Reviewerly/exHarmony huggingface dataset:
Impact of feature representation on supervised classifiers — A comparative analysis

Text mining refers to the process of deriving high-quality information from text. A number of approaches have been developed to represent and classify text documents. Most of the approach try to achieve good classification performance by considering a document only by words. Suitable method should be used for mapping text to best features. In this paper, performance analysis of three different supervised algorithms, as a text classifier, on the basis of two different feature representation methods-Count Vector and Term Frequency Inverse Document Frequency method is discussed.
Predicting Visual Memory Schemas with Variational Autoencoders

Visual memory schema (VMS) maps show which regions of an image cause that image to be remembered or falsely remembered. Previous work has succeeded in generating low resolution VMS maps using convolutional neural networks. We instead approach this problem as an image-to-image translation task making use of a variational autoencoder. This approach allows us to generate higher resolution dual channel images that represent visual memory schemas, allowing us to evaluate predicted true memorability and false memorability separately. We also evaluate the relationship between VMS maps, predicted VMS maps, ground truth memorability scores, and predicted memorability scores.
Cooperative Job Scheduling and Data Allocation for Busy Data-Intensive Parallel Computing Clusters

In data-intensive parallel computing clusters, it is important to provide deadline-guaranteed service to jobs while minimizing resource usage (e.g., network bandwidth and energy). Under the current computing framework (that first allocates data and then schedules jobs), in a busy cluster with many jobs, it is difficult to achieve these objectives simultaneously. We model the problem to simultaneously achieve the objectives using integer programming, and propose a heuristic Cooperative job Scheduling and data Allocation method (CSA). CSA novelly reverses the order of data allocation and job scheduling in the current computing framework, i.e., changing data-first-job-second to job-first-data-second. It enables CSA to proactively consolidate tasks with more common requested data to the same server when conducting deadline-aware scheduling, and also consolidate the tasks to as few servers as possible to maximize energy savings. This facilitates the subsequent data allocation step to allocate a data block to the server that hosts most of this data's requester tasks, thus maximally enhancing data locality and reduce bandwidth consumption. CSA also has a recursive schedule refinement process to adjust the job and data allocation schedules to improve system performance regarding the three objectives and achieve the tradeoff between data locality and energy savings with specified weights. We implemented CSA and a number of previous job schedulers on Apache Hadoop on a real supercomputing cluster. Trace-driven experiments in the simulation and the real cluster show that CSA outperforms other schedulers in supplying deadline-guarantee and resource-efficient services.
BEMServer: An Open Source Platform for Building Energy Performance Management

In the era of new technologies and with the growing need for reliable ecological energy supplies, researchers eyes are diverted towards reducing the energy consumption of buildings which becomes a major concern that requires a variety of actors being involved (e.g., building owners, energy/facility managers, occupants, data scientist), who have different expertise and need to use different tools This paper introduces BEMServer, an open source Building Energy Management system solution built to ease the integration of data analytic and visualization services into building and energy domain applications.It handles heterogeneous data in a transparent way by providing a clear and unambiguous semantic representation of the modeled resources.To do so, BEMServer integrates the OntoH2G formal ontology at its core.The architecture of BEM-Server, the design and use of OntoH2G in the solution, as well as the suitability of BEMServer as a middleware in the H2020 project Hit2Gap are described
Sistem Controling Engine Menggunakan Fingerprint Berbasis Arduino

The growth of motorcycles at this time is very fast. The case of motorcycle theft is still often the case, this happens because there is still a lack of security systems that exist in motor vehicles that only use the ignition key. In addition, the lack of human control systems are still often missed.&#x0D; Motorcycles are the main transportation at this time, so it needs to be made bicycle security system. Security is done in the form of a system with fingerprints in anticipation of the frequent mode of motorcycle theft, which can access by using fingerprints are certain people whose fingerprint data has been programmed into the system, so that the motorcycle can not live without the match between the fingerprint which is detected by the sensor by fingerprint.
Strongly secure quantum ramp secret sharing constructed from algebraic curves over finite fields

The first construction of strongly secure quantum ramp secret sharing by Zhang and Matsumoto had an undesirable feature that the dimension of quantum shares must be larger than the number of shares. By using algebraic curves over finite fields, we propose a new construction in which the number of shares can become arbitrarily large for fixed dimension of shares.
A Naïve Bayes Model using Semi-Supervised Parameters for Enhancing the Performance of Text Analytics

