Reviewerly/exHarmony huggingface dataset:
Qué piensan los docentes sobre la práctica experimental virtual

espanolEste trabajo analiza las respuestas vertidas en un cuestionario electronico, por tres cohortes de docentes que participaron en un taller sobre experimentos virtuales dictado por los autores. La encuesta realizada tiene por objetivo conocer lo que piensan los docentes sobre aspectos vinculados a las Tecnologia de la Informacion y la Comunicacion en general y a la Practica Experimental Virtual en particular y su relacion con la Ensenanza de las ciencias naturales. Dentro de las conclusiones a que arribamos podemos destacar que una amplia mayoria de los docentes que realizan actividades practicas en el aula no utilizan la modalidad virtual. EnglishThis paper analyzes the answers given in an electronic questionnaire, by three cohorts of teachers who participated in a workshop on virtual experiments dictated by the authors. The survey carried out aims to know what teachers think about aspects related to Information Technology and Communication in general and to Virtual Experimental Practice in particular and its relationship with the Teaching of Natural Sciences. Within the conclusions reached, we can highlight that a large majority of teachers who carry out practical activities in the class-room do not use the virtual modality.
Forecasting of Vannamei Shrimp Production Based on Weather Factors Using Radial Basis Function Neural Network Approach (Case Study: Lamongan District)

Abstract One of the economic activity sectors that can be influenced by uncertain weather conditions is aquaculture production. In Lamongan, aquaculture production especially vannamei shrimp is very dependent on the ideal weather conditions. Uncertainty of the weather conditions can cause irregular harvesting to the production of vannamei shrimp. These trend changes can have an impact on the activities of production supply chain, namely the fulfillment of the entry quota of vannamei shrimp production from agents or distributors to exporters of vannamei shrimp to meet market demand. This marketing result can increase the original income of Lamongan area. To find out the development of the trend required a forecasting process and appropriate classification based on past data using artificial neural networks. One structure of artificial neural networks that can be predicting and classifying is a radial basis function neural network (RBFNN). The structure of RBFNN is trained using K-means clustering and gradient descent method. We use average temperature, average humidity and rainfall each month starting from January 2013 until December 2017 as the actual datasets. From those datasets, the training datasets start from January 2013 until December 2016 and the remaining datasets are used as the testing datasets. Built in the Python program, the test results show that our forecasting and classification had an accuracy level with mean absolute percentage error (MAPE) 16.7%.
Detection of Compromised Models Using Bayesian Optimization

Modern AI is largely driven by machine learning. Recent machine learning algorithms such as deep neural networks (DNN) have become quite effective in many recognition tasks e.g., object recognition, face recognition, speech recognition, etc. Due to their effectiveness, these models are already catering to user needs in the real world. To handle the service requests from large number of users and meet round the clock demand, these models are usually hosted on cloud platforms (e.g., Microsoft Azure ML Studio). When hosting a model on the cloud, there may be security concerns. For example, during the transit of the model to the cloud, a malicious third party can alter the model or sometimes the cloud provider itself may use a lossy compression on the model to efficiently manage the server resources. We propose a method to detect such model compromises via sensitive samples. Finding the best sensitive sample boils down to an optimization problem where the sensitive sample maximizes the difference in the prediction between the original and the modified model. The optimization problem is challenging as (1) the altered model is unknown (2) we have to search a sensitive sample in high-dimensional data space and (3) the optimization problem is a non-convex problem. To overcome these challenges, we first use a variational autoencoder to transform high-dimensional data to a non-linear low-dimensional space and then uses Bayesian optimization to find the optimal sensitive sample. Our proposed method is capable of generating a sensitive sample that can detect model compromise without incurring much cost by multiple queries.
Study on a test scoring system for vocational secondary schools using Computerized Adaptive Testing (CAT)

This study aimed to determine: (1) the effectiveness of paper-based tests (PBT) and computer-based tests (CBT), (2) the best test assessment system, and (3) whether Computerized Adaptive Testing (CAT) is suitable for use in vocational secondary schools. This study is a literature review project focusing on a “Test Scoring System”. The sources reviewed in this study come from four major sources: (1) academic books from libraries, (2) peer-reviewed educational research journal articles from digital databases, (3) government documents online, and (4) other online electronic resources. The result of this study is a recommendation to create a test scoring system based on item response theory: Computerized Adaptive Testing (CAT). This test scoring system is using item response theory to know student’s ability.
Research On Key Dimension Detection Algorithm Of Auto Parts Based On Hough Transformation

