nils-herrmann/clean_scirepeval huggingface dataset:
Individual Differences in the Benefits of Feedback for Learning

Research on learning from feedback has produced ambiguous guidelines for feedback design--some have advocated minimal feedback, whereas others have recommended more extensive feedback that highly supported performance. The objective of the current study was to investigate how individual differences in cognitive resources may predict feedback requirements and resolve previous conflicted findings.



Cognitive resources were controlled for by comparing samples from populations with known differences, older and younger adults.To control for task demands, a simple rule-based learning task was created in which participants learned to identify fake Windows pop-ups. Pop-ups were divided into two categories--those that required fluid ability to identify and those that could be identified using crystallized intelligence.



In general, results showed participants given higher feedback learned more. However, when analyzed by type of task demand, younger adults performed comparably with both levels of feedback for both cues whereas older adults benefited from increased feedbackfor fluid ability cues but from decreased feedback for crystallized ability cues.



One explanation for the current findings is feedback requirements are connected to the cognitive abilities of the learner-those with higher abilities for the type of demands imposed by the task are likely to benefit from reduced feedback.



We suggest the following considerations for feedback design: Incorporate learner characteristics and task demands when designing learning support via feedback.
A Comprehensive Review of Semiconductor Ultraviolet Photodetectors: From Thin Film to One-Dimensional Nanostructures

Ultraviolet (UV) photodetectors have drawn extensive attention owing to their applications in industrial, environmental and even biological fields. Compared to UV-enhanced Si photodetectors, a new generation of wide bandgap semiconductors, such as (Al, In) GaN, diamond, and SiC, have the advantages of high responsivity, high thermal stability, robust radiation hardness and high response speed. On the other hand, one-dimensional (1D) nanostructure semiconductors with a wide bandgap, such as β-Ga2O3, GaN, ZnO, or other metal-oxide nanostructures, also show their potential for high-efficiency UV photodetection. In some cases such as flame detection, high-temperature thermally stable detectors with high performance are required. This article provides a comprehensive review on the state-of-the-art research activities in the UV photodetection field, including not only semiconductor thin films, but also 1D nanostructured materials, which are attracting more and more attention in the detection field. A special focus is given on the thermal stability of the developed devices, which is one of the key characteristics for the real applications.
Approach for Information System Maturity Assessment

Nowadays, many organizations face the issue of information and communication technology (ICT) management. The organization undertakes various activities to assess the state of their ICT or the entire information system (IS), such as IS audits, reviews, IS due diligence, or they already have implemented systems to gather information regarding their IS. For the organizations’ boards and managers there is an issue how to evaluate current IS maturity level and based on assessments, define the IS strategy how to reach the maturity target level. The problem is, what kind of approach to use for rapid and effective IS maturity assessment. This paper summarizes the research regarding IS maturity within different organizations where the authors have delivered either complete IS due diligence or made partial analysis by IS Mirror method. The main objective of this research is to present and confirm the approach which could be used for effective IS maturity assessment and could be provided quickly and even remotely. The paper presented research question, related hypothesis, and approach for rapid IS maturity assessment, and results from several case studies made on-site or remotely.
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives

An important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions. This allows humans to adapt easily to novel situations where AI fails catastrophically due to a lack of situation-specific rules and generalization capabilities. Commonsense knowledge also provides background information that enables humans to successfully operate in social situations where such knowledge is typically assumed. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. Previous versions of SenticNet were focused on collecting this kind of knowledge for sentiment analysis but they were heavily limited by their inability to generalize. SenticNet 4 overcomes such limitations by leveraging on conceptual primitives automatically generated by means of hierarchical clustering and dimensionality reduction.
Emotion recognition in the wild challenge 2016

The fourth Emotion Recognition in the Wild (EmotiW) challenge is a grand challenge in the ACM International Conference on Multimodal Interaction 2016, Tokyo. EmotiW is a series of benchmarking and competition effort for researchers working in the area of automatic emotion recognition in the wild. The fourth EmotiW has two sub-challenges: Video based emotion recognition (VReco) and Group-level emotion recognition (GReco). The VReco sub-challenge is being run for the fourth time and GReco is a new sub-challenge this year.
Reduced hippocampal volumes and memory loss in patients with early- and late-onset depression.

Hippocampal volume reduction has been reported inconsistently in people with major depression.



