nils-herrmann/clean_scirepeval huggingface dataset:
Distributed Representation-based Recommender Systems in E-commerce

Recommender system plays an important role in many e-commerce services, such as in Rakuten. In this paper, we focus on the item-to-item recommender and the user-to-item recommenders, which are two most widely used functions in online services for presenting relevant items given an item, or a particular user. We use a large amount of log data from one of Rakuten markets, and apply distributed representation method to that data for developing two types of recommender systems. The key idea of our approach is treating items as words, and users’ sessions as sentences, then training the Word2vec model and Doc2vec models based on those items and user’s information. Resulting item vectors from the Word2vec model can be used to calculate the cosine similarity between items, and find the similar items given an item. Similarly, Doc2vec model helps users find relevant items that might interest them using similarity between items and vectors. We also use the item vectors from both embedding models to build an additional user-to-item recommender, namely Item Vector-based system. The experiments show that our best system achieved a hit-rate of 24.17% for recommending items to users in testing data, which outperformed conventional approaches to a significant extent. Keyword Recommender System,Distributed Representation,Item Vector-based
A NoSQL Data Model for Scalable Big Data Workflow Execution

While big data workflows haven been proposed recently as the next-generation data-centric workflow paradigm to process and analyze data of ever increasing in scale, complexity, and rate of acquisition, a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. In the meanwhile, although NoSQL has emerged as a new category of data models, they are optimized for storing and querying of large datasets, not for ad-hoc data analysis where data placement and data movement are necessary for optimized workflow execution. In this paper, we propose a NoSQL data model that: 1) supports high-performance MapReduce-style workflows that automate data partitioning and data-parallelism execution. In contrast to the traditional MapReduce framework, our MapReduce-style workflows are fully composable with other workflows enabling dataflow applications with a richer structure, 2) automates virtual machine provisioning and deprovisioning on demand according to the sizes of input datasets, 3) enables a flexible framework for workflow executors that take advantage of the proposed NoSQL data model to improve the performance of workflow execution. Our case studies and experiments show the competitive advantages of our proposed data model. The proposed NoSQL data model is implemented in a new release of DATAVIEW, one of the most usable big data workflow systems in the community.
ARTIFICIAL NEURAL NETWORK ARCHITECTURE FOR SOLVING THE DOUBLE DUMMY BRIDGE PROBLEM IN CONTRACT BRIDGE

Card games are interesting for many reasons besides their connection with gambling. Bridge is being a game of imperfect information, it is a well defined, decision making game. The estimation of the number of tricks to be taken by one pair of bridge players is called Double Dummy Bridge Problem (DDBP). Artificial Neural Networks are Non – Linear mapping structures based on the function of the human brain. Feed Forward Neural Network is used to solve the DDBP in contract bridge. The learning methodology, supervised learning was used in Back – Propagation Network (BPN) for training and testing the bridge sample deal. In our study we compared back – Propagation algorithm and obtained that Resilient Back – Propagation algorithms by using Hyperbolic Tangent function and Resilient Back – Propagation algorithm produced better result than the other. Among various neural network architectures, in this study we used four network architectures viz., 26x4, 52, 104 and 52x4 for solving DDBP in contract bridge.
Supervised Dynamic and Adaptive Discretization for Rule Mining

Association rule mining is a well-researched topic in data mining. However, a common limitation with existing algorithms is that they mainly deal with categorical data. In this work we propose a methodology that allows adaptive discretization and quantitative rule discovery in large mixed databases. More specifically, we propose a top-down, recursive approach to find ranges of values for continuous attributes that result in high confidence rules. Our approach allows any given continuous attribute to be discretized in multiple ways. Compared to a global discretization scheme, our approach makes it possible to capture different intervariable interactions. We applied our algorithm to various synthetic and real datasets, including Intel manufacturing data that motivated our research. The experimental results and analysis indicate that our algorithm is capable of finding more meaningful rules for multivariate data, in addition to being more efficient than the state-of-the-art techniques.
Open Sesame!: a Look at Personal Assistants

