nils-herrmann/clean_scirepeval huggingface dataset:
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model updates are sent back and aggregated on the server to update the master model then redistributed to the clients. In this paradigm, the user data never leaves the client, greatly enhancing the user’ privacy, in contrast to the traditional paradigm of collecting, storing and processing user data on a backend server beyond the user’s control. In this paper we introduce, as far as we are aware, the first federated implementation of a Collaborative Filter. The federated updates to the model are based on a stochastic gradient approach. As a classical case study in machine learning, we explore a personalized recommendation system based on users’ implicit feedback and demonstrate the method’s applicability to both the MovieLens and an in-house dataset. Empirical validation confirms a collaborative filter can be federated without a loss of accuracy compared to a standard implementation, hence enhancing the user’s privacy in a widely used recommender application while maintaining recommender performance.
On the features and challenges of security and privacy in distributed internet of things

In the Internet of Things, services can be provisioned using centralized architectures, where central entities acquire, process, and provide information. Alternatively, distributed architectures, where entities at the edge of the network exchange information and collaborate with each other in a dynamic way, can also be used. In order to understand the applicability and viability of this distributed approach, it is necessary to know its advantages and disadvantages – not only in terms of features but also in terms of security and privacy challenges. The purpose of this paper is to show that the distributed approach has various challenges that need to be solved, but also various interesting properties and strengths.
Assessment of Micro-Enterprise Development through IT Adoption

While prior work has focused on the process through which IT may be adopted by micro-enterprises, this research takes it one step further by assessing the outcomes from IT adoption in micro-enterprises that have undergone technology, training and trust building interventions. It analyzes these using a systematic evaluation model that ties in the key components of development. The contribution of this paper is in providing insights into how researchers and practitioners can better stimulate micro-enterprise growth and economic development through IT adoption.
Review of automatic sarcasm detection

Sentiment Analysis has become a significant research matter for its probable in tapping into the vast amount of opinions generated by the people. Sentiment analysis deals with the computational conduct of opinion, sentiment within the text. People sometimes uses sarcastic text to express their opinion within the text. Sarcasm is a type of communication act in which the people write the contradictory of what they mean in reality. The intrinsically vague nature of sarcasm sometimes makes it hard to understand. Recognizing sarcasm can promote many sentiment analysis applications. Automatic detecting sarcasm is an approach for predicting sarcasm in text. In this paper we have tried to talk of the past work that has been done for detecting sarcasm in the text. This paper talk of approaches, features, datasets, and issues associated with sarcasm detection. Performance values associated with the past work also has been discussed. Various tables that present different dimension of past work like dataset used, features, approaches, performance values has also been discussed.
Non-negative Matrix Factorization Meets Word Embedding

Document clustering is central in modern information retrieval applications. Among existing models, non-negative-matrix factorization (NMF) approaches have proven effective for this task. However, NMF approaches, like other models in this context, exhibit a major drawback, namely they use the bag-of-word representation and, thus, do not account for the sequential order in which words occur in documents. This is an important issue since it may result in a significant loss of semantics. In this paper, we aim to address the above issue and propose a new model which successfully integrates a word embedding model, word2vec, into an NMF framework so as to leverage the semantic relationships between words. Empirical results, on several real-world datasets, demonstrate the benefits of our model in terms of text document clustering as well as document/word embedding.
Physical activity and mental health in a student population.

A growing body of literature indicates that physical activity can have beneficial effects on mental health. However, previous research has mainly focussed on clinical populations, and little is known about the psychological effects of physical activity in those without clinically defined disorders.



The present study investigates the association between physical activity and mental health in an undergraduate university population based in the United Kingdom.



One hundred students completed questionnaires measuring their levels of anxiety and depression using the Hospital Anxiety and Depression Scale (HADS) and their physical activity regime using the Physical Activity Questionnaire (PAQ).



Significant differences were observed between the low, medium and high exercise groups on the mental health scales, indicating better mental health for those who engage in more exercise.