Sentiment mining is an emerging area that utilizes the feedback of the users to make intelligent decisions in marketing.Consumer reviews helps the user to convert oral conversations to digital versions to be used in marketing analytics.Since e-commerce takes intrinsic forms in present day business, the need to get positive reviews for specific products is vital on an organization perspective.Both sentiment mining and TF-IDF (Term Frequency-Inverse Document Frequency) are text mining tasks but have their unique applications and characteristics.Sentiment mining classifies the documents into positive, negative or neutral opinions that are used to derive intelligent decisions.TF-IDF classifies the documents into sub-categories within the documents itself.Utilizing the TF-IDF as a feature for sentiment mining improves the performance of classification.In this research work a strategy is applied for feature preprocessing to evaluate the consumer's sentiments accurately.This strategy makes use of the semi-supervised parameter estimation Naïve Bayes Model.The experimental results demonstrate that TF-IDF tuning approach results in better optimized results.
Pattern recognition and machine learning

Artificial intelligence, robotics, and machine learning are not futuristic dreams anymore. The early consequences of these technologies are upon us already. Industrial robots, self-driving cars, an...
TOWARD OVERCOMING THE DISPROPORTION BETWEEN THE DEMAND FOR PROFESSIONALS AND THE PROVISION OF TRAINING IN CYBERSECURITY

TOWARD OVERCOMING THE DISPROPORTION BETWEEN THE DEMAND FOR PROFESSIONALS AND THE PROVISION OF TRAINING IN CYBERSECURITY
Enseñanza de la matemática para la justicia social en cursos de postgraduación

espanolEn este trabajo reportamos las reacciones de un grupo de profesores de matematica cursando estudios de postgraduacion en modalidad virtual, al solicitarseles disenar una secuencia de ensenanza desde la perspectiva de la Ensenanza de la Matematica para la Justicia Social, para ser aplicada en la formacion de profesores de matematica. Las evidencias recogidas dejan entrever las dificultades de los participantes para la comprension de la perspectiva, asi como para desarrollar disenos de ensenanza desde ella. El trabajo en foro de discusion permitio sortear algunas de estas dificultades debido a que las intervenciones de los participantes y de las docentes del curso aportaron retroalimentacion que favorecio la reflexion critica y el diseno creativo de situaciones de ensenanza. EnglishIn this article we report the reactions of a group of mathematics teachers studying postgraduate studies in virtual modality, when they were asked to design a teaching sequence from the perspective of Teaching Mathematics for Social Justice, to be applied in mathematics teacher training. The evidence collected reveals the difficulties of the participating teachers with the understanding of the perspective as well as to develop teaching designs from this perspective. The work in the discussion forum allowed them to overcome some of those difficulties because the interventions of the participants and the teachers of the course provided feedback that favored critical reflection and the creative design of teaching situations.
Asymmetric Quantum Multicast Network Coding: Asymmetric Optimal Cloning over Quantum Networks

Multicasting of quantum states is an essential feature of quantum internet. Since the noncloning theorem prohibits perfect cloning of an unknown quantum state, an appropriate protocol may depend on the purpose of the multicast. In this paper, we treat the multicasting of a single copy of an unknown state over a quantum network with free classical communication. We especially focus on protocols exactly multicasting an asymmetric optimal universal clone. Hence, these protocols are optimal and universal in terms of mean fidelity between input and output states, but the fidelities can depend on target nodes. Among these protocols, a protocol spending smaller communication resources is preferable. Here, we construct such a protocol attaining the min-cut of the network described as follows. Two (three) asymmetric optimal clones of an input state are created at a source node. Then, the state is divided into classical information and a compressed quantum state. The state is sent to two (three) target nodes using the quantum network coding. Finally, the asymmetric clones are reconstructed using LOCC with a small amount of entanglement shared among the target nodes and the classical information sent from the source node.
The Influence of Stress on Erythrocyte Sedimentary Rate (ESR) for the Staff of College of Science – Diyala University

This study was applied on 15 teaching staffs (10 men and 5 women) from Department of Chemistry, Diyala University to measure the effect of the stress during the working hours on the erythrocyte sedimentation rate (ESR). Blood samples were taken from the teaching staffs before and after the work, as well as another sample after the end of the working hours. The results were analyzed using a statistical program. Most of the results were within the normal level of ESR. There is no stress effect on teaching staffs during the working hours.
TopicSifter: Interactive Search Space Reduction Through Targeted Topic Modeling

