Reviewerly/exHarmony huggingface dataset:
Chatbots and Communication: The Growing Role of Artificial Intelligence in Addressing and Shaping Customer Needs

Objectives: This study examines the advantages of introducing chatbots and the different kinds of chatbot services in the e-commerce industry that are operating abroad.It also seeks to determine changes in future communication with companies and customers.Methods: The main characteristics of e-commerce chatbots will be explored through four examples: the chatbots used by 1-800-Flowers, Sephora, PVR Cinemas, and Vainu.Results: Sephora provides personalized services such as information delivery and recommendations through chatbots.1-800-Flowers provides useful information to customers through interactive commerce services, as well as voice orders through Alexa for a convenient purchasing experience.PVR Cinemas supports purchasing and reservation functions to quickly solve customer problems, while Vainu has demonstrated that it can acquire customer information and use it for marketing by asking various questions to customers.Through these four cases, this study confirmed that product search, upselling, and marketing functions are the main advantages that can be obtained through communication with customers through chatbots in e-commerce transactions.Conclusions: It was confirmed that chatbots contribute to improving the productivity and efficiency of existing business operations, and in particular, improving the convenience of customers' shopping, ordering, and payment experiences.In addition, as companies attempt to provide new shopping experiences to customers using chatbots, communication with customers through chatbots in the e-commerce field is expected to increase in the future.
Hyperfast second-order local solvers for efficient statistically preconditioned distributed optimization

Statistical preconditioning enables fast methods for distributed large-scale empirical risk minimization problems. In this approach, multiple worker nodes compute gradients in parallel, which are then used by the central node to update the parameter by solving an auxiliary (preconditioned) smaller-scale optimization problem. The recently proposed Statistically Preconditioned Accelerated Gradient (SPAG) method [1] has complexity bounds superior to other such algorithms but requires an exact solution for computationally intensive auxiliary optimization problems at every iteration. In this paper, we propose an Inexact SPAG (InSPAG) and explicitly characterize the accuracy by which the corresponding auxiliary subproblem needs to be solved to guarantee the same convergence rate as the exact method. We build our results by first developing an inexact adaptive accelerated Bregman proximal gradient method for general optimization problems under relative smoothness and strong convexity assumptions, which may be of independent interest. Moreover, we explore the properties of the auxiliary problem in the InSPAG algorithm assuming Lipschitz third-order derivatives and strongly convexity. For such problem class, we develop a linearly convergent Hyperfast second-order method and estimate the total complexity the InSPAG method with hyperfast auxiliary problem solver. Finally, we illustrate the proposed method's practical efficiency by performing large-scale numerical experiments on logistic regression models. To the best of our knowledge, these are the first empirical results on implementing high-order methods on large-scale problems, we work with data where the dimension is of the order of 3 million, and the number of samples is 700 million.
Thermal-aware adaptive VM allocation considering server locations in heterogeneous data centers

Virtualized data centers usually consist of heterogeneous servers which have different specifications (performance). Though there usually exist unused heterogeneous servers in such data centers, conventional DVFS (Dynamic Voltage and Frequency Scaling)-based DTM (Dynamic Thermal Management) techniques do not exploit the unused servers to cool down hot servers. In this paper, we propose a novel DTM technique which adaptively exploits external computing resources (unused servers with different performance) as well as internal computing resources (unused CPU cores in the server) available in heterogeneous data centers. Additionally, we also propose to consider locations of the servers when migrating VMs (Virtual Machines) among servers in a rack, which has a large impact on the on-chip temperatures and performance due to the heat conduction; when VMs run on the two closest servers in the rack, the ambient temperature of servers is up to 6.2° higher, compared to the case where VMs run on the two farthest servers, so that on-chip temperature more rapidly increases causing up to 13.5% of performance degradation due to more frequent thermal throttling. When the temperature of a CPU core in a server exceeds a pre-defined thermal threshold, our proposed technique estimates the impact of VM migrations on performance (e.g., performance degradation due to the physical machine migrations and/or core migrations of VMs). Depending on the estimated performance impact of VM migrations, our technique adaptively employs the following three methods: (1) a method that migrates a VM to another distant server with different performance, (2) a method that migrates VMs among CPU cores in the server, and (3) a DVFS-based method. In our experiments, our proposed technique improves performance by 15.1% and saves system-wide EDP by 22.9%, on average, compared to a state-of-the-art DVFS-based DTM technique, satisfying thermal constraints.
Performance Analysis of Real Estate Management Entities Based on DEA Model and BP Neural Network Model

