Reviewerly/exHarmony huggingface dataset:
Improving search and recommendation by asking clarifying questions

Recently, there has been renewed interest in interactive search and recommender systems through the development of systems that ask clarifying questions (CQs) to users to better understand their information needs. Although the significance and effectiveness of CQs have been recongnized, research on modelling CQs to improve search and recommender systems is limited, compared to traditional search and recommender systems. Also, understanding users' willingness or ability to answer these CQs, and the extent to which CQs impact users' behavior and their ability to identify relevant information remains relatively unexplored. The work in this thesis provides a series of models and online user studies for search and recommender systems that construct and use CQs. In the first part of the thesis, we focus on CQ-based search and recommendation algorithms. We first propose a novel CQ-based document search algorithm, Sequential Bayesian Search based method for Technology-Assisted Review (SBSTAR) and its extension, to efficiently retrieve the last few, but significant, relevant documents [Zou et al., 2018; Zou and Kanoulas, 2020]. The algorithm sequentially selects and directly asks CQs to users about the presence or absence of an entity in the missing relevant documents. Next, we propose a novel CQ-based product search algorithm, Question-based Sequential Bayesian Product Search (QSBPS), to effectively locate products that users are looking for [Zou and Kanoulas, 2019]. The model is based on a duet learning framework that learns product relevance and the reward of the potential CQ to be asked to the user. Furthermore, we propose a novel CQ-based recommendation algorithm, Question-based recommendation (Qrec), to assist users to find items interactively [Zou et al., 2020a]. The model is first trained offline by a novel matrix factorization algorithm, and then iteratively updates the user and item latent factors online based on the user's answers. We experimentally test the performance of the proposed models and demonstrate that they outperform state-of-the-art baselines. In the second part of the thesis, we focus on the evaluation of CQ-based search and recommendation. We first conduct an online user study by deploying a CQ-based system in the domain of online retail, to understand to what extent users can answer CQs. We explore the user willingness, ability, and user perception to provide answers to CQs [Zou et al., 2020b]. Next, we conduct a large user study on web search to understand the impact of CQ interactions on user search behavior and satisfaction, and how users interact with different levels of quality of CQs under different user perceptions and conditions. Awarded by : University of Amsterdam, Amsterdam, The Netherlands on 6 September 2021. Supervised by : Evangelos Kanoulas, Maarten de Rijke. Available at : https://hdl.handle.net/11245.1/3c46aa78-98af-4997-aef7-44db7ab2fa3d.
Using the modified k-mean algorithm with an improved teaching-learning-based optimization algorithm for feedforward neural network training

In this paper we proposed a novel procedure for training a feedforward neural network. The accuracy of artificial neural network outputs after determining the proper structure for each problem depends on choosing the appropriate method for determining the best weights, which is the appropriate training algorithm. If the training algorithm starts from a good starting point, it is several steps closer to achieving global optimization. In this paper, we present an optimization strategy for selecting the initial population and determining the optimal weights with the aim of minimizing neural network error. Teaching-learning-based optimization (TLBO) is a less parametric algorithm rather than other evolutionary algorithms, so it is easier to implement. We have improved this algorithm to increase efficiency and balance between global and local search. The improved teaching-learning-based optimization (ITLBO) algorithm has added the concept of neighborhood to the basic algorithm, which improves the ability of global search. Using an initial population that includes the best cluster centers after clustering with the modified k-mean algorithm also helps the algorithm to achieve global optimum. The results are promising, close to optimal, and better than other approach which we compared our proposed algorithm with them.
Research on Intelligent Recommendation Model of E-Commerce Commodity Based on Feature Selection and Deep Belief Network

Due to the complexity and uncertainty of customer demand behavior, it was often difficult to obtain satisfactory recommendation results by using the existing online commodity recommendation systems. Therefore, a network commodity intelligent recommendation model based on feature selection and deep belief network was proposed. Based on the basic structure and function of the existing recommendation systems, this paper expounded the interaction process between customers, e-commerce platforms, enterprises, and the recommendation system. By analyzing the internal relationship between customer demand and commodity recommendation, the relationship model between customer demand and commodity recommendation was established. After analyzing the characteristics of customers’ demand for goods, a data mining method was used to classify the characteristics of customers’ demand behavior, and a feature selection method based on deep belief network (DBN) was proposed to obtain the main information conducive to commodity recommendation. Finally, an e-commerce commodity recommendation algorithm based on feature selection and deep belief network was proposed. The experimental results showed that the network commodity recommendation model proposed in this paper can not only provide customers with satisfactory recommendation results but also has better performance than other traditional recommendation models. The recommendation model proposed in this paper can support different e-commerce website recommendation systems.
Complete and tractable machine-independent characterizations of second-order polytime

