nils-herrmann/clean_scirepeval huggingface dataset:
Localization Systems Using Passive UHF RFID

Since gathering spatial information of objects often provides a large number of extended functions in RFID based applications, many different localization systems have been developed in recent years. Determining and estimating the physical location of tagged objects in an interrogating area is known as localization. In this paper, we study properties of passive UHF RFID systems such as the relationship between distance and RSSI (Received Signal Strength Indictor), performance variations among the same type of passive tags, and readabilities of tags. We propose a received signal strength based Localization Algorithm using passive UHF RFID system. The localization uses the k-nearest neighbor algorithm to estimate the physical position of the target tag. To improve the accuracy of the passive tag attached object location, the properties and characteristics are applied to the localization algorithm. According to the analysis of experimentation, our proposed approach shows over 34% improvement compared with the k-Nearest Neighbor algorithm with the use of single reader and a single antenna, and 13 reference tags.
The Economic Cost of Publicly Announced Information Security Breaches: Empirical Evidence from the Stock Market

This study examines the economic effect of information security breaches reported in newspapers on publicly traded US corporations. We find limited evidence of an overall negative stock market reaction to public announcements of information security breaches. However, further investigation reveals that the nature of the breach affects this result. We find a highly significant negative market reaction for information security breaches involving unauthorized access to confidential data, but no significant reaction when the breach does not involve confidential information. Thus, stock market participants appear to discriminate across types of breaches when assessing their economic impact on affected firms. These findings are consistent with the argument that the economic consequences of information security breaches vary according to the nature of the underlying assets affected by the breach.
The Limited Effectiveness of Neural Networks for Simple Question Answering on Knowledge Graphs by

Simple factoid question answering (QA) is a task, where the questions can be answered by looking up a single fact in the knowledge base (KB). However, this QA task is difficult, since retrieving a single supporting fact involves searching many alternatives given a query expressed in natural language. We use a retrieval-based approach to QA. We decompose the problem into four sub-problems: entity detection, entity linking, relation prediction, and evidence integration. Entity detection and linking rely on detecting the entities in a question and linking them to the candidate entities in the KB. Relation prediction classifies a question as one of the relation types in the KB. Finally, evidence integration combines scores from entity linking and relation prediction to predict an (entity, relation) pair that answers the question. Most of the research community has explored complex neural network architectures for this task without establishing baselines to compare the results with ‘non-neural-network’ approaches. We explore several different models for entity detection and relation prediction; a few different scoring functions for entity linking and evidence integration. Our findings show that deep learning does help for the QA task, but not as much as the research community has portrayed it to be. We also present two simple yet very competitive baselines: one based on a simple neural network architecture and one that does not use any neural networks.
Six-phase electrically excited synchronous generator for More Electric Aircraft

This paper proposes an electromagnetic design of an asymmetrical six-phase salient-pole synchronous generator for the aircraft power system. The generator is coupled to the aircraft engine and it is connected to 270V DC bus via a dual three-phase voltage source inverter. An electrical excitation current is fed to the rotor coil in order to produce the induced voltage in the stator windings. The external excitation possesses the advantage of controlling the output voltage by adjusting the excitation current. The generator is designed based on the performance of the synchronous machine at different load conditions and at different rotor speeds. In order to achieve an improved performance and an increased efficiency of the machine, the design of the stator windings and the rotor geometry is studied. The selection of the windings topology and the number of stator slots is discussed where the requirements of the aircraft variable speed generator are considered. The electromagnetic design of the machine is implemented and the Finite Element Method is used to analyze the performance of the machine. The control system of the DC bus voltage is discussed based on the machine characteristics obtained from the simulation based on Finite Element Method. The cross-saturation of the machine is considered at overload condition.
Explorer Practical probabilistic programming with monads

The machine learning community has recently shown a lot of interest in practical probabilistic programming systems that target the problem of Bayesian inference. Such systems come in different forms, but they all express probabilistic models as computational processes using syntax resembling programming languages. In the functional programming community monads are known to offer a convenient and elegant abstraction for programming with probability distributions, but their use is often limited to very simple inference problems. We show that it is possible to use the monad abstraction for constructing probabilistic models, while still offering good performance of inference in challenging models. We use a GADT as an underlying representation of a probability distribution and apply Sequential Monte Carlo-based methods to achieve efficient inference. We define a formal semantics via measure theory and check the monad laws. We demonstrate a clean and elegant implementation that achieves performance comparable with Anglican, a state-of-the-art probabilistic programming system.
Ubiquitous Commerce : The New World of Technologies

