Reviewerly/exHarmony huggingface dataset:
Disruptive Engagements With Technologies, Robotics, and Caring

Human caring, founded on a Japanese caring perspective and nursing theory, fosters innovative and creative ideas for an aging society. The growing reality of health care dependency on technology presents a temptation to give robots utility as partners in nursing practice. Human caring expressed in human-to-human relationships, and also between humans and nonhumans, is a futuristic model for health care, with humanoid robots as major supporters. The purpose of this article is to explore this disruptive technology, along with its functions and characteristics. Tanioka's Transactive Relationship Theory of Nursing is shared along with its relevance for addressing technological disruptions in health care.
Performance-driven adaptive differential evolution with neighborhood topology for numerical optimization

This paper presents a novel differential evolution algorithm for numerical optimization by making full use of the neighborhood information to balance exploration and exploitation. To effectively meet the search requirement of each individual, a neighborhood-based adaptive mutation strategy is developed by using the ring topology to construct an elite individual set and adaptively choosing a suitable elite individual to guide its search according to its neighborhood performance. Then, a neighborhood-based adaptive parameter setting is designed to improve the suitability of parameters for each individual by utilizing the feedback information of population and its neighbors simultaneously. Furthermore, a restart mechanism is proposed to further enhance the performance of algorithm by adaptively strengthening the search abilities of unpromising individuals, removing the worse individuals and randomly replacing some individuals with Gaussian Walks. Differing from the existing DE variants, the proposed algorithm adaptively guides the search and suitably adjusts the parameters for each individual by using its neighborhood performance, and strengthens the exploitation and exploration by removing the worse individuals and randomly replacing some individuals. Then it could properly adjust the search ability of each individual, and effectively balance diversity and convergence. Compared with 16 typical algorithms, the numerical results on 30 IEEE CEC2014 benchmark functions show that the proposed algorithm has better performance.
Deep learning for noise-tolerant RDFS reasoning1

Since the 2001 envisioning of the Semantic Web (SW) (Scientific American 284(5) (2001) 34–43), the main research focus in SW reasoning has been on the soundness and completeness of reasoners. While these reasoners assume the veracity of input data, the reality is that the Web of data is inherently noisy. Although there has been recent work on noise-tolerant reasoning, it has focused on type inference rather than full RDFS reasoning. Even though RDFS closure generation can be seen as a Knowledge Graph (KG) completion problem, the problem setting is different – making KG embedding techniques that were designed for link prediction not suitable for RDFS reasoning. This paper documents a novel approach that extends noise-tolerance in the SW to full RDFS reasoning. Our embedding technique – that is tailored for RDFS reasoning – consists of layering RDF graphs and encoding them in the form of 3D adjacency matrices where each layer layout forms a graph word. Each input graph and its entailments are then represented as sequences of graph words, and RDFS inference can be formulated as translation of these graph words sequences, achieved through neural machine translation. Our evaluation on LUBM1 synthetic dataset shows 97% validation accuracy and 87.76% on a subset of DBpedia while demonstrating a noise-tolerance unavailable with rule-based reasoners.
AI Based Automatic Robbery/Theft Detection using Smart Surveillance in Banks

Deep learning is the segment of artificial intelligence which is involved with imitating the learning approach that human beings utilize to get some different types of knowledge. Analyzing videos, a part of deep learning is one of the most basic problems of computer vision and multi-media content analysis for at least 20 years. The job is very challenging as the video contains a lot of information with large differences and difficulties. Human supervision is still required in all surveillance systems. New advancement in computer vision which are observed as an important trend in video surveillance leads to dramatic efficiency gains. We propose a CCTV based theft detection along with tracking of thieves. We use image processing to detect theft and motion of thieves in CCTV footage, without the use of sensors. This system concentrates on object detection. The security personnel can be notified about the suspicious individual committing burglary using Real-time analysis of the movement of any human from CCTV footage and thus gives a chance to avert the same.
Experimental investigation of the effect of classical noise on quantum non-Markovian dynamics

We provide an experimental study of the relationship between the action of different classical noises on the dephasing dynamics of a two-level system and the non-Markovianity of the quantum dynamics. The two-level system is encoded in the photonic polarization degrees of freedom and the action of the noise is obtained via a spatial light modulator, thus allowing for an easy engineering of different random environments. The quantum non-Markovianity of the dynamics driven by classical Markovian and non-Markovian noise, both Gaussian and non-Gaussian, is studied by means of the trace distance. Our study clearly shows the different nature of the notion of non-Markovian classical process and non-Markovian quantum dynamics.
Breast Cancer Prediction using Feature Selection and Ensemble Voting

