Reviewerly/exHarmony huggingface dataset:
Adaptive Remediation with Multi-modal Content

Remediation is an integral part of adaptive instructional systems that provide a supplement to lectures in case the delivered content proves too difficult for a user to fully grasp in a single class session. To extend the delivery of current remediation methods from single type of sources to combinations of different material types, we propose an adaptive remediation system with multi-modal remediation content. The system operates in four main phases: ingesting a library of multi-modal content files into bite-sized chunks, linking them based on topical and contextual relevance, then modeling users’ real-time knowledge state when they interact with the delivered course through the system and determining whether remediation is needed, and finally identifying a set of remediation segments addressing the current knowledge weakness with the relevance links. We conducted two studies to test our developed adaptive remediation system in an advanced engineering course taught at an undergraduate institution in the US and evaluated our system on productivity. Both studies show that our system is effective in increasing the productivity by at least 50%.
Analysis of the Characteristics of Customer Defection on a Hair Salon Considering Individual Differences

In recent years, as the number of hair salons increases, and the scale of the hair salon market has declined. Hence, competition for hair salon acquisition will be intensified. It is important for a hair salon to specify the cause of customer defection and plan to settle that. In this study, we analyzed the POS data with customer distinguishing information of a hair salon chain. Concretely, we performed logistic regression and hierarchical Bayes logit model to identify the cause of customer defection on the hair salon. After that, we categorized and considered the customers using hierarchical cluster analysis. Using these models, we extract the characteristics of customer defection on a hair salon and propose marketing measures to prevent defection.
Evolutionary and swarm-intelligence algorithms through monadic composition

Reproducible experimental work is a vital part of the scientific method. It is a concern that is often, however, overlooked in modern computational intelligence research. Scientific research within the areas of programming language theory and mathematics have made advances that are directly applicable to the research areas of evolutionary and swarm intelligence. Through the use of functional programming and the established abstractions that functional programming provides, it is possible to define the elements of evolutionary and swarm intelligence algorithms as compositional computations. These compositional blocks then compose together to allow the declarations of an algorithm, whilst considering the declaration as a "sub-program". These sub-programs may then be executed at a later time and provide the blueprints of the computation. Storing experimental results within a robust data-set file format, which is widely supported by analysis tools, provides additional flexibility and allows different analysis tools to access datasets in the same efficient manner. This paper presents an open-source software library for evolutionary and swarm-intelligence algorithms which allows the type-safe, compositional, monadic and functional declaration of algorithms while tracking and managing effects (e.g. usage of a random number generator) that directly influences the execution of an algorithm.
Image Stitching Based on Binocular Vision

Abstract In the field of photography, image stitching is used to generate panorama. In most cases, the position and intrinsic parameters of the camera can not be obtained, so the transform matrix can only be calculated using the information of the image. In industrial machine vision detection system, the information of the camera can be obtained by calibration. It is feasible to improve the speed of image stitching algorithm by using the information of camera calibration. An image stitching algorithm based on binocular vision was proposed. Compared with the traditional stitching algorithm based on image information only, it greatly improved the running speed of the algorithm. The effectiveness of the algorithm was verified by experiments.
Reasoning on objects and grasping using description logics

In the growing interest about robot ontologies, the role of description logics (DL) used to query the ontology is often neglected. After discussing the representation problems of using ontologies for robotics problems, we develop two case studies relevant for dexterous manipulation, grasping unknown objects and understanding objects as the sum of their parts. We develop knowledge bases for solving those problems, and examine the possible queries that could be answered. We observe that the creation of such relationships in ontologies can follow an embodied approach, letting the robot to create new relationships from experiments, so making knowledge acquisition an embodied and iterative process. From those two cases we conclude that the creation of robot ontologies able to improve reasoning in manipulation is still a challenge itself. However ontologies are expected to be a necessary tool to share knowledge between humans and robots, and their role in integrating available symbolic knowledge and experimental data is still to be exploited.
SmartThings Event Export using SmartApps

The field of Digital Forensics must continuously reinvent and develop methods and practices to contend with emerging technologies. Nowhere is this truer than with home automation technology. This paper demonstrates the ability to harness developer tools for individual smart home systems, specifically Samsung SmartThings, and to apply their use to forensically sound data acquisition.
Accessible Interface for Context Awareness in Mobile Devices for Users With Memory Impairment

