Reviewerly/exHarmony huggingface dataset:
Simulating quantum circuits by adiabatic computation: Improved spectral gap bounds

Adiabatic quantum computing is a framework for quantum computing that is superficially very different to the standard circuit model. However, it can be shown that the two models are computationally equivalent. The key to the proof is a mapping of a quantum circuit to an adiabatic evolution, and then showing that the minimum spectral gap of the adiabatic Hamiltonian is at least inverse polynomial in the number of computational steps $L$. In this paper we provide two simplified proofs that the gap is inverse polynomial. Both proofs result in the same lower bound for the minimum gap, which for $L\ensuremath{\gg}1$ is ${min}_{s}{\mathrm{\ensuremath{\Delta}}}_{0}\ensuremath{\gtrsim}{\ensuremath{\pi}}^{2}/[8{(L+1)}^{2}]$, an improvement over previous bounds. Our first method is a direct approach based on an eigenstate ansatz, while the second uses Weyl's theorem to leverage known exact results into a bound for the gap. Our results suggest that it may be possible to use these methods to find bounds for spectral gaps of Hamiltonians in other scenarios.
Some remarks on non projective Frobenius algebras and linear codes

With a small suitable modification, dropping the projectivity condition, we extend the notion of a Frobenius algebra to grant that a Frobenius algebra over a Frobenius commutative ring is itself a Frobenius ring. The modification introduced here also allows Frobenius finite rings to be precisely those rings which are Frobenius finite algebras over their characteristic subrings. From the perspective of linear codes, our work expands one’s options to construct new finite Frobenius rings from old ones. We close with a discussion of generalized versions of the MacWilliams identities that may be obtained in this context.
Retrieving Linguistic Information from a Corpus on the Example of Negation in Chinese

The paper deals with corpus analysis of negation in Chinese, namely the negatives bù 不 and méi/ méiyǒu没/没有. The adverbs BU and MEI are two of the most frequent negatives in Chinese. The aim of this study is to present statistical data together with linguistics analysis. The results provide empirical evidence of discrepancy between “authentic” language data versus linguistic prescription with practical implications for second-language acquisition. The findings inter alia suggest a new approach to verb categorisation.
Soiling impact on direct normal irradiation measurements

Accurate and reliable solar radiation measurements are necessary to plan, finance and operate solar power projects. Soiling of sensors is an important source for underestimation of solar radiation. Earlier studies showed that especially pyrheliometers measuring direct normal irradiance (DNI) suffer from high susceptibility of dirt, while Rotating Shadowband Irradiometers (RSI) showed almost a factor of 10 better soiling behavior. This study extends the experiences by more extensive comparisons at another site in Spain and one site in Germany both covering one year. For the first time the soiling behavior of four different pyrheliometer makes are compared in parallel. At the Andasol 3 CSP plant in Southern Spain daily soiling rates vary between 0.1% and 0.4% with a mean of 0.14% for pyrheliometers, while RSI soiling rate on average was only 0.035%. At the site near Freiburg, Southern Germany, the soiling rates of pyrheliometers vary between 0.2% and 0.6% with a mean of 0.4%, while RSI soiling is about 0.1% per day. Thus, the resistance to soiling of the RSI in our experiments is only about a factor of 4 better than for pyrheliometers. This is significantly less than found earlier but still a good reason to better apply RSI instruments in case pyrheliometers cannot be cleaned with high frequency – ideally daily, but at least weekly or more in more dusty regions. In agreement with other studies we find a high dependence of soiling rates on the location and its characteristics. Comparisons between various pyrheliometer types show that pyrheliometers from various manufacturers show similar high soiling rates. But a protection cap lessens the soiling susceptibility, as the only pyrheliometer without such a hood shows significantly higher soiling rates. The specification of daily soiling rates presumes a linear behavior with time. From a longterm test where cleaning was omitted over a period of about five months it is shown that after several weeks saturation effects reduce the soiling process. From analysis if this experiment we conclude that the soiling of radiometers follows an exponential dependency. Until a total soiling of around 4% is reached a linear soiling rate is a good approximation.
Emotion Measurement from Attention Analysis on Imagery in Virtual Reality

Measuring human emotion from affective interaction is an important part of computing for mental health applications. Our work refers to non-obtrusive emotion measurement from eye tracking that gets increasingly ubiquitous, e.g., in Virtual Reality (VR) technology. We present a concept and first results of a feasibility study in which affectively weighted imagery of an image database is presented and integrated using discriminative observation, multi-object tracking and video-gaming. We measured attention preference for affective image classes and found relevant correlation with data extracted from a questionnaire on emotional states (MDBF) that would eventually substantiate basic valence classification from eye tracking data. The playful approach using the well-known concentration (pairs) game enables frequent repetition of the measurements in mental health care, therapeutic or pedagogical scenarios.
Using Sentiment Representation Learning to Enhance Gender Classification for User Profiling

