Reviewerly/exHarmony huggingface dataset:
A Seq2seq-Based Approach to Question Answering over Knowledge Bases

Semantic parsing, as an essential approach to question answering over knowledge bases KBQA), transforms a question into query graphs for further generating logical queries. Existing semantic parsing approaches in KBQA mainly focus on relations (called local semantics) with paying less attention to the relationship among relations (called global semantics). In this paper, we present a seq2seq-based semantic parsing approach to improving performance of KBQA by converting the identification problem of question types to the problem of machine translation. Firstly, we introduce a BiLSTM-based named entity recognition (NER) method to extract all classes of entities occurring in questions. Secondly, we present an attention-based seq2seq model to learn one type of a question by applying seq2seq model in extracting relationships among classes. Finally, we generate templates to adopt more question types for matching more complex questions. The experimental results on a real knowledge base Chinese film show that our approach outperforms the existing template matching model.
Fracture and internal structure detection of ceramic objects using improved digital radiography images

Radiography testing (RT) is an effective, non-invasive method for the detection of hidden structures, details and fracture regions of valuable ancient ceramic objects. To maximize detail extraction from the radiographs and to better interpret images, we use an efficient image processing method that reveals the design and defect regions of objects. For larger radiographic images, it is preferable that a desktop computer is capable of processing the algorithm. Algorithms that require a large memory size and CPU time necessitate the resizing of the images, resulting in lower quality output images. In this study, the fast bilateral filter (FBF) was implemented to enhance the visualization of the radiographs of ceramic objects from the archaeological museum of Burriana, Spain. The digital radiographs were provided by a computed radiography (CR) system at the Laboratory of Documentation and Registration in the University Institute for the Restoration of the Patrimony (IRP) of the Universitat Politècnica de València (UPV), Spain. The FBF method based on the Fourier series and the truncated Gaussian function was successfully implemented with radiographs of antique objects. The results show that the internal structures, manufacturing processes, and fractures are better visualized and the edges and fine details are preserved. The design details were shown to be better visualized than the original radiographs based on opinions and evaluations from experts in radiography and the conservation and restoration of cultural heritage.
Administering Artificial Intelligence

As AI increasingly features in everyday life, it is not surprising to hear calls to step up regulation of the technology. In particular, a turn to administrative law to grapple with the consequences of AI is understandable because the technology’s regulatory challenges appear facially similar to those in other technocratic domains, such as the pharmaceutical industry or environmental law. But AI is unique, even if it is not different in kind. AI’s distinctiveness comes from technical attributes—namely, speed, complexity, and unpredictability—that strain administrative law tactics, in conjunction with the institutional settings and incentives, or strategic context, that affect its development path. And this distinctiveness means both that traditional, sectoral approaches hit their limits, and that turns to a new agency like an “FDA for algorithms” or a “federal robotics commission” are of limited utility in constructing enduring governance solutions.This Article assesses algorithmic governance strategies in light of the attributes and institutional factors that make AI unique. In addition to technical attributes and the contemporary imbalance of public and private resources and expertise, AI governance must contend with a fundamental conceptual challenge: algorithmic applications permit seemingly technical decisions to de facto regulate human behavior, with a greater potential for physical and social impact than ever before. This Article warns that the current trajectory of AI development, which is dominated by large private firms, augurs an era of private governance. To maintain the public voice, it suggests an approach rooted in governance of data—a fundamental AI input—rather than only contending with the consequences of algorithmic outputs. Without rethinking regulatory strategies to ensure that public values inform AI research, development, and deployment, we risk losing the democratic accountability that is at the heart of public law.
A Hybrid User-Centric Approach for Efficient Web Service Selection

Over the last few years, e-commerce has exhibited explosive growth due to the ease of availability of the internet. E-commerce is rapidly changing the way in which businesses are interacting with each other as well as with their consumers. In each innovative e-commerce application, web services are being included as an important component. This leads to the availability of a huge number of web services that provide similar functionalities. The main challenge is to select the appropriate web service which fulfills the end user requirements. So, there is a need for a web service selection method that selects the web services not only based on their functionality, but also considers the nonfunctional requirements. This article proposes a method to preprocess web services using the J48 classification technique. After that, a hybrid weight evaluation mechanism is employed to obtain the weight values of each nonfunctional parameter. In the end, the web services that are near to user expectations are selected out using the ranking method.
Reasoning with words: A hedge algebra linguistic cognitive map approach

