Reviewerly/exHarmony huggingface dataset:
Applying Decision Graphs in the Context of Automated Driving

Techniques to enable automated driving currently receive a lot of attention in computer science research. Car automation requires realizing several cognitive functions by computers. One important functionality is environment perception. This consists of several sub-tasks which are complex and thus computation intensive when implemented. We propose the use of decision graphs to speed up the execution of a consistency check. This check is applied to the output of a of neural net which classifies regions in an environment image. The check consists in evaluating a set of probabilistic rules. The paper describes how the miAamics approach of pre-computing results of rule evaluations with decision graphs may be profitably used in this application.
Anomalous quantum correlations in the motion of a trapped ion

In a previous paper (Krumm and Vogel 2018 Phys. Rev. A 97, 043806) we presented a method to solve the nonlinear Jaynes-Cummings dynamics, describing the quantized motion of a trapped ion exactly, including detuning. Here we investigate this model with respect to nonclassical effects, such as squeezing and sub-Poisson statistics. We show that for the versatile model under study there exist quantum phenomena beyond squeezing and sub-Poisson statistics, such as anomalous quantum correlations of two non-commuting observables. In particular, it is shown that for the excitation of the zeroth sideband neither squeezing nor sub-Poisson statistics do occur, but anomalous correlations can be verified. Furthermore, it is shown how these anomalous correlation functions can be derived from measured data.
Enabling Accountable Collaboration in Distributed, Autonomous Systems by Intelligent Agents

High degree of distribution is one of the leading features in present computer systems. Cloud Computing, Internet of Things and Blockchains are nowadays very hot research topics and we are going to use these architectures in many critical domains, like e-health, conservation of documents and acts in accordance with the law, economic transactions and contracts etc. When dealing with such heterogeneous systems, it is really hard to understand if a distributed collaboration among agents fulfils requirements, rules and current laws. In addition, if something goes wrong during collaboration, assigning accountability is even more complicated. This work aims at introducing a novel methodology and a formal framework able to attribute liability of failures or incorrect design and implementation both to humans and software agents in autonomous, distributed systems.
Shaping the Reaction

When modelling for the social we need to consider more than one medium. Little is known as to how platform community characteristics shape the discussion and how communicators could best engage each community, taking into consideration these characteristics. We consider comments on TED videos featuring roboticists, shared at TED.com and YouTube. We find evidence of different social norms and importantly, approaches to comment writing. The emotional tone is more positive at TED; however, there is little emotional escalation in either platform. The study highlights the importance of considering the community characteristics of a medium, when communicating with the public in a case study of emerging technologies.
Using Semantic Modelling to Improve the Processing Efficiency of Big Data in the Internet of Things Domain

Introduction. The development of the Internet of Things (IoT), equipped with various electronic sensors and controllers that distantly operate with these things is an important step of a new technical revolution. In this article we look at the features of Big Data generated by the Internet of Things (IoT) technology, and also present the methodology for processing this Big Data with use of semantic modeling (ontologies) at all stages of the Big Data life cycle. Semantic modeling allows to eliminate such contradictions in these technologies as the heterogeneity of devices and things that causes the heterogeneity of the data types produced by them. Machine learning is used as an instrument for Big Data of analyzes: it provides logical inference of the rules that can be applied to processing of information generated by Smart Home system.The purpose of the article is to use deep machine learning, based on convolutional neural networks because this model of machine learning corresponds to processing of unstructured and complex nature of the IoT domain.Results.Proposed approach increases the efficiency of IoT Big Data processing and differs from traditional processing systems by using NoSQL database, distributed architectures and semantic modeling. 27
Towards a Model for Implicit Evaluation in the Wild on a Large Scale

Evaluation is an essential activity in HCI. Once a product is released, it may be of interest to continue its evaluation in an implicit and in the wild fashion on a large scale. In addition to data logs which can offer a view of the usage of the system, attitudinal data (e.g. satisfaction, emotional reaction) also contribute to understand the user's complete interaction. Compiling these data is a challenge when performing it implicitly and in the wild. This work proposes an approach to evaluate interactive systems with a large number of users (in the large), in real conditions (in the wild) and implicitly - without the users being aware that they are participating in the evaluation of the system.
Ultrasound Speckle noise Reduction Based on Motion Compounding Using Optimized Adaptive Rood Pattern Search

