Reviewerly/exHarmony huggingface dataset:
Accelerating Monte Carlo simulation of light propagation in tissue mimicking turbid medium based on generative adversarial networks

Monte Carlo (MC) simulation is the most frequently used method to numerically model the light propagation in biological tissues because of its high flexibility and precision. Although MC simulation is assumed to be capable of achieving any desired precision, larger number of photons are always necessary for more precise simulation, leading to its major limitation of intensive computation. In this work, the authors present a way to adapt generative adversarial networks (GAN) to accelerate MC simulation.The pix2pix network, a variant of GAN, was investigated to reconstruct precise MC simulation results from the results roughly modeled by small amount of photons, thus the computation time was expected to be significantly saved. The proposed method was tested on single-layer embedded tumor models to derive the absorption distribution maps.The results demonstrate that the absorption distribution maps reconstructed from the simulation of only 10 000 photons were very similar to those modeled by using 1 000 000 photons, based on the criterion of peak signal to noise ratio (PSNR) and percentage difference of power coupling efficiencies, and the simulation process was proved to be accelerated by approximately 102 times.For the first time, GAN was adapted to save computation time of MC simulation of light propagation. By achieving MC simulation with acceptable quality, the proposed method can speed up the computation by hundreds of times.
Super-resolution method for MR images based on multi-resolution CNN

High-Resolution (HR) Magnetic Resonance Images (MRI) can help physician diagnosis lesion more effectively. However, in practice, it is difficult to obtain HR-MRI due to equipment limitations, scanning time or patient comfort. Fortunately, with the development of information technology, HR-MRI could be obtained by some image post-processing approaches. This paper presents a novel HR-MRI generation approach based on convolution neural network (CNN) and multi-resolution analysis, which intends to improve the resolution of the low-resolution (LR) T2-weighted (T2w) images with the prior information provided by HR T1-weighted (T1w) reference images based on the inherent correlation between the two. Specifically, a novel 3D multi-resolution analysis multi-modality SR reconstruction network for MRI is built, which takes full advantage of structural similarity between the modalities. Different from other networks, the proposed reconstruction network fully combines the idea of multi-resolution analysis. Firstly, we built an associative memory network between HR-T1w and HR-T2w, which learns high-frequency feature from HR-T1w for HR-T2w at different scales. Subsequently, a progressive reconstruction approach is presented to combine the multi-scale high-frequency feature extracted by the first step from HR-T1w with the down-sampling 4x LR-T2w feature to generate 2× and 4× HR-T2w images, respectively. The experimental results on two real MRI dataset show the effectiveness of the proposed 3D SR reconstruction network, which achieves improved reconstruction performance compared with other single- and multi-modal networks. Even with 4x magnification, our method can effectively restore the edge details of the image.
Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation

Neural networks are being increasingly applied to control and decision making for learning-enabled cyber-physical systems (LE-CPSs). They have shown promising performance without requiring the development of complex physical models; however, their adoption is significantly hindered by the concerns on their safety, robustness, and efficiency. In this work, we propose COCKTAIL, a novel design framework that automatically learns a neural network based controller from multiple existing control methods (experts) that could be either model-based or neural network based. In particular, COCKTAIL first performs reinforcement learning to learn an optimal system-level adaptive mixing strategy that incorporates the underlying experts with dynamically-assigned weights, and then conducts a teacher-student distillation with probabilistic adversarial training and regularization to synthesize a student neural network controller with improved control robustness (measured by a safe control rate metric with respect to adversarial attacks or measurement noises), control energy efficiency, and verifiability (measured by the computation time for verification). Experiments on three non-linear systems demonstrate significant advantages of our approach on these properties over various baseline methods.
Adaptive Gradient Coding

This paper focuses on mitigating the impact of stragglers in distributed learning system. Unlike the existing results designated for a fixed number of stragglers, we develop a new scheme called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Gradient Coding (AGC)</i> with flexible communication cost for varying number of stragglers. Our scheme gives an optimal tradeoff between computation load, straggler tolerance and communication cost by allowing workers to send multiple signals sequentially to the master. In particular, it can minimize the communication cost according to the unknown real-time number of stragglers in practical environments. In addition, we present a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Group AGC (G-AGC)</i> by combining the group idea with AGC to resist more stragglers in some situations. The numerical and simulation results demonstrate that our adaptive schemes can achieve the smallest average running time.
Learnable Locality-Sensitive Hashing for Video Anomaly Detection

