Reviewerly/exHarmony huggingface dataset:
Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach

The primary objective of the present work is to introduce a new method, i.e., Theory of Experimentation for prediction of monthly average global solar radiation. Meteorological data for 15 years is accessed considering six input predictors (i.e., latitude, longitude, altitude, relative humidity, temperature, and sunshine hours). Global solar radiation model is developed using various input parameters, and the accuracy of the developed models is assessed using statistical errors. The established model forms are also compared with the models available in the literature. Also, Global Performance Indicator is employed to sort the models for the development of the ranking system. A five-variable global solar radiation model (M-06) is found the best amongst all the proposed models (on training dataset) where the determination coefficient is 0.9424, and the mean percentage error is −0.1524%; whereas, for validation dataset, a two-variable regression model was seen to be the best. The study reveals that the effectiveness of the developed Global solar radiation model does not increase with an increase in the input variables; however, altitude, relative humidity, and sunshine hours are the dominating parameter. The proposed method exhibits a high potential of use in the prediction of monthly average global solar radiation.
Blockchain-based mobility management for 5G

5G promises to expand the realm of mobile communication beyond LTE by enabling vehicles, sensors and other countless devices to interact and share data over the cellular network. In the era of 5G, it is expected that the amount of mobile data will surpass that of wired data. To unlock the full potential of 5G, it is imperative to address the shortcomings of LTE, particularly the security weakness in handover. In LTE, the key derivation scheme used in handover does not support full forward key separation, which makes the network not only vulnerable to attacks but also prone to latency. Therefore, a novel approach is introduced using blockchain to overcome the issues. Blockchain is a technology originally developed to support Bitcoin to securely make financial transactions without an intermediary like a bank. Some of the innate security features of blockchain are evolved to support a structurally secure key derivation scheme for handover in 5G with full forward key separation. The paper reviews extensively on the security benefits of the new key derivation scheme using blockchain and demonstrates the simulation results using a custom application written in JavaScript to verify the performance enhancement.
Augmented Reality Assisted Instrument Insertion and Tool Manipulation for the First Assistant in Robotic Surgery

In robotic-assisted laparoscopic surgery, the first assistant (FA) stands at the bedside assisting the intervention, while the surgeon sits at the console teleoperating the robot. Tasks for the FA include navigating new instruments into the surgeon's field-of-view and passing in or retracting materials from the body using hand-held tools. We previously developed ARssist, an augmented reality application based on an optical see-through head-mounted display, to aid the FA. In this paper, we refine the system and first perform a pilot study with three experienced surgeons for two specific tasks: instrument insertion and tool manipulation. The results suggest that ARssist would be especially useful for less experienced assistants and for difficult hand-eye configurations. We then perform a multi-user study with inexperienced subjects. The results show that ARssist can reduce navigation time by 34.57%, enhance insertion path consistency by 41.74%, reduce root-mean-square path deviation by 40.04%, and reduce tool manipulation time by 72.25%. Thus, ARssist has the potential to improve efficiency, safety and hand-eye coordination, especially for novice assistants.
A Sentiment Analysis Model for Faculty Comment Evaluation Using Ensemble Machine Learning Algorithms

Teacher evaluation is the systematic procedure done in educational institutions to review the performance of the teachers in a classroom. It aims to provide constructive feedback for teacher's professional growth which benefits students in their education. Students' feedback in the evaluation typically include textual comments which are unstructured but are rich with adequate information and insight about teacher's mastery of the course, teaching style, course content and learning experiences of the students. In this study, sentiment analysis or opinion mining was used to analyze the students' comments. An ensemble approach integrating five individual machine algorithms namely Naive Bayes, Logistic Regression, Support Vector Machine, Decision Tree and Random Forest algorithms were applied to classify the comments based on Majority Voting Principle. The experimental result shows that the ensemble classification system outperforms these individual classifiers with 90.32% accuracy. It helps to improve machine learning results producing better predictions compared to a single model.
Suppression of spectral diffusion by anti-Stokes excitation of quantum emitters in hexagonal boron nitride