Abstract The class of Basic Feasible Functionals $$\mathtt{BFF}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>BFF</mml:mi> </mml:math> is the second-order counterpart of the class of first-order functions computable in polynomial time. We present several implicit characterizations of $$\mathtt{BFF}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>BFF</mml:mi> </mml:math> based on a typed programming language of terms. These terms may perform calls to imperative procedures, which are not recursive. The type discipline has two layers: the terms follow a standard simply-typed discipline and the procedures follow a standard tier-based type discipline. $$\mathtt{BFF}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>BFF</mml:mi> </mml:math> consists exactly of the second-order functionals that are computed by typable and terminating programs. The completeness of this characterization surprisingly still holds in the absence of lambda-abstraction. Moreover, the termination requirement can be specified as a completeness-preserving instance, which can be decided in time quadratic in the size of the program. As typing is decidable in polynomial time, we obtain the first tractable ( i.e. , decidable in polynomial time), sound, complete, and implicit characterization of $$\mathtt{BFF}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>BFF</mml:mi> </mml:math> , thus solving a problem opened for more than 20 years.
Simulation of load balancing algorithms using cloud analyst for distinct regions IT resources

Cloud computing is currently on the rise as new technology and as a new business model, according to experts. Data archiving, servers (both real-world and virtual), telecommunications, machine learning tools for data analysis, and a cloud services provider's (CSP) data center are all examples of cloud computing resources that are available on demand over the internet. Cloud computing allows client devices to access data from faraway servers over the internet, which is referred to as "cloud computing." Rather than purchasing, operating, and maintaining physical data centers and servers, we can take advantage of technology services provided by third parties. A pay-per-use pricing structure is in place. It is spreading over the world simply because of its simple and service-oriented business approach. It delivers the greatest computing service to the user and makes the execution of large-scale activities much easier than before. Through the use of a cloud analyst tool, this study provides an overview of the evaluation of the response time and processing time of several existing algorithms that are used in cloud computing.
Machine Learning Approach to Sentiment Analysis in Data Mining

Widespread internet use and the web have brought about new ways of expressing individual sentiments. A sentiment is defined as an individual's view in which feelings, attitudes, and thoughts can be represented. When it comes to analysing and extracting Sentiment analysis and opinion mining are two of the most prominent disciplines of research. They derive insights using text data through numerous sources like Facebook and Twitter. Sentiment analysis frequently elicits information on how people feel about various events, brands, products, or businesses. Researchers collect and improvise replies from the general public to conduct evaluations. This paper looks into sentiment analysis for classifying Twitter subscriber tweets. This approach can help analysing the information gathered and stored in positive, neutral and negative opinions. This information is first pre-processed before creating feature vectors. On the basis of machine learning, classification methods were used. The study's algorithms are used Maximum Entropy, Naive Bayes and Support Vector Machine; they are used to categorize documents as positive or negative. The dataset for this paper are obtained from Twitter and includes subscribed tweets by using the API. Following pre-processing, machine learning methods are used to determine whether the tweets are positive or negative.
Calculation of a controller for a multi-channel object with non-stationary parameters containing delay links

Nonstationary linear systems or linear systems with variable parameters are systems that are described by linear differential equations with variable coefficients. Consideration of the controller synthesis problems for such systems may be dictated by the need to satisfy a certain margin of the automatic control system stability to minor disturbances both from sensors reading the state of the system and from changes in the parameters of the controlled object itself over time. In addition, a real controlled object, in comparison with its mathematical model, quite often contains some uncertainties that should not significantly affect the quality of control.This work considers an example of synthesizing a multichannel controller that would provide a good control quality (stability margin) if the controlled object contains some uncertainties or if its mathematical model with accuracy up to certain parameters is unknown. In addition, the control task is complicated by a link of delay in the structure of the object (that is, there is a certain period of time when there is no reaction from the regulated object to the control action).A modified algorithm of polynomial matrix decomposition of the matrix transfer function of the system, taking into account the conservation of the delay link poles, is chosen as a method of controller synthesis. To demonstrate the algorithm operation, a linear model of the unstable controlled object with a multi-channel structure consisting of two input and two output channels is taken. The description of the object is used in the form of the matrix transfer function, which shows the interaction in the multi-channel control system and the relationship between channels. The object model contains cross-links representing direct input-output connections. The delay links are present in all channels and are approximated using the Pade series with a limited number of terms. The non-stationary parameters of the object were obtained due to the multiplicative interference modeled in the Simulink Matlab package.It is necessary to achieve certain quality indicators of the output vector process when controlling two input influences. To ensure a stable transition process from the system, the poles preservation of the delay link will be necessary. Then the controller calculated for the system with the delay link in the form of the Pade series is applied to the system with an "ideal" delay.
Conversion of Sign Language into Devanagari Text Using CNN

