Reviewerly/exHarmony huggingface dataset:
Üniversite Öğrencilerinin Çevrimiçi Alışveriş Davranışlarına İlişkin Mikro Alan Çalışması

The rapid spread of the Internet worldwide has created new opportunities in trade. Electronic commerce is different from the traditional trade which provides consumers access to many products as online and irrespective of time and location. However, some consumers don’t prefer electronic commerce and persist to prefer tradional trade. This study has been conducted with the aim of searching online shopping tendencies of college students. For this purpose, a conceptual framework has been created by firstly searching the domestic and foreign literature related to the subject. The study group of research consist of 403 students who studiying in Babaeski Vocational School. To collect the data, a 21-item questionnaire form developed by the researchers was used and an on-site survey method was applied. The data obtained after the questionnaire were subjected to statistical analysis and the results were interpreted. As a result of the analysis of the data; It has been observed that students prefer electronic commerce especially because it is economical. It has been seen that students mostly prefer the textile industry in electronic commerce and generally use a well-known e-commerce site. There were significant differences between the online shopping behaviours of students and the demographic factors.
Interpolative Reasoning: Valid, Specificity-Gradual and Similarity-Based

In this paper, a recall of the similarity-based view of interpolative reasoning considered as an analogical scheme of reasoning introduced by Bernadette Bouchon-Meunier and her colleagues. Based on this, an extension of this replacement is presented that finely corrects the validity by choosing a similar observation that non only guarantees convexity, but also a gradual behaviour with respect to a specifity measure.
Engineering Semantic Self-composition of Services Through Tuple-Based Coordination

Service self-composition is a well-understood research area focusing on service-based applications providing new services by automatically combining pre-existing ones. In this paper we focus on tuple-based coordination, and propose a solution leveraging logic tuples and tuple spaces to support semantic self-composition for services. A full-stack description of the solution is provided, ranging from a theoretical formalisation to a technologically valuable design and implementation.
Processing and Visualization of Collected Data Based on Open-Source Tools and Principles

This paper demonstrates the use of opensource tools and libraries to process collected data and visualize analysis. These data were collected using a built-in assembly with a GPS receiver and an Arduino microcomputer, and in a second example with the help of a mobile device and a free Phyphox app. Using the Python programming language, the Matplotlib, NumPy and Pandas libraries analysis and visualization the collected data are made within the Jupyter notebook. In this paper, we demonstrate how it is possible to do a simple data analysis and apply it for learning purposes, but also in further research. Different graphical data representations are used in the paper to demonstrate the importance of the correct representations selected for different types of data. In an age when the amount of data collected is growing exponentially, it is important to be able to extract useful information from them, to make the data analysis results as accurate as possible. The complete software part of the work was created using an open-source tool and all the details and results of this work are freely available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Capture to Rendering Pipeline for Generating Dynamically Relightable Virtual Objects with Handheld RGB-D Cameras

We present a complete end-to-end pipeline for generating dynamically relightable virtual objects captured using a single handheld consumer-grade RGB-D camera. The proposed system plausibly replicates the geometry, texture, illumination, and surface reflectance properties of non-Lambertian objects, making them suitable for integration within virtual reality scenes that contain arbitrary illumination. First, the geometry of the target object is reconstructed from depth images captured using a handheld camera. To get nearly drift-free texture maps of the virtual object, a set of selected images from the original color stream is used for camera pose optimization. Our approach further separates these images into diffuse (view-independent) and specular (view-dependent) components using low-rank decomposition. The lighting conditions during capture and reflectance properties of the virtual object are subsequently estimated from the computed specular maps. By combining these parameters with the diffuse texture, the reconstructed model can then be rendered in real-time virtual reality scenes that plausibly replicate real world illumination at the point of capture. Furthermore, these objects can interact with arbitrary virtual lights that vary in direction, intensity, and color.
Research on the Path of IGV Mobile Car Robot Based on Computer in Dynamic Environment

Abstract The evolution of various behavior modes of mobile robots described by non-linear differential equation in the dynamic environment of computer is used to simulate human path planning. The problem of path planning for mobile robots in computer dynamic environment is studied by behavioral dynamics method. Firstly, the behavior variables and behavior modes of path planning are determined, the course angle dynamics model and velocity dynamics model of the robot are constructed, and the unstable fixed points are analyzed, and the corresponding solutions are given. Secondly, in order to get a better path planning, the competitive model is used to adjust the weight of the behavior mode adaptively. And the computer simulation results show that the model is correct. And the weight adjustment is effective.
Smart Derivatives: On-Chain Forwards for Digital Assets