Based on auto valve seat ring and the size of the spur gear slotting detection as an example, the visual inspection method is used to measure the key dimensions. Image feature recognition method based on Hough transform is put forward, and the size detecton algorithm is designed on this basis. this paper expounds the problems existing in the process of feature recognition, and adopt different methods to solve. Finally, the detection accuracy is verified. Inside of the valve seat diameter detection process, test results is not accurate because of the uncertainty in parameters, and multiple characteristics identification results. In order to solve this problem, in the algorithm design limiting detection range of valve seat ring diameter, the detection accuracy is higher, faster. Delimiting the scope of testing method has been used in solid round edge detection, this paper uses the method to detect hollow circular edge. In the feature recognition and dimension detection of spur gear kyway keyway, due to the similarity of some features of the keyway and spur gear teeth, which are straight lines, features of spur gear teeth appear in the identification results. The data that interfered with the size measurement of keyway appeared in the size measurement results. Because the size of the line segment on the tooth is much smaller than the size of the keyway, the detection result of the keyway size is obtained by using the data statistics method to exclude the data with too large difference from the actual value.
Jaringan Syaraf Tiruan untuk Pendugaan Porositas Tanah

Suharyatun S, Rahmawati W, Sugianti C. 2019. Artificial neural networks for estimating soil porosity. In : Herlinda S et al. ( Eds .), Prosiding Seminar Nasional Lahan Suboptimal 2019, Palembang  4-5 September 2019. pp. 424-429.  Palembang: Unsri Press. Texture is one of the physical properties of soil that is permanent and related to the other physical properties of soil. Its properties is included the ability to absorb the water which expressed by porosity. This study aims to develop an artificial neural network (ANN) model to estimate soil porosity based on soil texture. The research was conducted in several stages, namely: (1) measuring the physical properties of soil consisting of texture, moisture content, volume density, and particle density (2) calculating soil porosity, (3) Developing ANN models are correlated on textures and soil porosity. ANN models are made using 3 input layer neurons, 5 hidden layer 1 neurons, 5 hidden layer 2 neurons and 1 input layer neurom. The ANN model uses the logig-tansig-purelin activation function with a RMSE (Root Mean Square Error) value of 2.0242. Determination of the training model (R 2 ) was 0.957. The results of testing the validity of the models produce the same determination value R 2 = 0.957. Keywords: artificial neural network, the
The Sloan Digital Sky Survey as an archetypal mid-scale program

We describe the Sloan Digital Sky Survey (SDSS) as an archetypal mid-scale program, including the technical, scientific, and organizational aspects underpinning its success over the last twenty years. Based on the SDSS experience, we present six recommendations that the funding mechanisms for mid-scale programs should be designed to ensure.
Action recognition based on virtual simulation for prosthetic vision

For now, even the state-of-the-art of retinal prostheses is limited by low resolution and image processing algorithms. In order to find suitable parameters for prosthetic vision and help visual prosthesis wearers get on well with low-resolution gray-scale image correctly and effectively, the action recognition experiments based on virtual simulation were performed in this paper. Animated videos of skeletonized walking upright were structured in 3ds Max, then the action animation video clips were pixelized by MATLAB. The actions were classified into three categories: combined actions, simple actions and difficult actions. All action clips have 6 resolutions (6&times;16, 24&times;24, 32&times;32, 48&times;48, 64&times;64, 128&times;128). Twenty observers (classified by gender and experience) were recruited to participate in the test. They were asked to recognize actions at different resolutions. The results showed that there was no significant regular pattern of gender difference on the recognition accuracy. As for experience difference, the recognition accuracy of experienced observers was higher than the unexperienced ones. The conclusion was drawing that learning experience can improve recognition accuracy, experienced observers recognized an action requires lower resolution, 48&times;48 was a suitable resolution which has considerable latent capacity.
Using a Systematic Literature Review to Strengthen the Evidence Supporting a Simulation Model of Distributed Software Projects

In silico studies allow the use of computer models to simulate the environment, object, and subject behavior of an experiment. They are valuable tools to support the quantitative analysis of the behavior of different types of projects because they allow the use of different techniques such as stochastic and analytical methods to evaluate the impact of project decisions without the real execution costs. Recently, in silico studies have been used to study distributed software development projects. In this paper, we present a simulation model defined by a set of variables that describe the dynamics of Distributed Software Development (DSD) projects. The assessment of our simulation model is based on a Systematic Literature Review (SLR) where we seek evidence, from primary studies published until October 2018, that the variables and relationships in our model do exist in actual DSD projects. We use a SLR as a validation mechanism. Our results are twofold. First, there is a gap of studies on some variables including knowledge expertise and task allocation strategy in distributed software development projects. And, second, there still is a lack of data to support results related to productivity, especially testing activities, on DSD projects. Despite the problem of lack of common terminology for reporting the results on DSD studies, we argue that we need more studies to be able to clearly infer the dynamics of the relationship between variables on distributed software development. Our study brings one step closer to fulfill this gap.
A study of the exploration/exploitation trade-off in reinforcement learning : Applied to autonomous driving

