Reviewerly/exHarmony huggingface dataset:
Predicting Di-2-Ethylhexyl Phthalate Toxicity: Hybrid Integrated Harris Hawks Optimization With Support Vector Machines

Phthalic acid esters (PAEs) are organic pollutants and synthetic compounds and have adverse effects on human health. In this study, we investigated whether Di-2-Ethylhexyl phthalate (DEHP), one of many PAEs, has adverse effects on rats. Adult male Sprague-Dawley rats were treated daily by oral gavage with vehicle (corn oil) or DEHP at a dose of 3000 mg/kg/day for 15 days. The results showed that DEHP caused hepatotoxicity in rats. When compared with the control group, relative liver weights, and serum alanine, aminotransferase levels significantly increased after DEHP exposure. Hepatocyte swelling and degeneration were also found in DEHP-exposed rats. This study proposes an effective intelligence framework for the prediction of DEHP poisoning. The framework is designed by integrating an enhanced Harris hawks optimization (HHO) with a support vector machine (SVM), which is called SGLHHO-SVM. The core characteristic of the developed methodology is the SGLHHO algorithm that integrates the levy mechanism and two core operators abstracted from the salp swarm algorithm and grey wolf optimizer to enhance and restore the search capabilities of the HHO. The presented SGLHHO approach is used to tackle the key parameter pair optimization of the SVM, and it is also utilized to grab the optimal feature subset. Regarding the optimal feature subset and the pair parameter simultaneously, SGLHHO-SVM can autonomously predict the DEHP poisoning. The developed SGLHHO was conducted on 23 benchmark problems and compared with other state-of-the-art and competitive methods. The results demonstrate that the designed SGLHHO performs superior to other competitors on most benchmark problems. Furthermore, the proposed SGLHHO-SVM is also compared with other machine learning algorithms on a real-life DEHP sampled data. Statistical results verify the proposal can show better predictive property and higher stability on all for metrics. Therefore, the SGLHHO-SVM may be served as a potential computer-aided tool for the prediction of DEHP poisoning.
Adaptation of Machine Translation Models with Back-Translated Data Using Transductive Data Selection Methods

Data selection has proven its merit for improving Neural Machine Translation (NMT), when applied to authentic data. But the benefit of using synthetic data in NMT training, produced by the popular back-translation technique, raises the question if data selection could also be useful for synthetic data? In this work we use Infrequent n-gram Recovery (INR) and Feature Decay Algorithms (FDA), two transductive data selection methods to obtain subsets of sentences from synthetic data. These methods ensure that selected sentences share n-grams with the test set so the NMT model can be adapted to translate it. Performing data selection on back-translated data creates new challenges as the source-side may contain noise originated by the model used in the back-translation. Hence, finding n-grams present in the test set become more difficult. Despite that, in our work we show that adapting a model with a selection of synthetic data is an useful approach.
Effect Improved for High-Dimensional and Unbalanced Data Anomaly Detection Model Based on KNN-SMOTE-LSTM

High-dimensional and unbalanced data anomaly detection is common. Effective anomaly detection is essential for problem or disaster early warning and maintaining system reliability. A significant research issue related to the data analysis of the sensor is the detection of anomalies. The anomaly detection is essentially an unbalanced sequence binary classification. The data of this type contains characteristics of large scale, high complex computation, unbalanced data distribution, and sequence relationship among data. This paper uses long short-term memory networks (LSTMs) combined with historical sequence data; also, it integrates the synthetic minority oversampling technique (SMOTE) algorithm and K-nearest neighbors (kNN), and it designs and constructs an anomaly detection network model based on kNN-SMOTE-LSTM in accordance with the data characteristic of being unbalanced. This model can continuously filter out and securely generate samples to improve the performance of the model through kNN discriminant classifier and avoid the blindness and limitations of the SMOTE algorithm in generating new samples. The experiments demonstrated that the structured kNN-SMOTE-LSTM model can significantly improve the performance of the unbalanced sequence binary classification.
Weather Observations Website

WOW is a cloud-based platform that allows members of the public to share observations with the world. WOW is being used to combine crowdsourced data with observations from the Met Office's official network of automatic surface stations to produce analyses for nowcasting.
The influence of organisational culture and information security culture on employee compliance behaviour