Software agents, or softbots, are supposedly intelligent programs that assist the user in performing repetitive , boring, and time-consuming tasks. An overview of software agents, with special attention focused on the so-called personal assistants, is presented as an introduction. Following that, an in-depth look at one of the rst commercially available intelligent agents Open Sesame!, a personal assistant for managing the Macintosh desktop environment, is presented. The existing intelligent agent paradigms, we argue, break down when it comes to actively helping the end user because of aws inherent in the current approaches to intelligent agent design. To that end, we discuss the reasons why this is, particularly with respect to Open Sesame!, and promote the notion of situatedness to be taken into account more seriously for developing software agents that actively support the user for the duration of the task at hand. As an example for a more situated approach to personal assistant design, a quick overview of a project to develop a personal assistant for news ltering is discussed.
A Novel Broad-Band Chebyshev-Response Rat-Race Ring Coupler

A novel broad-band rat-race ring coupler with a unit element at each port and an ideal phase inverter at one of the ring arms is proposed and analyzed. Design equations based on Chebyshev equiripple functions are derived. The design curves of equal or unequal power division are also presented. Theoretical data show that the bandwidth of unequal power division with a specific return-loss value increases as the power division ratio increases or decreases from unity. For a 180 hybrid with equal power division (3 dB) and 15-dB return loss, the proposed rat-race ring may have a bandwidth of 4 : 1. Three circuits of this novel rat-race ring are realized using finite-groundplane coplanar waveguide. The measured results match very well with the theory.
Model predictive path-following control of an A.R. drone quadrotor

This paper addresses the design and implementation of the Extended Prediction Self-Adaptive Control (EPSAC) approach to Model Predictive Control (MPC) for path-following. Special attention is paid to the closed-loop performance in order to achieve a fast response without overshoot, which are the necessary conditions to ensure the desired tracking performance in confined or indoor environments. The performance of the proposed MPC strategy is compared to the one achieved using PD controllers. Experimental results using the low-cost quadrotor AR.Drone 2.0 validate the reliability of the proposed strategy for 2D and 3D movements.
Relaxed Lasso

The Lasso is an attractive regularisation method for high-dimensional regression. It combines variable selection with an efficient computational procedure. However, the rate of convergence of the Lasso is slow for some sparse high-dimensional data, where the number of predictor variables is growing fast with the number of observations. Moreover, many noise variables are selected if the estimator is chosen by cross-validation. It is shown that the contradicting demands of an efficient computational procedure and fast convergence rates of the 2-loss can be overcome by a two-stage procedure, termed the relaxed Lasso. For orthogonal designs, the relaxed Lasso provides a continuum of solutions that include both softand hard-thresholding of estimators. The relaxed Lasso solutions include all regular Lasso solutions and computation of all relaxed Lasso solutions is often identically expensive as computing all regular Lasso solutions. Theoretical and numerical results demonstrate that the relaxed Lasso produces sparser models with equal or lower prediction loss than the regular Lasso estimator for high-dimensional data.
MULTI-SKILLED MOTION CONTROL

Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. We extend policy distillation methods to the continuous action setting and leverage this technique to combine expert policies, as evaluated in the domain of simulated bipedal locomotion across different classes of terrain. We also introduce an input injection method for augmenting an existing policy network to exploit new input features. Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills. The combination of these methods allows a policy to be incrementally augmented with new skills. We compare our progressive learning and integration via distillation (PLAID) method against three alternative baselines.
The Mediator complex: a central integrator of transcription

The RNA polymerase II (Pol II) enzyme transcribes all protein-coding and most non-coding RNA genes and is globally regulated by Mediator — a large, conformationally flexible protein complex with a variable subunit composition (for example, a four-subunit cyclin-dependent kinase 8 module can reversibly associate with it). These biochemical characteristics are fundamentally important for Mediator's ability to control various processes that are important for transcription, including the organization of chromatin architecture and the regulation of Pol II pre-initiation, initiation, re-initiation, pausing and elongation. Although Mediator exists in all eukaryotes, a variety of Mediator functions seem to be specific to metazoans, which is indicative of more diverse regulatory requirements.
"My Roomba Is Rambo": Intimate Home Appliances

Robots have entered our domestic lives, but yet, little is known about their impact on the home. This paper takes steps towards addressing this omission, by reporting results from an empirical study of iRobot’s RoombaTM, a vacuuming robot. Our findings suggest that, by developing intimacy to the robot, our participants were able to derive increased pleasure from cleaning, and expended effort to fit Roomba into their homes, and shared it with others. These findings lead us to propose four design implications that we argue could increase people’s enthusiasm for smart home technologies.
Penile fracture - experience in 56 cases.

