Reviewerly/exHarmony huggingface dataset:
Actor–Critic Learning Control With Regularization and Feature Selection in Policy Gradient Estimation

Actor-critic (AC) learning control architecture has been regarded as an important framework for reinforcement learning (RL) with continuous states and actions. In order to improve learning efficiency and convergence property, previous works have been mainly devoted to solve regularization and feature learning problem in the policy evaluation. In this article, we propose a novel AC learning control method with regularization and feature selection for policy gradient estimation in the actor network. The main contribution is that ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularization is used on the actor network to achieve the function of feature selection. In each iteration, policy parameters are updated by the regularized dual-averaging (RDA) technique, which solves a minimization problem that involves two terms: one is the running average of the past policy gradients and the other is the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -regularization term of policy parameters. Our algorithm can efficiently calculate the solution of the minimization problem, and we call the new adaptation of policy gradient RDApolicy gradient (RDA-PG). The proposed RDA-PG can learn stochastic and deterministic near-optimal policies. The convergence of the proposed algorithm is established based on the theory of two-timescale stochastic approximation. The simulation and experimental results show that RDA-PG performs feature selection successfully in the actor and learns sparse representations of the actor both in stochastic and deterministic cases. RDA-PG performs better than existing AC algorithms on standard RL benchmark problems with irrelevant features or redundant features.
A Blockchain based Identity Management System Considering Reputation

In the era of the Internet, a user can perform some actions by being assigned with a pseudo identity, and most users' identities are stored by the certified third parties. This centralized identity management model has a potential safety concern. Once the database server is crashed or the data are leaked, the privacy of the user are exposed. In this paper, a smart contract technology on the blockchain is used to build a decentralized identity management system. The system is usercentric, allowing users to fully control their identity information, only authorized third parties allow access to their user information. Attribute-based authentication is used to achieve identity anonymity. In addition, a reputation model based on attribute reputation is proposed, which makes the user's identity credible even in a decentralized environment.
Triple-Complementary Network for RGB-D Salient Object Detection

Most of the existing RGB-D saliency detectors have tried different strategies to fuse RGB and depth information to generate better saliency detection results. However, the ability of only using RGB image to detect the salient object is ignored in RGB-D saliency detectors. In this paper, we propose a triple-complementary network for fully exploring the RGB and depth information. The first two sub-networks are used for extracting saliency from RGB image and RGB-D image pair, respectively. The third sub-network refines the saliency map by comprehensively considering RGB image, depth map and the refined saliency map of the first two sub-networks. We propose depth weighted refinement to suppress the high contrast objects with large depth values. The proposed method not only fully explores the information of RGB but also makes the RGB and depth tightly coupled. The experiments on five RGB-D saliency detection datasets show the superiority of the proposed method over the state-of-the-art RGB-D saliency detectors.
Language resources for Maghrebi Arabic dialects’ NLP: a survey

Diglossia is one of the main characteristics of Arabic language. In Arab countries, there are three forms of Arabic that co-exist: Classical Arabic (CA) which is mainly used in the Quran and in several classical literary texts, Modern Standard Arabic (MSA) that descends from CA and used as official language, and various regional colloquial varieties of Arabic that are usually referred to as Arabic dialects (AD). Deemed to be amongst low-resource languages, these dialects have aroused increased interest among the NLP community in recent years. Indeed, the various Arabic dialects are increasingly used on the social web and may be transcribed in both the Arabic and the Latin script. The latter is known as Arabizi and seems to be more frequently used for some of them. The AD NLP raises many challenges and requires the availability of large and appropriate language resources. In this study, we focus, in particular, on the Maghrebi Arabic dialects (MADs). We propose a thorough review of the language resources (LRs) that have been generated by the various work carried out on the MAD language processing. A survey of the currently online available MAD NLP dedicated-LRs is also compiled and discussed. LRs investigated in this work are essentially data-resources such as primary and annotated corpora, lexica, dictionaries, ontologies, etc.
Vocabulary and time based bug‐assignment: A recommender system for open‐source projects