Ultrasound (US) image quality is degraded due to many artifacts, speckle noise is the major one due to the coherent nature of sound wave. Many spatial and compounding image enhancement techniques are proposed. professional sonographers carefully use the image enhancement due to the loses of diagnostic details. Compound enhancement is proposed to enhance the image quality keeping enough texture details for diagnosis [1]. Many compounding methods are proposed frequency compounding, spatial compounding and motion compounding also.This paper uses the motion compounding to despeckle the ultrasound images. The speckle pattern will be decorrelated due to slightly moving the US probe or the organ being imaged. Direct image compounding of the US images will deform the tissue and blur the image. therefore, motion estimation and compensation must be take place before compounding.Optimized adaptive Rood Pattern search (O-ARPS) is proposed based on the original ARPS [2], ARPS is commonly used in video compression. O-ARPS Optimized for the ultrasound images and multicore processing platform.Experimental in vivo frames show that the speckle noise is significantly reduced without degrade the organs boundary, also better image perception achieved due to keeping a few texture details with Timing Performance close to be real time.
Large-Scale Machine Learning on Debugging Machine Learning Systems

A computation indicated applying Tensor Movement may be accomplished with minimum modify on a wide selection of heterogeneous methods, including cellular devices such as for example devices and pills around large-scale spread methods of a huge selection of products and 1000s of computational units such as for example GPU cards. Even with arrangement, it's frequent to find out restrictions of the design or improvements in the goal notion that necessitate improvements to working out information and parameters. But, by nowadays, there's number frequent knowledge by what these iterations contain, or what debugging resources are required to help the investigative process. As more information becomes accessible, more formidable issues may be tackled. Consequently, device understanding is commonly utilized in pc technology and different fields. But, establishing effective device understanding programs involves an amazing level of "dark art" that's difficult to find in textbooks. This short article summarizes a dozen critical classes that device understanding scientists and practitioners have learned. These calculations are useful for numerous applications like information mining, picture running, predictive analytics, etc. to call a few. The key benefit of applying device understanding is that, when an algorithm finds what direction to go with information, it may do their function automatically.
Preparation of the entanglement states by realizing the Hadamard and CNOT quantum gates

The preparation of the maximum entangled states, i.e., the Bell states is studied in this paper. The Cartan decomposition and the Lyapunov control methods are used to design the control laws for some operations in realizing the Hadamard and CNOT gates to prepare the Bell states. The numerical simulation experiments show that for some proper control parameters in the designed control laws, the Hadamard and CNOT gates are realized and the stable Bell states are prepared in a short time.
Development and Investigation of Data Stream Classifier Using Lagrangian Interpolation Method

Analyzing data streams floated over digitized organizations have become a notable challenge as it is often being obstructed by the incidence of concept drift and concept evolution. Conventional classification algorithms are neither accurate nor reliable on classifying dynamic data streams. For instance, Support Vector Machine (SVM) is a prominent classifier for performing supervised classification on static data; however, it becomes flabby when it is directly applied for data stream classification due to its intrinsic nature. In a bid to avoid this issue, this research work expounds a novel SVM-based Parallel Genetic Ensemble Model, which makes a series of optimization on SVM by synergizing it with Lagrangian interpolation method, fuzzy logic, and parallel genetic algorithm. The proposed model effectively classifies real-time data streams amid of concept drift and concept evolution. The exhaustive experiments probed on real-time data streams assert the efficacy of the proposed system in terms of various metrics.
Decentralizing Decision Making Process of Simultaneous Bilateral Negotiations of Large Multiagent Systems in e-Commerce Applications Using Holonic Structures

Automated strategic negotiation is a key technology which facilitates to reach agreements between self-interested agents. Existing mechanisms mainly practice principles of auction mechanisms in which a single seller handles many buyers. The key advantage of that approach is the facility for the guarantee to reach the optimal deal because of global knowledge the seller has regarding the demand at hand. However, the main limitation of such an approach is the difficulty of exercising different negotiation strategies with different opponents. Although concurrent multiagent negotiation approaches resolve many of these limitations, the existing frameworks and protocols are too simplistic to handle dynamicity of larger multi-agent negotiation systems situated in very dynamic environments. The dynamicity of a large number of simultaneous bilateral negotiations can be appropriately coordinated through a topological structure that decentralises the autonomy of decision-making process under centralised supervision with the help of appropriate scheduling and planning mechanisms. This paper presents an application of a framework which coordinates the dynamicity of simultaneous bi-lateral negotiations by using Holonic structures. The framework possesses a top-down hierarchical structure with a decentralised control which takes the bottom-up perspective for effective decision making. The framework was simulated in an e-commerce application and showed how practically our approach could be utilised to model real-world scenarios to handle simultaneous bi-lateral negotiations of large multiagent systems.
Explorable Instructions