User profiling means exploiting the technology of machine learning to predict attributes of users, such as demographic attributes, hobby attributes, preference attributes, etc. It’s a powerful data support of precision marketing. Existing methods mainly study network behavior, personal preferences and post texts to build user profile. Through our data analysis of micro-blog, we find that females show more positive and have richer sentiments than males in online social platform. This difference is very conducive to the distinction between genders. Therefore, we argue that sentiment context is important as well for user profiling. In this paper, we propose to predict one of the demographic labels: gender by exploring micro-blog user posts. Firstly we build a sentiment polarity classifier in advance by training a Long Short-Term Memory (LSTM) model. Next we extract sentiment representations from LSTM middle layer. Lastly we combine sentiment representations with virtual document vectors to train a basic MLP network for gender classification. We conduct experiments on a dataset provided by SMP CUP 2016 in China. Experimental results show that our approach can improve gender classification accuracy by 5.53%, compared with classical MLP gender classification without sentiment context.
Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity

Zeroth-order (a.k.a, derivative-free) methods are a class of effective optimization methods for solving complex machine learning problems, where gradients of the objective functions are not available or computationally prohibitive. Recently, although many zeroth-order methods have been developed, these approaches still have two main drawbacks: 1) high function query complexity; 2) not being well suitable for solving the problems with complex penalties and constraints. To address these challenging drawbacks, in this paper, we propose a class of faster zeroth-order stochastic alternating direction method of multipliers (ADMM) methods (ZO-SPIDER-ADMM) to solve the nonconvex finite-sum problems with multiple nonsmooth penalties. Moreover, we prove that the ZO-SPIDER-ADMM methods can achieve a lower function query complexity of $O(nd+dn^{\frac{1}{2}}\epsilon^{-1})$ for finding an $\epsilon$-stationary point, which improves the existing best nonconvex zeroth-order ADMM methods by a factor of $O(d^{\frac{1}{3}}n^{\frac{1}{6}})$, where $n$ and $d$ denote the sample size and data dimension, respectively. At the same time, we propose a class of faster zeroth-order online ADMM methods (ZOO-ADMM+) to solve the nonconvex online problems with multiple nonsmooth penalties. We also prove that the proposed ZOO-ADMM+ methods achieve a lower function query complexity of $O(d\epsilon^{-\frac{3}{2}})$, which improves the existing best result by a factor of $O(\epsilon^{-\frac{1}{2}})$. Extensive experimental results on the structure adversarial attack on black-box deep neural networks demonstrate the efficiency of our new algorithms.
The Research of Blended Learning Model of the “Apple Teacher” Program

The "Apple Teacher" program adopts a mixed learning mode in course teaching, combining network resources with the real environment to form "Online Learning + Teaching Assistant Support; Offline Class + Theme Learning; Exchange Discussion + Summary Reflection" in one of the blended learning modes and "Basic Knowledge-Self-testing-Theme Learning-Self-creation-Summary Reflection" form of learning activities."The national medium and long-term education reform and development program outline (2010-2020)" since its launch, pays more and more attention to education informatization in our country.In the "Internet+" era, information technology and teaching idea constantly updated.The combination of online blended learning mode has become a trend, and blended learning effect has already become the important direction of teaching reform in education field.In the "Apple Teacher" program, learners take an active part in learning, actively construct and realize the harmonious unity of "Individual Knowledge Construction-Learning Community".
XCMRC: Evaluating Cross-lingual Machine Reading Comprehension

We present XCMRC, the first public cross-lingual language understanding (XLU) benchmark which aims to test machines on their cross-lingual reading comprehension ability. To be specific, XCMRC is a Cross-lingual Cloze-style Machine Reading Comprehension task which requires the reader to fill in a missing word (we additionally provide ten noun candidates) in a sentence written in target language (English / Chinese) by reading a given passage written in source language (Chinese / English). Chinese and English are rich-resource language pairs, in order to study low-resource cross-lingual machine reading comprehension (XMRC), besides defining the common XCMRC task which has no restrictions on use of external language resources, we also define the pseudo low-resource XCMRC task by limiting the language resources to be used. In addition, we provide two baselines for common XCMRC task and two for pseudo XCMRC task respectively. We also provide an upper bound baseline for both tasks. We found that for common XCMRC task, translation-based method and multilingual sentence encoder-based method can obtain reasonable performance but still have much room for improvement. As for pseudo low-resource XCMRC task, due to strict restrictions on the use of language resources, our two approaches are far below the upper bound so there are many challenges ahead.
Geomorphology of Las Derrumbadas dome complex, Puebla Mexico