The aim of this work is to report the diagnostic and therapeutic options for 55 patients with clinical diagnosis of penile fracture.



The patients were retrospectively assessed between 1982 and 2002. The primary diagnostic evaluation method for 55 patients (56 fractures) was clinical history and physical exam. Ten (17.8%) cases required complementary exams. Ultrasound (US) was performed in 2 cases, and magnetic resonance imaging (MRI) in 1 case. Retrograde urethrocystogram was performed in suspicious urethral injury, which happened for 7 patients.



Of 56 assessed cases, 49 (89.5%) were submitted to surgical exploration, and only 7 were conservatively conducted. Surgical treatment was performed in 48 patients (49 fractures), in these cases, 47 (95.9%) presented tunica albuginea disruption and solely 2 (4.1%) evidenced lesion of dorsal vein. Ultrasonography confirmed disruption of tunica albuginea in 1 (50%) case, and in the other it was not possible to determinate the origin of the lesion, and the patient was submitted to surgical exploration, which confirmed the condition. MRI was used only in 1 case, confirming the lesion. Among 7 patients submitted to conservative management, until now, 3 (42.8%) required surgical intervention to correct penile chordee.



Penile fracture is an entity of eminently clinical diagnosis, which management should be surgical and immediate, avoiding thus complications related to erectile dysfunction. When suspecting an associated urethral injury, Urethrocystogram is recommended. In cases where there is diagnostic uncertainty, ultrasound and/or MRI may be used to reveal the condition.
Realistic and Fast Cloud Rendering

Clouds are an important aspect of rendering outdoor scenes. Thi paper describes a cloud system that extends texture splatting on par ticles to model a dozen cloud types (e.g., stratus, cumulus congestus, cumulonimb us), an improvement over earlier systems that modeled only one type of cumulus. W e also achieve fast real-time rendering, even for scenes of dense overcast cove rage, which was a limitation for previous systems. We present a new shading model that uses artist-driven contr ols rather than a programmatic approach to approximate lighting. This is sui table when fine-grained control over the look-and-feel is necessary, and artistic r esources are available. We also introduce a way to simulate cloud formation and dissipa tion using texture
Holistic data accuracy assessment using search & scored-based Bayesian network learning algorithms

Data accuracy is one of the central criteria for data quality. Bayesian network (BN) learning algorithm based on independence test is a promising approach for assessing data accuracy. However, it may get stuck in out-of-memory exception when confront with large network and small percentage of inaccurate data. In this paper, we extend the method for data accuracy assessment to search & scored-based BN learning algorithms. This category of algorithms can overcome the shortage of the former one, which will broaden the application of BN learning algorithms in assessing data accuracy. More indicators based on adjacent matrix are developed to view data accuracy from different perspectives. Two algorithms are tested to verify the efficacy of our research and the results show that the mainstream search & scored-based BN learning algorithms scale well with the strategy for data accuracy assessment.
EmailProfiler: Spearphishing Filtering with Header and Stylometric Features of Emails

Spearphishing is a prominent targeted attack vector in today's Internet. By impersonating trusted email senders through carefully crafted messages and spoofed metadata, adversaries can trick victims into launching attachments containing malicious code or into clicking on malicious links that grant attackers a foothold into otherwise well-protected networks. Spearphishing is effective because it is fundamentally difficult for users to distinguish legitimate emails from spearphishing emails without additional defensive mechanisms. However, such mechanisms, such as cryptographic signatures, have found limited use in practice due to their perceived difficulty of use for normal users. In this paper, we present a novel automated approach to defending users against spearphishing attacks. The approach first builds probabilistic models of both email metadata and stylometric features of email content. Then, subsequent emails are compared to these models to detect characteristic indicators of spearphishing attacks. Several instantiations of this approach are possible, including performing model learning and evaluation solely on the receiving side, or senders publishing models that can be checked remotely by the receiver. Our evaluation of a real data set drawn from 20 email users demonstrates that the approach effectively discriminates spearphishing attacks from legitimate email while providing significant ease-of-use benefits over traditional defenses.
Database Security