The Maker and DIY craft movements promote values of democratization, creativity, and customization [12]. While the associated informal learning structures - makerspaces, workshops, and DIY tutorials - support fabrication, they do not necessarily scaffold design learning [27]. Here, I present my work on design learning in formal and informal contexts and propose the re-design of DIY instructions to better support and understand informal design learning.
Bidirectional estimation between context and motion in motion sequence in which context changes

We describe a motion recognition method for reducing recognition errors. The method has a two-layer structure: a lower layer for motion recognition that is affected by the distribution of topics used as context information in the upper layer and an upper layer for the topic distribution that is affected by motion recognition in the bottom layer. We introduce an algorithm for the bottom layer to integrate the motion likelihood calculated using a hidden Markov model and motion appearance probabilities obtained by a topic model. We also use a set of particles to estimate and update contexts on the basis of the result of motion recognition in the bottom layer. The set of particles presents a probabilistic distribution of motion topics, and motion recognition and particle update procedures are performed on each particle. We experimented with two types of sequential motion: a sequence of 33 daily motions and complex motion sequences assuming actual observation. The results showed that the proposed method reduced recognition errors and tracked latent topics better than conventional methods did.
Type-2 Fuzzy Logic Augmentation of the Imperialist Competitive Algorithm with Dynamic Parameter Adaptation

In this paper we propose the utilization of type-2 fuzzy systems for the dynamic adjustment of parameters in the imperialist competitive algorithm (ICA), a type-1 fuzzy system was used as a basis, with decades as the input variable and the beta parameter as the output variable, then it was extended to interval type-2 fuzzy systems, and three variants with triangular, Gaussian and trapezoidal membership functions were performed. The imperialist competitive algorithm is based on the concept of imperialism, where the strongest countries absorb the weakest and make then their colonies. To measure the performance of the proposed method 10 mathematical functions with different number of decades are used and finally, a comparison was made between our variants and the results obtained with the type-1 fuzzy system to observe their behavior in the face of optimization problems.
Human Activity and Context Recognition using Lifted Marginal Filtering

Computational causal behavior models can be used for joint human activity recognition and reasoning about the context of an activity, like the location of used objects, which is relevant for assistive systems. Such models are computationally expensive due to the large number of different states that need to be considered. However, the distribution of these states is often highly symmetrical. Lifted Marginal Filtering (LiMa) is an inference algorithm that maintains a suitably factorized state distribution, such that symmetrical factors can be represented compactly. In this paper, we show for the first time the application of LiMa to a complex real-world activity recognition setting based on real IMU data. This is achieved by introducing an operation that prevents the distribution representation to grow indefinitely, by projecting the distribution back to an exchangeable distribution. We show that LiMa needs fewer states to represent the exact filtering distribution, and achieves a higher activity recognition accuracy when only limited resources are available to represent the state distribution.
Towards Adaptive Worked-Out Examples in an Intelligent Tutoring System

Worked-out examples (WOEs) have been shown to be effective for learning, but they need to be adapted to student characteristics. We experimented with three versions of our Intelligent Tutoring System for Computer Science, one that does not include WOEs, and two that differ as concerns WOE length and content. We found that shorter WOEs are more effective for advanced students, whereas novice students learned the same, no matter the presence or length of the WOE.
An Architecture with Mixed Complex Event Processing Technology For CPS

Abstract Cyber-Physical System (CPS) is an emerging intelligent systems, which is considered as a combination of computers and physical world. Events are usually as a natural way for interactions and communications, which occur in the physical world and can be responded in the cyber world. Additionally, using complex event processing (CEP) technology, some complex events can be processed that are better suited for complicated and intelligent CPS. At first, a component-model framework is proposed for modeling CPS architecture based on event-driven pattern operational semantics, which provides a tool to understand the transition based on event-driven pattern in CPS. In order to improve the immense workload on centralized CEP, a mixed master-slaves CEP is proposed. Meanwhile, an architecture of CPS with master-slaves CEP is constructed and its execution mechanism is introduced in detail.
Innovation of College English Teaching Model Based on Mobile Micro-learning Technology