A world initiative was set in motion for decreasing the amount of traffic accidents. Autonomous driving is a field which contributes to the initiative. Following report examines exploration/exploit ...
Encoding a qubit in a molecule

Quantum error-correcting codes are constructed embedding a finite-dimensional codespace in the infinite-dimensional state space of rotational degrees of freedom of rigid bodies. Such codes protect against orientational diffusion and kicks in the body’s angular momentum.
Safe and Efficient Path Planning for Omni-directional Robots using an Inflated Voronoi Boundary

Path planning algorithms for mobile robots are concerned with finding a feasible path between a start and goal location in a given environment without hitting obstacles. In the existing literature, important performance metrics for path planning algorithms are the path length, computation time and path safety, which is quantified by the minimum distance of a path from obstacles. The subject of this paper is the development of path planning algorithms for omni-directional robots, which have the ability of following paths that consist of concatenated line segments. As the main contribution of the paper, we develop three new sampling-based path planning algorithms that address all of the stated performance metrics. The original idea of the paper is the computation of a modified environment map that confines solution paths to the vicinity of the Voronoi boundary of the given environment. Using this modified environment map, we adapt the sampling strategy of the popular path planning algorithms PRM (probabilistic roadmap), PRM* and FMT (fast marching tree). As a result, we are able to generate solution paths with a reduced computation time and increased path safety. Computational experiments with different environments show that the proposed algorithms outperform state-of-the-art algorithms.
Deep Memory Network with Auxiliary Sequences for Chinese Implied Sentiment Analysis

Sentiment analysis is a hot topic and has various application scenarios. The polarity recognition of implied sentiment in a sentence can be achieved by the way of statistic and prediction. However, the polarity of sentiment is influenced by funny, humorous, and ironic Internet cultures, therefore it is hard to be verified. In this paper, we use a deep memory network with the auxiliary sequence to obtain the text feature vectors. Then the Emoji set and the special word set from the internet are imported, which are combined with the formal text feature vectors to form the classification feature vectors. At last a binary classifier is designed to get the final polarity prediction. Besides, an incremental online learning method with feedback adjustment is introduced to update the Emoji set and the special word set. Experiment results show that, on the IMDB datasets the prediction accuracy is about 85% and on the Chinese implied sentiment evaluation datasets the prediction accuracy is about 96%, which prove the effectiveness of the model.
Chinese dialect identification using prosodic classes and enhanced bigram model

A method based on prosodic classes is proposed for Chinese dialect identification in this paper. The prosodic classes are obtained from a large number of prosodic words which are the basic unit of the prosodic structure, and simultaneously include acoustic, phonotactic and prosodic feature to classify dialects. In addition, the pauses between prosodic words also are considered and described as a special prosodic class. The different between the Chinese dialects is distinguished by the prosodic classes and their order in the whole sentences. The enhanced bigram model (EBM) based on HMM technique is proposed to obtain the sequential statistics of sequences of prosodic classes, which is shown to yields better identification performance and outperform the universal HMM model. We implement the new method to illustrate the capability of identification and evaluate it on the corpus from the Project for the Protection of Language Resources of China. The experimental results show that our method provides competitive performance with the existing methods.
A Preliminary Framework for Strategic and Compliance Monitoring

The complexity of modern organisations drives the usage of enterprise architectures models and execution data as suitable tools to manage such complexity. To promote a successful enterprise management, this paper proposes the 2QUEN framework that uses enterprise architecture models to monitor the achievement of enterprise goals. In order to represent the relevant architectural domains, 2QUEN uses abstractions like goals, business processes and normative primitives. In order to gather data to monitor goal achievement, 2QUEN proposes the first steps of a methodology to access and query data of a legacy relational database with the purpose of querying norms states. The methodology starts from normative primitives (commitments, authorisations, prohibitions and powers) and uses the architecture provided by the 2-level version of OBDA [2] (2OBDA), a framework from the Semantic Web community, to semantically represent normative primitives and their states, and subsequently map those norms to the underlying legacy data.
Assessing the Adoption Level of Agile Development Within Software Product Lines: The AgiPL-AM Model