In this paper, we present a framework for the development of on-chain forwards (and futures). This utilises smart contracts to automate the custody of collateral and settlement of payouts on expiry. Importantly, our framework also enables forwards to be traded without counterparty risk or reliance on off-chain assets (such as fiat currencies). To achieve this, we build on our previous work on on-chain options and demonstrate how the relevant mathematical guarantees can be extended to forwards. In addition, we discuss recent trends in cryptoasset derivatives, capital requirements, and other design considerations (such as the use of split contracts). This paper will be of interest to academics and practitioners interested in financial smart contracts.
Hermite Rational Function Interpolation with Error Correction

We generalize Hermite interpolation with error correction, which is the methodology for multiplicity algebraic error correction codes, to Hermite interpolation of a rational function over a field \(\mathsf {K}\) from function and function derivative values.We present an interpolation algorithm that can locate and correct \(\le E\) errors at distinct arguments \(\xi \in \mathsf {K}\) where at least one of the values or values of a derivative is incorrect. The upper bound E for the number of such \(\xi \) is input. Our algorithm sufficiently oversamples the rational function to guarantee a unique interpolant. We sample \((f/g)^{(j)}(\xi _i)\) for \(0 \le j \le \ell _i\), \(1 \le i \le n\), \(\xi _i\) distinct, where \((f/g)^{(j)}\) is the j-th derivative of the rational function f/g, \(f,g\in \mathsf {K}[x]\), \(\text {GCD}(f,g)=1\), \(g\ne 0\), and where \(N = \sum _{i=1}^n (\ell _i+1) \ge D_f + D_g +1+2E + 2\sum _{k=1}^E \ell _k\); \(D_f\) is an upper bound for \(\deg (f)\) and \(D_g\) an upper bound for \(\deg (g)\), which are input to our algorithm. The arguments \(\xi _i\) can be poles, which is truly or falsely indicated by a function value \(\infty \) with the corresponding \(\ell _i=0\). Our results remain valid for fields \(\mathsf {K}\) of characteristic \(\ge 1 + \max _i \ell _i\). Our algorithm has the same asymptotic arithmetic complexity as that for classical Hermite interpolation, namely \(N (\log N)^{O(1)}\).For polynomials, that is, \(g=1\), and a uniform derivative profile \(\ell _1 = \cdots = \ell _n\), our algorithm specializes to the univariate multiplicity code decoder that is based on the 1986 Welch-Berlekamp algorithm.
Lightweight key management system for inter-node communication in IoT

Internet-of-Things (IoT) nodes need secure communication protocols for transmitting messages between each other via public networks. An indispensable building block of any such protocols is reliable cryptographic keys, which are used by algorithms for encrypting and authenticating messages. A key management system (KMS) is responsible for generating high quality keys, updating them when necessary and revoking compromised keys for various entities. The major challenges for a KMS for IoT nodes are security, flexibility and efficiency. In this paper, we provide general constructions of an efficient KMS for inter-node communications. The KMS is lightweight because 1) the key hierarchy is relatively flat with limited depth, 2) the key update protocols have the optimal number of messages for explicit authentication, and 3) all the computation of key materials and messages only involves efficient symmetric cryptography. We thoroughly evaluate each component of our KMS and exhibit its outstanding efficiency in terms of key generation and key update speed. In addition, our formal security analysis of key update protocols can also be of independent interest.
Design of electrical wheelchair navigation for disabled patient using convolutional neural networks on Raspberry Pi 3

Patients with physical disabilities such as losing an arm, hand, or paralysis will have difficulty moving from one place to another. They need someone or a device that can help their mobility. One of the tools that are often used by physically disabled patients to help their activities is an electric wheelchair. The main purpose of this article is to design a wheelchair for physically disabled patients that can be controlled by voice commands using the convolutional neural network method (CNN). CNN is employed to identify voice commands embedded on raspberry Pi 3. The recorded sound data is converted to spectrogram images before being fed to CNN. This method has been proven well in voice commands recognition with an accuracy of more than 90%. There are five different voice commands for wheelchair navigation, which are forward, backward, left, right and stop. Preliminary experimental results indicate that electrically designed wheelchairs can move using speech command.
Blind quantum computation with hybrid model

