nils-herrmann/clean_scirepeval huggingface dataset:
Entrepreneur or Intermediary: The Nature of the Relationship Manager's Job

A new liaison role between Information Systems (IS) and users, the relationship manager (RM), has recently emerged. Accolding to the prescriptive literature, RMs add value by deep understanding of the businesses they serve and technologyleadership. Uttle is known, however, about their actual work practices. Is the RM an intermediary, filtering information and sometimes misinformation, from clients to IS, or do they play more pivotal roles as entrepreneurs and change agents? This article addresses these questions by studying four RMs in four different industries. The RMs were studied using the structured observation methodology employed by Mintzberg (CEOs), Ives and Olson (MIS managers), and Stephens et at. (CIOs), l'he findings suggest that while RMs spend less time communicating with users than one would expect, they are leaders, often mavericks, in the entrepreneurial work practices necessary to build partnerships with clients and to make the IS infrastructure more responsive to client needs.
Reducing the Rank in Relational Factorization Models by Including Observable Patterns

Tensor factorization has become a popular method for learning from multirelational data. In this context, the rank of the factorization is an important parameter that determines runtime as well as generalization ability. To identify conditions under which factorization is an efficient approach for learning from relational data, we derive upper and lower bounds on the rank required to recover adjacency tensors. Based on our findings, we propose a novel additive tensor factorization model to learn from latent and observable patterns on multi-relational data and present a scalable algorithm for computing the factorization. We show experimentally both that the proposed additive model does improve the predictive performance over pure latent variable methods and that it also reduces the required rank — and therefore runtime and memory complexity — significantly.
Torque Ripple Reduction of a Direct-Drive Permanent-Magnet Synchronous Machine by Material-Efficient Axial Pole Pairing

This paper investigates a material-efficient axial pole pairing method for torque ripple reduction in a direct-drive outer-rotor surface-mounted permanent-magnet synchronous machine. The effects of the magnet pole arc width on the torque ripple characteristics of the machine are first established by both analytical and 2-D finite element approaches. Furthermore, the effectiveness of the axial pole pairing technique in mitigating the machine cogging torque, back electromotive force harmonics, and overall torque quality is comprehensively examined. Finally, 3-D finite element analysis and experiments are carried out to validate the proposed approach, and the results show that axial pole pairing can be cost efficiently implemented in terms of magnet material usage and assembly.
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities

Entities are essential elements of natural language. In this paper, we present methods for learning multi-level representations of entities on three complementary levels: character (character patterns in entity names extracted, e.g., by neural networks), word (embeddings of words in entity names) and entity (entity embeddings). We investigate state-of-theart learning methods on each level and find large differences, e.g., for deep learning models, traditional ngram features and the subword model of fasttext (Bojanowski et al., 2016) on the character level; for word2vec (Mikolov et al., 2013) on the word level; and for the order-aware model wang2vec (Ling et al., 2015a) on the entity level. We confirm experimentally that each level of representation contributes complementary information and a joint representation of all three levels improves the existing embedding based baseline for fine-grained entity typing by a large margin. Additionally, we show that adding information from entity descriptions further improves multi-level representations of entities.
Short term stock price prediction using deep learning

Short — term price movements, contribute a considerable measure to the unpredictability of the securities exchanges. Accurately predicting the price fluctuations in stock market is a huge economical advantage. The aforementioned task is generally achieved by analyzing the company, this is called as fundamental analysis. Another method, which is undergoing a lot of research work recently, is to create a predictive algorithmic model using machine learning. To train machines to take trading decisions in such short — period of time, the latter method needs to be adopted. Deep Neural Networks, being the most exceptional innovation in Machine Learning, have been utilized to develop a short-term prediction model. This paper plans to forecast these short — term prices of stocks. 10 unique stocks recorded on New York Stock Exchange are considered for this review. The review essentially focuses on the prediction of these short — term prices leveraging the power of technical analysis. Technical Analysis guides the framework to understand the patterns from the historical prices fed into it, and attempts to probabilistically forecast the fleeting future prices of the stock under review. The paper discusses about two distinct sorts of Artificial Neural Networks, Feed Forward Neural Networks and Recurrent Neural Networks. The review uncovers that Feed Forwards Multilayer Perceptron perform superior to Long Short-Term Memory, at predicting the short — term prices of a stock.
Beyond question answering : understanding the information need of the user

