Reviewerly/exHarmony huggingface dataset:
A polarity calculation approach for lexicon-based Turkish sentiment analysis

Sentiment analysis attempts to resolve the senses or emotions that a writer or speaker intends to send across to the people about an object or event. It generally uses natural language processing and/or artificial intelligence techniques for processing electronic documents and mining the opinion specified in the content. In recent years, researchers have conducted many successful sentiment analysis studies for the English language which consider many words and word groups that set emotion polarities arising from the English grammar structure, and then use datasets to test their performance. However, there are only a limited number of studies for the Turkish language, and these studies have lower performance results compared to those studies for English. The reasons for this can be incorrect translation of datasets from English into Turkish and ignoring the special grammar structures in the latter. In this study, special Turkish words and linguistic constructs which affect the polarity of a sentence are determined with the aid of a Turkish linguist, and an appropriate lexicon-based polarity determination and calculation approach is introduced for this language. The proposed methodology is tested using different datasets collected from Twitter, and the test results show that the proposed system achieves better accuracy than the previously developed lexical-based sentiment analysis systems for Turkish. The authors conclude that especially analysis of word groups increases the overall performance of the system significantly.
How You Act Tells a Lot: Privacy-Leaking Attack on Deep Reinforcement Learning

Machine learning has been widely applied to various applications, some of which involve training with privacy-sensitive data. A modest number of data breaches have been studied, including credit card information in natural language data and identities from face dataset. However, most of these studies focus on supervised learning models. As deep reinforcement learning (DRL) has been deployed in a number of real-world systems, such as indoor robot navigation, whether trained DRL policies can leak private information requires in-depth study.To explore such privacy breaches in general, we mainly propose two methods: environment dynamics search via genetic algorithm and candidate inference based on shadow policies. We conduct extensive experiments to demonstrate such privacy vulnerabilities in DRL under various settings. We leverage the proposed algorithms to infer floor plans from some trained Grid World navigation DRL agents with LiDAR perception. The proposed algorithm can correctly infer most of the floor plans and reaches an average recovery rate of 95.83% using policy gradient trained agents. In addition, we are able to recover the robot configuration in continuous control environments and an autonomous driving simulator with high accuracy. To the best of our knowledge, this is the first work to investigate privacy leakage in DRL settings and we show that DRL-based agents do potentially leak privacy-sensitive information from the trained policies.
Method for designing phase-retrieval algorithms for Ronchi phase-shifting lateral-shearing interferometry

We propose a general method of designing phase-analysis algorithms for Ronchi phase-shifting lateral-shearing interferometry. Based on the expression and method, three new phase-shifting algorithms are designed to eliminate negative error effects of interference from unwanted diffraction orders, which limits the accuracy of wavefront aberration measurement. The proposed eight-, 10-, and 13-frame algorithms can eliminate the effects of the first ±5, ±9, and ±15 multi-diffraction orders, respectively. Simulation works and experimental results verify the general expression and corresponding designed method.
Ship Detection for Optical Remote Sensing Images Based on Visual Attention Enhanced Network

Ship detection plays a significant role in military and civil fields. Although some state-of-the-art detection methods, based on convolutional neural networks (CNN) have certain advantages, they still cannot solve the challenge well, including the large size of images, complex scene structure, a large amount of false alarm interference, and inshore ships. This paper proposes a ship detection method from optical remote sensing images, based on visual attention enhanced network. To effectively reduce false alarm in non-ship area and improve the detection efficiency from remote sensing images, we developed a light-weight local candidate scene network( L 2 CSN) to extract the local candidate scenes with ships. Then, for the selected local candidate scenes, we propose a ship detection method, based on the visual attention DSOD(VA-DSOD). Here, to enhance the detection performance and positioning accuracy of inshore ships, we both extract semantic features, based on DSOD and embed a visual attention enhanced network in DSOD to extract the visual features. We test the detection method on a large number of typical remote sensing datasets, which consist of Google Earth images and GaoFen-2 images. We regard the state-of-the-art method [sliding window DSOD (SW+DSOD)] as a baseline, which achieves the average precision (AP) of 82.33%. The AP of the proposed method increases by 7.53%. The detection and location performance of our proposed method outperforms the baseline in complex remote sensing scenes.
User-centered fall detection using supervised, on-line learning and transfer learning