This paper comprises a geomorphologic map of the Las Derrumbadas (LDR) volcanic complex in Mexico. The complex stretches over more than 285 km2, the stratigraphic column encompasses a range age from Cretacious to Quaternary. The methodology used involved the analysis of digital morphometric models, satellite imagery and absolute datings. The result is a 1:40,000 scale map, which is divided into two large geomorphologic groups in terms of the dominant processes: exogenic and endogenic This, in turn, are divided into various subgroups representing the morphological complexity of the study area, principally related to volcanic processes and their coherence with slope processes.
Automated Evolutionary Design of Fault-Tolerant Logic Circuits

Currently, fault tolerance of electronic equipment requires special attention.Application field of integrated circuits expands; at the same time, permissible limits of destabilizing effects, which increase vulnerability of integrated circuits, grow.It is often necessary to take into account requirements for fault tolerance and apply various methods and tools for developing the most stable circuits as early as at the design stage.As the result, there is a large demand for design automation tools for failure-and faulttolerant integrated circuits.This paper presents a method for combinational circuits synthesis based on general principles of evolutionary algorithms.This method allows synthesizing comparatively small logic circuits that are resistant to random failures induced by hits of heavy charged particles.
Sentence Similarity Algorithm Based on Fused Bi-Channel Dependency Matching Feature

Many tasks of natural language processing such as information retrieval, intelligent question answering, and machine translation require the calculation of sentence similarity. The traditional calculation methods used in the past could not solve semantic understanding problems well. First, the model structure based on Siamese lack of interaction between sentences; second, it has matching problem which contains lacking position information and only using partial matching factor based on the matching model. In this paper, a combination of word and word’s dependence is proposed to calculate the sentence similarity. This combination can extract the word features and word’s dependency features. To extract more matching features, a bi-directional multi-interaction matching sequence model is proposed by using word2vec and dependency2vec. This model obtains matching features by convolving and pooling the word-granularity (word vector, dependency vector) interaction sequences in two directions. Next, the model aggregates the bi-direction matching features. The paper evaluates the model on two tasks: paraphrase identification and natural language inference. The experimental results show that the combination of word and word’s dependence can enhance the ability of extracting matching features between two sentences. The results also show that the model with dependency can achieve higher accuracy than these models without using dependency.
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely on its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language understanding in order to perform complex reasoning in the visual world. However, initial advancements combining standard vision and language methods with imitation and reinforcement learning algorithms have shown EQA might be too complex and challenging for these techniques. In order to investigate the feasibility of EQA-type tasks, we build the VideoNavQA dataset that contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task. We investigate several models, adapted from popular VQA methods, on this new benchmark. This establishes an initial understanding of how well VQA-style methods can perform within this novel EQA paradigm.
Teaching ICT Skills to Children and the Empowerment of Female College Students in STEM in Japan

According to the Japanese government, "Society 5.0" is defined as "a future society that Japan should aspire to" and we will be able to solve our social problems with the use of Big Data, IoT, AI, and robots there. For this reason, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) has focused its attention on teaching computing skills from early stages in formal education. It was decided to teach computing skills at elementary schools in 2020 and junior high schools in 2021. It will be particularly important to encourage female students to be involved in using computer skills in the future and this paper has its purpose to introduce a project of female students at a national institute of technology in Japan who teach programming a robot to younger generations. Their goal is to foster interest in STEM, particularly among female pupils and introduce them to role models through workshops because the number of female students is very low in this field in our country.
VizMe: An Annotation-Based Program Visualization System Generating a Compact Visualization

As the scale of software is getting large and complex, the development cost of software scales up to lots of man-months. The maintenance cost of complex software is also increasing and it is usually more than the development cost. Understanding the underlying logic and modifying the actual code consume most part of the maintenance. However, the first step, say the understanding of codes, is extremely hard for even professional developers especially when the code to modify is written by other programmers. Though several visualization methods, such as flowcharts and NS-charts, have been proposed up to now, it is still practically hard to adopt such methods in a real analysis of an existing code because the complexity of the code results in complex visualization results. The complexity of these results hinders the developers in drawing them directly or even in taking a look of them. This paper introduces VizMe, an automatic visualization tool generating a compact visualization. The key of the compact visualization is the adoption of the meta-tags, the annotations attributed to the code blocks. VizMe accepts the source code written in C, Java, or Scheme and produces the visual output including flowcharts, PADs (problem analysis diagrams), and pseudo codes.
Comparison of Four Nature Inspired Clustering Algorithms: PSO, GSA, BH and IWD