Intelligent interaction between humans and computers has been a dream of artificial intelligence since the beginning of digital era and one of the original motivations behind the creation of artificial intelligence. A key step towards the achievement of such an ambitious goal is to enable the Question Answering systems understand the information need of the user. In this thesis, we attempt to enable the QA system’s ability to understand the user’s information need by three approaches. First, an clarification question generation method is proposed to help the user clarify the information need and bridge information need gap between QA system and the user. Next, a translation based model is obtained from the large archives of Community Question Answering data, to model the information need behind a question and boost the performance of question recommendation. Finally, a fine-grained classification framework is proposed to enable the systems to recommend answered questions based on information need satisfaction.
A Survey on Latest DoS Attacks:Classificationand Defense Mechanisms

Distributed Denial of Service (DDoS) is defined as an attack in which mutiple compromised systems are made to attack a single target to make the services unavailable foe legitimate users.It is an attack designed to render a computer or network incapable of providing normal services. DDoS attack uses many compromised intermediate systems, known as botnets which are remotely controlled by an attacker to launch these attacks. DDOS attack basically results in the situation where an entity cannot perform an action for which it is authenticated. This usually means that a legitimate node on the network is unable to reach another node or their performance is degraded. The high interruption and severance caused by DDoS is really posing an immense threat to entire internet world today. Any compromiseto computing, communication and server resources such as sockets, CPU, memory, disk/database bandwidth, I/O bandwidth, router processing etc. for collaborative environment would surely endanger the entire application. It becomes necessary for researchers and developers to understand behaviour of DDoSattack because it affects the target network with little or no advance warning. Hence developing advanced intrusion detection and prevention systems for preventing, detecting, and responding to DDOS attack is a critical need for cyber space. Our rigorous survey study presented in this paper describes a platform for the study of evolution of DDoS attacks and their defense mechanisms.
The growing ubiquity of algorithms in society: implications, impacts and innovations

The growing ubiquity of algorithms in society raises a number of fundamental questions concerning governance of data, transparency of algorithms, legal and ethical frameworks for automated algorithmic decision-making and the societal impacts of algorithmic automation itself. This article, an introduction to the discussion meeting issue of the same title, gives an overview of current challenges and opportunities in these areas, through which accelerated technological progress leads to rapid and often unforeseen practical consequences. These consequences-ranging from the potential benefits to human health to unexpected impacts on civil society-are summarized here, and discussed in depth by other contributors to the discussion meeting issue.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.
Design and analysis of a cable-driven manipulator with variable stiffness

A manipulator with variable stiffness allows the manipulator to adjust its stiffness to fulfill different task requirements. In this paper, a cable-driven manipulator with the ability to significantly regulate its stiffness through tension manipulation is introduced. Variable stiffness is achieved by attaching a novel variable stiffness device along each driving cable, in which the stiffness of the device is a function the cable tension. As cable-driven manipulator has actuation redundancy, the tension distribution can be manipulated even at a stationary pose. Such property allows the cable-driven manipulator to adjust the stiffness of each variable stiffness device, thereby changing the stiffness of the manipulator. The design and analysis of the variable stiffness device is presented. The variable stiffness device uses commercial torsion springs, and has a compact and light-weight design. Experimental and simulation results verified that cable-driven manipulator with such variable stiffness devices is able to achieve significant stiffness regulation.
Deep active learning for object detection

Object detection methods like Single Shot Multibox Detector (SSD) provide highly accurate object detection that run in real-time. However, these approaches require a large number of annotated training images. Evidently, not all of these images are equally useful for training the algorithms. Moreover, obtaining annotations in terms of bounding boxes for each image is costly and tedious. In this paper, we aim to obtain a highly accurate object detector using only a fraction of the training images. We do this by adopting active learning that uses ‘human in the loop’ paradigm to select the set of images that would be useful if annotated. Towards this goal, we make the following contributions: 1. We develop a novel active learning method which poses the layered architecture used in object detection as a ‘query by committee’ paradigm to choose the set of images to be queried. 2. We introduce a framework to use the exploration/exploitation trade-off in our methods. 3. We analyze the results on standard object detection datasets which show that with only a third of the training data, we can obtain more than 95% of the localization accuracy of full supervision. Further our methods outperform classical uncertainty-based active learning algorithms like maximum entropy.
Clinical aspects of urea cycle dysfunction and altered brain energy metabolism on modulation of glutamate receptors and transporters in acute and chronic hyperammonemia.

