Reviewerly/exHarmony huggingface dataset:
Effect handlers via generalised continuations

Abstract Plotkin and Pretnar’s effect handlers offer a versatile abstraction for modular programming with user-defined effects. This paper focuses on foundations for implementing effect handlers, for the three different kinds of effect handlers that have been proposed in the literature: deep, shallow, and parameterised. Traditional deep handlers are defined by folds over computation trees and are the original construct proposed by Plotkin and Pretnar. Shallow handlers are defined by case splits (rather than folds) over computation trees. Parameterised handlers are deep handlers extended with a state value that is threaded through the folds over computation trees. We formulate the extensions both directly and via encodings in terms of deep handlers and illustrate how the direct implementations avoid the generation of unnecessary closures. We give two distinct foundational implementations of all the kinds of handlers we consider: a continuation-passing style (CPS) transformation and a CEK-style abstract machine. In both cases, the key ingredient is a generalisation of the notion of continuation to accommodate stacks of effect handlers. We obtain our CPS translation through a series of refinements as follows. We begin with a first-order CPS translation into untyped lambda calculus which manages a stack of continuations and handlers as a curried sequence of arguments. We then refine the initial CPS translation by uncurrying it to yield a properly tail-recursive translation and then moving towards more and more intensional representations of continuations in order to support different kinds of effect handlers. Finally, we make the translation higher order in order to contract administrative redexes at translation time. Our abstract machine design then uses the same generalised continuation representation as the CPS translation. We have implemented both the abstract machine and the CPS transformation (plus extensions) as backends for the Links web programming language.
Corpus for Emotion Detection on Roman Urdu

Language assets, like corpora, are essential for different natural language processing tasks. There are many useful applications of emotion analysis of text such as dialog systems, smart agents, mental disorder clinical diagnoses. In this research, an emotion labeled corpus for Roman Urdu is presented for evaluation of emotions in short text. We collected a sizable corpus with 10,000 manually annotated sentences in Roman Scripted Urdu to facilitate the development and evaluation of emotion detection systems for Roman Urdu. This corpus is the first of its kind to be created for the Roman Urdu.
Collaborative Health Care Plan through Crowdsource Data using Ambient Application

A collaborative health care plan using a multi-agent system assists adult individuals to live an independent healthy life by analyzing their routine life activities. Robust recognition of activities provides services such as health monitoring and fitness assessment. In this paper, we propose Collaborative Health Care Plan a novel system, to improve the independent living of an individual using a smartphone sensor, machine learning algorithm, multiple agents i.e. doctor, gym trainer, guardian, and intelligent ranker agent. The novelty of the devised approach is that it shares the daily life assessment of activities among care provides which in return provides a care plan or recommendation to ensure good health of the individual. We use a machine learning algorithm to recognize the adult individual's daily life physical activities. This system provides recommendations by care providers as well as suggest an optimal care plan decided by an intelligent autonomous agent using crowdsource data.
Performance Analysis of Object Detection and Tracking Algorithms for Traffic Surveillance Applications using Neural Networks

The single object detection has been performed by using the concepts of convolution layers. A neural network consists of several different layers such as the input layer, at least one hidden layer, and an output layer. The dataset used for single object detection is the on-road vehicle dataset. This dataset consists of three classes of images which are Heavy, Auto and Light. The dataset consists of images of varying illuminations. The performance metrics has been calculated for the day dataset, evening dataset and night dataset. Multiple object detection has been performed using the You Only Look Once (YOLOv3) algorithm. This approach encompasses a single deep convolution neural network dividing the input into a cell grid and each cell predicts a boundary box and classifies object directly. The dataset used for multiple object detection is the KITTI dataset. It consists of 80 classes out of which five classes has been considered for this project which are: car, bus, truck, and motorcycle and train. Using the Multiple Object Detection concepts, tracking of vehicles was further implemented. The first frame of the video was taken and Multiple object detection was performed and in the further frames of the video the object was tracked using its centroid position. This has been developed using OpenCV and Python using YOLOv3 algorithm for the object detection phase.
Utility maximisation for resource allocation of migrating enterprise applications into the cloud

