nils-herrmann/clean_scirepeval huggingface dataset:
A Memristor-Based Optimization Framework for AI Applications

Memristors have recently received significant attention as ubiquitous device-level components for building a novel generation of computing systems. These devices have many promising features, such as non-volatility, low power consumption, high density, and excellent scalability. The ability to control and modify biasing voltages at the two terminals of memristors make them promising candidates to perform matrix-vector multiplications and solve systems of linear equations. In this article, we discuss how networks of memristors arranged in crossbar arrays can be used for efficiently solving optimization and machine learning problems. We introduce a new memristor-based optimization framework that combines the computational merit of memristor crossbars with the advantages of an operator splitting method, alternating direction method of multipliers (ADMM). Here, ADMM helps in splitting a complex optimization problem into subproblems that involve the solution of systems of linear equations. The capability of this framework is shown by applying it to linear programming, quadratic programming, and sparse optimization. In addition to ADMM, implementation of a customized power iteration (PI) method for eigenvalue/eigenvector computation using memristor crossbars is discussed. The memristorbased PI method can further be applied to principal component analysis (PCA). The use of memristor crossbars yields a significant speed-up in computation, and thus, we believe, has the potential to advance optimization and machine learning research in artificial intelligence (AI).
Conditional Image Synthesis With Auxiliary Classifier GANs

Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128× 128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128 × 128 samples are more than twice as discriminable as artificially resized 32× 32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data.
Perspective - Tacit Knowledge and Knowledge Conversion: Controversy and Advancement in Organizational Knowledge Creation Theory

Nonaka’s paper [1994. A dynamic theory of organizational knowledge creation. ] contributed to the concepts of “tacit knowledge” and “knowledge conversion” in organization science. We present work that shaped the development of organizational knowledge creation theory and identify two premises upon which more than 15 years of extensive academic work has been conducted: (1) tacit and explicit knowledge can be conceptually distinguished along a continuum; (2) knowledge conversion explains, theoretically and empirically, the interaction between tacit and explicit knowledge. Recently, scholars have raised several issues regarding the understanding of tacit knowledge as well as the interaction between tacit and explicit knowledge in the theory. The purpose of this article is to introduce and comment on the debate about organizational knowledge creation theory. We aim to help scholars make sense of this debate by synthesizing six fundamental questions on organizational knowledge creation theory. Next, we seek to elaborate and advance the theory by responding to questions and incorporating new research. Finally, we discuss implications of our endeavor for organization science.
Text Mining For Information Systems Researchers: An Annotated Topic Modeling Tutorial

Analysts have estimated that more than 80 percent of today’s data is stored in unstructured form (e.g., text, audio, image, video)—much of it expressed in rich and ambiguous natural language. Traditionally, to analyze natural language, one has used qualitative data-analysis approaches, such as manual coding. Yet, the size of text data sets obtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challenges encountered when applying automated text-mining techniques in information systems research. In particular, we showcase how to use probabilistic topic modeling via Latent Dirichlet allocation, an unsupervised text-mining technique, with a LASSO multinomial logistic regression to explain user satisfaction with an IT artifact by automatically analyzing more than 12,000 online customer reviews. For fellow information systems researchers, this tutorial provides guidance for conducting text-mining studies on their own and for evaluating the quality of others.
Multimodal Brain MRI Tumor Segmentation via Convolutional Neural Networks

• Gliomas are the most commonly occurring type of brain tumors and are potentially very dangerous [2, 1], with about 90% of gliomas belonging to a highly aggressive class of cancerous tumors known as glioblastomas [4]. • Multimodality magnetic resonance imaging is the primary method of screening and diagnosis for gliomas . • Tumor regions currently are segmented manually by radiologists. • Segmentation algorithms based on convolutional neural networks (CNNs) have been shown to be at least as effective as other automated segmentation methods.
Insurer vs . Insurance Fraud : Characteristics and Detection

