Reviewerly/exHarmony huggingface dataset:
Smart Contract Protocol for Authenticity and Compliance with Anonymity on Hyperledger Fabric

We consider a new class of business-to-business (B2B) blockchain applications that require the execution of specific subroutines to simultaneously satisfy authenticity, compliance, and anonymity. Existing blockchain smart contract protocols do not, either directly or with minor modifications, ensure all the three properties. We present the ACAn smart contract protocol guaranteeing authenticity and compliance over a set of anonymous (unlinkable) subroutine executions. ACAn achieves this through a novel combination of zero-knowledge proofs and multiple Merkle-Tree commitments. We specifically focus on implementing ACAn on Hyperledger Fabric, a popular platform for B2B blockchain applications, which processes transactions in the execute-order-commit framework. The latter, however, leads to performance degradation due to read-write conflicts arising out of multiple clients independently executing the ACAn protocol. We propose enhancements to Hyperledger Fabric's smart contract API to support deferred changes to the shared ledger, allowing us to adapt ACAn so that such conflicts are effectively resolved. Our work provides evidence of significant performance gains due to the proposed enhancements, as well as experimental evaluation of the protocol's privacy preserving components.
QDRelu: An Activation Function Based On Quantum-Dot Spin-VCSELs

Activation function has become an indispensable part of artificial neural networks (ANNs). Nowadays, the realization of activation function mainly depends on electronic instrument, which need a lot of computing resources and power consumption. In this paper, we research quantum dot spin-polarized vertical-cavity surface-emitting laser and investigate the relationship between the total output intensity and normalized pump intensity through numerical simulation. Then QDRelu function based on the relationship is presented to realize the behavior of Relu function. At the same time, CIFAR10-based and MNIST-based recognition tasks are carried out to verify the feasibility of QDRelu function with respect to traditional Relu function, these tasks are implemented in several typical artificial neural network models. The results indicate that QDRelu function not only takes advantage of the merits of photonic technology such as high speed, low power consumption and high parallelism but also ensures accuracy of whole artificial neural network.
Road elevation map estimation based on affine transformation and stereo matching

Abstract This paper presents a road elevation map estimation method based on affine transformation. A geometric model of reference plane and stereo rig is proposed. The model showed that the height of 3-D points relative to the reference plane can be described by the proposed disparity relative to the reference plane. The reference plane is implicitly established by performing an affine transformation on target image. A local based stereo matching is applied to estimate subpixel disparity map. The elevation map can be easily estimated from subpixel disparity map using proposed elevation computation model. The experimental results on real-word road scenes show that the proposed method can provide accurate road elevation estimation.
Top Personalized Reviews Set Selection Based on Subject Aspect Modeling

People are checking different sites before doing their business to either purchase any item online or select any service or product. Many commercial sites rely on the reviews to evaluate their product and services. Other sites are especially designed for the users and reviews to e valuate any product or service. However, select the best review is still a big challenge for the user to select. Many works have been proposed to select the best reviews but with contain redundant information. The best personalized review that is really related to the main topic that the user is searching on is very important. For this reason, in this work, a new personalized reviews' selection is proposed. We based on the idea of that different point of view for the user causes different evaluation and revering. For this reason, searching on the best reviews in a specific subject gives more accurate and significant selection results. In this paper, design a new approach for the best personalized reviews' selection that is based on two stages. The first one in the predict the subject aspect modeling (distribution) based on using the A latent Dirichlet allocation (LDA) model. Second, we design a new weighted personalized reviews selection based subject aspect scoring function to select the top personalized reviews. The experimental results show our method selects reviews that are more focusing on the product or service subject aspect. The reviews that are more emphasizing on a different subject are selected.
FabricUnit: A Framework for Faster Execution of Unit Tests on Hyperledger Fabric

Enterprises and Governments, alike, are leveraging distributed ledger technologies to solve traditional problems across domains. They consider private blockchains such as Hyperledger Fabric as a safe bet for the obvious security and privacy reasons. However, the tools for software reliability are not yet matured. In this work, we propose FabricUnit, a unit testing framework for Hyperledger Fabric clients. FabricUnit identifies the safe methods that do not alter the state and re-uses the setup execution (deleting any stale data and reinitializes the data). Our experiment shows a reduction of approximately 30% in the tests execution time.
A Brief History of Piano Mechanics

