Reviewerly/exHarmony huggingface dataset:
DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node

Current state-of-the-art approximate nearest neighbor search (ANNS) algorithms generate indices that must be stored in main memory for fast high-recall search. This makes them expensive and limits the size of the dataset. We present a new graph-based indexing and search system called DiskANN that can index, store, and search a billion point database on a single workstation with just 64GB RAM and an inexpensive solid-state drive (SSD). Contrary to current wisdom, we demonstrate that the SSD-based indices built by DiskANN can meet all three desiderata for large-scale ANNS: high-recall, low query latency and high density (points indexed per node). On the billion point SIFT1B bigann dataset, DiskANN serves > 5000 queries a second with < 3ms mean latency and 95%+ 1-recall@1 on a 16 core machine, where state-of-the-art billion-point ANNS algorithms with similar memory footprint like FAISS and IVFOADC+G+P plateau at around 50% 1-recall@1. Alternately, in the high recall regime, DiskANN can index and serve 5 − 10x more points per node compared to state-of-the-art graph- based methods such as HNSW and NSG. Finally, as part of our overall DiskANN system, we introduce Vamana, a new graph-based ANNS index that is more versatile than the graph indices even for in-memory indices.
A System for Diacritizing Four Varieties of Arabic

Hamdy Mubarak, Ahmed Abdelali, Kareem Darwish, Mohamed Eldesouki, Younes Samih, Hassan Sajjad. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations. 2019.
Learning to Predict Novel Noun-Noun Compounds

We introduce temporally and contextuallyaware models for the novel task of predicting unseen but plausible concepts, as conveyed by noun-noun compounds in a time-stamped corpus.We train compositional models on observed compounds, more specifically the composed distributed representations of their constituents across a time-stamped corpus, while giving it corrupted instances (where head or modifier are replaced by a random constituent) as negative evidence.The model captures generalisations over this data and learns what combinations give rise to plausible compounds and which ones do not.After training, we query the model for the plausibility of automatically generated novel combinations and verify whether the classifications are accurate.For our best model, we find that in around 85% of the cases, the novel compounds generated are attested in previously unseen data.An additional estimated 5% are plausible despite not being attested in the recent corpus, based on judgments from independent human raters.
An adaptive nearest neighbor rule for classification

We introduce a variant of the $k$-nearest neighbor classifier in which $k$ is chosen adaptively for each query, rather than supplied as a parameter. The choice of $k$ depends on properties of each neighborhood, and therefore may significantly vary between different points. (For example, the algorithm will use larger $k$ for predicting the labels of points in noisy regions.) We provide theory and experiments that demonstrate that the algorithm performs comparably to, and sometimes better than, $k$-NN with an optimal choice of $k$. In particular, we derive bounds on the convergence rates of our classifier that depend on a local quantity we call the ``advantage'' which is significantly weaker than the Lipschitz conditions used in previous convergence rate proofs. These generalization bounds hinge on a variant of the seminal Uniform Convergence Theorem due to Vapnik and Chervonenkis; this variant concerns conditional probabilities and may be of independent interest.
Towards a Democratic Federation for Infrastructure Service Provisioning

The current implementations of federated clouds depend on a central broker that takes care of the resource allocation as well as scheduling and pricing for the shared resources under the federation. In this paper, we propose an alternate architecture for federated Infrastructure-as-a-service (IaaS) provisioning with the help of a completely decentralized marketplace designed using the blockchain technology. The proposed architecture is free from any central broker and supports decentralization, transparency of resource exchanges, autonomy of service providers, immutability in information exchange for dispute-free billing and fairness for service provisioning. An in-house implementation of the proposed architecture with three cloud service providers shows that CloudChain indeed supports resource allocation fairness while achieving almost similar service provisioning performance compared to a central broker based federation architecture.
The Usefulness of Imperfect Speech Data for ASR Development in Low-Resource Languages

When the National Centre for Human Language Technology (NCHLT) Speech corpus was released, it created various opportunities for speech technology development in the 11 official, but critically under-resourced, languages of South Africa. Since then, the substantial improvements in acoustic modeling that deep architectures achieved for well-resourced languages ushered in a new data requirement: their development requires hundreds of hours of speech. A suitable strategy for the enlargement of speech resources for the South African languages is therefore required. The first possibility was to look for data that has already been collected but has not been included in an existing corpus. Additional data was collected during the NCHLT project that was not included in the official corpus: it only contains a curated, but limited subset of the data. In this paper, we first analyze the additional resources that could be harvested from the auxiliary NCHLT data. We also measure the effect of this data on acoustic modeling. The analysis incorporates recent factorized time-delay neural networks (TDNN-F). These models significantly reduce phone error rates for all languages. In addition, data augmentation and cross-corpus validation experiments for a number of the datasets illustrate the utility of the auxiliary NCHLT data.
Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning

The automated generation of information indicating the characteristics of articles such as headlines, key phrases, summaries and categories helps writers to alleviate their workload.Previous research has tackled these tasks using neural abstractive summarization and classification methods.However, the outputs may be inconsistent if they are generated individually.The purpose of our study is to generate multiple outputs consistently.We introduce a multi-task learning model with a shared encoder and multiple decoders for each task.We propose a novel loss function called hierarchical consistency loss to maintain consistency among the attention weights of the decoders.To evaluate the consistency, we employ a human evaluation.The results show that our model generates more consistent headlines, key phrases and categories.In addition, our model outperforms the baseline model on the ROUGE scores, and generates more adequate and fluent headlines.
Real-Time Gestural Control of Robot Manipulator Through Deep Learning Human-Pose Inference

With the raise of collaborative robots, human-robot interaction needs to be as natural as possible. In this work, we present a framework for real-time continuous motion control of a real collaborative robot (cobot) from gestures captured by an RGB camera. Through deep learning existing techniques, we obtain human skeletal pose information both in 2D and 3D. We use it to design a controller that makes the robot mirror in real-time the movements of a human arm or hand.
An Introduction to AnyBURL

Current research on knowledge graph completion is often concerned with latent approaches that are based on the idea to embed a knowledge graph into a low dimensional vector space. At the same time symbolic approaches have attracted less attention [13]. However, such approaches have a big advantage: they yield an explanation in terms of the rules that trigger a prediction. In this paper we propose a bottom-up technique for efficiently learning logical rules from large knowledge graphs inspired by classic bottom-up rule learning approaches as Golem [8] and Aleph [10]. Our approach is called AnyBURL (Anytime Bottom-Up Rule Learning). We report on experiments where we evaluated AnyBURL on datasets that have been labelled as hard cases for simple (rule-based) approaches. Our approach performs as good as and sometimes better than most models that have been proposed recently. Moreover, the required resources in terms of memory and runtime are significantly smaller compared to latent approaches. This paper is an extended abstract of an IJCAI 2019 paper [6].
Deep Residual Learning for Image Compression

In this paper, we provide a detailed description on our approach designed for CVPR 2019 Workshop and Challenge on Learned Image Compression (CLIC). Our approach mainly consists of two proposals, i.e. deep residual learning for image compression and sub-pixel convolution as up-sampling operations. Experimental results have indicated that our approaches, Kattolab, Kattolabv2 and KattolabSSIM, achieve 0.972 in MS-SSIM at the rate constraint of 0.15bpp with moderate complexity during the validation phase.
An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval

The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance. One of the main bottlenecks of this technique is the quadratic growth of the kNN graph size due to the high-quantity of new connections between nodes in the graph, resulting in long computation times. Several strategies have been proposed to address this, but none are effective and efficient. Our novel technique, based on LSH projections, obtains the same performance as the exact kNN graph after diffusion, but in less time (approximately 18 times faster on a dataset of a hundred thousand images). The proposed method was validated and compared with other state-of-the-art on several public image datasets, including Oxford5k, Paris6k, and Oxford105k.
Compound Domain Adaptation in an Open World.

A typical domain adaptation approach is to adapt models trained on the annotated data in a source domain (e.g., sunny weather) for achieving high performance on the test data in a target domain (e.g., rainy weather). Whether the target contains a single homogeneous domain or multiple heterogeneous domains, existing works always assume that there exist clear distinctions between the domains, which is often not true in practice (e.g., changes in weather). We study an open compound domain adaptation (OCDA) problem, in which the target is a compound of multiple homogeneous domains without domain labels, reflecting realistic data collection from mixed and novel situations. We propose a new approach based on two technical insights into OCDA: 1) a curriculum domain adaptation strategy to bootstrap generalization across domains in a data-driven self-organizing fashion and 2) a memory module to increase the model's agility towards novel domains. Our experiments on digit classification, facial expression recognition, semantic segmentation, and reinforcement learning demonstrate the effectiveness of our approach.
Survey on Blockchain Incentive Mechanism

