Reviewerly/exHarmony huggingface dataset:
Multi-Entity Aspect-Based Sentiment Analysis with Context, Entity, Aspect Memory and Dependency Information

Fine-grained sentiment analysis is a useful tool for producers to understand consumers’ needs as well as complaints about products and related aspects from online platforms. In this article, we define a novel task named “Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA)”. It investigates the sentiment towards entities and their related aspects. It makes the well-studied aspect-based sentiment analysis a special case of this type, where the number of entities is limited to one. We contribute a new dataset for this task, with multi-entity Chinese posts in it. We propose to model context, entity, and aspect memory to address the task and incorporate dependency information for further improvement. Experiments show that our methods perform significantly better than baseline methods on datasets for both ME-ABSA task and ABSA task. The in-depth analysis further validates the effectiveness of our methods and shows that our methods are capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simplified.
Kernel modified optimal margin distribution machine for imbalanced data classification

Although the optimal margin distribution machine (ODM) has better generalization performance in pattern recognition than traditional classifiers, ODM as well as traditional classifiers often suffers from data imbalance. To address this, this paper proposes a kernel modified ODM (KMODM) to eliminate the side effect of imbalanced data. According to the mechanism of ODM, a novel conformal function is designed to scale the kernel matrix of ODM, this can increases the separability of the training data in the feature space. In addition, to eliminate the skew of the separator toward minority class, KMODM introduces two free parameters in conformal function to balance the influence of different training data on separating hyperplane. Experimental results on two-dimensional visualization data show that KMODM can alleviate the skew of the separating hyperplane caused by imbalanced data. For most of ten UCI data sets, KMODM can broad the margin of the minority class and achieve the highest average G-mean and F1 score. This means that KMODM has more balanced detection rate and better generalization performance compared to other baseline methods, especially in presence of heavily imbalanced training data.
Tetrapartite entanglement measures of GHZ state with uniform acceleration

Using the single-mode approximation, we calculate entanglement measures such as negativity (1 − 3, 1 − 1 and 2 − 2 tangles) and von Neumann entropy for a tetrapartite Greenberger–Horne–Zeilinger system in a non-inertial frame. We then analyze the whole entanglement measures, the residual π4 and geometric Π4 average of tangles. We find that the difference between them is very small or disappears with the increasing accelerated observers. The entanglement properties are compared among the different cases from one accelerated observer and others remaining stationary to all four accelerated observers. The results presented here show that entanglement still exists for the complete system even when acceleration r tends to infinity. The degree of entanglement is always equal to zero for the 1 − 1 tangle case. We also study the negativity 2 − 2 tangle for completeness. We reexamine the existence of the Unruh effect in non-inertial frames. It is also found that the von Neumann entropy increases with the increasing accelerated observers, and and first increase and then decrease with the acceleration parameter r, e.g. at r = 0.599881 and at r = 0.676196. This implies that the subsystems and are at first more disordered and then the disorder is reduced with the increasing acceleration parameter r.
Between-subclass piece-wise linear solutions in large scale kernel SVM learning

The paper proposes a novel approach for learning kernel Support Vector Machines (SVM) from large scale data with reduced computation time. The proposed approach, termed as Subclass Reduced Set SVM (SRS-SVM), utilizes the subclass structure of data to effectively estimate the candidate support vector set. Since the candidate support vector set cardinality is only a fraction of the training set cardinality, learning SVM from the former requires less time without significantly changing the decision boundary. SRS-SVM depends on a domain knowledge related input parameter, i.e., number of subclasses. To reduce the domain knowledge dependency and to make the approach less sensitive to the subclass parameter, we extend the proposed SRS-SVM to create a robust and improved hierarchical model termed as the Hierarchical Subclass Reduced Set SVM (HSRS-SVM). Since SRS-SVM and HSRS-SVM splits non-linear optimization problem into multiple (smaller) linear optimization problems, both of them are amenable to parallelization. The effectiveness of the proposed approaches is evaluated on four synthetic and six real-world datasets. The performance is also compared with traditional solver (LibSVM) and state-of-the-art approaches such as divide-and-conquer SVM, FastFood, and LLSVM. The experimental results demonstrate that the proposed approach achieves similar classification accuracies while requiring fewer folds of reduced computation time as compared to existing solvers. We further demonstrate the suitability and improved performance of the proposed HSRS-SVM with deep learning features for face recognition using Labeled Faces in the Wild (LFW) dataset.
Picture-level just noticeable difference for symmetrically and asymmetrically compressed stereoscopic images: Subjective quality assessment study and datasets