Sign language is an increasingly valuable interactive medium for both deaf and hearing disabled individuals. These languages are considered as a group of predefined languages that use the visual–manual mode to communicate knowledge. These languages are considered to be unitary for deaf people. This paper proposes a useful hand motion picture recognition design method to provide a simple way to interact with the visually impaired and the average person. This is accomplished by considering Devanagari Sign Language finger-spelling comprehension in real time. In this paper, authors have created dataset of Devanagari Sign Language by capturing the gestures through the Web camera. From these captured images, we found the region of interest (ROI). Convolutional neural network has been used for classification as it eases the process of feature creation. For training this network, a hand signal has been used as input and the resultant output produces character when a suitable match is found with an accuracy of 98% and testing accuracy of 99.59%.
MTSP-ACA for Multi-UAV Path Planning Applied in SAR Operations in Mountainous Terrain

UAV is considered as one of the most efficient approaches in SAR operations. A combined MTSP-ACA algorithm is developed and simulated in an envisaged environment and associated background information to acquire the optimum path of and a time-related index of the multiple UAVs executing SAR operation. The size and parameters of an element grid are generated based on flight performance constraints and operational requirements. Using numerous element grids, discretization of the operative area is accomplished for the following modelling of MTSP-ACA simulation. Next, the operation is abstracted as a new type of MTSP application after the previous disposal of the affected area, and the solving model is also established. ACA optimization model for the above SAR operation is constructed for the final simulation. After three separate steps, the problem solution is computed under the circumstances the number of the launched plane are 8, 9, 10,11 respectively. The result indicates that MTSP-ACA is proved effective for high viability.
What is the Counsellor’s Role in Overcoming the Zoom Fatigue Phenomenon?

The increasing use of technology-based learning media such as Zoom and Google Meet during the Covid-19 pandemic can trigger the emergence of mental health problems in students and teachers.One of the problems is the Zoom Fatigue phenomenon which can make the learning process less effective and optimal.This paper aims to (1) describe the phenomenon of Zoom Fatigue in the scope of learning; (2) Mapping the factors causing Zoom Fatigue and its impact on the learning process; (3) Explain the role of counselors in overcoming the problem of Zoom Fatigue in schools.Therefore, to obtain results that are in accordance with the research objectives, researchers use the literature review method on the latest scientific articles that are considered relevant.
Automatic Grammatical Evolution-Based Optimization of Matrix Factorization Algorithm

Nowadays, recommender systems are vital in lessening the information overload by filtering out unnecessary information, thus increasing comfort and quality of life. Matrix factorization (MF) is a well-known recommender system algorithm that offers good results but requires a certain level of system knowledge and some effort on part of the user before use. In this article, we proposed an improvement using grammatical evolution (GE) to automatically initialize and optimize the algorithm and some of its settings. This enables the algorithm to produce optimal results without requiring any prior or in-depth knowledge, thus making it possible for an average user to use the system without going through a lengthy initialization phase. We tested the approach on several well-known datasets. We found our results to be comparable to those of others while requiring a lot less set-up. Finally, we also found out that our approach can detect the occurrence of over-saturation in large datasets.
Research on the Effectiveness and Promotion Strategy of Overseas Returnees in Colleges and Universities under the Background of “Double First-Class” Construction