There is no need to explain the relevance of fall detection (FD) in the elderly population: the faster they receive help, the higher the probabilities of recovery. FD has been widely studied, and a number of solutions have been proposed in the literature, including commercial products with well-known trademarks. Basically, the majority of the solutions include an Event Detection method followed by a feature extraction block as the inputs to a classifier that labels the sample as Fall or as Not Fall. Nevertheless, the proposals always rely on user feedback because of the relatively high percentage of false positives. This study proposes two supervised user-centered solutions for FD which are evaluated using two publicly available data sets that include simulated falls with the sensory system placed on a wrist. Instead of focusing on learning a generalized model, in this study we focus on learning specific models for each user. To overcome the lack of data from the Fall class, an on-line learning method to group the events arising from the activities of daily life and the consequent analysis of how fall events can then be detected is included. In addition, a transfer learning stage produces a priori knowledge from previous experiences that can be introduced into the on-line learning classifier in order to enhance its performance. Complete experimentation is carried out on two publicly available data sets, analyzing the performance of the supervised classifiers, and the performance of the whole detection system composed of the fall detection plus the classifier. In the case of on-line learning, only the results for the classifier, either with or without transfer learning, are included. The results suggest that FD user-centered solutions have the capacity to adapt to the information of a specific user, mostly when using both on-line learning and transfer learning. Nevertheless, the transfer learning stage needs refinements in order to avoid the complexity that this might introduce.
Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities

Alexander Erdmann, David Joseph Wrisley, Benjamin Allen, Christopher Brown, Sophie Cohen-Bodénès, Micha Elsner, Yukun Feng, Brian Joseph, Béatrice Joyeux-Prunel, Marie-Catherine de Marneffe. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.
Measuring the effect of different types of unsupervised word representations on Medical Named Entity Recognition

This work deals with Natural Language Processing applied to the clinical domain. Specifically, the work deals with a Medical Entity Recognition (MER) on Electronic Health Records (EHRs). Developing a MER system entailed heavy data preprocessing and feature engineering until Deep Neural Networks (DNNs) emerged. However, the quality of the word representations in terms of embedded layers is still an important issue for the inference of the DNNs. The main goal of this work is to develop a robust MER system adapting general-purpose DNNs to cope with the high lexical variability shown in EHRs. In addition, given that EHRs tend to be scarce when there are out-domain corpora available, the aim is to assess the impact of the word representations on the performance of the MER as we move to other domains. In this line, exhaustive experimentation varying information generation methods and network parameters are crucial. We adapted a general purpose sequential tagger based on Bidirectional Long-Short Term Memory cells and Conditional Random Fields (CRFs) in order to make it tolerant to high lexical variability and a limited amount of corpora. To this end, we incorporated part of speech (POS) and semantic-tag embedding layers to the word representations. One of the strengths of this work is the exhaustive evaluation of dense word representations obtained varying not only the domain and genre but also the learning algorithms and their parameter settings. With the proposed method, we attained an error reduction of 1.71 (5.7%) compared to the state-of-the-art even that no preprocessing or feature engineering was used. Our results indicate that dense representations built taking word order into account leverage the entity extraction system. Besides, we found that using a medical corpus (not necessarily EHRs) to infer the representations improves the performance, even if it does not correspond to the same genre.
Domain Adaptive Inference for Neural Machine Translation

We investigate adaptive ensemble weighting for Neural Machine Translation, addressing the case of improving performance on a new and potentially unknown domain without sacrificing performance on the original domain. We adapt sequentially across two Spanish-English and three English-German tasks, comparing unregularized fine-tuning, L2 and Elastic Weight Consolidation. We then report a novel scheme for adaptive NMT ensemble decoding by extending Bayesian Interpolation with source information, and report strong improvements across test domains without access to the domain label.
Bayesian parameterisation of a regional photovoltaic model – Application to forecasting

