nils-herrmann/clean_scirepeval huggingface dataset:
Skipping Word: A Character-Sequential Representation based Framework for Question Answering

Recent works using artificial neural networks based on word distributed representation greatly boost the performance of various natural language learning tasks, especially question answering. Though, they also carry along with some attendant problems, such as corpus selection for embedding learning, dictionary transformation for different learning tasks, etc. In this paper, we propose to straightforwardly model sentences by means of character sequences, and then utilize convolutional neural networks to integrate character embedding learning together with point-wise answer selection training. Compared with deep models pre-trained on word embedding (WE) strategy, our character-sequential representation (CSR) based method shows a much simpler procedure and more stable performance across different benchmarks. Extensive experiments on two benchmark answer selection datasets exhibit the competitive performance compared with the state-of-the-art methods.
Evolving missions to create game spaces

This paper describes a search-based generative method which creates game levels by evolving the intended sequence of player actions rather than their spatial layout. The proposed approach evolves graphs where nodes representing player actions are linked to form one or more ways in which a mission can be completed. Initially simple graphs containing the mission's starting and ending nodes are evolved via mutation operators which expand and prune the graph topology. Evolution is guided by several objective functions which capture game design patterns such as exploration or balance; experiments in this paper explore how these objective functions and their combinations affect the quality and diversity of the evolved mission graphs.
Erasure coded storage systems for cloud storage — challenges and opportunities

Erasure coded storage schemes offer a promising future for cloud storage. Highlights of erasure coded storage systems are that these offer the same level of fault tolerance as that of replication, at lower storage footprints. In the big data era, cloud storage systems based on data replication are of dubious usability due to 200% storage overhead in data replication systems. This has prompted storage service providers to use erasure coded storage as an alternative to replication. Refinements are required in various aspects of erasure coded storage systems to make it a real contender against data replication based storage systems. Streamlining huge bandwidth requirements during the recovery of failed nodes, inefficient update operations, effect of topology in recovery and consistency requirements of erasure coded storage systems, are some areas which need attention. This paper presents an in-depth study on the challenges faced, and research pursued in some of these areas. The survey shows that more research is required to improve erasure coded storage system from being bandwidth crunchers to efficient storage systems. Another challenge that has emerged from the study is the requirement of elaborate research for upgrading the erasure coded storage systems from being mere archival storage systems by providing better update methods. Provision of multiple level consistency in erasure coded storage is yet another research opportunity identified in this work. A brief introduction to open source libraries available for erasure coded storage is also presented in the paper.
A ZVS Grid-Connected Three-Phase Inverter

A six-switch three-phase inverter is widely used in a high-power grid-connected system. However, the antiparallel diodes in the topology operate in the hard-switching state under the traditional control method causing severe switch loss and high electromagnetic interference problems. In order to solve the problem, this paper proposes a topology of the traditional six-switch three-phase inverter but with an additional switch and gave a new space vector modulation (SVM) scheme. In this way, the inverter can realize zero-voltage switching (ZVS) operation in all switching devices and suppress the reverse recovery current in all antiparallel diodes very well. And all the switches can operate at a fixed frequency with the new SVM scheme and have the same voltage stress as the dc-link voltage. In grid-connected application, the inverter can achieve ZVS in all the switches under the load with unity power factor or less. The aforementioned theory is verified in a 30-kW inverter prototype.
Life Cycle of Smart Contracts in Blockchain Ecosystems

This paper discusses the life cycle of decentralized smart contracts, i.e. digital and executable representations of rights and obligations in a multi-party environment. The life cycle relies on blockchain technology, i.e. a distributed digital ledger, to ensure proper implementation and integrity of the smart contracts. The life cycle consists of four subsequent phases: Creation, freezing, execution, and finalization. For each phase actors and technological services are identified and explained in detail. With the life cycle at hand, risks and limitations of smart contracts and the underlying blockchain technology are briefly discussed.
A Real-Time Matching System for Large Fingerprint Databases