Blind quantum computation (BQC) allows a client who has a few quantum abilities to interact and delegate her quantum computation to a server that has strong quantum computabilities, while the server learns nothing about client’s quantum inputs, algorithms, and outputs. In this article, a new BQC protocol with hybrid model is proposed, using the combination of rotation operators to construct arbitrary quantum gate. Our BQC protocol is divided into two phases. In the first phase, a new kind of function operation is designed and defined, that is, the client sends the classical message [Formula: see text] to the server and then the sever performs a corresponding function operation [Formula: see text], which has been defined. In the second phase, a rotation operator or identity operator is implemented by quantum gate teleportation where the server can’t know which quantum gate has been teleported. Combining these two phases, the server has no idea about client’s quantum algorithms. When the server performs the corresponding operation honestly, the client only needs to perform [Formula: see text] and [Formula: see text] operators.
Challenges in Designing Interfaces for Large Displays: the Practitioners’ Point of View

With the constant growth in readily-available screen sizes and proliferation of large screens in public spaces, interaction designers are faced with the challenge of creating interfaces for large surfaces more often than ever. Contrary to mobile and desktop interfaces, design guidelines available to designers working with large screens are limited thus complicating the design process. In this paper, we report on interviews performed with interaction design practitioners with experience in creating large-screen interfaces. Through analyzing the interviews, we identify a set of challenges that designers see as important for improving the user experience of large-screen systems. We then contrast these challenges with findings from Human-Computer Interaction (HCI) literature to highlight practice-motivated research challenges. Our work aims at filling the gap between research and practice in large-screen interfaces and develops a practice-motivated horizon for future development.
Separation results for boolean function classes

We show (almost) separation between certain important classes of Boolean functions. The technique that we use is to show that the total influence of functions in one class is less than the total influence of functions in the other class. In particular, we show (almost) separation of several classes of Boolean functions which have been studied in coding theory and cryptography from classes which have been studied in combinatorics and complexity theory.
An Application of IMM Based Sensor Fusion Algorithm in Train Positioning System

With their serious impact on the safe and economic operation of railway domains, train positioning systems play a crucial part in railway signalling. In this paper, we present a solution for such a train positioning system by making use of a tachometer, a Doppler radar and a magnetic positioning sensor (a.k.a tag). An IMM (Interacting Multiple Model) filter based sensor fusion algorithm has been used to calculate the velocity and position of the train using the above sensors. The algorithm has been developed with SCADE (Safety Critical Application Development Environment) which is a tool frequently used for development in safety-critical systems because it drastically simplifies and accelerates the certification process required of EN 50128.
SSP: Speeding up Small Flows for Proactive Transport in Datacenters

Proactive transports nowadays have drawn much attention because of fast convergence, near-zero queueing and low latency. Proactive protocols, however, need an extra RTT to allocate ideal sending rate for new flows. To solve this, some studies, such as pHost, Homa, send unscheduled packets with line rate in the first RTT, which will causes severe network congestion. To avoid the queue buildup, Aeolus directly drops unscheduled packets when congestion occurs. Nevertheless, based on our experiment, a considerable part of small flows (0-100 KB) will be completed in the first RTT under 100 Gbps network, so that dropping unscheduled packets will severely affect performance of the small flows. In this paper we propose SSP, a new scheme aimed to eliminate the extra RTT delay and improve the flow completion time (FCT) of small flows under the proactive mechanism. Like pHost and Homa, SSP sends unscheduled packets at line rate when new flow arrives. Different from Aeolus, SSP selectively drops scheduled packets once queue buildup happens in the switch, thus protecting unscheduled packets which are more likely belong to small flows. Besides, based on the short-job-first (SJF) principle, we give relative higher priorities for small flows at the sender. Our simulation results with realistic workloads show that SSP can improve the FCT of small flows significantly. Specifically, under Web Search workload, SSP facilitates nearly 63% of 0-100 KB flows to complete one RTT faster. Also, SSP reduces the tail FCT by 56.8% at the 99th percentile compared with Expresspass and 29.2% compared with Aeolus while not leads to large queue buildup.
An ANN-TLBO Model to Predict Cumulative Number of Failures in Software

Determining release time of software is an important task for the software project managers in software industry. They need information such as cumulative number of failures present in software after testing a certain period of time. An artificial neural network (ANN) based model is used in this paper to predict cumulative number of failures in software. An ANN-TLBO approach is proposed in which Teaching Learning Based (TLB) optimization technique is used to train the ANN. The proposed approach is experimented with three data sets available in literature. The experimental results are compared with existing models and found to provide better prediction accuracy than some existing models.
Naive Website Categorization Based on Text Coverage