This paper selects the literature related to the introduction of university returnees in China Academic Journal Publishing General Library from 2008 to 2022, collects the literature data as two retrieval items, and analyzes the knowledge structure and hot issues of domestic academic circles on the introduction of overseas returnees in colleges and universities by using bibliometric tools such as Citespace and CNKI visualization software.It is found that the establishment of the ternary interactive model of academic ecology of the returned teachers in colleges and universities will improve their work efficiency and relieve their work pressure.Establish IPO model of academic innovation of college returnees teachers, and obtain the influencing factors that promote individual academic innovation and team research innovation output; by analyzing the common relationship between academic ecology and academic innovation, the strategies to improve the effectiveness of overseas returnees in colleges and universities are obtained.
Optimisation of Hopfield networks for storage and recall: a decade review

Pattern storage and recall in an efficient and effective manner is a prominent task in the pattern recognition field. Recurrent (also called feedback) networks are most frequently used network that can store and recall the patterns. Recurrent networks have a capability of recalling noisy or partial patterns like brain. A detailed study of different neural network, it is found that Hopfield neural network outperforms as compared to others. In this paper, we illustrate the review of a decade on optimisation of Hopfield neural network to improve storage capacity and recalling of patterns.
From Fundamentals to Recent Advances: A Tutorial on Keyphrasification

Keyphrases represent the most important information of text which often serve as a surrogate for efficiently summarizing text documents. With the advancement of deep neural networks, recent years have witnessed rapid development in automatic identification of keyphrases. The performance of keyphrase extraction methods has been greatly improved by the progresses made in natural language understanding, enable models to predict relevant phrases not mentioned in the text. We name the task of summarizing texts with phrases keyphrasification. In this half-day tutorial, we provide a comprehensive overview of keyphrasification as well as hands-on practice with popular models and tools. This tutorial covers important topics ranging from basics of the task to the advanced topics and applications. By the end of the tutorial, participants will have a better understanding of 1) classical and state-of-the-art keyphrasification methods, 2) current evaluation practices and their issues, and 3) current trends and future directions in keyphrasification research. Tutorial-related resources are available at https://keyphrasification.github.io/ .
Automatic Product Copywriting for E-commerce

Product copywriting is a critical component of e-commerce recommendation platforms. It aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. In this paper, we report our experience deploying the proposed Automatic Product Copywriting Generation (APCG) system into the JD.com e-commerce product recommendation platform. It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening. For selected domains, the models are trained and updated daily with the updated training data. In addition, the model is also used as a real-time writing assistant tool on our live broadcast platform. The APCG system has been deployed in JD.com since Feb 2021. By Sep 2021, it has generated 2.53 million product descriptions, and improved the overall averaged click-through rate (CTR) and the Conversion Rate (CVR) by 4.22% and 3.61%, compared to baselines, respectively on a year-on-year basis. The accumulated Gross Merchandise Volume (GMV) made by our system is improved by 213.42%, compared to the number in Feb 2021.
Beyond Notations: Hygienic Macro Expansion for Theorem Proving Languages

In interactive theorem provers (ITPs), extensible syntax is not only crucial to lower the cognitive burden of manipulating complex mathematical objects, but plays a critical role in developing reusable abstractions in libraries. Most ITPs support such extensions in the form of restrictive "syntax sugar" substitutions and other ad hoc mechanisms, which are too rudimentary to support many desirable abstractions. As a result, libraries are littered with unnecessary redundancy. Tactic languages in these systems are plagued by a seemingly unrelated issue: accidental name capture, which often produces unexpected and counterintuitive behavior. We take ideas from the Scheme family of programming languages and solve these two problems simultaneously by proposing a novel hygienic macro system custom-built for ITPs. We further describe how our approach can be extended to cover type-directed macro expansion resulting in a single, uniform system offering multiple abstraction levels that range from supporting simplest syntax sugars to elaboration of formerly baked-in syntax. We have implemented our new macro system and integrated it into the new version of the Lean theorem prover, Lean 4. Despite its expressivity, the macro system is simple enough that it can easily be integrated into other systems.
A hand-arm teleoperation system for robotic dexterous manipulation

To perform dexterous robotic manipulation, we set up a multimodal teleoperation system, which combines vision-based control for the robotic hand and IMU-based control for the robot arm. A novel vision-based neural network, Transteleop, is proposed to estimate the joint commands of the robot hand. Integrating the image-to-image translation approach, Transteleop bridges the kinematic gap between the robot hand and the human hand. Combining with wearable IMU on the human arm, the robot hand-arm coordinately tracking is performed to follow the human's manipulation. We demonstrate several subtle and dexterous tasks to verify the reliability of the proposed system.