Reviewerly/exHarmony huggingface dataset:
Direction-sensitive relation extraction using Bi-SDP attention model

Relation extraction is a crucial task of natural language processing (NLP). It plays a key role in question answering, web search, and information retrieval and so on. Previous research on this task has verified the effectiveness of using attention mechanisms, shortest dependency paths (SDP) and LSTM. However, most of these methods focus on learning a semantic representation of the whole sentence, highlighting the importance of partial words, or pruning the sentence with SDP. They ignore the lose of information in these methods, such as the dependency relation of each word and preposition words to indicate the relation direction. Besides, the SDP-based approach is prone to over-pruning. Based on the above observations, this paper presents a framework with a Bi-directional SDP (Bi-SDP) attention mechanism to tackle these challenges. The Bi-SDP is a novel representation of SDP, including original SDP and its reverse. The attention mechanism, based on Bi-SDP, builds a parallel word-level attention to capture relational semantic words and directional words. Furthermore, we explored a novel pruning strategy to minimize the length of input instance and the number of RNN cells simultaneously. Moreover, experiments are conducted on two datasets: SemEval-2010 Task 8 dataset and KBP37 dataset. Compared with the previous public models, our method can achieve better competitive performance on the SemEval-2010 Task 8 dataset and outperform existing models on the KBP37 dataset. Additionally, our experimental results also evidence that the directional prepositions in sentences are useful for relation extraction and can improve the performance of relationship with apparent physical direction.
A secure blockchain‐based demurrage mechanism for energy trading in smart communities

This paper combines additive homomorphic encryption and consortium blockchain technology to provide privacy and trust. Additionally, a dynamic energy pricing model is formulated based on the demand response ratio (DRR) of the load demand of prosumers to address fixed energy pricing problems. The proposed dynamic pricing model includes demurrage fees, which is a monetary penalty imposed on a prosumer, if it failed to deliver energy within the agreed duration. Furthermore, this paper also designs and analyzes a threat model of the proposed system. Experimental evaluations show the effectiveness of the proposed model with regards to low transaction cost, the minimum execution time for block creation, the privacy of prosumers and dispute resolution of demurrage fees. Moreover, the proposed scheme reduces the average system overhead cost up to 66.67% as compared to 33.43% for an existing scheme. Additionally, the proposed blockchain proof-of-authority consensus average hash power is minimized up to 82.75% as compared to 60.34% for proof-of-stake and 56.89% for proof-of-work consensus mechanisms.
High-Speed Neuromorphic Reservoir Computing Based on a Semiconductor Nanolaser With Optical Feedback Under Electrical Modulation

A high-speed neuromorphic reservoir computing system based on a semiconductor nanolaser with optical feedback (SNL-based RC) under electrical modulation is proposed for the first time and demonstrated numerically. A Santa-Fe chaotic time series prediction task is employed to quantify the prediction performance of the SNL-based RC system. The effects of the Purcell cavity-enhanced spontaneous emission factor F and the spontaneous emission coupling factor β on the proposed RC system are analyzed extensively. It is found that, in general, increased F and β extend the range of good prediction performance of the SNL-based RC system. Moreover, the influences of bias current and feedback phase are also considered. Due to the ultra-short photon lifetime in SNL, the information processing rate of the SNL-based RC system reaches 10Gpbs. The proposed high-speed SNL-based RC system in this paper provides theoretical guidelines for the design of RC-based integrated neuromorphic photonic systems.
Measuring and Improving Agile Processes in a Small-Size Software Development Company

Context: Agile software development has become commonplace in software development companies due to the numerous benefits it provides. However, conducting Agile projects is demanding in Small and Medium Enterprises (SMEs), because projects start and end quickly, but still have to fulfil customers' quality requirements. Objective: This paper aims at reporting a practical experience on the use of metrics related to the software development process as a means supporting SMEs in the development of software following an Agile methodology. Method: We followed Action-Research principles in a Polish small-size software development company. We developed and executed a study protocol suited to the needs of the company, using a pilot case. Results: A catalogue of Agile development process metrics practically validated in the context of a small-size software development company, adopted by the company in their Agile projects. Conclusions: Practitioners may adopt these metrics in their Agile projects, especially if working in an SME, and customise them to their own needs and tools. Academics may use the findings as a baseline for new research work, including new empirical studies.
Deep Learning Techniques for Part of Speech Tagging by Natural Language Processing