Solid-state quantum emitters are garnering a lot of attention due to their role in scalable quantum photonics. A notable majority of these emitters, however, exhibit spectral diffusion due to local, fluctuating electromagnetic fields. In this work, we demonstrate efficient anti-Stokes (AS) excitation of quantum emitters in hexagonal boron nitride (hBN) and show that the process results in the suppression of a specific mechanism responsible for spectral diffusion of the emitters. We also demonstrate an all-optical gating scheme that exploits Stokes and anti-Stokes excitation to manipulate spectral diffusion so as to switch and lock the emission energy of the photon source. In this scheme, reversible spectral jumps are deliberately enabled by pumping the emitter with high energy (Stokes) excitation; AS excitation is then used to lock the system into a fixed state characterized by a fixed emission energy. Our results provide important insights into the photophysical properties of quantum emitters in hBN and introduce a strategy for controlling the emission wavelength of quantum emitters.
Developing secure bitcoin contracts with BitML

We present a toolchain for developing and verifying smart contracts that can be executed on Bitcoin. The toolchain is based on BitML, a recent domain-specific language for smart contracts with a computationally sound embedding into Bitcoin. Our toolchain automatically verifies relevant properties of contracts, among which liquidity, ensuring that funds do not remain frozen within a contract forever. A compiler is provided to translate BitML contracts into sets of standard Bitcoin transactions: executing a contract corresponds to appending these transactions to the blockchain. We assess our toolchain through a benchmark of representative contracts.
Quantum algorithms for Second-Order Cone Programming and Support Vector Machines

We present a quantum interior-point method (IPM) for second-order cone programming (SOCP) that runs in time $\widetilde{O} \left( n\sqrt{r} \frac{\zeta \kappa}{\delta^2} \log \left(1/\epsilon\right) \right)$ where $r$ is the rank and $n$ the dimension of the SOCP, $\delta$ bounds the distance of intermediate solutions from the cone boundary, $\zeta$ is a parameter upper bounded by $\sqrt{n}$, and $\kappa$ is an upper bound on the condition number of matrices arising in the classical IPM for SOCP. The algorithm takes as its input a suitable quantum description of an arbitrary SOCP and outputs a classical description of a $\delta$-approximate $\epsilon$-optimal solution of the given problem. Furthermore, we perform numerical simulations to determine the values of the aforementioned parameters when solving the SOCP up to a fixed precision $\epsilon$. We present experimental evidence that in this case our quantum algorithm exhibits a polynomial speedup over the best classical algorithms for solving general SOCPs that run in time $O(n^{\omega+0.5})$ (here, $\omega$ is the matrix multiplication exponent, with a value of roughly $2.37$ in theory, and up to $3$ in practice). For the case of random SVM (support vector machine) instances of size $O(n)$, the quantum algorithm scales as $O(n^k)$, where the exponent $k$ is estimated to be $2.59$ using a least-squares power law. On the same family random instances, the estimated scaling exponent for an external SOCP solver is $3.31$ while that for a state-of-the-art SVM solver is $3.11$.
VPE: Variational Policy Embedding for Transfer Reinforcement Learning

Reinforcement Learning methods are capable of solving complex problems, but resulting policies might perform poorly in environments that are even slightly different. In robotics especially, training and deployment conditions often vary and data collection is expensive, making retraining undesirable. Simulation training allows for feasible training times, but on the other hand suffer from a reality-gap when applied in real-world settings. This raises the need of efficient adaptation of policies acting in new environments.We consider the problem of transferring knowledge within a family of similar Markov decision processes. We assume that Q-functions are generated by some low-dimensional latent variable. Given such a Q-function, we can find a master policy that can adapt given different values of this latent variable. Our method learns both the generative mapping and an approximate posterior of the latent variables, enabling identification of policies for new tasks by searching only in the latent space, rather than the space of all policies. The low-dimensional space, and master policy found by our method enables policies to quickly adapt to new environments. We demonstrate the method on both a pendulum swing-up task in simulation, and for simulation-to-real transfer on a pushing task.
SAR: learning cross-language API mappings with little knowledge

To save effort, developers often translate programs from one programming language to another, instead of implementing it from scratch. Translating application program interfaces (APIs) used in one language to functionally equivalent ones available in another language is an important aspect of program translation. Existing approaches facilitate the translation by automatically identifying the API mappings across programming languages. However, these approaches still require large amount of parallel corpora, ranging from pairs of APIs or code fragments that are functionally equivalent, to similar code comments.
Personalized Travel Recommendation Based on Sentiment-Aware Multimodal Topic Model

