Reviewerly/exHarmony huggingface dataset:
Cognitive Effects of Visualization Techniques for Inconsistency Metrics on Monitoring Data-Intensive Processes

Due to the increasing amounts of process data, monitoring company processes is currently evolving into a data-intensive analytics problem. In this report, we present the results of an experiment analyzing the cognitive effects of different visualization techniques for inconsistency metrics in the scope of monitoring data-intensive processes. Our results indicate that a ranked overview of metrics is associated with better understanding efficiency and less mental effort compared to other visualization techniques.
Channel-Grouping Based Patch Swap For Arbitrary Style Transfer

The basic principle of the patch-matching based style transfer is to substitute the patches of the content image feature maps by the closest patches from the style image feature maps. Since the finite features harvested from one single aesthetic style image are inadequate to represent the rich textures of the content natural image, existing techniques treat the full-channel style feature patches as simple signal tensors and create new style feature patches via signal-level fusion. In this paper, we propose a channel-grouping based patch swap technique to group the style feature maps into surface and texture channels, and the new features are created by the combination of these two groups, which can be regarded as a semantic-level fusion of the raw style features. Experimental results demonstrate that the proposed method outperforms the existing techniques in providing more style-consistent textures while keeping the content fidelity.
Teaming With Self-Integrating Systems

Expecting systems to be able to integrate themselves into an existing or nascent team assumes they can provide a useful function for the team, and changes what kinds of capabilities they must have, in addition to what the team must be able to do, beyond those that make them desired or potentially useful team members. This includes at least operational knowledge about their own capabilities, limitations, and their knowledge thereof. Completely new classes of environment-and self-modeling, self-assessment, and behavior negotiation processes are also needed, along with continual self-and boundarymonitoring processes to ensure that the integration assumptions remain valid. This paper takes one class of example application problem domains (search / surveillance), describes a design process for such a system, and what must be changed in the SISSY (Self-Improving Self-Integrating) system context.
Scalable Sketch-Based Sport Video Retrieval in the Cloud

Content-based video retrieval in general and in sport videos in particular has attracted an increasing interest in the past few years, due to the growing interest in sports analytics. Especially sketch-based queries, enabling spatial search in video collections, are increasingly being demanded by coaches and analysts in team sports as an essential tool for game analysis. Although there has been great progress in the last years in the field of sketch-based retrieval in sports, most approaches focus on functional aspects and only consider just a very limited number of games. The problem is to scale these systems to allow for interactive video retrieval on a large game collection, beyond single games. In this paper, we show how SportSense, our sketch-based video retrieval system, can be deployed and scaled-out in the Cloud, allowing managers and analysts to interactively search for scenes of their choice within a large collection of games. In our evaluations, we show how the system can scale to a collection of the size of an entire season with response times that enable real-time analysis.
Digital Currency Investment Strategy Framework Based on Ranking

Quantitative research of digital currency is important in the financial sector. Existing quantitative research mainly focuses on pairs trade, multifactor models, and investment portfolios. Investment portfolios refer to the allocation of funds to different types of financial products to minimise investment risk when expected returns can be obtained, or maximise returns on investment when investment risks are controllable. Herein, we propose a ranking-based digital currency investment strategy framework for investment portfolios in the digital currency market. The framework mainly involves selecting digital currency attributes, pre-processing historical data, exporting the ranking model of the investment portfolio strategy, and parameter optimisation.
A Kinect-Based Gesture Acquisition and Reproduction System for Humanoid Robots

The paper illustrates a system that endows an humanoid robot with the capability to mimic the motion of a human user in real time, serving as a basis for further gesture based human-robot interactions. The described approach uses the Microsoft Kinect as a low cost alternative to expensive motion capture devices.
Cognitive Architecture for Video Games

There has been an increasing interest in Frame-oriented reinforcement learning (FORL) in recent year. However, most of the works in the literature show little inspiration from human's perception action reward cycle (PARC) and causation.Inspired by human's vision system and learning strategy, we propose a novel architecture for FORL that understands the content of raw frames. The architecture achieves four objectives: 1. Extracting information from the environment by exploiting only unsupervised learning and reinforcement learning. 2. Understanding the content of a raw frame. 3. Exploiting a Folvea vision strategy which is analogous to human's vision system. 4. Establishing self-awareness and collecting new training data subset automatically to learn new objects without forgetting previous ones..The architecture is developed in the Super Mario Brothers video game.. At first, Mario is the only object recognized by the architecture. After automatic data subset collection and memory update, the architecture can recognize both Goomba and Mario and classify them using incremental training.We exemplify performance of each piece of the architecture with snippets obtained from the video game.
Motion Rectification Network for Unsupervised Learning of Monocular Depth and Camera Motion