Purpose Organisational culture plays an important role in influencing employee compliance with information security policies. Creating a subculture of information security can assist in facilitating compliance. The purpose of this paper is to explain the nature of the combined influence of organisational culture and information security culture on employee information security compliance. This study also aims to explain the influence of organisational culture on information security culture. Design/methodology/approach A theoretical model was developed showing the relationships between organisational culture, information security culture and employee compliance. Using an online survey, data was collected from a sample of individuals who work in organisations having information security policies. The data was analysed with Partial Least Square Structural Equation Modelling (PLS-SEM) to test the model. Findings Organisational culture and information security culture have significant, yet similar influences on employee compliance. In addition, organisational culture has a strong causal influence on information security culture. Practical implications Control-oriented organisational cultures are conducive to information security compliant behaviour. For an information security subculture to be effectively embedded in an organisation's culture, the dominant organisational culture would have to be considered first. Originality/value This research provides empirical evidence that information security subculture is influenced by organisational culture. Compliance is best explained by their joint influence.
Design and data analysis of wearable sports posture measurement system based on Internet of Things

Aiming at sports and daily training of athletes in rehabilitation therapy, a wearable sports posture measurement system is proposed in combination with the Internet of Things technology. By choosing a professional coach to wear the system, the experiment was verified with common sports postures, and the actual measured sports information was analysed and processed. The experimental results show that the system can perform stable online monitoring of the exercise process, and the data analysis results have a certain reference value for the evaluation of the patient's physical condition during rehabilitation and the daily training evaluation of athletes.
Clustered Discriminant Regression for High-Dimensional Data Feature Extraction and Its Applications in Healthcare and Additive Manufacturing

The recent increase in applications of high-dimensional data poses a severe challenge to data analytics, such as supervised classification, particularly for online applications. To tackle this challenge, efficient and effective methods for feature extraction are critical to the performance of classification analysis. The objective of this work is to develop a new supervised feature extraction method for high-dimensional data. It is achieved by developing a clustered discriminant regression (CDR) to extract informative and discriminant features for high-dimensional data. In CDR, the variables are clustered into different groups or subspaces, within which feature extraction is performed separately. The CDR algorithm, which is a greedy approach, is implemented to obtain the solution toward optimal feature extraction. One numerical study is performed to demonstrate the performance of the proposed method for variable selection. Three case studies using healthcare and additive manufacturing data sets are accomplished to demonstrate the classification performance of the proposed methods for real-world applications. The results clearly show that the proposed method is superior over the existing method for high-dimensional data feature extraction. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article forwards a new supervised feature extraction method termed clustered discriminant regression. This method is highly effective for classification analysis of high-dimensional data, such as images or videos, where the number of variables is much larger than the number of samples. In our case studies on healthcare and additive manufacturing, the performance of classification analysis based on our method is superior over the existing feature extraction methods, which is confirmed by using various popular classification algorithms. For image classification, our method with elaborately selected classification algorithms can outperform a convolutional neural network. In addition, the computation efficiency of the proposed method is also promising, which enables its online applications, such as advanced manufacturing process monitoring and control.
Immersive virtual environments and embodied agents for e-learning applications

Massive Open Online Courses are a dominant force in remote-learning yet suffer from persisting problems stemming from lack of commitment and low completion rates. In this initial study we investigate how the use of immersive virtual environments for Power-Point based informational learning may benefit learners and mimic traditional lectures successfully. We examine the role of embodied agent tutors which are frequently implemented within virtual learning environments. We find similar performance on a bespoke knowledge test and metrics for motivation, satisfaction, and engagement by learners in both real and virtual environments, regardless of embodied agent tutor presence. Our results raise questions regarding the viability of using virtual environments for remote-learning paradigms, and we emphasise the need for further investigation to inform the design of effective remote-learning applications.
Edge Computing Intelligence Using Robust Feature Selection for Network Traffic Classification in Internet-of-Things

Internet-of-Things (IoT) devices are massively interconnected, which generates a massive amount of network traffic. The concept of edge computing brings a new paradigm to monitor and manage network traffic at the network’s edge. Network traffic classification is a critical task to monitor and identify Internet traffic. Recent traffic classification works suggested using statistical flow features to classify network traffic accurately using machine learning techniques. The selected classification features must be stable and can work across different spatial and temporal heterogeneity. This paper proposes a feature selection mechanism called Ensemble Weight Approach (EWA) for selecting significant features for Internet traffic classification based on multi-criterion ranking and selection mechanisms. Extensive simulations have been conducted using publicly-available traces from the University of Cambridge. The simulation results demonstrate that EWA is capable of identifying stable features subset for Internet traffic identification. EWA-selected features improve the mean accuracy up to 1.3% and reduce RMSE using fewer features than other feature selection methods. The smaller number of features directly contributes to shorter classification time. Furthermore, the selected features can train stable traffic classification generative models irrespective of the dataset’s spatial and temporal differences, with consistent accuracy up to 97%. The overall performance indicates that EWA-selected statistical flow features can improve the overall traffic classification.
Adversarial Generative Grammars for Human Activity Prediction