Memory problems usually appear because of aging or may happen because of a brain injury. Such problems prevent the person from performing daily activities. In this paper, the authors propose a framework to develop a smartphone solution to detect and recognize the user context. In order to build the context detection framework, the authors compare three different machine learning techniques (C.4.5, random, and BFTree) in terms of context detection accuracy. Then, the authors use the classification technique with the highest accuracy in a mobile application to help users by detecting their context. The authors develop two interfaces based on the suggested accessibility features for users with memory impairment. Two scenarios are used to evaluate the user interface, and the results prove the applicability and the usability of the proposed context detection framework.
Tethered Dual-Wheeled Robot for Exploration of Volcanic Fumaroles on Steep Slopes

The observation of volcanoes is critical in the estimation of volcanic activities and disaster prevention. Volcanic gas from fumaroles is an effective target for these observations. This is because the gas is highly mobile and quickly transmits underground information to the surface. However, as typical volcanic gas has high temperature and contains toxic chemical components, the manned observation of volcanic fumaroles is dangerous. Therefore, in this research, the authors propose an improved version of the dual-wheeled robot to obtain volcanic information from around fumaroles. This robot has a passive tether-guide module to improve its turning range on a slope, which enables the robot to change its traversal direction independent of the tether direction; thus, it has improved turning motion, diagonal traversal motion, and crossing motion on a steep slope. Indoor and field experiments proved the validity of these mechanisms. The lessons learned from these field experiments are also discussed.
Mobile Robot Trajectory Analysis with the Help of Vision System

We present a vision-based motion analysis method for single and multiple mobile robots which allows quantifying the robot’s behaviour. The method defines how often and for how much each of the robots turn and move straight. The motion analysis relies on the robot trajectories acquired online or offline by an external camera and the algorithm is based on iteratively performed a linear regression to detect straight and curved paths for each robot. The method is experimentally validated with the indoor mobile robotic system. Potential applications include remote robot inspection, rescue robotics and multi-robotic system coordination.
Depth Information Estimation-Based DIBR 3D Image Hashing Using SIFT Feature Points

Image hashing has been widely used for traditional 2D image authentication, content-based identification, and retrieval. Being different from the traditional 2D image system, virtual image pair is generated from the center image according to the corresponding depth image in the DIBR process. In one of the communication models for DIBR 3D image system, the content consumer side only receives the virtual images without performing DIBR operation. By this way, only a variety of copies for virtual image pairs could be distributed. This paper designs a novel DIBR 3D image hashing scheme based on depth information estimation using local feature points, by detecting the matched feature points in virtual image pair and dividing these feature points into different groups according to the corresponding depth information estimated to generate the hash vector. As the experiments shown, the proposed DIBR 3D image hashing is robust against most of the content-preserving operations.
Fast. Efficient Performance Predictions for Big Data Applications

In recent years we observe a rapid growth in the deployment of machine learning workloads on big data analytics frameworks like Apache Spark and Apache Flink. These workloads are typically represented as graphs, run on shared infrastructures and often have much more demanding resource requirements than those traditionally found in typical enterprise settings. However, predicting the execution times of the workloads is important as they often run on shared public or private infrastructures and, thus, their execution is greatly affected by the resource sharing, the hardware infrastructure utilized as well as the choice of the configuration parameters provided by the frameworks. In this work, we propose a fast and efficient performance prediction system to address the challenge of predicting the execution times of big data workloads, exploiting the fact that workloads are represented as processing graphs and often share similar structures and parameters. Thus, we can use the performance models we have built for already deployed workloads, to estimate the end-to-end execution time for a new workload. Previous works assume that a large number of profiling runs can be utilized for building the prediction models. However, this assumption is not always valid and more elaborate mechanisms need to be applied. Our detailed experimental evaluation on our local Spark cluster illustrates that our approach can predict accurately the execution time of a wide range of Spark workloads.
Research on Software Testing Technical Ability Training based on e-learning

Software testing is an important part of Software Quality Assurance. Improving the testing technical ability of software testers is the goal of software testing teaching. Based on e-learning and CDIO education model, this paper proposes a test technical ability training mode integrating courseware resources, exercise environment and training activities, which changes the status of traditional software testing course teaching which emphasizes theory over practice. Teaching practice has proved that the new training method can improve students' interest in learning and effectively promote the training of students' ability of software testing.
Research on Mobile Learning System of Colleges and Universities

