Reviewerly/exHarmony huggingface dataset:
Is Facebook’s Libra Project Already a Miscarriage?

Facebook’s claim of Libra blockchain as a decentralized peer-to-peer electronic cash system is a blunt lie; if Facebook overcomes the massive regulatory hurdle and receives appropriate approvals, Libra will initially start off as a decentralized permissioned blockchain with a trusted-third party. Unlike Bitcoin’s purely peer-to-peer decentralized permissionless blockchain without a trusted third party, all transactions on Libra blockchain will be governed by the Libra Association as a de facto central authority comprising 28 founding-members most of which are for-profit heavyweight firms from the United States such as Visa, MasterCard, PayPal, Facebook Calibra, and eBay. Facebook’s Libra coin is a recent invention, but with apparent signs of a miscarriage. Facebook’s already troubled past for violation of privacy and exploitation of users’ data (i.e. data disclosure scandals Cambridge Analytica) has intensified the opposition among authorities and skepticism among industry participants. In the midst of Facebook’s planned launch date of first half of 2020, Libra is still running impetuous into increasing opposition from all sides; central banks, regulators, law makers, and tax agencies. As with any new paradigm-shifting technology (i.e. blockchain), Libra will cause a serious disruption in the short-term to the existing ecosystem of more than 2,400 digital coins that has taken a decade to form; however in the long-run, Libra as a stable global crypto-currency promises to revolutionize electronic payment systems and money transfers by enhancing financial inclusion and global stability as a public good. To launch Libra cryptocurrency, Facebook should not attempt to satisfy all of the concerns or issues brought by different branches of governments because this is both impractical and implausible to accomplish. A decade has passed since Bitcoin’s debut in January 2009, and to this date still many countries throughout the world do not have any regulation dealing with cryptocurrencies. If Facebook Libra does not sputter out, it will spur central banks to launch their own cryptocurrency projects.
Adversarial Machine Learning -- Industry Perspectives

Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems. We leverage the insights from the interviews and we enumerate the gaps in perspective in securing machine learning systems when viewed in the context of traditional software security development. We write this paper from the perspective of two personas: developers/ML engineers and security incident responders who are tasked with securing ML systems as they are designed, developed and deployed ML systems. The goal of this paper is to engage researchers to revise and amend the Security Development Lifecycle for industrial-grade software in the adversarial ML era.
Gradual Channel Pruning While Training Using Feature Relevance Scores for Convolutional Neural Networks

The enormous inference cost of deep neural networks can be mitigated by network compression.Pruning connections is one of the predominant approaches used for network compression.However, existing pruning techniques suffer from one or more of the following limitations: 1) They increase the time and energy consumed by the compute-heavy training stage due to the addition of the pruning and fine-tuning steps, 2) They prune layer-wise based on local information about a particular layer's statistics, ignoring the effect of error propagation through the network, 3) They lack an efficient means to determine the global importance of channels, 4) Due to the use of unstructured pruning, they may not lead to any energy advantage when implemented on mainstream platforms (GPUs and TPUs), requiring specialized hardware to reap the benefits.To address the above issues, we present a simple-yet-effective methodology for gradual channel pruning while training using a data-driven metric referred to as feature relevance score.The proposed technique eliminates the need for additional retraining by pruning the least important channels in a structured manner at fixed intervals during the regular training phase.Pruning is guided by feature relevance scores, which help in efficiently evaluating the contribution of each channel towards the discriminative power of the network.We demonstrate the effectiveness of the proposed methodology on architectures such as VGG and ResNet using datasets such as CIFAR-10, CIFAR-100, and ImageNet, and successfully achieve significant model compression while trading off less than 1% accuracy INDEX TERMS Convolutional neural networks (CNNs), deep learning, efficient deep learning, neural networks, model architecture, model compression, network design, relevance scores, structured pruning.
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing

