Reviewerly/exHarmony huggingface dataset:
S$$^{2}$$ES: a stationary and scalable knowledge transfer approach for multiagent reinforcement learning

Abstract Knowledge transfer is widely adopted in accelerating multiagent reinforcement learning (MARL). To accelerate the learning speed of MARL for learning-from scratch agents, in this paper, we propose a Stationary and Scalable knowledge transfer approach based on Experience Sharing (S $$^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> ES). The mainframe of our approach is structured into three components: what kind of experience, how to learn, and when to transfer. Specifically, we first design an augmented form of experience. By sharing (i.e., transmitting) the experience from one agent to its peers, the learning speed can be effectively enhanced with guaranteed scalability. A synchronized learning pattern is then adopted, which reduces the nonstationarity brought by experience replay, and at the same time retains data efficiency. Moreover, to avoid redundant transfer when the agents’ policies have converged, we further design two trigger conditions, one is modified Q value-based and another is normalized Shannon entropy-based, to determine when to conduct experience sharing. Empirical studies indicate that the proposed approach outperforms the other knowledge transfer methods in efficacy, efficiency, and scalability. We also provide ablation experiments to demonstrate the necessity of the key ingredients.
Expression recognition based on multiple feature fusion-based convolutional neural network

The process of traditional convolutional neural network to deal with human expression features is often one-way, and it cannot make full use of the features of each layer in the convolutional network to retain valuable facial expression features. This paper proposes an improved convolutional neural network structure with a multi-feature fusion module to optimize facial expression recognition. Within this structure, three convolutional layers are divided into one area module. For each area module, the input of each convolutional layer is composed of the outputs of the previous two convolutional layers. At the same time, a dense connection structure is added between two different area modules to realize feature reuse, thereby relying on feature diversity to improve the accuracy of facial expression recognition. Our experimental results on the CK+ and FER2013 databases show that compared with existing convolutional neural networks, the method gives higher accuracy and lower structure complexity.
Automatic Re-orientation of 3D Echocardiographic Images in Virtual Reality Using Deep Learning

In 3D echocardiography (3D echo), the image orientation varies depending on the position and direction of the transducer during examination. As a result, when reviewing images the user must initially identify anatomical landmarks to understand image orientation – a potentially challenging and time-consuming task. We automated this initial step by training a deep residual neural network (ResNet) to predict the rotation required to re-orient an image to the standard apical four-chamber view). Three data pre-processing strategies were explored: 2D, 2.5D and 3D. Three different loss function strategies were investigated: classification of discrete integer angles, regression with mean absolute angle error loss, and regression with geodesic loss. We then integrated the model into a virtual reality application and aligned the re-oriented 3D echo images with a standard anatomical heart model. The deep learning strategy with the highest accuracy – 2.5D classification of discrete integer angles – achieved a mean absolute angle error on the test set of 9.0 $$^\circ $$ . This work demonstrates the potential of artificial intelligence to support visualisation and interaction in virtual reality.
PyDentity: A playground for education and experimentation with the Hyperledger verifiable information exchange platform

PyDentity lowers the entry barrier for parties interested in experimenting with the Hyperledger’s verifiable information exchange platform. It enables educators, developers and researchers to configure and initialise a set of actors easily as associated Hyperledger Aries agents. This allows them to focus on writing domain-specific business logic within a Jupyter notebook interface for each actor. Through this, actors can be customised to determine their connections with others, the messages they send, and how they respond to messages they received. This simple architecture allows for fast iteration and detailed exploration of the possible interactions and use cases that this platform supports. Additionally, notebooks can easily be made self-documenting and easily replicated by multiple parties. PyDentity has already been used in several research and education projects.
Structural and dynamic conditions for the formation of large orogenic gold deposits in Central and Northeast Asia

