Reviewerly/exHarmony huggingface dataset:
The Effect of Social Interaction on Facilitating Audience Participation in a Live Music Performance

Facilitating audience participation in a music performance brings with it challenges in involving non-expert users in large-scale collaboration. A musical piece needs to be created live, over a short period of time, with limited communication channels. To address this challenge, we propose to incorporate social interaction through mobile music instruments that the audience is given to play with, and examine how this feature sustains and affects the audience involvement. We test this idea with an audience participation music system, Crowd in C. We realized a participation-based musical performance with the system and validated our approach by analyzing the interaction traces of the audience at a performance. The result indicates that the audience members were actively engaged throughout the performance, with multiple layers of social interaction available in the system. We also present how the social interactivity among the audience shaped their interaction in the music making process.
PinText: A Multitask Text Embedding System in Pinterest

Text embedding is a fundamental component for extracting text features in production-level data mining and machine learning systems given textual information is the most ubiqutious signals. However, practitioners often face the tradeoff between effectiveness of underlying embedding algorithms and cost of training and maintaining various embedding results in large-scale applications. In this paper, we propose a multitask text embedding solution called PinText for three major vertical surfaces including homefeed, related pins, and search in Pinterest, which consolidates existing text embedding algorithms into a single solution and produces state-of-the-art performance. Specifically, we learn word level semantic vectors by enforcing that the similarity between positive engagement pairs is larger than the similarity between a randomly sampled background pairs. Based on the learned semantic vectors, we derive embedding vector of a user, a pin, or a search query by simply averaging its word level vectors. In this common compact vector space, we are able to do unified nearest neighbor search with hashing by Hadoop jobs or dockerized images on Kubernetes cluster. Both offline evaluation and online experiments show effectiveness of this PinText system and save storage cost of multiple open-sourced embeddings significantly.
A Cancellable Face Template Scheme Based on Nonlinear Multi-Dimension Spectral Hashing

The exposure of face templates potentially leads to severe security and privacy risks. For example, the attacker can utilize the compromised face template to masquerade the template owner. In addition, these concerns are aggravated since face is irreplaceable and irrevocable. In this paper, we propose a cancelable transform, namely nonlinear multi-dimension spectral hashing (NMDSH) to protect face template. Essentially, NMDSH utilizes a many-to-one function to transform real-valued deep face feature vector into binary code. The transformed template thus possesses strong non-invertible property. Next, a highly nonlinear softmod function is further adapted into the scheme to provide an additional layer of protection against similarity-based attack. The accuracy performance of NMDSH is evaluated. Experiment results suggest that NMDSH can preserve the accuracy performance largely. Properties including non-invertibility, revocability and resistance to similarity-based attack are also evaluated.
ACT: An Annotation Platform for Citation Typing at Scale

In this paper we introduce the Academic Citation Typing (ACT) Platform, a highly scalable online tool that takes as its input any full text research paper and which then enables rapid annotation and classification of in-text citations according to purpose and in uence. In contrast to previous work, we employ rst authors as annotators. Our evaluation shows that these authors are able to quickly classify the citations within their own papers. Over 200 authors have thus far annotated their papers using the ACT platform. This approach has already enabled the collection of the largest dataset of citations annotated according to their purposes and in uence on the citing paper. Furthermore, this process is ongoing and the dataset will continue to expand following this initial phase.
Comparison of Objective Video Quality Assessment Methods on Videos With Different Spatial Resolutions

A significant progress has been made in adaptive video streaming technology in recent years, but there is still room for improvement using different optimization methods. The goal is to ensure the highest possible video quality perceived by the end users, thus ensuring the highest possible user quality of experience (QoE), regardless of network conditions. Apart from different coding methods, the content itself and the user experience should be considered as optimization parameters. This paper provides a comparison of five objective video quality assessment (VQA) metrics using different video sequences encoded according to H.264/AVC norm at different spatial resolutions and with different target coding bitrates. The metrics included for comparison are Peak Signal-to-Noise Ratio (PSNR), Video Quality Metric (VQM), Structural Similarity index (SSIM), Mean Structural Similarity (MSSIM) index, and Visual Signal-tonoise Ratio (VSNR). The results of the objective VQA metrics have been compared to those of performed subjective VQA experiments. The results show that SSIM and MSSIM achieve the highest performance when evaluating the quality of video sequences of different contents, resolutions and coded with a wide range of coding parameters. Additionally, the results indicate that it is more difficult to efficiently compress the video sequences with higher spatial and temporal activity. Also, due to spatial and temporal masking of compression artifacts in high complexity videos, the analyzed objective VQA metrics achieve lower quality prediction accuracy for that videos.
1+ <i>ε</i> approximation of tree edit distance in quadratic time