In this paper, we try to solve the personalized travel recommendation problem by exploiting the multi-modal data available from the real world social media, and a probabilistic graph model so called Sentiment-aware Multi-modal Topic Model (SMTM) is proposed to mine the latent semantics of the multi-modal data on the online travel website. Distinguished from previous approaches, our proposed approach try to mine the topics from tourist and attraction domains separately for disclosing semantics for tourist topics and attraction themes. In addition, we analyze tourist's sentiments on attractions to further obtain the tourist's attitude over attractions and recommend the attraction with proper sentiment on the related attraction themes accordingly. Based on the proposed SMTM model, the documents in tourist domain and in attraction domain can be compared with each other after they were projected into the mutual topic space, and this latent space projection scheme can be further applied to two personalized traveling recommendations, that is, the single platform traveling recommendation and the inter-platform traveling recommendation. Evaluation results based on the real world online travel website have shown the improved performance of our method.
The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning

State-of-the-art deep neural network recognition systems are designed for a static and closed world. It is usually assumed that the distribution at test time will be the same as the distribution during training. As a result, classifiers are forced to categorise observations into one out of a set of predefined semantic classes. Robotic problems are dynamic and open world; a robot will likely observe objects that are from outside of the training set distribution. Classifier outputs in robotic applications can lead to real-world robotic action and as such, a practical recognition system should not silently fail by confidently misclassifying novel observations. We show how a deep metric learning classification system can be applied to such open set recognition problems, allowing the classifier to label novel observations as unknown. Further to detecting novel examples, we propose an open set active learning approach that allows a robot to efficiently query a user about unknown observations. Our approach enables a robot to improve its understanding of the true distribution of data in the environment, from a small number of label queries. Experimental results show that our approach significantly outperforms comparable methods in both the open set recognition and active learning problems.
Local gap density for clustering high-dimensional data with varying densities

Density-based clustering algorithms are for clustering the data with arbitrary shapes. However, most of these algorithms face difficulties in handling the high-dimensional data with varying densities; especially, they cannot well discover the clusters in sparse regions. In this paper, we define a new type of density, local gap density, in the k-NN graph which works well for high-dimensional data. The local gap density of each point considers not only the number of all points in its nearest neighbor but also the average distance from this point to all points in this nearest neighbor. In this way, the core points in sparse regions in the sense of existing density-based clustering have high densities in our density definition, so they can be easily detected. By the core points, the potential cross-cluster edges in the k-NN graph can be well identified. After deleting these edges, we group all the points in each component with large cardinality as a subcluster, and then, similar to density peaks clustering, assign each remaining point to its corresponding existing subcluster. Extensive experiments on eight publicly available datasets demonstrate the effectiveness of our clustering algorithm.
Generalization in fully-connected neural networks for time series forecasting

In this paper we study the generalization capabilities of fully-connected neural networks trained in the context of time series forecasting. Time series do not satisfy the typical assumption in statistical learning theory of the data being i.i.d. samples from some data-generating distribution. We use the input and weight Hessians, that is the smoothness of the learned function with respect to the input and the width of the minimum in weight space, to quantify a network's ability to generalize to unseen data. While such generalization metrics have been studied extensively in the i.i.d. setting of for example image recognition, here we empirically validate their use in the task of time series forecasting. Furthermore we discuss how one can control the generalization capability of the network by means of the training process using the learning rate, batch size and the number of training iterations as controls. Using these hyperparameters one can efficiently control the complexity of the output function without imposing explicit constraints.
VR Ski Coach: Indoor Ski Training System Visualizing Difference from Leading Skier

The training of skiing is difficult because of environmental requirements and teaching methods. Therefore, we propose a virtual reality ski training system using an indoor ski simulator. The system is based on a simple indoor ski simulator with two trackers to capture the motion of skis. Users can control the skis in the virtual ski slope we provided and train their skills with a replay of a professional skier. The training system consists of three modules: a coach replay system for reviewing pro-skiers's motion; a time control system that can be used to watch the detailed motion of both the coach and the user; and a visualization of the angle of the skis to compare the difference of motions between the users and the coach.
Visualization Techniques for Precise Alignment in VR: A Comparative Study

Many studies explored the effectiveness of augmented, virtual, and mixed reality for object placement tasks. Two main approaches for assisting users during object alignment exist: static visualization techniques and interactive guides. This paper presents a comparative evaluation of four static visualization techniques used to render virtual objects when precise alignment in 6 degrees of freedom (DoF) is required. The selection of these techniques is based on the amount of occlusion caused by the visual guides during the alignment task. To the best of our knowledge, no previous work exists that evaluates which visualization technique is most suitable to support users while precisely aligning objects in virtual environments. We designed a virtual reality scenario considering two conditions -with and without time constraints- in which users aligned pairs of objects. To evaluate the users performance, quantitative and qualitative scores were collected. Our results suggest that visualization techniques with low levels of occlusion can improve alignment performance and increase user acceptance.
GPU-based Ising computing for solving max-cut combinatorial optimization problems

