Reviewerly/exHarmony huggingface dataset:
Dara

Many designers and artists in India, because of limited domestic patronage, are interested in international opportunities. However, they experience difficulties in discovering such opportunities. Finding relevant grants, funds, residencies, jobs, and collaborators takes significant time and effort. Typically, this is best done via a human matchmaker - someone who knows of opportunities and makes introductions. However, this person might be limited by their own social circles and knowledge. The few websites available are usually intimidating and not personalized. Our vision is to design and develop Dara, a conversational chatbot that asks a series of questions to its users and is able to suggest relevant opportunities. As a first step we built a Wizard-of-Oz prototype and interviewed 9 users about their experience. We then designed the personality, conversation flow and referral nomination system for our next-level prototype. We conducted a preliminary evaluation with 14 users in Bangalore, India. We discuss the implications for design of chatbots targeted at the creative community in the developing world.
Textual Content Retrieval from Filled-in Form Images

Form processing refers to the process of extraction of information from filled-in forms. In this work, we have addressed three very crucial challenges of a form processing system, namely touching component separation, text non-text separation and handwritten-printed text separation. The proposed method is evaluated on a database having 50 filled-in forms written in Bangla, collected during an essay competition in a school. The experimental results are promising.
A Refined Analysis of the Cost for Solving LWE via uSVP

The learning with errors (LWE) problem (STOC’05) introduced by Regev is one of the fundamental problems in lattice-based cryptography. One standard strategy to solve the LWE problem is to reduce it to a unique SVP ( $$\mathrm {\textsc {u}SVP}$$ ) problem via Kannan’s embedding and then apply a lattice reduction to solve the $$\mathrm {\textsc {u}SVP}$$ problem. There are two methods for estimating the cost for solving LWE via this strategy: the first method considers the largeness of the gap in the $$\mathrm {\textsc {u}SVP}$$ problem (Gama-Nguyen, Eurocrypt’08) and the second method (Alkim et al., USENIX’16) considers the shortness of the projection of the shortest vector to the Gram-Schmidt vectors. These two estimates have been investigated by Albrecht et al. (Asiacrypt’16) who present a sound analysis and show that the lattice reduction experiments fit more consistently with the second estimate. They also observe that in some cases the lattice reduction even behaves better than the second estimate perhaps due to the second intersection of the projected vector with the Gram-Schmidt vectors. In this work, we revisit the work of Alkim et al. and Albrecht et al. We first report further experiments providing more comparisons and suggest that the second estimate leads to a more accurate prediction in practice. We also present empirical evidence confirming the assumptions used in the second estimate. Furthermore, we examine the gaps in $$\mathrm {\textsc {u}SVP}$$ derived from the embedded lattice and explain why it is preferable to use $$\mu = 1$$ for the embedded lattice. This shows there is a coherent relation between the second estimate and the gaps in $$\mathrm {\textsc {u}SVP}$$ . Finally, it has been conjectured by Albrecht et al. that the second intersection will not happen for large parameters. We will show that this is indeed the case: there is no second intersection as $$\beta \rightarrow \infty $$ .
A Survey of Methods and Tools for Large-Scale DNA Mixture Profiling

DNA typing or profiling is being widely used for criminal identification, paternity tests, and diagnosis of genetic diseases. DNA typing is considered one of the hardest problems in the forensic science domain, and it is an active area of research. The computational complexity of DNA typing increases significantly with the number of unknowns in the mixture and has been the major deterring factor holding its advancements and applications. In this chapter, we provide an extended review of DNA profiling methods and tools with a particular focus on their computational performance and accuracy. The process of DNA profiling within the broader context of forensic science and genetics is explained. The various classes of DNA profiling methods including general methods, and those based on maximum likelihood estimators, are reviewed. The reviewed DNA profiling tools include LRmix Studio, TrueAllele, DNAMIX V.3, Euroformix, CeesIt, NOCIt, DNAMixture, Kongoh, LikeLTD, LabRetriever, and STRmix. A review of high-performance computing literature in bioinformatics and HPC frameworks is also given. Faster interpretations of DNA mixtures with a large number of unknowns and higher accuracies are expected to open up new frontiers for this area.
Logging mechanism for cross-organizational collaborations using Hyperledger Fabric

Organizations nowadays are largely computerized, with a mixture of internal and external services providing them with on-demand functionality.In some situations (e.g.emergency situations), cross-organizational collaboration is needed, providing external users access to internal services.Trust between partners in such a collaboration can however be an issue.Although (federated) access control policies may be in place, it is unclear which data was requested and delivered after a collaboration has finished.This may lead to disputes between participating organizations.The open-source permissioned blockchain Hyperledger Fabric is utilized to create a logging mechanism for the actions performed by the participants in such a collaboration.This paper presents the architecture needed for such a logging mechanism and provides details on its operation.A prototype was designed in order to evaluate the performance of an asynchronous logging approach.Measurements show that the proposed logging mechanism enables organizations to create a log of service interactions with limited delay imposed on the data exchange process.
Data Trusts as an AI Governance Mechanism