With the current rapid growth in multimedia technology, there is an imminent need for efficient techniclues to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated iri a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexincl large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-,4SIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the
A Compositional Object-Based Approach to Learning Physical Dynamics

This paper presents the Neural Physics Engine (NPE), an object-based neural network architecture for learning predictive models of intuitive physics. The NPE draws on the strengths of both symbolic and neural approaches: like a symbolic physics engine, it is endowed with generic notions of objects and their interactions, but as a neural network it can also be trained via stochastic gradient descent to adapt to specific object properties and dynamics of different worlds. We evaluate the efficacy of our approach on simple rigid body dynamics in two-dimensional worlds of bouncing balls. By comparing to less structured architectures, we show that the NPE’s compositional representation of the causal structure in physical interactions improves its ability to predict movement, generalize to different numbers of objects, and infer latent properties of objects such as mass.
Evolved Machines Shed Light on Robustness and Resilience

In biomimetic engineering, we may take inspiration from the products of biological evolution: we may instantiate biologically realistic neural architectures and algorithms in robots, or we may construct robots with morphologies that are found in nature. Alternatively, we may take inspiration from the process of evolution: we may evolve populations of robots in simulation and then manufacture physical versions of the most interesting or more capable robots that evolve. If we follow this latter approach and evolve both the neural and morphological subsystems of machines, we can perform controlled experiments that provide unique insight into how bodies and brains can work together to produce adaptive behavior, regardless of whether such bodies and brains are instantiated in a biological or technological substrate. In this paper, we review selected projects that use such methods to investigate the synergies and tradeoffs between neural architecture, morphology, action, and adaptive behavior.
A hybrid recommendation approach for a tourism system

Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.
An anomaly-based network intrusion detection system using Deep learning

Recently, anomaly-based intrusion detection techniques are valuable methodology to detect both known as well as unknown/new attacks, so they can cope with the diversity of the attacks and the constantly changing nature of network attacks. There are many problems need to be considered in anomaly-based network intrusion detection system (NIDS), such as ability to adapt to dynamic network environments, unavailability of labeled data, false positive rate. This paper, we use Deep learning techniques to implement an anomaly-based NIDS. These techniques show the sensitive power of generative models with good classification, capabilities to deduce part of its knowledge from incomplete data and the adaptability. Our experiments with KDDCup99 network traffic connections show that our work is effective to exact detect in anomaly-based NIDS and classify intrusions into five groups with the accuracy based on network data sources.
The Domain Specificity of Academic Emotional Experiences

The authors analyzed the domain specificity of emotions and focused on experiences of enjoyment, anxiety, and boredom in the domains of mathematics, Latin, German, and English. On the basis of assumptions in R. Pekrun's (2000; in press) con· trol·value theory and findings of pilot studies, the authors hypothesized the existence of a largely domain-specific organization of emotional experiences. The sample consisted of 721 students from grades 7 to 10. Confirmatory multitrait-multimethod factor analysis of the 2-faceted dataset (emotions and domains) corroborated assumptions of domain specificity. Furthermore, using multilevel analysis, the authors found that emotions were significantly more domain-specific than students' grades, with enjoyment being the most domain-specific of the three emotions under investigation. The authors discuss implications for future research and practice.
The See-Through System: A VANET-enabled assistant for overtaking maneuvers

The use of wireless technology based on Vehicular Ad hoc Networks (VANETs) for information exchange can influence the drivers' behavior towards improving driving performance and reducing road accidents. This information can even be more relevant if it is presented as a video stream. In this paper we propose a system that relies on VANET and video-streaming technology: the See-Through System (STS). The system enhances driver's visibility and supports the driver's overtaking decision in challenging situations, such as overtaking a vision-obstructing vehicle. The use of the See-Through System provides the driver with an additional tool for determining if traffic conditions permit starting an overtaking maneuver thus reducing the risk of overtaking. We tested the See-Through System on an experimental vehicle on the road as well as in the context of a driving simulator for real world environment. Results are promising, since the use of the 802.11p standard wireless communication protocol allows a vehicle-to-vehicle connection without significant delay and the totality of the participants regarded the information provided by the STS as useful.
SCMA Codebook Design

