Reviewerly/exHarmony huggingface dataset:
An Improved Set-based Reasoner for the Description Logic 𝒟ℒD4,×†

We present a KE-tableau-based implementation of a reasoner for a decidable fragment of (stratified) set theory expressing the description logic 𝒟ℒ〈4LQSR,×〉(D) (𝒟ℒD4,×, for short). Our application solves the main TBox and ABox reasoning problems for 𝒟ℒD4,×. In particular, it solves the consistency and the classification problems for 𝒟ℒD4,×-knowledge bases represented in set-theoretic terms, and a generalization of the Conjunctive Query Answering problem in which conjunctive queries with variables of three sorts are admitted. The reasoner, which extends and improves a previous version, is implemented in C++. It supports 𝒟ℒD4,×-knowledge bases serialized in the OWL/XML format and it admits also rules expressed in SWRL (Semantic Web Rule Language).
Rapid Continuous Software Engineering - State of the Practice and Open Research Questions

We need to built software rapidly and with a high quality. These goals seem to be contradictory, but actually, implementing automation in build and deployment procedures as well as quality analysis can improve both the development pace and the resulting quality at the same time. Rapid Continuous Software Engineering describes novel software engineering approaches that focus on short release cycles, continuous deployment, delivery, and continuous improvement through rapid tool-assisted feedback to developers. To realize these approaches there is a need for research and innovation with respect to automation and tooling, and furthermore for research into the organizational changes that support high pace development. This paper reports on the results of the 6th International Workshop on Rapid Continuous Software Engineering (RCoSE 2020), which focuses on the challenges and potential solutions in the area of Rapid Continuous Software Engineering, before reporting on our discussions regarding the state of the practice and open research topics.
Text Generation by Learning from Demonstrations

Current approaches to text generation largely rely on autoregressive models and maximum likelihood estimation. This paradigm leads to (i) diverse but low-quality samples due to mismatched learning objective and evaluation metric (likelihood vs. quality) and (ii) exposure bias due to mismatched history distributions (gold vs. model-generated). To alleviate these problems, we frame text generation as a reinforcement learning (RL) problem with expert demonstrations (i.e., the training data), where the goal is to maximize quality given model-generated histories. Prior RL approaches to generation often face optimization issues due to the large action space and sparse reward. We propose GOLD (generation by off-policy learning from demonstrations): an easy-to-optimize algorithm that learns from the off-policy demonstrations by importance weighting. Intuitively, GOLD upweights confident tokens and downweights unconfident ones during training. According to both automatic and human evaluation, models trained by GOLD outperform those trained by MLE and policy gradient on summarization, question generation, and machine translation. Further, they are less sensitive to decoding algorithms and alleviate exposure bias.
View Materialization Over Big Data

Advances in technology have resulted in the generation of a large volume of heterogeneous big data for large enterprises engaged in e-commerce, healthcare, education, etc. This is being created at a rapid rate but is low in its veracity. This big data includes large sets of semi-structured and unstructured data and is stored over a distributed file system (DFS). This data can be processed in a fault tolerant manner using several frameworks, tools, and advanced database technologies. Big data can provide important information, which can be used for business decision making. View materialization, which has been widely studied for structured databases or data warehouse, has been extended to big data to enhance efficiency of big data query processing. This paper focuses on the selection of big data views for materialization. The big data views can be identified by extracting a set of query attributes from the set of query workload of an enterprise. The query attributes are interrelated resulting in the creation of alternate access paths for query evaluation. The cost of query processing using big data views involves the integrity of different data types of heterogeneous big data, frequency of queries, change in the size of big data, selected sets of big data materialized views, and updates on big data and these sets of materialized views. The cost of query processing is computed using the stored size of big data views on the DFS system, which is a consistent processing framework of DFS. A big data view selection algorithm that is capable of selecting views from structured, semi-structured, and unstructured data has been proposed in this paper. The proposed algorithm would select big data views that would result in faster processing of most user queries resulting in efficient decision making.
How much do we orient? A systematic approach to auditory distraction.

Data on orienting and habituation to irrelevant sound can distinguish between task-specific and general accounts of auditory distraction: Distractors either disrupt specific cognitive processes (e.g., Jones, 1993; Salamé & Baddeley, 1982), or remove more general-purpose attentional resources from any attention-demanding task (e.g., Cowan, 1995). Tested here is the prediction that there is no further auditory distraction effect on immediate serial recall with increments in the number of distractors beyond the "changing-state point" of two discrete distractors. A Bayes factor analysis refutes this nil hypothesis: This prediction, a key element of the strong changing-state hypothesis, is shown to be less likely than two competing alternatives. Quantitative predictions for distraction as a function of the number of distracters are derived for an orienting-response (OR) and a stimulus-mismatch (SMM) hypothesis, representing general and task-specific accounts respectively. The data are shown to be more likely under the SMM hypothesis. Prospects for a parametric account of auditory distraction are considered. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Game method for modelling with temporal intuitionistic fuzzy evaluations for locating the wildfire ignition point

