Results of the analysis show that learning orientation played a partial mediating role in the association of entrepreneurial orientation with innovation performance.