Multicarrier CDMA is a multiple access scheme in which modulated QAM symbols are spread over OFDMA tones by using a generally complex spreading sequence. Effectively, a QAM symbol is repeated over multiple tones. Low density signature (LDS) is a version of CDMA with low density spreading sequences allowing us to take advantage of a near optimal message passing algorithm (MPA) receiver with practically feasible complexity. Sparse code multiple access (SCMA) is a multi-dimensional codebook-based non-orthogonal spreading technique. In SCMA, the procedure of bit to QAM symbol mapping and spreading are combined together and incoming bits are directly mapped to multi-dimensional codewords of SCMA codebook sets. Each layer has its dedicated codebook. Shaping gain of a multi-dimensional constellation is one of the main sources of the performance improvement in comparison to the simple repetition of QAM symbols in LDS. Meanwhile, like LDS, SCMA enjoys the low complexity reception techniques due to the sparsity of SCMA codewords. In this paper a systematic approach is proposed to design SCMA codebooks mainly based on the design principles of lattice constellations. Simulation results are presented to show the performance gain of SCMA compared to LDS and OFDMA.
Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models.

This paper identifies several serious problems with the widespread use of ANOVAs for the analysis of categorical outcome variables such as forced-choice variables, question-answer accuracy, choice in production (e.g. in syntactic priming research), et cetera. I show that even after applying the arcsine-square-root transformation to proportional data, ANOVA can yield spurious results. I discuss conceptual issues underlying these problems and alternatives provided by modern statistics. Specifically, I introduce ordinary logit models (i.e. logistic regression), which are well-suited to analyze categorical data and offer many advantages over ANOVA. Unfortunately, ordinary logit models do not include random effect modeling. To address this issue, I describe mixed logit models (Generalized Linear Mixed Models for binomially distributed outcomes, Breslow & Clayton, 1993), which combine the advantages of ordinary logit models with the ability to account for random subject and item effects in one step of analysis. Throughout the paper, I use a psycholinguistic data set to compare the different statistical methods.
Pick-by-Vision comes on age: evaluation of an augmented reality supported picking system in a real storage environment

Order picking is one of the most important process steps in logistics. Because of their flexibility human beings cannot be replaced by machines. But if workers in order picking systems are equipped with a head-mounted display, Augmented Reality can improve the information visualization.
 In this paper the development of such a system -- called Pick-by-Vision - is presented. The system is evaluated in a user study performed in a real storage environment. Important logistics figures as well as subjective figures were measured. The results show that a Pick-by-Vision system can improve considerably industrial order picking processes.
Active Damping in DC/DC Power Electronic Converters: A Novel Method to Overcome the Problems of Constant Power Loads

Multi-converter power electronic systems exist in land, sea, air, and space vehicles. In these systems, load converters exhibit constant power load (CPL) behavior for the feeder converters and tend to destabilize the system. In this paper, the implementation of novel active-damping techniques on dc/dc converters has been shown. Moreover, the proposed active-damping method is used to overcome the negative impedance instability problem caused by the CPLs. The effectiveness of the new proposed approach has been verified by PSpice simulations and experimental results.
Proposing measures of flicker in the low frequencies for lighting applications

The IEEE Standards Working Group, IEEE PAR1789 "Recommending practices for modulating current in High Brightness LEDs for mitigating health risks to viewers" has been formed to advise the lighting industry and standards groups about the emerging concern of flicker in LED lighting. This paper intends to introduce new measures and definitions of lamp flicker in lighting. The discussion represents on-going work in IEEE PAR1789 that is vital to designing safe LED lamp drivers.
Preparation of Personalized-dose Salbutamol Sulphate Oral Films with Thermal Ink-Jet Printing