Nature uses an optimized approach to do all type of jobs. Thus many naturalSubhra Pal, Shankho phenomena are mathematically modelled as meta-heuristic optimization algorithms. These meta-heuristic optimization algorithms are used in different fields to find an optimalHira, Rupkatha minimum or maximum solution where deterministic algorithm can’t be used. Clustering is one such field where different meta-heuristic are used. Although various such algorithmsPal, Somnath claim superiority over others, in this work, four different meta-heuristic algorithm based clustering are compared to determine which one is superior.
PSO Optimal Parameters and Fitness Functions in an NLP Task

The present article investigates the optimal setup of a particle swarm algorithm for a specific natural language processing task. This task consists of identifying syntactic phrases in unconstrained natural language texts, by using attractive and repulsive forces between neighbouring words. Here, a number of avenues for improving the swarm effectiveness are investigated, clustered around two main research directions. The first direction involves the optimal choice of main swarm parameters, namely the inertia, cognitive and social parameters. The research question is if the values of these parameters may be selected to give a substantial improvement in the optimization process, and whether the best values recommended for other tasks are effective in the chosen task. The second direction involves the choice of fitness function which determines the fitness of each solution, and guides the optimization process. Lesser used fitness functions are applied, to determine whether they can generate an improved phrasing solution. Experiments show these two research directions to lead to a substantial improvement in the phrasing accuracy.
Performance of UV and IR Sensors for Inspections of Power Equipment

Electric power infrastructure, such as transmission lines or substations, is usually routinely inspected to assess its condition. The vast majority of typical defects in power transmission equipment manifests itself either through corona phenomena or through thermal effects. Therefore, an IR camera and a solar blind UV camera are sufficient for the detection of most defects in power transmission equipment. In the past, many network operators have relied mostly on manual inspections. In recent years, however, manned as well as unmanned aerial inspection methods, which are significantly more time effective, have become increasingly affordable and are therefore gaining in popularity rapidly.To obtain meaningful measurement results, many factors must be taken into account, which can even be difficult with conventional, static measurements. In the case of highly dynamic measurement practices (airborne or vehicle based), the combination of velocity and distance presents further challenges.This contribution is focused on the detection performance of UV and IR sensors under dynamic conditions. For this purpose, experiments were carried out with a typical IR and UV/corona camera at various distances to artificial defects. Additionally, a method for the automatic evaluation of UV und IR data based on machine learning is presented.
Identity-based Conditional Privacy-Preserving Authentication Scheme Resistant to Malicious Subliminal Setting of Ephemeral Secret

In this paper we propose a modification of the Identity Based Conditional Privacy-Preserving Authentication Scheme (CPPA), which is based on Schnorr Signature Scheme (SS). The applicability and the security of the scheme is mainly considered in Intelligent Transportation Systems. We discuss scenarios with subliminal malicious setting of a ephemeral secret. We present a new, stronger security model for the scheme in which we allow the adversary to choose random values used during signing process. We define the SS to be secure if the advantage of the adversary in this model is negligible. Finally we prove the security of the modified Identity Based CPPA in our stronger model.
Is Deep Reinforcement Learning Really Superhuman on Atari?

Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is not straightforward. In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed play time can lead to very different performance. In this work, we discuss the difficulties of comparing different agents trained on ALE. In order to take a step further towards reproducible and comparable DRL, we introduce SABER, a Standardized Atari BEnchmark for general Reinforcement learning algorithms. Our methodology extends previous recommendations and contains a complete set of environment parameters as well as train and test procedures. We then use SABER to evaluate the current state of the art, Rainbow. Furthermore, we introduce a human world records baseline, and argue that previous claims of expert or superhuman performance of DRL might not be accurate. Finally, we propose Rainbow-IQN by extending Rainbow with Implicit Quantile Networks (IQN) leading to new state-of-the-art performance. Source code is available for reproducibility.
English Translation of Construction Text: Illogic Expressions and the Application of Explication Strategy

The accuracy of English translation of construction texts ensures the implementation of cooperative projects with foreign parties.However, translators are prone to comprehension errors in the process of English translation, thus leading to illogic expressions in the target text.This paper first analyzes the illogic expression problems at lexical, syntactical and discourse level, then briefly introduces explication translation strategy.Finally, it proposes explication translation strategy in construction texts so as to achieve the effect of precise wording, concise expression and rigorous logic.