nils-herrmann/clean_scirepeval huggingface dataset:
Ride On ! Mobility Business Models for the Sharing Economy

The public perception of shared goods has changed substantially in the past few years. While co-owning properties has been widely accepted for a while (e.g., timeshares), the notion of sharing bikes, cars, or even rides on an on-demand basis is just now starting to gain widespread popularity. The emerging “sharing economy” is particularly interesting in the context of cities that struggle with population growth and increasing density. While sharing vehicles promises to reduce inner-city traffic, congestion, and pollution problems, the associated business models are not without problems themselves. Using agency theory, in this article we discuss existing shared mobility business models in an effort to unveil the optimal relationship between service providers (agents) and the local governments (principals) to achieve the common objective of sustainable mobility. Our findings show private or public models are fraught with conflicts, and point to a merit model as the most promising alignment of the strengths of agents and principals.
Relation Classification via Multi-Level Attention CNNs

Relation classification is a crucial ingredient in numerous information extraction systems seeking to mine structured facts from text. We propose a novel convolutional neural network architecture for this task, relying on two levels of attention in order to better discern patterns in heterogeneous contexts. This architecture enables endto-end learning from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures. Experiments show that our model outperforms previous state-of-the-art methods, including those relying on much richer forms of prior knowledge.
Labeling Complicated Objects: Multi-View Multi-Instance Multi-Label Learning

Multi-Instance Multi-Label (MIML) is a learning framework where an example is associated with multiple labels and represented by a set of feature vectors (multiple instances). In the formalization of MIML learning, instances come from a single source (single view). To leverage multiple information sources (multi-view), we develop a multi-view MIML framework based on hierarchical Bayesian Network, and derive an effective learning algorithm based on variational inference. The model can naturally deal with examples in which some views could be absent (partial examples). On multi-view datasets, it is shown that our method is better than other multi-view and single-view approaches particularly in the presence of partial examples. On single-view benchmarks, extensive evaluation shows that our method is highly competitive or better than other MIML approaches on labeling examples and instances. Moreover, our method can effectively handle datasets with a large number of labels.
How online trust influence B2C e-Commerce adoption? An empirical study among Asian online shoppers

Though many previous studies has proved the importance of trust from various perspectives, the researches about online consumer’s trust are fragmented in nature and still it need more attention from academics. Lack of consumers trust in online systems is a critical impediment to the success of e-Commerce. Therefore it is important to explore the critical factors that affect the formation of user’s trust in online environments. The main objective of this paper is to analyze the effects of various antecedents of online trust and to predict the user’s intention to engage in online transaction based on their trust in the Information systems. This study is conducted among Asian online consumers and later the results were compared with those from Non-Asian regions. Another objective of this paper is to integrate De Lone and McLean model of IS Success and Technology Acceptance Model (TAM) for measuring the significance of online trust in e-Commerce adoption. The results of this study show that perceived security, perceived privacy, vendor familiarity, system quality and service quality are the significant antecedents of online trust in a B2C e-Commerce context.
Neuroleptic malignant syndrome and atypical antipsychotic drugs.

The incidence of neuroleptic malignant syndrome (NMS) is not known, but the frequency of its occurrence with conventional antipsychotic agents has been reported to vary from 0.02% to 2.44%.



MEDLINE search conducted in January 2003 and review of references within the retrieved articles.



Our MEDLINE research yielded 68 cases (21 females and 47 males) of NMS associated with atypical antipsychotic drugs (clozapine, N = 21; risperidone, N = 23; olanzapine, N = 19; and quetiapine, N = 5). The fact that 21 cases of NMS with clozapine were found indicates that low occurrence of extrapyramidal symptoms (EPS) and low EPS-inducing potential do not prevent the occurrence of NMS and D(2) dopamine receptor blocking potential does not have direct correlation with the occurrence of NMS. One of the cardinal features of NMS is an increasing manifestation of EPS, and the conventional antipsychotic drugs are known to produce EPS in 95% or more of NMS cases. With atypical antipsychotic drugs, the incidence of EPS during NMS is of a similar magnitude.



