nils-herrmann/clean_scirepeval huggingface dataset:
A methodology proposal for developing adaptive cMOOC

Massive Open Online Courses or MOOCs, both in their major approaches xMOOC and cMOOC, are attracting a very interesting debate about their influence in the higher education future. MOOC have both great defenders and detractors. As an emerging trend, MOOCs have a hype repercussion in technological and pedagogical areas, but they should demonstrate their real value in specific implementation and within institutional strategies. Independently, MOOCs have different issues such as high dropout rates and low number of cooperative activities among participants. This paper presents an adaptive proposal to be applied in MOOC definition and development, with a special attention to cMOOC, that may be useful to tackle the mentioned MOOC problems.
Recognition by Linear Combinations of Models

Visual object recognition requires the matching of an image with a set of models stored in memory. In this paper, we propose an approach to recognition in which a 3-D object is represented by the linear combination of 2-D images of the object. IfJLk{M1,.” .Mk} is the set of pictures representing a given object and P is the 2-D image of an object to be recognized, then P is considered to be an instance of M if P= C~=,aiMi for some constants (pi. We show that this approach handles correctly rigid 3-D transformations of objects with sharp as well as smooth boundaries and can also handle nonrigid transformations. The paper is divided into two parts. In the first part, we show that the variety of views depicting the same object under different transformations can often be expressed as the linear combinations of a small number of views. In the second part, we suggest how this linear combination property may be used in the recognition process.
An Architecture to Support the Collection of Big Data in the Internet of Things

The Internet of Things (IoT) relies on physical objects interconnected between each others, creating a mesh of devices producing information. In this context, sensors are surrounding our environment (e.g., cars, buildings, smartphones) and continuously collect data about our living environment. Thus, the IoT is a prototypical example of Big Data. The contribution of this paper is to define a software architecture supporting the collection of sensor-based data in the context of the IoT. The architecture goes from the physical dimension of sensors to the storage of data in a cloud-based system. It supports Big Data research effort as its instantiation supports a user while collecting data from the IoT for experimental or production purposes. The results are instantiated and validated on a project named SMARTCAMPUS, which aims to equip the SophiaTech campus with sensors to build innovative applications that supports end-users.
Design of a 750,000 rpm switched reluctance motor for micro machining

This paper presents a detailed design process of an ultra-high speed, switched reluctance machine for micro machining. The performance goal of the machine is to reach a maximum rotation speed of 750,000 rpm with an output power of 100 W. The design of the rotor involves reducing aerodynamic drag, avoiding mechanical resonance, and mitigating excessive stress. The design of the stator focuses on meeting the torque requirement while minimizing core loss and copper loss. The performance of the machine and the strength of the rotor structure are both verified through finite-element simulations The final design is a 6/4 switched reluctance machine with a 6mm diameter rotor that is wrapped in a carbon fiber sleeve and exhibits 13.6 W of viscous loss. The stator has shoeless poles and exhibits 19.1 W of electromagnetic loss.
Free Space Optical Communication: Challenges and Mitigation Techniques

In recent years, free space optical (FSO) communication has gained significant importance owing to its unique features: large bandwidth, license free spectrum, high data rate, easy and quick deployability, less power and low mass requirement. FSO communication uses optical carrier in the near infrared (IR) and visible band to establish either terrestrial links within the Earth’s atmosphere or inter-satellite/deep space links or ground-to-satellite/satellite-to-ground links. It also find its applications in remote sensing, radio astronomy, military, disaster recovery, last mile access, back-haul for wireless cellular networks and many more. However, despite of great potential of FSO communication, its performance is limited by the adverse effects (viz., absorption, scattering and turbulence) of the atmospheric channel. Out of these three effects, the atmospheric turbulence is a major challenge that may lead to serious degradation in the bit error rate (BER) performance of the system and make the communication link infeasible. This paper presents a comprehensive survey on various challenges faced by FSO communication system for both terrestrial and space links. It will provide details of various performance mitigation techniques in order to have high link availability and reliability of FSO system. The first part of the paper will focus on various types of impairments that poses a serious challenge to the performance of FSO system for both terrestrial and space links. The latter part of the paper will provide the reader with an exhaustive review of various techniques used in FSO system both at physical layer as well as at the upper layers (transport, network or link layer) to combat the adverse effects of the atmosphere. Further, this survey uniquely offers the current literature on FSO coding and modulation schemes using various channel models and detection techniques. It also presents a recently developed technique in FSO system using orbital angular momentum to combat the effect of atmospheric turbulence.
A Semi-Parallel Successive-Cancellation Decoder for Polar Codes

