Reviewerly/exHarmony huggingface dataset:
A complete characterization of pretty good state transfer on paths

We give a complete characterization of pretty good state transfer on paths between any pair of vertices with respect to the quantum walk model determined by the XY-Hamiltonian. If n is the length of the path, and the vertices are indexed by the positive integers from 1 to n, with adjacent vertices having consecutive indices, then the necessary and sufficient conditions for pretty good state transfer between vertices a and b are that (a) a + b = n + 1, (b) n + 1 has at most one positive odd non-trivial divisor, and (c) if n = 2^t r - 1, for r odd and r \neq 1, then a is a multiple of 2^{t - 1}.
Strategies for stable merge sorting

We introduce new stable natural merge sort algorithms, called 2-merge sort and α-merge sort. We prove upper and lower bounds for several merge sort algorithms, including Timsort, Shiver's sort, α-stack sorts, and our new 2-merge and α-merge sorts. The upper and lower bounds have the forms c · n log m and c · n log n for inputs of length n comprising m runs. For Timsort, we prove a lower bound of (1.5−o(1))n log n. For 2-merge sort, we prove optimal upper and lower bounds of approximately (1.089 ± o(1))n log m. We state similar asymptotically matching upper and lower bounds for α-merge sort, when φ
Performance Evaluation and Review of Lightweight Cryptography in an Internet-of-Things Environment

Internet-of-Things is used in numerous applications including industrial, military systems and the platform like any other on the internet is vulnerable to security threats. However, due to the nature of the devices used, conventional security methods can incur an overhead and may obstruct the mission of the application due to resource constraints of the devices involved. To alleviate this problem, lightweight cryptographic algorithms have been proposed. Development and implementation of such algorithms continue to move forward; however, it remains a challenge to find suitable algorithms that match the specific needs of the IoT application implementer. In this paper, lightweight cryptography algorithms are categorized, and a few are chosen to be simulated and compared using metrics relevant to an Internet-of-Things environment.
Why does this Entity matter?

Our goal is to complement an entity ranking with human-readable explanations of how those retrieved entities are connected to the information need. While related to the problem of support passage retrieval, in this paper, we explore two underutilized indicators of relevance: contextual entities and entity salience. The effectiveness of the indicators are studied within a supervised learning-to-rank framework on a dataset from TREC Complex Answer Retrieval. We find that salience is a useful indicator, but it is often not applicable. In contrast, although performance improvements are obtained by using contextual entities, using contextual words still outperforms contextual entities.
Enhancing The Visitors Learning Experience in SMB II Museum Using Augmented Reality Technology

TThis study proposes Augmented Reality (AR) technology that brings virtual 3D object of museum collections into real life environment. Proposed AR model is designed, implemented, and evaluated in term of its effectiveness. Taking sample consists of 20 artefacts of Palembang cultural object in Museum of Sultan Mahmud Badaruddin II, AR for cultural heritage education is developed in Voforia and 3D Blender environment for implementation. The experimental results shows that the proposed AR effectively used for giving real and enhance learning experience for museum visitors in learning the artefacts.
Codesign-NAS

Field-programmable gate arrays (FPGAs) have become a popular compute platform for convolutional neural network (CNN) inference; however, the design of a CNN model and its FPGA accelerator has been inherently sequential. A CNN is first prototyped with no-or-little hardware awareness to attain high accuracy; subsequently, an FPGA accelerator is tuned to that specific CNN to maximize its efficiency. Instead, we formulate a neural architecture search (NAS) optimization problem that contains parameters from both the CNN model and the FPGA accelerator, and we jointly search for the best CNN model-accelerator pair that boosts accuracy and efficiency -we call this Codesign-NAS. In this paper we focus on defining the Codesign-NAS multiobjective optimization problem, demonstrating its effectiveness, and exploring different ways of navigating the codesign search space. For Cifar-10 image classification, we enumerate close to 4 billion model-accelerator pairs, and find the Pareto frontier within that large search space. Next we propose accelerator innovations that improve the entire Pareto frontier. Finally, we compare to ResNet on a highly-tuned accelerator, and show that using codesign, we can improve on Cifar-100 classification accuracy by 1.8% while simultaneously increasing performance/area by 41% in just 1000 GPU-hours of running Codesign-NAS, thus demonstrating that our automated codesign approach is superior to sequential design of a CNN model and accelerator.
Multi-Mediated Semi-Quantum Key Distribution