Video anomaly detection (VAD) mainly refers to identifying anomalous events that have not occurred in the training set where only normal samples are available. Existing works usually formulate VAD as a reconstruction or prediction problem. However, the adaptability and scalability of these methods are limited. In this paper, we propose a novel distance-based VAD method to take advantage of all the available normal data efficiently and flexibly. In our method, the smaller the distance between a testing sample and normal samples, the higher the probability that the testing sample is normal. Specifically, we propose to use locality-sensitive hashing (LSH) to map the samples whose similarity exceeds a certain threshold into the same bucket in advance. To utilize multiple hashes and further alleviate the computation and memory usage, we propose to use the hash codes rather than the features as the representations of the samples. In this manner, the complexity of near neighbor search is cut down significantly. To make the samples that are semantically similar get closer and those not similar get further apart, we propose a novel learnable version of LSH that embeds LSH into a neural network and optimizes the hash functions with contrastive learning strategy. The proposed method is robust to data imbalance and can handle the large intra-class variations in normal data flexibly. Besides, it has a good ability of scalability. Extensive experiments demonstrate the superiority of our method, which achieves new state-of-the-art results on VAD benchmarks.
Internet of Cross-Chains: Model-Driven Cross-Chain as a Service Platform for the Internet of Everything in Smart City

Modern consumer electronics include consumer robots, which are made by heterogeneous Internet of Things, and Internet of robotics things are integral members of the Internet of Everything (IoE). IoE plays an incumbent role in smart cities and generates big data. Moreover, smart city IoE maintains various levels of security provisions. However, those are still vulnerable to security and privacy breaches. Nonetheless, mobility increases the risk of security and privacy breaches. As a result, in the internet era, providing seamless security and privacy for inter- and intra-communication of heterogeneous things is a significant challenge. Furthermore, blockchain is one of the established communication techniques that ensure both security and privacy. On the other hand, blockchain inherits trivial constraints with respect to scalability, latency, and interoperability. Cross-chain is a blockchain concept that aims to alleviate the aforementioned concerns. Therefore, we proposed a multifactor aware (privacy, security, interoperability, and scalability) model-driven reference architecture for the Internet of cross-chains, also known as cross-chain as a service platform.
SoK: A Framework for Asset Discovery: Systematizing Advances in Network Measurements for Protecting Organizations

Asset discovery is fundamental to any organization's cybersecurity efforts. Indeed, one must accurately know which assets belong to an IT infrastructure before the infrastructure can be secured. While practitioners typically rely on a relatively small set of well-known techniques, the academic literature on the subject is voluminous. In particular, the Internet measurement research community has devised a number of asset discovery techniques to support many measurement studies over the past five years. In this paper, we systematize asset discovery techniques by constructing a framework that comprehensively captures how network identifiers and services are found. We extract asset discovery techniques from recent academic literature in security and networking and place them into the systematized framework. We then demonstrate how to apply the framework to several case studies of asset discovery workflows, which could aid research reproducibility. These case studies further suggest opportunities for researchers and practitioners to uncover and identify more assets than might be possible with traditional techniques.
An Open-Source Data Acquisition and Manual Segmentation System for Hand Gesture Recognition based on EMG

Due to lack of standardization in the data acquisition process, Hand Gesture Recognition literature has produced a high number of different but incompatible datasets. This paper presents a system for data acquisition of EMG signals and its manual segmentation. The system can be connected with the two most affordable wearable EMG armbands: Myo Armband and gForce Pro. The system allows to record a given number of samples per gesture during a given number of seconds. Twelve gestures were selected for being natural and the most reported in the literature. The system includes several features that enhance the quality of the dataset such as: strategies to maintain the volunteer attention, and the capability to resume recording in case of interruption. The system was evaluated using the Computer System Usability Questionnaire (CSUQ) over 10 data collectors. This questionnaire allowed to obtain System quality (85.5 %), Information quality (84.5 %) and Interface quality (89.5%) perceptions with an overall usability of 85.9%. These results show that the system is greatly designed, intuitive and of ease of use. The software is publicly available and was developed in Matlab.
Innovating Multi-user Volumetric Video Streaming through Cross-layer Design

Although existing work has demonstrated the feasibility of streaming volumetric content to a single user, there exist many appealing applications (e.g., classroom education and collaborative design) that involve multiple users who watch the same volumetric content simultaneously. In this paper, we first perform a scaling experiment to demonstrate the challenges of streaming high-quality volumetric videos to multiple users and reveal the viewport-similarity opportunity that we can leverage to effectively optimize the network resource utilization using multicast over mmWave. We then develop a holistic research agenda for improving the performance and quality of experience for multi-user volumetric video streaming on commodity devices. Our proposed research includes joint viewport prediction and blockage mitigation for multiple users, multicast grouping based on viewport similarity, customized mmWave beam design for efficient multicast, and mmWave-aware multi-user video rate adaptation. Finally, we discuss the open challenges of building a practical system with the proposed research roadmap.
Scalable Identity-Oriented Speech Retrieval

