Reviewerly/exHarmony huggingface dataset:
GINS: A Global intensifier-based N-Gram sentiment dictionary

Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.
ReliableBox: Secure and Verifiable Cloud Storage With Location-Aware Backup

While the prevalent cloud storage platforms are offering convenient services in support of diverse data-driven applications for clients, various security concerns raise in terms of data confidentiality, availability, and retrievability. Among them, servers' dishonesty on the location-specific data backup becomes a serious concern when the data stands out clients' control, considering the strict regulations imposed by many governments and organizations on data storage location. This article studies location-aware data backup verification for the data stored in clouds and aims to design a secure framework, named as ReliableBox, enabling the clients to verify if their data have been backed up on the remote servers with specific geolocation. In the design of ReliableBox, we leverage the prominent proof-of-storage techniques for data possession proof, and take advantage of multilateration geolocation and Intel SGX for the precise communication delay measurement and trust computing delay measurement, respectively. In ReliableBox, a client first computes integrity tags for the files and then outsources both the files and tags to the cloud storage server. In the later attestation, with the precise network delay and distance measurement from location-known verifiers, the client verifies that the outsourced files are intact and backed-up to hosts at the specific geolocation. With the customized design, ReliableBox can support the security needs in terms of both data integrity and backup location verification for clients, even when there exists potential dishonest cloud service providers who may manipulate the network delays or forge verification proofs. We provide security analysis to show the security property of ReliableBox in terms of data access, confidentiality, and verifications. In the end, we implement the system prototype and deploy it into several prevalent and commercial cloud platforms for performance evaluation. The experimental results demonstrate that ReliableBox is secure in support of data integrity checking and location-aware backup auditing, while it is robust to the data possession and location spoofing attacks.
Comparing the Neuro-Physiological Effects of Cinematic Virtual Reality with 2D Monitors

In this work, we explore if the immersion afforded by Virtual Reality can improve the cognitive integration of information in Cinematic Virtual Reality (CVR). We conducted a user study examining participants' cognitive activities (recall performance and cortical response) when consuming visual information of emotional and emotionally neutral scenes in a non-CVR environment (i.e. a monitor) versus a CVR environment (i.e. a head-mounted display). Cortical response was recorded using electroencephalography. The results showed that participants had greater early visual attention with neutral emotions in a CVR environment, and showed higher overall alpha power in a CVR environment. The use of CVR did not significantly affect participants' recall performance.
Approximating Edit Distance in Truly Subquadratic Time: Quantum and MapReduce

The edit distance between two strings is defined as the smallest number of insertions , deletions , and substitutions that need to be made to transform one of the strings to another one. Approximating edit distance in subquadratic time is “one of the biggest unsolved problems in the field of combinatorial pattern matching” [37]. Our main result is a quantum constant approximation algorithm for computing the edit distance in truly subquadratic time. More precisely, we give an <?TeX $O(n^{1.810})$?> quantum algorithm that approximates the edit distance within a factor of 3. We further extend this result to an <?TeX $O(n^{1.708})$?> quantum algorithm that approximates the edit distance within a larger constant factor. Our solutions are based on a framework for approximating edit distance in parallel settings. This framework requires as black box an algorithm that computes the distances of several smaller strings all at once. For a quantum algorithm, we reduce the black box to metric estimation and provide efficient algorithms for approximating it. We further show that this framework enables us to approximate edit distance in distributed settings. To this end, we provide a MapReduce algorithm to approximate edit distance within a factor of <?TeX $1+\epsilon$?> , with sublinearly many machines and sublinear memory. Also, our algorithm runs in a logarithmic number of rounds.
DSPNet: Deep Learning-Enabled Blind Reduction of Speckle Noise

Blind reduction of speckle noise has become a longstanding unsolved problem in several imaging applications, such as medical ultrasound imaging, synthetic aperture radar (SAR) imaging, and underwater sonar imaging, etc. The unwanted noise could lead to negative effects on the reliable detection and recognition of objects of interest. From a statistical point of view, speckle noise could be assumed to be multiplicative, significantly different from the common additive Gaussian noise. The purpose of this study is to blindly reduce the speckle noise under non-ideal imaging conditions. The multiplicative relationship between latent sharp image and random noise will be first converted into an additive version through a logarithmic transformation. To promote imaging performance, we introduced the feature pyramid network (FPN) and atrous spatial pyramid pooling (ASPP), contributing to a more powerful deep blind DeSPeckling Network (named as DSPNet). In particular, DSPNet is mainly composed of two subnetworks, i.e., Log-NENet (i.e., noise estimation network in logarithmic domain) and Log-DNNet (i.e., denoising network in logarithmic domain). Log-NENet and Log-DNNet are, respectively, proposed to estimate noise level map and reduce random noise in logarithmic domain. The multi-scale mixed loss function is further proposed to improve the robust generalization of DSPN et. The proposed deep blind despeckling network is capable of reducing random noise and preserving salient image details. Both synthetic and realistic experiments have demonstrated the superior performance of our DSPNet in terms of quantitative evaluations and visual image qualities.
TOMATE: A heuristic-based approach to extract data from HTML tables

