nils-herrmann/clean_scirepeval huggingface dataset:
Curated Sources The Basics : What is AI ? What ’ s the Difference between Artificial Intelligence , Machine Learning , and Deep Learning ?

So, what comes next? Where are we headed with AI, and what level of responsibility do the designers and providers have with managing AI technology? Will we control AI technology or will it control us? How do we handle the economic Curated by Andrew Boyarsky, MSM, PMP Clinical Associate Professor, and Academic Director of the MS in Enterprise Risk Management, Katz School of Graduate and Professional Studies, Yeshiva University “Every major player is working on this technology of artificial intelligence. As of now, it's benign... but I would say that the day is not far off when artificial intelligence as applied to cyber warfare becomes a threat to everybody.”
Design of circularly polarized Giuseppe Peano fractal patch antenna by the theory of Characteristic Mode

The Characteristic Mode (CM) Theory has been used in a variety of applications including radiation and scattering analysis and pattern synthesis. CM theory provides a systematic approach to antenna design and is used here to design the Giuseppe Peano (GP) fractal microstrip antennas. In this paper, three GP fractal antennas with linear and circular polarization are designed by CM theory. Each GP fractal antenna is first analyzed by CM theory to find the antenna CMs. Coaxial probe feed location is then selected appropriately to excite desired CMs with respect to their modal currents (surface currents). The goal of this work is to use the CM theory to inform the synthesis of dual band and circularly polarized GP fractal antennas. GP fractal antennas are fabricated and measured to evaluate and prove their stated properties and specifications.
Sparse Unsupervised Capsules Generalize Better

We show that unsupervised training of latent capsule layers using only the reconstruction loss, without masking to select the correct output class, causes a loss of equivariances and other desirable capsule qualities. This implies that supervised capsules networks can’t be very deep. Unsupervised sparsening of latent capsule layer activity both restores these qualities and appears to generalize better than supervised masking, while potentially enabling deeper capsules networks. We train a sparse, unsupervised capsules network of similar geometry to (Sabour et al., 2017) on MNIST (LeCun et al., 1998) and then test classification accuracy on affNIST1 using an SVM layer. Accuracy is improved from benchmark 79% to 90%.
Lessons from Dutch IT-outsourcing success and failure

We present the findings from a Dutch field study of 30 outsourcing deals totaling to more than 100 million Euro, where both customers and corresponding IT-outsourcing providers participated. The main objective of the study was to examine from a number of well-known factors whether they discriminate between IT-outsourcing success and failure in the early phase of service delivery and to determine their impact on the chance on a successful deal. We investigated controllable factors to increase the odds during sourcing and rigid factors as a warning sign before closing a deal. Based on 250 interviews we collected 28 thousand data points. From the data and the perceived failure or success of the closed deals we investigated the discriminative power of the determinants (ex post). We found three statistically significant controllable factors that discriminated in an early phase between failure and success. They are: working according to the transition plan, demand management and, to our surprise, communication within the supplier organisation (so not between client and supplier). These factors also turned out to be the only significant factors for a (logistic) model predicting the chance of a successful IT-outsourcing. Improving demand management and internal communication at the supplier increases the odds the most. Sticking to the transition plan only modestly. Other controllable factors were not significant in our study. They are managing the business case, transfer of staff or assets, retention of expertise and communication within the client organisation. Of the rigid factors, the motive to outsource, cultural differences, and the type of work were insignificant. The motive of the supplier was significant: internal motivations like increasing profit margins or business volume decreased the chance of success while external motivations like increasing market share or becoming a player increased the success rate. From the data we inferred that the degree of experience with sourcing did not show to be a convincing factor of success. Hiring sourcing consultants worked contra-productive: it lowered chances of success.
Secure and dependable software defined networks

The revolutionary concept of Software Defined Networks (SDNs) potentially provides flexible and wellmanaged next-generation networks. All the hype surrounding the SDNs is predominantly because of its centralized management functionality, the separation of the control plane from the data forwarding plane, and enabling innovation through network programmability. Despite the promising architecture of SDNs, security was not considered as part of the initial design. Moreover, security concerns are potentially augmented considering the logical centralization of network intelligence. Furthermore, the security and dependability of the SDN has largely been a neglected topic and remains an open issue. The paper presents a broad overview of the security implications of each SDN layer/interface. This paper contributes further by devising a contemporary layered/interface taxonomy of the reported security vulnerabilities, attacks, and challenges of SDN. We also highlight and analyze the possible threats on each layer/interface of SDN to help design secure SDNs. Moreover, the ensuing paper contributes by presenting the state-ofthe-art SDNs security solutions. The categorization of solutions is followed by a critical analysis and discussion to devise a comprehensive thematic taxonomy. We advocate the production of secure and dependable SDNs by presenting potential requirements and key enablers. Finally, in an effort to anticipate secure and dependable SDNs, we present the ongoing open security issues, challenges and future research directions. & 2015
Credit scoring using data mining techniques with particular reference to Sudanese banks