Developments in mobile communications and ubiquitous computing have heralded an era of ubiquitous commerce (ucommerce).This paper studies the concept of ubiquitous computing and its adaption to commerce with new issues associated. With the rapid advancement in field of networking and communications, no aspect of human life is untouched. Commercial activities are also affected by the new advancements. Any time/ always/ anywhere service providing is the key to this ultimate or ubiquitous commerce. Commerce that offers unique and personalized service to every customer individually using ubiquitous networks is called u-commerce. U-commerce is abbreviation that stands for ubiquitous commerce, also called ubicomp or ultimate commerce. U-commerce represents the next phase of commence, which is initiated by electronic commerce (e-commerce) and propagated by mobile commerce (m-commerce).In this paper, we look into the basic construct or building blocks of U-commerce and what are the obstacles faced in its implementations. U-commerce is combination of ecommerce, m-commerce, television, voice and silent commerce. It can be seen as extension of current types of commerce, but not as their replacement. Main u-commerce characteristics are described using the four u-constructs: ubiquity, universality, uniqueness, and unison. Although U-commerce brings many benefits the principal obstacle to u-commerce development and adoption are customer concerns for their privacy protection.
ARCHER: Aggressive Rewards to Counter bias in Hindsight Experience Replay

Experience replay is an important technique for addressing sample-inefficiency in deep reinforcement learning (RL), but faces difficulty in learning from binary and sparse rewards due to disproportionately few successful experiences in the replay buffer. Hindsight experience replay (HER) (Andrychowicz et al. 2017) was recently proposed to tackle this difficulty by manipulating unsuccessful transitions, but in doing so, HER introduces a significant bias in the replay buffer experiences and therefore achieves a suboptimal improvement in sample-efficiency. In this paper, we present an analysis on the source of bias in HER, and propose a simple and effective method to counter the bias, to most effectively harness the sample-efficiency provided by HER. Our method, motivated by counter-factual reasoning and called ARCHER, extends HER with a trade-off to make rewards calculated for hindsight experiences numerically greater than real rewards. We validate our algorithm on two continuous control environments from DeepMind Control Suite (Tassa et al. 2018) Reacher and Finger, which simulate manipulation tasks with a robotic arm in combination with various reward functions, task complexities and goal sampling strategies. Our experiments consistently demonstrate that countering bias using more aggressive hindsight rewards increases sample efficiency, thus establishing the greater benefit of ARCHER in RL applications with limited computing budget.
Randomized experiments to detect and estimate social influence in networks

Estimation of social influence in networks can be substantially biased in observational studies due to homophily and network correlation in exposure to exogenous events. Randomized experiments, in which the researcher intervenes in the social system and uses randomization to determine how to do so, provide a methodology for credibly estimating of causal effects of social behaviors. In addition to addressing questions central to the social sciences, these estimates can form the basis for effective marketing and public policy. In this review, we discuss the design space of experiments to measure social influence through combinations of interventions and randomizations. We define an experiment as combination of (1) a target population of individuals connected by an observed interaction network, (2) a set of treatments whereby the researcher will intervene in the social system, (3) a randomization strategy which maps individuals or edges to treatments, and (4) a measurement of an outcome of interest after treatment has been assigned. We review experiments that demonstrate potential experimental designs and we evaluate their advantages and tradeoffs for answering different types of causal questions about social influence. We show how randomization also provides a basis for statistical inference when analyzing these experiments.
Ontology Based Information Retrieval in Semantic Web: A Survey