With the continuous development of information technology, the combination of education and information technology has also brought about great changes in College English teaching. The rapid development of mobile communication technology and mobile terminal technology has promoted the wide application of mobile learning in College English teaching. The classroom teaching mode based on mobile Micro-learning can effectively use mobile technology to fully improve the efficiency of classroom teaching, help to build a student-centered personalized teaching mode full of vitality, make full use of fragmented time, and open up a new path for college English teaching.
Acceleration Technique in Particle-based Collision Detection Using Cone Area Based Dynamic Collision Regions

In this paper, we propose a framework that can perform acceleration collision detection efficiently by using a cone based collision area in a particle-based system which requires collision detection with many objects.Three conditions determine particle and cone-based collision regions: 1) If there is a cone position within the radius of the adjacent particle, 2) In the case where the position of the adjacent particle exists in the cone area, 3) When adjacent particles exist between two vectors forming a cone area.As a result, it is defined that when the above conditions are all satisfied, the particle and the region of a cone have collided.In this paper, we automatically update the area of the cone, which is the collision detection area, according to the particle movement.Determine the direction and length of the cone based on the position and velocity of the particle to calculate the dynamic change of the cone.Collision detection is performed quickly using only the particles in the finally calculated area.The acceleration method proposed in this paper is simple to implement because it is executed with a closed form equation instead of explicitly creating the tree data structure, and collision inspection performance is improved in all results 키워드: 충돌 검사, 동적인 충돌 영역, 입자 기반 충돌 감지
Memory Confidentiality and Integrity Protection Technology

In most existing computer systems, data transmission and preservation in the form of plaintext are vulnerable to various attacks. In this paper, we use Parralelized memory Confidentiality and Integrity Protection technology (PCIP) algorithm to ensure the confidentiality and integrity of memory data. On the basis of PCIP, we use PCIP Bonsai Merkle Tree (PCIP+BMT) to protect the counter values of off-chip to reduce system delay and overhead. PCIP is that uses counter mode encryption to encrypt data while adding redundant data for integrity checking. Finally, we use the SimpleScalar Tool to simulate the PCIP and PE-ICE algorithms. The results show that PCIP is encrypted more effectively than the PC-ICE. Compared with the Hash algorithm, it can reduce the system delay and reduce the internal memory overhead. The tree mechanism adopted in this paper reduces the impact on system performance.
Implement a Program with Contents of Measurement and Control for Elementary School Science Classes

In today’s day and age, we encounter a wide array of electronic devices such as personal computers, game machines, mobile phones, etc. in our daily life. Such devices are controlled through invisible sites. The measurement and control of these devices constitute such an essential technology for the modern society that they have to be formally studied at junior high school and beyond. To facilitate the smooth transition of students into the process of learning about such devices, education about them should preferably be introduced at the early stage of elementary school. However, that will make the elementary school’s curriculum too restrictive, as activities not described in the government course of studies cannot be implemented. In this study, therefore, we have implemented graduation work activities (teaching for measurement and control) in accordance with the government course of studies to teach science at elementary schools and have verified its effectiveness.
A new type of multilevel system for image sparse recovery

This work focuses on image sparse recovery problem. First, we construct a new kind of pseudo-directional multilevel system, which forms a tight frame in [Formula: see text]. Different from the widely used directional multilevel transforms curvelts and shearlets, whose subbands are neat and nonsensitive to the energy distribution of signals in Fourier domain, the proposed multilevel system is designed to have subbands with specific shape, the shape is oriented by the energy distribution. Thus, we can obtain more sparse structure of signals and low computation for the multilevel transform. Moreover, to detect directional singularities of signals effectively, a local directional gradient operator is introduced to catch the signal variation along different directions, it can be seen as the generalized gradient. Then we proposed a simple but efficient method for image sparse recovery, the split Bregman algorithm is employed to solve the proposed convex model which guarantees the global optimal solution. Some contrast experiments suggest that the sparse recovery by the proposed method performs well in artifacts’ suppressing and details’ extraction.
Development of Manipulation Purpose Small Agent Vehicle System for Cutting Mission for AUV

In this paper, small agent ROVs based manipulation system is proposed for underwater cutting mission such as a rope, fish net. AUV has small agent which cutter or flipper for the manipulation. The small agent system was developed to carry out the manipulation tasks. Internal vision and external image sonar were used for the sensing of the sophisticate task. For effective manipulation performance, the optimal agent's shape and layout of thrusters were studied. Indoor tank tests were conducted to study the feasibility of the cutting mission.