Summary Bug‐assignment (BA), the task of ranking developers in terms of the relevance of their expertise to fix a new bug report is time consuming, which is why substantial attention has been paid to developing methods for automating it. In this article, we describe a new BA approach that relies on two key intuitions. Similar to traditional BA methods, our method constructs the expertise profile of project developers, based on the textual elements of the bugs they have fixed in the past; unlike traditional methods, however, our method considers only the programming keywords in these bug descriptions, relying on Stack Overflow as the vocabulary for these keywords. The second key intuition of our method is that recent expertise is more relevant than past expertise, which is why our method weighs the relevance of a developer's expertise based on how recently they have fixed a bug with keywords similar to the bug at hand. We evaluated our BA method using a dataset of 93k bug‐report assignments from 13 popular GitHub projects. In spite of its simplicity, our method predicts the assignee with high accuracy, outperforming state‐of‐the‐art methods.
Distribution of rocks in Pınarbaşı Ophiolite from central Anatolia (Turkey) based on analysis of <scp>ASTER</scp> and Landsat‐8 data

The Late Cretaceous Pınarbaşı Ophiolite (PO), located at central Turkey is represented by distinct rock types (e.g., harzburgite, dunite, pyroxenite, gabbro) that can be observed in many ophiolites around the world. In this study, the classification of rock types of PO in the Eastern Tauride Belt (Turkey) was made using the advanced spaceborne thermal emission and reflection radiometer and Landsat‐8 satellite images. The developed colour–band ratio composite images together with principal component analysis and minimum noise fraction transformations implemented to the remote sensing data efficiently delineate the spatial distribution of pyroxenites, metamorphic sole rocks, and gabbros in the study area. In addition, a body of serpentinized dunites, pyroxenites, metamorphic sole rocks, and listwaenites that has not been recognized before was identified within PO using remote sensing techniques. The promising results of the image processing were verified by detailed field observations and petrographic analysis. The satellite‐based remote sensing approaches of this work can be applicable for mapping ophiolitic rock types in PO in particular as well as other ophiolites from similar geographical regions around the world.
Avatar-Based Learning and Teaching as a Concept of New Perspectives in Online Education in Post-Soviet Union Countries

In the article the avatar-based learning and teaching (A-BL&amp;T) as a concept of control and managing knowledge in modern socio-economic conditions is proposed to use for assessment a university's economic efficiency. It is shown that all elements, methods and techniques (tools) do not operate in isolation, but rather are interrelated, complementing each other. All of them are used in the process of management and, in a combination, are powerful tools for increasing efficiency of management. Based on the example of avatar-based learning and teaching in Russian universities as modern educational environments, a conceptual model, methodology, and methods have been obtained for the automation of planning and calculation of the academic load in the university.
Designing accessible, explainable AI (XAI) experiences

Explainable Artificial Intelligence (XAI) has taken off in recent years, a field that develops techniques to render complex AI and machine learning (ML) models comprehensible to humans. Despite the growth of XAI techniques, we know little about the challenges of leveraging such explainability capabilities in situated settings of use. In this article, we discuss some particular issues around the intersection between accessibility and XAI. We outline two primary concerns: one, accessibility at the interface; and two, tailoring explanations to individuals' diverse and changing explainability needs. We illustrate these issues by discussing two application areas for AI/Ml systems (aging-in-place and mental health support) and discuss how issues arise at the nexus between explainability and accessibility.
Research data management at an African medical university: Implications for academic librarianship

The study investigates and provides results on the current status of research data management at a medical school in a developing African country. The study focusses on four aspects of research data management: generation; preservation and backup; competencies; and challenges. Quantitative data collected using a questionnaire from 84 health researchers and 16 librarians was complemented by qualitative data gathered through conducting structured interviews with the Director of Research of the university. The study results provide the research community with a better understanding of research data management perspectives amongst health researchers in a developing country. This could help research data management stakeholders to adopt better strategies for championing research data management activities in this particular country and in other developing countries. The study notes that the current research data management status creates an opportunity for an academic library to consolidate and fortify its widely viewed natural role in research data management. In view of the findings, the study proposes that the adoption of research data management policies is essential in inspiring the conceptualisation, popularisation, adoption, and operationalisation of various research data management activities within the African university environment.
Using the Keras Model for Accurate and Rapid Gender Identification through Detection of Facial Features

As the need for person-aligned, effective, and ethical structures emerges, automatic identification of gender is gaining interest in the era of machine-man interactions. Most of the systems for gender detection were analysed using textual or audiovisual sources. Far too many suggested different approaches for automatic identification of gender using the characteristics acquired from people's bodies and/or behaviours. But the accuracy has always been a question or a drawback in automated gender detection. In the proposed research work, First, the faces and the facial features which includes eyes, mouth, and nose are detected using Haar Cascade based on Viola-Jones face detection algorithm. Before, the gender detection, the noise is reduced by applying adaptive filters, thereby increasing the accuracy. The obtained facial features are given as the input or test data to the neural network. The neural network is designed to obtain the features and acts as a classifier to detect the genders. Simply, the feature extraction and gender detection is performed using the open source neural network called keras.
Privacy preserving cloud based secure digital locker using Paillier based difference function and chaos based cryptosystem