Although unsupervised methods of monocular depth and camera motion estimation have made significant progress, most of them are based on the static scene assumption and may perform poorly in dynamic scenes. In this paper, we propose a novel framework for unsupervised learning of monocular depth and camera motion estimation, which is applicable to dynamic scenes. Firstly, the framework is trained to obtain initial inference results by assuming the scene is static, through minimizing a photometric consistency loss and a 3D transformation consistency loss. Then, the framework is fine-tuned by jointly learning with a motion rectification network (RecNet). Specifically, RecNet is designed to rectify the individual motion of moving objects and generate motion rectified images, enabling the framework to learn accurately in dynamic scenes. Extensive experiments have been done on the KITTI dataset. Results show that our method achieves state-of-the-art performance on both depth prediction and camera motion estimation tasks.
Class Design of the First Year Education that Introduced the Viewpoint of SDGs at College of Industrial Technology, Nihon University

With the adoption of the United Nations Sustainable Development Goals (SDGs) in 2015, now global sustainability benchmarks have been applied across diverse sectors and national contexts. Consideration of SDGs keywords is indispensable for the engineering education that plays an important role to achieve the sustainable society. Therefore, from the perspective of the SDGs, we designed the learning strategy for the first year students in accordance with the educational philosophy of our college, “Education for growing Engineers with business management skills”.
The Effect of Using Instant Messaging Communication on the Teaching and Learning Experience of Students

There has been recent interest in studying the use of instant messaging (IM) as a teaching tool for supervising student dissertations and tutoring undergraduate students as discussed by Yue (2014a, 2014b, 2015). The research studies on this area have indicated that students are very positive about the use of IM for communicating with their tutors or supervisors. This chapter aims to study the use of IM as a supplementary tool in tutoring undergraduate students. The use of IM was analysed, based on the teaching and learning feedback obtained from an end-of-course questionnaire survey. A correlation analysis was carried out to study the possible relationship between the use of IM and the teaching and learning experience of students. Other associated measures, such as the tutorial attendance rate and assessment marks, were also examined.
Efficient Return Address Verification Based on Dislocated Stack

Return-oriented programming (ROP) is a prevalent code reuse technique that hijacks a program's control flow by modifying its return addresses on the stack. Researchers have proposed some return address verification methods by using the message authentication code (MAC). But these approaches suffer from high performance overhead. In this article, we first propose Dislocated Stack, a new kind of stack layout in which a previous return address would be pushed onto the current stack frame and the current return address would be stored into a hardware buffer on function calls. Based on Dislocated Stack, we design two new verification approaches, Lazy Verification and Batch Verification. Lazy Verification does not verify a return address popped from stack until it is going to be used for return. Batch Verification verifies a couple of return addresses at one time. We implemented these two designs on RISC-V architecture and quantitatively analyzed their effect on QEMU. Our experiments show that Lazy Verification reduces over 99% verifications on function returns and incurs only 1.23% performance overhead; while in Batch Verification, the overhead is merely 0.78%. The result demonstrates that these two approaches are highly efficient for return address verification.
Soft multi-functional electronic skin for continuous eye motion monitoring

Eye movement detection is widely applied to safety driving areas for reducing the traffic accidents, which is related with the acquisition of different kinds of electrophysiological signals. Soft multi-functional electronic skin (SMFES) is created to collect skin temperature, sweating, and electrooculogram (EOG) signals simultaneously for drivers’ eye movement monitoring, which is conformal contact with the surface of skin for more stable and accurate biological data. The feature signals are extracted with different eye movements by SMFES which is helpful to recognize drivers’ eye movements and to predict the fatigue driving behaviors. Adaptive neuro fuzzy inference system (ANFIS) model is built to classify and recognize the typical eye movements for devoting the relationship between the eye movements (‘Up’, ‘Down’, ‘Left’, and ‘Right’) and the physiological signals. This work demonstrates an intelligent recognition algorithm embedded into SMFES seamlessly with existing driving environment for eye motion detection, expanding the applications of SMFES in safety driving and wearable electronics.
Learning Control for Autonomous Driving on Slippery Snowy Road Conditions

