Reviewerly/exHarmony huggingface dataset:
Introspective<i>Q</i>-learning and learning from demonstration

Abstract One challenge faced by reinforcement learning (RL) agents is that in many environments the reward signal is sparse, leading to slow improvement of the agent’s performance in early learning episodes. Potential-based reward shaping can help to resolve the aforementioned issue of sparse reward by incorporating an expert’s domain knowledge into the learning through a potential function. Past work on reinforcement learning from demonstration (RLfD) directly mapped (sub-optimal) human expert demonstration to a potential function, which can speed up RL. In this paper we propose an introspective RL agent that significantly further speeds up the learning. An introspective RL agent records its state–action decisions and experience during learning in a priority queue. Good quality decisions, according to a Monte Carlo estimation, will be kept in the queue, while poorer decisions will be rejected. The queue is then used as demonstration to speed up RL via reward shaping. A human expert’s demonstration can be used to initialize the priority queue before the learning process starts. Experimental validation in the 4-dimensional CartPole domain and the 27-dimensional Super Mario AI domain shows that our approach significantly outperforms non-introspective RL and state-of-the-art approaches in RLfD in both domains.
A New Low-Complexity Cipher Class for Clone-Resistant Identities

A new large cipher class based on new two variable self-inverse-permutation deploying Golden S-Boxes is presented. The mapping has particular properties, in that it is both self-inverse and uses arithmetic over the ring of integers modulo 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> . The key contribution of such cipher structures is in activating the use of passive hard-core multipliers (Math-Blocks) in modern FPGA devices. Such multipliers are often available for free when not utilized in for the system tasks. Standard ciphers do not usually use multiplications due to their high implementation complexity. The proposed cipher class is reactivating such free multipliers to create good quality ciphers at very low cost. The target application of such ciphers is in creating the so-called Secret Unknown Ciphers (SUCs). Such SUCs can serve as highly-resilient clone-resistant digital-identity-modules in future smart self-reconfiguring non-volatile FPGA devices. The security level of the resulting ciphers is relatively high and scalable. The main future application areas of such structures are expected to be for vehicular security applications. A sample implementation prototype demonstrates the complexity of the resulting physical identity and its performance.
Crowd Motion Pattern Detection at the Microscopic Level

In this paper we present a common-sense reasoning model for crowd motion pattern detection and behaviour analysis at the microscopic level. Information about the trajectories of individuals is represented with fuzzy predicates. The characteristic motion patterns and the behaviour of groups of individuals are described with fuzzy predicates and fuzzy functions, respectively. The usability of the proposed model is tested on a simulated crowd scenario. The obtained results show that the model supports the efficient representation of expert knowledge and detection of motion/behaviour patterns. The main contribution of this paper is a proposed model for motion pattern detection and classification based on fuzzy knowledge which can imitate common-sense reasoning.
Semivariogram Based Feature Extraction for Content Based Image Retrieval

Semivariogram is widely accepted as a powerful tool for the classification of texture for the analysis of satellite images. An experimental semivariogram technique is proposed to extract the texture property from the color images. Then texture feature is used to retrieve the similar images from the large database. Experimental results show the good performance of the CBIR system for the Corel database. Euclidean distance measure is used for image matching process. The proposed approach improves the performance of the retrieval process.
Learning classifier systems

tutorial Share on Learning classifier systems: from principles to modern systems Author: Anthony Stein University of Augsburg, Augsburg, Germany University of Augsburg, Augsburg, GermanyView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 747–769https://doi.org/10.1145/3319619.3323393Published:13 July 2019Publication History 2citation91DownloadsMetricsTotal Citations2Total Downloads91Last 12 Months0Last 6 weeks0 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
LHRNet: Lateral hierarchically refining network for salient object detection

Deep convolutional neural networks (CNNs) have shown outstanding performance in salient object detection. However, there exist two conundrums under-explored. 1) High-level features are beneficial to locate salient objects while low-level features contain fine-grained details. How to combine these t wo types of features to promote accuracy is the first conundrum. 2) Previous CNN-based methods adopt a convolutional layer after extracting features to infer saliency maps. While encountering images that are different greatly from training dataset, adopting a convolutional layer as a classifier is not robust enough to detect all salient objects. In addition, limited receptive field and lack of spatial correlation will cause salient objects to be incomplete while blurring their boundaries. In this paper, a Lateral Hierarchically Refining Network (LHRNet) is put forward for accurate salient object detection. Firstly, LHRNet efficiently integrates multi-level features, which simultaneously incorporates coarse semantics and fine details. Then a coarse saliency prediction is made from low-resolution features by convolution. Finally, a series of nearest neighbor classifiers are learned to hierarchically restore the missing parts of salient objects while refining their boundaries, yielding a more reliable final prediction. Comprehensive experiments demonstrate that this network performs favorably against state-of-the-art approaches on six datasets.
Product Feature Extraction via Topic Model and Synonym Recognition Approach

