nils-herrmann/clean_scirepeval huggingface dataset:
SoK: Uncentralisable Ledgers and their Impact on Voting Systems

As we observe a trend towards the recentralisation of the Internet, this paper raises the question of guaranteeing an everlasting decentralisation. We introduce the properties of strong and soft uncentralisability in order to describe systems in which all authorities can be untrusted at any time without affecting the system. We link the soft uncentralisability to another property called perfect forkability. Using that knowledge, we introduce a new cryptographic primitive called uncentralisable ledger and study its properties. We use those properties to analyse what an uncentralisable ledger may offer to classic electronic voting systems and how it opens up the realm of possibilities for completely new voting mechanisms. We review a list of selected projects that implement voting systems using blockchain technology. We then conclude that the true revolutionary feature enabled by uncentralisable ledgers is a self-sovereign and distributed identity provider.
Semantically Far Inspirations Considered Harmful?: Accounting for Cognitive States in Collaborative Ideation

Collaborative ideation systems can help people generate more creative ideas by exposing them to ideas different from their own. However, there are competing theoretical views on whether and when such exposure is helpful. Associationist theory suggests that exposing ideators to ideas that are semantically far from their own maximizes novel combinations of ideas. In contrast, SIAM theory cautions that systems should offer far ideas only when ideators reach an impasse (a cognitive state in which they have exhausted ideas within a particular category), and offer near ideas during productive ideation (a cognitive state in which they are actively exploring ideas within a category), which maximizes exploration within categories. Our research compares these theoretical recommendations. In an online experiment, 245 participants generated ideas for a themed wedding; we detected and validated participants' cognitive states using a combination of behavioral and neuroimaging data. Receiving far ideas during productive ideation resulted in slower ideation and less within-category exploration, without significant benefits for novelty, compared to receiving no inspirations. Participants were also more likely to hit an impasse when receiving far ideas during productive ideation. These findings suggest that far inspirational ideas can harm creativity if received during productive ideation.
Hardware based design and implementation of a bottle recycling machine using FPGA

Conventional reverse vending machines use complex image processing technology to detect the bottles which make it more expensive. In this paper the design of a Smart Bottle Recycle Machine (SBRM) is presented. It is designed on a Field Programmable Gate Array (FPGA) using an ultrasonic range sensor which is readily available at a low cost. The sensor was used to calculate the number of bottles and distinguish between them. The main objective of this project is to build a SBRM at a cheaper production cost. This project was implemented on Altera DE2-115 board using Verilog HDL. This prototype enables the user to recycle plastic bottles and receive reward points. FPGA was chosen because hardware based implementation on a FPGA is usually much faster than the software based implementation on a microcontroller. The former is also capable of executing concurrent parallel processes at a high speed where the latter can only do a limited amount of parallel execution. So, overall FPGAs are more efficient than the microcontrollers for development of reliable and real time applications. The developed project is environment friendly and cost effective.
Optimal Prediction of Burgers's Equation

We examine an application of the optimal prediction framework to the truncated Fourier–Galerkin approximation of Burgers’s equation. Under particular conditions on the density of the modes and the length of the memory kernel, optimal prediction introduces an additional term to the Fourier–Galerkin approximation which represents the influence of an arbitrary number of small wavelength unresolved modes on the long wavelength resolved modes. The modified system, called the t-model by previous authors, takes the form of a time-dependent cubic term added to the original quadratic system. Numerical experiments show that this additional term restores qualitative features of the solution in the case where the number of modes is insufficient to resolve the resulting shocks (i.e., zero or very small viscosity) and for which the original Fourier–Galerkin approximation is very poor. In particular, numerical examples are shown in which the kinetic energy decays in the same manner as in the exact solution, i.e., as t−2 when tshock t Re, even when a very small number of resolved modes is used. Correlation-like quantities related to the memory kernel are then computed, and these exhibit a t−3 tail for the same time period.
An Efficient Fraud Identification Method Combining Manifold Learning and Outliers Detection in Mobile Healthcare Services