This paper is a response to the Singapore Personal Data Protection Commission consultation on a draft AI Governance Framework. It analyses the five data trust models proposed by the UK Open Data Institute and identifies that only the contractual and corporate models are likely to be legally suitable for achieving the aims of a data trust.The paper further explains how data trusts might be used as in the governance of AI, and investigates the barriers which Singapore's data protection law presents to the use of data trusts and how those barriers might be overcome. Its conclusion is that a mixed contractual/corporate model, with an element of regulatory oversight and audit to ensure consumer confidence that data is being used appropriately, could produce a useful AI governance tool.
Review on Wikification methods

The paper reviews methods on automatic annotation of texts with Wikipedia entries. The process, called Wikification aims at building references between concepts identified in the text and Wikipedia articles. Wikification finds many applications, especially in text representation, where it enables o ne to capture the semantic similarity of the documents. Also, it can be considered as automatic tagging of the text. We describe typical approaches to Wikification, and identify their advantages and disadvantages. The main problem for wide usage of the Wikification method is the lack of open-sourced frameworks that enable researchers to work cooperatively on that problem. Also problematic is the lack of a unified platform for evaluation of the results proposed by different approaches.
Comparative Study on Object Tracking Algorithms for mobile robot Navigation in GPS-denied Environment

This paper presents a comparative study conducted on the performance of the commonly used object tracking and location prediction algorithms for mobile robot navigation in a dynamically cluttered and GPS-denied mining environment. The study was done to test the different algorithms for the same set criteria (such as accuracy and computational time) under the same conditions.The identified commonly used algorithms for object tracking and location prediction of moving objects used in this investigation are Kalman filter (KF), extended Kalman filter (EKF) and particle filter (PF). The study results of those algorithms are analyzed and discussed in this paper. A trade-off was apparent. However, in overall performance KF has shown its competitiveness.The result from the study has found that the KF based algorithm provides better performance in terms of accuracy in tracking dynamic objects under commonly used benchmarks. This finding can be used in development of an efficient robot navigation algorithm.
Application Research of Moving Target Detection Based on Optical Flow Algorithms

Abstract After many years of research, optical flow algorithm has achieved good results in detecting moving objects in simple scenes, but the detection effect in some complex scenes is not ideal, for example, in scenes with changing illumination and large displacement, the accuracy of moving objects detection is low. In order to solve this problem, this paper proposes texture decomposition of images, and applies texture image and pyramid technology to Lucas-Kanade optical flow algorithm. Relevant experiments show that this method can achieve better detection results for moving objects in static scenes.
Spam Domain Detection Method Using Active DNS Data and E-Mail Reception Log

E-mail is widespread and an essential communication technology in modern times. Since e-mail has problems with spam mails and spoofed e-mails, countermeasures are required. Although SPF, DKIM and DMARC have been proposed as sender domain authentication, these mechanisms cannot detect non-spoofing spam mails. To overcome this issue, this paper proposes a method to detect spam domains by supervised learning with features extracted from e-mail reception log and active DNS data, such as the result of Sender Authentication, the Sender IP address, the number of each DNS record, and so on. As a result of the experiment, our method can detect spam domains with 88.09% accuracy and 97.11% precision. We confirmed that our method can detect spam domains with detection accuracy 19.40% higher than the previous study by utilizing not only active DNS data but also e-mail reception log in combination.
A fast online spherical hashing method based on data sampling for large scale image retrieval

Hashing methods are used to perform the approximate nearest neighbor search due to the low storage for binary codes and the fast computation of Hamming distance. However, in most of the hashing methods, the learning process of hash functions has high cost in both time and storage. To overcome this issue, in this paper, a fast online unsupervised hashing method based on data sampling is proposed to learn the hypersphere-based hash functions from the streaming data. By maintaining a small-size data sample to efficiently preserve the properties of the streaming data, the hypersphere-based hash functions are learnt in an online fashion from the data sample and we can justify the hash functions by proving their theoretic properties. To further improve the search accuracy of our method, a new dimensionality reduction algorithm is proposed to learn the projection matrix from the data sample to construct a low-dimensional space. Then, the data sample is projected into the low-dimensional space, and our method can learn the hash functions online from the small-size projected data sample with low computational complexity and storage space. The experiments show that our method has a better search accuracy than other online hashing methods and runs faster in learning the hash functions.
Pseudo-Random Number Generation Using Generative Adversarial Networks