As e-commerce is becoming more and more popular, sentiment analysis of online reviews has become one of the most important studies in text mining. The main task of sentiment analysis is to analyze the user's attitude towards different product features. Product feature extraction refers to extracting the product features of user evaluation from reviews, which is the first step to achieve further sentiment analysis tasks. The existing product feature extraction methods do not address flexibility and randomness of online reviews. Moreover, these methods have defects such as low accuracy and recall rate. In this study, we propose a product feature extraction method based on topic model and synonym recognition. Firstly, we set a threshold that TF-IDF value of a product feature noun must reach to filter meaningless words in reviews, and select the threshold by grid search. Secondly, considering the occurrence rule of different product features in reviews, we propose a novel product similarity calculation, which also performs weighted fusion based on information entropy with a variety of general similarity calculation methods. Finally, compared with traditional methods, the experimental results show that the product feature extraction method proposed in this paper can effectively improve $$ F1 $$ and recall score of product feature extraction.
Automatic Speech Recognition Based on Clustering Technique

Automatic Speech Recognition (ASR) is defined as a computer-driven transcription of the spoken word into readable text. The main aim of ASR technology is to correctly identify the words spoken by a person. In this paper, we are using the clustering method in ASR to identify the digit spoken by the speaker. The importance of ASR is to allow a computer to recognize the words that are spoken by any human being independent of vocabulary size, noise, speaker characteristics or accent. This paper introduces ASR using clustering techniques to recognize the English digits. The clustering algorithms used here are K-means and Gaussian Expectation Maximization (GEM). We use Mel-frequency cepstral coefficients (MFCCs) for extracting features from speech. Performance is calculated for an individual digit using the hard threshold technique. This paper also compares the result using two different clustering techniques.
How Did Banks Evolve in Africa?

The chapter makes attempts to trace the historical development of financial institutions, and banks in particular. The focus is on how banking systems emerged in Africa. Pre- and post-colonial institutions are explored. Crucially, post-colonial institutions are examined in the context of how they are extending financial inclusion to the bottom pyramid of the economies. Furthermore, differences arising from colonial origins are analysed.
Predicting outcomes in heritage grammars

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Effective Emoticon Based Framework for Sentimental Analysis of Web Data

The Explosive development in the social media domain has created a platform for mass generation of textual and emoticon based web data from micro blogging sites. Sentimental Analysis refers to analysis of sentiments or emotions from such heterogeneous reviews are the present urge of the market. Thus, an effective emoticon based framework is proposed which generates scores of both textual and emoticons into seven layered categories using SentiWordNet and weighs performance of various machine learning techniques like SVM/SMO, K-Nearest Neighbor (IBK), Multilayer Perception (MLP) and Naive Bayes (NB). Using Jsoup crawler input reviews are obtained and processed with initial pre-processing model for emoticons and text data followed by stemming and POS tagger. Projected framework is investigated on college and hospital dataset obtaining upper attainment level by Kappa statistic metrics having 98.4% correctness and lesses bug value. Proposed Framework showcases greater competence score with lesser FP Rate based on weighted average of correctness measures. The investigational outcomes are tested on training data with Ten-Fold cross validation. The outcome reveals that suggested emoticon based framework for the task of Sentimental analysis can be efficaciously applied in online decision job.
Slow Replica and Shared Protection: Energy-Efficient and Reliable Task Assignment in Cloud Data Centers