Summary In this article, we introduce a method for reasoning with words based on hedge algebra using linguistic cognitive map. Our model consists of set of vertices and edges with value to be linguistic variables which are constrained by linguistic lattice. We figure out relationship between numeric and linguistic domain for fuzzifying data. We also prove convergence theorem on learning method for linguistic cognitive map.
GRAM: Gradient Rescaling Attention Model for Data Uncertainty Estimation in Single Image Super Resolution

In this paper, a new learning method to quantify data uncertainty without suffering from performance degradation in Single Image Super Resolution (SISR) is proposed. Our work is motivated by the fact that the idea of loss design for capturing uncertainty and that for solving SISR are contradictory. As to capturing data uncertainty, we often model the output of a network as a Euclidian distance divided by a predictive variance, negative log-likelihood (NLL) for the Gaussian distribution, so that images with high variance have less impact on training. On the other hand, in the SISR domain, recent works give more weights to the loss of challenging images to improve the performance by using attention models. Nonetheless, the conflict should be handled to make neural networks capable of predicting the uncertainty of a super-resolved image, without suffering from performance degradation. Therefore, we propose a method called Gradient Rescaling Attention Model (GRAM) that combines both attempts effectively. Since variance may reflect the difficulty of an image, we rescale the gradient of NLL by the degree of variance. Hence, the neural network can focus on the challenging images, similarly to attention models. We conduct performance evaluation using standard SISR benchmarks in terms of peak signal-noise ratio (PSNR) and structural similarity (SSIM). The experimental results show that the proposed gradient rescaling method generates negligible performance degradation compared to SISR outputs with the Euclidian loss, whereas NLL without attention degrades the SR quality.
Maximum Sustainable Throughput Evaluation Using an Adaptive Method for Stream Processing Platforms

The volume and type of streaming data is increasing rapidly, thus, real-time processing scenarios for streaming data have continued to increase. The inherent volatility of streaming data causes large changes in the throughput volatility of stream processing platforms, making evaluations of the maximum sustainable throughput (MST) for such platforms a challenge. To address these problems and improve the low efficiency of manual evaluations, a naïve method is typically used to evaluate the MST of platforms by periodically measuring throughput. The throughput volatility is detected using a skip sliding window that is defined as a bounded sequence in which a limited number of throughput values stored to measure volatility in a skipping fashion in each data growth cycle, which is a fixed time interval with respect to the continuous data rate increases. A skipping sliding window that is used to describe the procedure with which the MST is calculated in an approximate way is based on a sliding window, which is used usually in the context of stateful streaming operators. Then, according to the throughput volatility, the method judges whether the MST has been reached using a latency guarantee. However, because this method has low efficiency and a large error rate, we propose an adaptive MST evaluation method that adds a data-growth factor function to the naïve method cycle that dynamically and adaptively tunes the data rate for each data growth cycle. The experimental results from four open-source benchmarks running on three mainstream stream processing platforms show that the adaptive MST evaluation method has a lower error rate and executes faster compared with the naïve evaluation method. Moreover, the proposed method is insensitive to parameter variations and is suitable for mainstream stream processing platforms.
Hydrogenic entanglement

Is there any entanglement in the simplest ubiquitous bound system? We study the solutions to the time-independent Schr\"odinger equation for a Hydrogenic system and devise two entanglement tests for free and localised states. For free Hydrogenic systems, we compute the Schmidt basis diagonalisation for general energy eigenstates, and for a Hydrogenic system localised to a three-dimensional Gaussian wavepacket, we demonstrate that measuring its second moments is sufficient for detecting entanglement. Our results apply to any system that exhibits Hydrogenic structure.
Directive (EU) 2019/790 on copyright and related rights in the Digital Single Market and the question of approximation of Ukrainian legislation to its provisions

The article explores the provisions of the Directive (EU) 2019/790 on copyright and related rights in the Digital Single Market and the question of the adaptation of Ukrainian legislation to its provisions. It highlights importance of the Directive provisions for the development of national law on fair compensation to authors and performers for the use of works and performances.
Updating the goal model with user reviews for the evolution of an app