We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world Additive Manufacturing (AM) testbed. The dataset contains infrared (IR) images collected under both normal conditions and synthetic anomalies. We show that our encoder-decoder model is able to identify the injected anomalies in a modern AM manufacturing process in an unsupervised fashion. In addition, our approach also gives hints about the temperature non-uniformity of the testbed during manufacturing, which was not previously known prior to the experiment.
Privacy and Cryptocurrencies—A Systematic Literature Review

Our transaction history in the current centralized banking system has the ability to reveal a lot of private information for each spender, both to the banking system itself, but also to those entities that surround it (e.g., governments, industry etc).Examples of leaking information constitute the amounts spent, the goods on which the amounts were spent, the spending locations and the users we exchange money with.This knowledge is powerful in the hands of those who have it, and can be used in multiple ways, not always to our benefit.Cryptocurrencies, such as the famous Bitcoin, were proposed as a means to address the limitations of centralized banking systems and to offer its users privacy with regards to their transactional data.In this work, we perform a systematic literature review on the realm of privacy for electronic currencies.We present the development of digital money from electronic cash to cryptocurrencies and focus on the techniques that are employed to enhance user-privacy.Furthermore, we present flaws of the current cryptocurrency systems, which reduce the privacy of the cryptocurrency users.Finally, we describe three research directions to enhance privacy for cryptocurrencies: transaction propagation mechanisms, succinct ZK proof systems without a trusted setup, and specialised trustless zero-knowledge proofs.
K-Core Based Temporal Graph Convolutional Network for Dynamic Graphs

Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information. However, many existing methods focus on static graphs while ignoring evolving graph patterns. Inspired by the success of graph convolutional networks(GCNs) in static graph embedding, we propose a novel k-core based temporal graph convolutional network, the CTGCN, to learn node representations for dynamic graphs. In contrast to previous dynamic graph embedding methods, CTGCN can preserve both local connective proximity and global structural similarity while simultaneously capturing graph dynamics. In the proposed framework, the traditional graph convolution is generalized into two phases, feature transformation and feature aggregation, which gives the CTGCN more flexibility and enables the CTGCN to learn connective and structural information under the same framework. Experimental results on 7 real-world graphs demonstrate that the CTGCN outperforms existing state-of-the-art graph embedding methods in several tasks, including link prediction and structural role classification. The source code of this work can be obtained from \url{https://github.com/jhljx/CTGCN}.
A semantic and social‐based collaborative recommendation of friends in social networks

Summary The development of social media technologies has greatly enhanced social interactions. The proliferation of social platforms has generated massive amounts of data and a considerable number of persons join these platforms every day. Therefore, one of the current issues is to facilitate the search for the most appropriate friends for a given user. We focus in this article on the recommendation of users in social networks. We propose a novel approach which combines a user‐based collaborative filtering (CF) algorithm with semantic and social recommendations. The semantic dimension suggests the close friends based on the calculation of the similarity between the active user and his friends. The social dimension is based on some social‐behavior metrics such as friendship and credibility degree. The novelty of our approach concerns the modeling of the credibility of the user, through his/her trust and commitment in the social network. A social recommender system based on this approach is developed and experiments have been conducted using the Yelp social network. The evaluation results demonstrated that the proposed hybrid approach improves the accuracy of the recommendation compared with the user‐based CF algorithm and solves the sparsity and cold start problems.
Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts

Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.
A comprehensive study on disease risk predictions in machine learning

Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. Comprehensive survey on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavours have been shifted.
Alleviating New User Cold-Start in User-Based Collaborative Filtering via Bipartite Network

The recommender system (RS) can help us extract valuable data from a huge amount of raw information. User-based collaborative filtering (UBCF) is widely employed in practical RSs due to its outstanding performance. However, the traditional UBCF is subject to the new user cold-start issue because a new user is often extreme lack of available rating information. In this article, we develop a novel approach that incorporates a bipartite network into UBCF for enhancing the recommendation quality of new users. First, through the statistic and analysis of new users' rating characteristics, we collect niche items and map the corresponding rating matrix to a weighted bipartite network. Furthermore, a new weighted bipartite modularity index merging normalized rating information is present to conduct the community partition that realizes coclustering of users and items. Finally, for each individual clustering that is much smaller than the original rating matrix, a localized low-rank matrix factorization is executed to predict rating scores for unrated items. Items with the highest predicted rating scores are recommended to a new user. Experimental results from two real-world data sets suggest that without requiring additional complex information, the proposed approach is superior in terms of both recommendation accuracy and diversity and can alleviate the new user cold-start issue of UBCF effectively.
Quantum Optical Metrology of Correlated Phase and Loss