Engagement in physical activity can be an important contributory factor in the mental health of undergraduate students.
Silicon microring resonators

An overview is presented of the current state-of-theart in silicon nanophotonic ring resonators. Basic theory of ring resonators is discussed, and applied to the peculiarities of submicron silicon photonic wire waveguides: the small dimensions and tight bend radii, sensitivity to perturbations and the boundary conditions of the fabrication processes. Theory is compared to quantitative measurements. Finally, several of the more promising applications of silicon ring resonators are discussed: filters and optical delay lines, label-free biosensors, and active rings for efficient modulators and even light sources. Silicon microring resonators Wim Bogaerts*, Peter De Heyn, Thomas Van Vaerenbergh, Katrien De Vos, Shankar Kumar Selvaraja, Tom Claes, Pieter Dumon, Peter Bienstman, Dries Van Thourhout, and Roel Baets
Standby and Active Leakage Current Control and Minimization in CMOS VLSI Circuits

In many new high performance designs, the leakage component of power consumption is comparable to the switching component. Reports indicate that 40% or even higher percentage of the total power consumption is due to the leakage of transistors. This percentage will increase with technology scaling unless effective techniques are introduced to bring leakage under control. This article focuses on circuit optimization and design automation techniques to accomplish this goal. The first part of the article provides an overview of basic physics and process scaling trends that have resulted in a significant increase in the leakage currents in CMOS circuits. This part also distinguishes between the standby and active components of the leakage current. The second part of the article describes a number of circuit optimization techniques for controlling the standby leakage current, including power gating and body bias control. The third part of the article presents techniques for active leakage control, including use of multiple-threshold cells, long channel devices, input vector design, transistor stacking to switching noise, and sizing with simultaneous threshold and supply voltage assignment.
A METHOD FOR OPTIMISED ALLOCATION OF SYSTEM ARCHITECTURES WITH REAL-TIME CONSTRAINTS

Optimised allocation of system architectures is a well researched area as it can greatly reduce the developmental cost of systems and increase performance and reliability in their respective applications. In conjunction with the recent shift from federated to integrated architectures in automotive, and the increasing complexity of computer systems, both in terms of software and hardware, the applications of design space exploration and optimised allocation of system architectures are of great interest. This thesis proposes a method to derive architectures and their allocations for systems with real-time constraints. The method implements integer linear programming to solve for an optimised allocation of system architectures according to a set of linear constraints while taking resource requirements, communication dependencies, and manual design choices into account. Additionally, this thesis describes and evaluates an industrial use case using the method wherein the timing characteristics of a system were evaluated, and, the method applied to simultaneously derive a system architecture, and, an optimised allocation of the system architecture. This thesis presents evidence and validations that suggest the viability of the method and its use case in an industrial setting. The work in this thesis sets precedence for future research and development, as well as future applications of the method in both industry and academia.
A Novel Framework for Imputation of Missing Values in Databases

Many of the industrial and research databases are plagued by the problem of missing values. Some evident examples include databases associated with instrument maintenance, medical applications, and surveys. One of the common ways to cope with missing values is to complete their imputation (filling in). Given the rapid growth of sizes of databases, it becomes imperative to come up with a new imputation methodology along with efficient algorithms. The main objective of this paper is to develop a unified framework supporting a host of imputation methods. In the development of this framework, we require that its usage should (on average) lead to the significant improvement of accuracy of imputation while maintaining the same asymptotic computational complexity of the individual methods. Our intent is to provide a comprehensive review of the representative imputation techniques. It is noticeable that the use of the framework in the case of a low-quality single-imputation method has resulted in the imputation accuracy that is comparable to the one achieved when dealing with some other advanced imputation techniques. We also demonstrate, both theoretically and experimentally, that the application of the proposed framework leads to a linear computational complexity and, therefore, does not affect the asymptotic complexity of the associated imputation method.
Heart Rate Variability Anatomy and Physiology

Heart rate variability (HRV) is generated by the interaction of multiple regulatory mechanisms that operate on different time scales. This article examines the regulation of the heart, the meaning of HRV, Thayer and Lane’s neurovisceral integration model, the sources of HRV, HRV frequency and time domain measurements, Porges’s polyvagal theory, and resonance frequency breathing. The medical implications of HRV biofeedback for cardiovascular rehabilitation and inflammatory disorders are considered.
How the built environment affects physical activity: views from urban planning.