In this paper, a cloud based digital image locker system along with secure user authentication and novel image cryptosystem for preserving privacy of user images is proposed. Images require more storage space as compared to text. So, it is not feasible to store images locally in digital locker for everyone. By taking advantage of high speed Internet, it is feasible to use cloud for efficient storage of images. However, so many researchers feel that cloud technology is 'honest-but-curious'. Cloud service follows the specified protocol completely, but it traces and analyzes the data to get the user's important information. To overcome from such challenges, privacy of user's identity and data should be preserved. The proposed authentication protocol is designed using Paillier based difference function which is based on Homomorphic cryptosystem. The speciality of homomorphic function is that computation on encrypted data is possible without decrypting the cipher text. Because of this, authentication process is done in encrypted environment. A novel image cryptosystem is designed based on Fridrich model. In this cryptosystem, confusion is performed at bit-level and diffusion is performed at pixel-level. Keys are modified in each round of confusion to achieve more security. Simulation and cryptanalysis prove the effectiveness of the proposed system.
KeyRAM: A 0.34 uJ/decision 18 k decisions/s Recurrent Attention In-memory Processor for Keyword Spotting

This paper presents a 0.34 uJ/decision deep learning-based classifier for keyword spotting (KWS) in 65 nm CMOS with all weights stored on-chip. This work adapts a Recurrent Attention Model (RAM) algorithm for the KWS task, and employs an in-memory computing (IMC) architecture to achieve up to 9× savings in energy/decision and more than 23× savings in EDP of decisions over a state-of-the art IMC IC for KWS using the Google Speech dataset while achieving the highest reported decision throughput of 18.32 k decisions/s.
A Novel Approach for Semantic Similarity Measurement for High Quality Answer Selection in Question Answering using Deep Learning Methods

Question retrieval and high-quality answer retrieval is the main task in Question-answering system. It is a real-world application of NLP technologies. The major challenge of QA is the exact selection of high-quality responses w.r.t. given questions and by doing that it will also minimize the time of finding a similar question and high-quality answer. The colossal measure of information accessible, however getting the correct data open when required is significant. So, for a new question, it gives a list of similar and related questions, which could satisfy response, information needs to the user, without waiting for new questions to be answered by users. QA engines attempt to let you ask the question the way you normally ask. QA can have two domains Open Domain Question Answering and Closed Domain Question Answering. In Open-area there's no any unique area, customers are free to ask any question, the device will deliver the solution from the internet and deliver a respective solution to the person. In a closed domain, QA users are restricted to some particular domain. We have used four datasets name as STSB(Semantic textual similarity benchmark), MRPC(Microsoft research paraphrase corpus), SICK(Sentences involving compositional knowledge) and Wikipedia dataset. And we've got as compared our proposed machine with different contemporary strategies and our proposed technique offers the excellent result many of the other techniques.
A Mutually Attentive Co-Training Framework for Semi-Supervised Recognition

Self-training plays an important role in practical recognition applications where sufficient clean labels are unavailable. Existing methods focus on generating reliable pseudo labels to retrain a model, while ignoring the importance of improving model reliability to those inevitably mislabeled data. In this paper, we propose a novel Mutually Attentive Co-training Framework (MACF) that can effectively alleviate the negative impacts of incorrect labels on model retraining by exploring deep model disagreements. Specifically, MACF trains two symmetrical sub-networks that have the same input and are connected by several attention modules at different layers. Each attention module analyzes the inferred features from two sub-networks for the same input and feedback attention maps for them to indicate noisy gradients. This is realized by exploring the back-propagation process of incorrect labels at different layers to design attention modules. By multi-layer interception, the noisy gradients caused by incorrect labels can be effectively reduced for both sub-networks, leading to robust training to potential incorrect labels. In addition, a hierarchical distillation strategy is developed to improve the pseudo labels by aggregating the predictions from multi-models and data transformations. The experiments on six general benchmarks, including classification and biomedical segmentation, demonstrate that MACF is much robust to noisy labels than previous methods.
Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback

Current ongoing industry revolution demands more flexible products, including robots in household environments and medium-scale factories. Such robots should be able to adapt to new conditions and environments and be programmed with ease. As an example, let us suppose that there are robot manipulators working on an industrial production line and that they need to perform a new task. If these robots were hard coded, it could take days to adapt them to the new settings, which would stop production at the factory. Robots that non-expert humans could easily program would speed up the process considerably.
Scanning horizons in research, policy and practice

New and emerging environmental issues make policy and practice difficult. A pressing need to respond when knowledge of the problem is limited is added to an already challenging conservation agenda. Horizon-scanning is an evolving approach that draws on diverse information sources to identify early indications of poorly recognised threats and opportunities. There are many ways to conduct horizon scans, ranging from automated techniques that scan online content and mine text to manual methods that systematically consult large groups of people (often experts). These different approaches aim to sort through vast volumes of information to look for signals of change, for example the rise in microplastics or the use of mobile phones to gather data in remote forests. Identifying these new threats and opportunities is the first important step towards further researching and managing them. This chapter reviews different approaches to horizon-scanning, together with ways of encouraging uptake of scanning outputs. It concludes by introducing emerging technologies that will add value to horizon-scanning in the future.
A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem

The evolution of the Internet of Things is significantly affected by legal restrictions imposed for personal data handling, such as the European General Data Protection Regulation (GDPR). The main purpose of this regulation is to provide people in the digital age greater control over their personal data, with their freely given, specific, informed and unambiguous consent to collect and process the data concerning them. ADVOCATE is an advanced framework that fully complies with the requirements of GDPR, which, with the extensive use of blockchain and artificial intelligence technologies, aims to provide an environment that will support users in maintaining control of their personal data in the IoT ecosystem. This paper proposes and presents the Intelligent Policies Analysis Mechanism (IPAM) of the ADVOCATE framework, which, in an intelligent and fully automated manner, can identify conflicting rules or consents of the user, which may lead to the collection of personal data that can be used for profiling. In order to clearly identify and implement IPAM, the problem of recording user data from smart entertainment devices using Fuzzy Cognitive Maps (FCMs) was simulated. FCMs are an intelligent decision-making system that simulates the processes of a complex system, modeling the correlation base, knowing the behavioral and balance specialists of the system. Respectively, identifying conflicting rules that can lead to a profile, training is done using Extreme Learning Machines (ELMs), which are highly efficient neural systems of small and flexible architecture that can work optimally in complex environments.
Effective contact recommendation in social networks by adaptation of information retrieval models

We investigate a novel perspective to the development of effective algorithms for contact recommendation in social networks, where the problem consists of automatically predicting people that a given user may wish or benefit from connecting to in the network. Specifically, we explore the connection between contact recommendation and the text information retrieval (IR) task, by investigating the adaptation of IR models (classical and supervised) for recommending people in social networks, using only the structure of these networks. We first explore the use of adapted unsupervised IR models as direct standalone recommender systems. Seeking additional effectiveness enhancements, we further explore the use of IR models as neighbor selection methods, in place of common similarity measures, in user-based and item-based nearest-neighbors (kNN) collaborative filtering approaches. On top of this, we investigate the application of learning to rank approaches borrowed from text IR to achieve additional improvements. We report thorough experiments over data obtained from Twitter and Facebook where we observe that IR models, particularly BM25, are competitive compared to state-of-the art contact recommendation methods. We provide further empirical analysis of the additional effectiveness that can be achieved by the integration of IR models into kNN and learning to rank schemes. Our research shows that the IR models are effective in three roles: as direct contact recommenders, as neighbor selectors in collaborative filtering and as samplers and features in learning to rank.
Bubble Gaze Cursor + Bubble Gaze Lens: Applying Area Cursor Technique to Eye-Gaze Interface

We conducted two studies exploring how an area cursor technique can improve the eye-gaze interface. We first examined the bubble cursor technique. We developed an eye-gaze-based cursor called the bubble gaze cursor and compared it to a standard eye-gaze interface and a bubble cursor with a mouse. The results revealed that the bubble gaze cursor interface was faster than the standard point cursor-based eye-gaze interface. In addition, the usability and mental workload were significantly better than those of the standard interface. Next, we extended the bubble gaze cursor technique and developed a bubble gaze lens. The results indicated that the bubble gaze lens technique was faster than the bubble gaze cursor method and the error rate was reduced by 54.0%. The usability and mental workload were also considerably better than those of the bubble gaze cursor.