Cloud computing has attracted many enterprises to migrate or deploy their applications into the cloud platform so as to reduce their operational costs. In this paper, we investigate the reasonable resource allocation for migrating enterprise applications to the cloud. We first formulate a generalisation form of utility maximisation model for resource allocation of enterprise applications migration. The generalisation-form optimisation problem is difficult to resolve due to its non-strict convexity and non-separation. We apply the successive approximation method to study the generalisation-form model and present a gradient-based resource allocation scheme to solve the approximation problems. Then, we analyse its performance through mathematical method and also illustrate with some numerical examples.
A Very Deep Transfer Learning Model for Vehicle Damage Detection and Localization

Claims leakage is a major problem engendering tremendous losses for insurance companies. Those losses are due to the difference between the amount paid by insurance companies and the exact amount that should be spent, which cost millions of dollars yearly. Experts assert that these losses are caused by inefficient claims processing, frauds, and poor decision-making in the company. With the huge advances in Artificial Intelligence (AI), machine and deep learning algorithms, those technologies have started being used in insurance industry to solve such problems and cope with their negative consequences. In this paper, we propose automated and efficient deep learning-based architectures for vehicle damage detection and localization. The proposed solution combines deep learning, instance segmentation, and transfer learning techniques for features extraction and damage identification. Its objective is to automatically detect damages in vehicles, locate them, classify their severity levels, and visualize them by contouring their exact locations. Numerical results reveal that our transfer learning proposed solution, based on Inception-ResnetV2 pre-trained model followed by a fully connected neural network, achieves higher performances in features extraction and damage detection/localization than another pre-trained model, i.e., VGG16.
Steerable Self-Driving Data Visualization

In this work, we present a self-driving data visualization system, called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepEye</small> , that automatically generates and recommends visualizations based on the idea of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visualization by examples.</i> We propose effective visualization recognition techniques to decide which visualizations are meaningful and visualization ranking techniques to rank the good visualizations. Furthermore, a main challenge of automatic visualization system is that the users may be misled by blindly suggesting visualizations without knowing the user's intent. To this end, we extend <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepEye</small> to be easily steerable by allowing the user to use <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">keyword search</i> and providing click-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">faceted navigation</i> . Empirical results, using real-life data and use cases, verify the power of our proposed system.
Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation

Recently, sequential recommendation has attracted substantial attention from researchers due to its status as an essential service for e-commerce. Accurately understanding user intention is an important factor to improve the performance of recommendation system. However, user intention is highly time-dependent and flexible, so it is very challenging to learn the latent dynamic intention of users for sequential recommendation. To this end, in this paper, we propose a novel intention modeling from ordered and unordered facets (IMfOU) for sequential recommendation. Specifically, the global and local item embedding (GLIE) we proposed can comprehensively capture the sequential context information in the sequences and highlight the important features that users care about. We further design ordered preference drift learning (OPDL) and unordered purchase motivation learning (UPML) to obtain user’s the process of preference drift and purchase motivation respectively. With combining the users’ dynamic preference and current motivation, it considers not only sequential dependencies between items but also flexible dependencies and models the user purchase intention more accurately from ordered and unordered facets respectively. Evaluation results on three real-world datasets demonstrate that our proposed approach achieves better performance than the state-of-the-art sequential recommendation methods achieving improvement of AUC by an average of 2.26%.
Linking reaction mechanisms and quantum chemistry: An ontological approach

In this paper, a linked-data framework for connecting species in chemical kinetic reaction mechanisms with quantum calculations is presented. A mechanism can be constructed from thermodynamic, reaction rate, and transport data that has been obtained either experimentally, computationally, or by a combination of both. This process in practice requires multiple sources of data, which raises, inter alia, species naming and data inconsistency issues. A linked data-centric knowledge-graph approach is taken in this work to address these challenges. In order to implement this approach, two existing ontologies, namely OntoKin, for representing chemical kinetic reaction mechanisms, and OntoCompChem, for representing quantum chemistry calculations, are extended. In addition, a new ontology, which we call OntoSpecies, is developed for uniquely representing chemical species. The framework also includes agents to populate and link knowledge-bases created through the instantiation of these ontologies. In addition, the developed knowledge-graph and agents naturally form a part of the J-Park Simulator (JPS) – an Industry 4.0 platform which combines linked data and an eco-system of autonomous agents for cross-domain applications. The functionality of the framework is demonstrated via a use-case based on a hydrogen combustion mechanism.
A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification

High number of writers, small number of training samples per writer with high intra-class variability and heavily imbalanced class distributions are among the challenges and difficulties of the offline Handwritten Signature Verification (HSV) problem. A good alternative to tackle these issues is to use a writer-independent (WI) framework. In WI systems, a single model is trained to perform signature verification for all writers from a dissimilarity space generated by the dichotomy transformation. Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers, and hence of being used in a transfer learning context. In this work, we present a white-box analysis of this approach highlighting how it handles the challenges, the dynamic selection of references through fusion function, and its application for transfer learning. All the analyses are carried out at the instance level using the instance hardness (IH) measure. The experimental results show that, using the IH analysis, we were able to characterize "good" and "bad" quality skilled forgeries as well as the frontier region between positive and negative samples. This enables futures investigations on methods for improving discrimination between genuine signatures and skilled forgeries by considering these characterizations.
Cryptographically Secure Pseudo-Random Number Generator IP-Core Based on SHA2 Algorithm

In the context of growing the adoption of advanced sensors and systems for active vehicle safety and driver assistance, an increasingly important issue is the security of the information exchanged between the different sub-systems of the vehicle. Random number generation is crucial in modern encryption and security applications as it is a critical task from the point of view of the robustness of the security chain. Random numbers are in fact used to generate the encryption keys to be used for ciphers. Consequently, any weakness in the key generation process can potentially leak information that can be used to breach even the strongest cipher. This paper presents the architecture of a high performance Random Number Generator (RNG) IP-core, in particular a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG) IP-core, a digital hardware accelerator for random numbers generation which can be employed for cryptographically secure applications. The specifications used to develop the proposed project were derived from dedicated literature and standards. Subsequently, specific architecture optimizations were studied to achieve better timing performance and very high throughput values. The IP-core has been validated thanks to the official NIST Statistical Test Suite, in order to evaluate the degree of randomness of the numbers generated in output. Finally the CSPRNG IP-core has been characterized on relevant Field Programmable Gate Array (FPGA) and ASIC standard-cell technologies.
Improved DD-based Equivalence Checking of Quantum Circuits

Quantum computing is gaining considerable momentum through the recent progress in physical realizations of quantum computers. This led to rather sophisticated design flows in which the originally specified quantum functionality is compiled through different abstractions. This increasingly raises the question whether the respectively resulting quantum circuits indeed realize the originally intended function. Accordingly, efficient methods for equivalence checking are gaining importance. However, existing solutions still suffer from significant shortcomings such as their exponential worst case performance and an increased effort to obtain counterexamples in case of non-equivalence. In this work, we propose an improved DD-based equivalence checking approach which addresses these shortcomings. To this end, we utilize decision diagrams and exploit the fact that quantum operations are inherently reversible - allowing for dedicated strategies that keep the overhead moderate in many cases. Experimental results confirm that the proposed strategies lead to substantial speed-ups - allowing to perform equivalence checking of quantum circuits factors or even magnitudes faster than the state of the art.
ICT diffusion and the finance–growth nexus: a panel analysis on ECOWAS countries

Abstract In this study, we rely on the extension of the endogenous growth theory to consider the role of ICT diffusion in the finance–growth nexus theoretically informed via the supply-leading and demand-following theses. Motivated by current realities highlighting the significance of information and communication technologies (ICTs) to modern societies, the study evaluates the foregoing research objective in ECOWAS countries over the 2005–2016 period based on the justification of the ICT diffusion into the ECOWAS region around the early 2000s. Innovatively, the study constructs an ICT diffusion index covering fixed telephone lines, mobile cellular subscription and Internet penetration as relevant media for ICT dispersion. We adopt the pooled mean group estimator on a dynamic panel ARDL model for robust analysis of the subject matter. We find that financial development (its main and alternative measure) alone inhibits growth of ECOWAS countries, but its interaction with ICT diffusion promotes economic growth in the region. The implication of our research findings for the West African region is that financial sector development efforts have to be strengthened by investment in ICT infrastructure, improved Internet penetration and further integration of electronic finance policies.
Artificial Intelligence in Libraries

