Reviewerly/exHarmony huggingface dataset:
Castor: Programming with extensible generative visitors

Much recent work on type-safe extensibility for Object-Oriented languages has focused on design patterns that require modest type system features. Examples of such design patterns include Object Algebras, Extensible Visitors, Finally Tagless interpreters, or Polymorphic Embeddings. Those techniques, which often use a functional style, can solve basic forms of the Expression Problem. However, they have important limitations. This paper presents Castor: a Scala framework for programming with extensible, generative visitors. Castor has several advantages over previous approaches. Firstly, Castor comes with support for (type-safe) pattern matching to complement its visitors with a concise notation to express operations. Secondly, Castor supports type-safe interpreters (à la Finally Tagless), but with additional support for pattern matching and a generally recursive style. Thirdly, Castor enables many operations to be defined using an imperative style, which is significantly more performant than a functional style (especially in the JVM platform). Finally, functional techniques usually only support tree structures well, but graph structures are poorly supported. Castor supports type-safe extensible programming on graph structures. The key to Castor's usability is the use of annotations to automatically generate large amounts of boilerplate code to simplify programming with extensible visitors. To illustrate the applicability of Castor we present several applications and two case studies. The first case study compares the ability of Castor for modularizing the interpreters from the “Types and Programming Languages” book with previous modularization work. The second case study on UML activity diagrams illustrates the imperative aspects of Castor, as well as its support for hierarchical datatypes and graphs.
The Workflow Satisfiability Problem with User-Independent Constraints

The Workflow Satisfiability Problem (WSP) is a problem of interest in access control of information security. In its simplest form is the problem coincides with the Constraint Satisfiability Problem, where the number of variables is usually much smaller than the number of values. Wang and Li (2010) were the first to study the WSP as a problem parameterized by the number of variables. The paper initiated very fruitful research surveyed by Cohen, Crampton, Gutin and Wahlström (2017). In this paper, we briefly overview some more recent WSP algorithmic developments.
Active Training Trajectory Generation for Inverse Dynamics Model Learning with Deep Neural Networks

Inverse dynamics models have been used in robot control algorithms to realize a desired motion or to enhance a robot's performance. As robot dynamics and their operating environments become more complex, there is a growing trend of learning uncertain or unknown dynamics from data. While techniques such as deep neural networks (DNNs) have been successfully used to learn inverse dynamics, it is usually implicitly assumed that the learning modules are trained on sufficiently rich datasets. In practical implementations, this assumption typically results in a trial-and-error training process, which can be inefficient or unsafe for robot applications. In this paper, we present an active trajectory generation framework that allows us to systematically design informative trajectories for training DNN inverse dynamics modules. In particular, we introduce an episode-based algorithm that integrates a spline trajectory optimization approach with DNN active learning for efficient data collection. We consider different DNN uncertainty estimation techniques and active learning heuristics in our work and illustrate the proposed active training trajectory generation approach in simulation. We show that the proposed active training trajectory generation outperforms adhoc, intuitive training approaches.
Neural Machine Translation between Myanmar (Burmese) and Dawei (Tavoyan)

This work explores the first evaluation of the quality of neural machine translation between Myanmar (Burmese) and Dawei (Tavoyan). We also developed Myanmar-Dawei parallel corpus (around 9K sentences) based on the Myanmar language of ASEAN MT corpus. We implemented two prominent neural machine translation systems: Recurrent Neural Network (RNN) and Transformer with syllable segmentation. We also investigated various hyper-parameters such as batch size, learning rate and cell types (GRU and LSTM). We proved that LSTM cell type with RNN architecture is the best for Dawei-Myanmar and Myanmar-Dawei neural machine translation. Myanmar to Dawei NMT achieved comparable results with PBSMT and HPBSMT. Moreover, Dawei to Myanmar RNN machine translation performance achieved higher BLEU scores than PBSMT (+1.06 BLEU) and HPBSMT (+1.37 BLEU) even with the limited parallel corpus.
Transformation of Personnel Provision for Russian System of Higher Education

