Reviewerly/exHarmony huggingface dataset:
Fault-Tolerant Quantum Machine Learning

Here, we focus on more traditional approaches to quantum machine learning which try to speed up classical routines by making use of fault-tolerant quantum computers. We discuss quantum machine learning algorithms based on linear algebra subroutines such as matrix inversion, and those based on amplitude amplification or Grover search. We will then have a look at how classical probabilistic models like Bayesian nets and Boltzmann machines can be implemented on a quantum computer, and finish with an idea of how to use superposition to represent ensembles of classifiers.
Improved Zero-Knowledge Argument of Encrypted Extended Permutation

Extended permutation (EP) is a generalized notion of the standard permutation. Unlike the one-to-one correspondence mapping of the standard permutation, EP allows to replicate or omit elements as many times as needed during the mapping. EP is useful in the area of secure multi-party computation (MPC), especially for the problem of private function evaluation (PFE). As a special class of MPC problems, PFE focuses on the scenario where a party holds a private circuit C while all other parties hold their private inputs \(x_1, \ldots , x_n\), respectively. The goal of PFE protocols is to securely compute the evaluation result \(C(x_1, \ldots , x_n)\), while any other information beyond \(C(x_1, \ldots , x_n)\) is hidden. EP here is introduced to describe the topological structure of the circuit C, and it is further used to support the evaluation of C privately.For an actively secure PFE protocol, it is crucial to guarantee that the private circuit provider cannot deviate from the protocol to learn more information. Hence, we need to ensure that the private circuit provider correctly performs an EP. This seeks the help of the so-called zero-knowledge argument of encrypted extended permutation protocol. In this paper, we provide an improvement of this protocol. Our new protocol can be instantiated to be non-interactive while the previous protocol should be interactive. Meanwhile, compared with the previous protocol, our protocol is significantly (e.g., more than \(3.4\times \)) faster, and the communication cost is only around \(24\%\) of that of the previous one.
Integrated programmable controlled phase gate design for quantum information processing

Abstract An integrated programmable controlled-phase (CPHASE) gate has been proposed for quantum information processing applications. This gate can introduce arbitrary phase difference to the target qubit in the case of the control qubit being in the state of |1〉. As the desired phase difference can be utilized after fabrication, unlike the conventional controlled-phase gates that provide hard-coded phase shift, such an integrated gate is expected to pave the way for more versatile operations of current integrated optical circuits as well as possible new applications.
Trimming Data Sets: a Verified Algorithm for Robust Mean Estimation

The operation of trimming data sets is heavily used in AI systems. Trimming is useful to make AI systems more robust against adversarial or common perturbations. At the core of robust AI systems lies the concept that outliers in a data set occur with low probability, and therefore can be discarded with little loss of precision in the result. The statistical argument that formalizes this concept of robustness is based on an extension of the Chebyshev’s inequality first proposed by Tukey in 1960.
Miner Competition and Transaction Fees

In order to maintain the function of a decentralized financial system like Bitcoin, transaction fees are offered to engage miners in the transaction confirmation process. This paper investigates the effect of miner competition on the equilibrium transaction fees. We develop a game-theoretic model with costly entry into mining activities. We find that miners may strategically assemble fewer transactions into a block to reduce total fees, and as a result, to deter entry. Equilibrium transaction fees also depend on block rewards as a rise in total fees is accompanied by a drop in rewards. Our empirical analysis supports the model's predictions. We provide evidence on the existence of excess capacity in a block, taking into account the random confirmation process. The empirical findings demonstrate that heightened competition tends to increase the block size and total fees. Furthermore, the halving of rewards correlates to a fee hike.
Looking Farther in Parametric Scene Parsing with Ground and Aerial Imagery

Parametric models that represent layout in terms of scene attributes are an attractive avenue for road scene understanding in autonomous navigation. Prior works that rely only on ground imagery are limited by the narrow field of view of the camera, occlusions and perspective foreshortening. In this paper, we demonstrate the effectiveness of using aerial imagery as an additional modality to overcome the above challenges. We propose a novel architecture, Unified, that combines features from both aerial and ground imagery to infer scene attributes. We quantitatively evaluate on the KITTI dataset and show that our Unified model outperforms prior works. Since this dataset is limited to road scenes close to the vehicle, we supplement the publicly available Argoverse dataset with scene attribute annotations and evaluate on far-away scenes. We show both quantitatively and qualitatively, the importance of aerial imagery in understanding road scenes, especially in regions farther away from the ego-vehicle. All code, models, and data, including scene attribute annotations on the Argoverse dataset along with collected and processed aerial imagery, are available at https://bit.ly/2QsKNeR.
Fashionista: A Clothing Application with Collaborative Filtering Based Recommendation System

