Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Memory Efficient Hardware Architectures for Lifting Based and Non-Stationary Filter Based 5/3 2-D Inverse DWT

This paper presents comparative analysis of two different memory efficient hardware architectures for 5/3 two-dimensional (2-D) inverse discrete wavelet transform (DWT). The first one is based on lifting 5/3 inverse filters, while the second one is based on non-stationary 5/3 inverse filters. Both architectures require the lowest size of memory compared to other architectures published in the literature, but differs in terms of used logic resources. Both architectures are suitable for implementation in JPEG 2000 image decoder which uses 5/3 inverse DWT filters.
Watchman Routes for Robot Inspection

Inspection is a hot topic of robotics recently, and there are many different ways to solve the inspection problem. In this paper, we propose a new framework for a robust and efficient inspection of the entire workspace in a watchman route based on automatically generated waypoints. The framework architecture design includes several relevant technologies and refines algorithms such as medial axis transformation, shortest path approximation, and Monte-Carlo search for finding tours. This framework is evaluated in a client-server system: the simulation of the robot is run on Unity, while data processing is executed in a Python server. Experimenting with this approach, the measured inspection coverage of the workspace on random terrains was at least 99.6%.
Development of a System for Analyzing Hand-Raising Communication by Using a VR Headset to Synthesize Human-CG Character Interaction

Embodied Body motions and actions play an important role in human communication. Hand raising is one of the typical body motions that people have been encouraged to performed since their childhoods, but almost no studies address it from the viewpoint of human–computer interaction. By focusing on its importance, the authors have been developed hand-raising robots and Computer Graphics characters to analyze the communication effect. In this study, we developed an analyzing system using a Virtual Reality (VR) headset that can enable users to immerse themselves into CG. We performed an evaluation experiment to analyze the effects of the number of CG characters who raised their hands and the height of the hand raising. The scene in which all CG characters raised their hands to a low point and the scene in which one-third of the CG characters raised their hands to a high point were rated equally. This means that the effect of the number of CG characters in VR was larger than in front of a large screen. As a result, the participants’ impression of hand-raising communication changed.
The Granulation Building and Thinking of ICT-based Teaching Resources

Through focusing on the building of granulation resources, the essay analyzes online open course Recognition and Identification of Clothing Fabrics based on the granulation's connotation and feature, content, method and application of ICT-based teaching resources. It concludes that the granulation of ICT-based teaching resources is based on certain teaching objectives and reconstructed orderly by revolving around knowledge and skill with a relatively integrated and stable structure. The granulation of ICT-based teaching resources possesses characteristics of level and integrity. There exists diverse building methods, features and strategies for granulation's ICT-based teaching resources according to different teaching properties of course.
An Analysis of ProjectEdu: A Mobile Learning Application for Software Project Management Education

Learning applications play a key role in educational activities, both in academia and in industry. In this scenario, mobile learning has emerged as a new and promising learning modality, providing more attractiveness, agility, interactivity and flexibility to learners, tutors and teachers in carrying out educational activities and practices. In a different but related perspective, according to ACM/IEEE curricula, Software Project Management is an important topic to be taught in Computing degree programs. Despite its relevance, frequently, there is no specific course to teach Project Management, so its concepts are taught along with the other concepts of Software Engineering. However, even when there is a course dedicated to the subject, in its traditional format, the subject of Project Management is approached in a theoretical way. In this context, it is important to seek strategies that motivate the teaching-learning process, such as, for instance, mobile learning. In order to provide a more interesting approach to learn Software Project Management, we designed, prototyped and evaluated a tool entitled ProjectEdu. In general, users were enthusiastic and positive about the use of the mobile learning application, but they also pointed out some improvement points to make the tool more attractive.
Adaptive Aesthetic Photo Filter by Deep Learning

The paper proposes a deep end-to-end model with full differentiability, FilterNet, for image enhancement that could optimize the image filter parameters and recommend the best photo filter for a given image to achieve optimum aesthetic effect. The model learns the aesthetic distribution of images from evaluation network that is pretrained and identifies the binary aesthetic quality of an image, similar to the structure of GAN (generative adversarial network). The proposed model is weakly-supervised and one stage and accompanied by new training methods compared to other state of art models and conditional inputs that help the model to be more content-aware, yielding competitive results compared to professional photo editing. The performance of FilterNet is evaluated on both deep learning and traditional methods including online user studies.
Data Visualization Model Methods and Techniques