This paper presents an investigation of Gaussian Processes (GPs) in combination with model predictive control (MPC) for autonomous driving control on slippery snowy road conditions. A double lane change scenario with two different road friction coefficients is considered for learning the GP model. The model is then incorporated into the MPC algorithm development. The performance of the GP-MPC controller is evaluated and compared with conventional MPC controller. The validation is conducted based on a co-simulation platform that simulates high fidelity vehicle/tire dynamics and snowy traffic environment in different setting conditions, respectively. The results demonstrate that the GP-MPC controller can achieve better trajectory tracking performance and with less control input than the conventional MPC controller however with higher computation time.
n-Oscillator Neural Network based Efficient Cost Function for n-city Traveling Salesman Problem

Neural Networks have long been a mainstream technique to solve optimization problems. A classic example is the Travelling Salesman Problem (TSP) which NP-hard. Using a Hopfield-Tank representation, an n-city problem is mapped to a cost function of n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> interacting neural units. Stochastic gradient descent helps achieve the global minima. Due to the nature of the TSP problem, the cost function has to penalize invalid sub-routes (non-Hamiltonian cycles) and minimize the travel cost simultaneously. In addition, there is a starting point and travel direction associated `2n' degeneracy. Previously, a cellular neuronal approach was proposed where the neural units were replaced with oscillators. The phase relations determined the output solution. Multiphase clusters of these oscillators solved the degeneracy issue. This paper proposes an n-oscillator cost function for an n -city TSP. Since a group of single frequency oscillator phases are naturally ordered and circular in a system, the proposed method exploits the true potential of oscillator nodes. The sub-routes and degeneracy are eliminated by design in addition to massively increasing the scaling potential (n vs. n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). It was also found that the proposed n- mapping can converge to the optimum tour much faster (about 100 times for a 5-city problem) than for n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> mapping. Our approach projects hardware efficiency in terms of area footprint, computation time and energy. With coupled single device-based compact nanoscale oscillator systems becoming increasingly viable in hardware, efficient cost function mappings of hard problems using oscillator phases, as shown here, is critical to solving large graphical optimization problems.
Investigating the Control of Writing Skills

The issue of control of writing processes addresses for a specific area a general issue that is of increasing importance in education, namely, the control by the student of various mental activities and strategies that increase learning. Within education, this issue is often talked about in terms of study skills, awareness of comprehension failure, and learning how to learn. Much of the acquisition of writing skill requires not the learning of novel subskills but the assuming of control by the writer over already automatized language subskills. Given that control ability is essential for writing skill, one is still left with the problem of identifying whether a writer has it. Particular attention is paid to the structure of nonoptimal productions in analyzing the texts. To date, the method has been used to examine writing skills, primarily at the sentence level; however, the method should be readily extendable to examination of writing skills at discourse levels.
Prosodic addressee-detection

We analyze the addressee detection task for complexity-identical dialog for both human conversation and device-directed speech. Our recurrent neural model performs at least as good as humans, who have problems with this task, even native speakers, who profit from the relevant linguistic skills. We perform ablation experiments on the features used by our model and show that fundamental frequency variation is the single most relevant feature class. Therefore, we conclude that future systems can detect whether they are addressed based only on speech prosody which does not (or only to a very limited extent) reveal the content of conversations not intended for the system.
Transformation of Education: Major Trends

Aim. The presented study aims to identify the major trends and prospects for the development of digital technologies in education. Tasks. The authors comprehensively analyze the digital transformation of education, identify shortcomings in the modern education system and key technologies for the development of digital education. Methods. The authors use the systems approach, comparative and structural analysis, analytical and theoretical generalization to obtain the main results of the study, including works of Russian scientists on digital education development trends. Results. The major trends in the transformation of education in the context of digitalization are identified and determined, and prospects for the use of digital technologies in education are shown. Conclusions. The following trends in the transformation of education are considered particularly important: increasing the role of interactive forms of education, practice-oriented education, and reducing the role of the academic component in the educational process. The learning management system is classified according to a set of reasonable criteria. This study shows that rapid social changes call for the modernization of the education system and implementation of continuous education technologies in the context of digital transformation of education at all levels.
On l<sub>2</sub>-Sensitivity for Generalized Direct-Form II Structure of 2-D Separable-Denominator Filters