In recent years, with the rapid development of network communication technology and the widespread use of mobile devices. Mobile learning is getting more and more attention and research by scholars, becoming a new type of learning method. Students group is the highest popularization rate of smart phones, while college students also have strong ability to learn and accept new things quickly and so on. Therefore, under the new situation, colleges and universities should further study mobile learning and construct a mobile learning system that meets the needs of young students, which can provide a useful supplement for the current student education. The article introduces the research background and the status of mobile learning research. Then the article analyzes the characteristics of mobile learning and proposes a proposal for building a mobile learning system in colleges.
A Design of Augmented-Reality Smart Window Using Directive Information Fusion Technology for Exhibitions

In this paper, we present a design of augmented-reality (AR) smart window with directive information fusion technology. Typically, a showcase used in exhibitions is a glass cabinet used to display exhibits for viewing. However, if visitors want to get more information about the exhibit or identify a specific object in the showcase, they need to check label cards attached beside the showcase, which seriously distracts their attention to the exhibit at the moment. In order to create a more intuitive user's experience for museum visitors, an AR smart window system has been established. By the use of flexible transparent AMOLED display, the window is not only a case to display exhibits, but also a display to show their description. What's more, the AR smart window is integrated with directive information fusion technology which is based on eye tracking and object recognition technique, enabling the information to always show on the user's eyesight to the exhibit no matter how the user moves. This novel system not only makes exhibitions more intelligent and user-friendly but also brings a whole new interactive experience for visitors in exhibitions.
Automatic primary screening and real-time risk assessment method using deep learning to prevent the patients from falling in the hospital

Fall is one of the main factors causing the extension of the patient’s hospital stay. In some cases, fall leads the elderly patients to bone fracture, bedridden, and care state. Also in other cases, fall leads to fear, withdraw, and bedridden. However, the medical staff can’t keep eye on all the patients at all the time. Moreover, patient's condition and their environment change from moment to moment. Therefore, the primary screening is need to find the high risk patients and to focus attention on them. Furthermore, real-time risk assessment is need to prevent the patients from falling. In this paper, we propose and develop an automatic real-time fall risk assessment using deep learning as a primary screening of the patients in the hospital. By using our method, we construct a part of the nursing system to reduce the fall incidents and accidents of the patients, and the caregiving load of the medical staff. At the reception, proposed method recognizes patient’s appearance and estimates risks by classifying the fall risk levels according to a hospital’s clinical safety standard, and it stores those safety related information in the electric medical records of patients. While the patients stay in the hospital, medical staffs see the potential risks in the condition of the patients and their environment online through the wearable devices such as smart glasses, mobile terminals, and assistive mobile robots. We prepared a datasets including 16,000 images which collected from public datasets such as MIT places, educational video, actual incident location, and annotated them by ourselves for the clinical safety. The result show a fundamental experiment comparing with manual method by medical staffs and our proposed method surpasses real-time performance and coverage. These automatic fall risk assessment method using deep learning is novel to in clinical safety field.
Trademark Image Retrieval Based on Faster R-CNN

Abstract Automatic retrieval of digital trademark images is significant for improving the efficiency of trademark examination and management. In this paper, we proposed a method based on deep learning for trademark retrieval. Faster R-CNN was first applied to trademark retrieval. The global feature descriptor of the image is extracted by Faster R-CNN and the local feature of the image is extracted through the object proposal regions by the Region Proposal Networks (RPN). Retrieval strategies consist of initial ranking and spatial reranking. The experimental results show that our proposed method achieves remarkable performance in trademark image retrieval.
Principle of block chain technology and its application in electric power industry

Block chain is an important sub-concept of Bitcoin, which is essentially a comprehensive database without intermediary structure. Block chain technology, as a distributed data storage database technology, has broad application prospects in the power industry. Starting from the basic conceptual structure characteristics of block chain technology, this paper explains the principle and core technology of block chain technology, and explores the application of block chain technology in power transmission, power terminal services and energy fields.
A Novel Distributed Knowledge Reasoning Model