Abstract Goal model is an important model in requirements engineering, and it can describe features and their relationships for supporting the development of apps. Since an app evolves continually, the goal model also needs to be updated with new requirements to guide the whole process. As the feedback of users, reviews provide an abundant resource of user requirements for updating the goal model. In this paper, we propose an approach to help developers (a) analyze reviews to gain the information of user requirements by training a classifier and defining keyword‐based linguistic rules as well as grammar‐based rules and (b) update the goal model with the extracted information, including improving existing goals and extending the model with new goals. In addition, we design a framework to represent results so that they can be understood by developers easily. According to our experiments based on the data in Google Play, the F‐measure of classifier on reviews can reach 75.76%, and the average precision for extracting requirements‐related information from reviews is 84.04%, then we can map the information to goals with the F‐measure of 70.21%. Furthermore, the survey on 22 developers shows that the information provided by us is useful for updating the goal model.
Application of data anonymization in Learning Analytics

Thanks to the proliferation of academic services on the Web and the opening of educational content, today, students can access a large number of free learning resources, and interact with value-added services. In this context, Learning Analytics can be carried out on a large scale thanks to the proliferation of open practices that promote the sharing of datasets. However, the opening or sharing of data managed through platforms and educational services, without considering the protection of users' sensitive data, could cause some privacy issues. Data anonymization is a strategy that should be adopted during lifecycle of data processing to reduce security risks. In this research, we try to characterize how much and how the anonymization techniques have been used in learning analytics proposals. From an initial exploration made in the Scopus database, we found that less than 6% of the papers focused on LA have also covered the privacy issue. Finally, through a specific case, we applied data anonymization and learning analytics to demonstrate that both technique can be integrated, in a reliably and effectively way, to support decision making in educational institutions.
A Causal Semantics of IS Generics

Abstract The felicity, or acceptability, of IS generics, i.e. generic sentences with indefinite singulars, is considerably more restricted compared to BP generics, generics with bare plurals. The goal of this paper is to account for the limited felicity of IS generics compared to BP generics, on the one hand, while preserving the close similarity between the two types of generics, on the other. We do so by proposing a causal analysis of IS generics, and show that this corresponds closely with a probabilistic analysis of BP generics.
Noisy Subgraph Isomorphisms on Multiplex Networks

We address the problem of finding noisy subgraph isomorphisms on large multiplex networks. Our goal is to find as many subgraph matches as possible within a noise tolerance. We propose a novel approach based on the well-known A* search algorithm. Our approach employs new heuristics to estimate the number of missing edges of subgraph matches. This method is verified on one of the synthetic multiplex networks from the Modeling Adversarial Activity program of the Defense Advanced Research Projects Agency.
Performance Evaluation of Ethereum-based On-chain Sensor Data Management Platform for Industrial IoT

CIA (Confidentiality, Integrity and Availability) is getting more and more important in the cloud based manufacturing and IIoT (Industrial Internet-of-Things)/Indutry 4.0 domain. Recently, blockchain, a decentralized ledger technique, has attracted considerable attention to realize secure and robust data management systems for IIoT. Although several blockchain-based data management platforms for IIoT have been proposed, sensor data are stored outside of the blockchain, meaning that the data integrity and accessibility are not improved. In this paper, we take a different blockchain-based approach for sensor data management; any important sensor data are stored in the blockchain. For realizing this, a series of sensor data is compressed-then-stored in the blockchain by leveraging a fact that many sensor data often have a certain level of periodicity and stability. We have tested our idea against the real sensor data measured at our model factory, and evaluated several performance metrics such as compression ratio, compression/decompression time, process time required to store and retrieve sensor data from our Proof-of-Concept system with Ethereum and the expected blockchain size. From the results, it can be concluded that the proposed platform is viable for small factory cases but needs more technological advancement is required for large scale cases.
DAME WEB: DynAmic MEan with Whitening Ensemble Binarization for Landmark Retrieval without Human Annotation

In this work, we propose a simple yet effective module called DynAmic MEan (DAME) which allows a neural network to dynamically learn to aggregate feature maps at the pooling stage based on the input image, in order to generate global descriptors suitable for landmark retrieval. In contrast to Generalized Mean (GeM), which uses a predefined and static norm for pooling features into descriptors, we use a dynamic p-norm, with the p value being generated online by the model for each image. In addition, we utilize the introduced dynamic pooling method, to propose a novel feature whitening technique, Whitening Ensemble Binarization (WEB), to discover complementary information through multiple statistical projections. The memory cost of the proposed global binary descriptor is 8 times smaller than the state-of-the-art, while exhibiting similar or improved performance. To further demonstrate the power of DAME, we use it with features extracted from a fixed, pre-trained classification network, and illustrate that our dynamic p-norm is capable of learning to pool the classification features into global descriptors suitable for retrieval. Finally, by combining DAME with WEB, we achieve state-of-the-art results on challenging large-scale landmark retrieval benchmarks.
Domain adaptation-based transfer learning using adversarial networks