The amount of websites on the internet has grown tremendously in recent years. With the rapid increase in volume of available World Wide Web resources, a need arises for an efficient and automated website categorization mechanism. This research proposes and implements several machine learning models for general-purpose classification of website content. Using a linguistic principle which states that 3000 key words can account for up to 85% of written text coverage, a bag-of-words feature set of high-frequency words appearing across the surveyed websites was generated. The bag-of-words training set was initially fit to six different classification models, with and without feature selection. The models with applied feature selection were found to perform up to 15% better. Linear SVM, multinomial Naive Bayes and logistic regression showed the most promising results, with classification accuracies of over 70%. Using these three algorithms as base models, an additional ensemble voting model was constructed, reaching an accuracy of 76%. Lastly, the paper compares the achieved performance metrics with those of previous related research, highlights several limitations of the current model and suggests possible future improvements.
The Application of Continuous Time SRML

The SENSORIA Reference Modeling Language (SRML) aims at modeling composite services at a high level. Continuous time SRML extends SRML in that it extends the semantic domain of SRML with Service-Oriented Hybrid Doubly Labeled Transition Systems, and thus provides a way to model services with continuous time executions. In this paper, we apply continuous time SRML to a traffic control system, to show the approach for using continuous time SRML to model continuous time executions in a service-oriented context; and make comparison of our approach with other related works to show the advantages of it.
A Novel Hybrid Bacterial Foraging Optimization Algorithm Based on Reinforcement Learning

This paper proposes a novel hybrid BFO algorithm based on reinforcement learning (QLBFO), which combines Q-learning with the improved BFO operators. In the QLBFO algorithm, under the guidance of Q-learning mechanism, each bacterium has the chance to adaptively choose appropriate one from three chemotaxis mechanisms to adjust step size. In addition, to maintain the diversity of the whole bacterial population and promote the convergence speed of the algorithm, we also improved two operators. On the one hand, we add the learning communication mechanism in the chemotaxis operator, which can make the bacterium learn from the current best one during the searching process. On the other hand, to alleviate the premature problem, a novel mechanism is adopted into the process of elimination and dispersal for each bacterium. Finally, experimental results show that the proposed QLBFO performs better than four compared algorithms.
A Case Study of Transfer of Lesion-Knowledge

All organs in the human body are susceptible to cancer, and we now have a growing store of images of lesions in different parts of the body. This, along with the acknowledged ability of neural-network methods to analyse image data, would suggest that accurate models for lesions can now be constructed by a deep neural network. However an important difficulty arises from the lack of annotated images from various parts of the body. Our proposed approach to address the issue of scarce training data for a target organ is to apply a form of transfer learning: that is, to adapt a model constructed for one organ to another for which there are minimal or no annotations. After consultation with medical specialists, we note that there are several discriminating visual features between malignant and benign lesions that occur consistently across organs. Therefore, in principle, these features boost the case for transfer learning on lesion images across organs. However, this has never been previously investigated. In this paper, we investigate whether lesion knowledge can be transferred across organs. Specifically, as a case study, we examine the transfer of a lesion model from the brain to lungs and lungs to the brain. We evaluate the efficacy of transfer of a brain-lesion model to the lung, and the transfer of a lung-lesion model to the brain by comparing against a model constructed: (a) without model-transfer (i.e. random weights); and (b) using model-transfer from a lesion-agnostic dataset (ImageNet). In all cases, our lesion models perform substantially better. These results point to the potential utility of transferring lesion-knowledge across organs other than those considered here.
Managerial Computer Communication: Implementation of Applied Linguistics Approaches in Managing Electronic Communication

Managerial communication in the past few decades has been much influenced by new trends in computer science bringing new means of communication. The use of computers for communication is ubiquitous and the trend toward computerization will be even stronger soon. However, the intercultural aspect of this communication must not be neglected as it poses a potential threat to company management and all the communication processes in the company and global business environment. Applied linguistics presents a potentially useful tool which could be used for IT developers of communication tools and apps so that they take into consideration critical aspects of human communication which must be implemented in these modern communication tools and methods. Applied linguistics and its practical implementation can prove very useful and can reinforce managerial communication when using modern technological tools. ICT departments, in the past few years, have been focusing more on technological aspects of communication when creating these tools; however, now it is clearly visible that there is an urgent need to have more parameters embedded in the communication tools and one of them is the interculturality aspect because electronic or computer communication is now in its essence global and intercultural. The conducted research into the intercultural communication awareness in ICT departments proves that the need for bringing this topic to priority in education ICT students is crucial to further competitiveness and lossless transfer of information.