Reviewerly/exHarmony huggingface dataset:
An Improved YOLOv3-based Neural Network for De-identification Technology

In this paper, we proposed an improved YOLOv3-based neural network for De-identification technology. The existing YOLOv3 is a network with fast speed and performance recently. Most surveillance system using CCD cameras simultaneously store images from cameras installed in multiple locations. In such an environment, the use of deep learning requires a method of detecting objects through a single inference engine in a plurality of image. If the inference engine hardware is used for each camera channel, the cost of building a surveillance system increases significantly. Therefore, in the field of surveillance systems, a network structure with a high detection speed is required even if the detection performance is slightly degraded. This paper proposes a method to increase the detection speed by reducing the existing YOLOv3 network Architecture. 53 feature extractors, Darknet-53, are reduced to 24 layers. Therefore, a total of 106 layers is reduced to 39. And 53 YOLOv3 box detection parts are reduced to 15 layers. In order to verify the efficiency of the proposed algorithm, the WIDER FACE dataset and its own collected dataset, we compared the performance with the existing YOLOv3-tiny and YOLOv3. As a result, the result was 87.48% mAP improved by 19.55% compared to the conventional YOLOv3-tiny. And I got a slow result of 100.5 FPS speed than the existing speed. And Object De-identification technology has been applied according to the results of the detection box. Therefore, it is faster than YOLOv3, and it is similar to YOLOv3 detection accuracy, proving that it is better than YOLOv3-tiny in real time detection.
Guaranteed Active Constraints Enforcement on Point Cloud-approximated Regions for Surgical Applications

In this work, a passive physical human-robot interaction (pHRI) controller is proposed to intraoperatively ensure that sensitive tissues will not be damaged by the robot's tool. The proposed scheme uses the point cloud of the restricted region's surface as constraint definition and Artificial Potential fields for constraint enforcement. The controller is proven to be passive with respect to the interaction force and to guarantee constraint satisfaction in all cases. The proposed methodology is experimentally validated by the kinesthetic guidance of a KUKA LWR4+ robot's end-effector driving a virtual slave KUKA in the vicinity of a 3D point-cloud of a kidney and its adjacent vessels.
Residential links under the weather

Weather is a leading threat to the stability of our vital infrastructure. Last-mile Internet is no exception. Yet, unlike other vital infrastructure, weather's effect on last-mile Internet outages is not well understood. This work is the first attempt to quantify the effect of weather on residential outages.
Quality assessment of laser speckle patterns for digital image correlation by a Multi-Factor Fusion Index

Digital image correlation based on laser speckle has its unique advantages in ultra-high temperature deformation measurement. The traditional quality assessment criteria of artificial speckle which consider only the contrast of the speckle pattern or the morphology of the speckle particles are not suitable for laser speckle. Here, a new index, called Multi-Factor Fusion Index (MFFI), which takes the inhomogeneity of gray distribution, the mean square deviation of gray and the standard deviation of speckle particles size into consideration, is proposed to evaluate the quality of laser speckle patterns. Multi-Factor Fusion Index was compared with three commonly used criteria, the mean intensity gradient, the mean intensity of the second derivative, and the pattern quality metric combining the sum of square of subset intensity gradients (SSSIG) and the secondary auto-correlation peak height. The results showed that the proposed index had better capability of assessing the quality of laser speckle patterns. And the influence by external factors such as apertures, laser powers and temperatures and material factors such as roughness and light reflectance were analyzed by MFFI. Experiments on artificial speckle showed that MFFI was also suitable for artificial speckle patterns in most cases.
Combining Physical Simulators and Object-Based Networks for Control

Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Most physics engines therefore employ approximations that lead to a loss in precision. In this paper, we propose a hybrid dynamics model, simulator-augmented interaction networks (SAIN), combining a physics engine with an object-based neural network for dynamics modeling. Compared with existing models that are purely analytical or purely data-driven, our hybrid model captures the dynamics of interacting objects in a more accurate and data-efficient manner. Experiments both in simulation and on a real robot suggest that it also leads to better performance when used in complex control tasks. Finally, we show that our model generalizes to novel environments with varying object shapes and materials.
A Model for Detecting Cryptocurrency Transactions with Discernible Purpose