Abstract There is an implicit assumption in machine learning techniques that each new task has no relation to the tasks previously learned. Therefore, tasks are often addressed independently. However, in some domains, particularly reinforcement learning (RL), this assumption is often incorrect because tasks in the same or similar domain tend to be related. In other words, even though tasks are quite different in their specifics, they may have general similarities, such as shared skills, making them related. In this paper, a novel domain adaptation-based method using adversarial networks is proposed to do transfer learning in RL problems. Our proposed method incorporates skills previously learned from source task to speed up learning on a new target task by providing generalization not only within a task but also across different, but related tasks. The experimental results indicate the effectiveness of our method in dealing with RL problems.
Effects of the Audience Robot on Robot Interactive Theater Considering the State of Audiences

In recent years, social robots have been widely researched due to the advancement of electronics hardware and artificial intelligence learning systems. In this paper, we propose a novel multi-robot system inspired by the hero show and Kabuki shows in Japanese theater. The multi-robot system consists of multiple robots and each of the robots has its own role for instance, performer and audience. During the robot performance, audience robots tend to cheer up the performer robot through active feedbacks such as hands clapping, waving and laughing. The audience robot at the same time will lead human audiences to perform active feedback as well. As such, we believe that human audiences will be more attached to robot performance by communicating with audience robots. We also created a system where the number of robot performers adjusts to the number of audiences, which is observed by OpenPose. The effectiveness of the proposed multi-robot system is evaluated through a series of human-robot experiments.
Entanglement of multiphoton two-mode polarization Fock states and of their superpositions

Density matrices of pure multiphoton Fock polarization states and their resultant reduced density matrices of mixed states are expressed in similar ways in terms of matrices of correlators, defined as averaged products of equal numbers of creation and annihilation operators. The degree of entanglement of considered states is evaluated for various combinations of parameters of states and characters of their reduction.
Examining K-12 educators’ perception and instruction of online accessibility features

Previous research on online features embedded in language assessments have been limited to the use of dictionary or spell/grammar check. Few have examined the role of accessibility features in the Kindergarten to Grade 12 (K-12) English learner (EL) context in the U.S. This study examined how K-12 EL educators perceive and instruct online accessibility features in an annual English language proficiency assessment taken by over 2 million children. Educator perception of ELs' use of the features was also investigated. Of the ten features examined, five (Colored Overlays, Color Contrast, Highlighter, Line Guide, and Magnifier) were available across the four language domains (listening, speaking, reading, and writing) and five were specific to writing (Sticky Notes, Cut, Copy, Paste, and Underline). In a two-phase mixed-methods study, first 377 K-12 EL educators completed a survey and nine educators were further interviewed. Findings were analyzed according to educators' (1) perception of online accessibility features, (2) instruction of the features, and (3) perception of ELs' use of the features. Educators perceived the Highlighter, Line Guide, Copy, and Paste to be more important than others, which affected their instruction. Results provide practical implications for instructing and designing online accessibility features in K-12 English language proficiency assessments.
Improving the Accuracy of Collaborative Filtering-Based Recommendations by Considering the Temporal Variance of Top-N Neighbors

The accuracySingh, Pradeep Kumar of the recommendationSetta, Showmik processPramanik, Pijush Kanti Dutta based on neighborhood-based collaborativeChoudhury, Prasenjit filtering tends to diverge because the interests/preferences of the neighbors are likely to change along with time. The traditional recommendation methods do not consider the shifted likings of the neighbors; hence, the calculated set of neighbors does not always reflect the optimal neighborhood at any given point of time. In this paper, we propose a novel approach to calculate the similarity between users and find the similar neighbors of the target user in different time period to improve the accuracy in personalized recommendation. The performance of the proposed algorithm is tested on the MovieLens dataset using different performance metrics viz. MAE, RMSE, precision, recall, F-score, and accuracy.