Topic modeling is commonly used to analyze and understand large document collections. However, in practice, users want to focus on specific aspects or "targets" rather than the entire corpus. For example, given a large collection of documents, users may want only a smaller subset which more closely aligns with their interests, tasks, and domains. In particular, our paper focuses on large-scale document retrieval with high recall where any missed relevant documents can be critical. A simple keyword matching search is generally not effective nor efficient as 1) it is difficult to find a list of keyword queries that can cover the documents of interest before exploring the dataset, 2) some documents may not contain the exact keywords of interest but may still be highly relevant, and 3) some words have multiple meanings, which would result in irrelevant documents included in the retrieved subset. In this paper, we present TopicSifter, a visual analytics system for interactive search space reduction. Our system utilizes targeted topic modeling based on nonnegative matrix factorization and allows users to give relevance feedback in order to refine their target and guide the topic modeling to the most relevant results.
Word-Emotion Lexicon for Myanmar Language

In recent years, social media emerges and becomes mostly use all over the world. Social media users express their emotions by posting status with friends. Analysis of emotions of people becomes popular to apply in many application areas. So many researchers propose emotion detection systems by using Lexicon based approach. Researchers create emotion lexicons in their own languages to apply in emotional system. To detect Myanmar social media users’ emotions, lexicon does not available thus; a new word-emotion lexicon especially based on Myanmar language is needed to create. This paper describes the creation of Myanmar word-emotion lexicon, M-Lexicon that contains six basic emotions: happiness, sadness, fear, anger, surprise, and disgust. Facebook status written in Myanmar words are collected and segmented. Words in M-Lexicon is finally got by applying stop-words removal process. Finally, Matrices, Term-Frequency Inversed Document Frequency (TF-IDF), and unity-based normalization is used in lexicon creation. Experiment shows that the M-lexicon creation contains over 70% of correctly associated with six basic emotions.
Design of Convolutional Neural Network Based on Reticulated Convolution Module

Convolutional neural networks use convolutional layers for feature extraction. Due to the limited feature extraction capabilities of traditional convolutional layers, the performance of convolutional neural network models is constrained. This paper presents an easy-to-port convolution module called Reticulated Convolution Module, which is dedicated to improving the ability of convolutional neural networks to extract key features. It adopts a funnel-like mesh structure, which first refines the features and then performs grouping, combination and fusion operations. We validate our Reticulated Convolution Module through extensive experiments on different types of datasets such as 101_food, Caltech-256 and GTSRB. The experimental results show that the convolutional neural network designed by Reticulated Convolution Module has excellent performance.
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment

Empowerment is an information-theoretic method that can be used to intrinsically motivate learning agents. It attempts to maximize an agent's control over the environment by encouraging visiting states with a large number of reachable next states. Empowered learning has been shown to lead to complex behaviors, without requiring an explicit reward signal. In this paper, we investigate the use of empowerment in the presence of an extrinsic reward signal. We hypothesize that empowerment can guide reinforcement learning (RL) agents to find good early behavioral solutions by encouraging highly empowered states. We propose a unified Bellman optimality principle for empowered reward maximization. Our empowered reward maximization approach generalizes both Bellman's optimality principle as well as recent information-theoretical extensions to it. We prove uniqueness of the empowered values and show convergence to the optimal solution. We then apply this idea to develop off-policy actor-critic RL algorithms which we validate in high-dimensional continuous robotics domains (MuJoCo). Our methods demonstrate improved initial and competitive final performance compared to model-free state-of-the-art techniques.
Computational Intractability and Solvability for the Birds of a Feather Game

In this paper, we analyze Birds of a Feather (BoaF), a perfectinformation one-player card game that is the subject of the 2019 EAAI Undergraduate Research Challenge. We prove that the generalized N × N BoaF game is NP-complete, and then explore the one million deals in the 4×4 BoaF testbed. We present several graph-theoretic algorithms to prove that 1880 of these million deals are unsolvable, and conclude the paper with two search algorithms that efficiently show that all of the remaining 998,120 deals are in fact solvable.
Efficient PRNU Matching in the Encrypted Domain

Photoresponse Non-Uniformity (PRNU) is becoming particularly relevant within digital media forensics, as a means to effectively determine the source camera of a given image. Most of the practical applications in digital media forensics involve dealing with highly sensitive data whose content must be protected. In this context, several secure frameworks have been proposed to perform PRNU-based camera attribution while preserving the privacy of both the testing images and the PRNU fingerprint. The two most recent and relevant ones, independently proposed in 2018, are (a) Mohanty et al.&rsquo;s, who combine the use of a trusted environment (ARM TrustZone) to compute the PRNU fingerprint, with the Boneh-Goh-Nissim (BGN) cryptosystem to perform the matching, and (b) Pedrouzo-Ulloa et al.&rsquo;s, who propose a more flexible solution which can be fully implemented on a general purpose architecture and does not require access to a trusted environment. In this work, we revisit the existing frameworks and propose a general formulation for PRNU matching based on lattice cryptosystems which improves on the BGN-based solution in terms of efficiency, flexibility and privacy.
Structure Generation and Guidance Network for Unsupervised Monocular Depth Estimation

