Reviewerly/exHarmony huggingface dataset:
Field test study on reinforced soil steep slope in subgrade widening of Highways

Abstract To study the application effect of the reinforced soil steep slope structure in the subgrade widening project of highways under the condition of limited land occupation, based on the Xinyuan expressway reconstruction and expansion project, this paper conducts field test and research on the structural state of the reinforced soil structure, and selects a natural grading subgrade widening section to compare the widening effect. Through buried sensors to monitor the settlement of the cross-section of the foundation and analyze the change law of the data after the operation. Based on the research on the structural state of reinforced soil steep slope widening subgrade, this paper summarizes the structural deformation law, construction key technology and control method in the construction process and the initial operation stage.
Algebraic Bayesian Networks: Empirical Estimates of the Sensitivity of Local Posteriori Inference

The article is aimed at studying empirical estimates of the sensitivity of the second task of a posteriori inference in a knowledge pattern. The article presents the results of an experiment on finding the relationship between the distortion of incoming information and the results of learning a knowledge pattern. Formally, information distortions were achieved by changing the estimates of the fixed evidence and finding the norm of the difference between the vectors of the original and the resulting evidence. Obtaining empirical estimates is the first example of studying the second task of a posteriori inference in a knowledge pattern. The relevance of the study is emphasized by the growing popularity of machine learning and, most importantly, data preparation, since ABS is one of the few models that can work with inaccurate data.
Learning to Infer the Depth Map of a Hand from its Color Image

We present the first direct approach targeted explicitly on human hands that infers depth from monocular RGB images. We achieve this with a Convolutional Neural Network (CNN) that employs a stacked hourglass model as its main building block. Intermediate supervision is used in several outputs of the proposed architecture in a staged approach. To aid the process of training and inference, hand segmentation masks are also estimated in such intermediate supervision steps, and used to guide the subsequent depth estimation process. In order to train and evaluate the proposed method we compile and make publicly available HandRGBD, a new dataset of 20,601 views of hands, each consisting of an RGB image and an aligned depth map. Based on HandRGBD, we explore variants of the proposed approach in an ablative study and determine the most accurate one. The results of an extensive experimental evaluation demonstrate that hand depth estimation from a single RGB frame can be achieved with an accuracy of 22mm, which is comparable to the accuracy achieved by contemporary low-cost depth cameras. Such a 3D reconstruction of hands based on RGB information is valuable as a final result on its own right, but also as an input to several other hand analysis and perception algorithms that require depth input. In this context, the proposed approach bridges the gap between RGB and RGBD, by making all existing RGBD-based methods applicable to RGB input.
Instance-Based Ensemble Selection Using Deep Reinforcement Learning

Ensemble selection is a very active research topic in machine learning area. It aims to achieve a better performance by selecting a proper subset of the original ensemble, which is essentially a searching problem in large combinatorial spaces. In this paper, we propose an instance-based reinforcement learning (IBRL) model, that selects distinct subsets for different instances. Specifically, we use deep Q-network to approximate the optimal policy. Rather than considering the overall performance of each classifier, the network learns from the feedback of classifiers on individual instance, so that it generates non-static subsets for different instances. Experiments are conducted to compare our model against state-of-the-art approaches for both selection and combination. The proposed method generates promising results and it shows exceptional advantage in large scale distributed environment. Due to the environment-free characteristic of reinforcement learning, our model is adaptable to various real world tasks with minimal changes.
DRG2vec: Learning Word Representations from Definition Relational Graph

Even with larger and larger text data available, encoding linguistic knowledge into word embeddings directly from corpora is still difficult. The most intuitive way to incorporate knowledge into word embeddings is to use external resources, such as the largest datasource for describing words - natural language dictionaries. However, previous methods usually neglect the recursive nature of dictionaries and cannot capture the high-order relation. In this paper, we present DRG2vec, a novel and efficient approach for learning word representations, which exploits the inherent recursiveness of dictionaries by modeling the whole dictionary as a homogeneous graph based on the co-occurrence of entry and word in the definition. Moreover, a tailor-made sampling strategy is introduced to generate word sequences from the definition relational graph, and then, the generated sequences are fed to the Skip-gram model with semantic negative sampling for word representation learning. Extensive experiments on sixteen benchmark datasets show that leveraging the recursive dictionary graph indeed achieves better performance than other state-of-the-art methods, especially exhibits a weighted average improvement of 8.6% in the word similarity task.
Advising the Buffalo Police Advisory Board