Optical absorption measurements characterize a wide variety of systems from atomic gases to in vivo diagnostics of living organisms. Here we study the potential of nonclassical techniques to reduce statistical noise below the shot-noise limit in absorption measurements with concomitant phase shifts imparted by a sample. We consider both cases where there is a known relationship between absorption and a phase shift, and where this relationship is unknown. For each case we derive the fundamental limit and provide a practical strategy to reduce statistical noise. Furthermore, we find an intuitive correspondence between measurements of absorption and of lossy phase shifts, which both show the same analytical form for precision enhancement for bright states. Our results demonstrate that nonclassical techniques can aid real-world tasks with present-day laboratory techniques.
Virtual Reality Laboratories: A Way Forward for Schools?

In recent years, the applications of virtual reality (VR) in learning environments has received considerable attention. This attention occurs as a part of a wider trend seen since the early millennium. This trend is that of increasing attention being placed on modes of instruction that can supply greater realism and immersion in the science classroom. VR is used in this study as a digital learning environment support tool. VR is defined as the use of three-dimensional graphic systems in combination with various interfaces to provide the effect of immersion and interaction in computer generated environments. The purpose of this study was to investigate the barriers to content learning and immersion of a VR laboratory designed to replicated hands-on laboratory for a large university system. The primary means for data collection was the use of a combination multiple choice and open-ended survey response in conjunction with interviews. Twelve faculty and 285 students took part in a pilot program testing a VR based laboratory system as a part of an undergraduate life sciences class. Overall, the results suggest the VR system as it is currently implemented is not yet ready for large-scale implementation due to barriers related to immersion and interface in the classroom. This study also provides design recommendations that may assist in the further development for VR use in the classroom in future iterations.
Fusion-Extraction Network for Multimodal Sentiment Analysis

Multiple modality data bring new challenges for sentiment analysis, as combining varieties of information in an effective manner is a rigorous task. Previous works do not effectively utilize the relationship and influence between texts and images. This paper proposes a fusion-extraction network model for multimodal sentiment analysis. First, our model uses an interactive information fusion mechanism to interactively learn the visual-specific textual representations and the textual-specific visual representations. Then, we propose an information extraction mechanism to extract valid information and filter redundant parts for the specific textual and visual representations. The experimental results on two public multimodal sentiment datasets show that our model outperforms existing state-of-the-art methods.
Towards Process Improvement in DevOps

In recent years, the software release cost has been reduced dramatically due to the alteration from traditional shrink-wrapped software to software as a service. Organizations that can deliver their services continuously and with a high frequency have a higher ability to compete in the market. As a response to this, a substantial number of software companies acquired DevOps to establish a culture of effective communication and collaboration between development and operation teams and in order to enhance the production release frequency as well as to maintain the product quality. However, the DevOps environment requires a platform that aid in evaluating the performance of existing processes and provide improvement recommendations. On top of that, organizations can only achieve the perceived benefits of DevOps if their processes are mature and continuously measured. The objective of this research is to investigate the process improvement contributions made by researchers in the DevOps field. For this purpose, we performed a systematic literature review that resulted in several maturity models and best practices. Our ultimate aim is to develop a DevOps maturity model that can appraise and improve the processes in the DevOps environment.
VD-BERT: A Unified Vision and Dialog Transformer with BERT