Structure information is important to unsupervised depth learning from monocular videos. However, most existing methods focus on depth smoothing on planar regions, while other structure information, such as object shape and surface curvature, is ignored. In this work, we propose SGGN, a novel Structure Generation and Guidance Network to refine depth estimation under the guidance of extracted image structure. We introduce second-order Domain Transform filtering, which explores spatial depth variation by gradient propagation, to capture long-range dependence in the extracted structure for depth refinement. Then, several structure-aware constraints, as well as an attention mechanism, are applied to guide the training of SGGN, which leads to better depth estimation with structural guidance. Notably, our structure-aware constraints are designed in terms of different characteristics. Experiments on three benchmarks demonstrate the effectiveness of our structure-guided model and its state-of-the-art performance for unsupervised depth estimation.
Dual-View Learning for Detecting Web Query Intents

Automatically categorizing user intent behind web queries is a key issue not only for improving information retrieval tasks but also for designing tailored displays based on the underlying intention. In this article, a multiview learning method is proposed to recognize the user intent behind web searches.
Cyber-Physical Planning: Deliberation for Hybrid Systems with a Continuous Numeric State

Cyber-physical systems pose unique deliberation challenges, where complex strategies must be autonomously derived and executed in the physical world, relying on continuous state representations and subject to safety and security constraints. Robots are a typical example of cyber-physical systems where high-level decisions must be reconciled with motion-level decisions in order to provide guarantees on the validity and efficiency of the plan.In this work we propose techniques to refine a high-level plan into a continuous state trajectory. The refinement is done by translating a high-level plan into a nonlinear optimization problem with constraints that can encode the intrinsic limitations and dynamics of the system as well as the rules for its continuous control. The refinement process either succeeds or yields an explanation that we exploit to refine the search space of a domain-independent task planner. We evaluate our approach on existing PDDL+ benchmarks and on a more realistic and challenging rover navigation problem.
Un modelo analítico para caracterizar recursos tecnológicos basados en contenidos científicos

This paper presents an analytical model for the characterization of technological resources which mediate educa-tional processes in science. This model was developed by the secondary analysis of six previous studies carried out by some members of the research group EDUCEVA-CienciaTIC. The model proposes five analytical dimensions: Technical (languages, Internet connection requirements, possibility of open access, devices, download size, number of downloads, user information provided before the download); Multimedia (iconography, audio, access to other resources, didactic tools, level of multimodality); Virtual Environment (level of realism, usability); Content (contents which the resource allows to teach, content quality, epistemological and linguistic elements) and Cognitive (interactivity, level of feedback, cognitive processes, collaboration). It is considered that the model has potential for the development of documentary research aiming at describing these kinds of resources and for the selection of resources and the preparation of didactic proposals mediated by information and communication technologies in science education.
Pengamanan Restful API menggunakan JWT untuk Aplikasi Sales Order

The company has an important role in attracting the attention of customers by promoting its products and services so that it can be widely known by the community through the sales division (sales person). The biggest challenge faced by companies is related to marketing, namely stable and sustainable income growth, and customer loyalty. So that the application of information and communication technology is needed in the business world as a tool to win the main competition in marketing products or services. In the company, order data made by the sales person is still via telephone. You need an application to speed up the product ordering process to customers. To solve these problems, each sales person will use a sales order application through a smartphone device that will make product orders. And product ordering data will go to a web-based application run by admin for the invoice creation process. This application is designed using the RESTful API which is one of the implementation models of the web service and. In terms of data exchange security using the Json Web Token. With this sales order application, it can simplify the sales person's performance in marketing their products. This application work runs well on the web and android device so the process of ordering products becomes easier. The use or json web token authentification on the RESTful API makes the application more secure because this application can not be accessed if it does not use tokens.&#x0D;
Semi-supervised Skin Detection by Network with Mutual Guidance

In this paper we present a new data-driven method for robust skin detection from a single human portrait image. Unlike previous methods, we incorporate human body as a weak semantic guidance into this task, considering acquiring large-scale of human labeled skin data is commonly expensive and time-consuming. To be specific, we propose a dual-task neural network for joint detection of skin and body via a semi-supervised learning strategy. The dual-task network contains a shared encoder but two decoders for skin and body separately. For each decoder, its output also serves as a guidance for its counterpart, making both decoders mutually guided. Extensive experiments were conducted to demonstrate the effectiveness of our network with mutual guidance, and experimental results show our network outperforms the state-of-the-art in skin detection.