Edit distance is one of the most fundamental problems in computer science. Tree edit distance is a natural generalization of edit distance to ordered rooted trees. Such a generalization extends the applications of edit distance to areas such as computational biology, structured data analysis (e.g., XML), image analysis, and compiler optimization. Perhaps the most notable application of tree edit distance is in the analysis of RNA molecules in computational biology where the secondary structure of RNA is typically represented as a rooted tree.
A Secure Reversible Data Hiding and Encryption System for Embedding EPR in Medical Images

Background: Recent advances in medical associated technologies have drastically increased the amount of electronic medical records collected, stored and transferred through the network. Considering the significance and level of sensitivity of the collected medical data, the security of the transmitted data has become a very vital and challenging task for researchers. The protection of these medical images with embedded data is usually guaranteed using encryption or data hiding techniques. Conventional techniques that employ encryption or data hiding are often insecure and also time-consuming during transmission through the network. Materials and Methods: A method combining encryption and data hiding together can result in compression of data that reduces the transmission time and increases the security level. Reversible data hiding in images can reestablish the cover image after extracting the hidden embedded data exclusive of alterations. Here a new reversible crypto-watermarking system is proposed using cryptographic algorithms that encrypts and hides an Electronic Patient Record (EPR) into an image corresponding to that patient using Rhombus Prediction Scheme. It embeds a big amount of encrypted data into an image with hardly noticeable modification using spatial pixel manipulations based on prediction errors. The marked image is hashed using SHA-256 algorithm. Results and Conclusion: Hashing and cryptography increases the robustness and guarantees authenticity with integrity. The proposed method results in improved safety with a lower transmission time than the existing methods.
Supervised Deep Learning in Face Recognition

The three main challenging problems in face recognition, i.e., recognizing a face with different expressions, recognizing a face under different lighting conditions, and recognizing a face in different poses are considered here. In recent years, Convolutional Neural Network (CNN) has been widely applied to face recognition problem because of its good performance. The application of CNN architectures for the above three challenging problems is discussed in this chapter.
Image Segmentation Approach for Acute and Chronic Leukaemia Based on Blood Sample Images

Abstract The uncontrolled development of abnormal white blood cells (blast) in bone marrow is the starting point where leukaemia cancer begins. It does not die like it should. Instead, it goes on dividing and crowding out the healthy blood cells, making it difficult for these healthy bloods to function normally. The diagnosis process from haematologist consumes a lot of time. Therefore, a good segmentation procedure is necessary in order to successfully identify and diagnose leukaemia cells automatically from blood sample images. This paper proposes segmentation procedures which consist of contrast enhancement, extraction of hue component information, as well as segmentation based on Otsu’s thresholding and watershed technique. The experimental results shows that the proposed segmentation procedure has successfully segmented 200 images consisting of acute and chronic leukaemia with average accuracy, sensitivity and specificity obtained of 98.90%, 82.14% and 99.49%, respectively. The result of segmentation performance achieved shows the significant of this approach. Based on results obtained, this segmentation technique is applicable to segment both acute and chronic leukaemia images with good segmentation performance.
Resolving a common vulnerability in secret sharing scheme–based data outsourcing schemes

Summary Every day, the amount of generated data from different resources are increasing significantly and posing new serious challenges in terms of data storage and maintaining. As a solution, outsourcing data to a public cloud server with high storage and processing capacity sounds reasonable in comparison with storing in local data storage. Therefore, the database as a service (DaaS) paradigm has gained much popularity over the last decade since the introduction of cloud services. Storing confidential data into a third party arises some concerns regarding security and privacy of the data. One popular approach to address these problems in data outsourcing is utilizing secret sharing and distributing sensitive data among several databases. In this paper, we introduce a flaw that exhibits a deficiency relating to current schemes. This flaw indicates an intrinsic vulnerability to such methods and necessitates a reconsideration of these schemes. Moreover, a new way will be developed to resolve this problem in the previous schemes, which can be employed in all current schemes to mitigate the mentioned concern. Finally, a comprehensive implementation demonstrates that the proposed method is scalable to meet increasing demands in large databases.
A Comparative Study of Augmented Reality Assistant Tools in Assembly