With the explosive growth in the scale of cloud computing infrastructures, reliability and energy efficiency have become important concerns considering the great complexity of cloud data centers. There is an urgent need for efficient task assignment that can dispatch tasks to appropriate cloud data center servers, which is critical to achieve reliability and energy efficiency in current cloud data centers. Most of the research on task assignment focuses on only one of the objectives of reliability and energy efficiency, while the two objectives are intrinsically conflicting with each other. In this paper, we deal with the problem of task assignment in data centers, with the objective of minimizing the energy consumption while providing failure tolerance to task execution failure. We propose a reliability-aware and energy-efficient task replica assignment algorithm based on running task replicas at a low speed and enabling multiple task replicas to share the same server resources. Each task in a job processed by the cloud computing platform has two instances: main task and task replica (shadow). Each main task runs on an individual server, and the task replica associated with the main task is assigned on a different server. The main tasks run at the full server speed, while the task replicas run at a lower rate than the main tasks. The task replicas can be mapped onto dedicated backup servers or be assigned to the servers on which the main tasks are running. Multiple task replicas can share the same server resources to reduce the number of servers required. We conduct experiments through simulations. Experimental results demonstrate that the proposed algorithm can effectively reduce the energy consumption, while achieving a good balance between the number of servers used and job completion time.
Quantum Network Coding Based on Entanglement Distribution

Quantum network coding (QNC) is a new technology of quantum network communication. Since QNC can maximize the communication efficiency of quantum communication networks, it receives wide attention. As an important quantum communication resource, quantum entanglement plays a key role in the field of quantum communication and quantum computation, of course, including QNC. Several typical QNC schemes require quantum entanglement to achieve lossless quantum communication. However, none of these previous schemes mentioned the formation and distribution of quantum entanglement. Moreover, the entangled resources required by these schemes are more demanding and the required experimental environment is harsh, which is difficult to operate in practice. Therefore, with the help of entanglement distribution by separable states and probabilistic cloning, we propose a novel quantum network coding scheme based on entanglement distribution. This scheme can successfully achieve quantum entanglement distribution in the butterfly network. It is efficient in the use of quantum resources and has stronger resistance to environmental noise and other disturbances. We also point out that quantum discord, as a more general quantum communication resource, controls the realization of the whole communication process.
Sensor Fusion for Learning-based Tracking of Controller Movement in Virtual Reality

Inside-out pose tracking of hand-held controllers is an important problem in virtual reality devices. Current state-of-the-art combines a constellation of light-emitting diodes on controllers with a stereo pair of cameras on the head-mounted display (HMD) to track pose. These vision-based systems are unable to track controllers when they move out of the camera's field-of-view (out-of-FOV). To overcome this limitation, we employ sensor fusion and a learning-based model. Specifically, we employ ultrasound sensors on the HMD and controllers to obtain ranging information. We combine this information with predictions from an auto-regressive forecasting model that is built with a recurrent neural network. The combination is achieved via a Kalman filter across different positional states (including out-of-FOV). With the proposed approach, we demonstrate near-isotropic accuracy levels (~1.23 cm error) in estimating controller position, which was not possible to achieve before with camera-alone tracking.
Deep Conditional Variational Estimation for Depth-Based Hand Poses

We propose a novel and effective approach for 3D hand pose estimation on single depth image. Instead of doing deterministic regression from depth images, our model focuses on learning a latent distribution to model the high dimensional space of pose joints, which can also be interpreted as a kinematics model for human hands. Specifically, the proposed network combines the framework of conditional variational autoencoder which learns an encoder and a decoder with standard convolutional network. The encoder models the latent variable as a prior or a regularization for the pose joints. Then probabilistic inference is performed by the decoder to generate the output prediction conditioned on input depth images. In addition, we introduce a pool-convolution module to improve the localization regression of the network. The architecture can be trained end-to-end. In experiments, we demonstrate the effectiveness of our proposed approach in comparison to various state-of-art holistic regression approaches.
Joint Energy Optimization on the Server and Network Sides for Geo-Distributed Datacenters

With the rapid development of cloud computing, many cloud service providers have been deploying more and more datacenters to provide better reliability and quality of service. The energy optimization problem has become an emerging concern. The current researches focus on how to either reduce the energy consumption of servers or reduce the consumption of inter-datacenters data transmission. However, energy optimization of joint inter-datacenter and servers has not been explored. In this paper, we first introduced the background to the energy consumption problem of geographically distributed datacenters. Then, based on the Service Level Agreement (SLA), this paper proposes an online control framework to minimize the energy consummation cost. The online control framework can dynamically make decisions by adjusting geographically load balancing, capacity right-sizing, server speed scaling, and flow programming. Finally, the simulation verifies the effectiveness of the proposed framework in cost saving.
Openly revisiting derivative-free optimization