In this paper we propose an adversarial generative grammar model for future prediction. The objective is to learn a model that explicitly captures temporal dependencies, providing a capability to forecast multiple, distinct future activities. Our adversarial grammar is designed so that it can learn stochastic production rules from the data distribution, jointly with its latent non-terminal representations. Being able to select multiple production rules during inference leads to different predicted outcomes, thus efficiently modeling many plausible futures. The adversarial generative grammar is evaluated on the Charades, MultiTHUMOS, Human3.6M, and 50 Salads datasets and on two activity prediction tasks: future 3D human pose prediction and future activity prediction. The proposed adversarial grammar outperforms the state-of-the-art approaches, being able to predict much more accurately and further in the future, than prior work. Code will be open sourced.
Polarimetric Multi-view Inverse Rendering

A polarization camera has great potential for 3D reconstruction since the angle of polarization (AoP) of reflected light is related to an object’s surface normal. In this paper, we propose a novel 3D reconstruction method called Polarimetric Multi-View Inverse Rendering (Polarimetric MVIR) that effectively exploits geometric, photometric, and polarimetric cues extracted from input multi-view color polarization images. We first estimate camera poses and an initial 3D model by geometric reconstruction with a standard structure-from-motion and multi-view stereo pipeline. We then refine the initial model by optimizing photometric rendering errors and polarimetric errors using multi-view RGB and AoP images, where we propose a novel polarimetric cost function that enables us to effectively constrain each estimated surface vertex’s normal while considering four possible ambiguous azimuth angles revealed from the AoP measurement. Experimental results using both synthetic and real data demonstrate that our Polarimetric MVIR can reconstruct a detailed 3D shape without assuming a specific polarized reflection depending on the material.
On Bounded Distance Decoding with Predicate: Breaking the “Lattice Barrier” for the Hidden Number Problem

Lattice-based algorithms in cryptanalysis often search for a target vector satisfying integer linear constraints as a shortest or closest vector in some lattice. In this work, we observe that these formulations may discard non-linear information from the underlying application that can be used to distinguish the target vector even when it is far from being uniquely close or short. We formalize lattice problems augmented with a predicate distinguishing a target vector and give algorithms for solving instances of these problems. We apply our techniques to lattice-based approaches for solving the Hidden Number Problem, a popular technique for recovering secret DSA or ECDSA keys in side-channel attacks, and demonstrate that our algorithms succeed in recovering the signing key for instances that were previously believed to be unsolvable using lattice approaches. We carried out extensive experiments using our estimation and solving framework, which we also make available with this work.
Streaming Object Detection for 3-D Point Clouds

Autonomous vehicles operate in a dynamic environment, where the speed with which a vehicle can perceive and react impacts the safety and efficacy of the system. LiDAR provides a prominent sensory modality that informs many existing perceptual systems including object detection, segmentation, motion estimation, and action recognition. The latency for perceptual systems based on point cloud data can be dominated by the amount of time for a complete rotational scan (e.g. 100 ms). This built-in data capture latency is artificial, and based on treating the point cloud as a camera image in order to leverage camera-inspired architectures. However, unlike camera sensors, most LiDAR point cloud data is natively a streaming data source in which laser reflections are sequentially recorded based on the precession of the laser beam. In this work, we explore how to build an object detector that removes this artificial latency constraint, and instead operates on native streaming data in order to significantly reduce latency. This approach has the added benefit of reducing the peak computational burden on inference hardware by spreading the computation over the acquisition time for a scan. We demonstrate a family of streaming detection systems based on sequential modeling through a series of modifications to the traditional detection meta-architecture. We highlight how this model may achieve competitive if not superior predictive performance with state-of-the-art, traditional non-streaming detection systems while achieving significant latency gains (e.g. $$1/15^\text {th}$$ – $$1/3^\text {rd}$$ of peak latency). Our results show that operating on LiDAR data in its native streaming formulation offers several advantages for self driving object detection – advantages that we hope will be useful for any LiDAR perception system where minimizing latency is critical for safe and efficient operation.
Intersection types and (positive) almost-sure termination