Development of human capital nowadays in the XXi century stands out as the most important factor of transforming economy and society. With help of investments into development of human potential, Russia increases the rates of economic growth, creating the necessary foundation for strengthening its positions in terms of global completion. The quality of human capital is built by the system higher professional education which determines the potential and success in changing the social structure in technological and economic aspects. The authors examine the topical moments of transforming the personnel provision for the Russian system of higher professional education at the current stage of forming the innovative economic strategy of Russia’s development. Improving the quality of the Russian higher education is at present topical, presenting a strategic task, the solution of which will allow to provide economic stability of our country. The article brings forth the results of investigating the attitude of the Russian people to the Russian and foreign higher education, gives an assessment of the current state of the personnel provision for the Russian higher school. On the basis of these results, the article identifies main drawbacks of the personnel provision for the Russian system of higher professional education and offers directions of increasing the personnel potential of the Russian educational organizations of the higher professional education system. It draws a conclusion that the process of transforming the personnel provision for the higher professional education system in the Russian educational organizations dictates a complex of requirements to the professional role of the faculty members, bringing to the forefront the availability of functions in organizing both the academic and the project and research activities, to formation of educational practices. The present-day faculty member must serve as a consultant, a researcher, a manager of projects, must be «a navigator» in the educational and digital environment.
Detecting Drivers Smartphone: A Learned Features Approach using Aggregated Scalogram Images

In this paper, we propose an image representation approach for detecting driver mobile phone from the accelerometer signals produced by a set of smartphones in a vehicle. Rather than following the classic paradigm of classifying the signal as driver or non-driver, we propose an original paradigm whereby we aggregate the signals together and train a classifier to detect the driver signal in that aggregation. We do so by stacking-up the Scalograms images of the smartphone signals and training a CNN classifier to identify the driver's Scalograms instance in the Scalograms stack image. To the best our knowledge, this is the first time such an image-fusion and classification scheme is proposed for detecting driver's smartphone. Experiments performed with an in-house dataset confirms the potential and the merit of our approach.
Hierarchical Cluster Analysis of Interval-valued Data Using Width of Range Euclidean Distance

In this paper, we deal with hierarchical clustering for interval-valued data. Interval-valued data is typical of symbolic data. We propose hierarchical clustering for interval-valued using Width of Range Euclidean Distance, and apply this methods to real data. Different combinations of representative interval-valued distance measures and linkage methods are best for clusters of particular shapes. Each of these linkage algorithms can yield totally different results when used on the same dataset, as each has its specic properties.
Sensor Data Prediction techniques for nodes in IoT (poster)

As many Internet of Things devices rely on batteries, reducing energy consumption where battery replacement may be a problem may extend their lifetime and possible applications. This problem may be hindering the expansion of IoT devices usage in some applications. In this paper, we propose three data prediction and aggregation techniques to be applied to sensor readings and avoid unnecessary communication between a sensor node and Sink, so as to save energy and reduce network traffic. Since we have limited memory and processing capacity within sensors, this proposed approach collects data, compare the readings with expected value from one of the three techniques, and if the monitored data is within an ex-ante defined and acceptable range, no data transmission is necessary - the Sink would infer the actual reading within a uncertainty margin. Otherwise, last reading and estimated trend is transmitted and both sensor node and Sink are synchronized. The proposed techniques are (i) constant; (ii) linear; and (iii) using machine learning (Weightless Neural Network). We discuss pros and cons of each technique depending on the characteristics of the monitored data, taking into consideration the number of times the sensor reading had to be sent to Sink, and the average difference between estimated and actual reading.
Synthesis of Facial Image using Conditional Generative Adversarial Network

Face sketch is done by sketch artist for a suspected or missing person from the description of an eyewitness. These methods have been widely used by forensic investigators. It is difficult for the sketch artist to draw perfectly from such verbal descriptions given by eyewitness of scenes and hard for the informer to confirm whether the sketch looks like the real person. In this work, we proposed a conditional generative adversarial network (cGAN) for synthesizing real human face taking a sketch as an input image. The focus of our model is to generate realistic images that preserve the identity the target person verified by face recognition algorithms. The proposed cGAN has been verified on a variety of facial sketches, which confirms the effectiveness and improved facial recognition score.
Research on Space-time Anti Jam Filtering Technology for GPS Receiver

In order to promote the real-time signal dealing ability of the GPS receiver, adaptive reduced-rank filtering algorithm is put forward in the paper. That is, the input data is segmented adaptively according to the processor performance to promote the data convergence speed. Then a fast-reduced rank reduction algorithm based on iterative correlation subtraction based on multistage nested wiener filtering is used to reduce the computational burden. It can promote the space-time anti-jam filtering ability of the GPS receiver. Based on some experimental results, the anti-jam ability and convergence property of the algorithm discussed are analyzed finally.
An Alternative Derivation of Generalized Likelihood Tests for Track-to-Track Correlation