Abstract Based on the financial report data of Listed Companies in 2015 and taking profitability as the criterion, this paper selects 40 real estate enterprises as samples, and analyzes the operation performance of the real estate industry by using data envelopment analysis method and BP neural network model. The results show that the scale benefit of the industry still exists, but it is no longer significant, and the development mode of the industry is in place. In the period of obvious change, the operation and management of real estate enterprises are becoming more and more mature, and the optimal allocation of resources has been basically achieved at the macro level of resource utilization, but the industry needs to be further improved in fine management, practical human resources management, supervisory management and other aspects. Excellent management team is the key of success in enterprises development.
A Large Family of Maximum Scattered Linear Sets of $${{\,\mathrm{{PG}}\,}}(1,q^n)$$ and Their Associated MRD Codes

Linear sets in projective spaces over finite fields were introduced by Lunardon (Geom Dedic 75(3):245–261, 1999) and they play a central role in the study of blocking sets, semifields, rank-metric codes, etc. A linear set with the largest possible cardinality and rank is called maximum scattered. Despite two decades of study, there are only a limited number of maximum scattered linear sets of a line $${{\,\mathrm{{PG}}\,}}(1,q^n)$$ . In this paper, we provide a large family of new maximum scattered linear sets over $${{\,\mathrm{{PG}}\,}}(1,q^n)$$ for any even $$n\ge 6$$ and odd q. In particular, the relevant family contains at least $$\begin{aligned} {\left\{ \begin{array}{ll} \left\lfloor \frac{q^t+1}{8rt}\right\rfloor ,&{} \text { if }t\not \equiv 2\pmod {4};\\ \left\lfloor \frac{q^t+1}{4rt(q^2+1)}\right\rfloor ,&{} \text { if }t\equiv 2\pmod {4}, \end{array}\right. } \end{aligned}$$ inequivalent members for given $$q=p^r$$ and $$n=2t>8$$ , where $$p=\textrm{char}({{\mathbb {F}}}_q)$$ . This is a great improvement of previous results: for given q and $$n>8$$ , the number of inequivalent maximum scattered linear sets of $${{\,\mathrm{{PG}}\,}}(1,q^n)$$ in all classes known so far, is smaller than $$q^2\phi (n)/2$$ , where $$\phi $$ denotes Euler’s totient function. Moreover, we show that there are a large number of new maximum rank-distance codes arising from the constructed linear sets.
Faster Non-Convex Federated Learning via Global and Local Momentum

In this paper, we propose \texttt{FedGLOMO}, the first (first-order) FL algorithm that achieves the optimal iteration complexity (i.e matching the known lower bound) on smooth non-convex objectives -- without using clients' full gradient in each round. Our key algorithmic idea that enables attaining this optimal complexity is applying judicious momentum terms that promote variance reduction in both the local updates at the clients, and the global update at the server. Our algorithm is also provably optimal even with compressed communication between the clients and the server, which is an important consideration in the practical deployment of FL algorithms. Our experiments illustrate the intrinsic variance reduction effect of \texttt{FedGLOMO} which implicitly suppresses client-drift in heterogeneous data distribution settings and promotes communication-efficiency. As a prequel to \texttt{FedGLOMO}, we propose \texttt{FedLOMO} which applies momentum only in the local client updates. We establish that \texttt{FedLOMO} enjoys improved convergence rates under common non-convex settings compared to prior work, and with fewer assumptions.
Forward thinking on backward tracing