The Picture-level Just Noticeable Difference (PJND) for a given image and compression scheme reflects the smallest distortion level that can be perceived by an observer with respect to a reference image. Previous work has focused on the PJND of images and videos. In this paper, we study the PJND of symmetrically and asymmetrically compressed stereoscopic images for JPEG2000 and H.265 intra coding. We conduct interactive subjective quality assessment tests to determine the PJND point using both a pristine image and a distorted image as a reference. We find that the PJND points are highly dependent on the image content. In asymmetric compression, there exists a perceptual threshold in the quality difference between the left and right views due to the binocular masking effect. We generate two PJND-based stereo image datasets (one for symmetric compression and one for asymmetric compression) and make them accessible to the public.
Counter-Terrorism Video Analysis Using Hash-Based Algorithms

The Internet is becoming a major source of radicalization. The propaganda efforts of new extremist groups include creating new propaganda videos from fragments of old terrorist attack videos. This article presents a web-scraping method for retrieving relevant videos and a pHash-based algorithm which identifies the original content of a video. Automatic novelty verification is now possible, which can potentially reduce and improve journalist research work, as well as reduce the spreading of fake news. The obtained results have been satisfactory as all original sources of new videos have been identified correctly.
Detail-preserving image super-resolution via recursively dilated residual network

Convolutional neural network (CNN) methods have been successfully applied in single image super-resolution (SR). However, existing very deep CNN based SR methods face with the challenge of memory footprint and computational complexity for real-world applications. Besides, many previous methods lack flexible ability to emphasize local spatial informative areas, which is limited to recover the high-frequency detail of LR input. In this paper, to address these problems, we implement a spatial modulated residual unit (SMRU) upon the dilated residual unit and propose a recursively dilated residual network (RDRN) to reconstruct high-resolution (HR) images from low-resolution (LR) observations. The proposed RDRN can effectively exploit the contextual information over larger regions and pay attention to the high-frequency parts for image detail recovery. Furthermore, such spatial modulation mechanism (SPM) in SMRU can incorporate well with existing SR models for better reconstruction performance. Extensive evaluations on public benchmark datasets demonstrate that our proposed method achieves superior performance in terms of quantitative and qualitative assessments.
Post-quantum cryptography and 5G security

The Fifth Generation (5G) mobile broadband standards make a fundamental shift in cryptography. Prior generations based their security and privacy principally on symmetric key cryptography. The Subscriber Identity Module (SIM) and its successors contain a shared key used to authenticate the User Equipment (UE) to the network, and vice versa.
Random ensemble of fuzzy rule-based models

Fuzzy rule-based models, due to modular architecture, have attracted attention and resulted in some practical implications because of their nonlinear characteristics and substantial interpretability. Data-driven fuzzy modeling is one of the most prevailing approaches, and the performance of such fuzzy models has been directly affected by a fundamental bias–variance dilemma. The concept and ensuing topologies of the ensemble strategy (bagging and boosting) offer an efficient method for constructing models to address this dilemma and to achieve a sound tradeoff. In this study, we design an ensemble fuzzy rule-based model in the setting of random forest and boosting mechanisms. To demonstrate the feasibility of the proposed method, we focus on the regression type of models. First, we design a method for assembling fuzzy rule-based models to improve the prediction accuracy. Second, we quantify the performance of the ensemble mechanism. To illustrate the effectiveness and discuss the main features of the proposed method, a series of publicly available datasets are considered in the experimental studies.
Information, uncertainty and the manipulability of artificial intelligence autonomous vehicles systems

