Reviewerly/exHarmony huggingface dataset:
Perceptions or Actions? Grounding How Agents Interact Within a Software Architecture for Cognitive Robotics

One of the aims of cognitive robotics is to endow robots with the ability to plan solutions for complex goals and then to enact those plans. Additionally, robots should react properly upon encountering unexpected changes in their environment that are not part of their planned course of actions. This requires a close coupling between deliberative and reactive control flows. From the perspective of robotics, this coupling generally entails a tightly integrated perceptuomotor system, which is then loosely connected to some specific form of deliberative system such as a planner. From the high-level perspective of automated planning, the emphasis is on a highly functional system that, taken to its extreme, calls perceptual and motor modules as services when required. This paper proposes to join the perceptual and acting perspectives via a unique representation where the responses of all software modules in the architecture are generalized using the same set of tokens. The proposed representation integrates symbolic and metric information. The proposed approach has been successfully tested in CLARC, a robot that performs Comprehensive Geriatric Assessments of elderly patients. The robot was favourably appraised in a survey conducted to assess its behaviour. For instance, using a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree), patients reported an average of 4.86 when asked if they felt confident during the interaction with the robot. This paper proposes a mechanism for bringing the perceptual and acting perspectives closer within a distributed robotics architecture. The idea is built on top of the blackboard model and scene graphs. The modules in our proposal communicate using a short-term memory, writing the perceptual information they need to share with other agents and accessing the information they need for determining the next goals to address.
Inject Rubrics into Short Answer Grading System

Short Answer Grading (SAG) is a task of scoring students' answers in examinations.Most existing SAG systems predict scores based only on the answers, including the model (Riordan et al., 2017) used as baseline in this paper, which gives the-state-of-the-art performance.But they ignore important evaluation criteria such as rubrics, which play a crucial role for evaluating answers in real-world situations.In this paper, we present a method to inject information from rubrics into SAG systems.We implement our approach on top of word-level attention mechanism to introduce the rubric information, in order to locate information in each answer that are highly related to the score.Our experimental results demonstrate that injecting rubric information effectively contributes to the performance improvement and that our proposed model outperforms the state-of-the-art SAG model on the widely used ASAP-SAS dataset under low-resource settings.
Using Graphs for Word Embedding with Enhanced Semantic Relations

Word embedding algorithms have become a common tool in the field of natural language processing.While some, like Word2Vec, are based on sequential text input, others are utilizing a graph representation of text.In this paper, we introduce a new algorithm, named WordGraph2Vec, or in short WG2V, which combines the two approaches to gain the benefits of both.The algorithm uses a directed word graph to provide additional information for sequential text input algorithms.Our experiments on benchmark datasets show that text classification algorithms are nearly as accurate with WG2V as with other word embedding models while preserving more stable accuracy rankings.
Protecting the stack with PACed canaries

Stack canaries remain a widely deployed defense against memory corruption attacks. Despite their practical usefulness, canaries are vulnerable to memory disclosure and brute-forcing attacks. We propose PCan, a new approach based on ARMv8.3-A pointer authentication (PA), that uses dynamically-generated canaries to mitigate these weaknesses and show that it provides more fine-grained protection with minimal performance overhead.
Improvement of RRT*-Smart Algorithm for Optimal Path Planning and Application of the Algorithm in 2 &amp; 3-Dimension Environment

Optimal path planning refers to find the safe route to the destination at a low cost, is a major problem with regard to autonomous navigation. Sampling Based Planning(SBP) approaches, such as Rapidly-exploring Random Tree Star(RRT*), are the most influential algorithm in path planning due to their relatively small calculations and scalability to high-dimensional problems. RRT*-Smart introduced path optimization and biased sampling techniques into RRT* to increase convergent rate. This paper presents an improvement plan that has changed the biased sampling method to increase the initial convergent rate of the RRT*-Smart, which is specified as mRRT*-Smart. With comparison among RRT*, RRT*-Smart and mRRT*-Smart in 2 &amp; 3-D environments, mRRT*-Smart showed similar or increased initial convergent rate than RRT* and RRT*-Smart.
Helpfulness-aware review based neural recommendation

