nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Scale Convolutional Neural Networks for Time Series Classification

Time series classification (TSC), the problem of predicting class labels of time series, has been around for decades within the community of data mining and machine learning, and found many important applications such as biomedical engineering and clinical prediction. However, it still remains challenging and falls short of classification accuracy and efficiency. Traditional approaches typically involve extracting discriminative features from the original time series using dynamic time warping (DTW) or shapelet transformation, based on which an off-the-shelf classifier can be applied. These methods are ad-hoc and separate the feature extraction part with the classification part, which limits their accuracy performance. Plus, most existing methods fail to take into account the fact that time series often have features at different time scales. To address these problems, we propose a novel end-to-end neural network model, Multi-scale Convolutional Neural Network (MCNN), which incorporates feature extraction and classification in a single framework. Leveraging a novel multi-branch layer and learnable convolutional layers, MCNN automatically extracts features at different scales and frequencies, leading to superior feature representation. MCNN is also computationally efficient, as it naturally leverages GPU computing. We conduct comprehensive empirical evaluation with various existing methods on a large number of benchmark datasets, and show that MCNN advances the state-of-the-art by achieving superior accuracy performance than other leading methods.
Dynamic convolutional neural network for activity recognition

In this paper, a novel Dynamic Convolutional Neural Network (D-CNN) is proposed using sensor data for activity recognition. Sensor data collected for activity recognition is usually not well-aligned. It may also contains noises and variations from different persons. To overcome these challenges, Gaussian Mixture Models (GMM) is exploited to capture the distribution of each activity. Then, sensor data and the GMMs are screened into different segments. These segments form multiple paths in the Convolutional Neural Network. During testing, Gaussian Mixture Regression (GMR) is applied to dynamically fit segments of test signals into corresponding paths in the CNN. Experimental results demonstrate the superior performance of D-CNN to other learning methods.
Cooperative Intersection Management: A Survey

Intersection management is one of the most challenging problems within the transport system. Traffic light-based methods have been efficient but are not able to deal with the growing mobility and social challenges. On the other hand, the advancements of automation and communications have enabled cooperative intersection management, where road users, infrastructure, and traffic control centers are able to communicate and coordinate the traffic safely and efficiently. Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter. Cooperative methods, including time slots and space reservation, trajectory planning, and virtual traffic lights, are discussed in detail. Vehicle collision warning and avoidance methods are discussed to deal with uncertainties. Concerning vulnerable road users, pedestrian collision avoidance methods are discussed. In addition, an introduction to major projects related to cooperative intersection management is presented. A further discussion of the presented works is given with highlights of future research topics. This paper serves as a comprehensive survey of the field, aiming at stimulating new methods and accelerating the advancement of automated and cooperative intersections.
A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment.

Veterinary antibiotics (VAs) are widely used in many countries worldwide to treat disease and protect the health of animals. They are also incorporated into animal feed to improve growth rate and feed efficiency. As antibiotics are poorly adsorbed in the gut of the animals, the majority is excreted unchanged in faeces and urine. Given that land application of animal waste as a supplement to fertilizer is often a common practice in many countries, there is a growing international concern about the potential impact of antibiotic residues on the environment. Frequent use of antibiotics has also raised concerns about increased antibiotic resistance of microorganisms. We have attempted in this paper to summarize the latest information available in the literature on the use, sales, exposure pathways, environmental occurrence, fate and effects of veterinary antibiotics in animal agriculture. The review has focused on four important groups of antibiotics (tylosin, tetracycline, sulfonamides and, to a lesser extent, bacitracin) giving a background on their chemical nature, fate processes, occurrence, and effects on plants, soil organisms and bacterial community. Recognising the importance and the growing debate, the issue of antibiotic resistance due to the frequent use of antibiotics in food-producing animals is also briefly covered. The final section highlights some unresolved questions and presents a way forward on issues requiring urgent attention.
Graphene photonics and optoelectronics