Extracting data from user-friendly HTML tables is difficult because of their different layouts, formats, and encoding problems. In this article, we present a new proposal that first applies several pre-processing heuristics to clean the tables, then performs functional analysis, and finally applies some post-processing heuristics to produce the output. Our most important contribution is regarding functional analysis, which we address by projecting the cells onto a high-dimensional feature space in which a standard clustering technique is used to make the meta-data cells apart from the data cells. We experimented with two large repositories of real-world HTML tables and our results confirm that our proposal can extract data from them with an F1 score of 89.50% in just 0.09 CPU seconds per table. We confronted our proposal with several competitors and the statistical analysis confirmed its superiority in terms of effectiveness, while it keeps very competitive in terms of efficiency.
Quantum‐based exact pattern matching algorithms for biological sequences

In computational biology, desired patterns are searched in large text databases, and an exact match is preferable. Classical benchmark algorithms obtain competent solutions for pattern matching in time, whereas quantum algorithm design is based on Grover's method, which completes the search in time. This paper briefly explains existing quantum algorithms and defines their processing limitations. Our initial work overcomes existing algorithmic constraints by proposing the quantum-based combined exact (QBCE) algorithm for the pattern-matching problem to process exact patterns. Next, quantum random access memory (QRAM) processing is discussed, and based on it, we propose the QRAM processing-based exact (QPBE) pattern-matching algorithm. We show that to find all occurrences of a pattern, the best case time complexities of the QBCE and QPBE algorithms are , and the exceptional worst case is bounded by . Thus, the proposed quantum algorithms achieve computational speedup. Our work is proved mathematically and validated with simulation, and complexity analysis demonstrates that our quantum algorithms are better than existing pattern-matching methods.
A Framework for Constructing Thai Sentiment Corpus using the Cosine Similarity Technique

Unstructured data is growing rapidly due to the increase in various social media that allow individuals to express and write reviews in either a formal or informal style. Thus, in sentiment analysis, it becomes difficult to identify and analyze both positive and negative reviews. Thai is a low-resource language with few resources with which to conduct NLP research and the lack of a sentiment corpus. The main objective of this paper is to present a framework in which to construct a Thai sentiment corpus and sentiment polarity classification utilizing the cosine similarity technique. The proposed framework consists of three main steps: data collection; data preprocessing; and sentiment similarity measurement. Initially, data collection generates a sub-step that is a manual sentiment that classifies polarity into positive and negative. Data preprocessing is then applied. Moreover, we also created a special database in which to store text tokenization, convert abbreviations, check spelling errors, and conduct stop-word removal. Lastly, sentiment similarity measurement was conducted between two reviews with a combination of TF-IDF and the cosine similarity technique. We evaluated our framework by employing training data incorporating 3,129 reviews and testing data comprised of 1,000 reviews. The experiment results demonstrated that the proposed framework achieved an accuracy of 81.2%. We further observed that the data preprocessing step herein significantly affected the accuracy of the sentence similarity measurements of the two reviews.
Multi-view motion modelled deep attention networks (M2DA-Net) for video based sign language recognition

Currently, video-based Sign language recognition (SLR) has been extensively studied using deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In addition, using multi view attention mechanism along with CNNs could be an appealing solution that can be considered in order to make the machine interpretation process immune to finger self-occlusions. The proposed multi stream CNN mixes spatial and motion modelled video sequences to create a low dimensional feature vector at multiple stages in the CNN pipeline. Hence, we solve the view invariance problem into a video classification problem using attention model CNNs. For superior network performance during training, the signs are learned through a motion attention network thus focusing on the parts that play a major role in generating a view based paired pooling using a trainable view pair pooling network (VPPN). The VPPN, pairs views to produce a maximally distributed discriminating features from all the views for an improved sign recognition. The results showed an increase in recognition accuracies on 2D video sign language datasets. Similar results were obtained on benchmark action datasets such as NTU RGB D, MuHAVi, WEIZMANN and NUMA as there is no multi view sign language dataset except ours.
Automated Decentralized IoT Based Blockchain Using Ethereum Smart Contract for Healthcare