The link between the built environment and human behavior has long been of interest to the field of urban planning, but direct assessments of the links between the built environment and physical activity as it influences personal health are still rare in the field. Yet the concepts, theories, and methods used by urban planners provide a foundation for an emerging body of research on the relationship between the built environment and physical activity. Recent research efforts in urban planning have focused on the idea that land use and design policies can be used to increase transit use as well as walking and bicycling. The development of appropriate measures for the built environment and for travel behavior is an essential element of this research. The link between the built environment and travel behavior is then made using theoretical frameworks borrowed from economics, and in particular, the concept of travel as a derived demand. The available evidence lends itself to the argument that a combination of urban design, land use patterns, and transportation systems that promotes walking and bicycling will help create active, healthier, and more livable communities. To provide more conclusive evidence, however, researchers must address the following issues: An alternative to the derived-demand framework must be developed for walking, measures of the built environment must be refined, and more-complete data on walking must be developed. In addition, detailed data on the built environment must be spatially matched to detailed data on travel behavior.
Biomechanical characteristics and determinants of instep soccer kick.

Good kicking technique is an important aspect of a soccer player. Therefore, understanding the biomechanics of soccer kicking is particularly important for guiding and monitoring the training process. The purpose of this review was to examine latest research findings on biomechanics of soccer kick performance and identify weaknesses of present research which deserve further attention in the future. Being a multiarticular movement, soccer kick is characterised by a proximal-to-distal motion of the lower limb segments of the kicking leg. Angular velocity is maximized first by the thigh, then by the shank and finally by the foot. This is accomplished by segmental and joint movements in multiple planes. During backswing, the thigh decelerates mainly due to a motion-dependent moment from the shank and, to a lesser extent, by activation of hip muscles. In turn, forward acceleration of the shank is accomplished through knee extensor moment as well as a motion-dependent moment from the thigh. The final speed, path and spin of the ball largely depend on the quality of foot-ball contact. Powerful kicks are achieved through a high foot velocity and coefficient of restitution. Preliminary data indicate that accurate kicks are achieved through slower kicking motion and ball speed values. Key pointsSoccer kick is achieved through segmental and joint rotations in multiple planes and via the proximal-to-distal sequence of segmental angular velocities until ball impact. The quality of ball - foot impact and the mechanical behavior of the foot are also important determinants of the final speed, path and spin of the ball.Ball speed values during the maximum instep kick range from 18 to 35 msec(-1) depending on various factors, such as skill level, age, approach angle and limb dominance.The main bulk of biomechanics research examined the biomechanics of powerful kicks, mostly under laboratory conditions. A powerful kick is characterized by the achievement of maximal ball speed. However, maximal ball speed does not guarantee a successful kick: in each case, the ball must reach the target. As already explained, when the player is instructed to hit the ball accurately, joint and segment velocities are lower as opposed to a fast and powerful kick performance. It is therefore apparent that future research should focus on biomechanics of fast but accurate kicking.
Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data

The main goal of this research is to discover the structure of home appliances usage patterns, hence providing more intelligence in smart metering systems by taking into account the usage of selected home appliances and the time of their usage. In particular, we present and apply a set of unsupervised machine learning techniques to reveal specific usage patterns observed at an individual household. The work delivers the solutions applicable in smart metering systems that might: (1) contribute to higher energy awareness; (2) support accurate usage forecasting; and (3) provide the input for demand response systems in homes with timely energy saving recommendations for users. The results provided in this paper show that determining household characteristics from smart meter data is feasible and allows for quickly grasping general trends in data.
A Numerical-Based Attention Method for Stock Market Prediction With Dual Information

Stock market prediction is of great importance for financial analysis. Traditionally, many studies only use the news or numerical data for the stock market prediction. In the recent years, in order to explore their complementary, some studies have been conducted to equally treat dual sources of information. However, numerical data often play a much more important role compared with the news. In addition, the existing simple combination cannot exploit their complementarity. In this paper, we propose a numerical-based attention (NBA) method for dual sources stock market prediction. Our major contributions are summarized as follows. First, we propose an attention-based method to effectively exploit the complementarity between news and numerical data in predicting the stock prices. The stock trend information hidden in the news is transformed into the importance distribution of numerical data. Consequently, the news is encoded to guide the selection of numerical data. Our method can effectively filter the noise and make full use of the trend information in news. Then, in order to evaluate our NBA model, we collect news corpus and numerical data to build three datasets from two sources: the China Security Index 300 (CSI300) and the Standard & Poor’s 500 (S&P500). Extensive experiments are conducted, showing that our NBA is superior to previous models in dual sources stock price prediction.
Simultaneous localization and mapping (SLAM): part II