This paper surveys and compares a wide range of derivative-free optimization algorithms in an open source context. We also propose a genetic variant of differential evolution, an adaptation of population control for the multimodal noise-free case, new multiscale deceptive functions, and as a contribution to the debate on genetic crossovers, a test function with useless variables on which 2-points crossover achieves great performance. We include discrete and continuous and mixed settings; sequential and parallel optimization; rotated, separable and partially rotated settings; noisy and noise-free setting. We include real world applications and compare with recent optimizers which have not yet been extensively compared to the state of the art.
Design of Coimagination Support Dialogue System with Pluggable Dialogue System - Towards Long-Term Experiment

In this paper, we design a dialogue system which aims to support elderly people's cognitive function with a conversation by face to face. We have a hypothesis that a kind of group conversation method which is called Coimagination Method (CM) would have merit for a human at a perspective of cognitive function. However, current CM aims to be designed to provide balanced group conversation among participants. Hence, the method has mainly focuses the group conversation, hence it has some limitations such as user have to join the specific location. But, elderly people sometime face difficulty because of some physically or mental problems to do it. In this paper, we try to design a chat system which provides a one-to-one conversation between human and system which is based on the essence of CM and copes the above limitations. In order to design the system, at first we consider an experimental design which would be conducted remotely during long-term as a system requirement, then we design a system which meets the requirement. Moreover, we also develop a prototype system in order to confirm the feasibility of proposed system design.
Dynamic Generation of Assessment Items Using Wikidata

Automated generation of assessment items can provide large item pools for formative assessments with little effort. However, if the generation process produces self-contained items, these need to be updated or re-generated each time the data source used for generation changes. This paper describes and discusses an alternative approach that dynamically retrieves item content from Wikidata using SPARQL queries. The paper compares four different examples and discusses both benefits and limitations of this approach. Results show that the approach is usable for a broad range of different items for formative assessment scenarios and that limitations are manageable with acceptable effort.
Residual Attention Convolutional Network for Online Visual Tracking

Discriminative correlation filters (DCFs) have received much attention in visual tracking due to their high performance but suffered from unwanted boundary effects. Convolutional regression tracking reformulates the DCFs as a one layer convolutional network and avoids the boundary effects. However, this single convolutional network based algorithms' performance has been drastically limited by over fitness caused by data imbalance. In this paper, we propose a residual attention module to the one layer convolutional network to inhibit the descent of discriminative ability caused by over fitness. A bottom-up and top-down fully convolutional structure is designed in the residual attention module to form samples with bigger receptive field. After that, two types of activation function are applied to capture spatial attention and time attention. By combining the two types of attention, the residual attention can highlight the object and diminish the background response. We perform extensive experiments on two widely used datasets, namely, OTB-2013 and OTB-2015 and the results show that the proposed algorithm achieves favorable performance compared with the state-of-art trackers.
Visualizing the Correspondence of Feature Point Mapping between DICOM Images before and after Surgery

We extract feature point mapping between preoperative and postoperative Digital Imaging and Communications in Medicine (DICOM) images from magnetic resonance imaging (MRI) or from computer tomography (CT). The aim is to quantitatively investigate brain shift during intraoperative surgery. First, using 124 two-dimensional images constituting DICOM, a large number of 2D feature points are extracted as uniformly as possible inside the brain. Next, we extract one pair from the 124 preoperative images and the 124 postoperative images and construct map correspondences of similar feature points with a range of DICOM gray values. If the Euclidean distance between the two feature points in the 2D images is too large, the pair of feature points is deleted to prevent mis-mapping; brain shifts are usually less than 2-3 cm. Finally, we find image pairs with the highest number of mappings from DICOM images before and after surgery (two-dimensional stacked three-dimensional images), and generate graph representing correspondences between image pairs with the highest number. Finally, we visualize 3D correspondences between DICOM images before and after surgery.
#shutdownjnu vs #standwithjnu

Abstract In February 2016, one of the premier Universities of higher education in India, Jawaharlal Nehru University (JNU), suddenly came into the limelight for allegedly raising anti-India slogans at one of the protest cultural programmes in the campus. In this paper, I present a study of the co-emergence and spread of the two opposing hashtags in the context of the controversy – #shutdownjnu and #standwithjnu . The study is based on data collected from Twitter over a period of 2 years from February 2016 – February 2018. I present a quantitative as well as qualitative analysis of the emergence and use of these two hashtags on Twitter and understand these in terms of the process of enregisterment and how conflict (and consequently aggression) became normative and conventionalised in the context of these hashtags. I also take a comparative look at the enregisterment of the two hashtags and argue that despite similar conditions, enregisterment is not guaranteed.