In this paper, an alternative derivation for a generalized likelihood ratio test (GLRT) for pairwise track-to-track correlation is provided to be complementary to methods previously published. Hypothesis likelihoods for the case of a single target and the case for two targets are derived, and the generalized likelihood ratio is formed with the maximum likelihood estimates for the unknown parameter(s) under each hypothesis. A threshold is defined for a given probability of false alarms (i.e., miss-correlations). For more than two targets, each hypothesis has a varying number of true targets, and the generalized likelihoods are penalized using model selection criteria, such as minimum descriptor length (MDL) and Akaike information criteria (AIC) to determine the optimal number of targets (i.e., parameters) given the reported tracks to be correlated. Simulation results compare the generalized likelihood ratio to an extant procedure and the unrealistic clairvoyant detector for pairwise track-to-track correlation. For multi-sensor multi-target hypotheses, simulation results show the probability of correct correlation for the AIC and MDL methods for a varying number of targets and for various target separation.
Robust Denoising of Low-Dose CT Images using Convolutional Neural Networks

X-ray computed tomography (CT) images are widely used in medical diagnosis. A drawback of X-ray CT imaging is that the X-rays are harmful with high-dose. Reducing the X-ray dose can reduce the risks but introduce noise and artifacts in the reconstructed image. This paper presents a method, called FD-VGG for denoising of low-dose CT images. FD-VGG estimates the normal-dose image from the low-dose image and, hence, reduces noise and artifacts. In FD-VGG the loss function is defined by the combination of the mean square error (MSE) and perception loss. FD-VGG was trained on a dataset of 226200 low-dose and normal dose image pairs from 6 patients and evaluated on 100 low-dose images from 2 other patients. The corresponding normal dose images of these testing low-dose images are considered as standard images for quantitative evaluation. Two metrics namely PSNR and SSIM were used for objective evaluation. The experimental results showed that the proposed FD-VGG network was able to denoise low-dose images efficiently, in comparison with two state-of-the-art methods.
2D Statistical Shape Model for Lung Using Apex Anatomical Landmark-based Registration Criteria

Statistical Shape Models have been widely used in range of medical applications such as segmentation, registration, classification and interpretation of medical images. Advanced imaging systems such as 4DCT, 3DCT and PET scan have been used to analysis of diseases, however in addition to their high costs, expose the patients to the relatively high radiation doses. Shape representation using routine radiography imaging technique is desire as it does fast and inexpensive along with lower radiation doses. In this work, we propose a novel method to model 2D lung from a small number of Chest X-Ray images. The proposed method combines a contour-based shape descriptors technique and Apex Anatomical Landmark-based (AAL-based) registration criteria to build an optimal 2D statistical shape model of lung which can be used to better analysis of human lungs. The presented model in current work is compared to our prior model. The compactness of the proposed model is evaluated and compared to the prior model. The results show that the proposed model is able to explain more than 90% of total variations using only three principal component modes for both of the right and the left lungs.
Artificial Neural Networks Android-Based Interface Facial Recognition Systems

Face recognition system is a crucial issue these days. This research builds an Android-based facial recognition system in real time using the Gabor filter and artificial neural network (ANN) methods. The system can be implemented properly. The test results show that for testing in scenario 1, the largest accuracy is 90% in hidden layer 4 and 5. The smallest computation time is 0.46872 seconds for layer 2 and the biggest time is 0.63778 seconds for hidden layer 5. While the test results for scenario 2 shows the lowest accuracy is the trainrp training function for 76%, while the highest accuracy of 94% is in the traincgp training function.
Image Preprocessing Algorithm Based on K-Means

With the development of image acquisition and storage technology, the image data is greatly increased. How to process the increasing image data quickly has become the main problem of image processing. In this paper, the image data are processed by K-means algorithm in Python language environment, and the original image and the image processed by K-means algorithm are classified and trained in convolution neural network. The experimental results show that the time consumed by the image processed by K-means algorithm is 20 s to 30 s less than that of the original image in convolution neural network. It can effectively improve the efficiency of image processing.
Information Security Risk Management in Electronic Banking System

Information and communication technology stands up as an important and effective part of various organization in Myanmar. By use of telecommunication network and internet, the information security and IT risk management system become indispensable requirement of organization. The organization need to maintain their information from when internal or external threats are incident. All of threats might control the information security risks and organizations are managed their own information environment. So, the organization required that they maintain and cover with high level security system. This paper focuses on information security risk management in the some activity of electronic banking system (e-banking system). The electronic banking technology is allowed for a variety of implementing financial services between organizations and customers. It allows serving, business market to improve customer service all the time [3]. The use of e-banking system is high rate in developed countries and comparatively lower in less developed countries. The analysis of the evolution and existing standard of electronic banking made the economic opportunity grow for the government [4].
Privacy, Security Of Cookies In HTTP Transmission