Over the last decade, smart sensors known as Things are used to collect data from resources to provide real-time services that could be crucial by systems. These sensors are connected to the Internet for efficient data dissemination from anywhere and anytime so that it is called Internet of Things (IoT). Data can be processed in Cloud Computing (CC) for analysis and comparison. To reduce the latency of retrieving data from sensitive applications, Fog Computing (FC) plays a vital role. However, these technologies IoT, CC, and FC are a single point of failure which the systems may fail suddenly. On the other hand, traffic from Things has been increasing in the last few years. Due to a lot of patients suffer from a lack of medical services capabilities, IoT aids healthcare systems to improve the quality of life. However, IoT has several limitations and vulnerable to security threats. Blockchain technology enhances IoT challenges in a peer-to-peer network in terms of security and availability. This chapter proposes IoT based blockchain architecture, including five layers: Things, gateway, Fog, Cloud, and application for healthcare to enhance data sharing in a decentralized manner using Ethereum platform. Smart contract is programmed using Solidity to allow Things to communicate between each other automatically without intermediary and to store data in public/private blockchain. Three consensus algorithms, namely: Proof of Work (PoW), Proof of Stake (PoS), and Proof of Authority (PoA), are compared and evaluated of the proposed architecture in terms of CPU utilization, memory usage, and cost. PoA is most lightweight and suitable for IoT application due to energy-saving and its election process.
Complete Coverage Path Planning for Intelligent Sweeping Robot

As a universal optimization problem in automatic control, complete coverage path planning is one of the useful functions for intelligent sweeping robot, which makes sweeping robot complete sweeping more quickly and efficiently. Though conventional unit decomposition method has got solution, excessive decomposition for complex concave polygon environment and ignoring turning degrade the optimization effect. Considering the practical application of sweeping robot, improvements in three aspects are proposed that the cost function is added with the cost of turning, concave areas which can be combined planning with adjacent area are retained no decomposition, and global planning of unit path with transfer between units are considered by exhaustive search and ant colony algorithm, which can be applied to whole complex environment with better performance. Simulation experimental results show that the proposed approach can effectively reduce turning and total cost, make sweeping robot complete coverage more effectively.
High school Physics teachers’ perceptions of the learning revolution era 4.0 at training activities in Bener Meriah Regency

Abstract The constraints of the limited practicum facilities for Physics lessons in Bener Meriah Regency High School schools can be overcome by virtual laboratory practicum. Learning technology using a virtual laboratory experiment method based on Smart Phone is given to Physics teachers in the district so that they can use simple information technology applications in learning. The subject matter of physics used in this training was Waves and Optics. This activity is gradually carried out to pioneer the realization of physics learning in high schools following the demands of the industrial era 4.0. The output is expected to improve the pedagogical quality of teachers, the learning process, and to overcome the limitations of Physics practicum tools, and to support online learning during the Covid-19 pandemic. The activity was attended by 10 high school teachers in Bener Meriah Regency. All participants were able to master the transfer of technology provided. They are optimistic that this IT technology can be implemented in the learning of Wave and Optical material Physics in their schools on duty. 100% of trainees are satisfied with the service and expect will be any similar activities to be continued for other Physics materials.
RGLN: Robust Residual Graph Learning Networks via Similarity-Preserving Mapping on Graphs

Graph Convolutional Neural Networks (GCNNs) extend CNNs to irregular graph data domain, such as brain networks, citation networks and 3D point clouds. It is critical to identify an appropriate graph for basic operations in GCNNs. Existing methods often manually construct or learn one fixed graph based on known connectivities, which may be sub-optimal. To this end, we propose a residual graph learning paradigm to infer edge connectivities and weights in graphs, which is cast as distance metric learning under a low-rank assumption and a similarity-preserving regularization. In particular, we learn the underlying graph based on similarity-preserving mapping on graphs, which keeps similar nodes close and pushes dissimilar nodes away. Extensive experiments on semi-supervised learning of citation networks and 3D point clouds show that we achieve the state-of-the-art performance in terms of both accuracy and robustness.
Is there anisotropy in structural bias?