In VLSI physical design, many algorithms require the solution of difficult combinatorial optimization problems such as max/min-cut, max-flow problems etc. Due to the vast number of elements typically found in this problem domain, these problems are computationally intractable leading to the use of approximate solutions. In this work, we explore the Ising spin glass model as a solution methodology for hard combinatorial optimization problems using the general purpose GPU (GPGPU). The Ising model is a mathematical model of ferromagnetism in statistical mechanics. Ising computing finds a minimum energy state for the Ising model which essentially corresponds to the expected optimal solution of the original problem. Many combinatorial optimization problems can be mapped into the Ising model. In our work, we focus on the max-cut problem as it is relevant to many VLSI design automation problems. Our method is inspired by the observation that Ising annealing process is very amenable to fine-grain massive parallel GPU computing. We will illustrate how the natural randomness of GPU thread scheduling can be exploited during the annealing process to create random update patterns and allow better GPU resource utilization. Furthermore, the proposed GPU-based Ising computing can handle any general Ising graph with arbitrary connections, which was shown to be difficult for existing FPGA and other hardware based implementation methods. Numerical results show that the proposed GPU Ising max-cut solver can deliver more than 2000X speedup over the CPU version of the algorithm on some large examples, which shows huge performance improvement for addressing many hard optimization algorithms for solving practical VLSI design automation problems.
Quality of experience for unified communications: A survey

Summary This paper presents a quality of experience (QoE) conceptual model to the context of unified communications (UC) through summary, classification, and discussion of multiple influence factors (IFs) and dimensions affecting it. A deep and comprehensive understanding of the IFs and their impact on QoE for a given service is an essential precondition for successful QoE management with the overall goal of prominently optimizing end‐user QoE, while making efficient use of network resources and maintaining a satisfied user base. The proposed conceptual model was used to conduct a qualitative meta‐analytical review of selected papers. The results of the qualitative review include various IFs, QoE dimensions, and key findings in the form of research recommendations for QoE in the context of UC.
iVRNote: Design, Creation and Evaluation of an Interactive Note-Taking Interface for Study and Reflection in VR Learning Environments

In this contribution, we design, implement and evaluate the pedagogical benefits of a novel interactive note taking interface (iVRNote) in VR for the purpose of learning and reflection lectures. In future VR learning environments, students would have challenges in taking notes when they wear a head mounted display (HMD). To solve this problem, we installed a digital tablet on the desk and provided several tools in VR to facilitate the learning experience. Specifically, we track the stylus' position and orientation in the physical world and then render a virtual stylus in VR. In other words, when students see a virtual stylus somewhere on the desk, they can reach out with their hand for the physical stylus. The information provided will also enable them to know where they will draw or write before the stylus touches the tablet. Since the presented iVRNote featuring our note taking system is a digital environment, we also enable students save efforts in taking extensive notes by providing several functions, such as post-editing and picture taking, so that they can pay more attention to lectures in VR. We also record the time of each stroke on the note to help students review a lecture. They can select a part of their note to revisit the corresponding segment in a virtual online lecture. Figures and the accompanying video demonstrate the feasibility of the presented iVRNote system. To evaluate the system, we conducted a user study with 20 participants to assess the preference and pedagogical benefits of the iVRNote interface. The feedback provided by the participants were overall positive and indicated that the iVRNote interface could be potentially effective in VR learning experiences.
How to detect qubit-environment entanglement generated during qubit dephasing

We propose a straightforward experimental protocol to test whether qubit-environment entanglement is generated during pure dephasing of a qubit. The protocol is implemented using only measurements and operations on the qubit---it does not involve the measurement of the system-environment state of interest, but the preparation and measurement of the qubit in two simple variations. A difference in the time dependencies of qubit coherence between the two cases testifies to the presence of entanglement in the state of interest. Furthermore, it signifies that the environment-induced noise experienced by the qubit cannot be modeled as a classical stochastic process independent of the qubit state. We demonstrate the operation of this protocol on a realistically modeled nitrogen-vacancy center spin qubit in diamond interacting with a nuclear spin environment, and show that the generation of entanglement should be easily observable in this case.