Despite the tremendous concern in the insurance industry over insurance fraud by customers, the federal Insurance Fraud Prevention Act primarily targets internal fraud, or insurer fraud, in which criminal acts such as embezzlement could trigger an insurer’s insolvency, rather than fraud perpetrated by policyholders such as filing false or inflated claims—insurance fraud. Fraud committed against insurers by executives and employees is potentially one of the costliest issues facing the industry and attracts increasing attention from regulators, legislators, and the industry. One book includes reports on some 140 insurance executives convicted of major fraud in recent years. This study investigates whether insurers’ weapons against insurance fraud are also used effectively to combat insurer fraud. Several variables are tested—characteristics of perpetrators, schemes employed, and types of detection and investigation techniques utilized—to compare the characteristics of insurer fraud with those of insurance fraud and also with those in non-insurance industries. A detailed survey of 8,000 members of the Association of Certified Fraud Examiners provides the database; chisquare statistics, the Median (Brown-Mood) test, and the Kruskal-Wallis test were used to measure for significant differences. Most of the authors’ expectations were supported by the analysis, but some surprises were found, such as the relative ineffectiveness of insurer internal control systems at identifying employee fraud. Internal whistleblowing also was not as prevalent in the insurance industry as in other organizations. Insurers were more likely to prosecute their employees for fraud than were other industries, however.
Performance Evaluation of Different Active Noise Control ( ANC ) Algorithms for Attenuating Noise in a Duct

Adaptive filter algorithms are extensively use in active control applications and the availability of low cost powerful digital signal processor (DSP) platforms has opened the way for new applications and further research opportunities in e.g. the active control area. The field of active control demands a solid exposure to practical systems and DSP platforms for a comprehensive understanding of the theory involved. Traditional laboratory experiments prove to be insufficient to fulfill these demands and need to be complemented with more flexible and economic remotely controlled laboratories. The purpose of this thesis project is to implement a number of different adaptive control algorithms in the recently developed remotely controlled Virtual Instrument Systems in Reality (VISIR) ANC/DSP remote laboratory at Blekinge Institute of Technology and to evaluate the performance of these algorithms in the remote laboratory. In this thesis, performance of different filtered-x versions adaptive algorithms (NLMS, LLMS, RLS and FuRLMS) has been evaluated in a remote Laboratory. The adaptive algorithms were implemented remotely on a Texas Instrument DSP TMS320C6713 in an ANC system to attenuate low frequency noise which ranges from 0-200 Hz in a circular ventilation duct using single channel feed forward control. Results show that the remote lab can handle complex and advanced control algorithms. These algorithms were tested and it was found that remote lab works effectively and the achieved attenuation level for the algorithms used on the duct system is comparable to similar applications.
Fog Computing for Sustainable Smart Cities: A Survey

The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for smart cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable smart cities.
Text Similarity Using Google Tri-grams

The purpose of this paper is to propose an unsupervised approach for measuring the similarity of texts that can compete with supervised approaches. Finding the inherent properties of similarity between texts using a corpus in the form of a word n-gram data set is competitive with other text similarity techniques in terms of performance and practicality. Experimental results on a standard data set show that the proposed unsupervised method outperforms the state-of-the-art supervised method and the improvement achieved is statistically significant at 0.05 level. The approach is language-independent; it can be applied to other languages as long as n-grams are available.
Hierarchical Block Sparse Neural Networks

Sparse deep neural networks(DNNs) are efficient in both memory and compute when compared to dense DNNs. But due to irregularity in computation of sparse DNNs, their efficiencies are much lower than that of dense DNNs on general purpose hardwares. This leads to poor/no performance benefits for sparse DNNs. Performance issue for sparse DNNs can be alleviated by bringing structure to the sparsity and leveraging it for improving runtime efficiency. But such structural constraints often lead to sparse models with suboptimal accuracies. In this work, we jointly address both accuracy and performance of sparse DNNs using our proposed class of neural networks called HBsNN (Hierarchical Block sparse Neural Networks).
Automatic Argumentative-Zoning Using Word2vec

In comparison with document summarization on the articles from social media and newswire, argumentative zoning (AZ) is an important task in scientific paper analysis. Traditional methodology to carry on this task relies on feature engineering from different levels. In this paper, three models of generating sentence vectors for the task of sentence classification were explored and compared. The proposed approach builds sentence representations using learned embeddings based on neural network. The learned word embeddings formed a feature space, to which the examined sentence is mapped to. Those features are input into the classifiers for supervised classification. Using 10-cross-validation scheme, evaluation was conducted on the Argumentative-Zoning (AZ) annotated articles. The results showed that simply averaging the word vectors in a sentence works better than the paragraph to vector algorithm and by integrating specific cuewords into the loss function of the neural network can improve the classification performance. In comparison with the hand-crafted features, the word2vec method won for most of the categories. However, the hand-crafted features showed their strength on classifying some of the categories.
Quantification of training load during one-, two- and three-game week schedules in professional soccer players from the English Premier League: implications for carbohydrate periodisation.

