nils-herrmann/clean_scirepeval huggingface dataset:
Adversarial Evaluation of Dialogue Models

The recent application of RNN encoder-decoder models has re sulted in substantial progress in fully data-driven dialogue systems, but evalua tion remains a challenge. An adversarial loss could be a way to directly evaluate the ex t nt to which generated dialogue responses sound like they came from a human. Th is could reduce the need for human evaluation, while more directly evaluati ng on a generative task. In this work, we investigate this idea by training an RN N to discriminate a dialogue model’s samples from human-generated samples. A lthough we find some evidence this setup could be viable, we also note that ma ny issues remain in its practical application. We discuss both aspects and conc lude that future work is warranted.
Telecommunications Fraud : Problem Analysis-an Agent-based KDD Perspective

Telecommunications fraud is a problem that affects operators all around the world. Operators know that fraud cannot be completely eradicated. The solution to deal with this problem is to minimize the damages and cut down losses by detecting fraud situations as early as possible. Computer systems were developed or acquired, and experts were trained to detect these situations. Still, the operators have the need to evolve this process, in order to detect fraud earlier and also get a better understanding of the fraud attacks they suffer. In this paper the fraud problem is analyzed and a new approach to the problem is designed. This new approach, based on the profiling and KDD (Knowledge Discovery in Data) techniques, supported in a MAS (Multiagent System), does not replace the existing fraud detection systems; it uses them and their results to provide operators new fraud detection methods and new knowledge.
‘Who Likes What and, Why?’ Insights into Modeling Users’ Personality Based on Image ‘Likes’

The increased proliferation of data production technologies (e.g., cameras) and consumption avenues (e.g., social media) has led to images and videos being utilized by users to convey innate preferences and tastes. This has opened up the possibility of using multimedia as a source for user-modeling. This work attempts to model personality traits (based on the Five Factor Theory) of users using a collection of images they tag as ‘favorite’ (or like) on Flickr. First, a set of semantic features are proposed to be used for representing different concepts in images which influence users to like them. The addition of the proposed features led to improvement over state-of-the-art by 12 percent. Second, a novel machine learning approach is developed to model users’ personality based on the image features (resulting in upto 15 percent improvement). Third, efficacy of the semantic features and the modeling approach is shown in recommending images based on personality modeling. Using the modeling approach, recommendations are made regarding the factors that might influence users with different personality traits to like an image.
4 store : The Design and Implementation of a Clustered RDF Store

This paper describes the design and implementation of the 4store RDF storage and SPARQL query system with respect to its cluster and query processing design. 4store was originally designed to meet the data needs of Garlik, a UK-based semantic web company. This paper describes the design and performance characteristics of 4store, as well as discussing some of the trade-offs and design decisions. These arose both from immediate business requirements and a desire to engineer a scalable system capable of reuse in a range of experimental contexts where we were looking to explore new business opportunities.
Stacked Extreme Learning Machines

Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. It provides a unified solution that can be used directly to solve regression, binary, and multiclass classification problems. In this paper, we propose a stacked ELMs (S-ELMs) that is specially designed for solving large and complex data problems. The S-ELMs divides a single large ELM network into multiple stacked small ELMs which are serially connected. The S-ELMs can approximate a very large ELM network with small memory requirement. To further improve the testing accuracy on big data problems, the ELM autoencoder can be implemented during each iteration of the S-ELMs algorithm. The simulation results show that the S-ELMs even with random hidden nodes can achieve similar testing accuracy to support vector machine (SVM) while having low memory requirements. With the help of ELM autoencoder, the S-ELMs can achieve much better testing accuracy than SVM and slightly better accuracy than deep belief network (DBN) with much faster training speed.
Low-voltage-swing monolithic dc-dc conversion

A low-voltage-swing MOSFET gate drive technique is proposed in this paper for enhancing the efficiency characteristics of high-frequency-switching dc-dc converters. The parasitic power dissipation of a dc-dc converter is reduced by lowering the voltage swing of the power transistor gate drivers. A comprehensive circuit model of the parasitic impedances of a monolithic buck converter is presented. Closed-form expressions for the total power dissipation of a low-swing buck converter are proposed. The effect of reducing the MOSFET gate voltage swings is explored with the proposed circuit model. A range of design parameters is evaluated, permitting the development of a design space for full integration of active and passive devices of a low-swing buck converter on the same die, for a target CMOS technology. The optimum gate voltage swing of a power MOSFET that maximizes efficiency is lower than a standard full voltage swing. An efficiency of 88% at a switching frequency of 102 MHz is achieved for a voltage conversion from 1.8 to 0.9 V with a low-swing dc-dc converter based on a 0.18-/spl mu/m CMOS technology. The power dissipation of a low-swing dc-dc converter is reduced by 27.9% as compared to a standard full-swing dc-dc converter.
Smooth Loss Functions for Deep Top-k Classification