A semi-quantum key distribution (SQKD) protocol allows two users $A$ and $B$ to establish a shared secret key that is secure against an all-powerful adversary $E$ even when one of the users (e.g., $B$) is semi-quantum or classical in nature while the other is fully-quantum. A mediated SQKD protocol allows two semi-quantum users to establish a key with the help of an adversarial quantum server. We introduce the concept of a multi-mediated SQKD protocol where two (or more) adversarial quantum servers are used. We construct a new protocol in this model and show how it can withstand high levels of quantum noise, though at a cost to efficiency. We perform an information theoretic security analysis and, along the way, prove a general security result applicable to arbitrary MM-SQKD protocols. Finally, a comparison is made to previous (S)QKD protocols.
Uncovering Blockchain Research Publications in Asia Compared to the Rest of the World

A Web of Science (WoS) search suggests that Asian countries generated 45.2% of all blockchain-related research articles. However, blockchain studies have neglected to examine the intellectual development of blockchain technology in Asia. Mainland China has become the center of blockchain scholarship in Asia; South Korea and Singapore have also become important. Comparing blockchain articles published in Asia to those published outside of Asia indicates that both article types feature high levels of research focusing on computer science. However, Asian studies pay more attention to engineering technology issues, while non-Asian studies pay more attention to sociological issues such as those involving business and law. Moreover, blockchain R&D collaboration between universities, industry, and government is less salient in Asia than it is in the rest of the world. If the Asian region is to become a global blockchain center, it must promote research on the commercialization of blockchains and in the social science dimensions of the technology. Asian researchers should expand their partnerships with industry.
Opinion Spam Detection in Product Reviews Using Self-Training Semi-Supervised Learning Approach

The review of a product can influence a buyer's decision to buy the product. In addition to influencing buyer decisions, fake reviews can also confuse buyers who are looking for product information from honest and genuine reviews. We need a system that can filter spam to reduce the negative influence on product selling and product review writings. Spam that will be detected is the type of brand only spam and not a review. Those types get the initial label through manual labeling. Manual labeling requires a lot of time and effort. Therefore, in this paper, we proposed a self-training semi-supervised learning approach. This method labels spam from the prediction of the labeled training data. The best results were obtained with a scenario without stemming, merging of review centric features and bigram, SMOTE borderline1 oversampling and Polynomial SVM kernel that has accuracy 86.33%.
An Empirical Study of Software Exceptions in the Field using Search Logs

Background: Software engineers spend a substantial amount of time using Web search to accomplish software engineering tasks. Such search tasks include finding code snippets, API documentation, seeking help with debugging, etc. While debugging a bug or crash, one of the common practices of software engineers is to search for information about the associated error or exception traces on the internet. Aims: In this paper, we analyze query logs from Bing to carry out a large scale study of software exceptions. To the best of our knowledge, this is the first large scale study to analyze how Web search is used to find information about exceptions. Method: We analyzed about 1 million exception related search queries from a random sample of 5 billion web search queries. To extract exceptions from unstructured query text, we built a novel machine learning model. With the model, we extracted exceptions from raw queries and performed popularity, effort, success, query characteristic and web domain analysis. We also performed programming language-specific analysis to give a better view of the exception search behavior. Results: Using the model with an F1-score of 0.82, our study identifies most frequent, most effort-intensive, or less successful exceptions and popularity of community Q&A sites. Conclusion: These techniques can help improve existing methods, documentation and tools for exception analysis and prediction. Further, similar techniques can be applied for APIs, frameworks, etc.
Unbabel's Participation in the WMT20 Metrics Shared Task.

We present the contribution of the Unbabel team to the WMT 2020 Shared Task on Metrics. We intend to participate on the segmentlevel, document-level and system-level tracks on all language pairs, as well as the “QE as a Metric” track. Accordingly, we illustrate results of our models in these tracks with reference to test sets from the previous year. Our submissions build upon the recently proposed COMET framework: we train several estimator models to regress on different humangenerated quality scores and a novel ranking model trained on relative ranks obtained from Direct Assessments. We also propose a simple technique for converting segment-level predictions into a document-level score. Overall, our systems achieve strong results for all language pairs on previous test sets and in many cases set a new state-of-the-art.
Cryptocurrency Mining: Asymmetric Response to Price Movement

I model the entry and exit decisions of cryptocurrency miners. I argue that miners using Application Specific Integrated Circuit (ASIC) equipment, with a lower salvage value, have asymmetric reactions to price shocks, while miners using Graphics Processing Unit (GPU) equipment, with a higher salvage value, have more symmetric reactions to price shocks. Estimating the long-run equilibrium relationship between cryptocurrency price and hashrate (the aggregate computing power of miners), I show that Bitcoin miners, who use ASIC equipment, respond only to negative disequilibria (when hashrate is relatively low compared to price). Meanwhile, Ether miners, when using GPU equipment, respond symmetrically to disequilibria. These results have both practical and theoretical implications for cryptocurrency pricing and applications of blockchain technology.
Introduction to Normalizing Flows for Lattice Field Theory