The perpetration of financial fraud progresses parallel with the innovation in the field of finance. Consequently, the emergence of the blockchain technology has also manifested financial transaction obfuscation through the use of de-anonymization of the blockchain technology. This study identifies the suspicious transaction from Binance, an open-source cryptocurrency, through the means of defining and detecting the cryptocurrency wallets. By drawing the metadata of 38,526 wallets from etherscan.io, this study investigates the transactions with discernible purpose. This study performed an unsupervised learning expectation maximization (EM) algorithm to cluster the data set. Based on the features engineered from the unsupervised learning, we performed an anomaly detection using Random Forest (RF). In this study, we offered an insight into labeling the cryptocurrency wallets by providing a model for detecting the cryptocurrency with anomalous transactions. We advocate that labeling the wallets with discernible transactions may help financial institutions, private sectors, financial intelligence, and government agencies identify and detect the transactions with illicit activities.
Adversarial discriminative sim-to-real transfer of visuo-motor policies

Various approaches have been proposed to learn visuo-motor policies for real-world robotic applications. One solution is first learning in simulation then transferring to the real world. In the transfer, most existing approaches need real-world images with labels. However, the labeling process is often expensive or even impractical in many robotic applications. In this article, we introduce an adversarial discriminative sim-to-real transfer approach to reduce the amount of labeled real data required. The effectiveness of the approach is demonstrated with modular networks in a table-top object-reaching task where a seven-degree-of-freedom arm is controlled in velocity mode to reach a blue cuboid in clutter through visual observations from a monocular RGB camera. The adversarial transfer approach reduced the labeled real data requirement by 50%. Policies can be transferred to real environments with only 93 labeled and 186 unlabeled real images. The transferred visuo-motor policies are robust to novel (not seen in training) objects in clutter and even a moving target, achieving a 97.8% success rate and 1.8 cm control accuracy. Datasets and code are openly available.
Detecting "0-Day" Vulnerability: An Empirical Study of Secret Security Patch in OSS

Security patches in open source software (OSS) not only provide security fixes to identified vulnerabilities, but also make the vulnerable code public to the attackers. Therefore, armored attackers may misuse this information to launch N-day attacks on unpatched OSS versions. The best practice for preventing this type of N-day attacks is to keep upgrading the software to the latest version in no time. However, due to the concerns on reputation and easy software development management, software vendors may choose to secretly patch their vulnerabilities in a new version without reporting them to CVE or even providing any explicit description in their change logs. When those secretly patched vulnerabilities are being identified by armored attackers, they can be turned into powerful "0-day" attacks, which can be exploited to compromise not only unpatched version of the same software, but also similar types of OSS (e.g., SSL libraries) that may contain the same vulnerability due to code clone or similar design/implementation logic. Therefore, it is critical to identify secret security patches and downgrade the risk of those "0-day" attacks to at least "n-day" attacks. In this paper, we develop a defense system and implement a toolset to automatically identify secret security patches in open source software. To distinguish security patches from other patches, we first build a security patch database that contains more than 4700 security patches mapping to the records in CVE list. Next, we identify a set of features to help distinguish security patches from non-security ones using machine learning approaches. Finally, we use code clone identification mechanisms to discover similar patches or vulnerabilities in similar types of OSS. The experimental results show our approach can achieve good detection performance. A case study on OpenSSL, LibreSSL, and BoringSSL discovers 12 secret security patches.
Virtual hype meets reality: Users’ perception of immersive journalism

This study responds to recent calls to investigate how media users perceive virtual reality technology in journalism. Within a uses and gratifications framework, focus group participants were asked to experience recent immersive journalistic productions and discuss their reactions. Six distinct gratifications were identified, which cluster into gratifications related to experience, affect, and action. Despite clear reservations about the technology, users see great potential for journalistic use. If employed responsibly, users think virtual reality can add considerable value to mainstream journalistic productions, potentially boosting engagement and trust. Researchers and practitioners can benefit from this initial study of users’ impressions of and reactions to immersive journalism.
Forward–Backward Decoding Sequence for Regularizing End-to-End TTS