The top-k error is a common measure of performance in machine learning and computer vision. In practice, top-k classification is typically performed with deep neural networks trained with the cross-entropy loss. Theoretical results indeed suggest that cross-entropy is an optimal learning objective for such a task in the limit of infinite data. In the context of limited and noisy data however, the use of a loss function that is specifically designed for top-k classification can bring significant improvements. Our empirical evidence suggests that the loss function must be smooth and have non-sparse gradients in order to work well with deep neural networks. Consequently, we introduce a family of smoothed loss functions that are suited to top-k optimization via deep learning. The widely used cross-entropy is a special case of our family. Evaluating our smooth loss functions is computationally challenging: a naı̈ve algorithm would require O( ( n k ) ) operations, where n is the number of classes. Thanks to a connection to polynomial algebra and a divideand-conquer approach, we provide an algorithm with a time complexity of O(kn). Furthermore, we present a novel approximation to obtain fast and stable algorithms on GPUs with single floating point precision. We compare the performance of the cross-entropy loss and our margin-based losses in various regimes of noise and data size, for the predominant use case of k = 5. Our investigation reveals that our loss is more robust to noise and overfitting than cross-entropy.
Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer

Deep networks often perform well on the data manifold on which they are trained, yet give incorrect (and often very confident) answers when evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift. We propose Manifold Mixup which encourages the network to produce more reasonable and less confident predictions at points with combinations of attributes not seen in the training set. This is accomplished by training on convex combinations of the hidden state representations of data samples. Using this method, we demonstrate improved semi-supervised learning, learning with limited labeled data, and robustness to novel transformations of the data not seen during training. Manifold Mixup requires no (significant) additional computation. We also discover intriguing properties related to adversarial examples and generative adversarial networks. Analytical experiments on both real data and synthetic data directly support our hypothesis for why the Manifold Mixup method improves results.
The construct and criterion validity of emotional intelligence and its potential utility for management studies.

In this study, the authors reviewed the definition of emotional intelligence (EI) and argued that El is conceptually distinct from personality. In Study 1, the authors showed that EI was related to yet distinct from personality dimensions and that it had incremental predictive power on life satisfaction. The authors examined the construct validity of self-reports and others' ratings of EI using two samples in Study 2. In a student sample, parents' ratings explained additional variance in the students' life satisfaction and feelings of powerlessness after controlling for the Big Five personality dimensions. In the work sample, peer ratings were found to be significant predictors of job performance ratings provided by supervisors after controlling for the Big Five personality dimensions. Other implications for future research on EI are discussed.
A multi-source dataset of urban life in the city of Milan and the Province of Trentino

The study of socio-technical systems has been revolutionized by the unprecedented amount of digital records that are constantly being produced by human activities such as accessing Internet services, using mobile devices, and consuming energy and knowledge. In this paper, we describe the richest open multi-source dataset ever released on two geographical areas. The dataset is composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino. The unique multi-source composition of the dataset makes it an ideal testbed for methodologies and approaches aimed at tackling a wide range of problems including energy consumption, mobility planning, tourist and migrant flows, urban structures and interactions, event detection, urban well-being and many others.
A 7-to-18.3GHz compact transformer based VCO in 16nm FinFET

A dual-mode wide-band transformer based VCO is proposed. The two port impedance of the transformer based resonator is analyzed to derive the optimum primary to secondary capacitor load ratio, for robust mode selectivity and minimum power consumption. Fabricated in a 16nm FinFET technology, the design achieves 2.6× continuous tuning range spanning 7-to-18.3 GHz using a coil area of 120×150 μm2. The absence of lossy switches helps in maintaining phase noise of -112 to -100 dBc/Hz at 1 MHz offset, across the entire tuning range. The VCO consumes 3-4.4 mW and realizes power frequency tuning normalized figure of merit of 12.8 and 2.4 dB at 7 and 18.3 GHz respectively.
Distributed Secondary Control for Islanded Microgrids—A Novel Approach