To evaluate the interrelationships between hippocampal volumes, memory and key clinical, vascular and genetic risk factors.



Totals of 66 people with depression and 20 control participants underwent magnetic resonance imaging and clinical assessment. Measures of depression severity, psychomotor retardation, verbal and visual memory and vascular and specific genetic risk factors were collected.



Reduced hippocampal volumes occurred in older people with depression, those with both early-onset and late-onset disorders and those with the melancholic subtype. Reduced hippocampal volumes were associated with deficits in visual and verbal memory performance.



Although reduced hippocampal volumes are most pronounced in late-onset depression, older people with early-onset disorders also display volume changes and memory loss. No clear vascular or genetic risk factors explain these findings. Hippocampal volume changes may explain how depression emerges as a risk factor to dementia.
Machinima production tools: a vernacular history of a creative medium

In recent years, HCI has shown a rising interest in the creative practices associated with massive online communities, including crafters, hackers, DIY, and other expert amateurs. One strategy for researching creativity at this scale is through an analysis of a community's outputs, including its creative works, custom created tools, and emergent practices. In this paper, we offer one such case study, a historical account of World of Warcraft (WoW) machinima (i.e., videos produced inside of video games), which shows how the aesthetic needs and requirements of video making community coevolved with the community-made creativity support tools in use at the time. We view this process as inhabiting different layers and practices of appropriation, and through an analysis of them, we trace the ways that support for emerging stylistic conventions become built into creativity support tools over time.
Chains in Chains - Logic and Challenges of Blockchains in Supply Chains

Due to the disruptive role of the Bitcoin in the financial sector, both scholars and practitioners are increasingly wondering whether it is possible to replicate the impact of the Blockchain technology in the supply chain context. As a distributed ledger technology characterized by the decentralized consensus, Blockchain is touted by many as the proper platform to collect all the information about supply chains from the producer to the consumer. However, the current technology immaturity and the lack of successful supply chain implementations pave the way for doubt about the disruptive role of this technology in supply chains. To the authors’ knowledge, this work is one of the very first attempts to link the blockchain technology to supply chain and logistics. This paper investigates the state-of-the-art application of blockchain in supply chains, exploring both the literature and the industry initiatives, contributing to the increase of the managerial insight and providing a future research agenda.
Exploiting Sentence Similarities for Better Alignments

We study the problem of jointly aligning sentence constituents and predicting their similarities. While extensive sentence similarity data exists, manually generating reference alignments and labeling the similarities of the aligned chunks is comparatively onerous. This prompts the natural question of whether we can exploit easy-to-create sentence level data to train better aligners. In this paper, we present a model that learns to jointly align constituents of two sentences and also predict their similarities. By taking advantage of both sentence and constituent level data, we show that our model achieves state-of-the-art performance at predicting alignments and constituent similarities.
Data Mining techniques for the detection of fraudulent financial statements

This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. In accomplishing the task of management fraud detection, auditors could be facilitated in their work by using Data Mining techniques. This study investigates the usefulness of Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. The input vector is composed of ratios derived from financial statements. The three models are compared in terms of their performances.
DeepTraffic: Driving Fast through Dense Traffic with Deep Reinforcement Learning

We present a micro-traffic simulation (named “DeepTraffic”) where the perception, control, and planning systems for one of the cars are all handled by a single neural network as part of a model-free, off-policy reinforcement learning process. The primary goal of DeepTraffic is to make the hands-on study of deep reinforcement learning accessible to thousands of students, educators, and researchers in order to inspire and fuel the exploration and evaluation of DQN variants and hyperparameter configurations through large-scale, open competition. This paper investigates the crowd-sourced hyperparameter tuning of the policy network that resulted from the first iteration of the DeepTraffic competition where thousands of participants actively searched through the hyperparameter space with the objective of their neural network submission to make it onto the top-10 leaderboard.
Applications of Artificial Neural Networks in Financial Economics: A Survey

This paper is a survey on the application of artificial neural networks in forecasting financial market prices. The objective of this paper is to appraise the potential of using artificial neural networks to predict the financial system, as it is reflected in many relevant articles. It will provide some guidelines and references for the research and implementation. This paper begins with an introduction to the theory of artificial neural networks. Subsequently it focuses on the forecast of stock prices and option pricing based on a non-linear ANN model. It proceeded with a presentation of the application of ANN in predicting exchange rates. The paper then reviewed the theoretical literature on the prediction of banking and financial crisis based on artificial neural networks. In general artificial neural network is a valuable forecast tool in financial economics due to the learning, generalization and nonlinear behavior properties. Finally it identifies a number of important opportunities for future research on the application of neural networks in financial economics.
ESPRESSO: Explaining Relationships between Entity Sets