With the development of mobile Internet, various mobile applications have become increasingly popular. Many people are being benefited from the mobile healthcare services. Compared with the traditional healthcare services, patients’ medical behavior trajectories can be recorded by mobile healthcare services meticulously. They monitor the entire healthcare services process and help to improve the quality and standardization of healthcare services. By tracking and analyzing the patients’ medical records, they provide real-time protection for the patients’ healthcare activities. Therefore, medical fraud can be avoided and the loss of public health funds can be reduced. Although mobile healthcare services can provide a large amount of timely data, an effective real-time online algorithm is needed due to the timeliness of detecting the medical insurance fraud claims. However, because of the complex granularity of medical data, existing fraud detection approaches tend to be less effective in terms of monitoring the healthcare services process. In this paper, we propose an approach to deal with these problems. By means of the proposed SSIsomap activity clustering method, SimLOF outlier detection method, and the Dempster–Shafer theory-based evidence aggregation method, our approach is able to detect unusual categories and frequencies of behaviors simultaneously. Our approach is applied to a real-world data set containing more than 40 million medical insurance claim activities from over 40 000 users. Compared with two state-of-the-art approaches, the extensive experimental results show that our approach is significantly more effective and efficient. Our approach agent which provides decision support for the approval sender during the medical insurance claim approval process is undergoing trial in mobile healthcare services.
Pedestrian Recognition Using Second-Order HOG Feature

Histogram of Oriented Gradients (HOG) is a well-known feature for pedestrian recognition which describes object appearance as local histograms of gradient orientation. However, it is incapable of describing higher-order properties of object appearance. In this paper we present a second-order HOG feature which attempts to capture second-order properties of object appearance by estimating the pairwise relationships among spatially neighbor components of HOG feature. In our preliminary experiments, we found that using harmonic-mean or min function to measure pairwise relationship gives satisfactory results. We demonstrate that the proposed second-order HOG feature can significantly improve the HOG feature on several pedestrian datasets, and it is also competitive to other second-order features including GLAC and CoHOG.
Building an IPTV VoD recommender system: An experience report

Internet Protocol Television (IPTV) is an increasingly popular multimedia service which is used to deliver television, video, audio and other interactive content over proprietary IP-based networks. Video on Demand (VoD) is one of the most popular IPTV services, and is very important for IPTV providers since it represents the second most important revenue stream after monthly subscriptions. In addition to high-quality VoD content, profitable VoD service provisioning requires an enhanced content accessibility to greatly improve end-user experience. Moreover, it is imperative to offer innovative features to attract new customers and retain existing ones. To achieve this goal, IPTV systems typically employ VoD recommendation engines to offer personalized lists of VoD items that are potentially interesting to a user from a large amount of available titles. In practice, a good recommendation engine does not offer popular and well-known titles, but is rather able to identify interesting among less popular items which would otherwise be hard to find. In this paper we report our experience in building a VoD recommendation system. The presented evaluation shows that our recommendation system is able to recommend less popular items while operating under a high load of end-user requests.
Progress in Artificial Intelligence and Pattern Recognition

Many nice machine learning methods are black box producing very efficient rules but hard to be understandable by the users. The aim of this paper is to help user by tools allowing a better comprehension of these rules. These tools are based on characteristic properties of the original variables in order to remain in the natural language of the user. They are based on three principles, first on local models fitting at best clusters to be found, second on a symbolic description of these clusters and their Symbolic Data Analysis, third on characteristic criterion increasing the explanatory power of the rules by an adaptive process filtering explanatory sub populations.
A measure of interpersonal dependency.

Interpersonal dependency refers to a complex of thoughts, beliefs, feelings, and behaviors revolving around needs to associate closely with valued other people. Its conceptual sources include the psychoanalytic theory of object relations, social learning theories of dependency, and the ethological theory of attachment. A review of existing self-report inventories revealed none that adequately assessed interpersonal dependency. A new 48-item self-report inventory which assesses interpersonal dependency in adults was developed using a sample of 220 normals and 180 psychiatric patients. It was cross-validated on two additional samples. Three components of interpersonal dependency emerged: emotional reliance on another person, lack of social self-confidence, and assertion of autonomy. The relationship of these components with normals and patients was discussed, as well as with the concepts of attachment and dependency.
Characteristics of Sulfobacillus acidophilus sp. nov. and other moderately thermophilic mineral-sulphide-oxidizing bacteria.

Several isolates of Gram-positive, acidophilic, moderately thermophilic, ferrous-iron- and mineral-sulphide-oxidizing bacteria were examined to establish unequivocally the characteristics of Sulfobacillus-like bacteria. Two species were evident: Sulfobacillus thermosulfidooxidans with 48-50 mol% G+C and Sulfobacillus acidophilus sp. nov. with 55-57 mol% G+C. Both species grew autotrophically and mixotrophically on ferrous iron, on elemental sulphur in the presence of yeast extract, and heterotrophically on yeast extract. Autotrophic growth on sulphur was consistently obtained only with S. acidophilus.
Low-power static and dynamic high-voltage CMOS level-shifter circuits