Research subject. Large orogenic gold deposits in the fold belts of Central and Northeast Asia. Materials and methods. Geological mapping of various scales on a number of large orogenic gold deposits was conducted using the methods of structural-paragenetic analysis of metamorphic strata, accompanied by obligatory linking of ore mineralization manifestations to specific structures. In a number of cases, various statistical methods were used to geometrize mineralization, identify patterns of its location and determine the paths of paleofluid flows. Available publications on the objects under consideration were reviewed. The geological and structural features of large orogenic gold deposits – Muruntau, Kokpatas, Sukhoi Log and Pavlik – were considered. Results. The Muruntau, Kokpatas and the Sukhoi Log ore deposits are of shariyage-thrust type. Compared to these objects, the Pavlik field is confined to a zone of volumetric fracturing between a series of reverse faults, feathering a large deep fault and belonging to the transpression type. At the Muruntau and Pavlik deposits, the analysis of the location of the most intensive mineralization substantiated the paths of paleofluid flows, along which the fluid migration and ore formation took place. Conclusions. The distribution of ore mineralization in the Muruntau deposit obeys the orientation of planar (cleavage) and linear (orientation of fold hinges) elements. Apparently, the former (main) direction may indicate the orientation and position of the main migration route of ore-bearing fluids, while the latter corresponds to secondary channels, the position of which is due to the intersection of syn-napping structures with favourable lithological horizons. For the Pavlik deposit, the position of ore pillars is compared with the paths of paleofluid flows, the root parts of which are promising for identifying the most powerful and intense mineralization.
An Empirical Study of Online Terminology Translation Retrieval:Using Search Engines as a Tool

Abstract Online search engines and other computer-aided tools are playing an increasingly important role in technical translation, especially in the translation of out-of-vocabulary new terms. Dictionaries, at times, prove to be of little help to term translation where out-of-vocabulary terms rely on polysemy for heuristic effects. This paper explores the role of online search engines in terminology translation by analyzing the effectiveness of four terminology translation extraction methods which utilize online search engines as search tools, thereby generating suggestions on how to use search result for reference. An experiment with some new terms was conducted to examine the effectiveness of each method. This study revealed that template-based term translation extraction and dictionary-based translation extraction have the highest accuracy rate of 86% and 89% respectively while semantic prediction search method is the most flexible method with the highest retrieval rate of 95% although its results are not always accurate. Therefore, a verification model was proposed, which utilizes quantitative-and-qualitative context analysis to verify the accuracy of translation results obtained from a search engine.
Acoustic unit discovery using transient and steady-state regions in speech and its applications

Acoustic modelling in the absence of labelled audio is difficult in speech processing, especially in under-resourced languages. Ideas from theories of speech production and perception can aid acoustic modelling in such a setting. Several production and perception related studies have shown the importance of the dynamic nature of speech. In the present work, an attempt is made to discover and model the dynamic nature of the speech signal. Specifically, speech is modelled as a sequence of transient and steady-state units. Model initialisation, which is crucial for unsupervised acoustic modelling, is performed using the syllabic structure present in the speech signal. The proposed method has similarities with the distinctive region model (DRM) for speech production, where the dynamic regions are assumed to be contained within syllable-like segments. An analysis of the discovered units reveals that the units are of transient and steady-state forms. The steady-state units predominantly correspond to vowels. The transient units correspond to nasal, approximant, fricative, and stop transients. Finally, the effectiveness of the proposed method is explored by applying the acoustic units to zero-resource text-to-speech synthesis and unsupervised keyword spotting tasks.
Active Object Searching on Mobile Robot Using Reinforcement Learning

With the fast development of hardware computation, visual object detection has achieved a prosperous development in both industrial application and academic researching. Beneficial from the great success in the field of artificial intelligence, mobile robot research has received much inspiration for autonomy path planning. Although state-of-art object detection algorithms, such as YOLO, Mask-RCNN, can detect and classify targets from robot view, complicated tasks require an autonomous robot that has the capability to adjust its viewpoint and self-decide anchor targets to get better detection results. In this paper, a method is proposed to help an agent learn what actions to take according to detected objects in its viewpoint. The agent is also able to search objects in different rooms and indoor environments only with prior knowledge of potential objects in a room. Experimental results indicate that our active searching approach can help agents learn active room selection and adjust viewpoint to receive better object detection results.
A Metaheuristic Algorithm for Classification of White Blood Cells in Healthcare Informatics