My research in the Community-based User Experience Lab at the University at Buffalo takes a critical research approach to understanding how residents interact with and experience the Buffalo Police Advisory Board. This poster assesses whether the BPAB is user-centered and usable according to its publicized purpose: to create a space for community members’ voices to be heard and to then advocate their concerns.
Image inspired Chinese couplet generation

Chinese couplets, as one of the traditional Chinese culture, is the treasure of Chinese civilization and the inheritance of Chinese history. Given a sentence (namely an antecedent clause), people reply with another sentence (namely a subsequent clause) equal in length. Because of the complexity of the semantic and grammatical rules of couplet, it is not easy to create a suitable couplet that meets the requirements of sentence pattern, context, and flatness. With the development of neural models and natural language processing, automatic generation of Chinese couplets has drawn significant attention due to its artistic and cultural value, most of these works mainly focus on generating couplet by given text information, while visual inspirations for couplet generation have been rarely explored. In this paper, we design a Chinese couplet generation model based on NIC (Neural Image Caption), which can compose a piece of couplet suitable to the artistic conception in an image. At first, we use the improved VGG16 model to predict the input image. The content of the image can be automatically recognized and the corresponding description are generated and translated into Chinese keywords. Then, the encoder-decoder framework is used repeatedly to process these keywords, and finally the couplet can be generated. Moreover, to satisfy special characteristics of couplets, we incorporate the attention mechanism into the encoding-decoding process, which greatly improves the accuracy of couplets generated automatically.
Learner Profiling Towards Improving Learner Success

There have been numerous research projects on learner attrition in open and distance learning institutions. Open University Malaysia (OUM) has introduced many interventions to mitigate the first semester attrition rate where the dropout is highest, but improvements have yet to be seen. This chapter aims at gathering learner profiles to be used as a guide in an attrition study. The research uses a survey methodology, with the instrument consisting of learners’ demographic profile and three constructs of personality, attitude and motivation. The survey was conducted online. Out of the total population of 2970, 637 (21.4%) first-semester learners responded, but only 438 completed questionnaires were used for the analysis. Statistical analysis showed that the four-point scale inventory provided good reliability for all three constructs. Under the personality construct, more than 90%, 84.4% and 78.3% of the respondents were in the “high” category for openness to experience and conscientiousness; leadership; and the autonomous construct, respectively. In terms of attitude, more than 90% of the learners were “high” for both categories (attitude towards career and attitude towards education). Under motivation, affiliation and power had less influence on motivation compared to achievement. This outcome suggests that, while learners are generally open to experiences and conscientious, effort is needed to encourage and guide them in becoming more autonomous, which is a required characteristic for students in open and distance learning. The learners showed a positive attitude towards education and career. Programmes designed to meet working adults’ needs may increase the likelihood of their being actively engaged.
Lightweight «Mora» Hash-Function for ICS Field Devices Data Protection

The article proposes a solution to the problem of protecting communication channels between field devices and the PLC level. The authors determine the model of the intruder for such channels in non-critical ACS TP. The article also provides a list of the most likely attacks. The protection of data transmitted through a physically insecure communication channel can be ensured by various cryptographic means. Standardized cryptographic primitives provide a high level of security, however, their implementation may require significant computing resources. Field devices often do not provide these resources. Otherwise, field data has a short lifetime and size. In view of this, it would be interesting to consider the applicability of low-resource cryptographic tools to solve this problem. Algorithms of this class provide low-resource implementation and are able to grant protection in a certain period of time. Using such algorithms to ensure data protection during their lifetime from an attacker with a certain potential can solve this problem. As a solution, it is proposed to use the lightweight "Mora" hash function with a block size of 64 bits. This function is "Streebog"-like, which allows to provide good cryptographic qualities. The small block size allows to obtain good performance with a sufficient level of resistance in the formed model of the intruder. The article provides a full description of the hash function, as well as the results of the cryptanalysis. The experiment showed the 30 times superiority of the "Mora" in the speed of work against the "Streebog".
Vision Guided Robots. Calibration and Motion Correction

