Reviewerly/exHarmony huggingface dataset:
Time Series Prediction with Dual Reliability: Uncertainty and Explainability

In recent years, there has been substantial research on uncertainty and explainability to improve the reliability of deep learning predictions. In the field of image processing, previous methods can be intuitively understood by humans and can be used to evaluate the reliability of predictions. However, they cannot be applied to complex time series prediction because of the difficulty for humans in evaluating the reliability of predictions. Previous methods focused on either uncertainty or explainability; therefore, the evaluation of the methods and predictions depended only on human knowledge. In this paper, we propose a time series prediction method focusing on both uncertainty and explainability. Our method allows the evaluation of predictions from multiple perspectives by using uncertainty and explainability without relying solely on human knowledge. The explanation obtained from the unimodal distribution prediction model is unreliable because the predictive distribution of time series prediction can be an inherently multi-peaked. The unimodal model does not correspond to the actual phenomenon and does not match with human knowledge. Therefore, we solve the time series prediction as a multi-class classification problem. From the verification of the proposed model with real time series, we confirm the effectiveness of the (1) prediction based on the expected value of the class probability distribution, (2) confidence of the prediction, and (3) series importance based on the confidence. Moreover, we show that our model can improve the robustness of the time series prediction and evaluate its reliability.
Voice Assistant and Touch Screen Operated Intelligent Wheelchair for Physically Challenged People

Due to age, mishaps, or ailments, many people are suffering from temporary or permanent physical disabilities. They face a lot of difficulties in their day to day body movements. Here, Wheelchair is the mostly used assisting device for handicapped people. The initial Wheelchair (Mechanical) had some limitations as it required another person to operate. As a result, a smart Wheelchair was invented which can be managed by the patients independently. The smart Wheelchair has also provided a way to lead a more active life for impaired people. In this paper, we have proposed an Internet of Things based Intelligent Wheelchair for the physically challenged people with weak limbs and joints. The Wheelchair can be operated using the touch screen or smart voice assistants e.g. Google Assistant, Siri, Alexa, Cortana, and so on. The distinguishing feature of the wheelchair is that it can operate in both online and offline mode as well as it can send periodical and emergency notification of current and critical condition of the patients. Numerical results show the appropriate efficiency of controlling the Wheelchair using touch screen or smart voice assistants for both online and offline environments.
CAPS: Smoothly Transitioning to a More Resilient Web PKI

Many recent proposals to increase the resilience of the Web PKI against misbehaving CAs face significant obstacles to deployment. These hurdles include (1) the requirement of drastic changes to the existing PKI players and their interactions, (2) the lack of signaling mechanisms to protect against downgrade attacks, (3) the lack of an incremental deployment strategy, and (4) the use of inflexible mechanisms that hinder recovery from misconfiguration or from the loss or compromise of private keys. As a result, few of these proposals have seen widespread deployment, despite their promise of a more secure Web PKI. To address these roadblocks, we propose Certificates with Automated Policies and Signaling (CAPS), a system that leverages the infrastructure of the existing Web PKI to overcome the aforementioned hurdles. CAPS offers a seamless and secure transition away from today’s insecure Web PKI and towards present and future proposals to improve the Web PKI. Crucially, with CAPS, domains can take simple steps to protect themselves from MITM attacks in the presence of one or more misbehaving CAs, and yet the interaction between domains and CAs remains fundamentally the same. We implement CAPS and show that it adds at most 5% to connection establishment latency.
RELATIONSHIPS AMONG READING ANXIETY, READING SELF-EFFICACY, AND READING COMPETENCY IN THE VOCATIONAL HIGH SCHOOL IN SINGARAJA

The general objective pursued is to describe and to relate the students’ reading anxiety, reading self-efficacy to their reading competency in the Vocational High School in Singaraja. The design applied is a quantitative research. There were 90 students recruited using cluster quota sampling. The data were gathered using scales and reading test. The obtained data were analyzed in two phases: descriptively and inferentially. Whereas inferentially, the data were analyzed using pair-wise correlation and multiple correlation technique. The findings show that: 1) the students’ reading anxiety is at high level while reading self-efficacy and reading competency are at the low levels; 2) the relations pair-wisely are negative and positive significantly correlated; and 3) the simultaneous or multiple relationships of the reading anxiety and reading self-efficacy to the students’ reading competency is proven to be significant ( =0.81; α =0.01).
A Real-Time Object Tracking model based on Deeper Siamese Network