Polar codes are a recently discovered family of capacity-achieving codes that are seen as a major breakthrough in coding theory. Motivated by the recent rapid progress in the theory of polar codes, we propose a semi-parallel architecture for the implementation of successive cancellation decoding. We take advantage of the recursive structure of polar codes to make efficient use of processing resources. The derived architecture has a very low processing complexity while the memory complexity remains similar to that of previous architectures. This drastic reduction in processing complexity allows very large polar code decoders to be implemented in hardware. An N=217 polar code successive cancellation decoder is implemented in an FPGA. We also report synthesis results for ASIC.
Exploratory factor analysis for small samples.

Traditionally, two distinct approaches have been employed for exploratory factor analysis: maximum likelihood factor analysis and principal component analysis. A third alternative, called regularized exploratory factor analysis, was introduced recently in the psychometric literature. Small sample size is an important issue that has received considerable discussion in the factor analysis literature. However, little is known about the differential performance of these three approaches to exploratory factor analysis in a small sample size scenario. A simulation study and an empirical example demonstrate that regularized exploratory factor analysis may be recommended over the two traditional approaches, particularly when sample sizes are small (below 50) and the sample covariance matrix is near singular.
NEURAL NETWORK PARADIGM FOR CLASSIFICATION OF DEFECTS ON PCB

A new technique is proposed to classify the defects that could occur on the PCB using neural network paradigm. The algorithms to segment the image into basic primitive patterns, enclosing the primitive patterns, patterns assignment, patterns normalization, and classification have been developed based on binary morphological image processing and Learning Vector Quantization (LVQ) neural network. Thousands of defective patterns have been used for training, and the neural network is tested for evaluating its performance. A defective PCB image is used to ensure the function of the proposed technique.
A Wideband Circularly Polarized Rectangular Dielectric Resonator Antenna Excited by an Archimedean Spiral Slot

A wideband circularly polarized (CP) rectangular dielectric resonator antenna (DRA) is presented. An Archimedean spiral slot is used to excite the rectangular DRA for wideband CP radiation. The operating principle of the proposed antenna is based on using a broadband feeding structure to excite the DRA. A prototype of the proposed antenna is designed, fabricated, and measured. Good agreement between the simulated and measured results is attained, and a wide 3-dB axial-ratio (AR) bandwidth of 25.5% is achieved.
Business Process Modeling: Current Issues and Future Challenges

Business process modeling has undoubtedly emerged as a popular and relevant practice in Information Systems. Despite being an actively researched field, anecdotal evidence and experiences suggest that the focus of the research community is not always well aligned with the needs of industry. The main aim of this paper is, accordingly, to explore the current issues and the future challenges in business process modeling, as perceived by three key stakeholder groups (academics, practitioners, and tool vendors). We present the results of a global Delphi study with these three groups of stakeholders, and discuss the findings and their implications for research and practice. Our findings suggest that the critical areas of concern are standardization of modeling approaches, identification of the value proposition of business process modeling, and model-driven process execution. These areas are also expected to persist as business process modeling roadblocks in the future.
AND EVALUATION OF WELL-KNOWN METHODS FOR MOVING OBJECT DETECTION AND TRACKING IN VIDEOS

Moving object detection and tracking (D&T) are important initial steps in object recognition, context analysis and indexing processes for visual surveillance systems. It is a big challenge for researchers to make a decision on which D&T algorithm is more suitable for which situation and/or environment and to determine how accurately object D&T (real-time or non-real-time) is made. There is a variety of object D&T algorithms (i.e. methods) and publications on their performance comparison and evaluation via performance metrics. This paper provides a systematic review of these algorithms and performance measures and assesses their effectiveness via metrics.
Scene Text Recognition: No Country for Old Men?