The piano action mechanism transforms key motion into hammer swing, which creates sound by striking the strings. The mechanical design of the key action influences keyboard responsiveness, heaviness or lightness, which are critical playability parameters that can “make or break” an instrument for a pianist. From the rigid lever mechanism of 14th-century clavichords, key action mechanisms developed into the modern piano action, with dozens of bodies and compliant elements. This paper presents a brief history of this evolution, with an analysis of the most important designs from a technical perspective.
Private Fraud Investigations

This chapter suggests that investigative journalists in the media and fraud examiners at global auditing firms and local law firms seem to fill some of the gap between public government and private governance when it comes to detection of white-collar offenders (Button and Gee 2013; Schneider 2006; Williams 2005, 2008, 2014). Press reporters’ detection of misconduct and crime “represented an important ingredient of the nineteenth-century newspaper” (Taylor 2018: 346), and this is certainly also the case so far in the twenty-first century media.
Multi-label Feature Selection based on Label-specific Features

Multi-label learning algorithm handles cases in which each sample is related with several labels synchronously. As is known to all, each label might possess its own peculiarities, such as LIFT algorithm, i.e. multi-label learning with Label-specific Features. It constructs feature by performing cluster techniques based on negative and positive training samples of each label. However, the main drawback of this kind of algorithm is the large amounts of irrelevant features or redundant features in its feature space. To solve this problem, this paper puts forward an effective algorithm named LEFS, i.e. multi-label Feature Selection based on Label-specific features with fuzzy Entropy. The approaches proposed are examined on the two realistic multi-label benchmark data sets, which are compared with several multi-label learning approaches. A few features are selected from original features to fed classifier, but they remain the same or even slightly improve accuracy from 91.82% to 92.49% on data set- Medical. Results of another data sets are similar to that of the Medical. Experiment results show that these approaches can not only decrease the dimension of the construct features, but also gain an effective classification performance compared with three well-established multi-label learning approaches.
Comparative Pedagogical Analysis of Teacher Education Programs in France And The USA

This article presents the results of comparative pedagogical studies that aim at determining the general and specific features of teacher education programs in France and the US. The study is based on binary comparisons and classical methods of comparative pedagogy, including comparative, descriptive and inductive-deductive. In the course of the study, the authors of the article determined conditions for efficient teacher training that are common to the French and US systems of higher education; established the key factor causing the specific functioning of education systems in France and the US; indicated the priority directions of designing teacher education programs in France and the US. The authors have interpreted the study results with due regard to the competency-based educational paradigm and determined trends that condition the general laws of designing educational programs.
A Recommender System for Cold-start Items: A Case Study in the Real Estate Industry

The recommender systems provide users with what they prefer and filter unnecessary information. In the fierce marketing environment, it is crucial to recommend items to users in an early stage to keep user's interests and loyalty. With the fast product renewal, classical recommendation methods such as collaborative filtering cannot handle the cold-start item problem. In many real-world applications, content information of items or users is available and can be used to assist recommendation. Besides, user may interact with the items in different behaviors such as view, click or subscribe. How to use the complex content information and multiple user behaviors are real problems that are not well solved in applications. In this paper, we propose a content-based recommender system to deal with the practical problem. Boosting tree model also added to the system to avoid potential Spam. We applied our developed method to real estate application to recommend new property which just landed into the market to users. Experimental results with three data subsets and three recommendation scenarios demonstrate that the proposed method can outperform the baseline on recommendation accuracy. The results indicate that our method can effectively reduce potential Spam to users, so that user experience will be improved.
Hardening Permissioned Blockchains with Verifiable Randomness

Permissioned blockchains are networks with identifiable participants. A prominent example is Hyperledger Fabric, that introduces the execute-order-validate architecture. The execution phase enables defining application level trust assumption by setting an endorsement policy, namely a set of rules that define the peers (affiliated with organizations) who provide attestation of transaction correctness. Since the organizations hosting those peers might collude or be compromised, the number of organizations required by the endorsement policy is often increased to reduce susceptibility to attacks. However, doing so impairs the performance of the system and is impractical in networks comprising of a large number of organizations. In this paper we propose an alternative endorsement protocol that hardens the system's security without hindering performance. We provide a quantitative analysis of the suggested technique, and show that by incorporating verified randomness in endorsement policies, attacks that are based on collusion or malicious peers are effectively mitigated with overwhelming probability.
GIMO: A multi-objective anytime rule mining system to ease iterative feedback from domain experts

