Reviewerly/exHarmony huggingface dataset:
High Speed Recurrent Regression Network for Visual Tracking

For some recently released trackers, the spatial-temporal information of the target are processed separately, which is time consuming and inefficient for locating the target in the sequential data-videos. To solve this problem, we present a recurrent regression framework(RRNet), which leverages spatial and temporal information coherence on feature level simultaneously. The RRNet is composed of a regression network and a long short term memory network(LSTM). The regression network is learned on static image level for focusing on the spatial information of the target, and the whole framework is fine-tuned on videos by fixing the parameters of the regression network, which improves the per-frame regression by aggregation of recurrent prior. Especially, there is no need to online training for adapting the unseen targets. And the experimental results show that the proposed RRNet gets better performance than the compared trackers with a high speed (45 fps).
Ichiro Robots Winning RoboCup 2018 Humanoid TeenSize Soccer Competitions

This paper describes the Ichiro RoboCup Winner TeenSize Humanoid League from the Institut Teknologi Sepuluh Nopember Surabaya, Indonesia. In this paper, the mechatronic design of Ichiro robots, algorithms and robotic behavior will be discussed. At this year, for the first time we took part in a competition on the TeenSize category, our algorithm and robot behavior were simple, but we were able to maximize the algorithm and get the advantages of a fast-computational process and ease of debugging. Therefore, we were able to get four awards: First place Teensize Soccer Competition, Second place TeenSize Technical Challenge, Second place Drop-in Challenge, Third place Best Humanoid Award.
Probability Distribution Mining of Time-Sensitive Spectrum Sensing Data

In the multi-dimensional spectrum space, the spectrum has strong time-varying characteristics. In this way, it is of great significance to estimate the changes of spectral noise and channel quality over time. The traditional Spectrum sensing technology as a key component in cognitive radio technology groups has make significantly contribution to improve the wireless spectrum utilization efficiency. However, many are not enough to judge whether the channel is available. The judge problem can be improved by estimating the probability distribution of spectrum sensing data changes. In this paper, we focus on spectrum sensing data and channel sensing data which are collected over months in various terrain environments. We observe that all data are time sensitive and may conform to some probability distribution. By a series of hypothesis testing on these data, we found that 77% of the samples with spectral noise changing data obey the exponential distribution, and that most channel quality changing data are basically subject to exponential distribution. This result can help us to predict and select spectrum holes with sound confidence.
Improving Object Detection by Deep Networks with Class-Related Features

Context is an important additional information for object detection. Class-related features which mainly focus on the coexistence of various classes objects in a single image is a kind of context information. In this paper, we propose an object detection algorithm that makes use of the class-related features. Specifically, in order to improve the performance of object detection, we propose an end-to-end network which introduces the attention mechanism to learn the class-related features and combine it with convolutional neural network (CNN) visual features. The experimental results on PASCAL VOC and MS COCO show that our method get a better performance than the baseline method (Faster R-CNN). The introduction of spatial attention module and channel-wise attention module captures the class-related features greatly. Furthermore, the Gated Recurrent Unit (GRU) which is adopted to combine class-related features and visual features works well in our model.
Intelligent System for the Learning of Sign Language Based on Artificial Neural Networks

Sign language has become a form of communication for people who have some type of hearing impairment, in order to relate to the world around him and perform daily activities including education, work performance and personal development. In the knowledge society, the development of new Technologies for Information and Communication (TIC), they constitute a teaching tool that changes the conditions for learning and communication as the world is digitized. Therefore, the development of an Ecuadorian sign language learning system based on the use of a gesture sensor and an artificial neural network multilayer feed-forward, implementing the Backpropagation algorithm, which takes advantage of the parallel property of decreasing the time required by a processor to distinguish the existing relationship between given patterns, represents a solution for improving the teaching-learning process. The system consists of three main layers: The first is responsible for the acquisition of data through a gestural device; the second uses the library of the gesture sensor to perform the acquisition and storage of information of the hand and the position of the fingers. Finally, the output layer, once the training of the neuron is analyzed, generates the real-time recognition of sign language.
Technology for Efficient Encoding of Structural Components using the Multi-Agent Approach for Telecommunication Tools and Devices

An approach for images compression based on generalized coding of its component, taking into account coordinate, structural and linear-scale characteristics is considered. The main stages of developing the method for encoding arrays of a linear-scale component of an image fragment, which are presented in the form of positional numbers with uneven adjacent elements, are disclosed. The property of a one-dimensional position element with uneven neighboring elements, which consists in the fact that the upper limit of its code determined by the multiplication of its bases is preserved.
Open University Participation in Community Education Based on Smart Network and Artificial Intelligence