The concept of a temporal intuitionistic fuzzy pair is introduced. It is used for an evaluation of the results of a Game Method for Modelling applied over some areas of fire in a two-dimensional orthogonal grid of cells.
Consistent estimation of small masses in feature sampling

Consider an (observable) random sample of size n from an infinite population of individuals, each individual being endowed with a finite set of features from a collection of features (Fj)j≥1 with unknown probabilities (pj)j≥1, i.e., pj is the probability that an individual displays feature Fj. Under this feature sampling framework, in recent years there has been a growing interest in estimating the sum of the probability masses pj's of features observed with frequency r≥0 in the sample, here denoted by Mn,r. This is the natural feature sampling counterpart of the classical problem of estimating small probabilities in the species sampling framework, where each individual is endowed with only one feature (or “species). In this paper we study the problem of consistent estimation of the small mass Mn,r. We first show that there do not exist universally consistent estimators, in the multiplicative sense, of the missing mass Mn,0. Then, we introduce an estimator of Mn,r and identify sufficient conditions under which the estimator is consistent. In particular, we propose a nonparametric estimator M^n,r of Mn,r which has the same analytic form of the celebrated Good--Turing estimator for small probabilities, with the sole difference that the two estimators have different ranges (supports). Then, we show that M^n,r is strongly consistent, in the multiplicative sense, under the assumption that (pj)j≥1 has regularly varying heavy tails.
Natural- to formal-language generation using Tensor Product Representations

Generating formal-language represented by relational tuples, such as Lispprograms or mathematical expressions, from a natural-language input is anextremely challenging task because it requires to explicitly capture discretesymbolic structural information from the input to generate the output. Moststate-of-the-art neural sequence models do not explicitly capture suchstructure information, and thus do not perform well on these tasks. In thispaper, we propose a new encoder-decoder model based on Tensor ProductRepresentations (TPRs) for Natural- to Formal-language generation, calledTP-N2F. The encoder of TP-N2F employs TPR 'binding' to encode natural-languagesymbolic structure in vector space and the decoder uses TPR 'unbinding' togenerate a sequence of relational tuples, each consisting of a relation (oroperation) and a number of arguments, in symbolic space. TP-N2F considerablyoutperforms LSTM-based Seq2Seq models, creating a new state of the art resultson two benchmarks: the MathQA dataset for math problem solving, and theAlgoList dataset for program synthesis. Ablation studies show that improvementsare mainly attributed to the use of TPRs in both the encoder and decoder toexplicitly capture relational structure information for symbolic reasoning.
A Stacking Ensemble of Multi Layer Perceptrons to Predict Online Shoppers' Purchasing Intention

With the rapid development of the internet, the field of E-Commerce has seen tremendous growth. The easy accessibility of viewing and purchasing products, and having it delivered to your doorstep is what makes E-Commerce extremely successful. E-Commerce also caters to almost every possible field, ranging from electronics to fashion to groceries. The number of visitors on E-Commerce websites is growing at a rapid pace, but the number of purchases remains constant. A novel stacking ensemble system has been proposed, which makes use of Multi-Layer Perceptron's to detect the intention of a user on whether a product would be purchased or not. `Online Shoppers Purchasing Intention' Dataset has been used. The proposed stacking ensemble model achieved an accuracy of 94% in predicting whether a user will purchase a product or not, based on the session details. To evaluate the proposed system, it is compared to over 15 classification algorithms, as well as existing systems that make use of the dataset. The results obtained show that the proposed stacking classifier outperforms the various classification algorithms as well as existing systems that make use of the dataset.
Improving Quantum Annealing Performance on Embedded Problems

Recently, many researchers are investigating quantum annealing as a solver for real-world combinatorial optimization problems. However, due to the format of problems that quantum annealing solves and the structure of the physical annealer, these problems often require additional setup prior to solving. We study how these setup steps affect performance and provide insight into the interplay among them using the job-shop scheduling problem for our evaluation. We show that the empirical probability of success is highly sensitive to problem setup and that excess variables and large embeddings reduce performance. We then show that certain problem instances are unable to be solved without the use of additional post-processing methods. Finally, we investigate the effect of pausing during the anneal. Our results show that pausing within a certain time window can improve the probability of success, which is consistent with other work. However, we also show that the performance improvement due to pausing can be masked depending on properties of the embedding, and thus, special care must be taken for embedded problems.
End-to-end novel visual categories learning via auxiliary self-supervision

