nils-herrmann/clean_scirepeval huggingface dataset:
Semantic information activated during retrieval contributes to later retention: Support for the mediator effectiveness hypothesis of the testing effect.

Previous research has proposed that tests enhance retention more than do restudy opportunities because they promote the effectiveness of mediating information--that is, a word or concept that links a cue to a target (Pyc & Rawson, 2010). Although testing has been shown to promote retention of mediating information that participants were asked to generate, it is unknown what type of mediators are spontaneously activated during testing and how these contribute to later retention. In the current study, participants learned cue-target pairs through testing (e.g., Mother: _____) or restudying (e.g., Mother: Child) and were later tested on these items in addition to a never-before-presented item that was strongly associated with the cue (e.g., Father)--that is, the semantic mediator. Compared with participants who learned the items through restudying, those who learned the items through testing exhibited higher false alarm rates to semantic mediators on a final recognition test (Experiment 1) and were also more likely to recall the correct target from the semantic mediator on a final cued recall test (Experiment 2). These results support the mediator effectiveness hypothesis and demonstrate that semantically related information may be 1 type of natural mediator that is activated during testing.
Aesthetic botulinum A toxin in the mid and lower face and neck.

Botulinum toxin type A (BOTOX formulation) is used extensively for smoothing hyperkinetic lines in the upper face. The use of botulinum toxin for aesthetic indications in the mid and lower face and neck is now becoming increasingly popular.



To review our current approaches to botulinum toxin treatment for cosmetic indications in the mid and lower face and neck.



Procedures and outcomes are described for the primary and adjunctive use of botulinum toxin.



Cosmetic treatment with botulinum toxin successfully changes the contour of the palebral aperture; smoothes lines, including "bunny" lines, perioral rhytides, and horizontal neck lines; softens creases, including the mental crease and melomental folds; and alleviates facial asymmetry and nasal flare. The doses of botulinum toxin used in the mid and lower face are generally lower than those used in the upper face. Caution must be used in injecting botulinum toxin in the perioral area to avoid an incompetent mouth.



Botulinum toxin treatment is valuable for aesthetic improvements in the mid and lower face and neck. In some areas, particularly the perioral region, the use of botulinum toxin in combination with other therapeutic modalities provides optimal results.
Generating Predictive Movie Recommendations from Trust in Social Networks

Social networks are growing in number and size, with hundreds of millions of user accounts among them. One added benefit of these networks is that they allow users to encode more information about their relationships than just stating who they know. In this work, we are particularly interested in trust relationships, and how they can be used in designing interfaces. In this paper, we present FilmTrust, a website that uses trust in web-based social networks to create predictive movie recommendations. Using the FilmTrust system as a foundation, we show that these recommendations are more accurate than other techniques when the user’s opinions about a film are divergent from the average. We discuss this technique both as an application of social network analysis, as well as how it suggests other analyses that can be performed to help improve collaborative filtering algorithms of all types.
On language and connectionism: Analysis of a parallel distributed processing model of language acquisition

Does knowledge of language consist of mentally-represented rules? Rumelhart and McClelland have described a connectionist (parallel distributed processing) model of the acquisition of the past tense in English which successfully maps many stems onto their past tense forms, both regular (walk/walked) and irregular (go/went), and which mimics some of the errors and sequences of development of children. Yet the model contains no explicit rules, only a set of neuron-style units which stand for trigrams of phonetic features of the stem, a set of units which stand for trigrams of phonetic features of the past form, and an array of connections between the two sets of units whose strengths are modified during learning. Rumelhart and McClelland conclude that linguistic rules may be merely convenient approximate fictions and that the real causal processes in language use and acquisition must be characterized as the transfer of activation levels among units and the modification of the weights of their connections. We analyze both the linguistic and the developmental assumptions of the model in detail and discover that (1) it cannot represent certain words, (2) it cannot learn many rules, (3) it can learn rules found in no human language, (4) it cannot explain morphological and phonological regularities, (5) it cannot explain the differences between irregular and regular forms, (6) it fails at its assigned task of mastering the past tense of English, (7) it gives an incorrect explanation for two developmental phenomena: stages of overregularization of irregular forms such as bringed, and the appearance of doubly-marked forms such as ated, and (8) it gives accounts of two others (infrequent overregularization of verbs ending in t/d, and the order of acquisition of different irregular subclasses) that are indistinguishable from those of rule-based theories. In addition, we show how many failures of the model can be attributed to its connectionist architecture. We conclude that connectionists' claims about the dispensability of rules in explanations in the psychology of language must be rejected, and that, on the contrary, the linguistic and developmental facts provide good evidence for such rules. On Language and Connectionism:... http://www.ecs.soton.ac.uk/~harnad/Papers/Py104/pinker.conn.html 2 of 79 20/04/2004 16.17 On Language and Connectionism: Analysis of a Parallel Distributed Processing Model of Language Acquisition Steven Pinker and Alan Prince "If design govern in a thing so small." -Robert Frost
Smart Papers: Dynamic Publications on the Blockchain