Analyzing and explaining relationships between entities in a knowledge graph is a fundamental problem with many applications. Prior work has been limited to extracting the most informative subgraph connecting two entities of interest. This paper extends and generalizes the state of the art by considering the relationships between two sets of entities given at query time. Our method, coined ESPRESSO, explains the connection between these sets in terms of a small number of relatedness cores: dense sub-graphs that have strong relations with both query sets. The intuition for this model is that the cores correspond to key events in which entities from both sets play a major role. For example, to explain the relationships between US politicians and European politicians, our method identifies events like the PRISM scandal and the Syrian Civil War as relatedness cores. Computing cores of bounded size is NP-hard. This paper presents efficient approximation algorithms. Our experiments with real-life knowledge graphs demonstrate the practical viability of our approach and, through user studies, the superior output quality compared to state-of-the-art baselines.
A Modified RSA Encryption Technique Basedon Multiple public keys

In the today‟s world, security is required to transmit confidential information over the network. Security is also demanding in wide range of applications. Cryptographic algorithms play a vital role in providing the data security against malicious attacks.RSA algorithm is extensively used in the popular implementations of Public Key Infrastructures. In asymmetric key cryptography, also called Public Key cryptography, two different keys (which form a key pair) are used. One key is used for encryption & only the other corresponding key must be used for decryption. No other key can decrypt the message – not even the original (i.e. the first) key used for encryption. The beauty of this scheme is that every communicating party needs just a key pair for communicating with any number of other communicating parties. Once someone obtains a key pair, he /she can communicate with anyone else. In this paper, we have done an efficient implementation of RSA algorithm using two public key pairs and using some mathematical logic rather than sending the e value directly as a public key.Because if an attacker has opportunity of getting the e value they can directly find d value and decrypt the message.
Online Detection of Fire in Video

This paper describes an online learning based method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. Our fire detection method consists of weak classifiers based on temporal and spatial modeling of flames. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish temporal flame flicker process from motion of flame colored moving objects. Boundary of flames are represented in wavelet domain and high frequency nature of the boundaries of fire regions is also used as a clue to model the flame flicker spatially. Results from temporal and spatial weak classifiers based on flame flicker and irregularity of the flame region boundaries are updated online to reach a final decision. False alarms due to ordinary and periodic motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.
Matching user accounts based on user generated content across social networks

Matching user accounts can help us build better users’ profiles and benefit many applications. It has attracted much attention from both industry and academia. Most of existing works are mainly based on rich user profile attributes. However, in many cases, user profile attributes are unavailable, incomplete or unreliable, either due to the privacy settings or just because users decline to share their information. This makes the existing schemes quite fragile. Users often share their activities on different social networks. This provides an opportunity to overcome the above problem. We aim to address the problem of user identification based on User Generated Content (UGC). We first formulate the problem of user identification based on UGCs and then propose a UGC-based user identification model. A supervised machine learning based solution is presented. It has three steps: firstly, we propose several algorithms to measure the spatial similarity, temporal similarity and content similarity of two UGCs; secondly, we extract the spatial, temporal and content features to exploit these similarities; afterwards, we employ the machine learning method to match user accounts, and conduct the experiments on three ground truth datasets. The results show that the proposed method has given excellent performance with F1 values reaching 89.79%, 86.78% and 86.24% on three ground truth datasets, respectively. This work presents the possibility of matching user accounts with high accessible online data.
Augmented Reality Audio for Mobile and Wearable Appliances *

The concept of augmented reality audio characterizes techniques where a real sound environment is extended with virtual auditory environments and communications scenarios. A framework is introduced for mobile augmented reality audio (MARA) based on a specific headset configuration where binaural microphone elements are integrated into stereo earphones. When microphone signals are routed directly to the earphones, a user is exposed to a pseudoacoustic representation of the real environment. Virtual sound events are then mixed with microphone signals to produce a hybrid, an augmented reality audio representation, for the user. An overview of related technology, literature, and application scenarios is provided. Listening test results with a prototype system show that the proposed system has interesting properties. For example, in some cases listeners found it very difficult to determine which sound sources in an augmented reality audio representation are real and which are virtual.
Wiki-ly Supervised Part-of-Speech Tagging