With the prevalence of voice devices in our daily life, speech data is accumulated at an unprecedented speed, forming an invaluable database for security surveillance and financial risk management. In these applications, a key task is given a querying speech snippet to retrieve all speech snippets that are uttered by the same speaker as the querying one, namely Identity-Oriented Speech Retrieval (IO-SR). In this paper, we propose an accuracy and scalable system for IO-SR, which seamlessly integrates speaker modeling and deep indexing techniques. Evaluations on an industrial dataset containing millions of speech snippets show that our system achieves superior performance compared with the state-of-the-art methods.
No Door Handle, No Entry! Expressing Cues through a Shape-Changing Door

Computation is increasingly seen as a design material, finding its way into once mundane objects, surfaces and spaces. As such, demonstrations of the convergence of architectural elements and interactive systems are becoming commonplace in both research and industry. While eXtended Reality (XR) and projection mapping technologies seem to be ubiquitous in this field, shape-changing interfaces are under-represented. In this case study we explore the domain of shape-changing thresholds in the form of an interactive door panel. Corresponding to conditions of space occupancy, the design communicates through dynamic affordances and affordance elimination three ascribed social cues: entry invitation, entry permission, and entry prohibition. In a user evaluation study, all participants were able to correctly interpret the entry invitation and the entry prohibition cues. Based on our learnings and findings, we discuss aspects of our design and implications of shape-changing doors.
Crony capitalism in Nigeria: the case of patronage funding of the Peoples Democratic Party and the power sector reform, 1999–2015

ABSTRACT The article argues that cronyism in the funding of the Peoples Democratic Party (PDP) explains the dismal record of the recent power sector reforms in Nigeria. It implies that the reforms were packaged by the then PDP-led government to benefit their major campaign financiers with contracts; thus, within this period the party financiers were able to assume a commanding position in the sector. The article further contends that the funding regime in the party reinforces corruption as financiers leveraged on their contributions to the party to ensure that the reform processes and outcomes reflected their economic interests. The case exemplifies the crony relationship between the business and the political class (that ought to act as the regulatory body), which is skewed towards primitive accumulation.
Parallel Collaborative Motion Planning with Alternating Direction Method of Multipliers

Collaborative motion planning for multi-agent systems is a challenging problem because of the existence of highly nonlinear and nonconvex constraints. Such difficulties also lead to inavoidable computational inefficiency, which significantly prohibits applying the existing collaborative motion planning algorithms to complex scenarios. This paper proposes a parallel computational algorithm to achieve collaborative motion planning efficiently, considering the nonlinear dynamics model and the nonconvex collision-avoidance constraints. Specifically, the alternating direction method of multipliers (ADMM) framework is elegantly incorporated to separate the large-scale cooperative nonconvex planning problem as two tractable and manageable subproblems, where the two subproblems handle the dynamics constraints and collision-free constraints, respectively. In the proposed approach, the differential dynamic programming (DDP) method is utilized to effectively solve the nonlinear subproblem with the dynamics constraints; meanwhile, the interior point (IPOPT) method is employed to address the nonconvex subproblem derived from the collision-avoidance constraints. Finally, two simulation scenarios are successfully implemented to illustrate the effectiveness of the proposed algorithm.
Detecting Non-Aligned Double JPEG Compression Based on Amplitude-Angle Feature

Due to the popularity of JPEG format images in recent years, JPEG images will inevitably involve image editing operation. Thus, some tramped images will leave tracks of Non-aligned double JPEG ( NA-DJPEG ) compression. By detecting the presence of NA-DJPEG compression, one can verify whether a given JPEG image has been tampered with. However, only few methods can identify NA-DJPEG compressed images in the case that the primary quality factor is greater than the secondary quality factor. To address this challenging task, this article proposes a novel feature extraction scheme based optimized pixel difference ( OPD ), which is a new measure for blocking artifacts. Firstly, three color channels (RGB) of a reconstructed image generated by decompressing a given JPEG color image are mapped into spherical coordinates to calculate amplitude and two angles (azimuth and zenith). Then, 16 histograms of OPD along the horizontal and vertical directions are calculated in the amplitude and two angles, respectively. Finally, a set of features formed by arranging the bin values of these histograms is used for binary classification. Experiments demonstrate the effectiveness of the proposed method, and the results show that it significantly outperforms the existing typical methods in the mentioned task.
Categorizing transaction costs outcomes under uncertainty: a blockchain perspective for government organizations