Distributed Ledgers (DLs), also known as blockchains, provide decentralised, tamper-free registries of transactions among partners that distrust each other. For the scientific community, DLs have been proposed to decentralise and make more transparent each step of the scientific workflow. For the particular case of dissemination and peerreviewing, DLs can provide the cornerstone to realise open decentralised publishing systems where social interactions between peers are tamperfree, enabling trustworthy computation of bibliometrics. In this paper, we propose the use of DL-backed Smart Contracts to track a subset of social interactions for scholarly publications in a decentralised and reliable way, yielding Smart Papers. We show how our Smart Papers approach complements current models for decentralised publishing, and analyse cost implications.
SHIHbot: A Facebook chatbot for Sexual Health Information on HIV/AIDS

We present the implementation of an autonomous chatbot, SHIHbot, deployed on Facebook, which answers a wide variety of sexual health questions on HIV/AIDS. The chatbot's response database is compiled from professional medical and public health resources in order to provide reliable information to users. The system's backend is NPCEditor, a response selection platform trained on linked questions and answers; to our knowledge this is the first retrieval-based chatbot deployed on a large public social network.
A Comparative Performance Study of OFDM System with the Implementation of Comb Pilot-Based MMSE Channel Estimation

This paper presents a comparative performance analysis of wireless orthogonal frequency division multiplexing (OFDM) system with the implementation of comb type pilot-based channel estimation algorithm over frequency selective multi-path fading channels. The Minimum Mean Square Error (MMSE) method is used for the estimation of channel at pilot frequencies. For the estimation of channel at data frequencies different interpolation techniques such as low-pass, linear, and second order interpolation are employed. The OFDM system simulation has been carried out with Matlab and the performance is analyzed in terms of bit error rate (BER) for various signal mapping (BPSK, QPSK, 4QAM, 16QAM, and 64QAM) and channel (Rayleigh and Rician) conditions. The impact of selecting number of channel taps on the BER performance is also investigated.
Blockchain and smart contract for digital certificate

According to the Taiwan Ministry of Education statistics, about one million graduates each year, some of them will go to countries, high schools or tertiary institutions to continue to attend, and some will be ready to enter the workplace employment. During the course of study, the students' all kinds of excellent performance certificates, score transcripts, diplomas, etc., will become an important reference for admitting new schools or new works. As schools make various awards or diplomas, only the names of the schools and the students are input. Due to the lack of effective anti-forge mechanism, events that cause the graduation certificate to be forged often get noticed. In order to solve the problem of counterfeiting certificates, the digital certificate system based on blockchain technology would be proposed. By the unmodifiable property of blockchain, the digital certificate with anti-counterfeit and verifiability could be made. The procedure of issuing the digital certificate in this system is as follows. First, generate the electronic file of a paper certificate accompanying other related data into the database, meanwhile calculate the electronic file for its hash value. Finally, store the hash value into the block in the chain system. The system will create a related QR-code and inquiry string code to affix to the paper certificate. It will provide the demand unit to verify the authenticity of the paper certificate through mobile phone scanning or website inquiries. Through the unmodifiable properties of the blockchain, the system not only enhances the credibility of various paper-based certificates, but also electronically reduces the loss risks of various types of certificates.
A Hybrid Model Combining Convolutional Neural Network with XGBoost for Predicting Social Media Popularity

A hybrid model for social media popularity prediction is proposed by combining Convolutional Neural Network (CNN) with XGBoost. The CNN model is exploited to learn high-level representations from the social cues of the data. These high-level representations are used in XGBoost to predict the popularity of the social posts. We evaluate our approach on a real-world Social Media Prediction (SMP) dataset, which consists of 432K Flickr images. The experimental results show that the proposed approach is effective, achieving the following performance: Spearman's Rho: 0.7406, MSE: 2.7293, MAE: 1.2475.
Deep Mutual Learning