Breast cancer is the most common cause of cancer among women worldwide. This paper analyses the performance of supervised and unsupervised models for breast cancer classification. Data from Wisconsin Breast Cancer Dataset is used in this paper. Feature selection is processed through scaling and principal component analysis. Final results indicate that Ensemble Voting approach is ideal as a predictive model for breast cancer. The raw data has 569 cases of breast cancer. The data is split into training and testing sets in the ration 70:30, respectively. The benchmark model is then created using Random Forest method. Various models are trained and tested on the data after Feature Scaling and Principle Component Analysis. Cross-validation is performed which showed that our model is stable. Among all the evaluated models, only four models, i.e., Ensemble - Voting Classifier, Logistics Regression, SVM Tuning and AdaBoost returned with accuracy of at least 98%. Based on results of the precision and recall, ROC-AVC, Fl-measure and computational time of the models, the Ensemble showed the most potential in breast cancer classification of the given dataset.
Probabilistic exact universal quantum circuits for transforming unitary operations

This paper addresses the problem of designing universal quantum circuits to transform $k$ uses of a $d$-dimensional unitary input-operation into a unitary output-operation in a probabilistic heralded manner. Three classes of protocols are considered, parallel circuits, where the input-operations can be simultaneously, adaptive circuits, where sequential uses of the input-operations are allowed, and general protocols, where the use of the input-operations may be performed without a definite causal order. For these three classes, we develop a systematic semidefinite programming approach that finds a circuit which obtains the desired transformation with the maximal success probability. We then analyse in detail three particular transformations; unitary transposition, unitary complex conjugation, and unitary inversion. For unitary transposition and unitary inverse, we prove that for any fixed dimension $d$, adaptive circuits have an exponential improvement in terms of uses $k$ when compared to parallel ones. For unitary complex conjugation and unitary inversion we prove that if the number of uses $k$ is strictly smaller than $d-1$, the probability of success is necessarily zero. We also discuss the advantage of indefinite causal order protocols over causal ones and introduce the concept of delayed input-state quantum circuits.
GHI forecasting using Gaussian process regression: kernel study

In this paper, online Gaussian process regression (GPR) is used to model and forecast Global Horizontal Irradiance, at forecast horizons ranging from 30 min to 5 h. It is shown that the covariance function (or kernel) is a key element, deeply influencing forecast results. As a consequence, Gaussian processes with simple kernels and with more complex kernels have been tested and compared to the classic persistence model. Using two datasets of 45 days, it is shown that online GPR models based on quasiperiodic kernels outperform both the persistence model and GPR models based on simple kernels, including the widely used squared exponential kernel.
Deep generative ranking for personalized recommendation

Recommender systems offer critical services in the age of mass information. Personalized ranking has been attractive both for content providers and customers due to its ability of creating a user-specific ranking on the item set. Although the powerful factor-analysis methods including latent factor models and deep neural network models have achieved promising results, they still suffer from the challenging issues, such as sparsity of recommendation data, uncertainty of optimization, and etc. To enhance the accuracy and generalization of recommender system, in this paper, we propose a deep generative ranking (DGR) model under the Wasserstein autoencoder framework. Specifically, DGR simultaneously generates the pointwise implicit feedback data (via a Beta-Bernoulli distribution) and creates the pairwise ranking list by sufficient exploiting both interacted and non-interacted items for each user. DGR can be efficiently inferred by minimizing its penalized evidence lower bound. Meanwhile, we theoretically analyze the generalization error bounds of DGR model to guarantee its performance in extremely sparse feedback data. A series of experiments on four large-scale datasets (Movielens (20M), Netflix, Epinions and Yelp in movie, product and business domains) have been conducted. By comparing with the state-of-the-art methods, the experimental results demonstrate that DGR consistently benefit the recommendation system in ranking estimation task, especially for the near-cold-start-users (with less than five interacted items).
Where’s My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution

We provide the first computational treatment of fused-heads constructions (FHs), focusing on the numeric fused-heads (NFHs). FHs constructions are noun phrases in which the head noun is missing and is said to be “fused” with its dependent modifier. This missing information is implicit and is important for sentence understanding. The missing references are easily filled in by humans but pose a challenge for computational models. We formulate the handling of FHs as a two stages process: Identification of the FH construction and resolution of the missing head. We explore the NFH phenomena in large corpora of English text and create (1) a data set and a highly accurate method for NFH identification; (2) a 10k examples (1 M tokens) crowd-sourced data set of NFH resolution; and (3) a neural baseline for the NFH resolution task. We release our code and data set, to foster further research into this challenging problem.
PDMFRec