This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows. The ideas and approaches proposed in arXiv:1904.12072, arXiv:2002.02428, and arXiv:2003.06413 are reviewed and a concrete implementation of the framework is presented. We apply this framework to a lattice scalar field theory and to U(1) gauge theory, explicitly encoding gauge symmetries in the flow-based approach to the latter. This presentation is intended to be interactive and working with the attached Jupyter notebook is recommended.
Accelerating the simulation of annual bifacial illumination of real photovoltaic systems with ray tracing

Accurate modeling of bifacial illumination is critical to improve the prediction of the energy yield of bifacial solar systems. Monte Carlo ray tracing is the most powerful tool to accomplish this task. In this work, we accelerate Monte Carlo ray tracing of large solar systems by nearly 90%. Our model achieves root-mean-square error values of 7.9% and 37.2% for the front and rear irradiance compared against single-axis tracking field reference data, respectively. The rear irradiance modeling error decreases to 18.9% if suspected snow periods are excluded. Crucially, our full system simulations show that surrounding ground surfaces affect the rear irradiance deep into the system. Therefore, unit system simulations cannot necessarily ignore the influence of the perimeter of large installations to accurately estimate annual yield. Large-scale simulations involving high-performance supercomputing were necessary to investigate these effects accurately, calibrate our simplified models, and validate our results against experimental measurements.
Multiview Deep Graph Infomax to Achieve Unsupervised Graph Embedding

Unsupervised graph embedding aims to extract highly discriminative node representations that facilitate the subsequent analysis. Converging evidence shows that a multiview graph provides a more comprehensive relationship between nodes than a single-view graph to capture the intrinsic topology. However, little attention has been paid to excavating discriminative representations of each node from multiview heterogeneous networks in an unsupervised manner. To that end, we propose a novel unsupervised multiview graph embedding method, called multiview deep graph infomax (MVDGI). The backbone of our proposed model sought to maximize the mutual information between the view-dependent node representations and the fused unified representation via contrastive learning. Specifically, the MVDGI first uses an encoder to extract view-dependent node representations from each single-view graph. Next, an aggregator is applied to fuse the view-dependent node representations into the view-independent node representations. Finally, a discriminator is adopted to extract highly discriminative representations via contrastive learning. Extensive experiments demonstrate that the MVDGI achieves better performance than the benchmark methods on five real-world datasets, indicating that the obtained node representations by our proposed approach are more discriminative than by its competitors for classification and clustering tasks.
Touch keystroke dynamics for demographic classification

Soft biometric traits are not fully distinctive in recognition tasks, but they can effectively added to biometric recognition systems to improve the overall performance. In this work, the focus is on the analysis of touch keystroke dynamics of smartphone’s users for demographic classification in age, gender and user experience. Starting from the data collected in three publicly available datasets and using traditional lightweight machine learning classification algorithms, the results reported in this work shows that an effective demographic analysis can be achieved as well as continuous authentication could be improved. Moreover, the study emphasize a critical issue affecting the experimental protocols in soft biometric analysis, discussing how sensibly the performance of a system can increase on a not wise splitting of the samples in the datasets.
Online MRAC method using neural networks based on variable learning rate for non-linear systems

This paper presents a new algorithm of a neural network model reference adaptive controller that uses a variable learning rate. It illustrates how the learning rate affects directly training speed. The neural network training algorithms, such as backpropagation, suffer from low-convergence speed as time-consuming. While increasing the learning rate may help to proceed much faster, it can result in unstable training in terms of weights divergence. Therefore, we propose a neural controller training algorithm using a variable learning rate which is capable of speeding up the learning process significantly and it can provide simultaneously stability of the learning process. The results of simulation show that using variable learning rate has better effects both on response time and on tracking performance.
Early Detection of DDoS Attacks using Photonic Neural Networks

Deep Learning (DL) has been extensively used in challenging tasks including security applications such as Distributed Denial of Service (DDoS) attacks. However, the high speed requirements of such applications along with the high complexity of DL models restrict the practical use of DL in real systems. Photonic neuromorphic hardware provides several advantages over electronic counterparts since it can operate at very high frequencies with lower power consumption. To this end, in this paper, we propose employing a photonic neuromorphic lookaside accelerator, aiming to perform real-time traffic inspection, enabling us to detect port-scanning attacks, which are indicative of DDoS attacks. We have designed, trained, and evaluated a Photonic Neural Network (PNN) capable of detecting DDoS attacks and operating on such photonic neuromorphic lookaside accelerators. The experimental evaluation is performed on Transport Control Protocol (TCP) traces obtained by simulating a port scanning attack and demonstrates the effectiveness of the proposed approach.
A detection method of the rescue targets in the marine casualty based on improved YOLOv5s