Structural Bias (SB) is an important type of algorithmic deficiency within iterative optimisation heuristics. However, methods for detecting structural bias have not yet fully matured, and recent studies have uncovered many interesting questions. One of these is the question of how structural bias can be related to anisotropy. Intuitively, an algorithm that is not isotropic would be considered structurally biased. However, there have been cases where algorithms appear to only show SB in some dimensions. As such, we investigate whether these algorithms actually exhibit anisotropy, and how this impacts the detection of SB. We find that anisotropy is very rare, and even in cases where it is present, there are clear tests for SB which do not rely on any assumptions of isotropy, so we can safely expand the suite of SB tests to encompass these kinds of deficiencies not found by the original tests.
Mask4D: 4D Convolution Network for Light Field Occlusion Removal

Current light field (LF) occlusion removal approaches usually select only a part of sub-aperture images (SAIs) or simply stack all SAIs to reconstruct the center view, which destroys the spatial layout of SAIs. In this paper, we present a simple yet effective LF occlusion removal method name Mask4D, which is a 4D convolution-based encoder-decoder network. We propose to keep the spatial layout of SAIs and construct all SAIs as a 5D input tensor to fully exploit the spatial connection information between SAIs. In particular, except for center view reconstruction, we jointly predict the occlusion mask to disentangle the occlusion mask from the occluded content. Extensive evaluations demonstrate that our Mask4D surpasses the state-of-the-art approaches across different datasets. Moreover, visualizations show that Mask4D predicts the occlusion mask precisely and the reconstructed center view looks more realistic than other approaches. Our code will be publicly available.
Zero-Shot Text Classification with Semantically Extended Graph Convolutional Network

As a challenging task of Natural Language Processing(NLP), zero-shot text classification has attracted more and more attention recently. It aims to detect classes that the model has never seen in the training set. For this purpose, a feasible way is to construct connection between the seen and unseen classes by semantic extension and classify the unseen classes by information propagation over the connection. Although many related zero-shot text classification methods have been exploited, how to realize semantic extension properly and propagate information effectively are far from solved. In this paper, we propose a novel zero-shot text classification method called Semantically Extended Graph Convolutional Network (SEGCN). In the proposed method, the semantic category knowledge from ConceptNet is utilized to semantic extension for linking seen classes to unseen classes and constructing a graph of all categories. Then, we build upon Graph Convolutional Network (GCN) for predicting the textual classifier for each category, which transfers the category knowledge by the convolution operators on the constructed graph and is trained in a semi-supervised manner using the samples of the seen classes. The experimental results on Dbpedia and 20newsgroup datasets show that our method outperforms the state of the art zero-shot text classification methods.
Creating and Migrating Chatbots with Conga

Chatbots are agents that enable the interaction of users and software by means of written or spoken natural language conversation. Their use is growing, and many companies are starting to offer their services via chatbots, e.g., for booking, shopping or customer support. For this reason, many chatbot development tools have emerged, which makes choosing the most appropriate tool difficult. Moreover, there is hardly any support for migrating chatbots between tools. To alleviate these issues, we propose a model-driven engineering solution that includes: (i) a domain-specific language to model chatbots independently of the development tool; (ii) a recommender that suggests the most suitable development tool for the given chatbot requirements and model; (iii) code generators that synthesize the chatbot code for the selected tool; and (iv) parsers to extract chatbot models out of existing chatbot implementations. Our solution is supported by a web IDE called Conga that can be used for both chatbot creation and migration. A demo video is available at https://youtu.be/3sw1FDdZ7XY.
Differentiable Architecture Search-Based Automatic Modulation Classification

Automatic modulation classification (AMC) is an essential and meaningful technology in the development of cognitive radio. It can judge the modulation mode according to the signal acquired by the receiver. In recent years, the deep learning (DL) method has been used to take the place of modulation signal recognition based on decision theory and pattern recognition, which has achieved very effective results. The development of the neural network classification model focuses on architectural engineering. Discovering state-of-the-art neural network architectures requires substantial prior knowledge and effort of human experts. Neural architecture search (NAS) can be viewed as a subdomain of automatic machine learning (AutoML), which uses a neural network to automatically adjust the structures and parameters to obtain a network that researchers need by following search strategies that maximize performance. In this paper, we propose a differentiable architecture search (DARTS) based AMC method. In addition, we also consider six other methods, including convolutional neural network (CNN), simple recurrent unit (SRU), a convolutional-recurrent neural network (CRFN-CSS), Residual Networks (ResNet), Inception Modules (Inception) and MobileNet. Simulation results show that the proposed method can achieve the optimal classification accuracy at low parameters and floating-point operations (FLOPs) without manual architecture engineering.