Reviews contain rich information of user interests and item characteristics. Incorporating reviews into recommendation has attracted increasing attention in recent years, which can help learn more accurate user and item representations for recommendation. Existing review based recommendation methods usually utilize the content of reviews while ignoring the helpfulness scores associated with them. Since different reviews have different informativeness and many reviews are noisy and even misleading, incorporating the helpfulness information of reviews can help better exploit the reviews for recommendation. In this paper, we propose a helpfulness-aware review based recommendation approach. The core of our approach is a review encoder and a user/item encoder. In the review encoder we learn representations of reviews from their content in a hierarchical way. We first learn sentence representations from words and then learn review representations from sentences, using a hierarchical attention network to select important words and sentences. In the user/item encoder, we learn representations of users/items from their reviews using an attention network. The query vector of the attention network comes from the helpfulness scores of these reviews. Since many reviews do not have helpfulness scores, we propose a neural helpfulness prediction model to predict the helpfulness scores of these reviews. The neural helpfulness prediction model is trained on the limited reviews with helpfulness scores voted by users. Extensive experiments on four benchmark datasets show that incorporating the helpfulness of reviews can effectively improve the performances of review based neural recommendation methods.
Design and analysis of a general vector space model for data classification in Internet of Things

Abstract The amount of information increases explosively in Internet of Things, because more and more data are sensed by large amount of sensors. The explosive growth of information makes it difficult to access information efficiently, so it is an effective method to decrease the amount of information to be transferred on network by text classification. This paper proposes a new text classification algorithm based on vector space model. This algorithm improves the feature selection and weighting methods by introducing synonym replacement to traditional text classification algorithms. The experimental results show that the proposed classification algorithm has considerably improved the precision and recall of classification.
Qubit-environment negativity versus fidelity of conditional environmental states for a nitrogen-vacancy-center spin qubit interacting with a nuclear environment

We study the evolution of qubit-environment entanglement, quantified using negativity, for nitrogen-vacancy-center spin qubits interacting with an environment of partially polarized nuclear spins in the diamond lattice. We compare it with the evolution of the fidelity of environmental states conditional on the pointer states of the qubit, which can serve as a tool to distinguish between entangling and nonentangling decoherence in the pure-dephasing scenario considered here. The two quantities show remarkable agreement during the evolution in a wide range of system parameters, leading to the conclusion that the amount of entanglement generated between the qubit and the environment is likely to be proportional to the trace that the joint evolution leaves on the environment.
The Entropy-Based Time Domain Feature Extraction for Online Concept Drift Detection

Most of time series deriving from complex systems in real life is non-stationary, where the data distribution would be influenced by various internal/external factors such that the contexts are persistently changing. Therefore, the concept drift detection of time series has practical significance. In this paper, a novel method called online entropy-based time domain feature extraction (ETFE) for concept drift detection is proposed. Firstly, the empirical mode decomposition based on extrema symmetric extension is used to decompose time series, where features in various time scales can be adaptively extracted. Meanwhile, the end point effect caused by traditional empirical mode decomposition can be avoided. Secondly, by using the entropy calculation, the time-domain features are coarse-grained to quantify the structure and complexity of the time series, among which six kinds of entropy are used for discussion. Finally, a statistical process control method based on generalized likelihood ratio is used to monitor the change of the entropy, which can effectively track the mean and amplitude of the time series. Therefore, the early alarm of concept drift can be given. Synthetic data sets and neonatal electroencephalogram (EEG) recordings with seizures annotations data sets are used to validate the effectiveness and accuracy of the proposed method.
Single-Shot Imaging of Two-Wavelength Spatial Phase-Shifting Interferometry

In this investigation, we propose an effective method to measure 3D surface profiles of specimens with single-shot imaging. Based on the two-wavelength interferometric principle and spatial phase-shifting technique using a polarization pixelated camera, the proposed system can not only rapidly measure the phase, but also overcome the 2π-ambiguity problem of typical phase-shifting interferometry. The rough surface profile can be calculated by the visibility of the interference fringe and can compensate for the height discontinuity by phase jumps occurring in a fine height map. An inclined plane mirror and a step height specimen with 9 μm were used for the validation of capability of measuring continuously smooth surface and large step heights. The measurement results were in good agreement with the results of typical two-wavelength interferometry.
Self-Attention Temporal Convolutional Network for Long-Term Daily Living Activity Detection

