Reviewerly/exHarmony huggingface dataset:
A Local Temporal Difference Code for Distributional Reinforcement Learning

The successor representation (SR) allows for efficient and flexible value computation by representing states with their expected temporal evolution. Here, we present two extensions of the SR theory to belief state and to distribution overvalue. When states are partially observed, an optimal agent should rely on beliefstates indicating the probability of being in different states. Here, we first present an analytical expression for the SR of for fixed policies and for decision making problems with only one decision. We show that this expression also provide a good approximation to SR(b) in problems with multiple decisions such as noisy 2Dnavigation tasks. We then propose a neural network that approaches the optimal SR(b) in tasks with multiple decisions. Next, we extent the SR to distribution overvalue. In the process, we propose a new local code for distributional reinforcement learning which allows agents to recover the value distribution of a state given its SR, as well as the expected temporal evolution of the value distribution. Finally, we combine these advances into a SR model that jointly accounts for uncertainty over states and value, implemented in a biologically plausible neural network.
Secret image sharing based on multiphase retrieval algorithm

This paper proposes a multiphase retrieval algorithm and then presents a secret image sharing scheme. The proposed algorithm can divide a secret image into multiple phase-only encodings (shadows) and then overcome the shadow expansion problem. When the shadows are arranged in a certain order, the light intensity detector can directly acquire the secret image using parallel light, and the recovery process is simple and easy to operate. Furthermore, Fresnel transform parameters are used for authentication, which can avoid deceit. The differences in the initial random phase encodings make the same secret image produce different shadows, which can effectively resist statistical analysis attacks. Finally, theoretical analysis and a simulation are used to verify the validity and feasibility of the proposed scheme.
When Mental Models Grow (C)old: A Cognitive Perspective on Home Heating Automation

Mental models are internal representations of the physical world that allow people to understand, explain, and operate a target system and predict the outcomes of their actions. Mental models can be used to explain a person's behavior. Adopted from the field of cognitive psychology, they have frequently been applied in ergonomics and human-computer interaction. Home heating systems are important from both a thermal comfort and a sustainability view-point. According to the Department for Business, Energy, and Industrial Strategy, domestic use accounts for 29% of the UK's energy consump-tion, and 65% of this is for space heating. mental model approach to interface design can lead to increased control over the system, which can enable users to achieve their home-heating goals.
A Multi-objective Ant Colony Optimization Algorithm with Local Optimum Avoidance Strategy

Ant colony optimization (ACO) algorithm has rapid convergence speed and competitive performance in multi-objective optimization. However, for complicated multi-objective problems (MOPs), it faces the issue of the local optimum trap, which hinders the global optimization of the multi-objective ACO. This paper proposes a multi-objective ACO algorithm with a local optimum avoidance (LOA) strategy, called MACO/D-LOA. The proposed strategy uses a matrix structure to record the paths that ants have visited. Then it controls the selected probability of the recorded paths. This mechanism enhances the exploration research capability of the multi-objective ACO while retaining the exploitation ability. Experimental results on typical test cases indicate that the proposed algorithm obtains competitive convergence results than state-of-the-art algorithms.
Reconhecimento de Actos de Diálogo Hierárquicos e Multi-Etiqueta em Dados em Espanhol

Dialog acts reveal the intention behind the uttered words. Thus, their automatic recognition is important for a dialog system trying to understand its conversational partner. The study presented in this article approaches that task on the DIHANA corpus, whose three-level dialog act annotation scheme poses problems which have not been explored in recent studies. In addition to the hierarchical problem, the two lower levels pose multi-label classification problems. Furthermore, each level in the hierarchy refers to a different aspect concerning the intention of the speaker both in terms of the structure of the dialog and the task. Also, since its dialogs are in Spanish, it allows us to assess whether the state-of-the-art approaches on English data generalize to a different language. More specifically, we compare the performance of different segment representation approaches focusing on both sequences and patterns of words and assess the importance of the dialog history and the relations between the multiple levels of the hierarchy. Concerning the single-label classification problem posed by the top level, we show that the conclusions drawn on English data also hold on Spanish data. Furthermore, we show that the approaches can be adapted to multi-label scenarios. Finally, by hierarchically combining the best classifiers for each level, we achieve the best results reported for this corpus.
«The window on the past», or the new space of the Rare Books Department of A. M. Gorky Universal Library

