Reviewerly/exHarmony huggingface dataset:
A Supporting System for Teaching Assistant to Control Assistant Behavior by Adjusting Instruction Time

In this study, we propose a Teaching Assistant (TA) support system to assist the TA in instructing students more effectively. The TA is expected to support students properly without decreasing the learning impact of the students. Hints for exercises should not be given excessively to let the students work by themselves. The proposed system controls the TA’s behavior by preventing the TA from overrunning the instruction time. To measure the instruction time, the position data of the TA in the classroom is used. The system detects the beginning of a TA instruction based on the still position in a fixed period. When the instruction time overruns a specific time, the system notifies the TA by sending a signal to vibrate the mobile device worn by the TA. The TA is assumed to conclude the instruction and leave from the student when the mobile device vibrates. We introduced the experiment in the classes of basic programming course. From the results of the experiment, the notification affected the TA adequately and the instruction was concluded as anticipated.
Eye Like Landmarks Extraction and Patching for Face Detection Using Deep Neural Network

This paper presents a novel technique to extract facial Eye Like Landmarks (ELL) for face detection. The proposed technique pre-processes input images for skin segmentation using a minimal approach which segments all the skin regions in a fragmented manner. Morphological closing further enhances fragmented skin regions. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to enhance eye like regions. The ELL are extracted from skin regions By using divide and conquer method with a heuristically calculated threshold. While Extracting ELL the rotation and scale are taken into consideration. The pair of ELL which satisfy criteria to be a possible eye pair is then combined to form a patched image which is smaller in dimension, hence easier and faster to process. Patched images are used to train a Deep Neural Network. The classification accuracy on the face and non-face patches is 97%. In the result section, we discuss the training and validation loss. The Extracted Patches can be further processed to extract features for robust face detection.
Genetic algorithm-based feature selection for depression scale prediction

This study aimed to improve the performance of machine learning prediction through feature selection using a genetic algorithm (GA) for predicting depression of elderly people based on the survey data of Korean Longitudinal Study of Aging (KLoSA) performed in South Korea. The proposed feature selection method finds an optimized feature set through a fitness function design that maximizes the correlations between the features selected and the label to be predicted while minimizing the correlations between the selected features by using GA. The effectiveness of the proposed GA was shown through comparative experiments.
Developing Usability Heuristics for Recommendation Systems Within the Mobile Context

The rapid development of advanced mobile technologies and wireless Internet services has accelerated the explosion of the mobile service market. Such an extension encourages an increasing number of users engaging in mobile applications. However, the challenge of designing "useful" mobile applications still remains. To overcome this challenge, this study conducts a systematic review of mobile applications, identifying a set of heuristics and relevant features in mobile applications design. More specifically, seven usability heuristics, namely navigation, content, error handling, consistency, functionality, cognitive load and aesthetic design have been developed for mobile applications. The results identify a number of design features based on the proposed usability heuristics, and the associated requirements of usability heuristic to mobile physical constraints are described. The major contribution is to propose a systematic analysis of mobile application usability for mobile developers and designers.
How the Cognitive Styles Affect the Immersive Experience: A Study of Video-Watching Experience in VR

In VR, the analog cinema experience is a common design method for video-watching function. However, will this approach increase user experience? We conducted 2 phases of experiments. First, from user interview, we found that the users’ evaluation of the viewing experience in VR is polarization. Second, studies were carried out with 24 users to find what caused the big difference. Through experiments, we found that different cognitive styles affect the immersive experience of users in VR cinema video-watching, and the Spatial Presence has a more significant impact on the Sense of Being There than the other factors, thus, affecting immersion. Our findings suggest that the design of virtual scenes should consider the different cognitive styles of users, and our research provide insights into future research on user experience of video-watching in VR.
Research and Application of Deep Belief Network Based on Local Binary Pattern and Improved Weight Initialization

In order to extract the features of the image more accurately, a deep belief network (DBN) based image feature extraction method is proposed. However, when the deep belief network extracts the features of the image, it is easy to ignore the local texture features of the image. Then the block local local binary mode is introduced to extract the local texture features of the image. At the same time, to improve the slow learning speed of the network, the initial weight of the network is improved. Finally, the proposed network is tested on the ORL image dataset. The results show that the proposed method not only improves the recognition accuracy of the network, but also accelerates the convergence speed of the network to some extent.
Optimisation of crop configuration using NSGA-III with categorical genetic operators