How to combine artificial intelligence to promote colleges and universities to participate in community education in a smart network environment is a problem that many universities need to focus on. From the perspective of practical application, with reference to the literature analyzed by scholars at home and abroad, this paper integrated the development and improvement methods of current community education. At the same time, this paper analyzed the current situation of colleges and universities and the practice of community education, found out the problems affecting the development of community education, and proposed reasonable solutions, which is not only conducive to the promotion of community education, but also has certain practical significance for the transformation and upgrading of community education.
Finger-Print Image Super-Resolution via Gradient Operator based Clustered Coupled Sparse Dictionaries

In this paper, a novel approach is employed for fingerprint image super-resolution based on sparse representation over a set of coupled low and high-resolution dictionary pairs. The primary step of fingerprint super-resolution involves learning a pair of coupled low- and high-resolution sub-dictionaries for each cluster of patches sampled from training set of fingerprint images. The clusters are formulated based on patch sharpness and the dominant phase angle via the magnitude and phase of the gradient operator for each image patch. In the reconstruction stage, for the low-resolution patch the most appropriate dictionary pair is selected, and the sparse coding coefficients are calculated with respect to the low-resolution dictionary. The equality assumption of the sparse representation of the low and high-resolution patches is the link between the low and high-resolution features space. For the reconstruction of high resolution patch, the sparse coefficients calculated for low-resolution patch are directly multiplied with corresponding high-resolution dictionary. The conducted experiments over fingerprint images show that the algorithm is competitive with the state-of-art super-resolution algorithms.
Stability analysis in identification of interval type‐2 adaptive neuro‐fuzzy inference system: Contribution to a novel Lyapunov function

Abstract In recent years, control research has strongly highlighted the issue of training stability in the identification of non‐linear systems. This paper investigates the stability analysis of an interval type‐2 adaptive neuro‐fuzzy inference system (IT2ANFIS) as an identifier through a novel Lyapunov function. In so doing, stability analysis is initially conducted on the IT2ANFIS identifier, while performing the online training of both the antecedent and the consequent parameters by the gradient descent (GD) algorithm. In addition, the same stability analysis is carried out when the antecedent and the consequent parameters are trained by GD and forgetting factor recursive least square (FRLS) algorithms, respectively (GD + FRLS). A novel Lyapunov function is proposed in this study in order for the identifier stability to attain the required conditions. These conditions determine the permissible boundaries for the covariance matrix and the learning rates at every iteration of the identification procedure. Stability analysis reveals that wide range of learning rates is obtained. Furthermore, simulation results indicate that when the permissible boundaries are selected according to the proposed stability analysis, a stable identification process with appropriate performance is achieved.
Arithmetic Operation Recognition Method Based on BP Neural Network

At present, artificial neural network (ANN) is widely used in the development of recognition software because of its reliable theory and good versatility. In this paper, the recognition of handwritten ordinary arithmetic operations is studied, and a recognition method based on BP neural network is proposed. To achieve the purpose of recognition of handwritten ordinary arithmetic operations, and through the implementation of this system, the experimental results show that the recognition accuracy can reach 95% under the appropriate number of iterations, learning rate and the number of hidden layer neurons.
3D+T dense motion trajectories as kinematics primitives to recognize gestures on depth video sequences

RGB-D sensors have allowed attacking many classical problems in computer vision such as segmentation, scene representations and human interaction, among many others. Regarding motion characterization, typical RGB-D strategies are limited to namely analyze global shape changes and capture scene flow fields to describe local motions in depth sequences. Nevertheless, such strategies only recover motion information among a couple of frames, limiting the analysis of coherent large displacements along time. This work presents a novel strategy to compute 3D+t dense and long motion trajectories as fundamental kinematic primitives to represent video sequences. Each motion trajectory models kinematic words primitives that together can describe complex gestures developed along videos. Such kinematic words were processed into a bag-of-kinematic-words framework to obtain an occurrence video descriptor. The novel video descriptor based on 3D+t motion trajectories achieved an average accuracy of 80% in a dataset of 5 gestures and 100 videos.
Modelling Experiential Knowledge Ethically: An Artefact‐Based Approach to Visually Documenting a Participatory Design Process with Young People