SARS, MERS and now SARS-CoV-2 are unlikely to be the last emerging infections we face during our lifetimes. Tracing contacts both forward and backward through our heterogeneous populations will prove essential to future response strategies.
Quantum speed of evolution in a Markovian bosonic environment

We present explicit evaluations of quantum speed limit times pertinent to the Markovian dynamics of an open continuous-variable system. Specifically, we consider the standard setting of a cavity mode of the quantum radiation field weakly coupled to a thermal bosonic reservoir. The evolution of the field state is ruled by the quantum optical master equation, which is known to have an exact analytic solution. Starting from a pure input state, we employ two indicators of how different the initial and evolved states are, namely, the fidelity of evolution and the Hilbert-Schmidt distance of evolution. The former was introduced by del Campo et al. [Phys. Rev. Lett. 110, 050403 (2013)], who derived a time-independent speed limit for the evolution of a Markovian open system. We evaluate it for this field-reservoir setting, with an arbitrary input pure state of the field mode. The resultant formula is then specialized to the coherent and Fock states. On the other hand, we exploit an alternative approach that employs both indicators of evolution mentioned above. Their rates of change have the same upper bound, and consequently, provide a unique time-dependent quantum speed limit. It turns out that the associate quantum speed limit time built with the Hilbert-Schmidt metric is tighter than the fidelity-based one. As apposite applications, we investigate the damping of the coherent and Fock states by using the characteristic functions of the corresponding evolved states. General expressions of both the fidelity and the Hilbert-Schmidt distance of evolution are obtained and analyzed for these two classes of input states. In the case of a coherent state, we derive accurate formulas for their common speed limit and the pair of associate limit times. We also find exact expressions of the same quantities in the limiting case of thermalization of the vacuum state, as well as for dissipation of one- and two-photon states.
Evaluation of Sensor-Agnostic All-Source Residual Monitoring for Navigation

In recent years, the need for error characterization, fault detection, and exclusion of navigation sensors has increased. Environments where GNSS performance is degraded or denied require the knowledge and application of alternative sensors such as cameras, magnetometers, and small ranging radios operating outside the GNSS band. Currently, vehicles such as unmanned aerial systems (UAS) may operate with a suite of these ’alt-nav’ sensors performing measurements across multiple domains. However, the addition of such sensors has increased the likelihood of having a mismodeled and/or faulty sensor, affecting the accuracy and performance of the overall navigation solution. Unlike traditional two-sensor systems such as GPS-Inertial integration, systems involving three or more sensors present the problem of ambiguity as to which sensor is adversely affecting the navigation solution. While extensive research into fault detection and exclusion has been conducted for standalone sensors such as ARAIM for GNSS systems, or Kalman filtering used in integrated two-sensor GNSS-Inertial systems, robustness and error resiliency for multi-sensor (i.e. three or more) systems remains largely unresolved. This problem presents the need for a robust framework that can maintain navigation integrity despite the additional sensor modalities. One proposed framework to solve the multi-sensor resiliency problem is known as the Autonomous and Resilient Management of All-Source Sensors for Navigation (ARMAS) and its associated fault detection algorithm, SAARM (Sensor Agnostic All-Source Residual Monitoring). This work provides insight into the performance of these algorithms with real GNSS data. The original work evaluated the performance on only simulated data.
Research on the Construction of Evaluation System of Intelligent Classroom Teaching Quality in Colleges and Universities under the Information Technology Environment

Abstract Under the deep integration of modern information technology and university education, a new intelligent classroom teaching mode has been produced. As a new teaching mode, its concrete implementation and teaching quality must be paid great attention to. In this paper, the characteristics, advantages, process and teaching effect of wisdom classroom teaching are studied as an important reference for the construction of wisdom classroom teaching quality evaluation system.
Symmetry Breaking for k-Robust Multi-Agent Path Finding