611 Electrons propagating through the bidimensional structure of graphene have a linear relation between energy and momentum, and thus behave as massless Dirac fermions1–3. Consequently, graphene exhibits electronic properties for a two-dimensional (2D) gas of charged particles described by the relativistic Dirac equation, rather than the non-relativistic Schrödinger equation with an eff ective mass1,2, with carriers mimicking particles with zero mass and an eff ective ‘speed of light’ of around 106 m s–1. Graphene exhibits a variety of transport phenomena that are characteristic of 2D Dirac fermions, such as specifi c integer and fractional quantum Hall eff ects4,5, a ‘minimum’ conductivity of ~4e2/h even when the carrier concentration tends to zero1, and Shubnikov–de Haas oscillations with a π phase shift due to Berry’s phase1. Mobilities (μ) of up to 106 cm2 V–1 s–1 are observed in suspended samples. Th is, combined with near-ballistic transport at room temperature, makes graphene a potential material for nanoelectronics6,7, particularly for high-frequency applications8. Graphene also shows remarkable optical properties. For example, it can be optically visualized, despite being only a single atom thick9,10. Its transmittance (T) can be expressed in terms of the fi ne-structure constant11. Th e linear dispersion of the Dirac electrons makes broadband applications possible. Saturable absorption is observed as a consequence of Pauli blocking12,13, and nonequilibrium carriers result in hot luminescence14–17. Chemical and physical treatments can also lead to luminescence18–21. Th ese properties make it an ideal photonic and optoelectronic material.
Access methods for Big Data: current status and future directions

Heterogeneity, size, timeliness, difficulty & confidentiality problems with Big Data hinder advancement at all phases of the channel that can create value from data. Data analysis, organization, retrieval & modeling are initial challenges for Big Data. Data investigation is a clear traffic jam in many applications, both due to lack of scalability of the core algorithms and due to the difficulty of the data that needs to be analyzed. Despite this, the appearance of the results and its understanding by non-technical experts is vital to extracting actionable knowledge. To defeat these, there is a need for novel architectures, techniques, algorithms & analytics to deal with it as well as to retrieve the value and unseen knowledge. Further, we need to build up efficient and optimized access methods for countless reasons such as velocity of Big Data. In this article, we present a brief overview of the current status of access methods for Big data and discuss a few promising research directions.
Smart-cap for pedestrian safety

In the last few decades, the use of mobile phones have increased rapidly. Safety experts have considered dependency on mobile phones as a threat to the pedestrian safety. In this paper, a wearable smart-cap has been proposed to detect an obstacle in the path of a pedestrian mobile user. The vision of the proposed system is to provide a user-friendly, cost effective and a quick way to avoid pedestrian accidents due to mobile phones. The volunteers' feedback and testing results have been outstanding. The volunteers have reported that the proposed system is user-friendly and accurate.
A Self-Determination Theory Perspective on Parenting

This article describes research on parenting that supports children’s need for autonomy. First, the authors define parental autonomy support and distinguish it from permissiveness or independence promotion. The authors also define psychologically controlling parenting and distinguish it from bevavioral control (structure). Second, the authors present studies examining how parental autonomy support promotes healthy development. Indeed, clear and consistent positive effects arise from different types of studies, conducted with children of various ages. Parent observation studies suggest that parental autonomy support is associated with infants’ motivation and toddlers’ internalization. Parent interview studies reveal that an autonomy-supportive parental attitude relates to children’s adjustment at school. Children self-report studies demonstrate a clear link between perceptions of parental autonomy support and psychosocial functioning amongst adolescents. Third, the correlates and precursors of parental psychological control and autonomy support are presented, with a special focus on parents’ trust in their children’s ability to develop in an autonomous manner. Finally, ideas for future research are suggested. Although self-determination theory is not strictly a developmental theory, it seems highly pertinent to the socialization of children, their internalization and development.
Fingerprint image analysis: Role of orientation patch and ridge structure dictionaries