Computer security is concerned with the ability of a computer system to enforce a security policy governing the disclosure, modification, or destruction of information. The security policy may be specific to the organization, or may be generic. For example, the DoD mandatory security (or multilevel security) poficies restrict access to classified information to cleared personnel. Discretionary security policies, on the other hand, define access restrictions based on the identity of users (or groups), the type of access (e.g., select, update, insert, delete), the specific object being accessed, and perhaps other factors (time of day, which application program is being used, etc.). Different types of users (system managers, database administrators, and ordinary users) may have different access rights to the data in the system. The access controls commonly found in most database systems are examples of discretionary access controls. This paper discusses discretionary security and mandatory security for database systems. We outline the current state of research in database security and briefly discuss some open research issues.
An insight into imbalanced Big Data classification: outcomes and challenges

BigData applications are emerging during the last years, and researchers frommany disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a “de facto” solution. Basically, it carries out a “divide-andconquer” distributed procedure in a fault-tolerant way to adapt for commodity hardware.Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit theMapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current B Alberto Fernández alberto@decsai.ugr.es Sara del Río srio@decsai.ugr.es Nitesh V. Chawla nchawla@nd.edu Francisco Herrera herrera@decsai.ugr.es 1 Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain 2 Department of Computer Science and Engineering, 384 Fitzpatrick Hall, University of Notre Dame, Notre Dame, IN 46556, USA 3 Interdisciplinary Center for Network Science and Applications, 384 Nieuwland Hall of Science, University of Notre Dame, Notre Dame, IN 46556, USA research state of this area. Second, to analyze the behavior of standard pre-processing techniques in this particular framework. Finally, taking into account the experimental results obtained throughout this work, we will carry out a discussion on the challenges and future directions for the topic.
Emotional convergence between people over time.

The authors propose that people in relationships become emotionally similar over time--as this similarity would help coordinate the thoughts and behaviors of the relationship partners, increase their mutual understanding, and foster their social cohesion. Using laboratory procedures to induce and assess emotional response, the authors found that dating partners (Study 1) and college roommates (Studies 2 and 3) became more similar in their emotional responses over the course of a year. Further, relationship partners with less power made more of the change necessary for convergence to occur. Consistent with the proposed benefits of emotional similarity, relationships whose partners were more emotionally similar were more cohesive and less likely to dissolve. Discussion focuses on implications of emotional convergence and on potential mechanisms.
Identification of Flux Linkage Map of Permanent Magnet Synchronous Machines Under Uncertain Circuit Resistance and Inverter Nonlinearity

This paper proposes a novel scheme for the identification of the whole flux linkage map of permanent magnet synchronous machines, by which the map of dq-axis flux linkages at different load or saturation conditions can be identified by the minimization of a proposed estimation model. The proposed method works on a conventional three-phase inverter based vector control system and the immune clonal based quantum genetic algorithm is employed for the global searching of minimal point. Besides, it is also noteworthy that the influence of uncertain inverter nonlinearity and circuit resistance are cancelled during the modeling process. The proposed method is subsequently tested on two PMSMs and shows quite good performance compared with the finite element prediction results.
SOFIT: Sociotechnical and Organizational Factors for Insider Threat

Human behavioral factors have been insufficiently represented in structured models (e.g., ontology frameworks) of insider threat risk. This paper describes the design and development of a structured model that emphasizes individual and organizational sociotechnical factors while incorporating technical indicators from previous work. We compare this model with previous research and describe a use case to demonstrate how the model can be applied as an ontology. We also summarize results of an expert knowledge elicitation study to reveal relationships among indicators and to examine several quantitative models for assessing threat of cases comprising multiple indicators.
Impact of Trust on Internet Banking Adoption : A Literature Review

Banks spend a great amount of money on providing the Internet banking service to customers. Hence, the lack of adoption of Internet banking can bring negative consequences for the banks. As a result, banks continuously try to adapt their business strategies to increase customer satisfaction. This paper provides an overview of the extant research into the relationships between trust and Internet banking adoption from the customers’ perspective. The literature review covers empirical studies on trust and online trust, the factors of trust and the adoption of Internet banking. To facilitate the review and analysis of the studies reported in the literature, we propose a framework of customer factors and website factors which affect trust and organize the impact of trust on Internet banking adoption under the proposed framework. The findings indicate that trust has a positive effect on the adoption of Internet banking. Moreover, the results show that there are many factors related to trust in electronic services. The majority of the research has been conducted in the context of Internet banking. Drawing on these studies, the review presented in this paper offers an overview of the relevant variables that may affect people’s trust in Internet banking.