This paper presents a novel approach to conceive the secondary control in droop-controlled microgrids (MGs). The conventional approach is based on restoring the frequency and amplitude deviations produced by the local droop controllers by using an MG central controller (MGCC). A distributed networked control system is used in order to implement a distributed secondary control (DSC), thus avoiding its implementation in MGCC. The proposed approach is not only able to restore frequency and voltage of the MG but also ensures reactive power sharing. The distributed secondary control does not rely on a central control, so that the failure of a single unit will not produce the fail down of the whole system. Experimental results are presented to show the feasibility of the DSC. The time latency and data drop-out limits of the communication systems are studied as well.
Analyzing the Bitcoin Ponzi Scheme Ecosystem

This paper analyzes the supply and demand for Bitcoinbased Ponzi schemes. There are a variety of these types of scams: from long cons such as Bitcoin Savings & Loans to overnight doubling schemes that do not take off. We investigate what makes some Ponzi schemes successful and others less so. By scouring 11 424 threads on bitcointalk. org, we identify 1 780 distinct scams. Of these, half lasted a week or less. Using survival analysis, we identify factors that affect scam persistence. One approach that appears to elongate the life of the scam is when the scammer interacts a lot with their victims, such as by posting more than a quarter of the comments in the related thread. By contrast, we also find that scams are shorter-lived when the scammers register their account on the same day that they post about their scam. Surprisingly, more daily posts by victims is associated with the scam ending sooner.
Self-contained Position Tracking of Human Movement Using Small Inertial/Magnetic Sensor Modules

Numerous applications require a self-contained personal navigation system that works in indoor and outdoor environments, does not require any infrastructure support, and is not susceptible to jamming. Posture tracking with an array of inertial/magnetic sensors attached to individual human limb segments has been successfully demonstrated. The "sourceless" nature of this technique makes possible full body posture tracking in an area of unlimited size with no supporting infrastructure. Such sensor modules contain three orthogonally mounted angular rate sensors, three orthogonal linear accelerometers and three orthogonal magnetometers. This paper describes a method for using accelerometer data combined with orientation estimates from the same modules to calculate position during walking and running. The periodic nature of these motions includes short periods of zero foot velocity when the foot is in contact with the ground. This pattern allows for precise drift error correction. Relative position is calculated through double integration of drift corrected accelerometer data. Preliminary experimental results for various types of motion including walking, side stepping, and running document accuracy of distance and position estimates.
Extended Pie Menus for Immersive Virtual Environments

Pie menus are a well-known technique for interacting with 2D environments and so far a large body of research documents their usage and optimizations. Yet, comparatively little research has been done on the usability of pie menus in immersive virtual environments (IVEs). In this paper we reduce this gap by presenting an implementation and evaluation of an extended hierarchical pie menu system for IVEs that can be operated with a six-degrees-of-freedom input device. Following an iterative development process, we first developed and evaluated a basic hierarchical pie menu system. To better understand how pie menus should be operated in IVEs, we tested this system in a pilot user study with 24 participants and focus on item selection. Regarding the results of the study, the system was tweaked and elements like check boxes, sliders, and color map editors were added to provide extended functionality. An expert review with five experts was performed with the extended pie menus being integrated into an existing VR application to identify potential design issues. Overall results indicated high performance and efficient design.
Practical and secure dynamic searchable encryption via oblivious access on distributed data structure

Dynamic Searchable Symmetric Encryption (DSSE) allows a client to perform keyword searches over encrypted files via an encrypted data structure. Despite its merits, DSSE leaks search and update patterns when the client accesses the encrypted data structure. These leakages may create severe privacy problems as already shown, for example, in recent statistical attacks on DSSE. While Oblivious Random Access Memory (ORAM) can hide such access patterns, it incurs significant communication overhead and, therefore, it is not yet fully practical for cloud computing systems. Hence, there is a critical need to develop private access schemes over the encrypted data structure that can seal the leakages of DSSE while achieving practical search/update operations.
 In this paper, we propose a new oblivious access scheme over the encrypted data structure for searchable encryption purposes, that we call <u>D</u>istributed <u>O</u>blivious <u>D</u>ata structure <u>DSSE</u> (DOD-DSSE). The main idea is to create a distributed encrypted incidence matrix on two non-colluding servers such that no arbitrary queries on these servers can be linked to each other. This strategy prevents not only recent statistical attacks on the encrypted data structure but also other potential threats exploiting query linkability. Our security analysis proves that DOD-DSSE ensures the unlink-ability of queries and, therefore, offers much higher security than traditional DSSE. At the same time, our performance evaluation demonstrates that DOD-DSSE is two orders of magnitude faster than ORAM-based techniques (e.g., Path ORAM), since it only incurs a small-constant number of communication overhead. That is, we deployed DOD-DSSE on geographically distributed Amazon EC2 servers, and showed that, a search/update operation on a very large dataset only takes around one second with DOD-DSSE, while it takes 3 to 13 minutes with Path ORAM-based methods.
Short-term load forecasting in smart grid: A combined CNN and K-means clustering approach