Semi-supervised learning has largely alleviated the strong demand for large amount of annotations in deep learning. However, most of the methods have adopted a common assumption that there is always labeled data from the same class of unlabeled data, which is impractical and restricted for real-world applications. In this research work, our focus is on semi-supervised learning when the categories of unlabeled data and labeled data are disjoint from each other. The main challenge is how to effectively leverage knowledge in labeled data to unlabeled data when they are independent from each other, and not belonging to the same categories. Previous state-of-the-art methods have proposed to construct pairwise similarity pseudo labels as supervising signals. However, two issues are commonly inherent in these methods: (1) All of previous methods are comprised of multiple training phases, which makes it difficult to train the model in an end-to-end fashion. (2) Strong dependence on the quality of pairwise similarity pseudo labels limits the performance as pseudo labels are vulnerable to noise and bias. Therefore, we propose to exploit the use of self-supervision as auxiliary task during model training such that labeled data and unlabeled data will share the same set of surrogate labels and overall supervising signals can have strong regularization. By doing so, all modules in the proposed algorithm can be trained simultaneously, which will boost the learning capability as end-to-end learning can be achieved. Moreover, we propose to utilize local structure information in feature space during pairwise pseudo label construction, as local properties are more robust to noise. Extensive experiments have been conducted on three frequently used visual datasets, i.e., CIFAR-10, CIFAR-100 and SVHN, in this paper. Experiment results have indicated the effectiveness of our proposed algorithm as we have achieved new state-of-the-art performance for novel visual categories learning for these three datasets.
On-demand Serverless Video Surveillance with Optimal Deployment of Deep Neural Networks

We present an approach to optimally deploy Deep Neural Networks (DNNs) in serverless cloud architectures.A serverless architecture allows running code in response to events, automatically managing the required computing resources.However, these resources have limitations in terms of execution environment (CPU only), cold starts, space, scalability, etc.These limitations hinder the deployment of DNNs, especially considering that fees are charged according to the employed resources and the computation time.Our deployment approach is comprised of multiple decoupled software layers that allow effectively managing multiple processes, such as business logic, data access, and computer vision algorithms that leverage DNN optimization techniques.Experimental results in AWS Lambda reveal its potential to build cost-effective ondemand serverless video surveillance systems.
Text-Linguistic Analysis in Forensic Authorship Attribution

Authorship analysis of anonymous texts is one of the more frequently required tasks in a forensic setting. Its main purpose is either to gain information on the author’s linguistic and social background to support an investigation or to match an anonymous text to a suspect’s previous writings. One approach to authorship attribution is pragmatic stylistic analysis, which is grounded in text-linguistic research, cf. Brinker (2002), Sandig (2006), Puschel (2009), and Brinker et al. (2018), and holds a broad and holistic view on style. In its analyses, it focuses on the functional and pragmatic aspects of style as part of a communicative strategy. A central element, especially in Brinker’s approach, is the thematic text pattern. How individuals argue, how they arrange textual patterns and how they express their demands most certainly reveal aspects of their individuality (Brinker, 2002; Brinker et al., 2018), suggesting that these cannot be easily suppressed or disguised. The paper applies Brinker’s approach to three very short anonymous extortion letters illustrating how text-linguistic analysis can contribute to authorship attribution. Firstly, the text structure of the letters will be analyzed and secondly, the relations between text pattern and stylistic features will be examined in detail, and the relevance of the text patterns will be discussed with reference to the author’s idiolect. The paper aims to point out the possibilities of a more in-depth textual analysis beyond the analysis of surface structures, especially in cases of very small data sets. Cite as: Fobbe , JLL 9 (2020), 93–114, DOI: 10.14762/jll.2020.093
Cognitive Pervasive Service Composition Applied to Predatory Crime Deterrence

Pervasive service composition is useful in many scenarios, for instance, in urban planning or controlled harvest. Currently, there is no standard to develop solutions using pervasive service composition. However, big companies propose their frameworks to develop complex services, but their frameworks are appropriate in specific applications, such as home automation and agriculture. On the other hand, there are different very well-grounded academic proposals for pervasive service composition. However, these do not solve the problems of traditional approaches that are appropriate to specific areas of application, and adaptation is needed to deal with the dynamism of the environment. This article presents a cognitive approach for pervasive service composition where InfoCom devices and the implementation of cognitive functions interact to create pervasive composite services. Our central hypothesis is that cognitive theory can help solve actual problems requiring pervasive service composition, as it addresses the above-mentioned problems. To test our approach, in this article we present a case of urban insecurity. Specifically, in different countries, street robbery using firearms is one of the problems with a high impact because of its frequency. This article proposes to compose a pervasive service for deterring criminals from committing their crimes. The results obtained by simulating our proposal in our case study are promising. However, more research needs to be achieved before applying the proposed approach to actual problems. The research needed ought to address various problems, some of which are discussed in this article.
MYSEJAHTERA APP AND THE PRIVACY RIGHTS IN TIMES OF COVID 19: THE LEGAL AND SYARIAH PERSPECTIVES