Pseudo-NMOS level-shifters consume large static current making them unsuitable for portable devices implemented with HV CMOS. Dynamic level-shifters help reduce power consumption. To reduce on-current to a minimum (sub-nanoamp), modifications are proposed to existing pseudo-NMOS and dynamic level-shifter circuits. A low power three transistor static level-shifter design with a resistive load is also presented.
Gradient Boosting for Conditional Random Fields Report

Gradient Boosting for Conditional Random Fields Report Title In this paper, we present a gradient boosting algorithm for tree-shaped conditional random fields (CRF). Conditional random fields are an important class of models for accurate structured prediction, but effective design of the feature functions is a major challenge when applying CRF models to real world data. Gradient boosting, which can induce and select functions, is a natural candidate solution for the problem. However, it is non-trivial to derive gradient boosting algorithms for CRFs, due to the dense Hessian matrices introduced by variable dependencies. We address this challenge by deriving a Markov Chain mixing rate bound to quantify the dependencies, and introduce a gradient boosting algorithm that iteratively optimizes an adaptive upper bound of the objective function. The resulting algorithm induces and selects features for CRFs via functional space optimization, with provable convergence guarantees. Experimental results on three real world datasets demonstrate that the mixing rate based upper bound is effective for training CRFs with non-linear potentials. 2
Variational Bayesian extreme learning machine

Extreme learning machine (ELM) randomly generates parameters of hidden nodes and then analytically determines the output weights with fast learning speed. The ill-posed problem of parameter matrix of hidden nodes directly causes unstable performance, and the automatical selection problem of the hidden nodes is critical to holding the high efficiency of ELM. Focusing on the ill-posed problem and the automatical selection problem of the hidden nodes, this paper proposes the variational Bayesian extreme learning machine (VBELM). First, the Bayesian probabilistic model is involved into ELM, where the Bayesian prior distribution can avoid the ill-posed problem of hidden node matrix. Then, the variational approximation inference is employed in the Bayesian model to compute the posterior distribution and the independent variational hyperparameters approximately, which can be used to select the hidden nodes automatically. Theoretical analysis and experimental results elucidate that VBELM has stabler performance with more compact architectures, which presents probabilistic predictions comparison with traditional point predictions, and it also provides the hyperparameter criterion for hidden node selection.
Lateral crural turn-in flap in functional rhinoplasty.

To use the trimmed cartilage as a support material for both internal and external valves.



The lateral crural turn-in flap (LCTF) technique is simply to make cephalic trimming of the lateral crura and turn it into a pocket created under the remaining lateral crus. Twenty-four patients with lateral crura wider than 12 mm and in whom this technique was applied took part in this study. The trimmed cartilage was used to reshape and/or support the lateral crus and the internal valve by keeping the scroll intact. The support and suspension of the lateral crura "sandwich" helped not only to prevent stenosis of the internal valve angle but also to widen it in some cases.



The LCTF has been used in 24 patients to reshape and/or add structure to the lateral crus with great success. The internal valve was also kept open by keeping the scroll area intact, especially in 1 patient with concave lateral crura in whom this technique helped to widen the internal valve angle.



This study shows that the LCTF can be used to reshape and add structure to the lateral crus and to suspend the internal valve. Although it is a powerful technique by itself in functional rhinoplasty, it should be combined with other methods, such as spreader flaps/grafts or alar battens, to obtain the maximum functional result.
Getting the Most out of AMR Parsing

This paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment. We first build a Bidirectional LSTM based concept identifier that is able to incorporate richer contextual information to learn sparse AMR concept labels. We then extend an HMM-based word-to-concept alignment model with graph distance distortion and a rescoring method during decoding to incorporate the structural information in the AMR graph. We show integrating the two components into an existing AMR parser results in consistently better performance over the state of the art on various datasets.
In vitro propagation of the neotropical giant bamboo, Guadua angustifolia Kunth, through axillary shoot proliferation

Guadua angustifolia Kunth was successfully propagated in vitro from axillary buds. Culture initiation, bud sprouting, shoot and plant multiplication, rooting and acclimatization, were evaluated. Best results were obtained using explants from greenhouse-cultivated plants, following a disinfection procedure that comprised the sequential use of an alkaline detergent, a mixture of the fungicide Benomyl and the bactericide Agri-mycin, followed by immersion in sodium hypochlorite (1.5% w/v) for 10 min, and culturing on Murashige and Skoog medium containing 2 ml l−1 of Plant Preservative Mixture®. Highest bud sprouting in original explants was observed when 3 mg l−1 N6-benzylaminopurine (BAP) was incorporated into the culture medium. Production of lateral shoots in in vitro growing plants increased with BAP concentration in culture medium, up to 5 mg l−1, the highest concentration assessed. After six subcultures, clumps of 8–12 axes were obtained, and their division in groups of 3–5 axes allowed multiplication of the plants. Rooting occurred in vitro spontaneously in 100% of the explants that produced lateral shoots. Successful acclimatization of well-rooted clumps of 5–6 axes was achieved in the greenhouse under mist watering in a mixture of soil, sand and rice hulls (1:1:1).
Adversarial Attacks and Defences Competition