Neural end-to-end TTS such as Tacotron like network can generate very high-quality synthesized speech, and even close to human recording for similar domain text. However, it performs unsatisfactory when scaling it to some challenging test sets. One concern is that the encoder-decoder with attention-based network adopts autoregressive generative sequence model with the limitation of “exposure bias”: errors made early could be quickly amplified, harming subsequent sequence generation. To address this issue, we propose two novel methods, which aim at predicting future by improving the agreement between forward and backward decoding sequence. The first one (denoted as MRBA) is achieved by adding divergence regularization terms to model training objective to maximize the agreement between two directional models, namely L2R (which generates targets from left-to-right) and R2L (which generates targets from right-to-left). While the second one (denoted as BDR) operates on decoder-level and exploits the future information during decoding. By introducing regularization term into the training objective of forward-backward decoders, the forward-decoder's hidden states are forced to be close to the backward-decoder's. Thus, the hidden representations of a unidirectional decoder are encouraged to embed some useful information about the future. Moreover, in order to make forward and backward decoding to improve each other in an interactive process, a joint training method is designed. Experimental results on both English and Mandarin dataset show that our proposed methods especially the second one (BDR), lead to a significantly improvement on both robustness and overall naturalness, as achieving obvious preference advantages in a challenging test, and achieving state-of-the-art performance (outperforming baseline “the revised version of Tacotron2” with a gap of 0.13 and 0.12 for English and Mandarin in MOS, respectively) on a general test.
The Perceptual Proxies of Visual Comparison

Perceptual tasks in visualizations often involve comparisons. Of two sets of values depicted in two charts, which set had values that were the highest overall? Which had the widest range? Prior empirical work found that the performance on different visual comparison tasks (e.g., “biggest delta”, “biggest correlation”) varied widely across different combinations of marks and spatial arrangements. In this paper, we expand upon these combinations in an empirical evaluation of two new comparison tasks: the “biggest mean” and “biggest range” between two sets of values. We used a staircase procedure to titrate the difficulty of the data comparison to assess which arrangements produced the most precise comparisons for each task. We find visual comparisons of biggest mean and biggest range are supported by some chart arrangements more than others, and that this pattern is substantially different from the pattern for other tasks. To synthesize these dissonant findings, we argue that we must understand which features of a visualization are actually used by the human visual system to solve a given task. We call these perceptual proxies. For example, when comparing the means of two bar charts, the visual system might use a “Mean length” proxy that isolates the actual lengths of the bars and then constructs a true average across these lengths. Alternatively, it might use a “Hull Area” proxy that perceives an implied hull bounded by the bars of each chart and then compares the areas of these hulls. We propose a series of potential proxies across different tasks, marks, and spatial arrangements. Simple models of these proxies can be empirically evaluated for their explanatory power by matching their performance to human performance across these marks, arrangements, and tasks. We use this process to highlight candidates for perceptual proxies that might scale more broadly to explain performance in visual comparison.
Neural Machine Translation With Sentence-Level Topic Context

Traditional neural machine translation (NMT) methods use the word-level context to predict target language translation while neglecting the sentence-level context, which has been shown to be beneficial for translation prediction in statistical machine translation. This paper represents the sentence-level context as latent topic representations by using a convolution neural network, and designs a topic attention to integrate source sentence-level topic context information into both attention-based and Transformer-based NMT. In particular, our method can improve the performance of NMT by modeling source topics and translations jointly. Experiments on the large-scale LDC Chinese-to-English translation tasks and WMT'14 English-to-German translation tasks show that the proposed approach can achieve significant improvements compared with baseline systems.
Reflections on the Limited Pervasiveness of Augmented Reality in Industrial Sectors

The paper aims to investigate the reasons why Augmented Reality (AR) has not fully broken the industrial market yet, or found a wider application in industries. The main research question the paper tries to answer is: what are the factors (and to what extent) that are limiting AR? Firstly, a reflection on the state of art of AR applications in industries is proposed, to discover the sectors more commonly chosen for deploying the technology so far. Later, based on a survey conducted after that, three AR applications have been tested on manufacturing, automotive, and railway sectors, and the paper pinpoints key aspects that are conditioning its embedding in the daily working life. In order to compare whether the perception of employees from railway, automotive, and manufacturing sectors differs significantly, a one-way analysis of variance (ANOVA) has been used. Later, suggestions are formulated in order to improve these aspects in the industry world. Finally, the paper indicates the main conclusions, highlighting possible future researches to start.
Study of factors influencing the decision to adopt the blockchain technology in real estate transactions in Kosovo