Model distillation is an effective and widely used technique to transfer knowledge from a teacher to a student network. The typical application is to transfer from a powerful large network or ensemble to a small network, in order to meet the low-memory or fast execution requirements. In this paper, we present a deep mutual learning (DML) strategy. Different from the one-way transfer between a static pre-defined teacher and a student in model distillation, with DML, an ensemble of students learn collaboratively and teach each other throughout the training process. Our experiments show that a variety of network architectures benefit from mutual learning and achieve compelling results on both category and instance recognition tasks. Surprisingly, it is revealed that no prior powerful teacher network is necessary - mutual learning of a collection of simple student networks works, and moreover outperforms distillation from a more powerful yet static teacher.
THERMOMETER ENCODING: ONE HOT WAY TO RESIST

It is well known that it is possible to construct “adversarial examples” for neural networks: inputs which are misclassified by the network yet indistinguishable from true data. We propose a simple modification to standard neural network architectures, thermometer encoding, which significantly increases the robustness of the network to adversarial examples. We demonstrate this robustness with experiments on the MNIST, CIFAR-10, CIFAR-100, and SVHN datasets, and show that models with thermometer-encoded inputs consistently have higher accuracy on adversarial examples, without decreasing generalization. State-of-the-art accuracy under the strongest known white-box attack was increased from 93.20% to 94.30% on MNIST and 50.00% to 79.16% on CIFAR-10. We explore the properties of these networks, providing evidence that thermometer encodings help neural networks to find more-non-linear decision boundaries.
A Cross Sectional Study on Procrastination: Who Procrastinate More?

In the present research was to investigate the levels and the prevalence of academic procrastination on high school, undergraduate and graduate students. In this respect, Procrastination Assessment Scale-Student (PASS) was administered to a total of 448 students who were 149 (83 female; 66 male) high-school, 150 (80 female; 70 male) undergraduate and 148 (84 female; 64 male) graduate students. The average age was 15.5 years old (SD = .56) for High-school, 20.4 years old (SD = 1.71) for Undergraduate, and 25.5 years old (SD = 2.32) for Graduate students. Results showed a significant difference among the academic levels of the students. Specifically, undergraduate students claimed to procrastinate more than graduate and high school students. High school and undergraduate students claimed to be nearly always or always procrastinator on studying for exams, while graduate students procrastinate more on writing term papers.
The Characteristics of Cloud Computing

Cloud computing emerges as one of the hottest topic in field of information technology. Cloud computing is based on several other computing research areas such as HPC, virtualization, utility computing and grid computing. In order to make clear the essential of cloud computing, we propose the characteristics of this area which make cloud computing being cloud computing and distinguish it from other research areas. The cloud computing has its own conceptional, technical, economic and user experience characteristics. The service oriented, loose coupling, strong fault tolerant, business model and ease use are main characteristics of cloud computing. Clear insights into cloud computing will help the development and adoption of this evolving technology both for academe and industry.
Malicious URL Detection using Machine Learning: A Survey

Malicious URL, a.k.a. malicious website, is a common and serious threat to cybersecurity. Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. It is imperative to detect and act on such threats in a timely manner. Traditionally, this detection is done mostly through the usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have been explored with increasing attention in recent years. This article aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning. We present the formal formulation of Malicious URL Detection as a machine learning task, and categorize and review the contributions of literature studies that addresses different dimensions of this problem (feature representation, algorithm design, etc.). Further, this article provides a timely and comprehensive survey for a range of different audiences, not only for machine learning researchers and engineers in academia, but also for professionals and practitioners in cybersecurity industry, to help them understand the state of the art and facilitate their own research and practical applications. We also discuss practical issues in system design, open research challenges, and point out some important directions for future research.
Enhanced Network Embeddings via Exploiting Edge Labels

Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. While achieving competitive performance on a variety of network inference tasks such as node classification and link prediction, these methods treat the relations between nodes as a binary variable and ignore the rich semantics of edges. In this work, we attempt to learn network embeddings which simultaneously preserve network structure and relations between nodes. Experiments on several real-world networks illustrate that by considering different relations between different node pairs, our method is capable of producing node embeddings of higher quality than a number of state-of-the-art network embedding methods, as evaluated on a challenging multi-label node classification task.
Foundations of MRI phase imaging and processing for Quantitative Susceptibility Mapping (QSM).

