Reviewerly/exHarmony huggingface dataset:
STNS-CSG: Syntax Tree Networks with Self-Attention for Complex SQL Generation

Text-to-SQL tasks that translate natural questions into corresponding SQL queries aim to help users easily query vast amounts of data stored in relational databases. Most existing studies do not require generating complex SQL queries with subqueries or multiple clauses. However, these complex queries exist widely in real life. Syntax tree network model is proposed to generate complex SQL queries. It suffers from the low accuracy of column prediction. In this paper, we propose a novel model, Syntax Tree Networks with Self-attention for Complex SQL Generation, which develop the syntax tree network model in twofold. The one is applying self-attention mechanism to better capture information in questions, the other is a table predictor that can get table information from questions directly. Experimental results show that our method outperforms the previous state-of-the-art model by 1.9% in exact matching accuracy.
Dither NN: Hardware/Algorithm Co-Design for Accurate Quantized Neural Networks

Deep neural network (NN) has been widely accepted for enabling various AI applications, however, the limitation of computational and memory resources is a major problem on mobile devices. Quantized NN with a reduced bit precision is an effective solution, which relaxes the resource requirements, but the accuracy degradation due to its numerical approximation is another problem. We propose a novel quantized NN model employing the “dithering” technique to improve the accuracy with the minimal additional hardware requirement at the view point of the hardware-algorithm co-designing. Dithering distributes the quantization error occurring at each pixel (neuron) spatially so that the total information loss of the plane would be minimized. The experiment we conducted using the software-based accuracy evaluation and FPGA-based hardware resource estimation proved the effectiveness and efficiency of the concept of an NN model with dithering.
Self-Organizing Maps for Agile Requirements Prioritization

In building software systems, decisions at the specification phase will extremely affect the rest of the system life cycle. Well-defined requirements at this phase will increase the chance of achieving the ultimate goal of delivering a software that meets stakeholders needs. Given a limited sources of time and predefined budget, not all the requirements should be fulfilled with the same priority. Here comes the need for requirement prioritization RP techniques. This paper presents a new approach to deal with the dynamic nature of requirements prioritization process in agile development. Training a self-organizing map according to requirement's predefined features is the main process in the proposed approach. The trained map can produce a set of clusters. A farther rank is given to each requirement according to map resulting weights. The proposed approach was implemented using different variables related to requirements themselves and related to the self-organizing map to show its ability to prioritize requirements in agile development model.
Arabic Document Indexing for Improved Text Retrieval

Arabic document indexing is a challenging process due to the complex morphological nature of the Arabic language. Methods of document indexing in the literature relied on applying morphological schemes to extract terms. These morphological schemes mainly depend on root extraction and stemming. This paper proposes a simple document indexing method based on selecting only definite words (that have the prefix AL, or it is acceptable to have this prefix). The words that preceding (and/or) succeeding these definite words are also considered. The proposed method is tested using the TREC-2001/2002 Arabic test collection. The proposed method outperforms selecting all terms, either without stemming, or stemmed by the Light10 stemmer, for example, indexing documents by selecting definite words and words that come after them enhances the Mean average Precision of the Light10 by 4.4%, and at the same time decreases the index size by 6.1%.
Autoencoder Ensemble for Person Re-Identification

Person re-identification (re-id) aims to match people from non-overlapping multi-camera networks. Recently, with the advancement of deep learning techniques, the performance of re-id has been improved swiftly. However, most of the existing re-id methods need large number of samples for training due to which the models do not generalize well on smaller datasets and suffers from small sample size problem. Additionally, they focus on single scale appearance information while ignoring rich information that can be exploited from other scales. In this paper, we propose a simple yet effective autoencoder, comprising of an encoder and a sequential decoder. The goal of the network is two fold. First, the network learns features by introducing a generative task to the embedding layer so that it can make features more generalizable to the unknown test data to prevent from overfitting. Second, the encoder feature embedding is used as an input to decoder to reconstruct the input image with various scales to achieve robustness against scale variations. The effectiveness of our proposed method is validated on three public person re-identification datasets, Market-1501, DukeMTMC-reID and CUHK03.
Using Clustering in Eye-Tracking Calibration Task

