Reviewerly/exHarmony huggingface dataset:
Petri Net Model and Max-Plus Algebra on Queue in Clinic UNS Medical Center

Abstract Patients come to the clinic UNS (Universitas Sebelas Maret) Medical Center to get a medical treatment. The clinic provides two health services include dental polyclinics and general polyclinics. Majority of patients do not know exactly how long the process of service from patient registration to getting medicine. Moving queues to each service is an event and people who queue up at each service are state. It is clear that the sum of all possible states is finite. This problem includes a Discrete Event System (DES). One of the uses of petri net is to model a DES. In this research, service flow was built in the clinic using Petri Net and service scheduling was modeled using Max-Plus Algebra. Furthermore, a coverability tree was built to determine the liveness and deadlock of the patient service queue and analyze the petri net flow. The selection of service start time can be chosen randomly to simulate the scheduling and length of service time.
Enhancing Mole Trust Algorithm Based Analysis User Profile

Social networking is exploited for Internet-based marketers' purposes in which, advertising companies are seeking to engage customers for their products.
Golf Club-Type Device with Force Feedback for Modifying Club Posture

On the golf-swing, the trajectory and face angle (angle of the golf-club head) of the golf-club greatly affect the direction and trajectory of the ball. Though the instruction of the golf-swing has been conducted to the beginners by the instructor or the consumer device specialized in the golf-swing training, it was difficult to modify the posture of golf-club during the swing. This is because the users require time to understand and express the instruction. Also, these instructions cannot convey detailed information to the users. Therefore, we propose a golf club type device mounting ungrounded force feedback module to modify the posture of golf-club during the golf-swing. In this paper, we implemented a prototype of the proposed device and the application using the device. The prototype was evaluated its performance and the results suggested the prototype generate sufficient torque to move the golf-club grasped by the user.
Research on a high-speed programmable pattern generation method with deep storage

Aiming at digital system testing, the paper proposed a high-speed programmable pattern generation method with deep storage based on FPGA+DDR3 SDRAM. Among them, DDR3 solves the storage problem of large-capacity programmable pattern data. For the discontinuity of DDR3 data outputing, the asynchronous FIFO is inserted as a buffer after DDR3, and the FIFO input and output speed difference is used to continuously uniformize the bursts of uneven pattern data output from DDR3. For the problem that the pattern data rate is limited by the FPGA logic resources, the parallel pattern data is serialized by the dedicated high-speed serial transceiver integrated by the FPGA. And the data rate is multiple of its original data rate. For the high-speed serial pattern data jitter attenuation problem of dynamic data rate switching, the paper uses the jitter attenuation technology to reduce the random jitter of the reference clock of high-speed serial pattern data after generating the 8-bit resolution variable reference clock through DDS+PLL from 5ps rms to 1ps rms. In the paper, the multi-channel data rate reaches up to 3.35 Gbps. The pattern data storage depth is 4 Gbits, and the random jitter of the pattern signal is less than 3 ps rms.
Smartphone Operation in a Dark Environment for Semi-awake Users

Human-computer interaction is commonly designed for users who are awake and in a brightly lit environment. However, as computer technology becomes ubiquitous, opportunities for semi-awake users in dark environments to interact with computers. In this study, we propose a low stimulus and effortless interaction that does not prevent semi-awake users from falling asleep in dark bedrooms. We have developed a bedside box (BSB) that holds and charges a smartphone. The BSB has an optical lens that projects the smartphone display onto the ceiling. The green and low light intensity projection primarily stimulates human rod cells and provides low-intensity visual stimuli. An interface that allows the user to set an air conditioner, to adjust lighting, to set an alarm clock, to monitor a security camera, to make handwritten notes, and to lock an entrance door was designed, developed experimentally, and evaluated.
Academic Achievement Recognition and Verification Using Blockchain

Falsification of certificates is a growing concern and the verification process can be a lengthy and challenging one. In this research, we are proposing a distributed ledger-based solution for the storage and verification of academic qualifications. An entity that would want to verify certificates can make use of our API service that would, in turn, scan a certificate, find the matching certificate template, extract the necessary data and verify it from a blockchain stored copy. In this research, we also propose an improved manner of verifying the ownership of a blockchain public address which also does not allow a user to present an address of a third party, this being one of the common security concerns of similar solutions. We also calculate the possible costs to adopt this system in all EU countries taking into consideration different gas prices, which is a determining factor to the transaction cost of a blockchain network. We conclude that a blockchain based certificate verification system addresses various issues related to document forgery and is a viable solution even with the current state of technology.
Prediction of Solar Irradiance Based on BP Neural Network with Wavelet Decomposition