Biometric traits, such as palmprint (Duta et al. 2002) and fingerprint (Jain et al. 1997), refer to distinctive anotomical and behavioral characteristics for automatic human identification. Fingerprints, which are ridge and valley patterns on the tip of a human finger, are one of the most important biometric traits due to their known uniqueness and persistence properties (Maltoni et al. 2009). Since the advent of fingerprints for identifying and tracing criminals in Argentina in 1893 (Hawthorne 2008), fingerprints have been primarily used as evidence in law enforcement and forensics. After the first paper on automated fingerprint matching was published by Mitchell Trauring (Trauring 1963) in Nature in 1963, the Federal Bureau of Investigation (FBI) installed the first Automated Fingerprint Identification System (AFIS) in the 1980. Now large scale fingerprint recognition systems are not only used worldwide by law enforcement and forensic agencies, they are also beginning to be deployed in civilian applications, such as (i) the OBIM (formerly the US-VISIT) program by the Department of Homeland Security (Department of Homeland Security 2014), and (ii) India’s Aadhar project (Planning Commission, Goverment of India 2014). In 2013, the TouchID system (Apple, Inc. 2014) in the Apple iPhone 5s for authenticating mobile phone users launched the application of fingerprint in mobile devices. Some major milestones in the history of fingerprint recognition are illustrated in Fig.1.1. The purported uniqueness of fingerprints is characterized in terms of three levels of features (Maltoni et al. 2009) . Level 1 features include the general ridge flow and pattern configurations such as pattern type, ridge orientation and frequency fields, and singular points (core and delta points). While level 1 features are not sufficient for individualization, they can be used for exclusion (the outcomes of comparing a fingerprint pair are one of three possibilities: match, inconclusive and exclusion). Level 2 features mainly
A FRAMEWORK OF INFORMATION SECURITY CULTURE CHANGE 1

Establishing information security culture within an organization may include transformation of how employees interact with the information assets which may be challenged with resistance, fear or confusion. Change management skills could assist organization members to smoothly adapt to the new culture. The use of change management in information security culture has been rarely investigated in the literature and very few models have been offered. This paper reviews the available change management models that have been used in information security management. Then it integrates a set of change management principles that were proposed in the literature and combine them to a comprehensive multistep framework that support and guide the transition in information security culture change within organizations. Moreover, the principles will be the base of suggestion of the appropriate guideline to support the effective implementation of change in information security culture. The framework provides guidance to information security professionals and academic researchers in taking proactive steps and measures to facilitate the culture change.
UPTIME: Ubiquitous pedestrian tracking using mobile phones

The mission of tracking a pedestrian is valuable for many applications including walking distance estimation for the purpose of pervasive healthcare, museum and shopping mall guides, and locating emergency responders. In this paper, we show how accurate and ubiquitous tracking of a pedestrian can be performed using only the inertial sensors embedded in his/her mobile phone. Our work depends on performing dead reckoning to track the user's movement. The main challenge that needs to be addressed is handling the noise of the low cost low quality inertial sensors in cell phones. Our proposed system combines two novel contributions: a novel step count estimation technique and a gait-based accurate variable step size detection algorithm. The step count estimation technique is based on a lightweight finite state machine approach that leverages orientation-independent features. In order to capture the varying stride length of the user, based on his changing gait, we employ a multi-class hierarchical Support Vector Machine classifier. Combining the estimated number of steps with the an accurate estimate of the individual stride length, we achieve ubiquitous and accurate tracking of a person in indoor environments. We implement our system on different Android-based phones and compare it to the state-of-the-art techniques in indoor and outdoor testbeds with arbitrary phone orientation. Our results in two different testbeds show that we can provide an accurate step count estimation with an error of 5.72%. In addition, our gait type classifier has an accuracy of 97.74%. This leads to a combined tracking error of 6.9% while depending only on the inertial sensors and turning off the GPS sensor completely. This highlights the ability of the system to provide ubiquitous, accurate, and energy efficient tracking.
Overcoming catastrophic forgetting in neural networks

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially.
Dynamic Transfer Learning for Named Entity Recognition

State-of-the-art named entity recognition (NER) systems have been improving continuously using neural architectures over the past several years. However, many tasks including NER require large sets of annotated data to achieve such performance. In particular, we focus on NER from clinical notes, which is one of the most fundamental and critical problems for medical text analysis. Our work centers on effectively adapting these neural architectures towards lowresource settings using parameter transfer methods. We complement a standard hierarchical NER model with a general transfer learning framework consisting of parameter sharing between the source and target tasks, and showcase scores significantly above the baseline architecture. These sharing schemes require an exponential search over tied parameter sets to generate an optimal configuration. To mitigate the problem of exhaustively searching for model optimization, we propose the Dynamic Transfer Networks (DTN), a gated architecture which learns the appropriate parameter sharing scheme between source and target datasets. DTN achieves the improvements of the optimized transfer learning framework with just a single training setting, effectively removing the need for exponential search.
Can You Be Persuaded? Individual Differences in Susceptibility to Persuasion