Estimating and forecasting photovoltaic (PV) power generation in regions—e.g. the area controlled by the transmission system operator (TSO)—is a requirement for the operation of the electricity supply system. An accurate calculation of this quantity requires detailed information of the installed PV systems within the considered region; however, this information is not publicly available making forecasting difficult. Therefore, approximating the undefined PV systems information for use in a PV power model (parameterization) is of critical interest. In this paper, we propose a methodological approach for parameterization using time series of aggregated PV power generation. A Bayesian approach is used to overcome the significant number of unknown parameters in the problem. It regularizes the linear system by imposing constraints on deviations from an initial-guess and covariance matrices; the initial guess uses available statistical distributions of PV system metadata. The performance of the proposed forecasting approach is evaluated using estimates of the regional PV power generation from three TSOs and meteorological data from the IFS forecast model (ECMWF). The proposed forecasting approach without the Bayesian parameterization has RMSE of 3.90%, 4.25% and 4.64%, respectively; including the Bayesian approach gives RMSE of 3.82%, 4.23% and 4.51%. For comparison, we also deployed a multiple linear regression which gave RMSE of 3.89%, 4.12% and 4.54%; however, there are considerable downsides to such an approach. Our approach is competitive with TSO forecasting systems despite using far fewer input data and simpler implementation of NWP prediction. This is particularly promising as there are many avenues for future development.
Full-Body High-Resolution Anime Generation with Progressive Structure-Conditional Generative Adversarial Networks

We propose Progressive Structure-conditional Generative Adversarial Networks (PSGAN), a new framework that can generate full-body and high-resolution character images based on structural information. Recent progress in generative adversarial networks with progressive training has made it possible to generate high-resolution images. However, existing approaches have limitations in achieving both high image quality and structural consistency at the same time. Our method tackles the limitations by progressively increasing the resolution of both generated images and structural conditions during training. In this paper, we empirically demonstrate the effectiveness of this method by showing the comparison with existing approaches and video generation results of diverse anime characters at 1024 $$\times $$ 1024 based on target pose sequences. We also create a novel dataset containing full-body 1024 $$\times $$ 1024 high-resolution images and exact 2D pose keypoints using Unity 3D Avatar models.
Task-agnostic dynamics priors for deep reinforcement learning

While model-based deep reinforcement learning (RL) holds great promise for sample efficiency and generalization, learning an accurate dynamics model is often challenging and requires substantial interaction with the environment. A wide variety of domains have dynamics that share common foundations like the laws of classical mechanics, which are rarely exploited by existing algorithms. In fact, humans continuously acquire and use such dynamics priors to easily adapt to operating in new environments. In this work, we propose an approach to learn task-agnostic dynamics priors from videos and incorporate them into an RL agent. Our method involves pre-training a frame predictor on task-agnostic physics videos to initialize dynamics models (and fine-tune them) for unseen target environments. Our frame prediction architecture, SpatialNet, is designed specifically to capture localized physical phenomena and interactions. Our approach allows for both faster policy learning and convergence to better policies, outperforming competitive approaches on several different environments. We also demonstrate that incorporating this prior allows for more effective transfer between environments.
QoS Based Resource Provisioning in Cloud Computing Environment: A Technical Survey

Cloud computing is one of the computing methodology that can address the on demand requirement of most of the applications in efficient manner. This system also permits the pay-per usage rating model for the purpose of computing services which are delivered to the end users throughout the globe using Internet. Because of reduced resources it becomes very difficult for the cloud purveyors to offer all the end users their required services. One such challenge raised by the cloud application is management of Quality of Service (QoS) that is the matter of allocating resources to provide assured service on the basis of availability, performance and reliability. From the cloud vendors impression of cloud resources can be assigned in a reasonable manner. Thus it is at most importance to meet the QoS requirement and satisfaction level of cloud users. This paper aims at the study of various researchers in the field of Resource allocation and resource availability in cloud computing environment based on QoS requirements.
Development of Output Correction Methodology for Long Short Term Memory-Based Speech Recognition

This paper presents a correction methodology for Long Short Term Memory (LSTM) based speech recognition. A strategy that validates with a reference database was developed for LSTM. It is conceptually simple but requires a large keyword database to match test templates. The correction method is based on the “most matching method” that is finding the word in which the system output is closest among the “Referenced Template Database”. Each LSTM model recognition output was corrected with the proposed new concept. Thus, system recognition performance was improved by correcting faulty outputs. The effectiveness, efficiency, and contribution of this approach to system performance were demonstrated by experiments. Tests carried out using different speech-text datasets and LSTM models yielded an average performance increase of 2.25%. With some advanced models, this ratio rises to 3.84%.
Near-optimal method for highly smooth convex optimization