Despite significant recent work, purely unsupervised techniques for part-of-speech (POS) tagging have not achieved useful accuracies required by many language processing tasks. Use of parallel text between resource-rich and resource-poor languages is one source of weak supervision that significantly improves accuracy. However, parallel text is not always available and techniques for using it require multiple complex algorithmic steps. In this paper we show that we can build POS-taggers exceeding state-of-the-art bilingual methods by using simple hidden Markov models and a freely available and naturally growing resource, the Wiktionary. Across eight languages for which we have labeled data to evaluate results, we achieve accuracy that significantly exceeds best unsupervised and parallel text methods. We achieve highest accuracy reported for several languages and show that our approach yields better out-of-domain taggers than those trained using fully supervised Penn Treebank.
Data Storage Security and Privacy in Cloud Computing : A Comprehensive Survey

Cloud Computing is a form of distributed computing wherein resources and application platforms are distributed over the Internet through on demand and pay on utilization basis. Data Storage is main feature that cloud data centres are provided to the companies/organizations to preserve huge data. But still few organizations are not ready to use cloud technology due to lack of security. This paper describes the different techniques along with few security challenges, advantages and also disadvantages. It also provides the analysis of data security issues and privacy protection affairs related to cloud computing by preventing data access from unauthorized users, managing sensitive data, providing accuracy and consistency of data stored.
Lipedema: a frequently misdiagnosed and misunderstood fatty deposition syndrome.

To enhance the learner's competence in caring for patients with lipedema through understanding the differential diagnoses, pathophysiology, and treatment/management options.



This continuing education activity is intended for physicians and nurses with an interest in skin and wound care.



After participating in this educational activity, the participant should be better able to: 1. Differentiate lipedema from other similar diagnoses. 2. Tell patients with lipedema and their caregivers about treatment of this condition. 3. Construct assessments, treatment plans, and management options for patients with lipedema.
Fractal Physiology and the Fractional Calculus: A Perspective

This paper presents a restricted overview of Fractal Physiology focusing on the complexity of the human body and the characterization of that complexity through fractal measures and their dynamics, with fractal dynamics being described by the fractional calculus. Not only are anatomical structures (Grizzi and Chiriva-Internati, 2005), such as the convoluted surface of the brain, the lining of the bowel, neural networks and placenta, fractal, but the output of dynamical physiologic networks are fractal as well (Bassingthwaighte et al., 1994). The time series for the inter-beat intervals of the heart, inter-breath intervals and inter-stride intervals have all been shown to be fractal and/or multifractal statistical phenomena. Consequently, the fractal dimension turns out to be a significantly better indicator of organismic functions in health and disease than the traditional average measures, such as heart rate, breathing rate, and stride rate. The observation that human physiology is primarily fractal was first made in the 1980s, based on the analysis of a limited number of datasets. We review some of these phenomena herein by applying an allometric aggregation approach to the processing of physiologic time series. This straight forward method establishes the scaling behavior of complex physiologic networks and some dynamic models capable of generating such scaling are reviewed. These models include simple and fractional random walks, which describe how the scaling of correlation functions and probability densities are related to time series data. Subsequently, it is suggested that a proper methodology for describing the dynamics of fractal time series may well be the fractional calculus, either through the fractional Langevin equation or the fractional diffusion equation. A fractional operator (derivative or integral) acting on a fractal function, yields another fractal function, allowing us to construct a fractional Langevin equation to describe the evolution of a fractal statistical process. Control of physiologic complexity is one of the goals of medicine, in particular, understanding and controlling physiological networks in order to ensure their proper operation. We emphasize the difference between homeostatic and allometric control mechanisms. Homeostatic control has a negative feedback character, which is both local and rapid. Allometric control, on the other hand, is a relatively new concept that takes into account long-time memory, correlations that are inverse power law in time, as well as long-range interactions in complex phenomena as manifest by inverse power-law distributions in the network variable. We hypothesize that allometric control maintains the fractal character of erratic physiologic time series to enhance the robustness of physiological networks. Moreover, allometric control can often be described using the fractional calculus to capture the dynamics of complex physiologic networks.