Conventional approaches to matrix factorisation (MF) typically rely on a centralised collection of user data for building a MF model. This approach introduces an increased risk when it comes to user privacy. In this short paper we propose an alternative, user-centric, privacy enhanced, decentralised approach to MF. Our method pushes the computation of the recommendation model to the user's device, and eliminates the need to exchange sensitive personal information; instead only the loss gradients of local (device-based) MF models need to be shared. Moreover, users can select the amount and type of information to be shared, for enhanced privacy. We demonstrate the effectiveness of this approach by considering different levels of user privacy in comparison with state-of-the-art alternatives.
Conversational Interfaces for Explainable AI: A Human-Centred Approach

One major goal of Explainable Artificial Intelligence (XAI) in order to enhance trust in technology is to enable the user to enquire information and explanation directly from an intelligent agent. We propose Conversational Interfaces (CIs) to be the perfect setting, since they are intuitive for humans and computationally processible. While there are many approaches addressing technical and agent related issues of this human-agent communication problem, the user perspective appears to be widely neglected. With the goal of better requirement understanding and identification of implicit user expectations, a Wizard of Oz (WoZ) experiment was conducted, where participants tried to elicit basic information from a pretended artificial agent via Conversational Interface (What are your capabilities?). Chats were analysed by means of Conversation Analysis, where the hypothesis that users pursue fundamentally different strategies could be verified. Stated results illustrate the vast variety in human communication and disclose both requirements of users and obstacles in the implementation of protocols for interacting agents. Finally, we inferred essential indications for the implementation of such a CI. The findings show that existing intent-based design of Conversational Interfaces is very limited, even in a well-defined task-based interaction.
Towards Private, Robust, and Verifiable Crowdsensing Systems via Public Blockchains

Public blockchains have emerged as a promising direction in revolutionizing existing data-driven systems relying on centralized service providers. Among others, one kind of such systems is the popular crowdsensing systems which promise convenient data collection and aggregation. Although promising, leveraging public blockchains to build crowdsensing systems is non-trivial and has to overcome several barriers. First, public blockchains are transparent and lack support for data privacy. Second, participants from the open blockchain environment may misbehave in serving crowdsensing applications, like providing invalid data or doing aggregation incorrectly. Further, on-chain processing incurs monetary cost, so simply putting all workload on-chain is highly uneconomical and a delicate joint on-chain and off-chain design is required. In this paper, we take the first research attempt and explore a new design point to bridge public blockchains with crowdsensing systems. We propose a framework for building private, robust, and verifiable blockchain-empowered crowdsensing systems. It features an open service paradigm where blockchain nodes can rent out their computing resources to serve crowdsensing applications, with custom and full-fledged mechanisms to foster a healthy and economical ecosystem and to simultaneously tackle the challenges of data privacy, robustness against misbehaving participants, and service correctness assurance. Extensive experiments demonstrate our designs practicality.
Impact of design patterns on software quality: a systematic literature review

The impact of design patterns on quality attributes has been extensively evaluated in studies with different perspectives, objectives, metrics, and quality attributes, leading to contradictive and hard to compare results. The authors’ objective is to explain these results by considering confounding factors, practices, metrics, or implementation issues that affect quality. Furthermore, there is a lack of research that connects design patterns evaluations to patterns development studies. Accordingly, they also aim at providing an initiate on how patterns structure and implementation can be improved, to promote software quality. To achieve their goals, conducted a systematic literature review by searching the literature for related studies. The study covers the period between years 2000 and 2018. They identified 804 candidate papers. After applying inclusion and exclusion criteria, they were left with 50 primary studies. Their results show that documentation of patterns, size of pattern classes, and the scattering degree of patterns have clear impact on quality. In case studies, researchers used different metrics applied to different modules. Controlled experiments have major design differences. Reaching consensuses on the effect of patterns requires considering influencing factors, using unified metrics, and an agreement on what modules to measure. Studying how to improve patterns modularity is recommended for future research.
Bounds and Constructions of Codes Over Symbol-Pair Read Channels