In living organisms, nitrogen arise primarily as ammonia (NH3) and ammonium (NH4(+)), which is a main component of the nucleic acid pool and proteins. Although nitrogen is essential for growth and maintenance in animals, but when the nitrogenous compounds exceeds the normal range which can quickly lead to toxicity and death. Urea cycle is the common pathway for the disposal of excess nitrogen through urea biosynthesis. Hyperammonemia is a consistent finding in many neurological disorders including congenital urea cycle disorders, reye's syndrome and acute liver failure leads to deleterious effects. Hyperammonemia and liver failure results in glutamatergic neurotransmission which contributes to the alteration in the function of the glutamate-nitric oxide-cGMP pathway, modulates the important cerebral process. Even though ammonia is essential for normal functioning of the central nervous system (CNS), in particular high concentrations of ammonia exposure to the brain leads to the alterations of glutamate transport by the transporters. Several glutamate transporters have been recognized in the central nervous system and each has a unique physiological property and distribution. The loss of glutamate transporter activity in brain during acute liver failure and hyperammonemia is allied with increased extracellular brain glutamate concentrations which may be conscientious for the cerebral edema and ultimately cell death.
The Sparse Manifold Transform

We present a signal representation framework called the sparse manifold transform that combines key ideas from sparse coding, manifold learning, and slow feature analysis. It turns non-linear transformations in the primary sensory signal space into linear interpolations in a representational embedding space while maintaining approximate invertibility. The sparse manifold transform is an unsupervised and generative framework that explicitly and simultaneously models the sparse discreteness and low-dimensional manifold structure found in natural scenes. When stacked, it also models hierarchical composition. We provide a theoretical description of the transform and demonstrate properties of the learned representation on both synthetic data and natural videos.
Academic advising system using data mining method for decision making support

With the high drop-out rates of college students across the Sultanate of Oman, the government urges HEIs to undertake ongoing analysis of its student population profile and evaluate the effectiveness of their academic advisory services. It is for this reason that this study intends to introduce an Online Student Academic Advising System for higher education, particularly to the Oman Tourism College (OTC). The developed system facilitates academic advising activities and keeps track of student's academic performance. By utilizing data mining method, the system is also capable of generating the required data set that can be used for data analysis and data modeling to discover useful knowledge regarding students' profile, students' registration, and students' module marks. The results of data mining in graphs and tables are very essential for OTC to make timely decisions and implement appropriate actions. The study was carried out using several stages of Developmental Research Method. By utilizing Quantitative Methodology from close-ended statements of the feedback survey, student participants were asked to evaluate the compliance of the developed system in accordance to the Industry Standard Organization 9126-1 (SQUARE ISO/IEC 9126-1:2001) software quality standards. The results showed that the system is a significant improvement for OTC and had a high level of compliance with the established software quality standards.
3D printing based on imaging data: review of medical applications

Generation of graspable three-dimensional objects applied for surgical planning, prosthetics and related applications using 3D printing or rapid prototyping is summarized and evaluated. Graspable 3D objects overcome the limitations of 3D visualizations which can only be displayed on flat screens. 3D objects can be produced based on CT or MRI volumetric medical images. Using dedicated post-processing algorithms, a spatial model can be extracted from image data sets and exported to machine-readable data. That spatial model data is utilized by special printers for generating the final rapid prototype model. Patient–clinician interaction, surgical training, medical research and education may require graspable 3D objects. The limitations of rapid prototyping include cost and complexity, as well as the need for specialized equipment and consumables such as photoresist resins. Medical application of rapid prototyping is feasible for specialized surgical planning and prosthetics applications and has significant potential for development of new medical applications.
Generating Synthetic Data for Text Recognition

Generating synthetic images is an art which emulates the natural process of image generation in a closest possible manner. In this work, we exploit such a framework for data generation in handwritten domain. We render synthetic data using open source fonts and incorporate data augmentation schemes. As part of this work, we release 9M synthetic handwritten word image corpus which could be useful for training deep network architectures and advancing the performance in handwritten word spotting and recognition tasks.
Chemical and Physicochemical Pretreatment of Lignocellulosic Biomass: A Review