One of the main tasks in agriculture is deciding which crop should be planted on which field. Agricultural companies often cultivate dozens of crops on hundreds of fields, making this problem extremely computationally complex. It was solved within evolutionary many-objective optimisation (EMO) framework. Objective functions included: profit, yield risk, price risk, scatteredness, crop rotation and environmental impact (total amounts of fertiliser and pesticide used). As the decision variables were categories (crops) and not real values, NSGA-III was adapted by changing the genetic operators of mutation and crossover from numerical to categorical. Optimisation was performed on the dataset provided by a partnering agricultural company. Out of the resulting population of solutions, characteristic crop configurations were chosen and compared to the benchmark, i.e. company's current strategy.
Photo-Realistic Exemplar-Based Face Ageing

We propose a photo-realistic method for artificially ageing facial photographs by combining learned shape deformations with skin detail transfer between a donor and a receiver face. Facial ageing is a complicated process that most existing face models, such as 3d Morphable Models fail to express, due to lacking correspondence between the wrinkles of different individuals. We propose an exemplar-based approach to face ageing, where we transfer high-frequency details from an older face texture to a younger receiver. By warping the resulting image according to a learned shape ageing deformation, we obtain photo-realistic aged photographs. We evaluate the simulator with human perception experiments showing that we can indeed create results that are perceived to be real.
Constant- Time Approximate Sliding Window Framework with Error Control

Stream Processing is a crucial element for the Edge Computing paradigm, in which large amount of devices generate data at the edge of the network. This data needs to be aggregated and processed on-the-move across different layers before reaching the Cloud. Therefore, defining Stream Processing services that adapt to different levels of resource availability is of paramount importance. In this context, Stream Processing frameworks need to combine efficient algorithms with low computational complexity to manage sliding windows, with the ability to adjust resource demands for different deployment scenarios, from very low capacity edge devices to virtually unlimited Cloud platforms. The Approximate Computing paradigm provides improved performance and adaptive resource demands in data analytics, at the price of introducing some level of inaccuracy that can be calculated. In this paper we present the Approximate and Amortized Monoid Tree Aggregator (A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> MTA). It is, to our knowledge, the first general purpose sliding window programable framework that combines constant-time aggregations with error bounded approximate computing techniques. It is very suitable for adverse stream processing environments, such as resource scarce multi-tenant edge computing. The framework can compute aggregations over multiple data dimensions, setting error bounds on any of them, and has been designed to support decoupling computation and data storage through the use of distributed Key-Value Stores to keep window elements and partial aggregations.
An Implementation of CoAP-Based Semantic Resource Directory in Californium

Although Resource Directory (RD) is proposed and still under standardization by IETF CoRE WG for resource discovery in constrained networks, Californium as an open-source platform of CoAP is short of the RD functions and could not well support CoAP-based semantic applications to do data analysis and security related to discoveries and allocations. This paper demonstrates an implementation of RD entity with basic mechanisms including URI discovery, resource registration and resource lookup in Californium, and improves the semantic-based resource discovery through adding several sematic attributes such as reference ontology (ro), semantic description (sd) and annotation type (at). The experimental results show that the implementation works are feasible and consistent with CoRE WG’s RD specification. Also its performance is given and achieves satisfactory results in semantic application. By realizing the semantic RD functions in Californium, this paper could promote CoAP based on IoT semantic researches on the Californium framework in the future.
Encourage Self-exploration Through an Interactive Chinese Scroll Painting Design

Gamification design enhances the user’s interest in things and thus makes the culture more widely available and easier to understand. This work attempts to transform a traditional viewing experience of scroll paintings into an immersive and playful interaction, allowing viewers to better appreciate these scroll paintings and understand the history behind them. We designed an immersive cultural heritage digital experience device, taking the “Han Xizai evening banquet” as an example. We hope to find an interesting method to encourage viewers to explore these paintings proactively by the interactive exhibition. It also helps the public to understand the charm of Chinese traditional paintings better.
Benchmark problems for exhaustive exact maximum clique search algorithms

There are well established widely used benchmark tests to assess the performance of practical exact clique search algorithms. In this paper a family of further benchmark problems is proposed mainly to test exhaustive clique search procedures.
Digital Audio Watermarking by Quantization Embedding System