During Multi-Agent Path Finding (MAPF) problems, agentscan be delayed by unexpected events. To address suchsituations recent work describes k-Robust Conflict-BasedSearch (k-CBS): an algorithm that produces coordinated andcollision-free plan that is robust for up tokdelays. In thiswork we introducing a variety of pairwise symmetry break-ing constraints, specific tok-robust planning, that can effi-ciently find compatible and optimal paths for pairs of con-flicting agents. We give a thorough description of the newconstraints and report large improvements to success rate ina range of domains including: (i) classic MAPF benchmarks;(ii) automated warehouse domains and; (iii) on maps fromthe 2019 Flatland Challenge, a recently introduced railwaydomain wherek-robust planning can be fruitfully applied toschedule trains.
Emotion analysis of teaching evaluation system based on AI techno-logy towards Chinese texts

In order to improve the teaching quality of graduate students, a quick method was designed to analyze the teaching evaluation comments in Chinese from students in the teaching evaluation system based on Baidu AI technology .So that it can help the teaching management department analyze the emotional inclination of students towards each course and grasp the trend and distribution of teaching quality accurately.The result of this article has shown that this method can analyze the students’ evaluation text correctly and provide decision-making reference for teaching management departments or teachers themselves.
Privacy-Preserving Trajectory Data Publishing by Dynamic Anonymization with Bounded Distortion

Publication of trajectory data that contain rich information of vehicles in the dimensions of time and space (location) enables online monitoring and supervision of vehicles in motion and offline traffic analysis for various management tasks. However, it also provides security holes for privacy breaches as exposing individual’s privacy information to public may results in attacks threatening individual’s safety. Therefore, increased attention has been made recently on the privacy protection of trajectory data publishing. However, existing methods, such as generalization via anonymization and suppression via randomization, achieve protection by modifying the original trajectory to form a publishable trajectory, which results in significant data distortion and hence a low data utility. In this work, we propose a trajectory privacy-preserving method called dynamic anonymization with bounded distortion. In our method, individual trajectories in the original trajectory set are mixed in a localized manner to form synthetic trajectory data set with a bounded distortion for publishing, which can protect the privacy of location information associated with individuals in the trajectory data set and ensure a guaranteed utility of the published data both individually and collectively. Through experiments conducted on real trajectory data of Guangzhou City Taxi statistics, we evaluate the performance of our proposed method and compare it with the existing mainstream methods in terms of privacy preservation against attacks and trajectory data utilization. The results show that our proposed method achieves better performance on data utilization than the existing methods using globally static anonymization, without trading off the data security against attacks.
Monte Carlo Simulation Aided Quantitative Laboratory X-ray Fluorescence Analysis and Its Application in Provenancing Studies for Geo-Archeological Samples

A laboratory-based X-ray fluorescence (XRF) methodology is presented for standardless quantified analysis based on a monochromatic X-ray spectrometer coupled to Monte Carlo aided quantification. This procedure will be valuable for many scientific fields (e.g. archaeology, geology, etc.) where the unique nature of the investigated samples calls for the application of non-destructive techniques. To illustrate the value of the methodology, a case study is presented where flint artefacts from the Scheldt basin are analyzed in an attempt to provenance them. So far, little geochemical research has been done in this area. Our results contribute to the creation of a database that will help assign lithic artefacts to specific geological outcrops and will aid further research in this field.
LogME: Practical Assessment of Pre-trained Models for Transfer Learning