Recommender systems are used by various e-commerce sites to recommend different products to their customers. The products can be recommended based on the ratings given by the customers and various other implicit parameters, like purchase history or no of times a particular item was viewed. The recommendations in this project will be based on explicit feedback in the form of ratings. We use Matrix Factorization, which is a technique that discovers latent features and complex hidden relationship patterns among users or items with similar preferences or features, given a matrix to decompose. Gradient Descent is used to optimize the cost function. The e-commerce application will enable users to perform all the essential activities like rating, buying or adding a product to the wishlist and cart. Users can browse for items or explore the recommendations made to them.
Ant Colony Optimization Algorithms for Selection and Sequencing Problems

This paper is a simple tutorial for researchers interested in ant colony optimization (ACO) in general and max-min ant system (MMAS) in particular. The paper compares the differences in implementing these algorithms to solve sequencing and selection problems. For selection problems, we use the famous knapsack problem (KP) to demonstrate how MMAS can be used, wherease, for the sequencing problem, we use the famous travelling salesman problem (TSP). Results from the literature shows how a MMAS algorithm can outperform other meta-heuristics in solving these two types of problems.
FABERT: A Feature Aggregation BERT-Based Model for Document Reranking

In a document reranking task, pre-trained language models such as BERT have been successfully applied due to their powerful capability in extracting informative features from queries and candidate answers. However, these language models always generate discriminative features and pay less attention to generalized features which contain shared information of query-answer pairs to assist question answering. In this paper, we propose a BERT-based model named FABERT by integrating both discriminative features and generalized features produced by a gradient reverse layer into one answer vector with an attention mechanism for document reranking. Extensive experiments on the MS MARCO passage ranking task and TREC Robust dataset show that FABERT outperforms baseline methods including a feature projection method which projects existing feature vectors into the orthogonal space of generalized feature vector to eliminate common information of generalized feature vectors.
Multi-level Cohesion Information Modeling for Better Written and Dialogue Discourse Parsing

Discourse parsing has attracted more and more attention due to its importance on Natural Language Understanding. Accordingly, various neural models proposed and have achieved certain success. However, due to the scale limitation of corpus, outstanding performance still depends on additional features. Different from previous neural studies employing simple flat word level EDU (Elementary Discourse Unit) representation, we improve the performance of discourse parsing by employing cohesion information (In this paper, we regard lexical chain and coreference chain as cohesion information) enhanced EDU representation. In particular, firstly we use WordNet and a coreference resolution model to extract lexical and coreference chain respectively and automatically. Secondly, we construct EDU level graph based on the extracted chains. Finally, using Graph Attention Network, we incorporate the obtained cohesion information into EDU representation to improve discourse parsing. Experiments on RST-DT, CDTB and STAC show our proposed cohesion information enhanced EDU representation can benefit both written and dialogue discourse parsing, compared with the baseline model we duplicated.
An End-to-End Clustering Framework Based on Dynamic Threshold for SAR Images

Manually labeling SAR images is an extremely important but expensive task. Clustering SAR images without labels can effectively reduce the cost of labeling. The existing methods often fail to obtain the optimal solution because the combination of feature representation and clustering assignment, as well as the mining of various correlations behind the input image are ignored. To tackle these problems, an end-to-end deep clustering framework based on a dynamic threshold for SAR images is proposed. In this method, clustering is transformed into a binary pairwise classification problem, and the positive and negative image pairs with high confidence are selected gradually through a dynamic threshold to guide the network representation learning. Various correlations of images are mined and used to learn the most comprehensive representation by reconstructing and maximizing the mutual information between the deep and shallow layers of the network. Extensive experiments on MSTAR datasets show that this method achieves better clustering performance than some state-of-the-art methods.
Hybrid Car Trajectory by Genetic Algorithms with Non-Uniform Key Framing

In this chapter, we present a hybrid application of genetic algorithms to a problem of optimizing a car trajectory in a car race setting. The aim is to find a curve traversing the race track, or a trajectory, that minimizes the total distance, and hope that it allows a car to finish the race faster. For this, we start by mapping and segmenting the track and then setting non-uniform key frames based on significant intervals where the track is almost straight and on long curves going in a single direction. The trajectory’s value is anchored in the key frame points, insuring an overall smoothness. Then on each interval, we apply the genetic algorithm to compute the trajectory, and piece it together at the end. We compare the results with a procedurally computed trajectory and with trajectories obtained by the genetic algorithm with uniform key framing.
An Error Analysis in Writing Descriptive Text of Tenth Grades Students at SMK Antartika 1 Sidoarjo

