Reviewerly/exHarmony huggingface dataset:
Early Detection of Wheel-Spinning in ASSISTments

Persistence is a crucial trait for learners. However, a common issue in mastery learning is that persistence is not always productive, a construct termed wheel-spinning. In this paper, we extend on prior work to develop wheel-spinning detectors in the ASSISTments learning system that distinguish between non-persistence, productive persistence and wheel-spinning. To understand how quickly we can detect each state, we use data from different numbers of practice opportunities and compare model performance across student-problem set pairs. We identify that a model constructed using data from the first nine practice opportunities outperforms models using less practice data. However, it is possible to differentiate students who will eventually wheel-spin from learners who will persist productively using data from only the first three opportunities. Wheel-spinning can be differentiated from non-persistence from the first five opportunities, and non-persistence can be differentiated from productive persistence from the first seven opportunities. These results show that early differentiation between wheel-spinning and productive persistence is feasible. These detectors relied upon hint requests, the correctness of prior opportunities, and the amount of practice and time on the skill. Identifying predictive features offer insights into the impact of in-system behaviors on wheel-spinning and guide the system design.
Research on Chinese Text and Application Based on the Latent Dirichlet Allocation

Chinese text research has always been a hot and difficult point in natural language processing. On the one hand, Chinese and English are very different in grammar, resulting in the uniqueness of Chinese word segmentation. On the other hand, Chinese part of speech tagging methods are also different from English. To some extent, the above two points hinder the research and application of natural language processing technology in Chinese text. The paper presents a word segmentation algorithm for Chinese text, and then introduces how to use vector space model to represent Chinese text. At the same time, the topic number k is given according to different criteria for measuring text clustering in order to build the Latent Dirichlet Allocation model base on Guangdong Province consumer complaint text. Through the model to assist the administrative departments to formulate corresponding policies, to grasp the key points of complaints accurately and quickly for the administrative personnel.
Multi-Domain Information Exposure using ALTO

Multi-domain applications can benefit from network information exposure using the ALTO protocol framework. ALTO provides network state and capabilities to applications to be more flexible in terms of rate adaptation, transmission time, and server/path selection. However, different key issues arise when designing an ALTO solution for multi-domain environments. In this talk, we summarize such issues along with basic mechanisms to be considered to allow ALTO to expose network information across multiple domains.
Improving Collaboration in Industry 4.0: The Usage of Blockchain for Knowledge Representation

With the introduction of Industry 4.0 in many industrial environments, many changes are going to take place in the manufacturing processes. Blockchain can help the collaboration between organisations, by improving and leading the way for a decentralised future, where transactions can happen much faster while ensuring that a more knowledgeable and demanding consumer has its expectations fulfilled. In this work we propose a model that uses blockchain and multi-agent systems to help represent an organisation in a network of entities, as well to create a system that is capable of handling entity transactions and provide a way of improving decision-making by enabling decisions to be done faster in a rapidly changing environment.
Wayfinding Systems: Digital Versus Analogical Media

In today’s society space is overloaded with visual information, thus digital platforms are being increasingly used in design and could contribute to hinder this trend, assuming digital media and mobile platforms as an alternative to analogical media (paper support). In what concerns wayfinding systems, they need to allow more then to reach a point, starting from another, they should allow the experience and the knowledge of space, by allowing several layers of information, that people can use or not, depending on their interests and ambitions. This paper presents a study about the preferences and difficulties of people over 50 years old in the use of analogical and digital maps and discusses the possibility of the use of wayfinding systems in digital means for this age group, regarding the Design principles and inclusivity.
DCNN-Based Facial Expression Recognition Using Transfer Learning

Human facial expression recognition has been a lively area of research over the last few years. Traditional approaches perform soundly on datasets prepared under controlled environment but when it comes to unconventional datasets having image variations or frames with partial faces, the performance has declined profusely. With deep learning specifically convolutional neural network (CNN), a lot of models have been proposed to improve the performance. In this paper, transfer learning is employed to get learned weights and biases to upsurge the performance. Trailing layers of ResNet-50 (a state-of-the-art CNN architecture) trained on more than a million images from ImageNet Dataset are used as a part of transfer learning. Further, an additional convolutional layer is suffixed to the first 48 layers of Resnet-50. The entire model is then trained separately using five activation functions (ELU, RELU, Sigmoid, SELU, Exponential) and a comparative analysis is performed. Maximum accuracy (86.80%) with the proposed model is achieved when using SELU as an activation function. The exponential activation function has shown the minimum accuracy among the five activation function used.
CVIN: Connected Vehicle Information Network