Pseudo-random number generators (PRNG) are a fundamental element of many security algorithms. We introduce a novel approach to their implementation, by proposing the use of generative adversarial networks (GAN) to train a neural network to behave as a PRNG. Furthermore, we showcase a number of interesting modifications to the standard GAN architecture. The most significant is partially concealing the output of the GAN’s generator, and training the adversary to discover a mapping from the overt part to the concealed part. The generator therefore learns to produce values the adversary cannot predict, rather than to approximate an explicit reference distribution. We demonstrate that a GAN can effectively train even a small feed-forward fully connected neural network to produce pseudo-random number sequences with good statistical properties. At best, subjected to the NIST test suite, the trained generator passed around 99% of test instances and 98% of overall tests, outperforming a number of standard non-cryptographic PRNGs.
Hyperbolic Disk Embeddings for Directed Acyclic Graphs

Obtaining continuous representations of structural data such as directed acyclic graphs (DAGs) has gained attention in machine learning and artificial intelligence. However, embedding complex DAGs in which both ancestors and descendants of nodes are exponentially increasing is difficult. Tackling in this problem, we develop Disk Embeddings, which is a framework for embedding DAGs into quasi-metric spaces. Existing state-of-the-art methods, Order Embeddings and Hyperbolic Entailment Cones, are instances of Disk Embedding in Euclidean space and spheres respectively. Furthermore, we propose a novel method Hyperbolic Disk Embeddings to handle exponential growth of relations. The results of our experiments show that our Disk Embedding models outperform existing methods especially in complex DAGs other than trees.
Riemannian adaptive stochastic gradient algorithms on matrix manifolds

Adaptive stochastic gradient algorithms in the Euclidean space have attracted much attention lately. Such explorations on Riemannian manifolds, on the other hand, are relatively new, limited, and challenging. This is because of the intrinsic non-linear structure of the underlying manifold and the absence of a canonical coordinate system. In machine learning applications, however, most manifolds of interest are represented as matrices with notions of row and column subspaces. In addition, the implicit manifold-related constraints may also lie on such subspaces. For example, the Grassmann manifold is the set of column subspaces. To this end, such a rich structure should not be lost by transforming matrices to just a stack of vectors while developing optimization algorithms on manifolds. We propose novel stochastic gradient algorithms for problems on Riemannian matrix manifolds by adapting the row and column subspaces of gradients. Our algorithms are provably convergent and they achieve the convergence rate of order $\mathcal{O}(\log (T)/\sqrt{T})$, where $T$ is the number of iterations. Our experiments illustrate the efficacy of the proposed algorithms on several applications.
Controlled Quantum Teleportation of Superposed Coherent State Using GHZ Entangled Coherent State

Controlled quantum teleportation of superposed coherent states using GHZ entangled 3-mode coherent states is studied. Proposed scheme can be implemented experimentally using linear optical components such as a symmetric lossless beam splitter, two phase-shifters and two photon counters. Fidelity is found close to unity for appreciable mean number of photons in coherent states and is 0.99 for mean photon number equal to two.
ICOs, the next generation of IPOs

Purpose The purpose of this paper is to recognize the benefits of the initial coin offering (ICO) as a way of raising funds and to present a detailed comparison between the ICO and the initial public offering to realize the future possibilities that this new funding method holds. Design/methodology/approach It is an exhaustive review of the ICO, the mechanism of crowdfunding, the blockchain technology behind it, benefits and current shortcomings of the ICO, and the potential future development of the ICO as a convenient and efficient way of raising capital. Findings ICOs have brought billions of dollars of funding to startups and projects worldwide in less than two years. Concurrently, many successful ICOs yielded extremely high returns to investors and believers of this new way of funding businesses. Research limitations/implications While the ICO is a revolutionary vehicle for business funding, it has raised concerns among users as well as potential investors about its risk and lack of regulation. The future of this innovative funding method highly depends on further development and placement of appropriate regulatory supervision, better understanding of risk and benefits and attaining the confidence of users. Originality/value This is a review of the advantages and drawbacks of the ICO. If the current fraud, market and cybersecurity risks can be mitigated and standardized regulations are developed, the ICO has a future to become an established way of capital funding or even replace the existing options, regardless of the size and age of companies.
Interest Point Detectors Stability Evaluation on ApolloScape Dataset