Cassuto and Blaum recently studied the symbol-pair channel, a model where every two consecutive symbols are read together. This special channel structure is motivated by the limitations of the reading process in high density data storage systems, where it is no longer possible to read individual symbols. In this new paradigm, the errors are not individual symbol errors, but rather symbol-pair errors, where at least one of the symbols is erroneous. In this work, we study bounds and constructions of codes over the symbol-pair channel. We extend the Johnson bound and the linear programming bound for this channel and show that they improve upon existing bounds. We then propose new code constructions that improve upon existing results for pair-distance six, seven, and ten.
Towards Automated Auditing with Machine Learning

We present the Automated List Inspection (ALI) tool that utilizes methods from machine learning, natural language processing, combined with domain expert knowledge to automate financial statement auditing. ALI is a content based context-aware recommender system, that matches relevant text passages from the notes to the financial statement to specific law regulations. In this paper, we present the architecture of the recommender tool which includes text mining, language modeling, unsupervised and supervised methods that range from binary classification models to deep recurrent neural networks. Next to our main findings, we present quantitative and qualitative comparisons of the algorithms as well as concepts for how to further extend the functionality of the tool.
Distributed mining of high utility time interval sequential patterns using mapreduce approach

High Utility Sequential Pattern mining (HUSP) algorithms aim to find all the high utility sequences from a sequence database. Due to the large explosion of data, recently few distributed algorithms have been designed for mining HUSPs based on the MapReduce framework. However, the existing HUSP algorithms such as USpan, HUS-Span and BigHUSP are able to predict only the order of items, they do not predict the time between the items, that is, they do not include the time intervals between the successive items. But in a real-world scenario, time interval patterns provide more valuable information than conventional high utility sequential patterns. Therefore, we propose a distributed high utility time interval sequential pattern mining (DHUTISP) algorithm using the MapReduce approach that is suitable for big data. DHUTISP creates a novel time interval utility linked list data structure (TIUL) to efficiently calculate the utility of the resulting patterns. Moreover, two utility upper bounds, namely, remaining utility upper bound (RUUB) and co-occurrence utility upper bound (CUUB) are proposed to prune the unpromising candidates. We conducted various experiments to prove the efficiency of the proposed algorithm over both the distributed and non-distributed approaches. The experimental results show the efficiency of DHUTISP over state-of-the-art algorithms, namely, BigHUSP, AHUS-P, PUSOM and UTMining_A.
MCTE: Minimizes Task Completion Time and Execution Cost to Optimize Scheduling Performance for Smart Grid Cloud

Cloud computing is popular in nowadays for its convenient and cheap.Grid provides services of available everywhere, and shares everyone.Therefore, smart grid cloud is a good way to manage data for sharing with all power supply stations.Grid cloud task scheduling is one of the key technologies that affect resource allocation efficiency in cloud computing environment.The advantages and disadvantages of scheduling algorithms will directly affect the scheduling performance of both cloud computing and the stability of the entire system platform.Cloud task scheduling problem has been proved to be a NP-hard problem, The traditional task scheduling algorithm can no longer meet the actual needs of cloud task scheduling, but the Heuristic algorithm is an effective method to solve this problem.This paper studies and analyzes the application of heuristic algorithms in cloud task scheduling problems, and proposes a cloud task scheduling strategy to minimize the task completion time and execution cost (MCTE) for the smart grid cloud.Then, carry out mathematical modeling on the grid cloud task scheduling problem.The experimental results show MCTE is well for the smart grid cloud.
Predicting Crime Using Time and Location Data

To have a better response towards criminal activity, it is very important that one should understand the patterns in crime. We analyze this pattern by taking crime datasets from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. This dataset includes different blocks of the city of Chicago. The major aim of this mission is to expect which category of crime is most probably to take place at a detailed time and places in Chicago. Finally, this paper uses a different algorithm like Random Forest, Decision Tree and different ensemble methods such as Extra Trees, Bagging and AdaBoost to evaluate the accuracy given by each algorithm.
Detecting Toxicity Triggers in Online Discussions

Despite the considerable interest in the detection of toxic comments, there has been little research investigating the causes -- i.e., triggers -- of toxicity. In this work, we first propose a formal definition of triggers of toxicity in online communities. We proceed to build an LSTM neural network model using textual features of comments, and then, based on a comprehensive review of previous literature, we incorporate topical and sentiment shift in interactions as features. Our model achieves an average accuracy of 82.5% of detecting toxicity triggers from diverse Reddit communities.