This chapter discusses methods to measure the 3D coordinates of parts to be processed by robots and on methods to control robots for accurately processing them. The method of triangulation is introduced and explained with the help of real-world examples. Image processing and correction in order to remove distortion effects helps to increasing accuracy. In addition, methods to calibrate the robot controller coordinate systems with coordinate systems used for measurements by external sensors such as cameras are introduced. Positions measured by cameras need to be converted to the robot reference frames. Then the accurate coordinate frame is used for the physical parts to be properly located in this area. The system is deployed on a real-world robotic cell used for welding a vehicle door.
Distance-Guided Mask Propagation Model for Efficient Video Object Segmentation

Video object segmentation (VOS) is a significant yet challenging task in computer vision. In VOS, two challenging problems, including occlusions and distractions, are needed to be handled especially in multi-object videos. However, most existing methods have difficulty in efficiently tackling these two factors. To this end, a new semi-supervised VOS model, called Distance-Guided Mask Propagation Model (DGMPM), is proposed in this paper. Specifically, a novel embedding distance module, which is utilized to generate a soft cue for handling occlusions, is implemented by calculating distance difference between target features and the centers of foreground/background features. This non-parametric module that is based on global contrast between the target and reference features to detect object regions even if occlusions still exist, is less sensitive to the feature scale. The prior knowledge of the previous frame is applied as spatial guidance in the decoder to reduce the effect of distractions. In addition, spatial attention blocks are designed to strengthen the network to focus on the target object and rectify the prediction results. Extensive experiments demonstrate that the proposed DGMPM achieves competitive performance on accuracy and runtime in comparison with state-of-the-art methods.
Novel Use of Self-organizing Map for Q-matrix Calibration in Cognitive Diagnosis Assessment

Cognitive diagnostic assessment (CDA) is a newly emerged tool for educational and psychological measurement. Unlike traditional assessment, CDA can measure a participant's detailed mastering pattern of finer-grained skills and knowledge (a.k.a. attributes). An important component in CDA is the Q-matrix, which associates each item with particular attribute(s) and thus forms the basis for performing diagnosis. However, in real-world applications, the Q-matrix is often defined by hand, which induces high risks of subjective errors. The purpose of the current investigation is to describe how the self-organizing map (SOM), a typical kind of unsupervised artificial neural network, can be used to validate and calibrate the Q-matrix. To do so, we propose to convert the Q-matrix calibration problem into a clustering-labeling process: first cluster the participants' responses to identify groups of test items and then label the item(s) with unknown or obscure attribute(s) based on group similarity. Following this idea, a one-phase method and a two-phase method are proposed. The one-phase method employs a single SOM to implement Q-matrix calibration at a fast speed. The two-phase method, which adopts a double-SOM design, is a bit more time-consuming but can provide a diagnosis of participants as a by-product. Experimental results on simulated data generated under various conditions confirm the effectiveness of the proposed method. As a real-data illustration, the proposed methods are also applied to analyze large-scale educational reading assessment data and achieve promising results.
Finding New Multiword Expressions for Existing Thesaurus

In this paper we study the task of adding new multiword expressions (MWEs) into an existing thesaurus. Standard methods of MWE discovery (statistical, context, distributional measures) can efficiently detect the most prominent MWEs. However, given a large number of MWEs already present in a lexical resource those methods fail to provide sufficient results in extracting unseen expressions. We show that the information deduced from the thesaurus itself is more useful than observed frequency and other corpus statistics in detecting less prominent expressions. Focusing on nominal bigrams (Adj-Noun and Noun-Noun) in Russian, we propose a number of measures making use of thesaurus statistics (e.g. the number of expressions with a given word present in the thesaurus), which significantly outperform standard methods based on corpus statistics or word embeddings.
Graph Matching Approach between Endoscope Images for Non-Rigid Motion using Blood Vessel Structure

It is important to estimate the size and shape of the polyp from endoscope images in the medical diagnosis. Ref.[1] was proposed as a method to estimate the absolute size of the polyp, where movement of endoscope in depth direction was estimated from two endoscope images. Ref.[1] introduced template matching using blood vessel to determine the correspondence between two images. However, it is not appropriate for actual endoscope environment since actual motion includes not only the camera transition but also the camera rotation. Further object motion is non-rigid motion and it is difficult to determine correct correspondence points between two images. So, this paper proposes a new approach to extract a blood vessel structure based on the blood vessel region using a graph structure, where the graph is matched between two images. Here, a matching method of blood vessels is described, where camera rotation and non-rigid motion of the target object are under consideration. Effectiveness of the proposed approach is confirmed in the experiments.
A Sample Teaching Design of ‘Water Conductivity’ Unit