In the recent years, a number of novel, deep-learning based, interest point detectors, such as LIFT, DELF, Superpoint or LF-Net was proposed. However there’s a lack of a standard benchmark to evaluate suitability of these novel keypoint detectors for real-live applications such as autonomous driving. Traditional benchmarks (e.g. Oxford VGG) are rather limited, as they consist of relatively few images of mostly planar scenes taken in favourable conditions. In this paper we verify if the recent, deep-learning based interest point detectors have the advantage over the traditional, hand-crafted keypoint detectors. To this end, we evaluate stability of a number of hand crafted and recent, learning-based interest point detectors on the street-level view ApolloScape dataset.
Human Behavior Prediction Though Noninvasive and Privacy-Preserving Internet of Things (IoT) Assisted Monitoring

As the supporting technologies for Ambient Assistant Living (AAL) in Internet of Things (IoT) domain have become more powerful and more attractive, the related systems will be widely deployed and put into action. With all associated embedded IoT sensing devices, how to maintain users’ privacy and data security is a highly concerned problem. There are generally two approaches to protect privacy. One is to implement complex security protocols to guarantee the safety of sensing, storage and data transmission. Another one is to prevent the privacy issues and concerns from the source. This proposed research will provide a concept of a framework that can support behaviour monitoring through noninvasive and privacy preserving sensing. The data collected, transmitted and used for analysing in this framework is sensing information with low richness. This framework aims to increase the users’ perceived privacy in existing monitoring systems to avoid data over-collection and over exposure problems.
A Recommendation Model Based on Content and Social Network

Recommendation model are a popular trend in recent research in Internet technologies. However traditional content-based recommendation, social network-based recommendation and collaborative filtering recommendation have their own shortcomings. To overcome them, we proposed a recommendation model based on content and social network (RMBCS). First, we proposed a new distance to calculate the text similarity between long text and short text. Then, we proposed a new method to find the nearest neighbor group from user's social network quickly and conveniently. At last, we recommend the texts which user's nearest neighbor group had read to the user. Experimental results indicate that our model has a better performance.
Investigating on the Impact of Software Clones on Technical Debt

Code reuse by copying a code fragment with or without modification generates duplicate copies of exact or similar code fragments in a software system, known as code clones. The debate about the harmfulness of clone in ongoing in the literature, nevertheless, it is widely recognized that clones needs special considerations during software evolution. In this paper, it is proposed a quantitative analysis of technical debt values to understand if it is higher with cloned code than those without cloned code. Moreover, changes performed on these files have been analyzed by analyzing commit logs. According to our inspection on four subject systems, the technical debt of files with cloned code is significantly higher than those without cloned code. Moreover, as expected, files with cloned code are more impacted by changes.
DTO: Donkey Theorem Optimization

Metaheuristic optimization algorithms have been used in many applications in recent years. Most of these algorithms are inspired by physical processes or living beings' behaviors. A new optimization algorithm, called Donkey Theorem Optimization (DTO), that simulates the behavior of Donkeys is proposed in this paper. DTO is based on donkey theorem that mimics behavior of donkey for reach to food. Proposed algorithm is tested on 23 well-known benchmark test functions and its performance compared with eight optimization algorithms. The results show that DTO is able to provide better results as compared to the other well-known optimization algorithms.
Appearance-Based Gaze Tracking: A Brief Review

Human gaze tracking plays an important role in the field of Human-Computer Interaction. This paper presents a brief review on appearance-based gaze tracking. Based on the appearance of human eyes, input features can be classified into three categories according to the different ways of extracting human eyes features, namely, complete human eye image, pixel-based feature and 3D reconstruction image. The estimation process from human eye feature to fixation point mainly uses different mapping functions. In this paper, common mapping functions and related algorithms are described in detail: k-nearest neighbor (KNN), random forest (RF) regression, gaussian process (GP) regression, support vector machines (SVM) and artificial neural networks (ANN). This paper evaluates the performance of these gaze tracking algorithms using different mapping functions. Based on the results of the evaluation, potential challenges are summarized and the future directions of gaze estimation are prospected.
The Anatomy of a Cryptocurrency Pump-and-Dump Scheme

While pump-and-dump schemes have attracted the attention of cryptocurrency observers and regulators alike, this paper represents the first detailed empirical query of pump-and-dump activities in cryptocurrency markets. We present a case study of a recent pump-and-dump event, investigate 412 pump-and-dump activities organized in Telegram channels from June 17, 2018 to February 26, 2019, and discover patterns in crypto-markets associated with pump-and-dump schemes. We then build a model that predicts the pump likelihood of all coins listed in a crypto-exchange prior to a pump. The model exhibits high precision as well as robustness, and can be used to create a simple, yet very effective trading strategy, which we empirically demonstrate can generate a return as high as 60% on small retail investments within a span of two and half months. The study provides a proof of concept for strategic crypto-trading and sheds light on the application of machine learning for crime detection.