It is a generally accepted fact that Off-the-shelf OCR engines do not perform well in unconstrained scenarios like natural scene imagery, where text appears among the clutter of the scene. However, recent research demonstrates that a conventional shape-based OCR engine would be able to produce competitive results in the end-to-end scene text recognition task when provided with a conveniently preprocessed image. In this paper we confirm this finding with a set of experiments where two off-the-shelf OCR engines are combined with an open implementation of a state-of-the-art scene text detection framework. The obtained results demonstrate that in such pipeline, conventional OCR solutions still perform competitively compared to other solutions specifically designed for scene text recognition.
Simple Principles of Metalearning

The goal of metalearning is to generate useful shifts of inductive bias by adapting the current learning strategy in a \useful" way. Our learner leads a single life during which actions are continually executed according to the system's internal state and current policy (a modiiable, probabilistic algorithm mapping environmental inputs and internal states to outputs and new internal states). An action is considered a learning algorithm if it can modify the policy. EEects of learning processes on later learning processes are measured using reward/time ratios. Occasional backtracking enforces success histories of still valid policy modiications corresponding to histories of lifelong reward accelerations. The principle allows for plugging in a wide variety of learning algorithms. In particular, it allows for embedding the learner's policy modiication strategy within the policy itself (self-reference). To demonstrate the principle's feasibility in cases where conventional reinforcement learning fails, we test it in complex, non-Markovian, changing environments (\POMDPs"). One of the tasks involves more than 10 13 states, two learners that both cooperate and compete, and strongly delayed reinforcement signals (initially separated by more than 300,000 time steps). The biggest diierence between time and space is that you can't reuse time.
Identification and evaluation of corporations for merger and acquisition strategies using patent information and text mining

This paper proposes a framework to identify and evaluate companies from the technological perspective to support merger and acquisition (M&A) target selection decision-making. This employed a text mining-based patent map approach to identify companies which can fulfill a specific strategic purpose of M&A for enhancing technological capabilities. The patent map is the visualized technological landscape of a technology industry by using technological proximities among patents, so companies which closely related to the strategic purpose can be identified. To evaluate the technological aspects of the identified companies, we provide the patent indexes that evaluate both current and future technological capabilities and potential technology synergies between acquiring and acquired companies. Furthermore, because the proposed method evaluates potential targets from the overall corporate perspective and the specific strategic perspectives simultaneously, more robust and meaningful result can be obtained than when only one perspective is considered. Thus, the proposed framework can suggest the appropriate target companies that fulfill the strategic purpose of M&A for enhancing technological capabilities. For the verification of the framework, we provide an empirical study using patent data related to flexible display technology.
The need for and meaning of positive organizational behavior

This essay draws from the emerging positive psychology movement and the author’s recent articles on the need for and meaning of a positive approach to organizational behavior. Specifically, the argument is made that at this time, the OB field needs a proactive, positive approach emphasizing strengths, rather than continuing in the downward spiral of negativity trying to fix weaknesses. However, to avoid the surface positivity represented by the non-sustainable best-sellers, the case is made for positive organizational behavior (POB) to take advantage of the OB field’s strength of being theory and research driven.  Confidence, hope, and resiliency are offered as meeting such POB inclusion criteria. The overall intent of the essay is to generate some positive thinking and excitement for the OB field and ‘hopefully’ stimulate some new theory building, research, and effective application.
Analysing anaphoric ambiguity in natural language requirements

Many requirements documents are written in natural language (NL). However, with the flexibility of NL comes the risk of introducing unwanted ambiguities in the requirements and misunderstandings between stakeholders. In this paper, we describe an automated approach to identify potentially nocuous ambiguity, which occurs when text is interpreted differently by different readers. We concentrate on anaphoric ambiguity, which occurs when readers may disagree on how pronouns should be interpreted. We describe a number of heuristics, each of which captures information that may lead a reader to favor a particular interpretation of the text. We use these heuristics to build a classifier, which in turn predicts the degree to which particular interpretations are preferred. We collected multiple human judgements on the interpretation of requirements exhibiting anaphoric ambiguity and showed how the distribution of these judgements can be used to assess whether a particular instance of ambiguity is nocuous. Given a requirements document written in natural language, our approach can identify sentences that contain anaphoric ambiguity, and use the classifier to alert the requirements writer of text that runs the risk of misinterpretation. We report on a series of experiments that we conducted to evaluate the performance of the automated system we developed to support our approach. The results show that the system achieves high recall with a consistent improvement on baseline precision subject to some ambiguity tolerance levels, allowing us to explore and highlight realistic and potentially problematic ambiguities in actual requirements documents.
Blazing Angels or Resident Evil ? Can Violent Video Games Be a Force for Good ?

