nils-herrmann/clean_scirepeval huggingface dataset:
In-Session Personalization for Talent Search

Previous efforts in recommendation of candidates for talent search followed the general pattern of receiving an initial search criteria and generating a set of candidates utilizing a pre-trained model. Traditionally, the generated recommendations are final, that is, the list of potential candidates is not modified unless the user explicitly changes his/her search criteria. In this paper, we are proposing a candidate recommendation model which takes into account the immediate feedback of the user, and updates the candidate recommendations at each step. This setting also allows for very uninformative initial search queries, since we pinpoint the user's intent due to the feedback during the search session. To achieve our goal, we employ an intent clustering method based on topic modeling which separates the candidate space into meaningful, possibly overlapping, subsets (which we call intent clusters) for each position. On top of the candidate segments, we apply a multi-armed bandit approach to choose which intent cluster is more appropriate for the current session. We also present an online learning scheme which updates the intent clusters within the session, due to user feedback, to achieve further personalization. Our offline experiments as well as the results from the online deployment of our solution demonstrate the benefits of our proposed methodology.
Learning exploration strategies in model-based reinforcement learning

Reinforcement learning (RL) is a paradigm for learning sequential decision making tasks. However, typically the user must hand-tune exploration parameters for each different domain and/or algorithm that they are using. In this work, we present an algorithm called leo for learning these exploration strategies on-line. This algorithm makes use of bandit-type algorithms to adaptively select exploration strategies based on the rewards received when following them. We show empirically that this method performs well across a set of five domains. In contrast, for a given algorithm, no set of parameters is best across all domains. Our results demonstrate that the leo algorithm successfully learns the best exploration strategies on-line, increasing the received reward over static parameterizations of exploration and reducing the need for hand-tuning exploration parameters.
A Syntax-based Statistical Translation Model

We presenta syntax-basedstatistical translationmodel. Our model transforms a source-languageparse tree into a target-languagestring by applying stochasticoperationsat eachnode. Theseoperationscapturelinguistic differencessuchas word order and case marking. Model parametersare estimatedin polynomialtime usinganEM algorithm. The model producesword alignmentsthat are better than those producedby IBM Model5.
Using latent semantic analysis to improve access to textual information

This paper describes a new approach for dealing with the vocabulary problem in human-computer interaction. Most approaches to retrieving textual materials depend on a lexical match between words in users' requests and those in or assigned to database objects. Because of the tremendous diversity in the words people use to describe the same object, lexical matching methods are necessarily incomplete and imprecise . The latent semantic indexing approach tries to overcome these problems by automatically organizing text objects into a semantic structure more appropriate for matching user requests. This is done by taking advantage of implicit higher-order structure in the association of terms with text objects. The particular technique used is singular-value decomposition, in which a large term by text-object matrix is decomposed into a set of about 50 to 150 orthogonal factors from which the original matrix can be approximated by linear combination. Terms and objects are represented by 50 to 150 dimensional vectors and matched against user queries in this “semantic” space. Initial tests find this completely automatic method widely applicable and a promising way to improve users' access to many kinds of textual materials, or to objects and services for which textual descriptions are available.
The impact of emotionality and self-disclosure on online dating versus traditional dating

Online dating is unique in the pursuit of romance. The bond created between potential partners takes a different path than normal dating relationships. Online dating usually begins with a flurry of e-mail messages, each more intimate than the last. Traditional dating relationships that might take months to develop in the real world, take weeks or even days online. Much has been written about cyber-dating, but little research has been done. This series of four studies examines the online dating process, similarities and differences between online and traditional dating, and the impact of emotionality and self-disclosure on first (e-mail) impressions of a potential partner. Results indicate that the amount of emotionality and self-disclosure affected a person’s perception of a potential partner. An e-mail with strong emotional words (e.g., excited, wonderful) led to more positive impressions than an e-mail with fewer strong emotional words (e.g., happy, fine) and resulted in nearly three out of four subjects selecting the e-mailer with strong emotional words for the fictitious dater of the opposite sex. Results for self-disclosure e-mails were complex, but indicate that levels of self-disclosure led to different impressions. Low levels of self-disclosure were generally preferred in choosing for the fictitious dater, although these preferences differed by gender, education, and ethnic background. Results were discussed in terms of theories of computer-mediated communication.
Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems

Tensor decompositions and tensor networks are emerging and promising tools for data analysis and data mining. In this paper we review basic and emerging models and associated algorithms for large-scale tensor networks, especially Tensor Train (TT) decompositions using novel mathematical and graphical representations. We discus the concept of tensorization (i.e., creating very high-order tensors from lower-order original data) and super compression of data achieved via quantized tensor train (QTT) networks. The main objective of this paper is to show how tensor networks can be used to solve a wide class of big data optimization problems (that are far from tractable by classical numerical methods) by applying tensorization and performing all operations using relatively small size matrices and tensors and applying iteratively optimized and approximative tensor contractions.
Detection of Partially Occluded Face Using Support Vector Machines

Partially occluded face detection is need, because although the technology of the Automated Teller Machines and face detection is increased, we cannot control the people who wear sunglasses or mask for the crime. To reject the occluded face, we first trained the features of the normal faces and the occluded faces that wear sunglasses or mask using Principal Component Analysis and Support Vector Machines to reduce the dimension and classify eficiently. Then we decide that the detected face is normal or partially occluded face using the scheme that integrates the Principal Component Analysis and Support Vector Machines. In the experiments, we trained the 3200 normal face images that have the variations of illumination and expression and each 2900 and 4500 partially occluded face images that wear the sunglasses or mask with 60*25, and 60*35 resolution. We get the 95.2% and 98.8% partially occluded face detection ratio after face detection and 2.5% and 0% false alarm ratio from the experiments based on the Purdue University Face DB. The proposed algorithm which is incorporated the face detection system can help the security using the Digital Video Recorder System and face recognition
Trustworthy Medical Device Software

This report summarizes what the computing research community knows about the role of trustworthy software for safety and effectiveness of medical devices. Research shows that problems in medical device software result largely from a failure to apply well-known systems engineering techniques, especially during specification of requirements and analysis of human factors. Recommendations to increase the trustworthiness of medical device software include (1) regulatory policies that specify outcome measures rather than technology, (2) collection of statistics on the role of software in medical devices, (3) establishment of open-research platforms for innovation, (4) clearer roles and responsibility for the shared burden of software, (5) clarification of the meaning of substantial equivalence for software, and (6) an increase in FDA’s access to outside experts in software. This report draws upon material from research in software engineering and trustworthy computing, public FDA data, and accident reports to provide a high-level understanding of the issues surrounding the risks and benefits of medical device software.
A Non-Parametric Bayesian Method for Inferring Hidden Causes

We present a non-parametric Bayesian approach to structure learning with hidden causes. Previous Bayesian treatments of this problem define a prior over the number of hidden causes and use algorithms such as reversible jump Markov chain Monte Carlo to move between solutions. In contrast, we assume that the number of hidden causes is unbounded, but only a finite number influence observable variables. This makes it possible to use a Gibbs sampler to approximate the distribution over causal structures. We evaluate the performance of both approaches in discovering hidden causes in simulated data, and use our non-parametric approach to discover hidden causes in a real medical dataset.
IT innovation adoption by enterprises: Knowledge discovery through text analytics