One of the key success factors of lending organizations in general and banks in particular is the assessment of borrower credit worthiness in advance during the credit evaluation process. Credit scoring models have been applied by many researchers to improve the process of assessing credit worthiness by differentiating between prospective loans on the basis of the likelihood of repayment. Thus, credit scoring is a very typical Data Mining (DM) classification problem. Many traditional statistical and modern computational intelligence techniques have been presented in the literature to tackle this problem. The main objective of this paper is to describe an experiment of building suitable Credit Scoring Models (CSMs) for the Sudanese banks. Two commonly discussed data mining classification techniques are chosen in this paper namely: Decision Tree (DT) and Artificial Neural Networks (ANN). In addition Genetic Algorithms (GA) and Principal Component Analysis (PCA) are also applied as feature selection techniques. In addition to a Sudanese credit dataset, German credit dataset is also used to evaluate these techniques. The results reveal that ANN models outperform DT models in most cases. Using GA as a feature selection is more effective than PCA technique. The highest accuracy of German data set (80.67%) and Sudanese credit scoring models (69.74%) are achieved by a hybrid GA-ANN model. Although DT and its hybrid models (PCA-DT, GA-DT) are outperformed by ANN and its hybrid models (PCA-ANN, GA-ANN) in most cases, they produced interpretable loan granting decisions.
Goal Profiles, Mental Toughness and its Influence on Performance Outcomes among Wushu Athletes.

This study examined the association between goal orientations and mental toughness and its influence on performance outcomes in competition. Wushu athletes (n = 40) competing in Intervarsity championships in Malaysia completed Task and Ego Orientations in Sport Questionnaire (TEOSQ) and Psychological Performance Inventory (PPI). Using cluster analysis techniques including hierarchical methods and the non-hierarchical method (k-means cluster) to examine goal profiles, a three cluster solution emerged viz. cluster 1 - high task and moderate ego (HT/ME), cluster 2 - moderate task and low ego (MT/LE) and, cluster 3 - moderate task and moderate ego (MT/ME). Analysis of the fundamental areas of mental toughness based on goal profiles revealed that athletes in cluster 1 scored significantly higher on negative energy control than athletes in cluster 2. Further, athletes in cluster 1 also scored significantly higher on positive energy control than athletes in cluster 3. Chi-square (χ(2)) test revealed no significant differences among athletes with different goal profiles on performance outcomes in the competition. However, significant differences were observed between athletes (medallist and non medallist) in self- confidence (p = 0.001) and negative energy control (p = 0.042). Medallist's scored significantly higher on self-confidence (mean = 21.82 ± 2.72) and negative energy control (mean = 19.59 ± 2.32) than the non-medallists (self confidence-mean = 18.76 ± 2.49; negative energy control mean = 18.14 ± 1.91).  pointsMental toughness can be influenced by certain goal profile combination.Athletes with successful outcomes in performance (medallist) displayed greater mental toughness.
Spindle Net: Person Re-identification with Human Body Region Guided Feature Decomposition and Fusion

Person re-identification (ReID) is an important task in video surveillance and has various applications. It is non-trivial due to complex background clutters, varying illumination conditions, and uncontrollable camera settings. Moreover, the person body misalignment caused by detectors or pose variations is sometimes too severe for feature matching across images. In this study, we propose a novel Convolutional Neural Network (CNN), called Spindle Net, based on human body region guided multi-stage feature decomposition and tree-structured competitive feature fusion. It is the first time human body structure information is considered in a CNN framework to facilitate feature learning. The proposed Spindle Net brings unique advantages: 1) it separately captures semantic features from different body regions thus the macro-and micro-body features can be well aligned across images, 2) the learned region features from different semantic regions are merged with a competitive scheme and discriminative features can be well preserved. State of the art performance can be achieved on multiple datasets by large margins. We further demonstrate the robustness and effectiveness of the proposed Spindle Net on our proposed dataset SenseReID without fine-tuning.
A Case Study of Tertiary Students‟ Experiences Using Edmodo in Language Learning