Digital learning formats using Augmented Reality (AR) technologies have already been successfully used in industry. AR opens new possibilities to guide employees systematically through assembly processes, ensuring the practical relevance of the learning content and a saving of time in the learning process. In order to enhance this technology’s acceptance, user-centred designed AR applications are crucial. This paper aims at deriving conclusions on the usability of AR Head Mounted Displays (HMD) in the field of production. As the transferability of lab-based usability studies is still lacking, we conducted a mixed method approach in a laboratory as well as a field setting. This paper presents the current state of the art, the development of the AR applications and the main results of the study.
Benchmark Dataset: Offline Handwritten Gurmukhi City Names for Postal Automation

Handwriting recognition delineate the computer’s ability to convert human handwriting into text that can be processed by machine. Postal automation plays a significant role in image processing and pattern recognition field. Handwritten city name recognition is the part of postal automation. For assessing the performance of the existing techniques for handwritten city name recognition, a standardized dataset proves useful. But due to lack of publicly accessible benchmark dataset in Gurmukhi script, a systematic comparison of the existing techniques for Gurmukhi city name recognition is not feasible. In this paper, we have presented a dataset for Gurmukhi postal automation named as HWR-Gurmukhi_Postal_1.0 which contains total 40,000 samples of names of various cities which are written in Gurmukhi script. This dataset can be seen as a benchmark for comparison among existing techniques for handwritten city name recognition.
Is a Blockchain-Based Game a Game for Fun, or Is It a Tool for Speculation? An Empirical Analysis of Player Behavior in Crypokitties

The market growth and popularity of Blockchain technology in various fields has been astonishing. The gaming industry is one of the prominent industries that applies Blockchain technology. However, there is some concern about the speculative aspect of Blockchain-based games. Therefore, this paper examines the effects of speculative and enjoyable aspects on users’ playing behavior in Blockchain-based games. For this, we developed a web crawler and collected data from Cryptokitties, one of very first successful Blockchain-based games. To analyze our dataset, we conducted fixed panel regression. The results indicate that the external aspects of the game are negatively related to item selling, whereas the internal aspects of the game are positively related to item selling in the Blockchain-based game. This result suggests that playing Blockchain-based games is a mix of behavior with enjoyable and speculative aspects. As such, it remains unclear whether users play Blockchain-based games just for fun or as a form of speculation.
Social Media Data Mining Techniques: A Survey

Social Networking sites become very popular from last few decades. And that are become very useful for extracting the opinion of peoples regarding various things and topics. There are various techniques of data mining that are very much helpful for OSNs mining. In this survey, we focus on the concept of various algorithms and techniques of data mining that are used to mine online social network (OSNs), with special importance on latest topic of research area. There are several reasons which have made the study of OSNs gain importance by researchers. Here, we also look on the major issues that can occur at the time of mining social media data and how those are solved in last decades.
Non-Native Speaker Generation and Perception for Mixed-Cultural Settings

This paper presents an experiment evaluating the effects of virtual agents' language proficiency on whether they are perceived as a native speakers or not. Our first results indicate that beyond 10% word order mistakes and 25% infinitive mistakes, virtual agents are perceived as non-native speakers, even though their appearance and non-verbal behaviour were not altered. We believe these thresholds constitute interesting guidelines possibly simplifying the design of non-native speaker simulations.
Quantum independent-set problem and non-Abelian adiabatic mixing

We present an efficient quantum algorithm for independent-set problems in graph theory, based on non-Abelian adiabatic mixing. We illustrate the performance of our algorithm with analysis and numerical calculations for two different types of graphs, with the number of edges proportional to the number of vertices or its square. Our quantum algorithm is compared to the corresponding quantum circuit algorithms and classical algorithms. Non-Abelian adiabatic mixing can be a general technique to aid exploration in a landscape of near-degenerate ground states.
Increasing Trust in Tor Node List Using Blockchain

Tor is a low-latency free anonymization network based on onion routing. In Tor, directory servers maintain a list of all nodes. It is, however, possible for a powerful adversary (e.g., law enforcement agencies) to seize or compromise enough directory servers and thus forge that list. Therefore, clients that obtained such a forged list of nodes can be effectively deanonymized. As a countermeasure, we propose to utilize a permissioned blockchain with a single voting committee that is privately “elected” by a verifiable random function (VRF). Since the blockchain provides us with integrity guarantees by design, we increase trust in the directory servers by decentralizing management of Tor nodes present in the shared list. We apply skiplist as an optimization reducing a validation overhead of newly joined nodes and clients. The proposed approach has only a small performance impact on the existing Tor infrastructure.
Riemannian Curvature of Deep Neural Networks