In an avoidable harmful situation, autonomous vehicles systems are expected to choose the course of action that causes the less damage to everybody. However, this behavioral protocol implies some predictability. In this context, we show that if the autonomous vehicle decision process is perfectly known then malicious, opportunistic, terrorist, criminal and non-civic individuals may have incentives to manipulate it. Consequently, some levels of uncertainty are necessary for the system to be manipulation proof. Uncertainty removes the misbehavior incentives because it increases the risk and likelihood of unsuccessful manipulation. However, uncertainty may also decrease the quality of the decision process with negative impact in terms of efficiency and welfare for the society. We also discuss other possible solutions to this problem.
Emerging Threats in Internet of Things Voice Services

In this study, we conduct an empirical analysis of interpretation errors made by Amazon Alexa, the speech-recognition engine that powers the Amazon Echo family of devices. We show how common misinterpretations made by Alexa can be used to build a new class of attacks, called skill squatting attacks, and discuss its security implications.
Combining 2D Gabor and Local Binary Pattern for Facial Expression Recognition Using Extreme Learning Machine

The efficiency of facial expression recognition (FER) is important for human-robot interaction. Detection of the facial region, extraction of discriminative facial expression features, and identification of categories of facial expressions are all related to the recognition accuracy and time-efficiency. An FER framework is proposed, in which 2D Gabor and local binary pattern (LBP) are combined to extract discriminative features of salient facial expression patches, and extreme learning machine (ELM) is adopted to identify facial expression categories. The combination of 2D Gabor and LBP can not only describe multiscale and multidirectional textural features, but also capture small local details. The FER of ELM and support vector machine (SVM) is performed using the Japanese female facial expression database and extended Cohn-Kanade database, respectively, in which both ELM and SVM achieve an accuracy of more than 85%, and the computational efficiency of ELM is higher than that of SVM. The proposed framework has been used in the multimodal emotional communication based humans-robots interaction system, in which FER within 2 seconds enables real-time human-robot interaction.
Mining Weighted Frequent Patterns from Uncertain Data Streams

In recent years, data mining has become one of the most demanding areas of computer science. For instance, it helps to discover frequent patterns by applying intelligence tools, techniques and methodologies from various kinds of databases. However, with the rapid growth of modern technology, high volumes of data with different characteristics are generated by many applications. Situation has become much more challenging and sophisticated when datasets are uncertain in nature and flowing at high velocity. Many applications also demand real-time analysis of data depending on current characteristics. Several researches have been made to mitigate the challenges regarding uncertain data streams. However, as the datasets are streaming in nature, frequent patterns of those datasets may be huge in size, and thus may require further mining to find the interesting patterns. Interestingness of patterns can be measured by associating weight with each item. In this paper, we propose a novel tree-based approach called WFPMUDS (Weighted Frequent Patterns mining from Uncertain Data Streams)-growth, which is capable of capturing recent behavior of uncertain data streams and only produces significant (weighted) patterns.
Relevance-based entity selection for ad hoc retrieval

Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the [email protected], and [email protected] metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities.
Evaluation Model for Investment in Solar Photovoltaic Power Generation Using Fuzzy Analytic Hierarchy Process

Owing to rapid growth in the Asian solar photovoltaic (PV) power market, decision-making models are required to develop efficient investment strategies. Previous studies have largely focused on technological conditions and macroeconomic indicators, but not on the increasing needs of the financial sector. In this study, we developed an evaluation model of solar PV investment and financial factors at the project level. The model applies the fuzzy analytic hierarchy process and selects appropriate evaluation indicators for investment by emphasizing financial factors such as access to finance and exit strategies after the initial investment. Furthermore, we derived the relative importance of the indicators for each stakeholder. Stakeholder analysis enables comparison by quantifying the risks of each market participant. Economic factors (71.57%) were selected as the most important evaluation factors, followed by policy factors (16.26%), and technical factors (12.17%). Higher weights were assigned to indicators that directly affect profitability. Stakeholders showed significant differences in policy factors. Power generation companies showed higher weight values in policy factors than other groups. Quantification of differences in perceptions provides basic data for establishing effective investment strategies and developing policies. Therefore, the proposed model will contribute to more efficient solar PV project development.
Perceptually Optimized Bit-Allocation and Associated Distortion Measure for Block-Based Image or Video Coding