Visual dialog is a challenging vision-language task, where a dialog agent needs to answer a series of questions through reasoning on the image content and dialog history. Prior work has mostly focused on various attention mechanisms to model such intricate interactions. By contrast, in this work, we propose VD-BERT, a simple yet effective framework of unified vision-dialog Transformer that leverages the pretrained BERT language models for Visual Dialog tasks. The model is unified in that (1) it captures all the interactions between the image and the multi-turn dialog using a single-stream Transformer encoder, and (2) it supports both answer ranking and answer generation seamlessly through the same architecture. More crucially, we adapt BERT for the effective fusion of vision and dialog contents via visually grounded training. Without the need of pretraining on external vision-language data, our model yields new state of the art, achieving the top position in both single-model and ensemble settings (74.54 and 75.35 NDCG scores) on the visual dialog leaderboard. Our code and pretrained models are released at https://github.com/salesforce/VD-BERT.
Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension

Although Vietnamese is the 17th most popular native-speaker language in the world, there are not many research studies on Vietnamese machine reading comprehension (MRC), the task of understanding a text and answering questions about it. One of the reasons is because of the lack of high-quality benchmark datasets for this task. In this work, we construct a dataset which consists of 2,783 pairs of multiple-choice questions and answers based on 417 Vietnamese texts which are commonly used for teaching reading comprehension for elementary school pupils. In addition, we propose a lexical-based MRC method that utilizes semantic similarity measures and external knowledge sources to analyze questions and extract answers from the given text. We compare the performance of the proposed model with several baseline lexical-based and neural network-based models. Our proposed method achieves 61.81% by accuracy, which is 5.51% higher than the best baseline model. We also measure human performance on our dataset and find that there is a big gap between machine-model and human performances. This indicates that significant progress can be made on this task. The dataset is freely available on our website for research purposes.
Non-Markovian sensing of a quantum reservoir

Quantum sensing explores protocols using the quantum resource of sensors to achieve highly sensitive measurement of physical quantities. The conventional schemes generally use unitary dynamics to encode quantities into sensor states. In order to measure the spectral density of a quantum reservoir, which plays a vital role in controlling the reservoir-caused decoherence to microscopic systems, we propose a nonunitary-encoding optical sensing scheme. Although the nonunitary dynamics for encoding in turn degrades the quantum resource, we surprisingly find a mechanism to make the encoding time a resource to improve the precision and to make the squeezing of the sensor a resource to surpass the shot-noise limit. Our result shows that it is due to the formation of a sensor-reservoir bound state. Enriching the family of quantum sensing, our scheme gives an efficient way to measure the quantum reservoir and might supply an insightful support to decoherence control.
Fully Automatic Landmarking of Syndromic 3D Facial Surface Scans Using 2D Images

3D facial landmarks are known to be diagnostically relevant biometrics for many genetic syndromes. The objective of this study was to extend a state-of-the-art image-based 2D facial landmarking algorithm for the challenging task of 3D landmark identification on subjects with genetic syndromes, who often have moderate to severe facial dysmorphia. The automatic 3D facial landmarking algorithm presented here uses 2D image-based facial detection and landmarking models to identify 12 landmarks on 3D facial surface scans. The landmarking algorithm was evaluated using a test set of 444 facial scans with ground truth landmarks identified by two different human observers. Three hundred and sixty nine of the subjects in the test set had a genetic syndrome that is associated with facial dysmorphology. For comparison purposes, the manual landmarks were also used to initialize a non-linear surface-based registration of a non-syndromic atlas to each subject scan. Compared to the average intra- and inter-observer landmark distances of 1.1 mm and 1.5 mm respectively, the average distance between the manual landmark positions and those produced by the automatic image-based landmarking algorithm was 2.5 mm. The average error of the registration-based approach was 3.1 mm. Comparing the distributions of Procrustes distances from the mean for each landmarking approach showed that the surface registration algorithm produces a systemic bias towards the atlas shape. In summary, the image-based automatic landmarking approach performed well on this challenging test set, outperforming a semi-automatic surface registration approach, and producing landmark errors that are comparable to state-of-the-art 3D geometry-based facial landmarking algorithms evaluated on non-syndromic subjects.