Quantitative Susceptibility Mapping (QSM) is a novel MRI based technique that relies on estimates of the magnetic field distribution in the tissue under examination. Several sophisticated data processing steps are required to extract the magnetic field distribution from raw MRI phase measurements. The objective of this review article is to provide a general overview and to discuss several underlying assumptions and limitations of the pre-processing steps that need to be applied to MRI phase data before the final field-to-source inversion can be performed. Beginning with the fundamental relation between MRI signal and tissue magnetic susceptibility this review covers the reconstruction of magnetic field maps from multi-channel phase images, background field correction, and provides an overview of state of the art QSM solution strategies.
Gamification in E-Learning: Introducing Gamified Design Elements into E-Learning Systems

Daniel Strmečki Faculty of Organization and Informatics, Pavlinska 2, 42000 Varaždin, Croatia Email: danstrmecki@gmail.com  Gamification is the usage of game mechanics, dynamics, aesthetics and game thinking in non-game systems. Its main objective is to increase user’s motivation, experience and engagement. For the same reason, it has started to penetrate in e-learning systems. However, when using gamified design elements in e-learning, we must consider various types of learners. In the phases of analysis and design of such elements, the cooperation of education, technology, pedagogy, design and finance experts is required. This paper discusses the development phases of introducing gamification into e-learning systems, various gamification design elements and their suitability for usage in e-learning systems. Several gamified design elements are found suited for e-learning (including points, badges, trophies, customization, leader boards, levels, progress tracking, challenges, feedback, social engagement loops and the freedom to fail). Advices for the usage of each of those elements in e-learning systems are also provided in this study. Based on those advises and the identified phases of introducing gamification info e-learning systems, we conducted an experimental study to investigate the effectiveness of gamification of an informatics online course. Results showed that students enrolled in the gamified version of the online module achieved greater learning success. Positive results encourage us to investigate the gamification of online learning content for other topics and courses. We also encourage more research on the influence of specific gamified design elements on learner’s motivation and engagement.
Test methods for on-site corrosion rate measurement of steel reinforcement in concrete by means of the polarization resistance method

This Recommendation covers the description of nondestructive electrochemical test methods for the estimation in large size concrete structures of the instantaneous corrosion current density, icorr, expressed in A/cm, by means of the so-called Polarization Resistance technique, Rp, in order to assess the condition of embedded steel reinforcement related to its corrosion. The values of icorr, can be used to assess the rate of degradation of concrete structures affected by reinforcement corrosion. However, they cannot give information on the actual loss in steel cross section which, at present, only can be assessed by means of direct visual observation. Values of the free corrosion potential or half-cell potential, Ecorr [V], of the embedded reinforcing steel and of the electrical concrete resistance, Re [ ], are obtained as preliminary steps of the Rp measurements. Values of the concrete resistivity, [ m], can be calculated from Re values providing the geometrical arrangement of the electrodes enables this calculation. Both parameters, Ecorr and Re (or ) may be used to complement the reliability of the icorr measurements. 2.
Development of Low Profile Cavity Backed Crossed Slot Antennas for Planar Integration

Single fed low profile cavity backed crossed slot antennas for dual frequency dual linear polarization and circular polarization applications are first presented in this paper. By employing the substrate integrated waveguide (SIW) technique in the antenna designs, the low profile backed cavity structure can be realized by using only a single layer of low cost printed circuit board (PCB) substrate. A single grounded coplanar waveguide (GCPW) is employed as the feeding element to excite the TE 120 and TE 210 modes in the SIW cavity. A crossed slot structure is used as the radiating element in order to radiate the desired dual linearly or circularly polarized wave. From the measurement results, it is seen that these novel antennas retain the advantages of conventional metallic cavity backed antennas, including high gain, high front-to-back ratio (FTBR), and low cross polarization level (CPL). Furthermore, the proposed antennas also possess the advantages of low profile, light weight, low fabrication cost, and easy integration with planar circuits.
2007 DARPA Urban Challenge The

This paper describes the design, construction, and testing of an autonomous ground vehicle by the Ben Franklin Racing Team for the DARPA Urban Challenge. After analyzing the performance demands required of an autonomous vehicle traveling in an uncertain urban environment, we have designed our sensing, planning, navigation, and actuation systems to meet these demands. We have chosen an array of GPS/INS, LADAR’s, and stereo cameras to provide timely information about the surrounding environment at the appropriate ranges. This sensor information is incorporated into a probabilistic dynamic map that can handle GPS dropouts and errors. Our planning algorithms consist of a high-level mission planner that uses information from the provided RNDF and MDF to select routes, while the lower level planner uses the latest dynamic map information to optimize a feasible trajectory to the next waypoint. The vehicle is actuated by a cost-based controller that efficiently handles steering, throttle, and braking maneuvers in both forward and reverse directions. Our software modules are integrated within a hierarchical architecture that allows rapid development and testing of the system performance. Quantitative evaluations show that our vehicle and algorithms are able to meet the demanding requirements needed to successfully complete the Urban Challenge.