The past decade has seen the rapid development of technology in online learning. Its development has led many researchers to investigate the use of Web 2.0, where learning is present not only in the four-walls of the classroom. This paper, therefore, reports on students‟ experiences using a Web 2.0 tool, namely Edmodo. In particular, the study aims at identifying their perceptions of using the platform in language learning and their views on the possibility of using it to supplement face-to-face discussions in English language classes. The study involved 24 samples who undergone focus group interview as the method of collecting data. In general, results of the study revealed mixed reviews, where some students agreed with the use of Edmodo while others expressed negative opinions towards its use. In addition, two broad themes emerged from analysis of the data. These findings have significant implications in that the teacher, in the first place, needs to be equipped with the knowledge of using the platform to benefit the students. Nevertheless, this research extends our knowledge for the understanding of how to integrate Web 2.0 technologies in language classes.
Tracking of Enriched Dialog States for Flexible Conversational Information Access

Dialog state tracking (DST) is a crucial component in a task-oriented dialog system for conversational information access. A common practice in current dialog systems is to define the dialog state by a set of slot-value pairs. Such representation of dialog states and the slot-filling based DST have been widely employed, but suffer from three drawbacks. (1) The dialog state can contain only a single value for a slot, and (2) can contain only users' affirmative preference over the values for a slot. (3) Current task-based dialog systems mainly focus on the searching task, while the enquiring task is also very common in practice. The above observations motivate us to enrich current representation of dialog states and collect a brand new dialog dataset about movies, based upon which we build a new DST, called enriched DST (EDST), for flexible movie information access. The EDST supports the searching task, the enquiring task and their mixed task. We show that our new EDST method not only achieves good results on Iqiyi dataset, but also outperforms other state-of-the-art DST methods on the traditional dialog datasets, WOZ2.0 and DSTC2.
Smart tourism: foundations and developments

Smart tourism is a new buzzword applied to describe the increasing reliance of tourism destinations, their industries and their tourists on emerging forms of ICT that allow for massive amounts of data to be transformed into value propositions. However, it remains ill-defined as a concept, which hinders its theoretical development. The paper defines smart tourism, sheds light on current smart tourism trends, and then lays out its technological and business foundations. This is followed by a brief discussion on the prospects and drawbacks of smart tourism. The paper further draws attention to the great need for research to inform smart tourism development and management.
Goos THE IMPACT OF ARTIFICIAL INTELLIGENCE ON EMPLOYMENT

Technological development, and in particular digitalisation, has major implications for labour markets. Assessing its impact will be crucial for developing policies that promote efficient labour markets for the benefit of workers, employers and societies as a whole. Rapid technological progress and innovation can threaten employment. Such a concern is not new but dates back at least to the 1930s, when John Maynard Keynes postulated his ‘technological unemployment theory’ – technological change causes loss of jobs (Keynes 1937). Technological innovations can affect employment in two main ways:
Sample Path Generation for Probabilistic Demand Forecasting

The state of the art in probabilistic demand forecasting [40] minimizes Quantile Loss to predict the future demand quantiles for different horizons. However, since quantiles aren’t additive, in order to predict the total demand for any wider future interval all required intervals are usually appended to the target vector during model training. The separate optimization of these overlapping intervals can lead to inconsistent forecasts, i.e. forecasts which imply an invalid joint distribution between different horizons. As a result, inter-temporal decision making algorithms that depend on the joint or step-wise conditional distribution of future demand cannot utilize these forecasts. In this work, we address the problem by using sample paths to predict future demand quantiles in a consistent manner and propose several novel methodologies to solve this problem. Our work covers the use of covariance shrinkage methods, autoregressive models, generative adversarial networks and also touches on the use of variational autoencoders and Bayesian Dropout.
Self-Supervised Deep Image Denoising

We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a “blind spot” in the receptive field, we address two of its shortcomings: inefficient training and somewhat disappointing final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the selfsupervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise.
Rotating Component Modal Analysis and Resonance Avoidance - An Update

Rotating disk and blade fatigue failures are usually a low percentage of failures in most machinery types, but other than coupling / shaft end failures remain some of the most problematic for extensive repairs. High-cycle fatigue failures of rotating disks and blades are not common in most machinery types, but when they occur, they require extensive repairs and resolution can be problematic. This paper is an update of the tutorial given at the 2004 Turbomachinery Symposium focusing on high-cycle fatigue failures in steam turbines, centrifugal and axial gas compressors in refineries and process plants. The failure theories and many of the descriptions for cases given in 2004 have been updated to include blade resonance concerns for potential flow as well as vane and blade wake effects. Disk vibratory modes can be of concern in many machines, but of little concern in others as will be explained. In addition, vibratory modes are included where blades are coupled via communication with the main disk. Over the past decade, fluid-structure-interaction computational methods and modal testing have improved and have been applied to failure theories and problem resolution in the given cases. There is also added information on the effects of mistuning blades and disks, some beneficial and some with serious concerns for increased resonant amplification. Finally, knowledge about acoustic pressure pulsation excitation, particularly for centrifugal impellers at rotating blade passing frequency, has been greatly expanded. A review of acoustics calculations for failure prevention, mainly for high-pressure applications is covered here.
Sign Language Recognition Using Convolutional Neural Networks