In this paper, we address the detection of daily living activities in long-term untrimmed videos. The detection of daily living activities is challenging due to their long temporal components, low inter-class variation and high intra-class variation. To tackle these challenges, recent approaches based on Temporal Convolutional Networks (TCNs) have been proposed. Such methods can capture long-term temporal patterns using a hierarchy of temporal convolutional filters, pooling and up sampling steps. However, as one of the important features of convolutional networks, TCNs process a local neighborhood across time which leads to inefficiency in modeling the long-range dependencies between these temporal patterns of the video. In this paper, we propose Self-Attention - Temporal Convolutional Network (SA-TCN), which is able to capture both complex activity patterns and their dependencies within long-term untrimmed videos. We evaluate our proposed model on DAily Home LIfe Activity Dataset (DAHLIA) and Breakfast datasets. Our proposed method achieves state-of-the-art performance on both DAHLIA and Breakfast dataset.
Does your code need comment?

Summary Code comments convey information about the programmers' intention in a more explicit but less rigorous manner than source code. This information can assist programmers in various tasks, such as code comprehension, reuse, and maintenance. To better understand the properties of the comments existing in the source code, we analyzed more than 450 000 comments across 136 popular open‐source software systems coming different domains. We found that the methods involving header comments and internal comments were shown low percentages in software systems, ie, 4.4% and 10.27%, respectively. As an application of our findings, we propose an automatic approach to determine whether a method needs a header comment, known as commenting necessity identification. Specifically, we identify the important factors for determining the commenting necessity of a method and extract them as structural features, syntactic features, and textual features. Then, by applying machine learning techniques and noise‐handling techniques, we achieve a precision of 88.5% on eight open‐source software from GitHub. The encouraging experimental results demonstrate the feasibility and effectiveness of our approach.
Parameter Estimation for PMSM based on a Back Propagation Neural Network Optimized by Chaotic Artificial Fish Swarm Algorithm

Permanent Magnet Synchronous Motor(PMSM) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. Aiming at the premature convergence of traditional Back Propagation Neural Network(BPNN) in PMSM motor parameter identification, a new method of PMSM motor parameter identification is proposed. It uses Chaotic Artificial Fish Swarm Algorithm(CAFSA) to optimize the initial weights and thresholds of BPNN, and then strengthens training by BPNN algorithm. Thus, the global optimal network parameters are obtained by using the global optimization of CAFSA and the local search ability of BPNN. The simulation results and experimental data show that the initial value sensitivity of the network model optimized by CAFS-BPNN Algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. Compared with other intelligent algorithms, such as RSL and PSO, CAFS-BPNNA has high identification accuracy and fast convergence speed for PMSM motor parameters.
Effectiveness evaluation without human relevance judgments: A systematic analysis of existing methods and of their combinations

In test collection based evaluation of retrieval effectiveness, it has been suggested to completely avoid using human relevance judgments. Although several methods have been proposed, their accuracy is still limited. In this paper we present two overall contributions. First, we provide a systematic comparison of all the most widely adopted previous approaches on a large set of 14 TREC collections. We aim at analyzing the methods in a homogeneous and complete way, in terms of the accuracy measures used as well as in terms of the datasets selected, showing that considerably different results may be achieved considering different methods, datasets, and measures. Second, we study the combination of such methods, which, to the best of our knowledge, has not been investigated so far. Our experimental results show that simple combination strategies based on data fusion techniques are usually not effective and even harmful. However, some more sophisticated solutions, based on machine learning, are indeed effective and often outperform all individual methods. Moreover, they are more stable, as they show a smaller variation across datasets. Our results have the practical implication that, when trying to automatically evaluate retrieval effectiveness, researchers should not use a single method, but a (machine-learning based) combination of them.
ML<sup>2</sup>S-SVM: multi-label least-squares support vector machine classifiers

Purpose Image classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance. Design/methodology/approach Many multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM (ML 2 S-SVM). Findings Experimental results on the emotions, scene, yeast and bibtex data sets indicate that the ML 2 S-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of ML 2 S-SVM, so it is necessary for users to identify proper parameters in advance. Originality/value On the basis of MTLS-SVM, a novel multi-label classification approach, ML 2 S-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification.
Integrating Virtual Reality and Augmented Reality in a Collaborative User Interface