Siamese networks have obtained widespread attention in the field of visual tracking. In this paper, we propose a high-performance model based on a deep Siamese network (SiamFC-R22) for real-time visual tracking. In response to the problem that most existing Siamese trackers cannot take advantage of the more abundant feature representation provided by deep networks, we construct a deep backbone network architecture with reasonable receptive field and stride by stacking redesigned residual modules. Furthermore, we propose a multi-layer aggregation module (MLA) to fuse a series of features effectively of different layers. MLA consists of the RAC branch and the IL branch. RAC is used to boost the ability to learn the representation of high-level semantic features. IL is applied to capture the better expression of low-level features that contain more detailed information. The comprehensive experiments on the OTB2015 benchmark illustrate that our proposed SiamFC-R22 achieves an AUC of 0.667. Meanwhile, it runs at over 60 frames per second, exceeding state-of-the-art competitors with significant advantages.
Using Quantum Algorithms to Solve Optimal Control Problems

We use quantum algorithms to solve optimal control problems. Compared with the gradient method usually used in classical algorithms, the time complexity down from O(N i ) to O(N i/2 log(N)).
Literature Review with Study and Analysis of the Quality Challenges of Recommendation Techniques and Their Application in Movie Ratings

During the past few decades, the web-based services and organizations like Amazon, Netflix and YouTube have been raised aggressively. These web services have shown the demand for the recommender systems and their growing place in our lives. More steps deeper, we noticed that the severity of the quality and accuracy of these recommendation systems is very high to match users with same interests. For that reason and for being in competitive position with the most outstanding recommendation web services, these recommendation systems should be always monitored and evaluated from a quality perspective. However, due to the steep growth rate of the available web-based services, new challenges like data sparsity, scalability problem and cold start issue have been burst and threaten the performance and the quality of the predicted recommendations. Accordingly, many data scientists and researchers got excited to figure out ways for these challenges especially if they are in scaled environments and distributed systems. These solutions could be achieved using multiple approaches such as machine learning and data mining.
A Framework for Designing Contextualized Computing Curriculum

Contextualized computing is a two-decades-old approach to providing foundations in computer science. The intent has always been to promote a diversity of approaches and to cast a wide net through which to bring under-represented groups into the field. Much of the curriculum design is ad hoc. This experience paper presents a disciplined model for curriculum design that provides a structure and methodology for integrating computing with another discipline. A framework for analysis is presented that abstracts the curriculum development process. It is grounded in 50 years of scholarship on instructional design. It extends established practice to concurrently develop goals, objectives, activities, and assessments across at least two domains. The methodology is applied in two non-traditional domains: an industry-based work-integrated learning program at a `software as a service' company, and in an alternative high school mathematics class for students at risk. This approach is unique in its emphasis on promoting a principled framework for curriculum design. It extends traditional instructional design by applying methodologies of agile development to identify, execute, and assess instructional computing.
Inverted Dirichlet and Related Distributions Based Feature Mapping for Data Classification

In this paper, we propose a distribution based feature mapping technique to improve the baseline accuracy of SVM kernels in different computer vision tasks. The proposed technique is based on learning parameters from the data and use that parameters to make inference from new data. The learned parameters can be thought of as prior knowledge about the data representation. Utilizing such prior knowledge about the data distribution increases the discriminative power of the classifier. Our proposed feature mapping technique is based on inverted Dirichlet, generalized inverted Dirichlet and inverted Beta Liouville distributions. These distributions are efficient in modelling semi-bounded data which are prevalent in computer vision problems. Our experimental results demonstrate the effectiveness of the proposed method in texture recognition, natural scene recognition and human action recognition in videos.
Friend-or-Foe Deep Deterministic Policy Gradient

One of the toughest challenges in the multi-agent deep reinforcement learning (MADRL) is that when the opponents' policies change rapidly, the collaborative agents can't learn well to respond to the opponents' policies effectively. This may lead to a local optimum w.r.t. the learned policy of the collaborative agents may be only locally optimal to the opponents' current policies. To address this problem, we propose a novel algorithm termed Friend-or-Foe Deep Deterministic Policy Gradient (FD2PG), in which the cooperative agents can be trained more robust and have stronger cooperation ability in continuous action space. These collaborative agents can generalize easily and respond correctly, even if their opponents' policies alter. Inspired by the classic Friend-or-Foe Q-learning algorithm (FFQ), we introduce the idea of minimizing the foes and maximizing the friends into the centralized training distributed execution framework, multi-agent deep deterministic policy gradient algorithm (MADDPG), to enhance collaborative agents' robustness and cooperativity. Besides, we introduce a Minimax Multi-Agent Learning (MMAL) method to explore two special equilibriums (the adversarial equilibrium and the coordination equilibrium), which can guarantee the convergence of FD2PG and improve optimization. Extensive fine-grained experiments, including four representative scenario experiments and two scale-performance correlation experiments, were conducted to demonstrate the superior performance of FD2PG comparing with existing baselines.
Hand Gesture Recognition Based on the Fusion of Visual and Touch Sensing Data