Although many methods are available to forecast short-term electricity load based on small scale data sets, they may not be able to accommodate large data sets as electricity load data becomes bigger and more complex in recent years. In this paper, a novel machine learning model combining convolutional neural network with K-means clustering is proposed for short-term load forecasting with improved scalability. The large data set is clustered into subsets using K-means algorithm, then the obtained subsets are used to train the convolutional neural network. A real-world power industry data set containing more than 1.4 million of load records is used in this study and the experimental results demonstrate the effectiveness of the proposed method.
Deep MIMO detection

In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction to deep learning and propose a modern neural network architecture suitable for this detection task. First, we consider the case in which the MIMO channel is constant, and we learn a detector for a specific system. Next, we consider the harder case in which the parameters are known yet changing and a single detector must be learned for all multiple varying channels. We demonstrate the performance of our deep MIMO detector using numerical simulations in comparison to competing methods including approximate message passing and semidefinite relaxation. The results show that deep networks can achieve state of the art accuracy with significantly lower complexity while providing robustness against ill conditioned channels and mis-specified noise variance.
Churn Prediction in MMORPGs Using Player Motivation Theories and an Ensemble Approach

In this paper, we investigate the problem of churn prediction in Massively multiplayer online role-playing games (MMORPGs) from a social science perspective and develop models incorporating theories of player motivation. The ability to predict player churn can be a valuable resource to game developers designing customer retention strategies. The results from our theory-driven model significantly outperform a diffusion-based churn prediction model on the same dataset. We describe the synthesis between a theory-driven approach and a data-driven approach to a problem and examine the trade-offs involved between the two approaches in terms of prediction accuracy, interpretability and model complexity. We observe that even though the theory-driven model is not as accurate as the data-driven one, the theory-driven model itself can be more interpretable to the domain experts and hence, more preferable over a complex data-driven model. We perform lift analysis of the two models and find that if a marketing effort is restricted in the number of customers it can contact, the theory-driven model would offer much better return-on-investment by identifying more customers among that restricted set who have the highest probability of churn. Finally, we use a clustering technique to partition the dataset and then build an ensemble on the partitioned dataset for better performance. Experiment results show that the ensemble performs notably better than the single classifier in terms of its recall value, which is a highly desirable property in the churn prediction problem.
GPRC5B activates obesity-associated inflammatory signaling in adipocytes.

A genome-wide association study identified a strong correlation between body mass index and the presence of a 21-kb copy number variation upstream of the human GPRC5B gene; however, the functional role of GPRC5B in obesity remains unknown. We report that GPRC5B-deficient mice were protected from diet-induced obesity and insulin resistance because of reduced inflammation in their white adipose tissue. GPRC5B is a lipid raft-associated transmembrane protein that contains multiple phosphorylated residues in its carboxyl terminus. Phosphorylation of GPRC5B by the tyrosine kinase Fyn and the subsequent direct interaction with Fyn through the Fyn Src homology 2 (SH2) domain were critical for the initiation and progression of inflammatory signaling in adipose tissue. We demonstrated that a GPRC5B mutant lacking the direct binding site for Fyn failed to activate a positive feedback loop of nuclear factor κB-inhibitor of κB kinase ε signaling. These findings suggest that GPRC5B may be a major node in adipose signaling systems linking diet-induced obesity to type 2 diabetes and may open new avenues for therapeutic approaches to diabetic progression.
Joint Discovery of Skill Prerequisite Graphs and Student Models

Skill prerequisite information is useful for tutoring systems that assess student knowledge or that provide remediation. These systems often encode prerequisites as graphs designed by subject matter experts in a costly and time-consuming process. In this paper, we introduce Combined student Modeling and prerequisite Discovery (COMMAND), a novel algorithm for jointly inferring a prerequisite graph and a student model from data. Learning a COMMAND model requires student performance data and a mapping of items to skills (Q-matrix). COMMAND learns the skill prerequisite relations as a Bayesian network (an encoding of the probabilistic dependence among the skills) via a two-stage learning process. In the first stage, it uses an algorithm called Structural Expectation Maximization to select a class of equivalent Bayesian networks; in the second stage, it uses curriculum information to select a single Bayesian network. Our experiments on simulations and real student data suggest that COMMAND is better than prior methods in the literature.