This paper studies task adaptive pre-trained model selection, an underexplored problem of assessing pre-trained models for the target task and select best ones from the model zoo \emph{without fine-tuning}. A few pilot works addressed the problem in transferring supervised pre-trained models to classification tasks, but they cannot handle emerging unsupervised pre-trained models or regression tasks. In pursuit of a practical assessment method, we propose to estimate the maximum value of label evidence given features extracted by pre-trained models. Unlike the maximum likelihood, the maximum evidence is \emph{immune to over-fitting}, while its expensive computation can be dramatically reduced by our carefully designed algorithm. The Logarithm of Maximum Evidence (LogME) can be used to assess pre-trained models for transfer learning: a pre-trained model with a high LogME value is likely to have good transfer performance. LogME is \emph{fast, accurate, and general}, characterizing itself as the first practical method for assessing pre-trained models. Compared with brute-force fine-tuning, LogME brings at most $3000\times$ speedup in wall-clock time and requires only $1\%$ memory footprint. It outperforms prior methods by a large margin in their setting and is applicable to new settings. It is general enough for diverse pre-trained models (supervised pre-trained and unsupervised pre-trained), downstream tasks (classification and regression), and modalities (vision and language). Code is available at this repository: \href{https://github.com/thuml/LogME}{https://github.com/thuml/LogME}.
Anti-Counterfeiting for Polymer Banknotes Based on Polymer Substrate Fingerprinting

Polymer banknotes are the trend for printed currency and have been adopted by more than fifty countries worldwide. However, over the past years, the quantity of polymer counterfeits has been increasing, so has the quality of counterfeits. This shows that the initial advantage of bringing a new polymer technology to fight against counterfeiting is reducing. To maintain one step ahead of counterfeiters, we propose a novel anti-counterfeiting technique called Polymer Substrate Fingerprinting (PSF). Our technique is built based on the observation that the opacity coating, a critical step during the production of polymer notes, is a stochastic manufacturing process, leaving uneven thickness in the coating layer and the random dispersion of impurities from the ink. The imperfections in the coating layer result in random translucent patterns when a polymer banknote is back-lit by a light source. We show these patterns can be reliably captured by a commodity negative-film scanner and processed into a compact fingerprint to uniquely identify each banknote. Using an extensive dataset of 6,200 sample images collected from 340 UK banknotes, we show that our method can reliably authenticate banknotes, and is robust against rough daily handling of banknotes. Furthermore, we show the extracted fingerprints contain around 900 bits of entropy, which makes it extremely scalable to identify every polymer note circulated globally. As compared with previous or existing anti-counterfeiting mechanisms for banknotes, our method has a distinctive advantage: it ensures that even in the extreme case when counterfeiters have procured the same printing equipment and ink as used by a legitimate government, counterfeiting banknotes remains infeasible because of the difficulty to replicate a stochastic manufacturing process.
TEACHERS' READINESS TO USE E-PORTFOLIOS

An e-portfolio is one of the tools for monitoring and evaluating the professional activity of school teachers and their achievements. An e-portfolio can be used for teachers’ professional development. It provides openness and transparency of both teachers’ and educational institutions’ work. The purpose of the study is to analyze teachers' readiness to use e-portfolios for both self-evaluation of their own professional experience and continuous professional development. The article analyzes various approaches to the structure of e-portfolios based on the analysis of international experience, the legislation of Ukraine and the survey of teachers. The purpose, tasks, requirements of normative documents on the use of e-portfolios are generalized.The multi-level structure of an e-portfolio is defined and the requirements for its dynamic content are substantiated. Teachers' needs for advanced training in using a variety of IT tools for creating e-portfolios have been systematized. It has been determined that the use of e-portfolio can satisfy both the needs of evaluating the effectiveness of teachers’ work and the formation of self-esteem and reflection skills, facilitate teachers’ continuous professional development, dissemination of advanced pedagogical experience and teaching practices. Different types of e-portfolios have been analyzed. IT tools for creating an e-portfolio have been identified. The content of the corresponding educational module for teachers in the system of postgraduate pedagogical education has been substantiated. The content of a portfolio is defined by the goals of its creation. For teachers, the emphasis in e-portfolio creation is not on learning, as it is for students, but on professional activity, self-assessment, and marketing. The results can be teachers' understanding of why and how they have learned throughout their careers, the importance of reflection for future professional development.