We analyze deep neural networks using the theory of Riemannian geometry and curvature. The objective is to gain insight into how Riemannian geometry can characterize and predict the trained behavior of neural networks. We define a method for calculating Riemann and Ricci curvature tensors, and Ricci scalar curvature values for a trained neural net, in such a way that the output classifier softmax values are related to the input transformations, through the curvature equations. We also measure these curvature tensors experimentally for different networks which are pretrained with stochastic gradient descent and offer a way of visualizing and understanding the measurements to gain insight into the effect curvature has on behavior the neural networks locally, and possibly predict their behavior for different transformations of the test data. We also analyze the effect of variation in depth of the neural networks as well as how it behaves for different choices of data set.
Discovering common bug‐fix patterns: A large‐scale observational study

Abstract Background : Automatic program repair aims to reduce costs associated with defect repair. The detection and characterization of common bug‐fix patterns in software repositories play an important role in advancing this field. Aim: In this paper, we characterize the occurrence of known bug‐fix patterns in Java repositories at an unprecedented large scale. Furthermore, we propose a novel automatic technique for unveiling frequent and isolated repair actions corresponding to realistic bug fixes in Java. Method: The study was conducted for Java GitHub projects organized in two distinct data sets. The first data set (Boa) contains more than 4 million bug‐fix commits from 101 471 projects. The second data set (Defects4J) contains 369 real bug fixes from five open‐source projects. Results: We characterized the prevalence of the five most common bug‐fix patterns (identified in the work of Pan et al ) in those bug fixes. The combined results showed direct evidence that developers often forget to add IF preconditions in the code. Conclusion: We discover a total of 155 repair actions from Defects4J patches and discuss 10 pervasive repair actions that occur across all analyzed Java projects. Moreover, the overall Precision and Recall values for the clustering approach were 0.62 and 0.64, respectively.
HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMo

We present our results for OffensEval: Identifying and Categorizing Offensive Language in Social Media (SemEval 2019 - Task 6). Our results show that context embeddings are important features for the three different sub-tasks in connection with classical machine and with deep learning. Our best model reached place 3 of 75 in sub-task B with a macro F1 of 0.719. Our approaches for sub-task A and C perform less well but could also deliver promising results.
Exploiting CPU Voltage Margins to Increase the Profit of Cloud Infrastructure Providers

Energy efficiency is a major concern for cloud computing, with CPUs accounting a significant fraction of datacenter nodes power consumption. CPU manufacturers introduce voltage margins to guarantee correct operation. However, these are unnecessarily wide for real-world execution scenarios, and translate to increased power consumption. In this paper, we investigate how such margins can be exploited by infrastructure operators, by selectively undervolting nodes, at the controlled risk of inducing failures and activating service-level agreement (SLA) violation penalties. We model the problem in a formal way, capturing the most important aspects that drive VM management and system configuration decisions. Then, we introduce XM-VFS policy that reduces infrastructure operator costs by reducing voltage margins, and compare it with the state-of-the-art which employs dynamic voltage-frequency scaling (DVFS) and workload consolidation. We perform simulations to quantify the cost reduction, considering the energy consumption and potential SLA violations. Our results show significant gains, up to 17.35% and 16.32% for the energy and cost reduction respectively. In our simulations, we use realistic assumptions for voltage margins, energy consumption and performance degradation of applications due to frequency scaling, based on the characterization of commercial Intel-and ARM-based machines. Our model and scheduling policy are generic and scalable.
Privacy-aware Document Ranking with Neural Signals

The recent work on neural ranking has achieved solid relevance improvement, by exploring similarities between documents and queries using word embeddings. It is an open problem how to leverage such an advancement for privacy-aware ranking, which is important for top K document search on the cloud. Since neural ranking adds more complexity in score computation, it is difficult to prevent the server from discovering embedding-based semantic features and inferring privacy-sensitive information. This paper analyzes the critical leakages in interaction-based neural ranking and studies countermeasures to mitigate such a leakage. It proposes a privacy-aware neural ranking scheme that integrates tree ensembles with kernel value obfuscation and a soft match map based on adaptively-clustered term closures. The paper also presents an evaluation with two TREC datasets on the relevance of the proposed techniques and the trade-offs for privacy and storage efficiency.
Implicit Entity Recognition, Classification and Linking in Tweets

Linking phrases to knowledge base entities is a process known as entity linking and has already been widely explored for various content types such as tweets. A major step in entity linking is to recognize and/or classify phrases that can be disambiguated and linked to knowledge base entities, i.e., Named Entity Recognition and Classification. Unlike common entity recognition and linking systems, however, we aim to recognize, classify, and link entities which are implicitly mentioned, and hence lack a surface form, to appropriate knowledge base entries. In other words, the objective of our work is to recognize and identify core entities of a tweet when those entities are not explicitly mentioned; this process is referred to as Implicit Named Entity Recognition and Linking.