a r t i c l e i n f o Enterprise adoption of information technology (IT) innovations has been a topic of tremendous interest to both practitioners and researchers. The study of technological, managerial, strategic, and economic factors as well as adoption processes and contexts has led the field to become a rich tapestry of many theoretical and conceptual foundations. This paper provides a comprehensive multidisciplinary classification and analysis of the scholarly development of the enterprise-level IT innovation adoption literature by examining articles over the past three decades (1977–2008). We identify 472 articles and classify them by functional discipline, publication, research methodology, and IT type. The paper applies text analytic methods to this document repository to (1) identify salient adoption determinants and their relationships, (2) discover research trends and patterns across disciplines, and (3) suggest potential areas for future research in IT innovation adoption at the enterprise level. Adoption of information technology (IT) innovations has been a topic of significant interest to researchers and practitioners over the past three decades (e.g. [18,53,56]). Broadly, there are two complementary perspectives on IT innovation adoption: the first and more frequently examined perspective includes the adoption of IT innovations by the individual user. Often referred to as the bottom–up view, individual IT innovation adoption research has focused on user characteristics, behavioral motivation, and contextual elements. Enterprise IT innovation adoption on the other hand focuses on the firm and firm-level characteristics. 1 This perspective has gained particular interest due to enterprises' increasing dependence on IT as well as some highly publicized successes and failures over the past two decades. Consequently enterprise-level IT innovation adoption studies have focused on why, how, and under what conditions enterprises have succeeded or failed in adopting and implementing IT innovations. These issues have been examined for a wide range of different IT innovations, including enterprise information systems, electronic commerce, database management systems, network and telecommunications infrastructure, computer hardware, enterprise architecture components, and business productivity applications, among many others. As a result, previous studies have identified drivers and inhibitors, explored the influence of important technological, individual, organizational, strategic, economic, and managerial , and environmental factors, and examined key processes and stages associated with the adoption of IT innovations. Because IT touches upon virtually all aspects of an enterprise's value chain, researchers have drawn on theories, frameworks and models from a variety of complementary academic reference disciplines such as information systems, …
An End-to-End Machine Learning System for Harmonic Analysis of Music

We present a new system for the harmonic analysis of popular musical audio. It is focused on chord estimation, although the proposed system additionally estimates the key sequence and bass notes. It is distinct from competing approaches in two main ways. First, it makes use of a new improved chromagram representation of audio that takes the human perception of loudness into account. Furthermore, it is the first system for joint estimation of chords, keys, and bass notes that is fully based on machine learning, requiring no expert knowledge to tune the parameters. This means that it will benefit from future increases in available annotated audio files, broadening its applicability to a wider range of genres. In all of three evaluation scenarios, including a new one that allows evaluation on audio for which no complete ground truth annotation is available, the proposed system is shown to be faster, more memory efficient, and more accurate than the state-of-the-art.
Natural Language Inference by Tree-Based Convolution and Heuristic Matching

In this paper, we propose the TBCNNpair model to recognize entailment and contradiction between two sentences. In our model, a tree-based convolutional neural network (TBCNN) captures sentencelevel semantics; then heuristic matching layers like concatenation, element-wise product/difference combine the information in individual sentences. Experimental results show that our model outperforms existing sentence encoding-based approaches by a large margin.
Trading structure for randomness in wireless opportunistic routing

Opportunistic routing is a recent technique that achieves high throughput in the face of lossy wireless links. The current opportunistic routing protocol, ExOR, ties the MAC with routing, imposing a strict schedule on routers' access to the medium. Although the scheduler delivers opportunistic gains, it misses some of the inherent features of the 802.11 MAC. For example, it prevents spatial reuse and thus may underutilize the wireless medium. It also eliminates the layering abstraction, making the protocol less amenable to extensions to alternate traffic types such as multicast.
 This paper presents MORE, a MAC-independent opportunistic routing protocol. MORE randomly mixes packets before forwarding them. This randomness ensures that routers that hear the same transmission do not forward the same packets. Thus, MORE needs no special scheduler to coordinate routers and can run directly on top of 802.11. Experimental results from a 20-node wireless testbed show that MORE's median unicast throughput is 22% higher than ExOR, and the gains rise to 45% over ExOR when there is a chance of spatial reuse. For multicast, MORE's gains increase with the number of destinations, and are 35-200% greater than ExOR.
Predicting WWW surfing using multiple evidence combination

The improvement of many applications such as web search, latency reduction, and personalization/ recommendation systems depends on surfing prediction. Predicting user surfing paths involves tradeoffs between model complexity and predictive accuracy. In this paper, we combine two classification techniques, namely, the Markov model and Support Vector Machines (SVM), to resolve prediction using Dempster’s rule. Such fusion overcomes the inability of the Markov model in predicting the unseen data as well as overcoming the problem of multiclassification in the case of SVM, especially when dealing with large number of classes. We apply feature extraction to increase the power of discrimination of SVM. In addition, during prediction we employ domain knowledge to reduce the number of classifiers for the improvement of accuracy and the reduction of prediction time. We demonstrate the effectiveness of our hybrid approach by comparing our results with widely used techniques, namely, SVM, the Markov model, and association rule mining.
The BUGS project: Evolution, critique and future directions.