Purpose In response, the purpose of this paper is to provide theoretical frameworks about the organizational uncertainty behind what and when to adopt blockchain technology and their implications on transaction costs. The immature nature and the absence of standards in blockchain technology lead to uncertainty in government organizations concerning the adoption (“what to adopt”) and the identification of the right time (“when to start”). Design/methodology/approach Using transaction cost theory and path dependency theory, this paper proposes two frameworks: to assess transaction cost risks and opportunities costs; and to depict four different types of transaction costs outcomes regarding blockchain adoption. Findings This paper identifies various theoretical concepts that influence blockchain adoption and combine the two critical constructs of “bounded rationality” and the “lock-in effect” to categorize the multiple transaction costs outcomes for blockchain adoption. Research limitations/implications Although existing research in blockchain highlights mainly the potential benefits of blockchain applications, only a little attention has been given to frameworks that categorize potential transaction costs outcomes under uncertainty, in particular from organizational theorists. Originality/value Both frameworks advance the understanding of the decision-making behind blockchain adoption and synthesize the current literature to offer conceptual clarity regarding the varied implications and outcomes linked to the uncertainty regarding transactions costs stemming from blockchain technology.
Adaptive Warm-Start MCTS in AlphaZero-Like Deep Reinforcement Learning

AlphaZero has achieved impressive performance in deep reinforcement learning by utilizing an architecture that combines search and training of a neural network in self-play. Many researchers are looking for ways to reproduce and improve results for other games/tasks. However, the architecture is designed to learn from scratch, tabula rasa, accepting a cold-start problem in self-play. Recently, a warm-start enhancement method for Monte Carlo Tree Search was proposed to improve the self-play starting phase. It employs a fixed parameter \(I^\prime \) to control the warm-start length. Improved performance was reported in small board games. In this paper we present results with an adaptive switch method. Experiments show that our approach works better than the fixed \(I^\prime \), especially for “deep”, tactical, games (Othello and Connect Four). We conjecture that the adaptive value for \(I^\prime \) is also influenced by the size of the game, and that on average \(I^\prime \) will increase with game size. We conclude that AlphaZero-like deep reinforcement learning benefits from adaptive rollout based warm-start, as Rapid Action Value Estimate did for rollout-based reinforcement learning 15 years ago.
Non-interactive Privacy Preserving Recurrent Neural Network Prediction with Homomorphic Encryption

Neural networks have enabled many new and interesting applications in a wide variety of domains. However, deep learning models are very computationally expensive and outsourcing the computation to the Cloud creates privacy concerns. Homomorphic encryption (HE) allows computation on encrypted data, thus preserving its privacy. Common neural networks architectures, such as CNNs and fully connected networks, have been adapted for HE. However, there is very little work on recurrent neural networks (RNNs). Existing solutions for RNNs over encrypted data require interaction between the data owner and the Cloud. In this paper, we present parallel RNN blocks, an RNN architecture that can be run on encrypted data without client-server interaction. We describe our proposed approach and evaluate it on a real-world dataset of online product reviews and IMDb movie reviews. Our results are promising; we can achieve 88.8% F1 score on the product reviews. The model generalizes well to the IMDb data set with a 74.36% F1 score using our proposed architecture. The performance is within 3 percentage points of our baseline.
Extraction of Key-Frames From Endoscopic Videos by Using Depth Information

Early detection of colorectal cancer (CRC) can reduce the risk of death. Polyps are the precursor to such cancer. Analyzing the polyps from the most significant frames out of thousands of endoscopy frames is vital for diagnosing and understanding disease. In this article, a deep learning-based monocular depth estimation (MDE) technique is proposed to select the most informative frames (key-frames) of an endoscopic video. In most cases, ground truth depth maps of polyps are not readily available, and that is why the transfer learning approach is adopted in our method. An endoscopic modality generally captures thousands of frames. In this scenario, it is quite essential to discard low-quality and clinically irrelevant frames of an endoscopic video while the most informative frames should be retained for clinical diagnosis. In this view, a key-frame selection strategy is proposed by utilizing the depth information of polyps. In our method, image moment, edge magnitude, and key points are considered for adaptively selecting the key-frames. One important application of our proposed method could be the 3D reconstruction of polyps with the help of extracted key-frames. It gives a surgeon a real-time 3D view of the polyp surface for resection which involves detaching the polyp from its mucosa layer. Also, polyps are localized with the help of extracted depth maps.