Blockchain technology has been widely explored in the automotive sector for a variety of applications. In particular, Mobility Open Blockchain Initiative (MOBI)-a non-profit alliance with over 70% of the world's large automakers-is actively developing a vehicle identity standard, which will serve as the foundation for other blockchain use cases such as vehicle-tovehicle payments and automotive financing. Despite the great potential of the vehicle identity standard as outlined in MOBI's official documents, a cost-effective design and implementation is still missing in the literature. In response, we propose CVIN (Connected Vehicle Information Network) as our technical design and implementation of MOBI's vehicle identity standard. First, we explain why Hyperledger Indy and Aries can be used as a basic building block of CVIN and describe how other building blocks can be integrated with Indy and Aries in order to offer additional advantages. Second, we propose to use the AURORA Connected Vehicle Testbed to evaluate the performances of CVIN with a particular focus on its security, privacy, latency, and interoperability. Third, we identify two major technical challenges and propose several solutions by leveraging edge computing and sidechain technology. Finally, we plan to opensource parts or all of CVIN, and we believe that our contribution together with the AURORA testbed will support our research community for rapid innovation.
Traceable policy-based signatures and instantiation from lattices

Policy-based signatures (PBS) were proposed by Bellare and Fuchsbauer (PKC 2014) to allow an authorized member of an organization to sign a message on behalf of the organization. The user’s authorization is determined by a policy managed by the organization’s trusted authority, while the signature preserves the privacy of the organization’s policy. Signing keys in PBS do not include user identity information and thus can be passed to others, violating the intention of employing PBS to restrict users’ signing capability. In this work, we introduce the notion of traceability for PBS by including user identity in the signing key such that the trusted authority will be able to open a suspicious signature and recover the signer’s identity should the needs arise. We provide rigorous definitions and stringent security notions of traceable PBS (TPBS), capturing the properties of PBS suggested by Bellare-Fuchsbauer and resembling the “full traceability” requirement for group signatures put forward by Bellare-Micciancio-Warinschi (Eurocrypt 2003). As a proof of concept, we provide a modular construction of TPBS, based on a signature scheme, an encryption scheme and a zero-knowledge proof system. Furthermore, to demonstrate the feasibility of achieving TPBS from concrete, quantum-resistant assumptions, we give an instantiation based on lattices.
Leukemia Cancer Detection Using Image Analytics : (Comparative Study)

Leukemia is a cancer of white blood cells (WBC). It can be fatal if not detected early. Microscopic images are studied by hematologists for detecting cancer. This manual detection becomes very tedious and time-consuming process. Leukemia if detected in earlier stages, can be cured. But traditional process causes late detection of cancerous cells. Hence in order to minimize the death caused due to late detection, an automated system can be used. This paper proposes an automated system which uses image analytics. Based on image analytics and classification algorithms performed on cell image samples of patients, the proposed system will give correct output. The dataset for experimentation is obtained from TCIA (The Cancer Imaging Archive) repository. The dataset is already pre-processed. An open source tool, "Orange-Data Mining" is used for predictions. In this comparative study, it was found that K-means clustering performs well for segmentation phase and also Neural Networks gives better results for classification phase. We have achieved AUC (area under curve) 0.865, Calculation accuracy (0.838), precision (0.835) and F1(0.836) for neural networks.
An Analysis of Haskell Parallel Programming Model in the HaLVM

Abstract Recently, cloud computers are widely used due to the development of computing environment and network speed. Unikernel is considered to be an attractive operating system in the manycore environment that effectively provides cloud computers. HalVM is a unikernel coded in Haskell. In this paper, we analyze the performance of Haskell running in a unikernel, which has shown good parallel programming performance in a manycore environment. In this paper, we analyze the performance of Haskell running in a unikernel, while Haskell has shown good performance in a manycore environment. To do this, we select HaLVM as the target operating system and analyze the performance by testing various Haskell parallel programming models. Test result shows that HaLVM is scalable only on IVC. We also implement a library to make programming with IVC more convenient, and use this library to compare extensibility experiments. Our test shows HaLVM’s scalability is 112.42% higher than Linux environment.
A NOTE ON THE SEQUENT CALCULI

Abstract We show that the replacement rule of the sequent calculi ${\bf G3[mic]}^= $ in [8] can be replaced by the simpler rule in which one of the principal formulae is not repeated in the premiss.
Light Weight Security and Authentication in Wireless Body Area Network(Wban)