S imultaneous localization and mapping (SLAM) is the process by which a mobile robot can build a map of the environment and, at the same time, use this map to compute its location. The past decade has seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. The great majority of work has focused on improving computational efficiency while ensuring consistent and accurate estimates for the map and vehicle pose. However, there has also been much research on issues such as nonlinearity, data association , and landmark characterization, all of which are vital in achieving a practical and robust SLAM implementation. This tutorial focuses on the recursive Bayesian formulation of the SLAM problem in which probability distributions or estimates of absolute or relative locations of landmarks and vehicle pose are obtained. Part I of this tutorial ( surveyed the development of the essential SLAM algorithm in state-space and particle filter form, described a number of key implementations, and cited locations of source code and real-world data for evaluation of SLAM algorithms. Part II of this tutorial (this article), surveys the current state of the art in SLAM research with a focus on three key areas: computational complexity, data association, and environment representation. Much of the mathematical notation and essential concepts used in this article are defined in Part I of this tutorial and, therefore, are not repeated here. SLAM, in its naive form, scales quadratically with the number of landmarks in a map. For real-time implementation, this scaling is potentially a substantial limitation in the use of SLAM methods. The complexity section surveys the many approaches that have been developed to reduce this complexity. These include linear-time state augmentation, sparsifica-tion in information form, partitioned updates, and submapping methods. A second major hurdle to overcome in the implementation of SLAM methods is to correctly associate observations of landmarks with landmarks held in the map. Incorrect association can lead to catastrophic failure of the SLAM algorithm. Data association is particularly important when a vehicle returns to a previously mapped region after a long excursion, the so-called loop-closure problem. The data association section surveys current data association methods used in SLAM. These include batch-validation methods that exploit constraints inherent in the SLAM formulation, appearance based methods, and multihypothesis techniques. The third development discussed in this tutorial is …
lsemantica : A Stata Command for Text Similarity based on Latent Semantic Analysis

The lsemantica command, presented in this paper, implements Latent Semantic Analysis in Stata. Latent Semantic Analysis is a machine learning algorithm for word and text similarity comparison. Latent Semantic Analysis uses Truncated Singular Value Decomposition to derive the hidden semantic relationships between words and texts. lsemantica provides a simple command for Latent Semantic Analysis in Stata as well as complementary commands for text similarity comparison.
A Scale-Out Blockchain for Value Transfer with Spontaneous Sharding

Bitcoin, as well as many of its successors, require the whole transaction record to be reliably acquired by all nodes to prevent double-spending. Recently, many blockchains have been proposed to achieve scale-out throughput by letting nodes only acquire a fraction of the whole transaction set. However, these schemes, e.g., sharding and off-chain techniques, suffer from a degradation in decentralization or the capacity of fault tolerance. In this paper, we show that the complete set of transactions is not a necessity for the prevention of double-spending if the properties of value transfers is fully explored. In other words, we show that a value-transfer ledger like Bitcoin has the potential to scale-out by its nature without sacrificing security or decentralization. Firstly, we give a formal definition for the value-transfer ledger and its distinct features from a generic database. Then, we introduce the blockchain structure with a shared main chain for consensus and an individual chain for each node for recording transactions. A locally executable validation scheme is proposed with uncompromising validity and consistency. A beneficial consequence of our design is that nodes will spontaneously try to reduce their transmission cost by only providing the transactions needed to show that their transactions are not double spend. As a result, the network is sharded as each node only acquires part of the transaction record and a scale-out throughput could be achieved, which we call "spontaneous sharding".
Review of maglev train technologies

This paper reviews and summarizes Maglev train technologies from an electrical engineering point of view and assimilates the results of works over the past three decades carried out all over the world. Many researches and developments concerning the Maglev train have been accomplished; however, they are not always easy to understand. The purpose of this paper is to make the Maglev train technologies clear at a glance. Included are general understandings, technologies, and worldwide practical projects. Further research needs are also addressed.