Muscle glycogen is the predominant energy source for soccer match play, though its importance for soccer training (where lower loads are observed) is not well known. In an attempt to better inform carbohydrate (CHO) guidelines, we quantified training load in English Premier League soccer players (n = 12) during a one-, two- and three-game week schedule (weekly training frequency was four, four and two, respectively). In a one-game week, training load was progressively reduced (P < 0.05) in 3 days prior to match day (total distance = 5223 ± 406, 3097 ± 149 and 2912 ± 192 m for day 1, 2 and 3, respectively). Whilst daily training load and periodisation was similar in the one- and two-game weeks, total accumulative distance (inclusive of both match and training load) was higher in a two-game week (32.5 ± 4.1 km) versus one-game week (25.9 ± 2 km). In contrast, daily training total distance was lower in the three-game week (2422 ± 251 m) versus the one- and two-game weeks, though accumulative weekly distance was highest in this week (35.5 ± 2.4 km) and more time (P < 0.05) was spent in speed zones >14.4 km · h(-1) (14%, 18% and 23% in the one-, two- and three-game weeks, respectively). Considering that high CHO availability improves physical match performance but high CHO availability attenuates molecular pathways regulating training adaptation (especially considering the low daily customary loads reported here, e.g., 3-5 km per day), we suggest daily CHO intake should be periodised according to weekly training and match schedules.
Bandwidth and Gain Enhancement of Microstrip Patch Antennas Using Reflective Metasurface

Bandwidth and gain enhancement of microstrip patch antennas (MPAs) is proposed using reflective metasurface (RMS) as a superstrate. Two different types of the RMS, namelythe double split-ring resonator (DSR) and double closed-ring resonator (DCR) are separately investigated. The two antenna prototypes were manufactured, measured and compared. The experimental results confirm that the RMS loaded MPAs achieve high-gain as well as bandwidth improvement. The desinged antenna using the RMS as a superstrate has a high-gain of over 9.0 dBi and a wide impedance bandwidth of over 13%. The RMS is also utilized to achieve a thin antenna with a cavity height of 6 mm, which is equivalent to λ/21 at the center frequency of 2.45 GHz. At the same time, the cross polarization level and front-to-back ratio of these antennas are also examined. key words: wideband, high-gain, metamaterial, Fabry-Perot cavity (FPC), frequency selective surface (FSS)
DCN+: Mixed Objective and Deep Residual Coattention for Question Answering

Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate. We propose a mixed objective that combines cross entropy loss with self-critical policy learning. The objective uses rewards derived from word overlap to solve the misalignment between evaluation metric and optimization objective. In addition to the mixed objective, we improve dynamic coattention networks (DCN) with a deep residual coattention encoder that is inspired by recent work in deep self-attention and residual networks. Our proposals improve model performance across question types and input lengths, especially for long questions that requires the ability to capture long-term dependencies. On the Stanford Question Answering Dataset, our model achieves state-of-the-art results with 75.1% exact match accuracy and 83.1% F1, while the ensemble obtains 78.9% exact match accuracy and 86.0% F1.
A direct adaptive method for faster backpropagation learning: the RPROP algorithm

A new learning algorithm for multilayer feedforward networks, RPROP, is proposed. To overcome the inherent disadvantages of pure gradient-descent, RPROP performs a local adaptation of the weight-updates according to the behaviour of the errorfunction. In substantial difference to other adaptive techniques, the effect of the RPROP adaptation process is not blurred by the unforseeable influence of the size of the derivative but only dependent on the temporal behaviour of its sign. This leads to an efficient and transparent adaptation process. The promising capabilities of RPROP are shown in comparison to other wellknown adaptive techniques.
Reinforcement Learning with Deep Energy-Based Policies