In eye-tracking experiments with primates, infants or non-cooperating subjects, the data used during the calibration step generally suffers from high contamination. This contamination typically behaves as clusters, corresponding to the periods in which the subject was not actually looking at the calibration target. In this type of multimodal samples, most techniques used to estimate the central point of the distribution usually fail, resulting in errors when mapping the gaze from the camera to the screen coordinates. In this manuscript, we analyze the viability of using clustering algorithms to find the center of the main cluster, corresponding to the main mode of a multivariate distribution, which will serve as the reference position for calibration. Those algorithms were compared using simulated data mimicking the spatial characteristics of eye-tracking typical patterns. More specifically mixtures of bi-variate Gaussians and Uniform distribution, with different number of clusters, variances, co-variances, percentage of samples in the main cluster and proportion of uniform noise. Then, the validation of these clustering-based techniques was conducted on experimental data from eye-tracking calibrations performed by Capuchin monkeys.
The Causal Relationship Between Returns and Trading Volume in Cryptocurrency Markets: Recursive Evolving Approach

This chapter examines the time-varying causal relationship between trading volume and returns in cryptocurrency markets. The chapter employs a novel Granger causality framework based on a recursive evolving window procedure. The procedures allow detecting changes in causal relationships among time series by considering potential conditional heteroskedasticity and structural shifts through recursive subsampling. The chapter analyzes the return-volume relationship for Bitcoin and seven other altcoins: Dash, Ethereum, Litecoin, Nem, Stellar, Monero, and Ripple. The results suggest rejecting the null hypothesis of no causality, indicating bi-directional causality between trading volume and returns for Bitcoin and the altcoins except Nem and Stellar. The findings also highlight that the causal relations in cryptocurrency markets are subject to change over time. The chapter may conclude that trading volume has predictive power on returns in cryptocurrency markets, implying potential benefits of constructing volume-based trading strategies for investors and considering trading volume information in developing pricing models to determine the fundamental value of the cryptocurrencies.
Temporal Continuity Based Unsupervised Learning for Person Re-identification

Most existing person re-identification (re-id) methods generally require a large amount of, difficult to collect, identity-labeled data to act as discriminative guideline for representation learning. To overcome this problem, we propose an unsupervised center-based clustering approach capable of progressively learning and exploiting the underlying re-id discriminative information from temporal continuity within a camera. We call our framework Temporal Continuity based Unsupervised Learning (TCUL). Specifically, TCUL simultaneously does center based clustering of unlabeled (target) dataset and fine-tunes a convolutional neural network (CNN) pre-trained on irrelevant labeled (source) dataset to enhance discriminative capability of the CNN for the target dataset. Furthermore, it exploits temporally continuous nature of images within-camera jointly with spatial similarity of feature maps across-cameras to generate reliable pseudo-labels for training a re-identification model. Extensive experiments on three large-scale person re-id benchmark datasets demonstrate superiority of TCUL over existing state-of-the-art unsupervised person re-id methods.
A Black-Box Approach to Generate Adversarial Examples Against Deep Neural Networks for High Dimensional Input

Generating adversarial samples is gathering much attention as an intuitive approach to evaluate the robustness of learning models. Extensive recent works have demonstrated that numerous advanced image classifiers are defenseless to adversarial perturbations in the white-box setting. However, the white-box setting assumes attackers to have prior knowledge of model parameters, which are generally inaccessible in real world cases. In this paper, we concentrate on the hard-label black-box setting where attackers can only pose queries to probe the model parameters responsible for classifying different images. Therefore, the issue is converted into minimizing non-continuous function. A black-box approach is proposed to address both massive queries and the non-continuous step function problem by applying a combination of a linear fine-grained search, Fibonacci search, and a zeroth order optimization algorithm. However, the input dimension of a image is so high that the estimation of gradient is noisy. Hence, we adopt a zeroth-order optimization method in high dimensions. The approach converts calculation of gradient into a linear regression model and extracts dimensions that are more significant. Experimental results illustrate that our approach can relatively reduce the amount of queries and effectively accelerate convergence of the optimization method.
Segmentation and classification of mast cells in histological images with deep learning

Mast cells are an important part of the human immune system. They are also found in pathological pathways of various diseases. Their activity in tissues may be analyzed with a histopathological examination. In this preliminary study, we apply a deep learning approach for analysis of an original dataset of mast cells stained by toluidine blue. UNet and a convolutional neural network are applied for cell segmentation and classification, respectively. The result allows for the proposal of a methodological consideration for the subsequent full study. Basic deep learning approaches reach 66.64% segmentation accuracy when measured with the Dice coefficient and 81.36% class accuracy when the training dataset is measured with the categorical accuracy metric.
Joint Grouping and Labeling via Complete Graph Decomposition