This paper proposes a novel model and device based on distributed knowledge reasoning, for predicting the environmental risk degree for an abnormal event (e.g., fire) in the Internet of Things (IoT) environment, which includes: (i) determining the predictive environmental information of the current node based on its historical environmental information and the predefined time-series prediction algorithm; (ii) determining the probability distribution function (PDF) of the environmental information obtained at each time corresponding to the historical environmental information; (iii) determining the deviated environment information based on the current environmental information and the probability distribution function mentioned above; and (iv) determining the environmental risk degree of the current node, based on the current environmental information, the predictive environmental information and the deviated environmental information. The wireless node of in the proposed model has the ability of perceiving and reasoning for the occurrence of an abnormal event. According to the current environmental information, the predictive environmental information and the deviated environmental information, the environmental risk degree of the current node could be determined jointly.
Operation Situation Assessment of Intelligent Distribution Station Based on Internet of Things

Abstract In order to solve the problem of online monitoring and early warning evaluation of intelligent distribution station, an operation situation assessment of intelligent distribution station based on internet of things is proposed. A multi-source data information matrix is established for the distribution station, and describes the temperature information, humidity information, water intrusion information, smoke information, switch telemetry information, and switch remote control information of the intelligent distribution station within a period of time before the current time. The distribution station multi-source information matrix is quantitatively evaluated through the distribution station multi-source evaluation matrix. Then, the distribution station multi-source evaluation matrix is quantitatively integrated for each column to generate a distribution station multi-source single evaluation matrix. Finally, based on the weighted average method, the distribution station multi-source single evaluation matrix is integrated to generate the distribution station operation situation evaluation index to describe the overall operation situation assessment of intelligent distribution station. The feasibility of the proposed method is verified by the example analysis, which can meet the actual needs of the field project.
A Novel Threshold Signature Scheme Based on Elliptic Curve with Designated Verifier

Aiming at the security threats and computational complexity of existing threshold signature schemes, this paper proposes a threshold signature scheme based on elliptic curve. The scheme render no less than t legitimate and honest members to form a valid signature, where the signature is combined with the public key of the designated receiver, thus only the designated receiver can verify and decrypt the information by using his or her private key. According to the analysis, the proposed scheme has the characteristics of unforgeability, confidentiality and integrity of messages. Moreover, the scheme has good robustness, consumes less computing resources, and lower communication bandwidth.
Flower Pollination Algorithm-Based FIR Filter Design for Image Denoising

This paper proposes flower pollination algorithm (FPA)-based two-dimensional FIR filter for the denoising of images. The efficacy of proposed FIR filter is tested on few standard images like Lena, Einstein, Cameraman, and Barbara and the noises include Gaussian, salt and pepper, and speckle. The results achieved by proposed filtering technique demonstrate a clear explanation of the performance improvement. Comparison with other available results in the literature indicates that the proposed method exhibits an average of 13% improvement in PSNR, 17% improvement in IQI, and 23% improvement in SSIM for Barbara image corrupted with Gaussian noise.
A Hybrid Approach of Wavelet Transform Using Lifting Scheme and Discrete Wavelet Transform Technique for Image Processing

Now a modern time many area such as company, medical, research and file require large number of image for general-purpose application to solve the complex problem. Image contain more information that require more storage space and transmission bandwidths, so the image compression is required to store only important information and reduce the different types of redundancy of image for storing and transmission in an efficient manner, because uncompressed image required more data storage capacity and transmission time. In the present work the storing space used is very less ecuase it help in reducing the processing time. For image compression, different transform technique is used. Image can be represented as a matrix of pixel values and after compression by applying different method there is no change or little change between pixel values. This present work is uses haar method and Lifting Wavelet Transform for image compression for increase the efficiency of Discrete Wavelet Transform (DWT).
Evolution and self-teaching in neural networks

Previous work presented a technique called evolving self-taught neural networks - neural networks that can teach themselves, intrinsically motivated, without external supervision or reward [3]. In an autonomous multi-agent setting in which the agent is primitively set to know little or nothing about its environment, self-teaching was shown to give rise to intelligence, whereas an evolutionary algorithm alone fails since it has no way to search without gradient information. In this paper, we conduct another comparative experiment in which the foraging agent is built more conscious of its environment beforehand. Experimental results show that the more conscious primitive design can let evolution alone be able to search. Yet the combination of evolution and self-teaching still outperforms the alternative. Indications for future work on evolving intelligence are also presented.