Persuasive technologies are growing in popularity and many designers create systems which intentionally change users attitudes or behaviors. This study shows that peoples individual differences in susceptibility to persuasion, as implemented using the six persuasion principles proposed by Cialdini , relates to their compliance to a persuasive request which is accompanied by a persuasive cue. This result is a starting point for designers to start incorporating individual differences in susceptibility to persuasive cues in their adaptive persuasive systems.
A framework for dynamically adaptable augmented reality systems

In this dissertation I present a software framework for adaptable Augmented Reality systems in intelligent environments: the Distributed Wearable Augmented Reality Framework (DWARF). The framework is a reusable basis for the development of Augmented Reality applications and gives developers a system structure to customize existing or develop new components. Augmented Reality is a human-computer interaction paradigm offering users new possibilities to interact with their environment. By tracking the position and viewing direction of the user and of real world objects in the environment, real world objects can be augmented with virtual objects. The most prominent example is the overlay of real world objects with computer graphics in a head-mounted display. DWARF models Augmented Reality applications in four abstraction layers: application layer, inter-application layer, solution domain layer, and architectural style layer. For the architectural style layer, I specify a contract-based peer-to-peer style. A system is a configuration of mutually interdependent distributed services. The connections between services are established by an active middleware based on an abstract service specification. Existing solutions for Augmented Reality are usually designed as monolithic or client/ server systems. DWARF uses an extension of component-oriented software engineering. Components on the user’s computer and in the environment are both used as building blocks for Augmented Reality systems. Further on, I extend the component-based system approach to a service-based approach. An application is not built from instances of components, but from a configuration of services available on the user’s computer and in the environment. An active middleware enables the dynamic adaptation of the system to changes in the environment by a reconfiguration of the system. For the solution domain layer, I present an abstract reference architecture and a system of patterns for Augmented Reality systems. The foundation for them is a broad analysis of existing Augmented Reality systems. Finally, as a case study for the applicability of the framework, I present the sub framework MARF for mobile maintenance systems and a navigation application as part of such a mobile Augmented Reality maintenance system, DWARF Pathfinder. This case study covers the application layer, the inter-application layer, and the solution domain layer.
The dependency paradox in close relationships: accepting dependence promotes independence.

Using multiple methods, this investigation tested the hypothesis that a close relationship partner's acceptance of dependence when needed (e.g., sensitive responsiveness to distress cues) is associated with less dependence, more autonomous functioning, and more self-sufficiency (as opposed to more dependence) on the part of the supported individual. In two studies, measures of acceptance of dependency needs and independent functioning were obtained through couple member reports, by observing couple members' behaviors during laboratory interactions, by observing responses to experimentally manipulated partner assistance provided during an individual laboratory task, and by following couples over a period of 6 months to examine independent goal striving as a function of prior assessments of dependency acceptance. Results provided converging evidence in support of the proposed hypothesis. Implications of the importance of close relationships for optimal individual functioning are discussed.
"No-Touch" Technique for Lip Enhancement.

The purpose of this study was to examine the anatomical principles of lip structure as they relate to individualized lip enhancement procedures and to describe a technique that does not violate lip mucosa during injection.



A retrospective analysis of patients undergoing lip enhancement procedures between 2001 and 2014 was performed. Preprocedural and postprocedural photographs were analyzed for lip subunit changes. A stepwise treatment algorithm targeting specific anatomical subunits of lip is described.



Four hundred ten patients were treated with a "no-touch" technique for lip enhancement. Lip profile is determined by the position of the white roll. The white roll is accessed by a 30-gauge needle at a point 5 mm lateral to the oral commissure and at the base of the philtral columns. Lip projection is established by vermilion formation contributing to the arc of the Cupid's bow. To improve projection, the labial commissure is entered with a 25-gauge cannula and tunneled into the submucosal space between the white and red rolls. Lip augmentation is a direct reflection of the prominence of the red line and can be approached in a perpendicular fashion with a needle or cannula descending to the level of the wet-dry junction.



Accurate assessment of the white and red rolls, arc of Cupid's bow, philtrum, and gingival show can guide the injector on the proper enhancement that individual patients require. The no-touch technique minimizes mucosal trauma. Tailoring treatment toward lip profile, projection, and/or augmentation can yield predictable and reproducible outcomes in this commonly performed cosmetic procedure.