We introduce the complete graph decomposition approach for joint grouping and labeling. Our framework takes into consideration both how to group subjects and how to assign labels to them in a joint manner, without knowing the number of groups beforehand. We model the relations of different targets via a complete graph, which is decomposed into a set of complete subgraphs to represent distinct groups. We implement this joint framework by fusing both deep features and rich contextual cues with model parameters learned from data. We propose an alternating search algorithm to solve the relevant inference problem efficiently. We evaluate the effectiveness of the proposed approach on human activity understanding, and show the proposed approach is competitive compared against the state-of-the-art.
Cross-sectional study of digital competences in the school trajectory higher education students (e-skills)

The need for evolution in the educational and technological part of higher level educational institutions is evident, as it is the last step for students oriented to their working life. Having the skills in digital knowledge is essential to expand their abilities and attitudes. In this research, we seek to identify the perception of students regarding the degree of knowledge and their reality, in relation to digital skills and their affinity towards them, in addition, it focuses on research methods to make a diagnosis of perception about digital skills of the students of the Facultad de Contaduria y Administración of the Universidad Veracruzana, and then, identify the transversality of the contents of the subjects and understand the current status. Starting from a methodology, conceptual and theoretical, allowed to analyze, under a deductive ways, the process of conceptualization and, later, its operationalization, where the evaluation dimensions are stipulated, with which an instrument with validity and reliability is presented, applied to a stratified sample to evaluate the five dimensions. The contribution of this research is based on the results of the statistical diagnosis, since with them you can generate strategies in the universities to identify the digital competences of the students and provide better tools focused on achieving a transversal objective in the school career, Implement new learning models and facilitate the acquisition of knowledge with better results.
Adaptive Message Embedding in Raw Images

In this paper, we propose an adaptive approach to message embedding in raw images. The cover image is divided into blocks where the top left pixels are dedicated as control pixels used to identify blocks carrying message information. Message bits are embedded while restricting the number of pixel changes in a cover segment to one change only. This is achieved through a concept of overloading the embedding indices; therefore, a maximum of one pixel value is changed per block for message embedding. The proposed solution is assessed in terms of percentage of changed pixels, PSNR, SSIM, histogram changes, and blind steganalysis. Comparison with existing work reveals that the proposed solution reduces both the image distortions and pixel change rates. It is also shown that the proposed solution is less detectable when tested with blind steganalysis in comparison to existing solutions.
Influences of Neural Network Structures on an Efficient Reinforcement Learning Policy Search

The Black-box Data-efficient RObot Policy Search algorithm, also known as Black-DROPS, is one of the most data-efficient algorithms for Reinforcement Learning for robotics. The algorithm is based on studying the dynamical model of uncertainty of robots, learning and optimizing the corresponding policy to maximize the reward. Black-DROPS does not limit the reward function, so it can have a wide range of applications. The algorithm is an optimization algorithm using the numerical approach, which is more efficient than the analytical approach in the case of multi-core operation. But the default one layer neural network does not have enough power to meet some difficult problems, and when faced with a high dimensional problem the algorithm will take an unusually long time to solve it. In this paper, we explore different Neural Network structures (layers, neurons, and computer cores) to study their influence on the speed from Black-DROPS on a Double Inverted Pendulum simulation. We demonstrate that although the performance (number of iterations to reach optimality) is affected by structural changes to a lesser extent, the computation time (time per iteration) is drastically affected by such changes, and an optimal structure size seems to exist, as we highlight in our results.
Vehicle Attribute Recognition for Normal Targets and Small Targets Based on Multitask Cascaded Network

The interference of the complex background and less information of the small targets are two major problems in vehicle attribute recognition. In this paper, two cascaded networks of vehicle attribute recognition are established to solve the two problems. For vehicle targets with normal size, the multitask cascaded convolution neural network MC-CNN-NT uses the improved Faster R-CNN as the location subnetwork. The vehicle targets in the complex background are extracted by the location subnetwork to the classification subnetwork CNN for the classification. The implementation of this task decomposition strategy effectively eliminates the interference of the complex background in target detection. For vehicle targets with small size, the multitask cascaded convolution neural network MC-CNN-ST applies the network compression strategy and the multilayer feature fusion strategy to extract the feature maps. These strategies enrich the location information and semantic information of the feature maps. In order to optimize the nonlinear mapping ability and the hard-to-detect samples mining ability of the networks, the activation function and the loss function in the two cascaded networks are improved. The experimental results show that MC-CNN-NT for the normal targets and MC-CNN-ST for the small targets achieve the state-of-the-art performance compared with other attribute recognition networks.
Dark Data Analytics Using Blockchain Technology