Purpose The purpose of this paper is to explore, from the view of buyers and sellers, the relationship between the blockchain technology and various important aspects of real estate transactions such as transparency, security and cost reduction. Design/methodology/approach The present study uses a quantitative research method. For the purpose of this study, a questionnaire with close-ended questions is used. The questionnaire was distributed to both buyers and sellers alike. The study included 1,000 people using the stratified probability sampling. The study uses factor analysis to analyze the relationship between the blockchain technology and other research variables such as such as transparency, security and cost reduction. Findings The findings of this study indicate that the buyers and sellers perceive that the transparency and cost reduction have the highest influence on the intention to adopt blockchain technology in a real estate transactions system, followed by the security of transactions. Practical implications The study has great implications for the real estate transactions system in Kosovo and society in general. The study shows that the blockchain technology can provide for a transparent and errorless interaction between buyers and sellers of real estate. Since smart contracts eliminate the need for third parties in real estate transactions, the cost of transactions shall be considerably reduced due to elimination of intermediaries and due to process speed and efficiency. Finally, the blockchain technology can increase the trust between parties and shall serve as a means to fight corruption and money laundering in real estate investments. Originality/value The study is the first quantitative study that studies the causal link between the blockchain technology and important aspects of real estate transactions system such as transparency, security and transaction costs.
Temporary Censorship Attacks in the Presence of Rational Miners

Smart contracts allow for exchange of coins according to program rules. While it is well known that so called bribery contracts can influence the incentive mechanism of a Nakamotostyle consensus, we present a more fine-grained bribery attack incentivizing a temporary censorship against a specific account. To this end, we introduce three different bribery contracts on the blockchain where each uniquely manipulates the rewards that a rational miner would receive. Additionally, we formalize the established bribery mechanisms as a Markov game and show for each game the existence of equilibria leading to successful censorships. Finally, we compare the bribery mechanisms with respect to the scalability of the attack costs and the strategic dominance. Our work is motivated by off-chain protocols including payment and state channels which require to publish transactions within a certain amount of time. In such off-chain protocols a temporary censorship attack can result into significant financial damage.
An energy flow simulation tool for incorporating short-term PV forecasting in a diesel-PV-battery off-grid power supply system

One of the primary technical challenges of integrating high levels of PV generation into standalone off-grid power supply systems is their variable power output characteristics. In dealing with this issue, the integration of reliable PV forecasting techniques and preferably energy storage, are highly effective. Applying a short-term PV forecasting method, together with a compensatory controllable resource, can help in the management of system operation. This study incorporates the development of an energy flow modelling tool that has been used to analyse the benefits of 1-min ahead PV forecasting and battery storage for different system configurations. Based on the five days of 1-min ahead forecasting results analysed, it is found that PV forecasting enables the prosumer to install more than double the PV capacity, compared to the allowed installed PV capacity when no forecasting is employed. This additional PV capacity saves around 24–25% (on average) of diesel fuel per day for the diesel-PV-battery configuration. The outcomes evidently indicate that incorporating 1-min ahead PV forecasting enables a significant increase of PV hosting capacity of the system, without compromising the reliability of the system.
FabZK: Supporting Privacy-Preserving, Auditable Smart Contracts in Hyperledger Fabric

On a Blockchain network, transaction data are exposed to all participants. To preserve privacy and confidentiality in transactions, while still maintaining data immutability, we design and implement FabZK. FabZK conceals transaction details on a shared ledger by storing only encrypted data from each transaction (e.g., payment amount), and by anonymizing the transactional relationship (e.g., payer and payee) between members in a Blockchain network. It achieves both privacy and auditability by supporting verifiable Pedersen commitments and constructing zero-knowledge proofs. FabZK is implemented as an extension to the open source Hyperledger Fabric. It provides APIs to easily enable data privacy in both client code and chaincode. It also supports on-demand, automated auditing based on encrypted data. Our evaluation shows that FabZK offers strong privacy-preserving capabilities, while delivering reasonable performance for the applications developed based on its framework.
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection

We study two problems in high-dimensional robust statistics: \emph{robust mean estimation} and \emph{outlier detection}. In robust mean estimation the goal is to estimate the mean $\mu$ of a distribution on $\mathbb{R}^d$ given $n$ independent samples, an $\epsilon$-fraction of which have been corrupted by a malicious adversary. In outlier detection the goal is to assign an \emph{outlier score} to each element of a data set such that elements more likely to be outliers are assigned higher scores. Our algorithms for both problems are based on a new outlier scoring method we call QUE-scoring based on \emph{quantum entropy regularization}. For robust mean estimation, this yields the first algorithm with optimal error rates and nearly-linear running time $\tilde{O}(nd)$ in all parameters, improving on the previous fastest running time $\tilde{O}(\min(nd/\e^6, nd^2))$. For outlier detection, we evaluate the performance of QUE-scoring via extensive experiments on synthetic and real data, and demonstrate that it often performs better than previously proposed algorithms.