The experience of the new library site is discussed; the importance of creative use of library space is substantiated. The «antic-style» library set up in Perm Regional Library Rare Book Department influences positively on the department work and stimulates interest towards rare and valuable books being displayed and promotes one of the most interesting library collections.
Historical Learning Media Based on Mobile Learning

Abstract The selection of instructional media that can be something urgent in the current era of information and communication technology development. The purpose of this study is to determine whether the learning media based on mobile learning can be applied to historical subjects. The method used in this research is literature study. Based on the results of the discussion, it can be concluded that the use of historical learning media based on mobile learning is needed to support the learning process and achieve learning objectives. while answering the question that, mobile learning based learning media can be applied to history subjects.
Adapting bug prediction models to predict reverted commits at Wayfair

Researchers have proposed many algorithms to predict software bugs. Given a software entity (e.g., a file or method), these algorithms predict whether the entity is bug-prone. However, since these algorithms cannot identify specific bugs, this does not tend to be particularly useful in practice. In this work, we adapt this prior work to the related problem of predicting whether a commit is likely to be reverted. Given the batch nature of continuous integration deployment at scale, this allows developers to find time-sensitive bugs in production more quickly. The models in this paper are based on features extracted from the revision history of a codebase that are typically used in bug prediction. Our experiments, performed on the three main repositories for the Wayfair website, show that our models can rank reverted commits above 80% of non-reverted commits on average. Moreover, when given to Wayfair developers, our models reduce the amount of time needed to find certain kinds of bugs by 55%. Wayfair continues to use our findings and models today to help find bugs during software deployments.
Responsiveness of Academic Libraries in South Africa to Research Support in the 4th Industrial Revolution: A Preliminary study

At the World Economic Forum in Davos, Switzerland, in 2016, the concept of the 4th Industrial revolution (4IR) was coined by Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, with the reference that it would be building on «the Third, the digital revolution» and would be «characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological». Thus, the ‘smart’ technologies will spearhead the revolution. We acknowledge that the 4IR will impact on everything, everywhere, including research and libraries. In this paper we conceptualize 4IR, and compare current academic/university library services/trends in South Africa with 4IR requirements through the analysis of 26 public university library websites. The findings show that the libraries are responding well to the revolution through their services, with remarkable of innovation and creativity on display. The study expects library services to be responsive, resourced and accessible anytime and anywhere, and provides a framework for further research and exploration in the region and perhaps elsewhere.
Sistem Pengolahan Data Jamaah Umroh Berbasis Web Menggunakan Metode Interpolation Seacrh pada PT. Kaisa Rossie Semarang

The use of software as a supporting tool to input data in a company is needed. The software that is used of course must also be able to work effectively and efficiently so that it can support the performance of administrative staff. Every company, private institution or government has certainly done a filing process to store files or data that are considered important. Archiving data on pilgrims performed at this company is still centralized in several Microsoft Excel files for all staff staff. Search is needed to find specific information from a table when the exact location of that information was previously unknown.When the array data is ordered with key values,searching data can be useful using method interpolation search for efficient to find a key value from data array. The system development method used in this system is the Research And Development (R &amp; D) method. This study purpose is intended to be able to assist the task of the cashier in inputting data and admin in making reports given to the leadership, and information appropriate to the position of the employee.
Mimicking the Hadamard discrete-time quantum walk with a time-independent Hamiltonian

We implement the discrete-time quantum walk model using the continuous-time evolution of the Hamiltonian that includes both the shift and the coin generators. Based on the Trotter-Suzuki first-order approximation, we consider an optimization problem in which the Hellinger distance between the walker probability distributions resulted from the evolution of such Hamiltonian and the quantum walk dynamics is minimized. The phase space implementation of the quantum walk is considered where the walker state is encoded on the coherent state of a resonator and the coin on the two-level state of a qubit. In this approach, no mechanism for switching between the coin and the shift operators is included. We show the Hellinger distance is bounded for large number of time steps. The distance is small when we deviate from the standard quantum walk model, namely, when the walker is allowed to move in between the sites. In simulating the standard quantum walk model, the distance is large but bounded by $26\%$ for a relevant number of time steps. Even so, the system evolution shows the essential characteristics of the standard quantum walk dynamics, namely, the ballistic evolution of the probability distribution and the linear growth of the corresponding standard deviation. Moreover, the entanglement generated in this approach is approximately the same as the entanglement generated in the standard quantum walk dynamics. Finally, the system dynamics under the influence of the decoherence also shows the similar quantum-to-classical transition as in the standard quantum walk dynamics.
The Transaction Graph for Modeling Blockchain Semantics