To evaluate the use of thermal ink-jetting as a method for dosing drugs onto oral films. A Hewlett-Packard printer cartridge was modified so that aqueous drug solutions replaced the ink. The performance of the printer as a function of print solution viscosity and surface tension was determined; viscosities between 1.1 and 1.5 mm2 s-1 were found to be optimal, while surface tension did not affect deposition. A calibration curve for salbutamol sulphate was prepared, which demonstrated drug deposition onto an acetate film varied linearly with concentration (r2 = 0.9992). The printer was then used to deposit salbutamol sulphate onto an oral film made of potato starch. It was found that when doses were deposited in a single pass under the print head, then the measured dose was in good agreement with the theoretical dose. With multiple passes the measured dose was always significantly less than the theoretical dose. It is proposed that the losses arise from erosion of the printed layer by shearing forces during paper handling. The losses were predictable, and the variance in dose deposited was always less than the BP limits for tablet and oral syrup salbutamol sulphate preparations. TIJ printing offers a rapid method for extemporaneous preparation of personalized-dose medicines.
EyeTell: Video-Assisted Touchscreen Keystroke Inference from Eye Movements

Keystroke inference attacks pose an increasing threat to ubiquitous mobile devices. This paper presents EyeTell, a novel video-assisted attack that can infer a victim's keystrokes on his touchscreen device from a video capturing his eye movements. EyeTell explores the observation that human eyes naturally focus on and follow the keys they type, so a typing sequence on a soft keyboard results in a unique gaze trace of continuous eye movements. In contrast to prior work, EyeTell requires neither the attacker to visually observe the victim's inputting process nor the victim device to be placed on a static holder. Comprehensive experiments on iOS and Android devices confirm the high efficacy of EyeTell for inferring PINs, lock patterns, and English words under various environmental conditions.
Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale

Research into social networking addiction has greatly increased over the last decade. However, the number of validated instruments assessing addiction to social networking sites (SNSs) remains few, and none have been validated in the Italian language. Consequently, this study tested the psychometric properties of the Italian version of the Bergen Social Media Addiction Scale (BSMAS), as well as providing empirical data concerning the relationship between attachment styles and SNS addiction.  A total of 769 participants were recruited to this study. Confirmatory factor analysis (CFA) and multigroup analyses were applied to assess construct validity of the Italian version of the BSMAS. Reliability analyses comprised the average variance extracted, the standard error of measurement, and the factor determinacy coefficient.  Indices obtained from the CFA showed the Italian version of the BSMAS to have an excellent fit of the model to the data, thus confirming the single-factor structure of the instrument. Measurement invariance was established at configural, metric, and strict invariances across age groups, and at configural and metric levels across gender groups. Internal consistency was supported by several indicators. In addition, the theoretical associations between SNS addiction and attachment styles were generally supported.  This study provides evidence that the Italian version of the BSMAS is a psychometrically robust tool that can be used in future Italian research into social networking addiction.
In good company: how social capital makes organizations work [Book Review]

Don Cohen and Laurence Prusak define "social capital" in terms of connections among people. It emerges, they write, from "trust, mutual understanding, and shared values and behaviors that bind the members of human networks and communities and make more cooperative action possible." Continuing a theme that Prusak, executive director of the IBM Institute for Knowledge Management, has been developing, they offer examples of how social capital is created and destroyed and the role it plays in collective human endeavors, particularly in modern business. The authors suggest that this understanding is essential for developing models that respect and nourish human relationships. Seekers of a rigorous, quantified theoretical framework won't find it here. Instead, the book explores the role of trust, community, connectedness and loyalty in making companies work today.--Alan S. Kay
Dual band (28/38 GHz) CPW slot directive antenna for future 5G cellular applications

A design and analysis of a 28/38 GHz dual-band CPW slot directive antenna are presented. To obtain a dual band at 28/38 GHz that covers 5G applications, chambered rectangular patches are printed on a low permittivity “εr2=2.2” grounded substrates with a 0.254-mm-thick where it is fed by a 50Ω microstrip line on the backside through a staircase rectangular apertures etched in the ground plane. The simulated results show that the designed antenna has dual band at 28 and 38 GHz for 5G system. The antenna provides almost directional patterns with acceptable gain values at both frequency band of operation.