Artificial intelligence (AI) is one of the emerging trends and applications of computing in libraries. It involves programming computers to do things, which if done by humans, would be said to require intelligence. The ultimate promise of artificial intelligence in libraries is to develop computer systems or machines that think, behave, and in fact rival human intelligence, and this clearly has major implications on librarianship. The application of artificial intelligence in the library has become pervasive. They include expert systems for reference services, book reading and shelf-reading robots, virtual reality for immersive learning among others. Although the incorporation of artificial intelligence in libraries can be perceived to alienate librarians from their users, it will probably help libraries do more rather than taking over the jobs of librarians. It will enhance their services delivery. Artificial intelligence will greatly improve library operations and services and will upgrade and heighten the relevance of libraries in an ever-changing digital society.
The autofeat Python Library for Automated Feature Engineering and Selection

This paper describes the autofeat Python library, which provides a scikit-learn style linear regression model with automated feature engineering and selection capabilities. Complex non-linear machine learning models such as neural networks are in practice often difficult to train and even harder to explain to non-statisticians, who require transparent analysis results as a basis for important business decisions. While linear models are efficient and intuitive, they generally provide lower prediction accuracies. Our library provides a multi-step feature engineering and selection process, where first a large pool of non-linear features is generated, from which then a small and robust set of meaningful features is selected, which improve the prediction accuracy of a linear model while retaining its interpretability.
Unsupervised Discriminative Projection for Feature Selection

Feature selection is one of the most important techniques to deal with the high-dimensional data for a variety of machine learning and data mining tasks, such clustering, classification, and retrieval, etc. Fuzziness is a widespread nature of data in nature human society. However, most existing feature selection methods ignore the existence of fuzziness in the data, resulting in sub-optimal feature subsets. To address the problem, we propose a novel unsupervised feature selection method, called Unsupervised Discriminative Projection for Feature Selection (UDPFS) to select discriminative features by conducting fuzziness learning and sparse learning, simultaneously. Specifically, we use projection matrix transform data as its low-dimensional representation, which are partitioned into clusters by using membership matrix with sparse constraint. In addition, <inline-formula><tex-math notation="LaTeX">$\ell _{2, 1}$</tex-math></inline-formula> -norm regularization is applied to the projection matrix. Then, a discriminative projection matrix with row sparse is obtained by perform fuzziness learning and sparse learning, simultaneously. An effective alternative optimization algorithm is proposed to solve the objective function. Evaluate experimental results on several real-world datasets show the effectiveness and superiority of the proposed unsupervised feature selection method.
TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines

Machine Learning (ML) research has primarily focused on improving the accuracy and efficiency of the training algorithms while paying much less attention to the equally important problem of understanding, validating, and monitoring the data fed to ML. Irrespective of the ML algorithms used, data errors can adversely affect the quality of the generated model. This indicates that we need to adopt a data-centric approach to ML that treats data as a first-class citizen, on par with algorithms and infrastructure which are the typical building blocks of ML pipelines. In this demonstration we showcase TensorFlow Data Validation (TFDV), a scalable data analysis and validation system for ML that we have developed at Google and recently open-sourced. This system is deployed in production as an integral part of TFX - an end-to-end machine learning platform at Google. It is used by hundreds of product teams at Google and has received significant attention from the open-source community as well.
Blockchain Based Dynamic Spectrum Access of Non-Real-Time Data in Cyber-Physical-Social Systems

Big data sharing in Cyber-Physical-Social Systems (CPSSs) relies on wireless transmission between numerous devices, causing a serious scarcity of radio spectrum resources. Although license-free spectrum access has great potential to alleviate the growing scarcity of spectrum resources, spectrum competition is more intense due to lower access requirements. A blockchain technology may solve this competition problem by introducing a dynamic cycle of “competition-verification-synchronization-competition”. In this paper, we propose a general framework for license-free spectrum resource management in CPSSs based on blockchain technologies and smart contracts. The management framework is mainly used for edge computing of non-real-time data. In particular, we divide spectrum of a local cell into multiple channels and each channel corresponds to a blockchain. Then, we propose a blockchain-KM protocol that may improve transaction processing speed without losing typical attributes of a general blockchain. For the proposed Blockchain-KM protocol, the entire private chain becomes a multi-ring blockchain and users rely on mining or leasing to access wireless spectrum. Different from the traditional mining process, the reward in our mining process is not only virtual currency but also a spectrum access license. Once a miner obtain a spectrum access license, it will exploit the license to transmit its messages over wireless links. Also, the miner may sell its license by an auction when it does not want to transmit messages. In the auction, we introduce a virtual currency, called as Xcoin, for spectrums or other trading (e.g., paid edge computing services).