Agile Product Lines are combinations of agile and product-line techniques. Introducing agile software development methods into software product lines makes the development processes evolve from predictive to iterative and incremental and offers flexibility to react on customers’ changing requirements and market demand and deliver high quality software [1]. However, this combination is still challenging and the maturity of an agile adoption is often hard to determine. Assessing the current situation regarding the combination is thus an essential step towards a successful integration of agile methods into software product lines. Following a specific research approach, we have built an assessment model called AgiPL-AM allowing self-evaluations within the team in order to determine the current state of agile software development in combination with software product lines. AgiPL-AM, our model for assessing organizational agility of Agile Product Line approaches, is comprised of six categories (five are related to agile principles and one to product line architecture) and five levels of maturity. The assessment results demonstrate that AgiPL-AM has the ability to reveal and pinpoint agile product-line approach strengths and weaknesses. It makes recommendations to improve the status and may give a guideline for this improvement.
Combinatorial Designs and Cryptography, Revisited

In the study of cryptography and information security, combinatorial structures arise in a natural and essential way, especially in the context of unconditional security (also termed information-theoretic security). In this chapter, the author discusses several interesting examples of interactions between cryptography and combinatorics. In 1993, he gave an invited talk at the Fourteenth British Combinatorial Conference. The paper he wrote to accompany his talk was entitled “Combinatorial Designs and Cryptography.” The purpose of this paper is to elucidate some of these connections. This is not intended to be an exhaustive survey, but rather a sampling of some research topics in which he have a personal interest. The author also discusses the connections between the One-time Pad, perfect secrecy and latin square. He focuses on three topics of continuing and/or recent interest, namely, threshold and ramp schemes, all-or-nothing transforms, and algebraic manipulation codes.
Information Assurance: The Way to Introduce in PG Education in India—A Policy Framework

Information Assurance (IA) is an important and emerging concepts related to the information security. Information Assurance (IA) is also required for the managing and implementing information security related policies. There are two types of security; firstly traditional security and another one is computational security. Traditional security is about the manual documents and content related security whereas; computational security is deals with various components based security viz. Network Security, Web Security, Database Technology, Multimedia Technology, etc. Information Security is very close with the Information Assurance (IA), as it is also deals with Manual and Computational areas; though it is additionally deals with the policies, regulation and guidelines of security. Many universities internationally offered degrees and academic programs on Information Assurance (IA) and allied fields viz. Information Assurance and Security, IT and Information Assurance, Computer and Information Assurance, IT Security and Assurance, etc. The degrees are offered in different levels viz. Bachelors Degree, Masters Degree, PG Diploma, PG Certificate, Doctoral and Post Doctoral level; there are different universities. In India, a very few universities have started security related degrees and most of these are offering traditional nomenclatures. There are potentialities to offer the degrees at UG levels with little initiatives and policies. This paper talks about such potentiate and situation in detail.
Combining Cross Entropy Loss with Manually Defined Hard Example for Semantic Image Segmentation

Semantic image segmentation has been one of the fundamental tasks in computer vision, which aims to assign a label to each pixel in an image. Nowadays, approaches based on fully convolutional network (FCN) have shown state-of-the-art performance in this task. However, most of them adopt cross entropy as the loss function, which will lead to poor performance in regions near object boundary. In this paper, we introduce two region-based metrics to quantitatively evaluate the performance of segmentation detail, which provides insights about the bottleneck of model. Based on this analysis, by use of a modified multi-task learning scheme, we combine cross entropy loss with manually defined hard example to propose a simple yet effective loss function named $$\mathcal {L}_\mathrm{{cehe}}$$, which helps model focus on the learning of segmentation detail. Experiments show that model using $$\mathcal {L}_\mathrm{{cehe}}$$ can better utilize spatial information comparing with the conventional cross entropy loss $$\mathcal {L}_\mathrm{{ce}}$$. Statistically, metrics indicate that the proposed method outperforms the widely used $$\mathcal {L}_\mathrm{{ce}}$$ by $$1.12\%$$ in terms of MIoU on Cityscapes validation set, and by $$4.15\%$$ in terms of the region-based metric MIoUiER proposed in this paper, proving that $$\mathcal {L}_\mathrm{{cehe}}$$ performs better in segmentation detail.
Development of a Multi-attribute Convolutional Neural Network to Seismic Facies Classification

Seismic facies classification contributes to the interpretation and geological modeling of subsurface.The complexity of geological environments still challenges any standardization of patterns recognition methods for estimating seismic facies and the search for an ideal method, since this step can be complex depending on the purpose and the available dataset.The convolutional neural networks (CNN) were developed to serve as the computer vision, seeking to imitate the functioning of human vision.Despite the potential of this technology, the convolutional networks have not yet been successfully adapted to work with seismic data.The main objective is to develop a workflow based on multi-attribute convolutional neural network for seismic facies characterization.The workflow is based on four main steps: facies classification; data extraction; CNN train and prediction.Aiming to verify the efficiency of this new method, it was elaborated a synthetic image and it was also applied to a real seismic data.In addition, seismic attributes have been calculated to enhance characteristics of the original dataset.The method demonstrates exciting results achieving accuracies above 90% over the expected facies model.