For NMS associated with atypical antipsychotic drugs, the mortality rate was lower than that with conventional antipsychotic drugs. However, the mortality rate may simply be a reflection of physicians' awareness and ensuing early treatment.
Flexible Combinatory Categorial Grammar Parsing Using the CYK Algorithm and Answer Set Programming

Combinatory Categorial Grammar (CCG) is a grammar formalism used for natural language parsing. CCG assigns structured lexical categories to words and uses a small set of combinatory rules to combine these categories in order to parse sentences. In this work we describe and implement a new approach to CCG parsing that relies on Answer Set Programming (ASP) — a declarative programming paradigm. Different from previous work, we present an encoding that is inspired by the algorithm due to Cocke, Younger, and Kasami (CYK). We also show encoding extensions for parse tree normalization and best-effort parsing and outline possible future extensions which are possible due to the usage of ASP as computational mechanism. We analyze performance of our approach on a part of the Brown corpus and discuss lessons learned during experiments with the ASP tools dlv, gringo, and clasp. The new approach is available in the open source CCG parsing toolkit AspCcgTk which uses the C&C supertagger as a preprocessor to achieve wide-coverage natural language parsing.
Image Selection in Photo Albums

The selection of the best photos in personal albums is a task that is often faced by photographers. This task can become laborious when the photo collection is large and it contains multiple similar photos. Recent advances on image aesthetics and photo importance evaluation has led to the creation of different metrics for automatically assessing a given image. However, these metrics are intended for the independent assessment of an image, without considering the possible context implicitly present within photo albums. In this work, we perform a user study for assessing how users select photos when provided with a complete photo album---a task that better reflects how users may review their personal photos and collections. Using the data provided by our study, we evaluate how existing state-of-the-art photo assessment methods perform relative to user selection, focusing in particular on deep learning based approaches. Finally, we explore a recent framework for adapting independent image scores to collections and evaluate in which scenarios such an adaptation can prove beneficial.
Applications of Deep Learning to Sentiment Analysis of Movie Reviews

Sentiment analysis is one of the main challenges in natural language processing. Recently, deep learning applications have shown impressive results across different NLP tasks. In this work, I explore performance of different deep learning architectures for semantic analysis of movie reviews, using Stanford Sentiment Treebank as the main dataset. Recurrent, Recursive, and Convolutional neural networks are implemented on the dataset and the results are compared to a baseline Naive Bayes classifier. Finally the errors are analyzed and compared. This work can act as a survey on applications of deep learning to semantic analysis.
Matlab-Based Intelligent Voiceprint Recognition System

In this paper, a complete voiceprint recognition based on Matlab was realized, including speech processing and feature extraction at early stage, and model training and recognition at later stage. For speech processing and feature extraction at early stage, Mel Frequency Cepstrum Coefficient (MFCC) was taken as feature parameter. For speaker model method, DTW model was adopted to reflect the voiceprint characteristics of speech, converting voiceprint recognition into speaker speech data evaluation, and breaking up complex speech training and matching into model parameter training and probability calculation. Simulation experiment results show that this system is effective to recognize voiceprint.
Bridging Languages through Etymology: The case of cross language text categorization

We propose the hypothesis that word etymology is useful for NLP applications as a bridge between languages. We support this hypothesis with experiments in crosslanguage (English-Italian) document categorization. In a straightforward bag-ofwords experimental set-up we add etymological ancestors of the words in the documents, and investigate the performance of a model built on English data, on Italian test data (and viceversa). The results show not only statistically significant, but a large improvement – a jump of almost 40 points in F1-score – over the raw (vanilla bag-ofwords) representation.
LinkBench: a database benchmark based on the Facebook social graph

Database benchmarks are an important tool for database researchers and practitioners that ease the process of making informed comparisons between different database hardware, software and configurations. Large scale web services such as social networks are a major and growing database application area, but currently there are few benchmarks that accurately model web service workloads.
 In this paper we present a new synthetic benchmark called LinkBench. LinkBench is based on traces from production databases that store "social graph" data at Facebook, a major social network. We characterize the data and query workload in many dimensions, and use the insights gained to construct a realistic synthetic benchmark. LinkBench provides a realistic and challenging test for persistent storage of social and web service data, filling a gap in the available tools for researchers, developers and administrators.
Developmental robotics: a survey

Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions. 1.  Developmental robotics is an emergent area of research at the intersection of robotics and developmental sciences—in particular developmental psychology and developmental neuroscience. It constitutes an interdisciplinary and two-pronged approach to robotics, which on one side employs robots to instantiate and investigate models originating from developmental sciences, and on the other side seeks to design better robotic systems by applying insights gained from studies on ontogenetic development.
Artificial Intelligence: What Works and What Doesn't?

research and development dollars for decades now, and people are beginning to ask hard questions: What really works? What are the limits? What doesn’t work as advertised? What isn’t likely to work? What isn’t affordable? This article holds a mirror up to the community, both to provide feedback and stimulate more selfassessment. The significant accomplishments and strengths of the field are highlighted. The research agenda, strategy, and heuristics are reviewed, and a change of course is recommended to improve the field’s ability to produce reusable and interoperable components.
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We also co-design a training procedure to preserve end-to-end model accuracy post quantization. As a result, the proposed quantization scheme improves the tradeoff between accuracy and on-device latency. The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.
Tensor completion using total variation and low-rank matrix factorization

In this paper, we study the problem of recovering a tensor with missing data. We propose a new model combining the total variation regularization and low-rank matrix factorization. A block coordinate decent (BCD) algorithm is developed to efficiently solve the proposed optimization model. We theoretically show that under some mild conditions, the algorithm converges to the coordinatewise minimizers. Experimental results are reported to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme.
Learning rotation invariant convolutional filters for texture classification

We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN). We encode rotation invariance directly in the model by tying the weights of groups of filters to several rotated versions of the canonical filter in the group. These filters can be used to extract rotation invariant features well-suited for image classification. We test this learning procedure on a texture classification benchmark, where the orientations of the training images differ from those of the test images. We obtain results comparable to the state-of-the-art. Compared to standard shallow CNNs, the proposed method obtains higher classification performance while reducing by an order of magnitude the number of parameters to be learned.
On the Wisdom of Experts vs. Crowds: Discovering Trustworthy Topical News in Microblogs

Extracting news on specific topics from the Twitter microblogging site poses formidable challenges, which include handling millions of tweets posted daily, judging topicality and importance of tweets, and ensuring trustworthiness of results in the face of spam. To date, all scalable approaches have relied on crowd wisdom, i.e., keyword-matching on the global tweet stream to gather relevant tweets, and crowd- endorsements to judge the importance of tweets. We propose a fundamentally different methodology -- for a given topic, we identify trustworthy experts on the topic, and extract news-stories that are most popular among the experts. Comparing the crowd-based and expert-based methodologies, we demonstrate that the news-stories obtained by our methodology (i) have higher relevance for a wide variety of topics, (ii) achieve very high coverage of important news-stories posted globally in Twitter, and (iii) are far more trustworthy. Using our methodology, we implemented and publicly deployed a topical news system for Twitter, which can extract news-stories on thousands of topics.
Gender differences and psychological factors associated with suicidal ideation among youth in Malaysia

Suicide is a global phenomenon that has been showing an upward trend in recent years. It is the second leading cause of death among youth. Studies on suicidal ideation warrant greater attention, as it leads to suicide attempts and other health risk behaviors. Thus, the objective of this study was to compare gender differences in suicidal ideation and determine the predictors of suicidal ideation among youth. This cross-sectional study was carried out among 232 youths aged between 15 and 25 years from selected urban areas in Malaysia. The results showed that suicidal ideation was higher among male participants compared with female participants. Age was the predictor of suicidal ideation for males, while depression and loss of motivation, as components of hopelessness, were the predictors of suicidal ideation among females. Hence, it is important that professionals conduct early identification tests for suicidality among young people. This will facilitate the early detection of depression and hopelessness, which is important, in order to prevent suicidal behaviors or other problems before these occur.
Efficient bipedal robots based on passive-dynamic walkers.