Diagnostic imaging is in itself an important technological evolution, which represented an advance in the way diseases are diagnosed, originating from the digital age in diagnostic medicine, assessing the current digital transformation experienced by health organizations. It allows, with the use of Artificial Intelligence (AI) in diagnostic medicine, in order to speed up and make the diagnosis of patients more accurate, anticipating medical outcomes, capable of improving internal processes of health services, allowing efficiency gains in diagnostic investigation and reduction of waste of resources. In this context, Blood is investigated through blood count through manual and/or automated procedure, however, in search of innovations in the field of medicine, this chapter presents the development of a Deep Learning framework for classifying white blood cell subtypes in digital images achieving criteria for efficiency and reliability, while making the methodology more accessible to less favored populations. Reflecting on laboratory medical exams generally present costs that are inaccessible to populations of underdeveloped and developing countries. Faced with this, it is of enormous value to create tools that facilitate the procurement of medical reports with high reliability and low cost. For this, it was implemented in python employing Jupyter notebook and essay conducted utilizing a dataset containing 12,500 digital images of human blood smear fields consisting of non-pathological leukocytes and as a result, the accuracy of the approach was 85.72% evidencing the high reliability of the proposed methodology. Therefore, the proposal is evaluated as an accurate, reliable, and inexpensive method that can be utilized as a third feasible proceeding for blood count in often underprivileged populations of underdeveloped and developing countries.
Manifold regularization ensemble clustering with many objectives using unsupervised extreme learning machines

Spectral clustering has been an effective clustering method, in last decades, because it can get an optimal solution without any assumptions on data’s structure. The basic key in spectral clustering is its similarity matrix. Despite many empirical successes in similarity matrix construction, almost all previous methods suffer from handling just one objective. To address the multi-objective ensemble clustering, we introduce a new ensemble manifold regularization (MR) method based on stacking framework. In our Manifold Regularization Ensemble Clustering (MREC) method, several objective functions are considered simultaneously, as a robust method for constructing the similarity matrix. Using it, the unsupervised extreme learning machine (UELM) is employed to find the generalized eigenvectors to embed the data in low-dimensional space. These eigenvectors are then used as the base point in spectral clustering to find the best partitioning of the data. The aims of this paper are to find robust partitioning that satisfy multiple objectives, handling noisy data, keeping diversity-based goals, and dimension reduction. Experiments on some real-world datasets besides to three benchmark protein datasets demonstrate the superiority of MREC over some state-of-the-art single and ensemble methods.
Framework for Spam Detection Using Multi-objective Optimization Algorithm

Electronic spam is a big problem for huge international corporations like AOL, Google, Yahoo and Microsoft. Spam creates many issues and may cause economic damage. Spam causes many problems. Spam creates issues in traffic and bottlenecks, reducing memory, processing capacity and distance. We can describe classification output optimization in computer vision as a multi-objective optimization problem. This paper presents an evolutionary multi-objective optimization algorithm (E-MOA) of the anti-spam filtering issue that discusses both e-mail classification requirements (FP and FN error rates) and e-mail classification times (minimisation). Test findings using a freely accessible model corpus have enabled us to draw significant aspects concerning both the effectiveness of rule-based classification filtering and the appropriateness of a spam filtering two-way classification system. This dataset also uses the DNN classification method, and this classification involves cross-validation. Finally, the e-mail spam classifier is defined based on error rate, accuracy and recall.
Developing Interactive Company Presentations in the 3D Glue Virtual Reality Environment: A Collaborative Educational Approach

Recent advances in availability of immersive technologies such as virtual reality (VR), large screen display systems, and tangible user interfaces (TUIs) [1], have enabled new ways of exploring, understanding, interacting with, and presenting information. This study presents an immersive virtual collaboration platform, 3D Glue, which can be used to develop company presentations and collaborative educational applications together with students. The paper describes the results of the workshop with n = 14 participants (8 students, 1 teacher, 4 company representatives, and 1 person from the local Chamber of Commerce). The collected data (video recordings and digital survey) were analyzed through content analysis. In conclusion, the development of company presentations in the virtual environment in combination with a co-creation workshop enabled the creation of comprehensive user experiences, enhancing collaborative learning, extending social communication and envisioning additional features for a successful workshop in a virtual reality environment.
Exploring and Analysing Surface, Deep, Dark Web and Attacks