Web application and web browsers generally use cookies to capture the data which is being transmitted in the sequent communication that provides continuity and state across HTTP connections. A cookie is a file which contains an end user's information and which sent by website (web server). Cookies allow us to get round the statelessness of the hypertext transfer protocol by storing information at the client-side. Due to highly sensitive nature of information involved in e-commerce, extra steps must be taken to make sure that information is secure. A preparatory form of communication enables us to avoid exploitation of cookies. Securing the cookies provide possible answer to the present drawback. As cookies are transferred in plain text there are high possibilities of that cookies can be manipulated. This paper proposes a new mechanism which helps to encrypt the cookies and protect them from possible threats.
Guidance for Specific Target Doors in Hallway using the Computer Vision for Autonomous Vehicles

Computer Vision would complement the autonomous robotics goals of accuracy and precision by adding “eyes” to the performance of the robot. We will be researching on computer vision techniques and experimenting their benefits with autonomous robotics. We investigated both Arduino MegaTM Controller Board and Raspberry Pi for analyzing the flexibility of chipset for computer vision using the 4-wire dual data bus Pixi Camera (CMU's 5th version). Both C/C++ and Python libraries were implemented to test the vision camera using I2C and USB communication. Pixi camera scans at a rate of 50 frames per second. We investigated the object recognition, object classification and object tracking capabilities using various algorithms for detecting the specific target doors in a hallway. The autonomous robot is going to maneuver in the hall way by avoiding the obstacles and stop precisely at a door-front of the assigned door number. This is possible by associating the door numbers with color codes.
A Simple and Efficient Algorithm Design for Improving the Infrared Tracking Accuracy of Smart Cars

The smart car collects the signal through the infrared sensor, and obtains the gray map in front of the car, according to the gray map, the tracking is judged.Due to the complexity of the environment, many gray-scale maps are difficult to trace, and even cannot be judged, which brings great uncertainty to the operation of the smart car.The paper designs a method to improve the infrared tracking accuracy of smart cars.First, determine the level of contrast in this graph.By improving the brightness in the grayscale image, the gray signal around the tracking line is eliminated, and then the tracking line is highlighted by increasing the contrast.Therefore, it effectively overcomes the shortcomings of poor practicality of smart cars and improves the infrared tracking accuracy of smart cars.
Applying prerequisite structure inference to adaptive testing

Modeling student knowledge is important for assessment design, adaptive testing, curriculum design, and pedagogical intervention. The assessment design community has primarily focused on continuous latent-skill models with strong conditional independence assumptions among knowledge items, while the prerequisite discovery community has developed many models that aim to exploit the interdependence of discrete knowledge items. This paper attempts to bridge the gap by asking, "When does modeling assessment item interdependence improve predictive accuracy?" A novel adaptive testing evaluation framework is introduced that is amenable to techniques from both communities, and an efficient algorithm, Directed Item-Dependence And Confidence Thresholds (DIDACT), is introduced and compared with an Item-Response-Theory based model on several real and synthetic datasets. Experiments suggest that assessments with closely related questions benefit significantly from modeling item interdependence.
Contrasting Human Opinion of Non-factoid Question Answering with Automatic Evaluation

Evaluation in non-factoid question answering tasks generally takes the form of computation of automatic metric scores for systems on a sample test set of questions against human-generated reference answers. Conclusions drawn from the scores produced by automatic metrics inevitably lead to important decisions about future directions. Metrics commonly applied include ROUGE, adopted from the related field of summarization, BLEU and Meteor, both of the latter originally developed for evaluation of machine translation. In this paper, we pose the important question, given that question answering is evaluated by application of automatic metrics originally designed for other tasks, to what degree do the conclusions drawn from such metrics correspond to human opinion about system-generated answers? We take the task of machine reading comprehension (MRC) as a case study and to address this question, provide a new method of human evaluation developed specifically for the task at hand.
Combining primitive DQNs for improved reinforcement learning in Minecraft

We ask whether a reinforcement learning agent learns better by first learning the skills to perform smaller tasks in a complex environment or by learning the skills in the complex environment from the start. This is investigated empirically in a non-trivial game environment. We use the premise of curriculum learning where an agent learns different skills in independent and isolated environments referred to as dojos in this paper. The skills learned in the dojos are then used as different actions as the agent decides which skill to perform that best applies to the current game state. We evaluate this with experiments conducted in the Minecraft gaming environment. We find that dojo learning is able to achieve better performance with faster training time in certain environments. The main benefit of this approach is that the reward functions can be finely tuned in the dojos for each action as compared to the traditional methods. However, the skills learned in the individual dojos become the limiting factor in performance as the agent is unable to combine these skills effectively when put in certain complex environments. This can be mitigated if the dojo modules are further trained to result in similar results as the standard method.