The aimed of this study to identify the error in writing descriptive text made by the tenth grade students of SMK Antartika 1 Sidoarjo. The researcher utilizes quantitative research method. The population of this study is the tenth grade students of SMK Antartika 1 Sidoarjo. The researcher uses total sampling as the sampling technique. The researcher takes 34 students of X TPM 2. The data instrument of this study is students’ test result. In this study, the researcher used 3 types of analyzed data based on Miles and Huberman theory. The researcher analyzed the errors made by the students use the theory of Corder (1973). The result showed that The tenth graders’ of SMK 1 Antartika Sidoarjo still made error in writing descriptive text in four kinds of error based on Corder (1973) theory, which are omission, addition, selection, and ordering. The students made error in omission 67 times or 30% of total error, 21 times or 10% made error in addition, 103 times or 47% made error in selection, and 30 times or 13% made error in ordering. The most error the students made is selection error. It means that the students still do not know how to use correct word that is appropriated with the context. The student still translate Indonesia into English directly without considering the context.
Analysis and Research on the Performance of Solar Concentration Based on Big Data and Machine Learning

Empirical formulas for PTC optical efficiency calculation are difficult and costly to obtain from rigorous comparative experiments, whereas simpler optical modeling methods inadequately incorporate realistic optical effects. In this article, algorithms are respectively developed to calculate the geometric concentration ratio (Cg) of linear Cassegrainian solar concentrators (CSC) with a secondary flat mirror based on the way of edge rays from solar sources to a flat-plate receiver. On the basis of the large amount of data generated, machine learning and Python language programming methods are used to analyze and process the data, and the functional relationship between the concentration ratio and each parameter is obtained. The learning and training effect is good, and the ideal result is achieved.
Distributed Storage and Query Method of Satellite Image Data based on HBase

With the rapid development of aerospace industry, satellite image data has shown a blowout growth. At present, the annual data reception capacity of a satellite has reached TB level, and the data size of a satellite image can reach about 2GB. This poses a serious challenge. Based on the Hadoop framework, this paper studies the HBase-based satellite image big data solution, and provides three query methods as HBase, Hive, and Impala according to the application scenario.
Research on the Intelligent Question Answering based on knowledge graph

Intelligent question answering based on knowledge graphs has been a research hotspot in recent years. The realization of intelligent question answering based on knowledge graphs is introduced from four aspects: template-based, semantic analysis, deep learning, and knowledge graph embedding. problem. Combined with the current development of artificial intelligence technology, the focus is on intelligent question answering based on deep learning, which will help more researchers to devote themselves to intelligent question answering research, develop question answering systems suitable for different fields according to the needs of different industries, and improve the level of intelligent information service in society.
Cultivation of Humanistic Accomplishment of SPORT Majors in Colleges and Universities in the Information Age

Education is the foundation of a country’s long-term development. The level of education and teaching is an important criterion that affects the country’s cultural soft power and overall national strength. At this stage, with the increasing development of the social and economic environment and the long-term inertial use of the exam-oriented education model with exams as the core, not only students, teaching concepts are backward, lack of cultural literacy, and the concept of utilitarianism has greatly affected the social mentality of citizens. The formation of humanism is also a necessary condition for the development of humanism. This article mainly introduces the research on the cultivation of humanistic literacy of college physical education students in the information age. A prerequisite for the level. The exSPORTrimental results of this paSPORTr show that the research on the cultivation of humanistic literacy of college physical education students in the information age has increased the comprehensive quality of college physical education students by 9%. The limitations of the research on the cultivation of humanistic literacy of college physical education students in the information age, for Provide good indoor path planning methods and approaches for the application of ant colony algorithm to analyze, discuss and summarize, so as to enrich the academic research results.
Constrained Path Planning and Guidance in General Wind Fields

This paper presents an optimal planning/guidance approach for a UAV point-to-point navigation in 2D, under wind influence, with the imposition of a general desired airspeed profile. A cost function weighting the travel time and the control effort is minimized through the Pontryagin’s Minimum Principle. An analytical expression for the optimal heading to assure minimum-time flight is also derived. A general wind case in constrained path navigation is solved through an iterative procedure, using artificial potential fields.
Facilitating Student Co-Authored Papers in LIS Education Research: A Case Study from the LIS Classroom