Data is termed as huge asset in today’s World. In this paper, an introduction to WWW, classification of different kinds of web, i.e. surface web, deep web and dark web is discussed along with differences among them. Trending research on deep and dark web is discussed focusing on benefits of deep web. The significance of searching deep web data underneath the surface web aids in getting access to gigantic data as 96% of data is hidden inside the deep web and it is freely available. TOR is a tool to access the deep data and how this works along with its benefits are deliberated and is the objective of this chapter. Deep web accessing method is described in detail with suitable examples. Ongoing research in deep web is discussed and later, attacks faced by the deep web and how cyber criminals use the dark web is emphasized. An overview of web, types of web and how it works is discussed focusing on surface web, deep web and dark web. Distinguish characteristics between deep and dark web are portrayed well with suitable examples. Attacks faced by the deep web are explained and the need to secure individuals system when accessing data hidden deeply inside the web and necessary measures to be considered are discussed.
ProgressiveNN: Achieving Computational Scalability with Dynamic Bit-Precision Adjustment by MSB-first Accumulative Computation

Computational scalability allows neural networks on embedded systems to provide desirable inference performance while satisfying severe power consumption and computational resource constraints. This paper presents a simple yet scalable inference method called ProgressiveNN, consisting of bitwise binary (BWB) quantization, accumulative bit-serial (ABS) inference, and batch normalization (BN) retraining. ProgressiveNN does not require any network structure modification and obtains the network parameters from a single training. BWB quantization decomposes and transforms each parameter into a bitwise format for ABS inference, which then utilizes the parameters in the most-significant-bit-first order, enabling progressive inference. The evaluation result shows that the proposed method provides computational scalability from 12.5% to 100% for ResNet18 on CIFAR-10/100 with a single set of network parameters. It also shows that BN retraining suppresses accuracy degradation of training performed with low computational cost and restores inference accuracy to 65% at 1-bit width inference. This paper also presents a method to dynamically adjust the bit-precision of the ProgressiveNN to achieve a better trade-off between computational resource use and accuracy for practical applications using sequential data with proximity resemblance. The evaluation result indicates that the accuracy increases by 1.3% with an average bit-length of 2 compared with only the 2-bit BWB network.
A Survey of Chinese Anaphora Resolution

Chinese anaphora resolution technology has been widely used in many natural language processing tasks, such as machine translation, information extraction and automatic text summarization. In this paper, we first introduce the resources for anaphora resolution, and then present the existing works on Chinese noun phrase resolution based on machine learning, deep learning and reinforcement learning techniques by analyzing the similarities and differences among them. Finally, we discuss the future development trend of Chinese anaphora resolution.
Parametrisation Independence of the Natural Gradient in Overparametrised Systems

In this paper we study the natural gradient method for overparametrised systems. This method is based on the natural gradient field which is invariant with respect to coordinate transformations. One calculates the natural gradient of a function on the manifold by multiplying the ordinary gradient of the function by the inverse of the Fisher Information Matrix (FIM). In overparametrised models, the FIM is degenerate and therefore one needs to use a generalised inverse. We show explicitly that using a generalised inverse, and in particular the Moore-Penrose inverse, does not affect the parametrisation independence of the natural gradient. Furthermore, we show that for singular points on the manifold the parametrisation independence is not even guaranteed for non-overparametrised models.
A Framework Using Artificial Intelligence for Vision-Based Automated Firearm Detection and Reporting in Smart Cities

For a few decades, mega-cities are facing some huge challenges. Among them, the prevention of crime seems to be more challenging than others. The safety of citizens in the dense urban population with conventional practices are unable to control the increasing crime rate. This work is aimed to develop a framework for the autonomous surveillance of public places, with visual-based handheld arms detection in a near real-time. It scans all the objects that come in front of the camera and when any type of weapon comes in contact with a lens it gives an alert, locks that object and the person holding it and identifies the person using facial recognition. If the alert does not get responded in a few minutes, the system will automatically notify the 3rd person or agency about the incident. It can also manually highlight any object in a frame to keep track of its movement for security purposes. Machine and Deep Learning techniques were used to train models for object detection and facial recognition. The model achieved an accuracy of 97.33% in object detection and 90% in facial recognition.
Statistical analyses for meta-heuristic stochastic optimization algorithms

tutorial Statistical analyses for meta-heuristic stochastic optimization algorithms Authors: Tome Eftimov Jožef Stefan Institute, Ljubljana, Slovenia Jožef Stefan Institute, Ljubljana, SloveniaView Profile , Peter Korošec Jožef Stefan Institute, Ljubljana, Slovenia Jožef Stefan Institute, Ljubljana, SloveniaView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 770–785https://doi.org/10.1145/3449726.3461438Published:08 July 2021Publication History 0citation87DownloadsMetricsTotal Citations0Total Downloads87Last 12 Months18Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access