To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the Alexey Kurakin, Ian Goodfellow, Samy Bengio Google Brain Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu Department of Computer Science and Technology, Tsinghua University Cihang Xie, Zhishuai Zhang, Alan Yuille Department of Computer Science, The Johns Hopkins University Jianyu Wang Baidu Research USA
Encryption is Not Enough: Inferring User Activities on KakaoTalk with Traffic Analysis

Many people started being concerned about their privacy in delivering private chats, photographs, contacts and other personal information through mobile instant messaging services. Fortunately, in the majority of mobile instant messaging services, encrypted communication channels (e.g., using the SSL/TLS protocols) are used by default to protect delivered messages against eavesdropping attacks. In this paper, however, we show that encryption is not enough. For example, in a real world service named KakaoTalk, many users’ online activities can effectively be identified with 99.7% accuracy even though traffic is encrypted. We present a practical traffic analysis attack using a supervised machine learning technique.
The factors predicting stress, anxiety and depression in the parents of children with autism.

The factors predicting stress, anxiety and depression in the parents of children with autism remain poorly understood. In this study, a cohort of 250 mothers and 229 fathers of one or more children with autism completed a questionnaire assessing reported parental mental health problems, locus of control, social support, perceived parent-child attachment, as well as autism symptom severity and perceived externalizing behaviours in the child with autism. Variables assessing parental cognitions and socioeconomic support were found to be more significant predictors of parental mental health problems than child-centric variables. A path model, describing the relationship between the dependent and independent variables, was found to be a good fit with the observed data for both mothers and fathers.
Toward Non-Intuition-Based Machine Ethics

We propose a deontological approach to machine ethics that avoids some weaknesses of an intuition-based system, such as that of Anderson and Anderson. In particular, it has no need to deal with conflicting intuitions, and it yields a more satisfactory account of when autonomy should be respected. We begin with a “dual standpoint” theory of action that regards actions as grounded in reasons and therefore as having a conditional form that is suited to machine instructions. We then derive ethical principles based on formal properties that the reasons must exhibit to be coherent, and formulate the principles using quantified modal logic. We conclude that deontology not only provides a more satisfactory basis for machine ethics but endows the machine with an ability to explain its actions, thus contributing to transparency in AI.
Matching Structure and Semantics: A Survey on Graph-Based Pattern Matching

The task of matching patterns in graph-structured data has applications in such diverse areas as computer vision, biology, electronics, computer aided design, social networks, and intelligence analysis. Consequently, work on graph-based pattern matching spans a wide range of research communities. Due to variations in graph characteristics and application requirements, graph matching is not a single problem, but a set of related problems. This paper presents a survey of existing work on graph matching, describing variations among problems, general and specific solution approaches, evaluation techniques, and directions for further research. An emphasis is given to techniques that apply to general graphs with semantic characteristics.
Reinforcement Learning approach for Real Time Strategy Games Battle city and S3

In this paper we proposed reinforcement learning algorithms with the generalized reward function. In our proposed method we use Q-learning and SARSA algorithms with generalised reward function to train the reinforcement learning agent. We evaluated the performance of our proposed algorithms on two real-time strategy games called BattleCity and S3. There are two main advantages of having such an approach as compared to other works in RTS. (1) We can ignore the concept of a simulator which is often game specific and is usually hard coded in any type of RTS games (2) our system can learn from interaction with any opponents and quickly change the strategy according to the opponents and do not need any human traces as used in previous works.
Data mining and visualization of data-driven news in the era of big data

With the continuous deepening of study of data mining, the application area of data mining gradually expanded, its influence also spread to the media industry. Data visualization technology has changed the traditional narrative mode, make the news becomes a product that be produced. This paper analyzes the history of computer aided reporting to data news, the main models of data news visualization, and the process of data news production through data mining. The study found that data news focuses on the way of data processing in the entire news workflow, it involves not only classical computer graphics technology, image-processing technology and computer audio technology, but also more data analysis and visual processing technologies based on new media and cloud computing involved in. Research data mining and visualization of data-driven journalism can help journalists use big data to do news work better, deepen people’s cognition of news events, find the logic which cannot be reflected in traditional news, and maximize the connotation of news report.