In ongoing year, the expanding number of wearable sensors on human can fill for some, needs like crisis care, medicinal services remote observing, individual diversion and correspondence and so forth. The WBAN empowers the clinical applications to be created utilizing electronic gadgets and sensors. The WBAN is made by wearing little sensors on the human body. A significant issue is the means by which to protect security and protection of patient's clinical social insurance data over remote correspondence. The vitality utilization and information security are as yet significant difficulties in medicinal services applications. In this paper we proposed an algorithm to secure patients data from eavesdropping attack with help of RSA secret key encryption technique. The target of this paper is to propose a remote sensor organize framework in which both pulse and internal heat level of various patients can screen on PC simultaneously utilizing RF arrange. The proposed model framework incorporates two sensor hubs and beneficiary hub (base station). The sensor hubs can transmit information to collector utilizing remote nRF handset module. The nRF handset module is utilized to move the information from microcontroller to PC and a graphical UI (GUI) is created to show the deliberate information and spare to database. Numerous advances have demonstrated their productivity in supporting WBANs applications, for example, remote observing, biofeedback and helped living by reacting to their particular nature of administration (QoS) necessities. Because of various accessible innovations, choosing the fitting innovation for a clinical application is being a difficult undertaking. In this paper, the distinctive clinical applications are introduced. The most widely recognized advances utilized in WBANs are featured. At long last, a coordinating between every application and the relating reasonable innovation is considered.
The Case for Domain-Specialized Branch Predictors for Graph-Processing

Branch prediction is believed by many to be a solved problem, with state-of-the-art predictors achieving near-perfect prediction for many programs. In this article, we conduct a detailed simulation of graph-processing workloads in the GAPBS benchmark suite and show that branch mispredictions occur frequently and are still a large limitation on performance in key graph-processing applications. We provide a detailed analysis of which branches are mispredicting and show that a few key branches are the main source of performance degradation across the graph-processing benchmarks we looked at. We also propose a few ideas for future work to improve branch prediction accuracy on graph workloads.
The use of Arduino software and hardware in a school physical experiment

The article considers the use of hardware and software Arduino in order to involve students in the study of such subjects as: physics and computer science; provide an opportunity improve and develop their own engineering ideas. The article proves that the use of hardware and software Arduino complex in teaching and research activity is an effective tool for improvement interest in such areas of activity as computer science, engineering, physics. The study found that an integrated approach allows arousing students’ interest in the study of natural sciences and mathematics, solving modern problems engineering and electronics, and developing them creativity. Work on your own projects allows children to show abilities and present their projects in various competitions, in addition motivates young researchers. Developed by students devices can significantly increase accuracy measurements during the experiment, increase the level of theoretical preparation for laboratory work, increase the general interest to perform laboratory work by students for due to the modernization of equipment and form new ones ideas about physical phenomena and processes. The results of students’ research activities can used during the teaching of physics in a specialized school, especially during school experiment.
A Novel Low Contrast Image Enhancement Using Adaptive Multi-Resolution Technique and SVD

An image is called visually rich when its brightness and contrast are properly adjusted. But it was reported in different applications that the contrast of an image was affected by the external interferences. Due to the above phenomena, the image quality degrades. To improve the quality of the image, contrast enhancement method has been used. This technique adjusts the intensity distribution of the low-quality image to solve the problem. But the old techniques were not suitable for all kinds of images. To overcome the limitations, this paper presents a novel variational mode decomposition (VMD)-based enhancement technique associated with singular value decomposition (SVD). Initially, the reference images and the processed image were decomposed into various modes using variational mode decomposition (VMD). Then in the second step, the maximum value of the singular matrix was calculated for the selected mode of each image. An image-dependent correction factor was calculated using the singular value matrix (SVM). Finally, the image is reconstructed by simply adding the β-corrected mode image with the unprocessed modes of the reference image. To avoid the over-enhancement, a new weighted factor was applied to the original as well as to the enhanced image. To validate the algorithm, the proposed method was tested on different types of publicly available low contrast images. The experimental result shows that the proposed algorithm gives significant enhancement in terms of contrast over other state-of-the-art methods.
Design, Analysis, and Implementation of Efficient Framework for Image Annotation