The use of computers has evolved so rapidly that our daily lives revolve around it. With the advancement of computer science and technology, the interaction between humans and computers is not limited to mice and keyboards. The whole-body interaction is the trend supported by the newest techniques. Hand gesture becomes more and more common, however, is challenged by lighting conditions, limited hand movements, and the occlusion of the hand images. The objective of this paper is to reduce those challenges by fusing vision and touch sensing data to accommodate the requirements of advanced human-computer interaction. In the development of this system, vision and touchpad sensing data were used to detect the fingertips using machine learning. The fingertips detection results were fused by a K-nearest neighbor classifier to form the proposed hybrid hand gesture recognition system. The classifier is then trained to classify four hand gestures. The classifier was tested in three different scenarios with static, slow motion, and fast movement of the hand. The overall performance of the system on both static and slow-moving hand are 100% precision for both training and testing sets, and 0% false-positive rate. In the fast-moving hand scenario, the system got a 95.25% accuracy, 94.59% precision, 96% recall, and 5.41% false-positive rate. Finally, using the proposed classifier, a real-time, simple, accurate, reliable, and cost-effective system was realised to control the Windows media player. The outcome of fusing the two input sensors offered better precision and recall performance of the system.
A Contour Detection Method Based on Image Saliency

The object contour detection in natural scenes is an important research problem in computer vision. The difficulty is that the integrity of contour extracted is interfered with a large number of texture edges in the background seriously. In recent years, some researchers have introduced image contour detection method with biological visual features and achieved preferable results. Such as, based on the fact of visual outer region suppression, they can suppress a certain amount of texture edge while extracting the contour of the object, and obtain the proper contour edge. However, when this method is used for contour detection in some image which the texture is similar to the contour, the texture edge still cannot be removed well, and the result is unsatisfactory. In consideration of the merit and demerit of the feature suppressed method, we propose an improved algorithm for object contour detection in natural scene based on image saliency. Firstly, the initial contour is obtained by using the suppression effect of non-classical receptive field, and the saliency map is obtained by using the hyper-complex Fourier transform image saliency detection method, and then the initial contour and the saliency map are integrated into the final contour. Compared with the traditional contour detection method, the contour extraction algorithm based on image saliency can remove more background information and improve the accuracy and integrity of object contour extracted in natural scenes.
Pedestrian Walking Direction Prediction Using Generative Adversarial Networks

Pedestrian walking direction prediction is a useful technique in several applications such as a car, the surveillance cameras and so on. This research focuses on the prediction of pedestrian walking direction by using the algorithm based on Generative Adversarial Networks (GANs). Microsoft KINECT, shortly, KINECT, is used for taking the successive images of walking pedestrians, which are used for the research. From successive images of pedestrians are taken as input data, the present algorithm determines the image following to the successive ones. The present algorithm is defined by means of the Generative Adversarial Networks (GANs). The numbers of the input and the output images are 8 and 2, respectively. GANs model has the best performance on prediction with the evaluation of PSNR and accuracy.
A Novel Framework for Enhancement of Diagnostic Information in MRI using Deep Super-Resolution

Magnetic resonance imaging (MRI) is one of the preferred medical imaging modality for soft tissue imaging. Besides its several advantages, it has a fundamental limitation i.e., slow imaging, which restrict its high resolution (HR) applications. One alternative solution is the super-resolution (SR) to obtain the HR image from the acquired low resolution (LR) image. HR image reconstruction from an LR image with low computational time without sacrificing the quality of the HR image is the main challenging task in MRI for clinical applications. Most of the well-known SR methods are not designed for clinical applications and also requires a significant amount of computational time, which is not clinically feasible. In this paper, we have proposed a region-of-interest based framework using deep learning, which not only enhances the resolution but also improves the diagnostic quality of MR images. Several experiments have been carried out with a set of pathological MR images to check the performance of the proposed technique. From the experimental results, it is observed that the proposed method might be a good candidate for clinical implementation to enhance the diagnostic information in MR images.
Secure Calculation for Position Information of IoT Device with Few Communication and Small Secret Information