Application that adopts collaborative system allows multiple users to interact with other users in the same virtual space either in Virtual Reality (VR) or Augmented Reality (AR). This paper aims to integrate the VR and AR space in a Collaborative User Interface that enables the user to cooperate with other users in a different type of interfaces in a single shared space manner. The gesture interaction technique is proposed as the interaction tool in both of the virtual spaces as it can provide a more natural gesture interaction when interacting with the virtual object. The integration of VR and AR space provide a cross-discipline shared data interchange through the network protocol of client-server architecture.
Proposal-Refined Weakly Supervised Object Detection in Underwater Images

Recently, Convolutional Neural Networks (CNNs) have achieved great success in object detection due to their outstanding abilities of learning powerful features on large-scale training datasets. One of the critical factors of their success is the accurate and complete annotation of the training dataset. However, accurately annotating the training dataset is difficult and time-consuming in some applications such as object detection in underwater images due to severe foreground clustering and occlusion. In this paper, we study the problem of object detection in underwater images with incomplete annotation. To solve this problem, we propose a proposal-refined weakly supervised object detection method, which consists of two stages. The first stage is a weakly-fitted segmentation network for foreground-background segmentation. The second stage is a proposal-refined detection network, which uses the segmentation results of the first stage to refine the proposals and therefore can improve the performance of object detection. Experiments are conducted on the Underwater Robot Picking Contest 2017 dataset (URPC2017) which has 19967 underwater images containing three kinds of objects: sea cucumber, sea urchin and scallop. The annotation of the training set is incomplete. Experimental results show that the proposed method greatly improves the detection performance compared to several baseline methods.
Challenges of Teaching Academic Writing Skills in ESL Classroom (Based on International Teaching Experience)

The painstaking effort to teach academic writing skills to EFL/L2 students is rewarding when learners are aware of the need to improve their English language skills and gain the mastery of key academic genres required both for their academic and professional life.The majority of Western communities/employers first evaluate novice specialists by their writing ability: CVs, statement of purpose, personal statement, cover letter, etc.This article draws a modest comparative analysis of approaches to teaching academic writing skills in European universities (Ukrainian and Slovak) based on personal experience; it makes an attempt to engrasp key principles of teaching some challenging aspects of academic writing skills in European university classroom, highlights most common students" mistakes, and ways of avoiding them.The purpose of this study has been to identify the most challenging writing aspects for L2 learners, mainly a range of Ukrainian and Slovak EFL students.To do so, the most frequent errors of the students" essays were collected and analysed based on language study discrepancies, which affect L2 learners" acquisition of literacy of the target language.The most palpable gaps appear in register and punctuation fields of the students" knowledge, which implies an application of powerful tools to deal with these errors.
Conditional Generative Denoising Autoencoder

We present a generative denoising autoencoder model that has an embedded data classifier in its architecture in order to take advantage of class-based discriminating features and produce better data samples. The proposed model is a conditional generative model and is sampled with a Markov chain Monte Carlo (MCMC) process according to a label that denotes the desired (or undesired) class or classes. In this sense, any chosen predefined class or characteristic may have a positive or negative effect on the image generation process, meaning that it can be instructed to be present or absent from the generated sample. We argue that allowing discriminative information in the form of feature detectors to be present in the latent representation of the autoencoder can be generally beneficial. This technique is an alternative approach to variational autoencoders (VAEs) that enforce a prior on the latent distribution. We further claim that supervised learning may be generally able to serve unsupervised learning through an interaction between the two paradigms. However, the extreme majority of research done on the interaction of the two learning regimes has the goal of using unsupervised learning to improve supervised learning. In this article, we explore the two learning paradigms' interaction in the opposite direction.
From Educational Failure to Social Success

The article presents an analysis of educational failure of Russian students, as well as the resources needed to solve it. The urgency of the problem is revealed. Methodological approaches to its sociological research are substantiated. Among them – the theory of human capital, the resource approach, the theory of social community. The results of the authors’ researches of higher education conducted in 2011–2017 have provided the empirical base of the study. The article gives an interpretation of the concepts of “educational failure” and “problem students” and dwells on the social causes of the phenomenon of educational failure. The authors argue that educational failure can be traced from school to college and University and then – to labour market. An unsuccessful student with a high probability will be an unsuccessful worker. The authors present a typology of modern students based on two criteria – the level of educational motivation formation and the readiness to study at University. Special attention is paid to the risks arising from the growing number of unsuccessful students in higher education institutions of provincial Russia. The article shows the prospects for overcoming educational failure of students.