Data extracted from software repositories is used intensively in Software Engineering research, for example, to predict defects in source code. In our research in this area, with data from open source projects as well as an industrial partner, we noticed several shortcomings of conventional data mining approaches for classification problems: (1) Domain experts’ acceptance is of critical importance, and domain experts can provide valuable input, but it is hard to use this feedback. (2) Evaluating the quality of the model is not a matter of calculating AUC or accuracy. Instead, there are multiple objectives of varying importance with hard to quantify trade-offs. Furthermore, the performance of the model cannot be evaluated on a per-instance level in our case, because it shares aspects with the set cover problem. To overcome these problems, we take a holistic approach and develop a rule mining system that simplifies iterative feedback from domain experts and can incorporate the domain-specific evaluation needs. A central part of the system is a novel multi-objective anytime rule mining algorithm. The algorithm is based on the GRASP-PR meta-heuristic but extends it with ideas from several other approaches. We successfully applied the system in the industrial context. In the current article, we focus on the description of the algorithm and the concepts of the system. We make an implementation of the system available.
End-to-End Multi-task Learning for Allusion Detection in Ancient Chinese Poems

Much efforts have been devoted to research about ancient Chinese poems. However, tasks around allusions, a fundamental element of ancient Chinese poetry, has received little attention. To mitigate this gap, we introduce three allusion tasks: allusion entity recognition (AER), allusion source identification (ASI), and allusion entity classification (AEC). For each task, we create a large corpus extracted from allusion dictionary. We explore the performance of two learning strategies: single-task model and allusion hierarchical multi-task learning (AHMTL) model. Compared with the single-task model, experimental results show that the AHMTL model improves each task's overall performance by formulating relationship between tasks. In addition, poem readability, a downstream task of allusion tasks, is combined to gain improvement in the F1-score by 1.4%.
Privacy-Preserving Claims Exchange Networks for Virtual Asset Service Providers

In order for VASPs to fulfill the regulatory requirements from the FATF and the Travel Rule, VASPs need truthful information regarding subjects, such as originators, beneficiaries and other VASPs involved in a virtual asset transfer. However, given that data about subjects are siloed in various organizations and institutions, there needs to be a practical way for VASPs to obtain information from these entities without direct access to the siloed data. In this paper we describe the Open Algorithms approach as a means for data holders to make insights about subjects available to Claims Providers based on vetted algorithms. A Claims Provider delivers signed claims to VASPs regarding the relevant subject, thereby relieving the VASP from having to deal with data, algorithms and analytics. We also propose a consortium arrangement for VASPs to establish a Claims Exchange Network, in which VASPs can deliver signed claims (obtained from their Claims Providers) and public-key information or certificates to other VASPs in a secure and confidential manner.
A Data Anonymization Method to Mitigate Identity Attack in Transactional Database Publishing

Publishing transactional database becomes more recognized for many institutions such as retails and groceries. Many of them share or publish their data to other institutions as an effort to gain more revenue for their business. However, publishing such a database is problematic since irresponsible parties may associate records in database with specific individuals to disclose personal identity known as identity attack. Data anonymization is an effective technique to protect database from the threat. Unfortunately, applying data anonymization method in transaction database using generalization and suppression based techniques may reduce data utility significantly and cause severe distortion to database properties. A solution to mitigate such drawbacks has been proposed by replacing item with another item instead of applying those techniques. However, selecting an item to replace another item causes other problems specifically when the selected item for the replacement process is not the optimum one. Therefore, in this paper we propose a data anonymization method which performs item replacement that utilizes weighted scoring method to select an optimal item with respect to minimize information loss and maintaining database properties. Experimental results show that the proposed method guarantee higher privacy protection compared with an existing method and it successfully generates an anonymized database while at the same time it maintains data utility by minimizing information loss more than 50% compared with that of an existing method. In addition, the data property of the anonymized database can be well maintained.
Motion-Attentive Network for Detecting Abnormal Situationsin Surveillance Video

Recently, numerous studies have utilized deep-learning-based approaches to detect anomalies in surveillance cameras. However, while several of these studies used motion features to detect abnormal situations, detection problems can arise due to the sparse information and irregular patterns in certain abnormal situations. We propose a means of preserving motion patterns in abnormal situations through a network called MA-Net, which solves representation problems caused by a loss of sparse information and irregular patterns. We show through experiments that the proposed method is superior to state-of-the-art methods.
Robust Distortion Estimation of Fisheye Cameras under Stereographic Projection Model