We propose a near-optimal method for highly smooth convex optimization. More precisely, in the oracle model where one obtains the $p^{th}$ order Taylor expansion of a function at the query point, we propose a method with rate of convergence $\tilde{O}(1/k^{\frac{ 3p +1}{2}})$ after $k$ queries to the oracle for any convex function whose $p^{th}$ order derivative is Lipschitz.
DMRAN:A Hierarchical Fine-Grained Attention-Based Network for Recommendation

The conventional methods for the next-item recommendation are generally based on RNN or one- dimensional attention with time encoding. They are either hard to preserve the long-term dependencies between different interactions, or hard to capture fine-grained user preferences. In this paper, we propose a Double Most Relevant Attention Network (DMRAN) that contains two layers, i.e., Item level Attention and Feature Level Self- attention, which are to pick out the most relevant items from the sequence of user’s historical behaviors, and extract the most relevant aspects of relevant items, respectively. Then, we can capture the fine-grained user preferences to better support the next-item recommendation. Extensive experiments on two real-world datasets illustrate that DMRAN can improve the efficiency and effectiveness of the recommendation compared with the state-of-the-art methods.
From Embodiment of a Point-Light Display in Virtual Reality to Perception of One's Own Movements

Humans can recognize living organisms and understand their actions solely on the basis of a small animated set of well-positioned points of light, i.e. by recognizing biological motion. Our aim was to determine whether this type of recognition and integration also occurs during the perception of one's own movements. The participants (60 females) were immersed with a virtual reality headset in a virtual environment, either dark or illuminated, in which they could see a humanoid avatar from a first-person perspective. The avatar's forearms were either realistic or represented by three points of light. Embodiment was successfully achieved through a 1-min period during which either the realistic or point-light avatar's forearms faithfully reproduced voluntary flexion–extension movements. Then, the "virtual mirror paradigm" was used to evoke kinesthetic illusions. In this paradigm, a passive flexion–extension of the participant's left arm was coupled with the movements of the avatar's forearms. This combined visuo-proprioceptive stimulation, was compared with unimodal stimulation (either visual or proprioceptive stimulation only). We found that combined visuo-proprioceptive stimulation with realistic avatars evoked more vivid kinesthetic illusions of a moving right forearm than unimodal stimulations, regardless of whether the virtual environment was dark or illuminated. Kinesthetic illusions also occurred with point-light avatars, albeit less frequently and a little less intense, and only when the visual environment was optimal for slow motion detection of the point-light display (lit environment). We conclude that kinesthesia does not require visual access to an elaborate representation of a body segment. Access to biological movement can be sufficient.
1D Barcode Detection via Integrated Deep-Learning and Geometric Approach

Vision-based 1D barcode reading has been the subject of extensive research in recent years due to the high demand for automation in various industrial settings. With the aim of detecting the image region of 1D barcodes, existing approaches are both slow and imprecise. Deep-learning-based methods can locate the 1D barcode region fast but lack an adequate and accurate segmentation process; while simple geometric-based techniques perform weakly in terms of localization and take unnecessary computational cost when processing high-resolution images. We propose integrating the deep-learning and geometric approaches with the objective of tackling robust barcode localization in the presence of complicated backgrounds and accurately detecting the barcode within the localized region. Our integrated real-time solution combines the advantages of the two methods. Furthermore, there is no need to manually tune parameters in our approach. Through extensive experimentation on standard benchmarks, we show that our integrated approach outperforms the state-of-the-art methods by at least 5%.
Galex: Exploring the Evolution and Intersection of Disciplines

Revealing the evolution of science and the intersections among its sub-fields is extremely important to understand the characteristics of disciplines, discover new topics, and predict the future. The current work focuses on either building the skeleton of science, lacking interaction, detailed exploration and interpretation or on the lower topic level, missing high-level macro-perspective. To fill this gap, we design and implement Galaxy Evolution Explorer (Galex), a hierarchical visual analysis system, in combination with advanced text mining technologies, that could help analysts to comprehend the evolution and intersection of one discipline rapidly. We divide Galex into three progressively fine-grained levels: discipline, area, and institution levels. The combination of interactions enables analysts to explore an arbitrary piece of history and an arbitrary part of the knowledge space of one discipline. Using a flexible spotlight component, analysts could freely select and quickly understand an exploration region. A tree metaphor allows analysts to perceive the expansion, decline, and intersection of topics intuitively. A synchronous spotlight interaction aids in comparing research contents among institutions easily. Three cases demonstrate the effectiveness of our system.