The current research on the blockchain includes study on network architecture and the incentive. In this paper, an introduction to the architecture of information technology was given and the goal and research status of the incentive layer of blockchain were illustrated with digital economy development as the backdrop. The existing issuance of token was elaborated from computation, storage and transmission, three core facets of network technology. The development of the token allocation and the path was analyzed to confirm its rationality. It discusses the possible directions and challenges of the future research on blockchain incentive.
A crisis for the future of forensic science: Lessons from the UK of the importance of epistemology for funding research and development

This study presents analysis of forensic science research funded by UK Research and Innovation (UKRI) research councils (2009–2018), representing 150 projects with a cumulative value of £56.1 m (0.01% of the total UKRI budget over this time period). The findings indicate that dedicated forensic science funding represents only 46.0% of the projects included in the dataset. Research focussed on developing technological outputs represented 69.5% of the total funding (£37.2 m) in comparison to foundational research which represented 19.2% (£10.7 m). Traditional forensic science evidence types such as fingerprints and DNA received 1.3% and 5.1% of the total funding respectively, in comparison to digital and cyber projects which received 25.7%. These data offer insight into the scale of the funding crisis in forensic science in the UK, and the need to increase the resources available, to develop ways of articulating value and to ensure that both technological and foundational research are enabled.
Latency-Aware Secure Elastic Stream Processing with Homomorphic Encryption

Abstract Increasingly organizations are elastically scaling their stream processing applications into the infrastructure as a service clouds. However, state-of-the-art approaches for elastic stream processing do not consider the potential threats of exposing their data to third parties in cloud environments. We present the design and implementation of an Elastic Switching Mechanism for data stream processing which is based on homomorphic encryption (HomoESM). The HomoESM not only elastically scales data stream processing applications into public clouds but also preserves the privacy of such applications. Using a real-world test setup, which includes an E-mail Filter benchmark and a Web server access log processor benchmark (EDGAR), we demonstrate the effectiveness of our approach. Experiments on Amazon EC2 indicate that the proposed approach for homomorphic encryption provides a significant result which is 10–17% improvement in average latency in the case of E-mail Filter benchmark and EDGAR benchmark, respectively. Furthermore, EDGAR add/subtract operations, multiplication, and comparison operations showed up to 6.13%, 7.81%, and 26.17% average latency improvements, respectively. Finally, we evaluate the potential of scaling the homomorphic stream processor in the public cloud. These results indicate the potential for real-world deployments of secure elastic data stream processing applications.
Rectification of Partitioned Covariance Intersection

Linear combination of two estimates is a cornerstone of decentralised estimation/fusion. Under unknown or partially known correlation of estimation errors, a conservative fusion constructs upper bounds of mean square error (MSE) matrices of the fused estimate. This paper points out a defect in the Partitioned Covariance Intersection fusion rule. By explicitly parametrising the admissible MSE matrices, their tight upper bounds are found. The new bounds are used to rectify the existing fusion rule. A virtue and interpretation of the new fusion rule are discussed and an illustrative example is presented.
A Collaborative Decision Support Tool for Managing Chronic Conditions.

This paper describes work to assess the feasibility of using a decision support tool to help patients with chronic conditions, specifically stroke, manage their condition in collaboration with their carers and the health care professionals who are looking after them. The system contains several novel elements: the integration of data from commercial wellness sensors, electronic health records and clinical guidelines; the use of computational argumentation to track the source of data and to resolve conflicts and make recommendations; and argumentation-based dialogue to support interaction with patients. The proposed approach is implemented as an application that can run on smart devices (e.g. tablets). The users have personalised dashboards where they can visualise their health data and interact with a conversational chatbot that provides further explanations about their overall well-being.
Symmetric-Key Corruption Detection: When XOR-MACs Meet Combinatorial Group Testing

We study a class of MACs, which we call corruption detectable MAC, that is able to not only check the integrity of the whole message, but also detect a part of the message that is corrupted. It can be seen as an application of the classical Combinatorial Group Testing (CGT) to message authentication. However, previous work on this application has an inherent limitation in its communication cost. We present a novel approach to combine CGT and a class of linear MACs (XOR-MAC) that breaks this limit. Our proposal, $$\textsf {XOR}\text {-}\textsf {GTM} $$ , has a significantly smaller communication cost than any of the previous corruption detectable MACs, while keeping the same corruption detection capability. Our numerical examples for storage application show a reduction of communication by a factor of around 15 to 70 compared with previous schemes. $$\textsf {XOR}\text {-}\textsf {GTM} $$ is parallelizable and is as efficient as standard MACs. We prove that $$\textsf {XOR}\text {-}\textsf {GTM} $$ is provably secure under the standard cryptographic assumptions.
An Outlier Degree Shilling Attack Detection Algorithm Based on Dynamic Feature Selection