The advent of Bitcoin paved the way for a plethora of blockchain systems supporting diverse applications beyond cryptocurrencies.Although in-depth studies of the consensus protocols as well as the privacy of blockchain transactions are available, there is no formal model of the transaction semantics that a blockchain is supposed to guarantee.In this work, we fill this gap, motivated by the observation that the semantics of transactions in blockchain systems can be captured by a directed acyclic graph.Such a transaction graph, or TDAG, generally consists of the states and the transactions as transitions between the states, together with conditions for the consistency and validity of transactions.We instantiate the TDAG model for three prominent blockchain systems: Bitcoin, Ethereum, and Hyperledger Fabric.We specify the states and transactions as well as the validity conditions of the TDAG for each one.This demonstrates the applicability of the model and formalizes the transaction-level semantics that these systems aim for.
Segmentation and Enhancement of Mammograms for the Detection of Cancer Using Gradient Weight Map and Decorrelation Stretch

To deal with computer aided detection of breast cancer in mammograms, an efficient automated preprocessing stage is the most vital step which assists radiologist’s decision. The approach proposes a model for the segmentation and enhancement of the breast region. The proposed method includes gradient weight map followed by region property-based extraction and morphological operations for background suppression and artifacts removal. Intensity adjustment and Otsu’s thresholding methods were adopted for breast region extraction. Finally, enhancement of the breast region is accomplished by Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Decorrelation stretch. Experimentation conducted on mini Mammographic Image Analysis Society (miniMIAS) dataset shows segmentation accuracy of about 97.64%. Enhancement contributes with better feature discrimination with high Peak Signal Noise Ratio (PSNR) and low Root Mean Square Error (RMSE).
Extracting Implicitly Asserted Propositions in Argumentation

Argumentation accommodates various rhetorical devices, such as questions, reported speech, and imperatives.These rhetorical tools usually assert argumentatively relevant propositions rather implicitly, so understanding their true meaning is key to understanding certain arguments properly.However, most argument mining systems and computational linguistics research have paid little attention to implicitly asserted propositions in argumentation.In this paper, we examine a wide range of computational methods for extracting propositions that are implicitly asserted in questions, reported speech, and imperatives in argumentation.By evaluating the models on a corpus of 2016 U.S. presidential debates and online commentary, we demonstrate the effectiveness and limitations of the computational models.Our study may inform future research on argument mining and the semantics of these rhetorical devices in argumentation. 1
Real-Time Hand Gesture Recognition Using KNN-DTW and Leap Motion Controller

The advances in artificial intelligence have been boosting the development of applications in different fields. Some of these fields work in real-time and based on development and implementation in machine learning models. The problems presented by these fields of study involve pattern recognition, which consists of selecting a label and specify the instant of time that defines the gesture. In this context, the paper presents a specific model of real-time hand gesture recognition using the leap motion controller and machine learning algorithms. This model recognizes five static gestures. The gestures are open hand, fist, wave in, wave out, and pinch. The modules used to develop the model are data acquisition, pre-processing, feature extraction, classification, and postprocessing. The input signals to the model are the spatial positions and the direction. These signals are pre-processed by a translation and rotation matrix. Also, these are normalized and smoothed by a Butterworth filter. For the feature extraction module, we use the windows division technique. The model was tested with a window of 7 and 10 features. This signal is delivered to the KNN classifier. It tested the model with k = 1, and k = 3, and with DTW as a distance metric. We are varying the signal warping parameter in w = 1, 5, and w = 7. Finally, the model report 92.22% of classification accuracy and 77.64% of recognition accuracy in 287 ms.
Grey wolf optimisation-based colour image watermarking

In recent years, multimedia information on the internet has increased exponentially and the multiple manipulation instruments can readily manipulate these data. This grows the requirements for a robust watermarking technique to avoid the illicit exchange and allocation of multimedia information and processing, especially for images. Even though the majority images are accessible in colour format, the contemporary digital image watermarking techniques classically embrace the binary or grey watermark in the host image. Furthermore, a correlated colour space that restraint the potentiality of watermarking techniques uses many current techniques for the digital image watermarking, as only single colour channel is accessible for implanting the watermark. This paper, therefore proposes a fresh technique of marking the colour watermark based on the steerable pyramid transformation, using an Uncorrelated Colour Space (UCS) and Grey Wolf Optimisation (GWO) for the host colour image. The findings indicate that the proposed technique is superior to the other techniques in the class against distinct attacks.
Summary of Pedestrian Traffic Statistics Research