BUGS is a software package for Bayesian inference using Gibbs sampling. The software has been instrumental in raising awareness of Bayesian modelling among both academic and commercial communities internationally, and has enjoyed considerable success over its 20-year life span. Despite this, the software has a number of shortcomings and a principal aim of this paper is to provide a balanced critical appraisal, in particular highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects. We also present a historical overview of the BUGS project and some future perspectives.
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Several large scale data mining applications, such as text c ategorization and gene expression analysis, involve high-dimensional data that is also inherentl y directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hyperspher e. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characteri zing such data. This paper proposes a generative mixture-model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. In particular, we derive and analyze two variants of the Expectation Maximiza tion (EM) framework for estimating the mean and concentration parameters of this mixture. Nume rical estimation of the concentration parameters is non-trivial in high dimensions since it i nvolves functional inversion of ratios of Bessel functions. We also formulate two clustering algorit hms corresponding to the variants of EM that we derive. Our approach provides a theoretical basis fo r the use of cosine similarity that has been widely employed by the information retrieval communit y, and obtains the spherical kmeans algorithm (kmeans with cosine similarity) as a special case of both variants. Empirical results on clustering of high-dimensional text and gene-expression d ata based on a mixture of vMF distributions show that the ability to estimate the concentration pa rameter for each vMF component, which is not present in existing approaches, yields superior resu lts, especially for difficult clustering tasks in high-dimensional spaces.
Joint learning and dictionary construction for pattern recognition

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.
Learning from labeled and unlabeled data on a directed graph

We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The time complexity of the algorithm derived from this framework is nearly linear due to recently developed numerical techniques. In the absence of labeled instances, this framework can be utilized as a spectral clustering method for directed graphs, which generalizes the spectral clustering approach for undirected graphs. We have applied our framework to real-world web classification problems and obtained encouraging results.
LoSt: location based storage

For certain types of sensitive data (such as health records) it is important to know the geographic location of the file, e.g. that it is stored on servers within the USA. This is particularly important for determining applicable laws and regulations. In this paper we discuss the problem of verifying the location of files within distributed file storage systems such as the cloud. We consider a general setup for a distributed storage system and show that verifying location when such a system is fully malicious, is impossible. We then make plausible assumptions about the behavior of the system and provide a formal definition for Proofs of Location (PoL) in our setting. We show secure and efficient PoL schemes can be constructed by using a geolocation scheme and a Proof of Retrievability (PoR) scheme with a new added property that we call re-coding, which is of independent interest.
Measuring e-government service value with the E-GOVSQUAL-RISK model

The purpose of this paper is to study the e-government service quality and risk perceptions of personal income taxpayers on e-government service value.  The study uses qualitative in-depth interview and content analysis to explore the determinants of e-government service quality and risk dimensions of e-government service value.  The findings suggest that perceived value of e-government service is e-government service quality, which consists of service design, web site design, technical support, and customer support quality. On the other hand, the three perceived risk concerns are performance, privacy, and financial audit risk.  The study interviews the small samples of income taxpayers to develop the determinants of e-government service value, future studies should utilize a quantitative study to strengthen the results. Future researchers could also expand the results to other groups of taxpayers (e.g. corporate tax) to explore and compare factors that contribute to e-government service value.  The results can assist e-government service design not only to increase electronic service quality but also to reduce risk facets in order to enhance e-government service value and enlarge acceptance from income taxpayers. E-government service providers can use the research model to detect electronic service weaknesses and risks so that the appropriate resources can be allocated to improve the system more effectively.  This study outlines e-government service value in terms of e-government service quality and risk perspectives or the E-GOVSQUAL-RISK model which contributes to the different knowledge on e-government service.
An Empirical Study of Learning to Rank for Entity Search

This work investigates the effectiveness of learning to rank methods for entity search. Entities are represented by multi-field documents constructed from their RDF triples, and field-based text similarity features are extracted for query-entity pairs. State-of-the-art learning to rank methods learn models for ad-hoc entity search. Our experiments on an entity search test collection based on DBpedia confirm that learning to rank methods are as powerful for ranking entities as for ranking documents, and establish a new state-of-the-art for accuracy on this benchmark dataset.