Recommender system is widely used in various fields for dealing with information overload effectively, and collaborative filtering plays a vital role in the system. However, recommender system suffers from its vulnerabilities by malicious attacks significantly, especially, shilling attacks because of the open nature of recommender system and the dependence on data. Therefore, detecting shilling attack has become an important issue to ensure the security of recommender system. Most of the existing methods of detecting shilling attack are based on user ratings, and one limitation is that they are likely to be interfered by obfuscation techniques. Moreover, traditional detection algorithms cannot handle different types of shilling attacks flexibly. In order to solve the problems, we proposed an outlier degree shilling attack detection algorithm by using dynamic feature selection. Considering the differences when users choose items, we combined rating-based indicators with user popularity, and utilized the information entropy to select detection indicators dynamically. Therefore, a variety of shilling attack models can be dealt with flexibility in this way. The experiments show that the proposed algorithm can achieve better detection performance and interference immunity.
A blockchain-based trusted security zone architecture

Purpose The purpose of this paper is to propose a trusted security zone architecture that uses a blockchain technology to provide secure sharing of data in the security zone while maintaining the integrity, confidentiality and availability of data. The blockchain uses a distributed network to ensure data availability and uses public ledgers to ensure the integrity and confidentiality of data. Design/methodology/approach The proposed architecture uses a blockchain technology to provide secure sharing of data in the security zone while maintaining the integrity, confidentiality and availability of data. The blockchain uses a distributed network to ensure data availability and uses public ledgers to ensure the integrity and confidentiality of data. Findings Analysis of the proposed architecture with a use case scenario demonstrates that it provides a robust security measure against unauthorized network intrusions. Originality/value Unlike the existing security zone, this paper adopts a method of storing data by using blockchain. It meets the need to study integrated authentication management methods of future research.
Build-a-Radar Self-Paced Massive Open Online Course (MOOC)

Electromagnetic education can be greatly enhanced by providing students with a practical hands-on project that helps illustrate the different theoretical concepts covered. This paper introduces a unique build-a-radar course for this purpose, and is offered online for free in a self-paced massively open online format. The site can be accessed at https://llx.mit.edu, and provides students with an understanding of radar through the use of pre-recorded video lectures, review questions and associated reference materials. This course also contains a detailed set of instructions to guide students through the radar construction using a hardware kit that is also available online. Together, these combined offerings present a novel teaching tool for all students interested in radar and electromagnetics as a whole.
Fundamentals of Convolutional Neural Networks

In this chapter, we will look at the main components of a convolutional neural network (CNN): kernels and pooling layers. We will then look at how a typical network looks. We will then try to solve a classification problem with a simple convolutional network and try to visualize the convolutional operation. The purpose of this is to try to understand, at least intuitively, how the learning works.
Data-Driven Structuring of the Output Space Improves the Performance of Multi-Target Regressors

The task of multi-target regression (MTR) is concerned with learning predictive models capable of predicting multiple target variables simultaneously.MTR has attracted an increasing attention within research community in recent years, yielding a variety of methods.The methods can be divided into two main groups: problem transformation and problem adaptation.The former transform a MTR problem into simpler (typically single target) problems and apply known approaches, while the latter adapt the learning methods to directly handle the multiple target variables and learn better models which simultaneously predict all of the targets.Studies have identified the latter group of methods as having competitive advantage over the former, probably due to the fact that it exploits the interrelations of the multiple targets.In the related task of multi-label classification, it has been recently shown that organizing the multiple labels into a hierarchical structure can improve predictive performance.In this paper, we investigate whether organizing the targets into a hierarchical structure can improve the performance for MTR problems.More precisely, we propose to structure the multiple target variables into a hierarchy of variables, thus translating the task of MTR into a task of hierarchical multi-target regression (HMTR).We use four data-driven methods for devising the hierarchical structure that cluster the real values of the targets or the feature importance scores with respect to the targets.The evaluation of the proposed methodology on 16 benchmark MTR datasets reveals that structuring the multiple target variables into a hierarchy improves the predictive performance of the corresponding MTR models.The results also show that data-driven methods produce hierarchies that can improve the predictive performance even more than expert constructed hierarchies.Finally, the improvement in predictive performance is more pronounced for the datasets with very large numbers (more than hundred) of targets.