For many natural language processing applications, part of speech (POS) tagging remains as a preliminary task. Marathi, is observed as a popular language in India but it only has limited tools and corpus for NLP applications. An accurate POS tagger is essential for many NLP tasks like sentiment analysis, named entity recognition, dependency parsing, etc. This research work proposes a deep learning model and bidirectional long short-term memory (Bi-LSTM) model to perform POS tagging for Marathi text. We achieved an accuracy of 85% for the deep learning model and 97% for the Bi-LSTM model. Our contribution here is based on three folds - building a deep learning model, building the Bi-LSTM model, comparison with machine learning techniques for the same dataset.
TextTricker: Loss-based and gradient-based adversarial attacks on text classification models

Adversarial examples are generated by adding infinitesimal perturbations to legitimate inputs so that incorrect predictions can be induced into deep learning models. They have received increasing attention recently due to their significant values in evaluating and improving the robustness of neural networks. While adversarial attack algorithms have achieved notable advancements in the continuous data of images, they cannot be directly applied for discrete symbols such as text, where all the semantic and syntactic constraints in languages are expected to be satisfied. In this paper, we propose a white-box adversarial attack algorithm, TextTricker, which supports both targeted and non-targeted attacks on text classification models. Our algorithm can be implemented in either a loss-based way, where word perturbations are performed according to the change in loss, or a gradient-based way, where the expected gradients are computed in the continuous embedding space to restrict the perturbations towards a certain direction. We perform extensive experiments on two publicly available datasets and three state-of-the-art text classification models to evaluate our algorithm. The empirical results demonstrate that TextTricker performs notably better than baselines in attack success rate. Moreover, we discuss various aspects of TextTricker in details to provide a deep investigation and offer suggestions for its practical use.
Visual Approaches for Exploratory Data Analysis: A Survey of the Visual Assessment of Clustering Tendency (VAT) Family of Algorithms

Exploratory data analysis (EDA) using data clustering is extremely important for understanding the basic characteristics of a novel data set before developing complex statistical models and testing the various hypotheses. A preliminary step to clustering is deciding whether the data contain any clusters and, if so, how many clusters to seek. This is the clustering-tendency-assessment problem, which has not received much attention in the pattern-recognition literature. An important category of algorithms in this domain includes visual approaches, represented here by the visual assessment of tendency (VAT) algorithm, which reorders the pairwise dissimilarity matrix and then generates a reordered dissimilarity image (RDI) or cluster heat map that shows possible clusters in the data by dark blocks along the diagonal.
Boosting label weighted extreme learning machine for classifying multi-label imbalanced data

As a flexible and efficient cost sensitive learning algorithm, the label weighted extreme learning machine (LW-ELM) has been proposed to address the class imbalance learning problem on the multi-label data. However, due to the adoption of empirical costs, the classification performance of LW-ELM can't be guaranteed enough. To solve this problem, an improved algorithm called BLW-ELM, which integrates LW-ELM into the Boosting ensemble learning framework, is presented in this paper. Specifically, BLW-ELM designates the appropriate cost for each training label belonging to each training instance according to the iterative feedbacks of the training results, further gets rid of exploring the intricate distribution of multi-label data directly. That is to say, BLW-ELM is an universal and self-adapting algorithm that can promotes the robustness of classification regardless of the data distribution types. Twelve multi-label data sets are used to verify the effectiveness and superiority of the proposed algorithm. Experimental results indicate that the proposed BLW-ELM algorithm is significantly superior to LW-ELM algorithm and many other state-of-the-art multi-label imbalance learning algorithms, as well it generally needs far less training time than those sophisticated algorithms.
AI Vision: Smart speaker design and implementation with object detection custom skill and advanced voice interaction capability

Amazon has provided a cloud-based developer console to write and deploy custom skills that can integrate with Alexa Voice Service device SDK running on the host hardware. When this deployed custom skill is invoked using a specific voice request, the host hardware performs the requested task and sends back its response. Currently, developers have implemented AI algorithms as a part of their custom skills on existing Alexa devices which focuses on voice-based applications and not the real-time image or video-based applications since current Alexa devices are not camera enabled. Hence there is a need to design an advanced camera-enabled Alexa smart speaker platform and provide it to the open-source community to facilitate implementing image/video based AI algorithms as a part of their custom skills. This article describes the design and development of a state-of-the-art camera-enabled, Linux-based modern Alexa smart speaker prototype. A microphone array with on-chip advanced DSP is interfaced with the prototype to provide speech algorithms processed voice data to Alexa Voice Service for achieving a seamless, full-duplex user-Alexa interaction. Finally, the main Deep Learning & Computer Vision-based object detection custom skill is realized and tested on the developed prototype.
OOS-SSS: An Efficient Online/Offline Subtree-Based Short Signature Scheme Using Chebyshev Chaotic Maps for Wireless Sensor Network