To improve the precision of solar irradiation (SI) prediction, this paper presents an SI prediction method based on BP neural network (BPNN) with wavelet decomposition (WD). Firstly, analyze data by wavelet, and WD applied to train input set, training target set, and testing target set. Then combined with BPNN to train, predict, and normalize the decomposed data. Finally, the anticipated results of the two methods are output as predicted values. The prediction results show that the proposed manner is consistent with the expected value curve when predicting SI, which improves the prediction accuracy and provides a basis for SI prediction of photovoltaic power plants.
DACNN

Recently, news recommendation has attracted great attentions as it helps users quickly find news satisfying their preferences. It is important to note that both the interests of users and the news readers change over time. Although existing news recommendation methods have achieved promising performance, they treat the representation of news as static and ignore the dynamic nature of news, i.e. news attracts different users at different times and thus may have different features. In this paper, we propose the Dynamic Attentive Convolution Neural Network (DACNN) to solve the above issues. Specifically, we extract features from the clicked history of news as dynamic representations of news and from the browsed history of users as dynamic representations of users. Moreover, we propose to employ a shared CNN with inner-attention to learn user-item interactions from the dynamic representation. Extensive experiments are conducted on two real-world datasets and have proved the superiority of our model.
Cooperative deployment of Shonabondhu

Technology intervention can play an important role during natural disasters. Flash flood, a major natural disaster taking place in Bangladesh, can be supported by continuous water level monitoring and notification based system. A low-cost sensor based system to handle flash flood required consideration of policy and local community support at its infancy. Later the system was designed using community inputs and deployed using authority support where the entire team worked with researchers cooperatively to have the ownership of the problem as well as the technology intervention.
Self-configuring genetic programming algorithm for solving symbolic regression problems

Abstract In most cases, the only way to study and solve practical problems is to investigate them with the help of models. However, this method is also to be a complicated problem where a significant part of the effort is aimed at finding the functional dependencies between input and output variables. But a great number of methods of numerical identification solve problems by developing a model that, in fact, is a black box. The reduction different types of problems to the problem of symbolic regression allow us to overcome the lack of these methods. The algorithm of genetic programming is applied for solving the problems of symbolic regression. The given paper considers an algorithmic complex that includes a genetic algorithm and an algorithm for genetic programming for solving symbolic regression problems. The uniform crossover operator is applied for these methods; it ensures the flexibility of the algorithm due to the greater diversity of structures resulting from crossover. The opportunity for selection two or more parents for recombination is realized. To automate the selection of the algorithms parameters a self-configuring procedure at the population level is realized and the efficiency of its application is proved for test problems of symbolic regression. The practical implementation of algorithms for solving classification problems and differential equations is carried out.
Singular Terms, Identity, and the Creation of Fictional Characters

Abstract How to interpret singular terms in fiction? In this paper, we address this semantic question from the perspective of the Artifactual Theory of Fiction (ATF). According to the ATF, fictional characters exist as abstract artifacts created by their author, and preserved through the existence of copies of an original work and a competent readership. We pretend that a well-suited semantics for the ATF can be defined with respect to a modal framework by means of Hintikka’s world lines semantics. The question of the interpretation of proper names is asked in relation to two inference rules, problematic when applied in intensional contexts: the Substitution of Identicals and Existential Generalization. The former fails because identity is contingent. The latter because proper names are not necessarily linked to well-identified individuals. This motivates a non-rigid interpretation of proper names in fiction, although cross-fictional reference (e.g. to real entities) is made possible by the interpretative efforts of the reader.
Optimal Combination of SVM and Bayesian Density Model Using Dempster-Shafer Theory

In pattern classification, the diversity among classifiers is known to be able to provide complementary knowledge and improve classification performance, if properly exploited. In this paper, we propose to integrate the support vector machine (SVM) and the Bayesian density model by utilizing their respective posterior classification probabilities. The Dempster-Shafer (DS) theory was adopted for fusing the two classifiers. The effectiveness of the proposed method was verified using three datasets. The performance of the proposed approach was shown to be superior to that of the benchmark methods.
Crime Story as a Tool for Scientific and Technological Outreach

In a world where public engagement is increasingly important, where there is the urge of leaving the research laboratories to tell people what is being done, and where the effort from Academy is still limited, we propose Death on the Nile, a novel experience to get people in touch with innovative interactive technologies. In our exhibition, visitors are invited to solve a crime while they get in touch with the new frontiers of Human-Computer Interaction. Also, participants can experience the different stages of the research process through the metaphor of the investigation. An empirical study (N=969) shows that Death on the Nile is engaging and effective as a method through which to present HCI research. Finally, we conceptualize the experience into a framework that can be used with potentially any interactive technology.
Tracing shapes with eyes