It is well known that input-invariant quantization in perceptual image or video coding often leads to visually suboptimal results and that quantization parameter adaptation (QPA) based on a model of the human visual system can improve subjective coding quality. This paper introduces a simple low-complexity QPA algorithm, controlled using a block-wise perceptually weighted distortion measure representing a generalization of the PSNR metric. The weighting scheme of this WPSNR metric is based on a psychovisual model. It directly leads to a perceptually adapted scaling of the block-wise Lagrange parameter used in the bit-allocation process in the encoder and, consequently, to a block-wise QPA. Unlike prior QPA approaches, the proposal avoids classifications of picture regions and easily extends from still-image or grayscale to video or chromatic coding. The WPSNR metric also uses fewer algorithmic operations than e. g. the multiscale structural similarity measure (MS-SSIM). Due to the results of two formal subjective tests indicating its visual benefit, the QPA proposal has been adopted into VTM, the currently developed Versatile Video Coding (VVC) reference software.
Phase triangulation method with spatial modulation frequency optimization

The paper proposes the phase triangulation method with an optimization of spatial modulation frequency. The optimal frequency is calculated based on the analysis of the amplitude-frequency characteristics of the measuring optical path according to the data obtained during the formation of structured images encoding the binary Gray code. The proposed approach makes it possible to use the optimal frequency of optical source spatial modulation in the 3D geometry measurements using phase steps triangulation and binary Gray code approaches. As a result, the method provides the minimum level of measurement error, regardless to parameters of the used optical-electronic elements of the measuring system.
An integrated highly synchronous, high resolution, real time eye tracking system for dynamic flight movement

Electronic surveillance systems are being used rapidly today, ranging from a simple video camera to a complex biometric surveillance system for facial patterns and intelligent computer vision based surveillance systems, which are applied in many fields such as home monitoring, security surveillance of important places and mission critical tasks like air traffic control surveillance. Such systems normally involve a computer system and a human surveillance operator, who looks at the dynamic display to perform his surveillance tasks. Exploitation of shared information between these physical heterogeneous data capture systems with human operated functions is one emerging aspect in electronic surveillance that has yet to be addressed deeply. Hence, an innovative interaction interface for such knowledge extraction and representation is required. Such an interface should establish a data activity register frame which captures information depicting various surveillance activities at a specified spatial and time reference. This paper presents a real time eye tracking system, which integrates two sets of activity data in a highly dynamic changing and synchronous manner in real-time with respect to both spatial and time frames, through the “Dynamic Data Alignment and Timestamp Synchronisation Model”. This model matches the timestamps of the two data streams, aligns them to the same spatial reference frame before fusing them together into a data activity register frame. The Air Traffic Control (ATC) domain is used to illustrate this model, where experiments are conducted under simulated radar traffic situations with participants and their radar input data. Test results revealed that this model is able to synchronise the timestamp of the eye and dynamic display data, align both of these data spatially, while taking into account dynamic changes in space and time on a simulated radar display. This system can also distinguish and show variations in the monitoring behaviour of participants. As such, new knowledge can be extracted and represented through this innovative interface, which can then be applied to other applications in the field of electronic surveillance to unearth monitoring behaviour of the human surveillance operator.
Delegated Authorization Framework for EHR Services Using Attribute-Based Encryption

Medical organizations find it challenging to adopt cloud-based Electronic Health Records (EHR) services due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient-centric approach for EHR management, where the responsibility of authorizing data access is handled at the patients end. This creates a significant overhead for the patient who must authorize every access of their health record. This is not practical given that multiple personnel are typically involved in providing care and that the patient may not always be in a state to provide this authorization. Hence there is a need to develop a proper authorization delegation mechanism for safe, secure and easy to use cloud-based EHR Service management. We present a novel, centralized, attribute-based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHRs access authority to medical providers.