The aim of this paper was to present a sample teaching design of “Water Conductivity” through digital educational video. The teaching of the thematic unit “Water Conductivity” causes difficulties and alternative conceptions to many students because of its abstract nature. The purpose of this research is twofold and results from the desire to understand the difficult perceivable concepts, a fact that is also related to the absence of the strongest teaching tool of a chemist, which is no other than the experiment.
Image Denoising Using Various Image Enhancement Techniques

The main aim of the image enhancement technique is to process any image given as input and to obtain the resultant outcome more accurately than the existing image. The level of accuracy of an image can be restored in different forms using image enhancement techniques. The choices of choosing different image enhancement techniques may vary depending upon the quality of the picture, task, and atmospheric conditions. The different algorithms used for enhancement and the concepts are discussed in this paper. The frequency and spatial domain of the image can be enhanced by using these processes.
Visual Exploration of Time-Series Forecasts Through Structured Navigation

Evaluating the forecasting ability of time-series involves observations of multiple charts representing different aspects of model accuracy. However, the sequence of the charts observed by users is not controlled and it is difficult for users to discover relations among charts. Therefore, we propose a method for constructing a navigation structure that shows these relations based on the syntax and semantics of the charts. An excerpt from the structure is used as a context menu that allows users to navigate through a series of charts and explore their relations in a structured way. A qualitative study is conducted to evaluate the system and the results show that our approach helps users explore the connections among charts and enhances the understanding of time-series forecasting performance.
Reliable Temporally Consistent Feature Adaptation for Visual Object Tracking

Correlation Filter (CF) based trackers have been the frontiers on various object tracking benchmarks. Use of multiple features and sophisticated learning methods have increased the accuracy of tracking results. However, the contribution of features are often fixed throughout the video sequence. Unreliable features lead to erroneous target localization and result in tracking failures. To alleviate this problem, we propose a method for online adaptation of feature weights based on their reliability. Our method also includes the notion of temporal consistency, to handle noisy reliability estimates. The two objectives are coupled to model a convex optimization problem for robust learning of feature weights. We also propose an algorithm to efficiently solve the resulting optimization problem, without hindering tracking speed. Results on VOT2018, TC128 and NfS30 datasets show that proposed method improves the performance of baseline CF trackers.
The Latest Technologies for Mobile Learning

This chapter reviews the latest technologies and their applications in facilitating mobile learning in the new age of education. It introduces and explains the key functions of various technologies and discusses the possible challenges in their applications faced by users. The results provide insights into the planning and preparation for blended learning, with mobile learning integrated into the conventional way of curriculum delivery.
The BlockChain Neural Network: Neuron as a Service

The BlockChain Neural Network increments gradually neurons as user information or data increases, offering a biologically inspired "Neuron as a Service" (NaaS) solution. The additional neurons codify both the new information to be added to the "neural block" and the previous neurons potential to form the "neural chain". This configuration provides the proposed algorithm with the same properties found in the BlockChain: security and decentralization with the same validation process: mining the input neurons until the neural network solution is found. The mathematical model of the Neural Networks in BlockChain configuration is presented in this paper with evidences of its stable learning convergence during its learning process. The main advantage of this research proposal is the biological simplicity of the solution, however it suffers high computational cost when the number of neurons increase. Experimental results that validate the proposed method are presented with optimistic conclusions; this paper provides the biologically inspired mathematical model as a digital step forward to avoid physical currencies, documentation and contracts.
Study on Human Cognition: A study challenging Gambling Businesses using a Cognitive Model for Casinos

It has been Two years since the bill to legalize casinos in Japan was enacted. Since casinos are avoided by many people in Japan, the promoters try to keep gambling images out of their publicity as much as possible. These promoters are relatively irreverent as to the effect of their promotions in order to enter the Japanese market successfully. Therefore, in this study, the perceptive aspects of those who do not have positive thoughts about casinos have been explored. This was done by designing a hypothetical human cognitive model. Data obtained from a total of 400 subjects through questionnaire survey was analyzed. From the results, it was found that the existing conventional PR methods of promoting casinos as resorts did not necessarily reduce the resistance and aversion to casinos in the minds of their detractors. The initiatives were discussed in order to make the opposition change their minds effectively through educating them to a level of understanding of what casinos are in terms of rules etc., as well as letting them try gambling so as to measure their practical feelings.