Passive-dynamic walkers are simple mechanical devices, composed of solid parts connected by joints, that walk stably down a slope. They have no motors or controllers, yet can have remarkably humanlike motions. This suggests that these machines are useful models of human locomotion; however, they cannot walk on level ground. Here we present three robots based on passive-dynamics, with small active power sources substituted for gravity, which can walk on level ground. These robots use less control and less energy than other powered robots, yet walk more naturally, further suggesting the importance of passive-dynamics in human locomotion.
Deep Learning for Hate Speech Detection in Tweets

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ∼18 F1 points.
Anomaly-based network intrusion detection : Techniques , systems and challenges

The Internet and computer networks are exposed to an increasing number of security threats. With new types of attacks appearing continually, developing flexible and adaptive security oriented approaches is a severe challenge. In this context, anomaly-based network intrusion detection techniques are a valuable technology to protect target systems and networks against malicious activities. However, despite the variety of such methods described in the literature in recent years, security tools incorporating anomaly detection functionalities are just starting to appear, and several important problems remain to be solved. This paper begins with a review of the most well-known anomaly-based intrusion detection techniques. Then, available platforms, systems under development and research projects in the area are presented. Finally, we outline the main challenges to be dealt with for the wide scale deployment of anomaly-based intrusion detectors, with special emphasis on assessment issues. a 2008 Elsevier Ltd. All rights reserved.
An Energy Blockchain, a Use Case on Tendermint

The recent advances in distributed energy systems require new models for exchanging energy among prosumers in microgrids. The blockchain technology promises to solve the digital issues related to distributed systems without a trusted authority and to allow quick and secure energy transactions, which are verified and cryptographically protected. Transactions are approved and subsequently recorded on all the machines participating in the blockchain. This work demonstrates how users, which are nodes of the energy and digital networks, exchange energy supported by a customized blockchain based on Tendermint. We focus on the procedures for generating blocks and defining data structures for storing energy transactions.
Provenance-Aware Query Optimization

Data provenance is essential for debugging query results, auditing data in cloud environments, and explaining outputs of Big Data analytics. A well-established technique is to represent provenance as annotations on data and to instrument queries to propagate these annotations to produce results annotated with provenance. However, even sophisticated optimizers are often incapable of producing efficient execution plans for instrumented queries, because of their inherent complexity and unusual structure. Thus, while instrumentation enables provenance support for databases without requiring any modification to the DBMS, the performance of this approach is far from optimal. In this work, we develop provenancespecific optimizations to address this problem. Specifically, we introduce algebraic equivalences targeted at instrumented queries and discuss alternative, equivalent ways of instrumenting a query for provenance capture. Furthermore, we present an extensible heuristic and cost-based optimization (CBO) framework that governs the application of these optimizations and implement this framework in our GProM provenance system. Our CBO is agnostic to the plan space shape, uses a DBMS for cost estimation, and enables retrofitting of optimization choices into existing code by adding a few LOC. Our experiments confirm that these optimizations are highly effective, often improving performance by several orders of magnitude for diverse provenance tasks.
Bit Adjusting Image Steganography in Blue Channel using AES and Secured Hash Function

Transmitting secret information through internet requires more security because of interception and improper manipulation by eavesdropper. One of the most desirable explications of this is “Steganography”. This paper proposes a technique of steganography using Advanced Encryption Standard (AES) with secured hash function in the blue channel of image. The embedding system is done by dynamic bit adjusting system in blue channel of RGB images. It embeds message bits to deeper into the image intensity which is very difficult for any type improper manipulation of hackers. Before embedding text is encrypted using AES with a hash function. For extraction the cipher text bit is found from image intensity using the bit adjusting extraction algorithm and then it is decrypted by AES with same hash function to get the real secret text. The proposed approach is better in Pick Signal to Noise Ratio (PSNR) value and less in histogram error between stego images and cover images than some existing systems. KeywordsAES-128, SHA-512, Cover Image, Stego image, Bit Adjusting, Blue Channel
Chemical methods for the production of graphenes.

Interest in graphene centres on its excellent mechanical, electrical, thermal and optical properties, its very high specific surface area, and our ability to influence these properties through chemical functionalization. There are a number of methods for generating graphene and chemically modified graphene from graphite and derivatives of graphite, each with different advantages and disadvantages. Here we review the use of colloidal suspensions to produce new materials composed of graphene and chemically modified graphene. This approach is both versatile and scalable, and is adaptable to a wide variety of applications.