This article describes the process and tools used to facilitate a collaborative student co-authored paper that was recently published as a short communication in the Journal of Education for Library and Information Science. As the instructor of the course in which the paper project took place, I provide direction here for other LIS instructors on how to successfully facilitate publishable-quality student co-authored papers as an in-class activity using online collaborative teaching tools. This paper project is especially relevant to courses or projects that include social justice topics.
Voxplan: A 3D Global Planner using Signed Distance Function Submaps

The ability to safely navigate through complex and cluttered environments is required for a wide range of robotics applications. This paper introduces a framework to compute safe global paths in maps represented as collections of 3D Signed Distance Function (SDF) submaps. Such maps are able to maintain global consistency in spite of odometry drift. However, computationally efficient global path planning in this context remains a challenging problem. We present a planning approach based on pre-computed local graphs, computed in each submap, that are linked to form a global path at planning time. To ensure globally safe paths, planning algorithms make frequent queries to the submap collection, which grows over time as the agent collects observational data. We present an efficient algorithm for performing these queries, through the use of a spatial hash table. We analyze the performance of our proposal extensively in simulation and real-world environments, and compare our approach to state- of-the-art planning approaches designed for monolithic maps, extended to submap-based maps. We show the efficacy of our method at adapting to global map deformations, while significantly reducing the planning time to an average of ~1.2 seconds, a reduction by 90 % compared to classical monolithic approaches.
BERT-based regression model for micro-edit humor classification task

Choosing the more humorous edited headline is a subfield of humor detection and generation tasks. This paper tries to deal with the second subtask of the SemEval-2020 shared task, “Assessing Humor in Edited News Headlines”. It aims to determine how machines can understand humor generated by an atomic edit to the original headline and automatically pick up the funnier version among two different edits. Given that both substitute words on the same original text are scored using crowdsourcing, we attempt not only classification but also the regression model on this special task. As for the training process, we first consider using two different embedding approaches, including GloVe and BERT, then further use different forms of neural network such as a fully connected layer, BiLSTM, and GRU. According to the experimental results, our BERT-based model gets a 64% accuracy performance, ranking second in the competition over 50 teams. Furthermore, by comparing the result and performance of the above models, we pick up some classic wrongly predicted samples and analyze the potential reasons for future study. The experimental results illustrate that mainly the revised sentence accounts for edit humor, whereas the original sentence does not have any effect. Besides, the combination of the revised and original sentence as input receives the best output, which shows that edit humor is probably produced from the edited sentence and the difference before and after modification.
New Quantum Image Manipulation System (NQIMS)

Quantum Image Processing (QIP) is focused on extending conventional image processing tasks and operations to quantum computing framework. It is utilizing quantum computing technologies to capture, manipulate, and recover quantum images in different formats and purposes. This paper proposes new technique using quantum principles and Bloch’s sphere applied on the color image directly using HSV color system. This new mapping technique between HSV color system and Bloch’s sphere uses for object detection. Also, this paper shows how to use usefully this new technique solving different image processing applications and important problems such as one of the recent challenges for finding 3D image from single 2D color image. The main challenge of this proposes technique that the image is not 3-D image and it avoiding the quantum measuring mathematical complexity.
Lightbulbs and ducks: The power of tech platforms to make waste disappear

Large technology platform companies have created data centres, filled with banks of data storage and processing equipment to collect, store and process the data created in the last two decades. These consume vast amounts of electricity and emit proportional amounts of heat as waste. This article considers the power of the big technology platforms to direct attention away from these by-products. The interaction of law and power raises novel questions about how to hold power to account. If the law is to effectively respond, it must confront the myriad ways that power is exercised
Towards Remote Pixelless Displays

Next generation displays have to resolve major design challenges for providing frictionless user experiences. To address these issues, we introduce two concepts named as “Beaming Displays” and “Patch Scanning Displays”.
Automated Replication of Tuple Spaces via Static Analysis

Coordination languages for tuple spaces can offer significant advantages in the specification and implementation of distributed systems, but often do require manual programming effort to ensure consistency. We propose an experimental technique for automated replication of tuple spaces in distributed systems. The system of interest is modelled as a concurrent Go program where different threads represent the behaviour of the separate components, each owning its own local tuple repository. We automatically transform the initial program by combining program transformation and static analysis, so that tuples are replicated depending on the components' read-write access patterns. In this way, we turn the initial system into a replicated one where the replication of tuples is automatically achieved, while avoiding unnecessary replication overhead. Custom static analyses may be plugged in easily in our prototype implementation. We see this as a first step towards developing a fully-fledged framework to support designers to quickly evaluate many classes of replication-based systems under different consistency levels.