In order to meet the requirements of high-dimensional data processing in the information field, this paper aims to explore methods and techniques for visualizing general data resource clustering data. Through the visual mapping of dimensionality reduction and high-dimensional data, a visual learning model for visual influencing factors is established. The visual system model approach was tested using the IRIS dataset from the University of California Irvine database (UCL) database. The results show that the model can effectively analyze the data set, visualize the characteristics of IRIS data in real time, achieve the expected results, and point the way for other data visualization models.
Inferring structure and parameters of dynamic systems using particle swarm optimization

Inferring models of dynamic systems from their time series data is a challenging task for optimization algorithms due to its potentially expensive computational cost and underlying large search space. In this study we aim to infer both the structure and parameters of a dynamic system model simultaneously by Particle Swarm Optimization (PSO), enhanced by effective stratified sampling strategies. More specifically we apply Latin Hyper Cube Sampling (LHS) with PSO. This leads to two novel swarm-inspired algorithms, LHS-PSO which can be used efficiently to learn the structure and parameters of simple and complex dynamic system models. We used a complex biological cancer model called Kinetochores, for assessing the performance of PSO and LHS-PSO. The experimental results demonstrate that LHS-PSO can find promising solutions with corresponding structure and parameters, and it outperforms PSO during our experiments.
Improved Moving Target Detection Based on Multi-Model Mean Model

Aiming at the problem of low detection accuracy of multi-mode mean model in complex scenarios, an improved detection method of moving target based on multi-mode mean model is proposed.Firstly, the background model is constructed using the multi-mode mean value model. According to different scene information, different thresholds are set and adjusted adaptively. The foreground image obtained by background difference method is detected by frame difference method, and the experiment is compared and analyzed. The detection rate and the error rate are reduced, and the detection accuracy is improved. Finally, the simulation results of three-segment video verify the effectiveness of the proposed method.
Art as a Living Interface

Serving a concise characterization of interactive systems in the context of aesthetics-motivated interaction, we suggest a chart containing relevant keywords. We consider the nature of responsive systems providing predictable reactive responses vs. truly interactive systems offering unpredictable but coherent life-like behavior, underpinned by the notion of a living interface. A wider context is developed to appreciate various styles of interaction in contemporary art. Two case studies describe the implementation of the author’s ideas. Initially, the pioneering work of Pask and Ihnatowicz is disclosed as exemplary and utterly significant for today’s practice.
Conclusions, reporting, and testimony

The end goal of any forensic document examination is to report the findings and explain what they mean in terms of the matter at hand. This chapter discusses issues pertaining to conclusions and reporting, exploring different terminology, philosophies, and approaches to these issues. Various options for conclusions are discussed along with reporting writing and testimony through deposition or in a trial setting.
Micro Touch Board Specially Designed for SliT that Is the Japanese Character Input Method for Smartwatches

We developed a touch board to solve the problem of smartwatches that screen is hidden by the finger during character input. It is used attached to the watchband under the screen. The board is optimized for the character input method named SliT (Slide-in and Tap method). Advantage of SliT is that the input speed of the novice is fast and the screen occupancy rate is low. Specifically, the speed is 28.7 [CPM (Characters Per Minute)] and the rate is 26.4%. In SliT, Japanese hiragana characters are input by combining 12 kinds of strokes and 8 tapping points. The board is designed to detect only those strokes and the taps. Eight touch sensors are arranged so as to surround the circular area placed at the center of the touch board. Between the sensors, numbers from 1 to 8 are allocated clockwise. Only when two adjacent sensors are touched at the same time, the number assigned to their boundary is entered. In stroke input, a fingertip is slid until passing through three boundaries, and three continuous numbers are entered. However, in rare cases, the last number may be lost. So, the stroke is judged from the first two numbers. The stroke recognition rate by this method is 98.7%. In tap input, users touch only one boundary. However, in a very few cases, an unnecessary number is added. In this case, the tap is judged from the first number. The tap recognition rate by this method is 99.4%.
Large Scale Dynamics Visualization On GPU

Simulating and visualizing dynamics of systems consisting of large amount of particles on GPU can be generally difficult due to limitation on data size and programmability. We propose a scheme for developing such algorithms when there exists certain kind of independence and similarity between subsystems. We have instantiated the scheme into algorithms and have integrated them into a real-world game engine. Empirical results show that these algorithms incur little overhead with tens of thousands of particles.
Proposal for Pervasive Elderly Care