Sign language is a lingua among the speech and the hearing impaired community. It is hard for most people who are not familiar with sign language to communicate without an interpreter. Sign language recognition appertains to track and recognize the meaningful emotion of human made with fingers, hands, head, arms, face etc. The technique that has been proposed in this work, transcribes the gestures from a sign language to a spoken language which is easily understood by the hearing. The gestures that have been translated include alphabets, words from static images. This becomes more important for the people who completely rely on the gestural sign language for communication tries to communicate with a person who does not understand the sign language. We aim at representing features which will be learned by a technique known as convolutional neural networks (CNN), contains four types of layers: convolution layers, pooling/subsampling layers, nonlinear layers, and fully connected layers. The new representation is expected to capture various image features and complex non-linear feature interactions. A softmax layer will be used to recognize signs. Convolutional Neural Networks, Softmax (key words) __________________________________________________*****_________________________________________________
Coherent Oscillations inside a Quantum Manifold Stabilized by Dissipation

S. Touzard, A. Grimm, Z. Leghtas, S. O. Mundhada, P. Reinhold, C. Axline, M. Reagor, K. Chou, J. Blumoff, K. M. Sliwa, S. Shankar, L. Frunzio, R. J. Schoelkopf, M. Mirrahimi, and M. H. Devoret Department of Applied Physics and Physics, Yale University, New Haven, , USA Yale Quantum Institute, Yale University, New Haven, , USA and QUANTIC team, INRIA de Paris, 2 Rue Simone Iff, 75012 Paris, France
Fuzzy Observer Design for a Class of Takagi-Sugeno Descriptor Systems

This paper deals with the problem of observers design for descriptor systems. The approach can be used to solve this problem in this paper 150 Mohamed Essabre et al. consists in designing an observer described by differential equations only. The idea of the proposed approach is to separate the dynamic relations of the static relations in the descriptor model. First, the method used for decomposed the differential part of the algebraic part is developed, secondly we give an observer design permitting to estimate the unknown state. Two classes of systems are considered. The first one concern the descriptor linear systems. An observer design of this class is given. The second class concern the descriptor nonlinear systems. First, the Takagi-Sugeno fuzzy model is employed to approximate the descriptor nonlinear systems. Next, a fuzzy observer design is designed to estimate the unknown state. The convergence of the state estimation error is studied using the Lyapunov theory and the stability conditions are given in terms of Linear Matrix Inequalities (LMIs). Finally, to illustrate the proposed methodology, a nonlinear heat exchanger pilot model is considered.
External Memory Enhanced Sequence-to-Sequence Dialogue Systems

In Natural Language Processing the sequence-to-sequence, encoder-decoder model is very successful in generating sentences, as are the tasks of dialogue, translation and question answering. On top of this model an attention mechanism is often used. The attention mechanism has the ability to look back at all encoder outputs for every decoding step. The performance increase of attention shows that the final encoded state of an input sequence alone is too poor to successfully generate a target. In this paper more elaborate forms of attention, namely external memory, are investigated on varying properties within the range of dialogue. In dialogue, the target sequence is much more complex to predict than in other tasks, since the sequence can be of arbitrary length and can contain any information related to any of the previous utterances. External memory is hypothesized to improve performance exactly because of these properties of dialogue. Varying memory models are tested on a range of context sizes. Some memory modules show more stable results with an increasing context size.
Dynamic Daylight Performance Metrics for Sustainable Building Design

The objective of this document is to promote the use of dynamic daylight performance measures for sustainable building design. The paper initially explores the shortcomings of conventional, static daylight performance metrics which concentrate on individual sky conditions, such as the common daylight factor. It then provides a review of previously suggested dynamic daylight performance metrics, discussing the capability of these metrics to lead to superior daylighting designs and their accessibility to nonsimulation experts. Several example offices are examined to demonstrate the benefit of basing design decisions on dynamic performance metrics as opposed to the daylight factor. Keywords—–daylighting, dynamic, metrics, sustainable buildings