Wireless sensor network (WSN) is a network system that involves spatially distributed devices such as wireless sensor nodes.As the data collected by the sensor nodes and transmitted through WSNs are mostly sensitive, confidential, or personal data, secure information transmission is a critical challenge, and one of the most significant security requirements is authentication.The digital signature plays a key role in ensuring data integrity, authentication and non-repudiation.In this article, we shall present an efficient, high security level online/offline subtree-based short signature scheme (OOS-SSS) using Chebyshev chaotic maps for WSN fuzzy user data sharing over a Galois field.The proposed scheme is secure in an environment of random oracle unforgeability under chosen message attack (UF-SBSS-CMA).Notably, our new design has made multiple-time usage of offline storage possible, enabling the signer to reuse offline pre-info in polynomial time instead of having only one single attempt as in the currently available online/offline signing schemes.In addition, based on our OOS-SSS design, we can build up an aggregation scheme for wireless sensor network settings.Also, the proposed scheme can be extended with some applications attached to it to allow users to register messages and implement them on WSN.Lastly, our performance comparison reveals that the proposed scheme has the lowest computational cost among six competing schemes.
Human activity recognition using improved complete ensemble EMD with adaptive noise and long short-term memory neural networks

The recognition of human activities is a topic of great relevance due to its wide range of applications. Different approaches have been proposed to recognize human activities, ranging from the comparison of signals with thresholds to the application of deep and machine learning techniques. In this work, the classification of six human activities (walking, walking downstairs, walking upstairs, standing, sitting, and lying down) is performed using bidirectional LSTM networks that exploit intrinsic mode function (IMF) representation of inertial signals. Records with inertial signals (accelerometer and gyroscope) of 2.56 s, available at the UCI Machine Learning Repository, were collected from 30 subjects using a smartphone. First, inertial signals were standardized to take them to the same scale and were decomposed into IMF using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). IMF were then segmented (split) into nine segments of 1.28 s with 12.5% overlap and introduced to a first network with four outputs to identify the dynamic activities and the statics as a single class called "statics", giving 98.86% accuracy. Then, the non-segmented IMF of the records assigned to the statics class were introduced to a second network to classify their three activities, giving an accuracy of 88.46%. In total, 92.91% accuracy was obtained to classify the six human activities. This performance is because ICEEMDAN allowed the extraction of information that was embedded in the signal, and the segmentation of the IMF allowed the network to discriminate between static and dynamic activities.
Infinite Families of Optimal Linear Codes Constructed From Simplicial Complexes

A linear code is optimal if it has the highest minimum distance of any linear code with a given length and dimension. We construct infinite families of optimal binary linear codes C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Δc</sub> constructed from simplicial complexes in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , where Δ is a simplicial complex in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> and Δ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sup> the complement of Δ. We first find an explicit computable criterion for C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Δc</sub> to be optimal; this criterion is given in terms of the 2-adic valuation of Σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j=1</sub> 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">|Ai|-1</sup> , where the At's are maximal elements of Δ. Furthermore, we obtain much simpler criteria under various specific conditions on the maximal elements of Δ. In particular, we find that C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Δc</sub> is a Griesmer code if and only if the maximal elements of Δ are pairwise disjoint and their sizes are all distinct. Specially, when f has exactly two maximal elements, we explicitly determine the weight distribution of C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Δc</sub> .We present many optimal linear codes constructed by our method, and we emphasize that we obtain at least 32 new optimal linear codes.
Fighting Against Deepfake: Patch&amp;Pair Convolutional Neural Networks (PPCNN)

In this paper, we propose a novel Patch&Pair Convolutional Neural Networks (PPCNN) to distinguish Deepfake videos or images from real ones. Through the comprehensive evaluations on public datasets, we demonstrate that our model performs better than existing detection methods and show better generalization.
Attentive Cross-Modal Fusion Network for RGB-D Saliency Detection

In this paper, an attentive cross-modal fusion (ACMF) network is proposed for RGB-D salient object detection. The proposed method selectively fuses features in a cross-modal manner and uses a fusion refinement module to fuse output features from different resolutions. Our attentive cross-modal fusion network is built based on residual attention. In each level of ResNet output, both the RGB and depth features are turned into an identity map and a weighted attention map. The identity map is reweighted by the attention map of the paired modality. Moreover, the lower level features with higher resolution are adopted to refine the boundary of detected targets. The entire architecture can be trained end-to-end. The proposed ACMF is compared with state-of-the-art methods on eight recent datasets. The results demonstrate that our model can achieve advanced performance on RGB-D salient object detection.
An Optimal Locality-Aware Task Scheduling Algorithm Based on Bipartite Graph Modelling for Spark Applications