Eye tracking systems can provide people with severe motor impairments a way to communicate through gaze-based interactions. Such systems transform a user's gaze input into mouse pointer coordinates that can trigger keystrokes on an on-screen keyboard. However, typing using this approach requires large back-and-forth eye movements, and the required effort depends both on the length of the text and the keyboard layout. Motivated by the idea of sketch-based image search, we explore a gaze-based approach where users draw a shape on a sketchpad using gaze input, and the shape is used to search for similar letters, words, and other predefined controls. The sketch-based approach is area efficient (compared to an on-screen keyboard), allows users to create custom commands, and creates opportunities for gaze-based authentication. Since variation in the drawn shapes makes the search difficult, the system can show a guide (e.g., a 14-segment digital display) on the sketchpad so that users can trace their desired shape. In this paper, we take a first step that investigates the feasibility of the sketch-based approach, by examining how well users can trace a given shape using gaze input. We designed an interface where participants traced a set of given shapes. We then compared the similarity of the drawn and traced shapes. Our study results show the potential of the sketch-based approach: users were able to trace shapes reasonably well using gaze input, even for complex shapes involving three letters; shape tracing accuracy for gaze was better than `free-form' hand drawing. We also report on how different shape complexities influence the time and accuracy of the shape tracing tasks.
Self-Tracking for Fertility Care: A Holistic Approach

With the recent popularity of self-tracking, individuals are increasingly using and interacting with their personal health data. Fertility is a health issue in which people often track and interact with diverse health-related data potentially associated with their fertility cycles. Fertility tracking is often impacted by different social factors and taboos, and it has been progressively assisted by consumer health technologies. My dissertation research combines multiple studies focusing on understanding the data practices, the influence of technology, and the consequences of using fertility-related personal data and technology in self-tracking for fertility. Based on my findings, I plan to explore how design and technology can be used to reinforce positive experiences, avoid negative emotional burden, and support holistic tracking for fertility.
Combining Embedded Computation and Image Tracking for Composing Tangible Augmented Reality

This work proposes a combination of embedded computation and marker tracking to provide more robust augmentations for composed objects in Tangible Augmented Reality. By integrating conductive elements into the tangibles' sides, communication between embedded microprocessors is enabled, such that a connected composition can be computed without relying on any marker tracking information. Consequently, the virtual counterparts of the tangibles can be aligned, and this virtual composition can be attached to a single marker as a whole, increasing the tracking robustness towards occlusions and perspective distortions. A technical evaluation shows that this approach provides more robust augmentations if a tangible block in a composition is occluded by at least 50% or perspectively distorted by at least 40 to 50 degrees, depending on the block's size. Additionally, a test with users relying on the use case of a couch configuration tool shows promising results regarding usability and user experience.
Effcient Nearest-Neighbor Search for Dynamical Systems with Nonholonomic Constraints

Nearest-neighbor search dominates the asymptotic complexity of sampling-based motion planning algorithms and is often addressed with k-d tree data structures. While it is generally believed that the expected complexity of nearest-neighbor queries is \(O(\text {log}(N))\) in the size of the tree, this paper reveals that when a classic k-d tree approach is used with sub-Riemannian metrics, the expected query complexity is in fact \(\varTheta (N^{p} \text {log}(N))\) for a number \(p\in \) 2 [0, 1) determined by the degree of nonholonomy of the system. These metrics arise naturally in nonholonomic mechanical systems, including classic wheeled robot models. To address this negative result, we propose novel k-d tree build and query strategies tailored to sub-Riemannian metrics and demonstrate significant improvements in the running time of nearest-neighbor search queries.
Pixel sampling by clustering

In this paper, we describe Pixel Sampling Clustering Technique (PSCT), a data-driven sampling procedure used to reduce pixel sets. We view the pixels in an image as a high redundancy 3D space. We also refer to this space as our color model. Our method aims to retain a relevant sample of the data so it can act as a new smaller, hence more efficient, color model. PSCT uses a pair of fast density-based clustering algorithms in tandem. First, it applies Birch and then DBSCAN to keep the most densely represented colors. We cluster the resulting color model and use the labels to segment images. We also complement the sampling method with a refinement algorithm intended to improve color representation. In our paper, we show how to reconstruct images using our reduced color model. We also show that reconstructed images have enough information to perform image related learning tasks with almost the same accuracy than the original images but with only a small fraction of the data. We test our sampling method in three image related supervised and unsupervised tasks and compare them with state-of-the-art methods. For our experiments, we use two image datasets: MIT’s Vision Texture Dataset and Berkeley’s BSD500.
Identity Discovery in Bitcoin Blockchain