In present age of computers, there are various resources for gathering information related to given query like Radio Stations, Television, Internet and many more. Among them, Internet is considered as major factor for obtaining any information about a given domain. When a user wants to find some information, he/she enters a query and results are produced via hyperlinks linked to various documents available on web. But the information that is retrieved to us may or may not be relevant. This irrelevance is caused due to huge collection of documents available on web. Traditional search engines are based on keyword based searching that is unable to transform raw data into knowledgeable representation data. It is a cumbersome task to extract relevant information from large collection of web documents. These shortcomings have led to the concept of Semantic Web (SW) and Ontology into existence. Semantic Web (SW) is a well defined portal that helps in extracting relevant information using many Information Retrieval (IR) techniques. Current Information Retrieval (IR) techniques are not so advanced that they can be able to exploit semantic knowledge within documents and give precise result. The terms, Information Retrieval (IR), Semantic Web (SW) and Ontology are used differently but they are interconnected with each other. Information Retrieval (IR) technology and Web based Indexing contributes to existence of Semantic Web. Use of Ontology also contributes in building new generation of webSemantic Web. With the help of ontologies, we can make content of web as it will be markup with the help of Semantic Web documents (SWD’s). Ontology is considered as backbone of Software system. It improves understanding between concepts used in Semantic Web (SW). So, there is need to build an ontology that uses well defined methodology and process of developing ontology is called Ontology Development.
An advertising analytics framework using social network big data

Since 2000, the Internet has become a primary advertising and marketing channel for businesses. With the recent advance of mobile computing and wireless networking, mobile advertising is now becoming popular because mobile devices provide an effective advertising platform. People today believe that big data analytics provides new opportunities and needs for advertisers. However, most existing advertisement solutions primarily use Behavior Targeting (BT) technology to provide static services, which cannot satisfy the real-time, fast big data processing requirements. The purpose of this project is to develop a new big data analytics service in advertising and marketing based on emergent big data technologies, data mining algorithms, and machine learning solutions. The primary objective of this project is to provide real-time and static on demand services for advertisers and publishers to decide when, what, where, who, and how to place advertisements. In addition, this project requires solutions to analyze the collected big advertising data, discover customers behavior patterns, and establish an innovative model for advertising recommendation and trend prediction. The system will be developed based on advanced machine learning and data mining algorithms, NoSQL database technologies, and visualization techniques. This service will allow advertisers and publishers to reduce their costs while improving their effectiveness.
Private Genome Analysis through Homomorphic Encryption

The rapid development of genome sequencing technology allows researchers to access large genome datasets. However, outsourcing the data processing o the cloud poses high risks for personal privacy. The aim of this paper is to give a practical solution for this problem using homomorphic encryption. In our approach, all the computations can be performed in an untrusted cloud without requiring the decryption key or any interaction with the data owner, which preserves the privacy of genome data.



We present evaluation algorithms for secure computation of the minor allele frequencies and χ2 statistic in a genome-wide association studies setting. We also describe how to privately compute the Hamming distance and approximate Edit distance between encrypted DNA sequences. Finally, we compare performance details of using two practical homomorphic encryption schemes--the BGV scheme by Gentry, Halevi and Smart and the YASHE scheme by Bos, Lauter, Loftus and Naehrig.



The approach with the YASHE scheme analyzes data from 400 people within about 2 seconds and picks a variant associated with disease from 311 spots. For another task, using the BGV scheme, it took about 65 seconds to securely compute the approximate Edit distance for DNA sequences of size 5K and figure out the differences between them.



The performance numbers for BGV are better than YASHE when homomorphically evaluating deep circuits (like the Hamming distance algorithm or approximate Edit distance algorithm). On the other hand, it is more efficient to use the YASHE scheme for a low-degree computation, such as minor allele frequencies or χ2 test statistic in a case-control study.
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization

We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents.
Narcissism and the narcissistic personality disorder: a comparison of the theories of Kernberg and Kohut.