The year of 2020 marks the most challenging year when a global health crisis due to the outbreak of COVID-19 pandemic worldwide. In Malaysia, the government took an approach by developing a contact-tracing application, known as MySejahtera to assist the government in managing the outbreaks and to enable the Ministry of Health to slow down the spread of the virus. Nevertheless, the technology has raised the data privacy concern where the app requires various users’ personal details to be inserted before the app can be used. Based on qualitative and doctrinal methods, this article examines whether the government can collect personal information of its citizens in times of pandemic, and to what extent such data should be protected. To do, this paper will be focusing on the invasion of the right to privacy by the government from the legal and Syariah perspectives. The finding suggests that in the time of  pandemic COVID-19, it is legal for the government to collect confidential information of its citizens based on the concept of public interest (Maslahah ‘Ammah) grounded from the Syariah principle. Alternatively, it is vital to set the timeline and the data should be destroyed at a certain period. It concludes that the contact tracing apps is inevitable and as an essential tool in combating the outbreaks amidst privacy invasion
Hamiltonian estimation based on adaptive weak value amplification

Abstract Hamiltonian estimation is a cardinal task in applications of quantum metrology, quantum computation, quantum simulation, etc. When the evolution time is short in order to suppress noise and enhance sensitivity, the measurement and postprocessing may incur high cost. In this paper, we propose an adaptive weak measurement scheme for Hamiltonian estimation, which adjusts the pre- and post-selection according to the difference of the mean shift between two orthogonal post-selected states. According to the angle of the post-selected state, the proposed scheme can change from weak value amplification (WVA) to joint weak measurement (JWM). A study of the quantum Fisher information shows that the JWM situation is always optimal. Meanwhile, the WVA approach loses a little information but it can inherit the WVA’s technical advantages, which are suitable for short evolution times. The widely applicable scope of the proposed scheme makes us believe that it offers a new path for the understanding of unknown Hamiltonians.
3D Fetal Face Reconstruction from Ultrasound Imaging

The fetal face contains essential information in the evaluation of congenital malformations and the fetal brain function, as its development is driven by genetic factors at early stages of embryogenesis.Three-dimensional ultrasound (3DUS) can provide information about the facial morphology of the fetus, but its use for prenatal diagnosis is challenging due to imaging noise, fetal movements, limited field-of-view, low soft-tissue contrast, and occlusions.In this paper, we propose a fetal face reconstruction algorithm from 3DUS images based on a novel statistical morphable model of newborn faces, the BabyFM.We test the feasibility of using newborn statistics to accurately reconstruct fetal faces by fitting the regularized morphable model to the noisy 3DUS images.The algorithm is capable of reconstructing the whole facial morphology of babies from one or several ultrasound scans to handle adverse conditions (e.g.missing parts, noisy data), and it has the potential to aid in-utero diagnosis for conditions that involve facial dysmorphology.
Semi-supervised Learning with a Teacher-Student Paradigm for Histopathology Classification: A Resource to Face Data Heterogeneity and Lack of Local Annotations

Training classification models in the medical domain is often difficult due to data heterogeneity (related to acquisition procedures) and due to the difficulty of getting sufficient amounts of annotations from specialized experts. It is particularly true in digital pathology, where models do not generalize easily. This paper presents a novel approach for the generalization of models in conditions where heterogeneity is high and annotations are few. The approach relies on a semi-supervised teacher/student paradigm to different datasets and annotations. The paradigm combines a small amount of strongly-annotated data, with a large amount of unlabeled data, for training two Convolutional Neural Networks (CNN): the teacher and the student model. The teacher model is trained with strong labels and used to generate pseudo-labeled samples from the unlabeled data. The student model is trained combining the pseudo-labeled samples and a small amount of strongly-annotated data. The paradigm is evaluated on the student model performance of Gleason pattern and Gleason score classification in prostate cancer images and compared with a fully-supervised learning approach for training the student model. In order to evaluate the capability of the approach to generalize, the datasets used are highly heterogeneous in visual characteristics and are collected from two different medical institutions. The models, trained with the teacher/student paradigm, show an improvement in performance above the fully-supervised training. The models generalize better on both the datasets, despite the inter-datasets heterogeneity, alleviating the overfitting. The classification performance shows an improvement both in the classification of Gleason pattern at patch level (\(\kappa \,=\,0.6129\, \pm \, 0.0127\) from \(\kappa \,=\,0.5608\pm 0.0308\)) and at in Gleason score classification, evaluated at WSI-level \(\kappa \,=\,0.4477\pm 0.0460\) from \(\kappa \,=\,0.2814\pm 0.1312\)).