Abstract This article contributes to the growing debate within art and design education research about the role, legitimacy and accountability of artefacts in the creation and generation of knowledge. I present an artefact‐based approach to visually documenting and disseminating my doctoral practice‐based fieldwork that protected the anonymity of the participants whilst supporting my exploration of the situational, interactional and tacit dimensions of participation. Constructing a three‐dimensional scaled model of the fieldwork setting and using narratives from my field notes, I undertook a cyclical reflective process where I reconstructed critical moments that took place. This was used as both a tool for communicating my participatory design educational practice and for evaluating my findings with the participants. By sharing this sense‐making process, I offer an ethical means of visually documenting fieldwork when working in sensitive contexts and with vulnerable participants, setting out the dual role this design artefact played as both a producer and carrier of experiential knowledge.
A 4900×m<sup>2</sup> 839Mbps Side-Channel Attack Resistant AES-128 in 14nm CMOS with Heterogeneous Sboxes, Linear Masked MixColumns and Dual-Rail Key Addition

A.A 4900μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> side-channel attack (SCA) resistant AES accelerator in 14nm CMOS achieves 1200x higher minimumtime-to-disclosure (MTD) than an unprotected AES. Randomized byte-order shuffling using heterogeneous Sboxes, linear masked MixColumns and dual-rail key addition enable 9.2x lower correlation between current traces and HD/HW power models. The accelerator achieves 839Mbps throughput with 0.7% performance overhead.
Target Tracking based on Multi Templates and Regression Network

This paper proposes a tracking algorithm of generic target based on multi templates and regression network. In order to improve the robustness and accuracy, the proposed tracking algorithm keeps a template set to remember target in different states, and uses the motion information of target in the last image frame to crop a searching region from current image frame. Then, the templates and the searching region are input to a two-branch network (regression network), and the predicted status information of target and confidence are output by the network. To keep the template set adaptable to the state change of target, a special template updating method is proposed. Some training methods are also used for training the networker to get a more effective tracker. Experimental results demonstrate that the methods employed in the proposed tracking algorithm improve the robustness and accuracy. The tracker based on the proposed target tracking algorithm is better than other trackers in term of robustness and accuracy, and it still can meet the real-time requirement.
A Blended Learning Approach to EFL Pronunciation Training

In this paper, we propose that traditional classroom-based pronunciation training for English-as-a-Foreign-Language (EFL) learners can be productively augmented with computer-supported blended learning. As current approaches to Computer Assisted Pronunciation Training (CAPT) are still lacking in flexibility, functionality and accuracy, a blended learning environment with computer-assisted adaptive training and peer assessment can be beneficial to pronunciation teaching and learning. With an innovative web-based training platform directly integrated with the learning environment, we deploy adaptive pronunciation training and collaborative peer assessment to enable EFL students to engage in autonomous learning and receive timely, personalized feedback. We describe the set-up of our technology-mediated learning environment and present how we integrate the online platform into our existing curriculum. The positive results from our initial experiments and user survey demonstrate the feasibility and benefits of the blended approach to EFL pronunciation training.
Accountable Protocols in Abductive Logic Programming.

Finding the entity responsible for an unpleasant situation is often difficult, especially in artificial agent societies.SCIFF is a formalization of agent societies, including a language to describe rules and protocols, and an abductive proof procedure for compliance checking. However, how to identify the entity responsible for a violation is not always clear.In this work, a definition of accountability for artificial societies is formalized in SCIFF. Two tools are provided for the designer of interaction protocols: a guideline, in terms of syntactic features that ensure accountability of the protocol, and an algorithm (implemented in a software tool) to investigate if, for a given protocol, nonaccountability issues could arise.
Integration of Senior Software Project Courses in an Undergraduate Software Engineering Program

Undergraduate software engineering programs typically include courses covering traditional models of the software development lifecycle. Following the SWEBOK guidelines, these may involve in the most straightforward case courses on Software Requirements Specification, Software Design, Software Construction or Implementation, and Software Testing. In practice, these courses are organized in a vertical sequence, with some being prerequisites to others, according to the individual stages in the development cycle, and are rarely intertwined or considered together on a cross-section basis. The contribution of this work is to demonstrate a practical way and share respective experience of integrating the four courses into a cohesive software development project block, additionally including a maintenance component with a fifth senior level course: Software Project in Computer Networks. The three essential benefits of such approach include: (1) increasing the size of the senior software project making it more realistic from an industrial perspective, (2) strengthening the understanding of software engineering concepts by applying them across the entire block rather than in isolation in individual courses, as well as (3) reducing project overload that may arise if each course were to require its own project.
A Travel Decision Support Algorithm: Landmark Activity Extraction from Japanese Travel Comments