As an important research direction in the field of computer vision and intelligent security, pedestrian traffic statistics have received more and more attention from the industry. This paper reviews the research on the important aspects of pedestrian flow statistics technology: feature extraction, target detection and pedestrian counting. Firstly, the typical methods of feature extraction are classified and compared according to the characteristics, and then the research on target detection and recognition is carried out. After summarizing, the pedestrian target count is introduced from the traditional method and the deep learning method respectively, and finally the future development is expected.
COVCOR20 at WNUT-2020 Task 2: An Attempt to Combine Deep Learning and Expert rules

In the scope of WNUT-2020 Task 2, we developed various text classification systems, using deep learning models and one using linguistically informed rules.While both of the deep learning systems outperformed the system using the linguistically informed rules, we found that through the integration of (the output of) the three systems a better performance could be achieved than the standalone performance of each approach in a cross-validation setting.However, on the test data the performance of the integration was slightly lower than our best performing deep learning model.These results hardly indicate any progress in line of integrating machine learning and expert rules driven systems.We expect that the release of the annotation manuals and gold labels of the test data after this workshop will shed light on these perplexing results.
Analysing and Predicting the Adoption of Anonymous Transactions in Cryptocurrencies

With the appearance of cryptocurrencies more than ten years back, many have been thinking that it is rather a short-term novelty that would only interest few enthusiasts and die shortly after. The history, however, has shown that not only cryptocurrencies itself are alive, but also the blockchain technology started to be applied in a variety of domains. This success required the governments to take action with respect to implementing regulations in a number of areas and, as a result of that, apart from implementing Know Your Customer (KYC) procedure, many exchanges delisted the cryptocurrencies that support anonymous transactions. In this work, we focus on the analysis of one of the cryptocurrencies offering anonymous transactions, Zcash. We are interested in answering the question of how the adoption of anonymous transactions in Zcash changes over time and whether the decisions of exchanges and new wallets in the ecosystem impacted it anyhow. Secondly, we investigate whether is it possible to predict if certain addresses that have been previously making only transparent transactions will be performing anonymous ones. The results indicate that the adoption of shielded transactions is independent of external events and it is possible to predict the involvement of an address in semi-shielded transactions.
What's in an accent?: the impact of accented synthetic speech on lexical choice in human-machine dialogue

The assumptions we make about a dialogue partner's knowledge and communicative ability (i.e. our partner models) can influence our language choices. Although similar processes may operate in human-machine dialogue, the role of design in shaping these models, and their subsequent effects on interaction are not clearly understood. Focusing on synthesis design, we conduct a referential communication experiment to identify the impact of accented speech on lexical choice. In particular, we focus on whether accented speech may encourage the use of lexical alternatives that are relevant to a partner's accent, and how this is may vary when in dialogue with a human or machine. We find that people are more likely to use American English terms when speaking with a US accented partner than an Irish accented partner in both human and machine conditions. This lends support to the proposal that synthesis design can influence partner perception of lexical knowledge, which in turn guide user's lexical choices. We discuss the findings with relation to the nature and dynamics of partner models in human machine dialogue.
Human Behavior Feature Representation and Recognition Based on Depth Video

With the continuous development of computer artificial intelligence technology, various applications based on artificial intelligence emerge in an endless stream, among which video image recognition technology is the most widely used in life. This article starts from the process of image recognition, based on the composite characteristics of artificial intelligence and video images, to discuss human gesture recognition technology.This article uses the feature extraction algorithm for image composite feature extraction as a method, and conducts human body movement collection experiments, analyzes the database and The gesture recognition step. This paper mainly introduces the extraction method of image composite features and the basic requirements of gesture recognition, and through the algorithm calculation of feature extraction, the function of human gesture recognition video and image composite features is completed, and the human action collection experiment is carried out to confirm. The results of images and data show the advantages of the algorithm support used in this article. We will Dmti. MsHOG is compared with other methods in the three subsets. In terms of the accuracy of all tests, our method performs better than other methods. The results show that the MSHOG (Multi-scale Histogram of Oriented Gradients) descriptor can represent the unique characteristics of human behavior, reflecting the effectiveness of our proposed method. In particular, this method achieved 100% recognition accuracy in Test, with an average recognition accuracy of 94.91%, which is significantly better than existing methods.