Overcoming the recalcitrance (resistance of plant cell walls to deconstruction) of lignocellulosic biomass is a key step in the production of fuels and chemicals. The recalcitrance is due to the highly crystalline structure of cellulose which is embedded in a matrix of polymers-lignin and hemicellulose. The main goal of pretreatment is to overcome this recalcitrance, to separate the cellulose from the matrix polymers, and to make it more accessible for enzymatic hydrolysis. Reports have shown that pretreatment can improve sugar yields to higher than 90% theoretical yield for biomass such as wood, grasses, and corn. This paper reviews different leading pretreatment technologies along with their latest developments and highlights their advantages and disadvantages with respect to subsequent hydrolysis and fermentation. The effects of different technologies on the components of biomass (cellulose, hemicellulose, and lignin) are also reviewed with a focus on how the treatment greatly enhances enzymatic cellulose digestibility.
A Framework for Understanding Chatbots and Their Future

Chatbots have rapidly become a mainstay in software development. A range of chatbots contribute regularly to the creation of actual production software. It is somewhat difficult, however, to precisely delineate hype from reality. Questions arise as to what distinguishes a chatbot from an ordinary software tool, what might be desirable properties of chatbots, and where their future may lie. This position paper introduces a starting framework through which we examine the current state of chatbots and identify directions for future work.
An alternative to the dark matter paradigm : relativistic MOND gravitation

MOND, invented by Milgrom, is a phenomenological scheme whose basic premise is that the visible matter distribution in a galaxy or cluster of galaxies alone determines its dynamics. MOND fits many observations surprisingly well. Could it be that there is no dark matter in these systems and we witness rather a violation of Newton’s universal gravity law ? If so, Einstein’s general relativity would also be violated. For long conceptual problems have prevented construction of a consistent relativistic substitute which does not obviously run afoul of the facts. Here I sketch TeVe S, a tensor-vectorscalar field theory which seems to fit the bill: it has no obvious conceptual problems and has a MOND and Newtonian limits under the proper circumstances. It also passes the elementary solar system tests of gravity theory. Dedicated to Moti Milgrom who started it all Here I discuss a modified gravity theory. Modified gravity is often mentioned today in relation with dark energy in cosmology. But I concentrate on the first systematic modified gravity approach to the mass discrepancy in galaxies and clusters, Milgrom’s modified Newtonian dynamics (MOND) [1, 2, 3], and report on a relativistic implementation of it . My approach is phenomenologically motivated, not theory motivated.
Mining Implicit Data Association from Tripadvisor Hotel Reviews

In this paper, we analyse a dataset of hotel reviews. In details, we enrich the review dataset, by extracting additional features, consisting of information on the reviewers’ profiles and the reviewed hotels. We argue that the enriched data can gain insights on the factors that most influence consumers when composing reviews (e.g., if the appreciation for a certain kind of hotel is tied to specific users’ profiles). Thus, we apply statistical analyses to reveal if there are specific characteristics of reviewers (almost) always related to specific characteristics of hotels. Our experiments are carried out on a very large dataset, consisting of around 190k hotel reviews, collected from the Tripadvisor website.
Improving Image Captioning by Leveraging Knowledge Graphs

We explore the use of a knowledge graphs, that capture general or commonsense knowledge, to augment the information extracted from images by the state-of-the-art methods for image captioning. We compare the performance of image captioning systems that as measured by CIDEr-D, a performance measure that is explicitly designed for evaluating image captioning systems, on several benchmark data sets such as MS COCO. The results of our experiments show that the variants of the state-of-the-art methods for image captioning that make use of the information extracted from knowledge graphs can substantially outperform those that rely solely on the information extracted from images.
Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition.  following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.
An Introduction to Machine Learning Communications Systems

We introduce and motivate machine learning (ML) communications systems that aim to improve on and to even replace the vast expert knowledge in the field of communications using modern machine learning techniques. These have recently achieved breakthroughs in many different domains, but not yet in communications. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about radio communications system design as an end-to-end reconstruction optimization task that seeks to jointly optimize transmitter and receiver components in a single process. We further present the concept of Radio Transformer Networks (RTNs) as a means to incorporate expert domain knowledge in the ML model and study the application of convolutional neural networks (CNNs) on raw IQ time-series data for modulation classification. We conclude the paper with a deep discussion of open challenges and areas for future investigation.