We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before. We apply our method to learning maximum entropy policies, resulting into a new algorithm, called soft Q-learning, that expresses the optimal policy via a Boltzmann distribution. We use the recently proposed amortized Stein variational gradient descent to learn a stochastic sampling network that approximates samples from this distribution. The benefits of the proposed algorithm include improved exploration and compositionality that allows transferring skills between tasks, which we confirm in simulated experiments with swimming and walking robots. We also draw a connection to actorcritic methods, which can be viewed performing approximate inference on the corresponding energy-based model.
Intuitionistic Fuzzy Cycles and Intuitionistic Fuzzy Trees

Connectivity has an important role in neural networks, computer network, and clustering. In the design of a network, it is important to analyze connections by the levels. The structural properties of intuitionistic fuzzy graphs provide a tool that allows for the solution of operations research problems. In this paper, we introduce various types of intuitionistic fuzzy bridges, intuitionistic fuzzy cut vertices, intuitionistic fuzzy cycles, and intuitionistic fuzzy trees in intuitionistic fuzzy graphs and investigate some of their interesting properties. Most of these various types are defined in terms of levels. We also describe comparison of these types.
Using Genetic Algorithm for Network Intrusion Detection

This paper describes a technique of applying Genetic Algorithm (GA) to network Intrusion Detection Systems (IDSs). A brief overview of the Intrusion Detection System, genetic algorithm, and related detection techniques is presented. Parameters and evolution process for GA are discussed in detail. Unlike other implementations of the same problem, this implementation considers both temporal and spatial information of network connections in encoding the network connection information into rules in IDS. This is helpful for identification of complex anomalous behaviors. This work is focused on the TCP/IP network protocols.
When grandiosity and vulnerability collide: Implicit and explicit self-esteem in patients with narcissistic personality disorder.

Narcissistic personality disorder (NPD) is characterized by reports of grandiosity including exaggerated illusions of superiority and entitlement (DSM-IV-TR, APA, 2000). Based on clinical theories (e.g., Kernberg, 1975), many researchers argue that high explicit self-esteem in narcissists masks underlying implicit vulnerability (low implicit self-esteem). Conversely, based on social learning theories (i.e., Millon, 1981), people with NPD are characterized by implicit grandiosity (high implicit self-esteem). We test these competing hypotheses in patients diagnosed with NPD.



The present study examined implicit self-esteem (using an Implicit Association Test) and explicit self-esteem (using a self-report questionnaire) in patients with NPD in comparison to non-clinical and clinical, non-NPD (Borderline Personality Disorder, BPD) control groups.



Patients with NPD scored lower on explicit self-esteem than non-clinical controls. In comparison to patients with BPD, NPD patients scored higher on explicit and implicit self-esteem. Moreover, within the group of NPD patients, damaged self-esteem (i.e., low explicit, high implicit) was associated with higher narcissistic psychopathology.



In both clinical groups we included participants seeking psychiatric treatment, which might influence explicit self-esteem. Longitudinal studies are needed to further assess self-esteem stability in NPD patients in comparison to the control groups.



Our findings are indicative of vulnerable facets in patients with NPD (i.e., low explicit self-esteem). Furthermore, damaged self-esteem is connected to specific psychopathology within the NPD group. Implications for research on NPD are discussed.
A Survey on Semantic Parsing

A significant amount of information in today’s world is stored in structured and semistructured knowledge bases. Efficient and simple methods to query these databases are essential and must not be restricted to only those who have expertise in formal query languages. The field of semantic parsing deals with converting natural language utterances to logical forms that can be easily executed on a knowledge base. In this survey, we examine the various components of a semantic parsing system and discuss prominent work ranging from the initial rule based methods to the current neural approaches to program synthesis. We also discuss methods that operate using varying levels of supervision and highlight the key challenges involved in the learning of such systems.
Image generation using generative adversarial networks and attention mechanism

For image generation, deep neural networks are trained to extract high-level features on natural images and to reconstruct the images from the features. However it is difficult to learn to generate images containing enormous contents. To overcome this difficulty, a network with an attention mechanism has been proposed. It is trained to attend to parts of the image and to generate images step by step. This enables the network to deal with the details of a part of the image and the rough structure of the entire image. The attention mechanism is implemented by recurrent neural networks. Additionally, the Generative Adversarial Networks (GANs) approach has been proposed to generate more realistic images. In this study, we present image generation where leverages effectiveness of attention mechanism and the GANs approach. We show our method enables the iterative construction of images and more realistic image generation than standard GANs and the attention mechanism of DRAW.