This paper designs a digital audio watermarking scheme using a quantization embedding system in the wavelet domain. In this paper, the relationship between the watermarked coefficients and the original coefficients is first presented and then the quantization-based embedding rules are combined into a quantization-based embedding system that incorporates a system state. For the embedding in this system, the information includes synchronization codes and the watermarks are embedded into the DWT lowest frequency sub-band coefficients, which can be described by a matrix form. Moreover, the SNR is rewritten as a performance index which is a quadratic form. Then, a nonlinear function connecting the performance index and the embedding system is derived. Accordingly, an optimization-based embedding system is proposed. Finally, the Lagrange Principle is used to obtain the optimal solution which is an important formula for watermarking. In addition, the hidden information can be extracted without knowledge of the original audio. In the experiments, the performance of the proposed system is tested and the results verify the high SNR and strong robustness against multimedia signal processing or malicious attacks.
Regularized extreme learning machine–based intelligent adaptive control for uncertain nonlinear systems in networked control systems

Since the learning ability of adaptive control (including multi-model adaptive control) is actually very limited and the persistent excitation (PE) condition is also not satisfied in the design of uncertain nonlinear system controllers, an improved networked control method based on regularized extreme learning machine for a class of nonlinear systems. The networked controller is built to compensate for the modeling error and system uncertainties. In the designed controller, its hidden node parameters are modified using the recently proposed regularized extreme learning machine (ELM), where they are assigned random values. However, different from the original ELM algorithm, the output weight is updated based on the L1/L2 norm penalty to guarantee the stability of the overall control system. The proposed model is finally applied to the internal model control in networked control system. The simulation results demonstrate good tracking performance of the proposed control scheme and have better control performance, anti-interference ability, and robustness for nonlinear system.
Intermediate Data Placement Strategy for Different Data Skew Levels Based on Random Sampling in Spark

In recent years, the Apache Spark had been widely used in processing large-scale data. However, when the input data onto MapReduce was skewed, the default intermediate data placement algorithm of the Apache Spark could not efficiently handle the skewed data. In this paper, in order to achieve load balancing with all reducers, the focus on our attentions was how to process data efficiently when the input data onto MapReduce was skewed. So we defined a data skew model to measure the skew degree of the intermediate data. We also applied a sampling algorithm based on reservoir model. When the skew of input data was severe, we proposed a fine-grained data placement algorithm based on splitting cluster. When the skew of input data was slight, we proposed a coarse-grained data placement algorithm that did not split the clusters. In our experiment, we first chose the appropriate sampling rate. Then, we determined the optimal value of the parameter that measures the two degrees of the skewed input data. At last, we compared the average execution time of several algorithms and the load balancing degree of reducers under different conditions. The experiments confirmed the efficiency of the two intermediate data placement algorithms proposed in this paper in their respective usage scenarios.
Reconfigurable Wavelet Based Real Time Imaging System for Low Bit Rate Telemetry Space Missions

The design of a Wavelet -based Imaging System for Space missions is discussed in this paper. Capturing images of interest in launch vehicles is a multidisciplinary area involving teams from various branches of engineering; however, this paper specifically focuses on the design of the space -qualified imaging system that can work at low bit rates. The features of the system, design of various parameters, compression algorithm and its parameter selection, tests carried out to make the system space worthy are explained. The system is being used in various missions of ISRO to capture multiple events of interest. Finally, this paper shows some examples of the successful application of this system to recent missions.
Using Mobile Eye Tracking to Evaluate the Satisfaction with Service Office

Eye tracking can be applied to a variety of scenarios as a means of measuring visual attention and interpreting visual solution strategies. In this article, we use mobile eye-tracker to collect information to evaluate user satisfaction with the tax service office. Mobile eye tracking can collect precise information concerning the users’ visual attention and interactions in authentic environments. Unlike screen-based eye-tracker using a laboratory or stationary computer, mobile eye tracking also can be used effectively in a walk a round scene where users could walk around and interact with diverse resources. In the progress of eye tracking data analysis, fixations and gaze points, areas of interest (AOIs), heat-maps play an important role. This annotation is typically the most time-consuming step of the analysis process. To reduce processing time and human effort, we introduced the latest computer vision techniques (i.e., You Only Look Once, YOLO) based on a convolutional neural network (CNN) to detect and recognize specific objects in recorded video. We propose a new method to evaluate the user satisfaction of a service system by implicating mobile eye tracker. In addition we gave a new idea of using CNN-based object detection technique to annotate video data collected by mobile eye tracker, which could be followed up for further analysis.
A Novel Facial Expression Recognition Scheme Based on Deep Neural Networks