Blockchain-based systems such as the one proposed to support the Bitcoin protocol are primarily used to enable the execution of financial transactions in a decentralized manner. The characteristics of blockchains have inspired the development of new types of applications that are shifting from its original purpose. Besides supporting the recording of crypto-currency transactions blockchains are also being exploited as mediums of recording arbitrary chunks of data. One technique for embedding such data on the public Bitcoin blockchain is using the OP_RETURN opcode creating an unspendable transaction.
Towards Real-time Webpage Relevance Prediction UsingConvex Hull Based Eye-tracking Features

Browsing the web for finding answers to questions has become pervasive in our everyday lives. When users search the web to satisfy their information-needs, their on-screen eye movements can serve as a source of implicit relevance feedback. We analyze data collected from two eye-tracking studies, wherein participants read online news-articles, and judged whether they contained answers to factual questions. We propose two eye-tracking features, derived from the area of the convex hull of their eye fixations. We demonstrate that these features can well distinguish between eye-movements on news-articles perceived to be relevant vs. irrelevant, for containing the answer to a question. These features can potentially be used for predicting the user's perceived-relevance in real-time. F1 scores as high as 0.80 are obtained using these proposed features only, and the performance is comparable to the combined predictive power of fifteen eye-tracking features established by prior literature.
The path planning method for anthropomorphic manipulator for avoidance an obstacle approximated by a parallelepiped

Abstract There are various methods of planning paths for anthropomorphic manipulators that allow to bypass the obstacle and to work in real time. One of these methods was developed by the authors for circumventing an obstacle, which was approximated by a sphere. The approximation by a sphere of elongated obstacles overstates their size that leads to an excessive length of the path of avoiding the obstacle. The developed method combines analytical and numerical solutions with relatively low computational complexity, allowing it to work in real time. The effectiveness of the method is illustrated by a numerical example. The trajectory of the anthropomorphic manipulator required for its movement can be obtained using the existing methods of generating a trajectory based on the path of movement calculated using the proposed method. The developed method can be implemented in the systems of intellectual or supervisory control by anthropomorphic manipulators.
Static Target Recognition of Indoor Mobile Robot Based on Improved SSD Algorithm

In the indoor application environment, in order to improve the overall efficiency of the service robot in the target detection, combined with the deep learning target detection theory, the SSD target detection algorithm and a lightweight neural network mobilenetv2 are combined to design a new target detection algorithm, which improves the target detection efficiency to a certain extent. The main improvements of this paper are as follows. Firstly, using SSD for reference, multi-scale feature extraction is carried out for the target image, and different sizes of targets are detected at different scales; Secondly, in the process of matching the actual target detection region with the algorithm prediction region, the positive and negative samples are processed properly to ensure the stability of the model; Thirdly, using the idea of mobilenet for reference, the traditional convolution is replaced by the deep separable convolution, which greatly reduces the calculation of data processing and improves the processing speed of the model while ensuring the accuracy of the model. Fourthly, in view of the time-consuming and laborious situation of manually marking data sets, a method of automatically marking data sets is proposed, which improves the efficiency of data set preparation and reduces the workload of manual marking. Through the improvement of the above algorithm, the detection speed of the system model has been greatly improved compared with SSD algorithm, and the overall recognition efficiency of indoor service robot has been improved to a certain extent.
Supporting Circuit Design with a Block-Based, Generator Language

Modern electronic design automation (EDA) tooling tends to focus on either the system-level design or the low-level electrical connectivity between physical components on a printed circuit board (PCB). We believe that a usable and functional system for circuit design needs to be able to interleave both levels of abstraction seamlessly and allow designers to transition between them freely. Existing work has experimented with approaches like circuit synthesis, functional characterization, or fine grained physical modeling. Each of these approaches augment the design process as it exists today, with its fundamental split between various levels of abstraction. We notice that hierarchical block diagrams can capture both high-level system structure as well as fine grained physical connectivity, and use that symmetry to construct a model for electronic circuits that can span the entire design process. Additionally, we construct user interfaces for our model that can support users of different skill levels throughout a design task. We discuss the design of our system, detailing both fundamental abstractions and usability trade-offs, and demonstrate its current capabilities through the design of example electronics projects.