The imaging geometry of a real imaging system always deviates from the ideal projection models. This deviation is treated as distortion, which is always estimated along with other parameters when calibrating a camera. Although the perspective projection model is chosen in most existing distortion estimation methods, it can't be used to describe a fish-eye imaging system. In this paper, we proposed a method to estimate the distortion of the fish-eye cameras in which the stereographic projection model is being used. Images of spheres and sets of parallel lines are utilized to estimate the distortion parameters. The method is validated on synthetic, simulated, and real images. Experimental results show that our method is robust to noise, and a subpixel accuracy is achieved. By applying estimated parameters, undistorted fisheye images can be treated as a stereographic projection of the world, which will be highly convenient for computer vision tasks.
FreshGraph: A Spam-Aware Recommender System for Cold Start Problem

Recommender systems provide personalized recommendation to help users levitating from information overload. Collaborative filtering based recommendation methods are playing a dominant role in the industry because of its versatility and simplicity. However, its performance suffers from sparse data, and being less effective in cold-start problem settings. In real world scenario, when users are recommended with items, it is very easy to overwhelm the target users with impersonalized information, which drives away valuable audience. In this paper, we propose a two-steps spam aware recommendation framework to effectively recommend new items to target users. By utilizing heterogeneous information graph structure, we first use item-user Meta-Path similarity measure for user candidate selection. Then we use entropy encoding measurement to identify false positive from candidate list to prevent possible spam from happening. The proposed method leverages the semantic information that persists inside the graph structure, which not only considers item content features, but also take user activeness into account for more effective audience targeting. The proposed method produces an explainable top-K user list for the new item, while K is a trailed number to each given item individually. Meanwhile, the proposed method is also adaptive to data change overtime, while capable of processing requests in a real-time fashion.
A Hybrid Model with Pre-trained Entity-Aware Transformer for Relation Extraction

Distantly supervised relation extraction is an efficient method to extract novel relational facts from unstructed text. Most previous neural methods adopt Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) to encode sentences. However, CNN is difficult to learn long-range dependencies and the parallelization of training RNN is precluded by its sequential nature. In this paper, we propose a novel hybrid model that combines Piece-wise Convolutional Neural Network (PCNN) and Entity-Aware Transformer to extract local features and learn the dependencies between distant positions jointly. The entity-aware Transformer is able to take semantic and syntax information under consideration and acquire entity-specific representations. The inner-sentence attention mechanism is then used over Transformer to alleviate the noise caused by irrelevant words. We concatenate outputs of PCNN and Transformer with word embeddings of entity mentions and then send them to the classifier, which can boost the performance of our model further. A transfer learning based strategy is applied, where the entity-aware Transformer is initialized with a priori knowledge learned from the related task of entity typing to improve the robustness of our model. The experimental results on a large-scale benchmark dataset show that our hybrid model with the pre-training strategy gets AUC score of 0.432 and outperforms the state-of-the-art baselines.
Improving Low-Resource Chinese Event Detection with Multi-task Learning

Chinese Event Detection (CED) aims to detect events from unstructured sentences. Due to the difficulty of labeling event detection datasets, previous approaches suffer from severe data sparsity problem. To address this issue, we propose a novel Lattice LSTM based multi-task learning model. On one hand, we utilize multi-granularity word information via Lattice LSTM to fully exploit existing datasets. On the other hand, we employ the multi-task learning mechanism to improve CED with datasets from other tasks. Specifically, we combine Name Entity Recognition (NER) and Mask Word Prediction (MWP) as two auxiliary tasks to learn both entity and general language information. Experiments show that our approach outperforms the six SOTA methods by 1.9% on ACE2005 benchmark. The source code is released on https://github.com/tongmeihan1995/MLL-chinese-event-detection .
A Perceptually Optimized Foveation Wavelet Visible Difference Predictor Quality Metric Based on Psychovisual Properties of the Human Visual System (HVS)

Region of interest (ROI) image and video compression techniques have been widely used in visual communication applications in an effort to deliver good quality images and videos at limited bandwidths. Foveated imaging exploits the fact that the spatial resolution of the human visual system (HVS) is highest around the point of fixation (foveation point) and decreases dramatically with increasing eccentricity. Exploiting this fact, the authors have developed an appropriate metric for the assessment of ROI coded images, adapted to foveation image coding based on psycho-visual quality optimization tools, which objectively enable us to assess the visual quality measurement with respect to the region of interest (ROI) of the human observer. The proposed metric yields a quality factor called foveation probability score (FPS) that correlates well with visual error perception and demonstrating very good perceptual quality evaluation.