In this article, a general framework of image annotation is proposed by involving salient object detection (SOD), feature extraction, feature selection, and multi-label classification. For SOD, Augmented-Gradient Vector Flow (A-GVF) is proposed, which fuses benefits of GVF and Minimum Directional Contrast. The article also proposes to control the background information to be included for annotation. This article brings about a comprehensive study of all major feature selection methods for a study on four publicly available datasets. The study concludes with the proposition of using Fisher’s method for reducing the dimension of features. Moreover, this article also proposes a set of features that are found to be strong discriminants by most of the methods. This reduced set for image annotation gives 3--4% better accuracy across all the four datasets. This article also proposes an improved multi-label classification algorithm C-MLFE.
Tuning Playfully: Composed and Adaptive Tunings in bitKlavier

Abstract Combining a new software system with the familiar interface of the MIDI keyboard, bitKlavier is a versatile instrument for exploring the nature of tuning and temperament. We describe a number of approaches it facilitates, including composed tunings, moving fundamental systems, and a novel system for adaptive tuning. All of these are characterized by the overarching design priority for bitKlavier to be a context for musical play and exploration, as opposed to finding singular, “correct” solutions to particular tuning “problems,” as has often been the case historically.
Towards the Development of a Robust Path Planner for Autonomous Drones

Path planning is a major challenge surrounding the development of autonomous drones. For a practical solution, a computationally inexpensive and efficient path planning algorithm needs to be utilized to ensure the smooth operation of drones during long distance missions. Randomly Exploring Random Trees (RRT) and RRT* are sampling based path planning algorithms that have been widely used to solve high dimensional complex problems. RRT* ensures asymptotic optimality; however, it requires a long time to converge to a near optimum solution. RRT* variants have been proposed to improve the rate of convergence. Although many RRT* variants have been proposed, to the best of our knowledge, there has not been a comprehensive analysis comparing the performance of these algorithm. In this study, we perform a detailed comparison of a select group of RRT* variants with RRT and RRT* to determine its potential to be used as a path planner for autonomous drones. We review each algorithm and evaluate its performance by investigating the path cost, execution time and the number of nodes required to generate a path. Experimental results suggest that the performance of the RRT* variants is generally dependent on the type of the environment.
Application of Expert System with Web-Based Forward Chaining Method in Diagnosing Corn Plant Disease

Abstract Corn plants are crops in agriculture which are widely cultivated, especially in the Jerowaru area. The empowerment of corn plants is often constrained by disease. Diseases in maize plants are caused by fungi, bacteria, viruses, soil conditions, and weather conditions. Diseases that attack corn plants can result in decreased productivity of corn plants. On the other hand, the limited number of agricultural extension workers makes farmers unable to quickly consult, therefore it is necessary to create a system that can provide information, make decisions and provide solutions regarding web-based corn plant diseases can diagnose corn plant diseases based on symptoms experienced corn plant. This expert system is designed using the Forward Chaining method consisting of several stages, namely, the stages of assessment, knowledge acquisition, design, and testing. Based on the results of tests conducted, this method can provide solutions in dealing with problems related to corn diseases and pests.
Visual Elements for the Design of a Robotic Head in Si-Robotics Project

For a human user it is fundamental to recognize the synthetic emotion being presented as feedback on a robotic head, that can appear spontaneous or activated in reaction to the user’s action or request. The design of such feedbacks can greatly lower the learning curve of users who need to interact with automated systems, reducing the time and effort that they need to put into learning how to use the system. The paper will present our findings regarding the design of facial expressions and empathic aspects of a robotic head and the resulting interface that will be implemented on the robotic system that is being developed for the European-funded Si-Robotics project.
Magnetostatic Active Contour Model with Classification Method of Sparse Representation

The active contour model is widely used to segment images. For the classical magnetostatic active contour (MAC) model, the magnetic field is computed based on the detected points by using an edge detector. However, noise and nontarget points are always detected. Thus, MAC is nonrobust to noise and the extracted objects may be deviant from the real objects. In this paper, a magnetostatic active contour model with a classification method of sparse representation is proposed. First, rough edge information is obtained with some edge detectors. Second, the extracted edge contours are divided into two parts by sparse classification, that is, the target object part and the redundant part. Based on the classified target points, a new magnetic field is generated, and contours evolve with MAC to extract the target objects. Experimental results show that the proposed model could decrease the influence of noise and robust segmentation results could be obtained.
Reasoning About Algebraic Structures with Implicit Carriers in Isabelle/HOL

We prove Chen and Grätzer’s construction theorem for Stone algebras in Isabelle/HOL. The development requires extensive reasoning about algebraic structures in addition to reasoning in algebraic structures. We present an approach for this using classes and locales with implicit carriers. This involves using function liftings to implement some aspects of dependent types and using embeddings of algebras to inherit theorems. We also formalise a theory of filters based on partial orders.