This chapter reports how interaction between family members and caregivers as perceived by family members could be improved via context-aware, imperceptible internet of things (IoT)-based solutions. The qualitative study focused on investigating experiences of the family members and the communication between caretakers in sheltered accommodation. Interviews including both open and closed questions revealed that there is high need for improving the communication, adding to the sparse earlier knowledge. The study revealed that the family members were willing to adopt an application to improve the communication that currently was experienced as too limited and vague. The results provide a fruitful base for further actions to improve communication between family members and professional caretakers.
Light-Weight DCNN for Face Tracking

Face tracking methods are increasingly critical for many expression mapping analysis applications, along its research track, deep convolutional neural network (DCNN-based) search techniques have attracted broad interests due to their high efficiency in 3D feature points. In this paper, we focus on the problem of 3D feature point’s extraction and expression mapping using a light-weight deep convolutional neural network (LW-DCNN) search and data conversion model, respectively. Specifically, we proposed novel light-weight deep convolutional neural network for 3D feature point’s extraction to solve the great initial shape errors in regression cascaded framework and the slow processing speed in traditional CNN. Furthermore, an effective data conversion model is proposed to generate the deformation coefficient to realize the expression mapping. Extensive experiments on several benchmark image databases validate the superiority of the proposed approaches.
Random Sampling on an High-Dimensional Sphere for Solving SVP

In this paper, we present a random sampling algorithm to solve the shortest vector problem (SVP). The idea of our algorithm comes from the Gaussian hypothesis, which is one of the most important and commonly used hypotheses in solving SVP. The main idea of our method is to generate the points which approximately satisfy Gaussian hypothesis of a given lattice. To find the shortest vector, we set some rules to make the result of this sampling algorithm get shorter progressively. We evaluate our algorithm by statistical and geometrical tools and give a basic analysis of the efficiency of algorithm. According to our analysis, parameters can be optimized to achieve high efficiency. Practical experiments have been conducted to validate our result where we solve SVP instances with dimension from 80 to 100.
Assessing the Status of Technical Documents as Textual Materials for Translation Training in Terms of Technical Terms

In this paper, we examine how methods for evaluating corpora in terms of technical terms can be used for characterising technical documents used as textual materials in translation training in a translation education setup. Technical documents are one of the standard types of textual materials used in translation training courses, and choosing suitable materials for learners is an important issue. In technical documents, technical terms play an essential role. Assessing how terms are used in these documents, therefore, would help translation teachers to choose relevant documents as training materials. As corpus-characterisation methods, we used self-referring measurement of the occurrence of terminology and measurement of the characteristic semantic scale of terms. To examine the practical applicability of these methods to assessing technical documents, we prepared a total of 12 short English texts from the six domains of law, medicine, politics, physics, technology and philosophy (two texts were chosen from each domain), whose lengths ranged from 300 to 1,150 words. We manually extracted terms from each text, and using those terms, we evaluated the nature and status of the textual materials. The analysis shows that even for short texts, the corpus-characterisation methods we provide useful insights into assessing textual materials.
Digital evidence and computer forensics

The National Institute of Justice (NIJ) defines digital evidence as information and data of value to an investigation that is stored on, received, or sent by a digital-related device or attachment. This evidence can be collected when digital-related devices or attachments are seized and secured for examination. The Scientific Working Group on Digital Evidence and the Scientific Working Group on Imaging Technology (SWGDE/SWGIT) indicate Digital Multimedia Evidence (DME) is any information of probative value that is either stored or transmitted in a digital form including, but not limited to; film, tape, magnetic and optical media, and/or the information contained therein. Computer Forensics is a fast-growing discipline and practice that blends many areas of expertise together. It includes investigation of cyber crimes, terrorism, child pornography, fraud, e-scam, network intrusion, drug/human trafficking, and traditional crimes. With the increase in e-mail phishing, scams, and fraud attempts, forensic investigators need to understand how to review and analyze the unique content of e-mail messages.
Unsupervised Image Feature Extraction Based on Scattering Transform and Self-supervised Learning with Highly Training Efficiency

Abstract Convolutional neural networks (CNN) can effectively extract high-level semantic features of images, however, to learn these features, a large number of labelled data is required. In order to extract image features without labelled data, this paper proposes an unsupervised image feature extraction method based on self-supervised learning. To learn image features, we train the neural network to identify the two-dimensional rotation applied to the image. The first few layers of the convolution network are replaced with a scattering network to speed up training process, and get good image features in the last few layers of the convolutional network. We input the extracted features into convolutional network for supervised learning, and take recognition accuracy as a criterion of feature validity. The experimental result show that the recognition accuracy gets 84% on CIFAR10, reaching the mainstream results of unsupervised method; and 62.17% on CIFAR100, which is very close to the rate of supervised learning. This method can be applied to the applications that do not have massive labelled training data and have limited computing resources.
Enhancing Cultural Engagement: A Heuristic Evaluation of World Cultural Heritage Websites