A new expression of evaluating l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -sensitivity for generalized direct-form II structure of two-dimensional (2-D) separable-denominator digital filters is derived and analyzed. Unlike the previous work, l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -sensitivity is analyzed for the filter structure instead of its state-space realization. Then the resulting l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -sensitivity measure is compared with that deduced in a recent study of generalized direct-form II state-space realization of 2-D separable in denominator digital filters. In a numerical example, the new l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -sensitivity measure is minimized with respect to free parameters subject to l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -scaling constraints by an exhaustive search over a set of discrete values of finite cardinality, and the results are compared with those obtained by existing techniques.
Self-attention-based Question Answering over Knowledge Graphs

This paper proposes a model for Simple Question Answering over Knowledge Graphs. Our model includes a Question Named Entity Recognition (NER) Model and an Attribute Classification Model. First, we utilize the BERT model, BiLSTM, and CNN in the Question NER Model. The BERT model is used to represents the question. The BilSTM and CNN are employed to extract question features. Second, we introduce the answer information and self-attention mechanism in the Attribute Classification Model. The self-attention mechanism is used to assign the weight of the attribute and the answer automatically. The experimental results show that our approach can obtain competitive results compared with state-of-the-art approaches.
An Image Segmentation Model Using a Level Set Method Based on Improved Signed Pressure Force Function SPF

Segmentation is a vital aspect of medical imaging. It aids in the visualization of medical data and diagnostics of various dieses. In this paper, we present a novel Level Set Method driven by New Signed Pressure Force function (SPF) for image segmentation. The advantages of this method are Firstly, the Function of Signed Pressure can efficiently stop the contours at weak or blurred edges, Second, exterior and interior boundaries can be detected no matter where the initial contour starts. We compare this method with Chan Vese (C-V) model and Geodesic Active Contours (GAC). The performance of each method can be evaluated either visually, or from similarity measurements between a reference and the results of the segmentation. We have applied each method for different medical images. Through simulated results, we have demonstrated that the best results are achieved by the our method.
Improving the Style Adaptation for Unsupervised Cross-Domain Person Re-identification

Most existing person re-identification (Re-ID) methods are based on supervised learning, in which a large amount of labeled data are required for training. However, it remains a challenge task for adapting a model trained in a labeled source domain to an unlabeled target domain, due to the domain gap. To alleviate this problem, we design an unsupervised person style transfer adaptation pipeline for the task of unsupervised domain adaptation (UDA) Re-ID. Following the pipeline, we first apply an image translator to generate style-transferred images. To preserve the ID-related information after translation, we introduce the intra-class similarity and inter-domain diversity, which are crucial properties for Re-ID. In this way, a Cross-domain Similarity Generative Adversarial Network (CSGAN) is proposed to bridge the domain gap. CSGAN is learned by jointly optimizing an image translator and a domain-invariant feature representation network (DIFRN), which constrains the CSGAN to maintain the intra-class similarity and inter-domain diversity during image-image translation. Comparison with current competitive methods demonstrates that the effectiveness of the proposed method under the setting of unsupervised domain adaptation.
Interactive Knowledge-Guided Learning

The manufacturing industry is faced with the challenge of how to produce parts with the targeted quality in the face of constantly changing environmental factors and raw materials using the least amount of resources. This challenge is currently met by a socio-technical system in which trained domain experts unilaterally adjust manifold process parameters to try to achieve the targeted quality (cf. Figure 1). Since machines produce data as well as products there is a natural interest to address this challenge using data-driven approaches [1]-[3]. In practice, however, data quantity is secondary to data quality since machine learning algorithms perform best on evenly distributed amounts of well-structured data, which in the case of supervised learning systems have to be labelled as well. Several techniques such as data augmentation and simulation exist to circumvent this problem. However, data augmentation reduces sample quality by introducing noise[4]-[6] and simulating manufacturing processes with their multitude of physical interactions to the required accuracy proves challenging [7]. As such, efficiency in regards to data usage is considered paramount in manufacturing scenarios. To achieve this, this work sets out to combine the knowledge of domain experts with learning systems. With this combination we can improve existing socio-technical systems and enhance the interactions between machine and operator introducing self-attention (cf. [8]) which furthers self-improvement and increases self-adaptiveness.