Violent video games have been a source of controversy in the United States and elsewhere for several decades. Considerable concern has been raised in the public and scientific communities about the alleged deleterious effects of violent games. These concerns may coincide with periodic moral panics about media’s influence, particularly on youth. This paper argues that the negative effects of violent games have been exaggerated by some elements of the scientific community, fitting with past cycles of media-focused moral panics. By contrast, potential positive effects of violent video game play have been ignored in the debate on violent games. The current paper considers research in several areas, including aggression, but also the nascent research fields of visuospatial cognition, social networking, and use as educational tools. It is argued that the debate on video game violence should be broadened to include both potential negative and positive effects.
Fuzzy identification of systems and its applications to modeling and control

A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented. The premise of an implication is the description of fuzzy subspace of inputs and its consequence is a linear input-output relation. The method of identification of a system using its input-output data is then shown. Two applications of the method to industrial processes are also discussed: a water cleaning process and a converter in a steel-making process.
Multi-criteria service recommendation based on user criteria preferences

Research in recommender systems is now starting to recognise the importance of multiple selection criteria to improve the recommendation output. In this paper, we present a novel approach to multi-criteria recommendation, based on the idea of clustering users in "preference lattices" (partial orders) according to their criteria preferences. We assume that some selection criteria for an item (product or a service) will dominate the overall ranking, and that these dominant criteria will be different for different users. Following this assumption, we cluster users based on their criteria preferences, creating a "preference lattice". The recommendation output for a user is then based on ratings by other users from the same or close clusters. Having introduced the general approach of clustering, we proceed to formulate three alternative recommendation methods instantiating the approach: (a) using the aggregation function of the criteria, (b) using the overall item ratings, and (c) combining clustering with collaborative filtering. We then evaluate the accuracy of the three methods using a set of experiments on a service ranking dataset, and compare them with a conventional collaborative filtering approach extended to cover multiple criteria. The results indicate that our third method, which combines clustering and extended collaborative filtering, produces the highest accuracy.
The safety critical electric machines and drives in the more electric aircraft: A survey

In order to improve aircraft efficiency, reliability and maintainability, the aerospace world has found in the progressive electrification of on-board services the way to reduce or to remove the presence of the hydraulic, mechanical and the bleed air/pneumatic systems. This concept is called More Electric Aircraft (MEA), which involves the introduction of the Electromechanical actuators (EMAs) and the electro-hydraulics actuators (EHAs) for the actuation of the flight surfaces of wide-body aircraft, moving from the “fly-by wire” to the “power-by wire” concept. The resulting step change in aircraft electrical loading will have far reaching implications for the electrical generation system. Considerable effort is being directed towards realizing the so-called More Electric Engine (MEE), which foresee an integration of the electrical generator directly inside the main gas turbine engine. Also the entire electrical distribution system is subject to radical revisiting, with a trend which is leaving the constant frequency AC energy distribution in favor of variable frequency or DC solutions. Hence, it is evident the MEA trend increments the technical challenges and research topics for the electrical engineering in the aeronautic applications. The aim of the paper is the presentation, from the electrical engineering point of view, of some of the most challenging application of electric machines and drives in the incoming new aircraft generation. There are too many on board electric components and systems to be analyzed: the authors centre the attention on the most safety critical drives: flight surface actuators, fuel pumps and generators.
Image-Based Appraisal of Real Estate Properties

Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buyers and sellers as the basis for negotiation and transaction. Traditionally, the repeat sales model has been widely adopted to estimate real estate prices. However, it depends on the design and calculation of a complex economic-related index, which is challenging to estimate accurately. Today, real estate brokers provide easy access to detailed online information on real estate properties to their clients. We are interested in estimating the real estate price from these large amounts of easily accessed data. In particular, we analyze the prediction power of online house pictures, which is one of the key factors for online users to make a potential visiting decision. The development of robust computer vision algorithms makes the analysis of visual content possible. In this paper, we employ a recurrent neural network to predict real estate prices using the state-of-the-art visual features. The experimental results indicate that our model outperforms several other state-of-the-art baseline algorithms in terms of both mean absolute error and mean absolute percentage error.