To help people smoothly and efficiently make travel decisions, we utilize the advantages of travel comments posted by thousands of other travelers. Bao,Siya In this paper, we analyze the feasibility of exploring landmark activity queries and representative examples from Japanese travel comments. Yanagisawa,Masao Contributions in this paper include a framework for extracting activity concerned keywords and queries, quantifying the relationship between landmark activities and comment contents. Togawa,Nozomu An evaluation of activity-example extraction is conducted in two case studies through 18,939 travel comments.
Design of Multi-CPU Automatic Sorting System Based on Machine Vision

In view of the problems of low efficiency and the poor sorting effect caused by manual sorting in quality detection of small workpieces, a low-cost multi-CPU automatic sorting system based on machine vision is designed. In the system, the Raspberry Pie processes the workpiece image which is captured by an industrial camera, and it calculates the position of the workpiece. And then the PLC controls the robotic arm to locate and grasp the workpiece and completes the sorting task. Meanwhile through the LAN to achieve the link between the Raspberry Pi and the PC, the system realizes remote monitoring and decision-making. The experiments show that the system effectively improves the recognition effect and the accuracy of the sorting comparing with other sorting systems. This research also has a good reference value for other related applications.
Cross Modality Alignment of Medical Volumes using Spatio-Semantic Attentive Cycle-GAN

Lack of large available datasets fully annotated is a fundamental bottleneck in pulmonary nodule detection, especially when the corresponding computed tomography(CT) images obtained are device-dependent. We propose a spatio-semantic attentive CycleGAN (SSA-CycleGAN) capable of aligning modalities, as well as distinguishing nodule vs. non-nodule, which in turn achieves effective data augmentation. Specifically, a novel training loss function is established, providing a constraint for semantic preservation and local fidelity of nodule regions. Extensive experimental results on varied datasets demonstrate the proposed framework achieves significant performance gain on pulmonary nodule detection.
A Quantum Algorithm for Trading Strategies with Position Limits

The number of unique trading strategies with a limited number of futures contracts on short and long positions exponentially grows with the number of ticks. The latter reaches today in daily sessions hundreds of thousands. Evaluation of the set properties of strategies may require quantum computations. A quantum algorithm for the set is suggested.
MBNet: multi-scale bilinear convolutional neural networks for fine-grained visual classification towards real-time tasks

Fine-grained visual classification (FGVC) is difficult due to the under-utilization of low-level features. This paper proposes a real-time method MBNet based on multi-stream multi-scale cross bilinear CNN that contributes to solving the problem. First, each layer of the multi-stream CNN is extracted by basic network such as VGGNet and others, followed by calculating multi-stream cross bilinear vector and bottom bilinear vector of low and high level features respectively. The FGVC results are predicted after feature fusion, which solves the problem that small and low-level details in the original image are easily overlooked. In the widely used datasets Caltech-UCSD Birds, Stanford Cars and Aircraft, the proposed method shows that the accuracy is significantly improved compared to the existing methods, reaching to state of the art level of 88.51%, 94.73% and 92.41%. It also meets the requirements of real-time tasks.
Searching for Economic Minerals

In regional investigations, one and the same set of methods can in principle be employed to study objects of various classes that control different types of mineral resources. Of special interest is a shared approach to predicting the presence of ore and oil and gas on the basis of geophysical data, by relating discoveries of nonconventional oil-and-gas deposits in mountainous regions to igneous and metamorphic findings. Quantitative criteria for integrated interpretation are considered in the previous Chapters (mainly in Chapter 4). It is simply worth noting that routine qualitative criteria are also integrated, since geological problems that can be solved using one geophysical method are very rare, whereas a combination of only two methods considerably expands the possibilities of geophysical data interpretation. A number of papers dealing, for the most part, with the principles of joint interpretation of gravity and magnetic fields have made this point clear-cut. For instance, it is expedient to consider qualitative criteria to single out and trace faults, which are among the main targets of geophysical investigation. It is possible not only to isolate and trace the zones of fracture by some typical indicators in the magnetic and gravity fields but also to assess their types.
Review of Data Security Frameworks for Secure Cloud Computing

The most important thing for cloud computing is to offer real time data security for not only small sets of data but in petabytes. Cloud is majorly used because of cost savings and the agile business models it provides. Unfortunately, cloud comes with certain data security challenges. Data security of a user is given the highest priority and hence this paper provides a few methods to mitigate them. The paper discusses layers of CCAF and how addition of excess data validates it. Solutions have been provided to overcome security breach when there is a large increase of data. Another framework is proposed in which each threat is considered individually and a solution is specified to overcome it. The third framework allows providers of cloud management system to define and enforce complex security policies to protect their data. This paper consists of five sections: Introductions, Problems, Solutions, Analysis and Conclusion.