Automated emotion recognition in the wild from facial images remains a challenging problem. Although recent advances in Deep Learning have supposed a significant breakthrough in this topic, strong changes in pose, orientation and point of view severely harm current approaches. With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. In this paper, we propose a Low-rank Multimodal Fusion (LMF) method, which performs multimodal fusion using low-rank tensors to improve efficiency. Experimental results performed on emotion datasets demonstrate that our proposed method outperforms state-of-the-art.
Independent Core Observer Model Research Program Assumption Codex

This document contains taxonomical assumptions, as well as the assumption theories and models used as the basis for all ICOM related research as well as key references to be used as the basis for and foundation of continued research as well as supporting any one that might attempt to find fault with our fundamentals in the hope that they do find flaw in or otherwise better inform the ICOM research program.
A Novel Content Based Image Retrieval Scheme in Cloud Computing

Image and multimedia data produced by individuals and enterprises is increasing in recent years. The need of outsourcing such intensive image feature detection tasks to cloud computing continues to grow. However, the concerns over the effective protection of private image and multimedia data when outsourcing it to cloud platform become the major barrier that impedes the further implementation of cloud computing techniques over massive amount of image and multimedia data. To address this challenge, a new scheme that supports Content Based Image Retrieval (CBIR) over the encrypted images without revealing the sensitive information to the cloud server is proposed. The novel scheme is based on complex networks theory and Speeded Up Robust Features (SURF) technique. SURF is one of the important local feature detection algorithms and has been broadly employed in different areas, including object recognition, image matching, robotic mapping, and so on. A high-performance privacy-preserving SURF feature detection system is analyzed and modeled for the secure computation purpose. The coordinates of the key-points (features) obtained from SURF detector are used to generate the complex network work. The security analysis and experiments show the security and efficiency of the proposed scheme.
Trajectory Smoothing Constraint and Hard Negative Mining for Distractor-Aware Regression Tracking

Recently, convolutional regression networks have drawn great attention in the tracking community. Convolutional regression trackers formulate the regression network as one convolutional layer and take advantages of end-to-end learning. However, existing convolutional regression trackers regress the input feature to Gaussian-like soft labels, which still assign a large label to semantic backgrounds in online model fine-tuning. As a result, in the presence of background distractors, convolutional regression trackers tend to drift toward regions, which exhibit a similar appearance compared to the object of interest. In this paper, we propose to achieve distractor-aware regression tracking with trajectory smoothing constraint and hard negative mining. The trajectory smoothing constraint measures motion distance and motion direction between the adjacent frames to discard distractors that fail to meet trajectory smoothness. On this basis, distractors are fed into convolutional regression networks as hard negative samples, which boosts the discriminability of convolutional regression trackers against background distractors. The experimental results on the OTB100 and VOT2016 benchmarks show that the proposed algorithm performs favorably in terms of both accuracy and robustness when compared with state-of-the-art trackers.
Cognitive Agents for Adaptive Training in Cyber Operations

To support training for offensive and defensive cyber operations, we focus on giving the trainee a realistic ecosystem to train in. This ecosystem includes models of attackers, defenders, and users. The high-level goals for adaptation in this ecosystem are of two types: realism in behavior and tailoring of training. In terms of realism, real-world cyber operations are highly adaptive. Attackers constantly innovate new attack techniques and adapt existing techniques to take advantage of emerging vulnerabilities. Defenders must adapt to ever-changing attack tactics and vulnerabilities. Users continuously adapt to rapidly changing technology. A realistic training ecosystem requires those adaptations to be reflected in the models of the synthetic actors. In terms of tailoring, training systems often require ecosystem actors to step outside of what would “realistically” happen and instead create artifices to focus the trainee’s experience on particular learning objectives. In support of these high-level adaptation goals, the CyCog (CYber COGnitive) framework currently supports three types of adaptivity. These include adaptation of tactics and techniques (for example, innovating a new attack or defense), adaptation of level of sophistication (for example, to make an attacker more or less aggressive, or to limit or expand a defender’s awareness to focus training), and adaptation of personality parameters (for example, to tune the preferences of various types of users in the ecosystem). To maintain maximum training flexibility, we use a mixed-autonomy approach that allows all forms of adaptation to be controlled on a spectrum from automated tuning to manual manipulation by human instructors.