In the distributed computing framework of Spark, cross-node/rack data transfer produced by map tasks and reduce tasks are common problems resulting in performance degradation, such as prolonging of entire execution time and network congestion. To address these problems, this article utilizes the bipartite graph modelling to propose an optimal locality-aware task scheduling algorithm. By considering global optimality, the algorithm can generate the optimal scheduling solution for both the map tasks and the reduce tasks for data locality. Because of the different communication modes, this article uses a unified graph to model the map task scheduling and the reduce task scheduling respectively. Then, by calculating the communication cost matrix of tasks, we formulate an optimal task scheduling scheme to minimize overall communication cost and transform the problem as the well-known graph problem: minimum weighted bipartite matching (MWBM), which can be resolved by Kuhn-Munkres algorithm. In addition, this article proposes a locality-aware executor allocation strategy to improve the data locality further. We implement our algorithm and strategy in Spark-2.4.1 and evaluate its performance using several representative micro-benchmarks, macro-benchmarks, and HiBench benchmark suite. The experimental results verify that by reducing the network traffic and access latency, the proposed algorithm can improve the job performance substantially compared to some other task scheduling algorithms.
Compact Adaptively Secure ABE from k-Lin: Beyond $$\mathsf {NC}^1$$ and Towards $$\mathsf {NL}$$

We present a new general framework for constructing compact and adaptively secure attribute-based encryption (ABE) schemes from k-Lin in asymmetric bilinear pairing groups. Previously, the only construction [Kowalczyk and Wee, Eurocrypt '19] that simultaneously achieves compactness and adaptive security from static assumptions supports policies represented by Boolean formulae. Our framework enables supporting more expressive policies represented by arithmetic branching programs. Our framework extends to ABE for policies represented by uniform models of computation such as Turing machines. Such policies enjoy the feature of being applicable to attributes of arbitrary lengths. We obtain the first compact adaptively secure ABE for deterministic and non-deterministic finite automata (DFA and NFA) from k-Lin, previously unknown from any static assumptions. Beyond finite automata, we obtain the first ABE for large classes of uniform computation, captured by deterministic and non-deterministic logspace Turing machines (the complexity classes $$\mathsf {L}$$ and $$\mathsf {NL}$$) based on k-Lin. Our ABE scheme has compact secret keys of size linear in the description size of the Turing machine M. The ciphertext size grows linearly in the input length, but also linearly in the time complexity, and exponentially in the space complexity. Irrespective of compactness, we stress that our scheme is the first that supports large classes of Turing machines based solely on standard assumptions. In comparison, previous ABE for general Turing machines all rely on strong primitives related to indistinguishability obfuscation.
An interval uncertain optimization method using back-propagation neural network differentiation

An interval uncertain optimization method is proposed for engineering uncertain optimization problems. The first-order derivative information may be unavailable in practical engineering systems. To overcome this drawback, the method to calculate the first-order partial derivatives with a back-propagation neural network is investigated, and a numerical example is presented to demonstrate its fine precision. In the optimization process, the back-propagation neural network and its first-order derivatives are introduced into the interval perturbation method and combined with the subinterval method, the extrema of the uncertain objective function and uncertain constraints can be approximated without much computational cost. Thus the most challenging problem in current interval optimization, namely low optimization efficiency caused by nested optimization can be solved to a certain extent. A two-bar structure system is adopted to validate its fine precision and high computational efficiency in interval bounds estimation. Based on the interval order relation and interval possibility degree model, an interval uncertain optimization model can be transformed into a deterministic optimization model, and be solved by some auxiliary optimization algorithm. A numerical example and an engineering application have indicated that this method is efficient for uncertain optimization problems.
Immersive virtual field trips and elementary students’ perceptions

Abstract In response to the increased attention and interest related to the application of virtual reality with head‐mounted displays in education, this study examined the elementary students’ perceptions of using immersive virtual field trips (VFTs) in the classroom. Reflection papers from 25 students were analyzed and triangulated with field notes from class observations and teacher interviews. The content analysis revealed that VFTs were perceived to be efficient with regard to time and cost, to expand learning opportunities, but lacked physical interaction. Elementary students also suggested immersive VFTs are engaging, real, and provide an increased perception of virtual presence. However, they also expressed concerns about health and safety, psychological side effects, technical malfunctions, and low social interaction.