These days, data is growing at a frantic pace in almost all fields such as E-Commerce, education, health industry, banking and many more, and we are not able to reap its value fully due to which it remains unprocessed or unstructured and hence turns into Dark Data. Dark Data is the data that an organisation collects and then fails to harness its value which then adds to the biggest portion of Big Data. This paper unlocks the potential of the upcoming revolution in digital economy, especially in the field of global financial markets: The Blockchain Technology. It is the engine behind the most popular, the very first cryptocurrency called the Bitcoin by Satoshi Nakamoto in 2008. We have explored various reasons for this technology to be a disruptive force in future, its current trends, various advantages and limitations. Also intersection of Blockchain Technology, Machine learning and Artificial Intelligence when used to handle Dark Data (unstructured or unused data) will not only upgrade the value of data, but will provide us with more transparent and efficient results, since Blockchain is nothing but a secure database of online transactions in itself.
Dehazed Image Enhancement by a Gamma Correction with Global Limits

Single image haze removal has been an active research topic in the image enhancement or image restoration community. Most of researchers have put their effort to improve the dehazing performance through modifying algorithm itself. In this paper, we propose a post-processing scheme, which is a gamma correction, to enhance the visual quality of a dehazed image after some dehazing algorithm. It is well-known that the conventional gamma correction (CGC) severely suffers from the due distortion in many cases. Thus, it hinders the application of the CGC to dehazed image enhancement. By our observations, the problem is caused by the local limits, which are found in the RGB components separately. To relieve the problem, a gamma correction with global limits (GCGL) is introduced, where the global limits are obtained by the minimum and maximum in the RGB components. The proposed GCGL is justified and applied to dehazed image enhancement. The simulation results indicate that the visual quality of dehazed images have been generally improved in the given examples.
A Research on Vehicle and UAV Routing Problem during Distribution Based on IAMMAS

Abstract According to the characteristic of vehicle and unmanned aerial vehicle (UAV) in distribution service, a vehicle and UAV distribution mode were put forward in this paper to further expand the three-dimensional space of distribution, improve the efficiency of distribution and relieve the pressure of distribution personnel. Based on the consideration of real-time road condition, real-time driving speed, maximum driving distance and mixing time windows in distribution practice, a two-stage nested VRPTW model under vehicle and UAV distribution mode was constructed in this paper. A hybrid algorithm of genetic algorithm and adaptive max-min ant system algorithm (GA-AMMAS) was designed to solve the above problems. In addition, the actual road traffic conditions and vehicle actual travel time were added to the heuristic information in this algorithm. Data experiments shows that the vehicle and UAV routing problem can be effectively solved by GA-AMMAS. In addition, compared with GA and ACA, GA-AMMAS shows obvious advantages in quality solution and speed of convergence.
An Educational Data Mining with Bayesian Networks for Analyzing Variables Affecting Parental Attachment

Despite of its usefulness, Bayesian Networks have been hardly adopted in the educational domain. In this study, we conduct data mining with Bayesian Networks to find which variables are in charge of parental attachment and how much the variables affect. The Hill-Climbing method is selected to learn a Bayesian Network and Markov Blanket is used to extract the related variables to parental attachment. To evaluate the effects of the variables, prediction and diagnostics analyses are performed with the learned Bayesian Network. This paper shows Bayesian Networks are quite effective in finding the related variables and examining the variables’ effects in the educational domain.
Post-Material Aesthetics: A Conceptualization of Digital Objects

The article conceptualizes the ontology and aesthetics of digital objects. In the context of the article, digital objects are defined as designed objects that incorporate and employ digital technology. They include such products as smartphones, tablet computers and smart speakers, among others. The main point made in the article is that digital technology is changing object design. Digitization has created a new ontological constitution of objects that are simultaneously material and digital and whose function often relies on their connection to other objects via wireless technologies. Building upon this point, the article proposes a concept of post-material aesthetics which comprehends the ontological constitution of digital objects in relation to materiality, function and conceptual values. Employing the concept of post-material aesthetics, the article investigates how digital objects, in their particular constitution, impose new conditions for framing human experience.
Object Dynamics from Video Clips using YOLO Framework

In this paper we present a real-time object detection in the warehouse, to predict collision arising from moving forklifts and raising alarm when necessary. There are many research using YOLO for real-time object detection like detecting person or cars for Advanced Driver Assistance System(ADAS). There are many cargoes in the warehouse. Employees need to collect them and deliver to other place. When the employees are driving the forklift with many cargoes or big shipments that may block there vision to see objects in front of them. The employee driving the forklift might not see cargoes stored at the corner while turning, causing accident. The system will identify objects in real-time, received through surveillance camera set at a height from where it can clearly capture required frames to predict collision. If the camera predict that there will be an imminent collision, it will sound the alarm.