In recent years, with the deep exploitation of marine resources and the development of maritime transportation, ship collision accidents occur frequently, which leads to the increasingly heavy task of maritime Search and Rescue (SAR). Unmanned Aerial Vehicles (UAVs) have the advantages of flexible maneuvering, robust adaptability and extensive monitoring, which have become an essential means and tool for emergency rescue of maritime accidents. However, the current UAVs-based drowning people detection technology has insufficient detection ability and low precision for small targets in high-altitude images. Moreover, limited by the load capacity, UAVs do not have enough computing power and storage space, resulting in the existing object detection algorithms based on deep learning cannot be directly deployed on UAVs. To solve the two issues mentioned above, this paper proposes a lightweight deep learning detection model based on YOLOv5s, which is used in the SAR task of drowning people of UAVs at sea. First, an extended small object detection layer is added to improve the detection effect of small objects, including the extraction of shallow features, a new feature fusion layer and one more prediction head. Then, the Ghost module and the C3Ghost module are used to replace the Conv module and the C3 module in YOLOv5s, which enable lightweight network improvements that make the model more suitable for deployment on UAVs. The experimental results indicate that the improved model can effectively identify the rescue targets in the marine casualty. Specifically, compared with the original YOLOv5s, the improved model mAP@0.5 value increased by 2.3% and the mAP@0.5:0.95 value increased by 1.1%. Meanwhile, the improved model meets the needs of the lightweight model. Specifically, compared with the original YOLOv5s, the parameters decreased by 44.9%, the model weight size compressed by 39.4%, and Floating Point Operations (FLOPs) reduced by 22.8%.
Reconciling Observations of Solar Irradiance Variability With Cloud Size Distributions

Clouds cast shadows on the surface and locally enhance solar irradiance by absorbing and scattering sunlight, resulting in fast and large solar irradiance fluctuations on the surface. Typical spatiotemporal scales and driving mechanisms of this intra-day irradiance variability are not well known, hence even one day ahead forecasts of variability are inaccurate. Here we use long term, high frequency solar irradiance observations combined with satellite imagery, numerical simulations, and conceptual modelling to show how irradiance variability is linked to the cloud size distribution. Cloud shadow sizes are distributed according to a power law over multiple orders of magnitude, deviating only from the cloud size distribution due to cloud edge transparency at scales below 750 meters. Locally cloud-enhanced irradiance occurs as frequently as shadows, and is similarly driven mostly by boundary layer clouds, but distributed over a smaller range of scales. We reconcile studies of solar irradiance variability with those on clouds, which brings fundamental understanding to what drives irradiance variability. Our findings have implications for not only for weather and climate modelling, but also for solar energy and photosynthesis by vegetation, where detailed knowledge of surface solar irradiance is essential.
Components and Development in Big Data System: A Survey

With the growth of distributed computing systems, the modern Big Data analysis platform products often have diversified characteristics. It is hard for users to make decisions when they are in early contact with Big Data platforms. In this paper, we discussed the design principles and research directions of modern Big Data platforms by presenting research in modern Big Data products. We provided a detailed review and comparison of several state-of-the-art frameworks and concluded into a typical structure with five horizontal and one vertical. According to this structure, this paper presents the components and modern optimization technologies developed for Big Data, which helps to choose the most suitable components and architecture from various Big Data technologies based on requirements.
A geometric-based approach to the maximum-speed state and output variables for some class of IMC structures

In this paper a new geometric-based approach to the maximum-speed state- and output-variable pair under perfect control design is proposed. Up to now, the maximum-speed perfect control has been connected with the pole-free instance having all closed-loop control system poles located at zero. The new results obtained through this paper show that the maximum-speed perfect control algorithm should be associated with the stable-pole case. The analytical results supported by the simulation example confirm the truthness of presented here innovative approach.
A VR Study on Freehand vs. Widgets for 3D Manipulation Tasks

We present in this article the results of a study based on a 3D user interface toolkit we developed which can be easily adapted and reused in VR applications featuring hand-tracking. We evaluated hand-based and widget-based manipulation techniques as well as different multimodal cues for 3D manipulation and system-control tasks. Our study compared the techniques in terms of performance and user acceptance. We found that free-hand manipulation is faster and preferred by the participants. We also analyzed the influence of the multimodal cues, finding valuable insights to integrate these cues and improve the user experience in 3D manipulation tasks.
LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC).

This paper describes the LIAAD system that was ranked second place in the Word-in-Context challenge (WiC) featured in SemDeep-5. Our solution is based on a novel system for Word Sense Disambiguation (WSD) using contextual embeddings and full-inventory sense embeddings. We adapt this WSD system, in a straightforward manner, for the present task of detecting whether the same sense occurs in a pair of sentences. Additionally, we show that our solution is able to achieve competitive performance even without using the provided training or development sets, mitigating potential concerns related to task overfitting