Narcissism has been a perennial topic for psychoanalytic papers since Freud's 'On narcissism: An introduction' (1914). The understanding of this field has recently been greatly furthered by the analytical writings of Kernberg and Kohut despite, or perhaps because of, their glaring disagreements. Despite such theoretical advances, clinical theory has far outpaced clinical practice. This paper provides a clarification of the characteristics, diagnosis and development of the narcissistic personality disorder and draws out the differing treatment implications, at various levels of psychological intensity, of the two theories discussed.
A Convolutional Encoder Model for Neural Machine Translation

The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. In this paper we present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the entire source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT’16 English-Romanian translation we achieve competitive accuracy to the state-of-theart and we outperform several recently published results on the WMT’15 EnglishGerman task. Our models obtain almost the same accuracy as a very deep LSTM setup on WMT’14 English-French translation. Our convolutional encoder speeds up CPU decoding by more than two times at the same or higher accuracy as a strong bi-directional LSTM baseline.
Stuck between Surplus and Shortage : Demand for Skills in

In order to remain competitive, firms need to keep the quantity and composition of jobs close to optimal for their given output. Since the beginning of the transition period, Russian industrial firms have been widely reporting that the quantity and composition of hired labour is far from being close to optimal. This paper discusses what kinds of firms in the Russian manufacturing sector are unable to optimize their employment and why. The main conclusion is that the key issue is an excess of non-viable firms and a shortage of highly efficient firms because of weak selection mechanisms. The major solution is seen in creating institutional conditions that stimulate a more efficient reallocation of labour. The analysis presented in this paper is based on data from a large-scale survey of Russian manufacturing firms.
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling

This paper presents our system submitted to the EmotionX challenge. It is an emotion detection task on dialogues in the EmotionLines dataset. We formulate this as a hierarchical network where network learns data representation at both utterance level and dialogue level. Our model is inspired by Hierarchical Attention network (HAN) and uses pre-trained word embeddings as features. We formulate emotion detection in dialogues as a sequence labeling problem to capture the dependencies among labels. We report the performance accuracy for four emotions (anger, joy, neutral and sadness). The model achieved unweighted accuracy of 55.38% on Friends test dataset and 56.73% on EmotionPush test dataset. We report an improvement of 22.51% in Friends dataset and 36.04% in EmotionPush dataset over baseline results.
The immunopathogenesis of seropositive rheumatoid arthritis: from triggering to targeting

Patients with rheumatoid arthritis can be divided into two major subsets characterized by the presence versus absence of antibodies to citrullinated protein antigens (ACPAs) and of rheumatoid factor (RF). The antibody-positive subset of disease, also known as seropositive rheumatoid arthritis, constitutes approximately two-thirds of all cases of rheumatoid arthritis and generally has a more severe disease course. ACPAs and RF are often present in the blood long before any signs of joint inflammation, which suggests that the triggering of autoimmunity may occur at sites other than the joints (for example, in the lung). This Review summarizes recent progress in our understanding of this gradual disease development in seropositive patients. We also emphasize the implications of this new understanding for the development of preventive and therapeutic strategies. Similar temporal and spatial separation of immune triggering and clinical manifestations, with novel opportunities for early intervention, may also occur in other immune-mediated diseases.
Understanding Convolutional Neural Networks with A Mathematical Model

This work attempts to address two fundamental questions about the structure of the convolutional neural networks (CNN): 1) why a nonlinear activation function is essential at the filter output of all intermediate layers? 2) what is the advantage of the two-layer cascade system over the one-layer system? A mathematical model called the “REctified-COrrelations on a Sphere” (RECOS) is proposed to answer these two questions. After the CNN training process, the converged filter weights define a set of anchor vectors in the RECOS model. Anchor vectors represent the frequently occurring patterns (or the spectral components). The necessity of rectification is explained using the RECOS model. Then, the behavior of a two-layer RECOS system is analyzed and compared with its one-layer counterpart. The LeNet-5 and the MNIST dataset are used to illustrate discussion points. Finally, the RECOS model is generalized to a multilayer system with the AlexNet as an example.
Introduction to Convolutional Neural Networks

6 The convolution layer 11 6.1 What is convolution? . . . . . . . . . . . . . . . . . . . . . . . . . 11 6.2 Why to convolve? . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 6.3 Convolution as matrix product . . . . . . . . . . . . . . . . . . . 15 6.4 The Kronecker product . . . . . . . . . . . . . . . . . . . . . . . 17 6.5 Backward propagation: update the parameters .  Even higher dimensional indicator matrices . . . . . . . . . . . . 19 6.7 Backward propagation: prepare supervision signal for the previous layer .  20 6.8 Fully connected layer as a convolution layer . . . . . . . . . . . . 22