Tourists usually visit cultural heritage websites before they travel to the actual sites. The design of the websites thus influences how an individual expect from the historical sites. This study aimed to investigate current cultural heritage websites and to identify the missing features/information that could potentially impact viewers’ cultural engagement. A heuristic evaluation was conducted with eight subject matter experts (each having more than two years of cultural product/web design experiences) on ten world cultural heritage websites (e.g., the official ones for Tower of London, Taj Mahal, and Statue of Liberty, etc.). For the purpose of the evaluation, the goals of cultural web applications (proposed by the Minerva Working Group 5) were provided to the experts. Ratings and design recommendations were collected and analyzed. Results showed numerous misalignments with the goals that made the websites unable to effectively deliver cultural values to viewers. This paper provides insight and contributes to cultural heritage web design.
Design and Research of New English Listening and Speaking Teaching Mode under the Background of Mobile Internet

In The rapid development of mobile technology has provided a new opportunity for the reform of college English listening and speaking teaching mode. This paper will focus on the big data impact brought by the rapid development of mobile internet and the spread of information explosion. Through the feasibility analysis of smartphone-based mobile learning in supporting English teaching, a multi-dimensional English listening and speaking teaching mode under the background of mobile internet is proposed, which provides a realistic basis for the design of mobile learning teaching mode with smartphones as terminals. This research is supported by constructivist learning theory, which solves the problem of lack of contextual interaction in traditional English teaching and limitations of learning time and place. This can lay a solid foundation for improving teaching quality and personnel training efficiency.
The 4-Variance Linear Complexity Distribution with 2n-Periodical Binary Sequences

In this paper, the method of calculating the k-variance linear complexity distribution with 2n-periodical sequences by the Games-Chan algorithm and sieve approach is affirmed for its generality. The main idea of this method is to decompose a binary sequence into some subsequences of critical requirements, hence the issue to find k-variance linear complexity distribution with 2n-periodical sequences becomes a combinatorial problem of these binary subsequences. As a result, we compute the whole calculating formulas on the k-variance linear complexity with 2n-periodical sequences of linear complexity less than 2n for k = 4, 5. With combination of results in the whole calculating formulas on the 3-variance linear complexity with 2n-periodical binary sequences of linear complexity 2n, we completely solve the problem of the calculating function distributions of 4-variance linear complexity with 2n-periodical sequences elegantly, which significantly improves the results in the relating references.
Decisional DNA based intelligent knowledge model for flexible manufacturing system

Modeling an effective mechanism for design and control strategies for the implementation of a flexible manufacturing system (FMS) has been a challenge. Consequently, to overcome this issue various techniques have applied in the past but most of these models are effective only for some specific situ ation or an element of FMS. In this study, the knowledge representation technique of Decisional DNA (DDNA) is applied to FMS to develop a generic model to achieve effective scheduling and manufacturing flexibility. Decisional DNA based Virtual Engineering Objects (VEO) are used as communicating media between machines, equipment and works pieces. The concept of Virtual Engineering Process (VEP) is applied for modeling routing flexibility. VEOs combined with VEPs form FMS-DDNA model, which facilitates in enhancing the performance of FMS, by inducing intelligence based on its own previous experience thus making it practical and smart.
Mining individualized context-dependent behavioral rules from smartphone data

This PhD research formulates the problem of mining contextual behavioral rules for individual users utilizing their smartphone data. In particular, we propose a dynamic time-series segmentation technique for behavioral data clustering, a machine learning rule-based classification technique for cont ext-aware usage behavior modeling and prediction, and a recency-based rule learning approach for dynamic updating and management of the discovered behavioral rules, to achieve our goal.
Innovations Using Blockchain–Part 1

& ANY NEW TECHNOLOGY experiences the well- known "hype-curve."We have seen this in cloud computing, Internet of Things (IoT), the transformation to mobile apps, wireless, smartphones, etc.New technologies, if successful, often become quickly ubiquitous, and oftentimes, most users and consumers know little about them, and new technologies often morph into commodities that are less and less discussed in the literature.Today, we are seeing two new technologies emerge that may go down this path to commodization: 5G and blockchain.This issue of IT Professional focuses on blockchain.Blockchain is a distributed ledger technology that "supposedly" enforces assumptions that are related to trust in transactions and data.But recognize that trust is always "in the eye of the beholder."A few common trust assumptions for blockchain include