Randomized higher-order computation can be seen as being captured by a lambda calculus endowed with a single algebraic operation, namely a construct for binary probabilistic choice. What matters about such computations is the probability of obtaining any given result, rather than the possibility or the necessity of obtaining it, like in (non)deterministic computation. Termination, arguably the simplest kind of reachability problem, can be spelled out in at least two ways, depending on whether it talks about the probability of convergence or about the expected evaluation time, the second one providing a stronger guarantee. In this paper, we show that intersection types are capable of precisely characterizing both notions of termination inside a single system of types: the probability of convergence of any lambda-term can be underapproximated by its type, while the underlying derivation's weight gives a lower bound to the term's expected number of steps to normal form. Noticeably, both approximations are tight -- not only soundness but also completeness holds. The crucial ingredient is non-idempotency, without which it would be impossible to reason on the expected number of reduction steps which are necessary to completely evaluate any term. Besides, the kind of approximation we obtain is proved to be optimal recursion theoretically: no recursively enumerable formal system can do better than that.
Causal Strategic Linear Regression

In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' propensity to game the decision rule by changing their features so as to receive better decisions. Whereas the strategic classification literature has previously assumed that agents' outcomes are not causally affected by their features (and thus that strategic agents' goal is deceiving the decision-maker), we join concurrent work in modeling agents' outcomes as a function of their changeable attributes. As our main contribution, we provide efficient algorithms for learning decision rules that optimize three distinct decision-maker objectives in a realizable linear setting: accurately predicting agents' post-gaming outcomes (prediction risk minimization), incentivizing agents to improve these outcomes (agent outcome maximization), and estimating the coefficients of the true underlying model (parameter estimation). Our algorithms circumvent a hardness result of Miller et al. (2020) by allowing the decision maker to test a sequence of decision rules and observe agents' responses, in effect performing causal interventions through the decision rules.
A Guide-line and Key-point based A-star Path Planning Algorithm For Autonomous Land Vehicles

This paper presents a novel path planning algorithm for Autonomous Land Vehicles (ALVs), which makes two significant improvements to the traditional A-star algorithm. An evaluation standard is first introduced to measure the performance of different algorithms and to select appropriate parameters for the proposed algorithm. Then, a guide-line based A-star algorithm is presented, in which the guide-line is employed to develop the heuristic function to overcome the shortcoming of traditional A-star algorithms. Further, for improving the obstacle avoidance performance, a novel key-point based algorithm is presented, which would guide the planning path to avoid the obstacle much earlier than the traditional one. Combination of these two improvements, this improved A-Star based path planning algorithm is valid. Experimental results show that the performance of the proposed algorithm is robust and stable. Compared with the state-of-the-art techniques, the performance is better in both simulation and real application.
Intent Mining from past conversations for Conversational Agent

Conversational systems are of primary interest in the AI community.Organizations are increasingly using chatbot to provide round-the-clock support and to increase customer engagement.Many commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize user input.These frameworks require a collection of user utterances and corresponding intent to train an intent model.Collecting a substantial coverage of training data is a bottleneck in the bot building process.In cases where past conversation data is available, the cost of labeling hundreds of utterances with intent labels is time-consuming and laborious.In this paper, we present an intent discovery framework that can mine a vast amount of conversational logs and to generate labeled data sets for training intent models.We have introduced an extension to the DBSCAN (Ester et al., 1996) algorithm and presented a density-based clustering algorithm ITER-DBSCAN for unbalanced data clustering.Empirical evaluation on one conversation dataset, six intent dataset, and one short text clustering dataset show the effectiveness of our hypothesis.We release the datasets and code for future evaluation at
Relatively complete verification of probabilistic programs: an expressive language for expectation-based reasoning

We study a syntax for specifying quantitative assertions —functions mapping program states to numbers—for probabilistic program verification. We prove that our syntax is expressive in the following sense: Given any probabilistic program C , if a function f is expressible in our syntax, then the function mapping each initial state σ to the expected value of evaluated in the final states reached after termination of C on σ (also called the weakest preexpectation wp[ C ]( f )) is also expressible in our syntax. As a consequence, we obtain a relatively complete verification system for reasoning about expected values and probabilities in the sense of Cook: Apart from proving a single inequality between two functions given by syntactic expressions in our language, given f , g , and C , we can check whether g ≼ wp[ C ]( f ).