Since IoT devices have small computing power and limited battery size, it is desirable that the number of communications be as small as possible. To solve such a request, we propose a calculation method based on triangulation. The feature of our method is that the number of communications is one round trip, and sufficient computational security is realized with small entropy secret information using W-OTS+ and HMAC. In this paper, "small entropy" means a 8-digit integer (about 24 [bit]) that can be memorized by humans. We show an example calculation and analyze the relationship between accuracy of position measurement and security. From the results, we conclude that our method has sufficient security and enough practicality using small entropy secret information.
Methods of organizing competence-oriented classes with students of pedagogical areas of training

Object: to describe the methods of organizing competence-oriented classes with students of pedagogical areas of training. Methods: the leading research methods were the generalization and analysis of pedagogical experience and the processing of quantitative data on the dynamics of indicators of the effectiveness of using the methods of organizing competence-oriented classes with students of pedagogical areas of training. Findings: principles of organizing competence-oriented training with students of pedagogical areas of training (systematic, flexibility, interaction, conscious perspective, professional expediency, activity, activity, ideality, humanism, feedback, reflexivity); revealed the advantages of using competence-oriented classes with students of pedagogical areas of training; formulated types of competence-oriented classes with students of pedagogical areas of training; methods of organizing lecture, seminar and practical competence-oriented classes have been developed; identified types of activities (areas of work) for inclusion in the rating indicators; The indicators for assessing the effectiveness of the described methods of work were determined. Conclusions: the article presents a list of competencies, examples of competence-oriented tasks aimed at their formation and planned learning outcomes; The indicators for assessing the effectiveness of the described methods of work were determined. The article materials can be useful for heads of educational structures of various levels, teachers of higher educational institutions, teachers of schools, parents.
Digital education technologies – some problems

Abstract The situation developed in spring of this year caused an active discussion on the topic of distance learning, due to which strengths and weaknesses of this type of education were identified. The authors dwelled on two of the most important, in their view, points: problem of possible assessing students’ understanding of educational material and possibilities of implementing the second most important component of educational process–upbringing. The use of various digital technologies tools in education is extremely relevant and necessary to expand the teacher’s capabilities to communicate to the student the current information volume, they make the learning process truly interactive, expand the student’s involvement in educational process. However, it is necessary to consider that any technologies can only implement those tasks that were formally set at their development time, but in reality, we need a flexible, sometimes spontaneous reaction of the teacher to assess the student’s understanding of the material passed through, their ability to use the acquired knowledge in practice, not to mention the fact that educational influence and adequate reaction of the teacher are possible only in contact interaction.
Environment and The Students’ Motivation in Studying Indonesian as a Foreign Language at GoBali Program, The University of Udayana-Bali

This study focuses on analysing the students’motivation for studying Indonesian as a foreign language at Go Bali, a one-semester international study program at the University of Udayana, particularly investigating the students’motivation during the course and analyse how the environment influences their learning motivation. This study involved 76 foreign students and 4 instructors in four Indonesian Language classrooms at the program and the data were collected through survey techniques, classroom observations and interviews. The results show that the students’ motivation in learning Bahasa Indonesia is varied and in general is relatively low. However, the environment where they study the language has actually given an advantages for most students in which they become more motivated to pick up the knowledge of the language for their daily lives in Bali, apart from whether it will give further benefits for their future or not.
Heterogeneous Multiple Unmanned Air Vehicles Collaborative Mission Planning with Complex Constraints

Heterogeneous multiple Unmanned Air Vehicles cooperate to execute mission in the form of team, which can improve the efficiency of mission execution and better adapt to complex mission requirements. Mission planning is the key to heterogeneous Unmanned Air Vehicle team resource scheduling. In this paper, a hybrid discrete genetic algorithm is proposed for solving heterogeneous multiple Unmanned Air Vehicles collaborative mission planning problem with complex constraints. The feasible encoding method is proposed, which effectively expressed the problem model under multiple constraints. Simulated annealing mechanism was introduced to prevent the algorithm from falling into local optimum and improve the convergence accuracy of the algorithm. The initial temperature and fitness adjustment functions are designed. Simulations verify the effectiveness of the proposed algorithm to deal with complex constrained mission planning problems. Through the comparative analysis of the performance of the algorithm, the superiority of the algorithm performance is verified.