nils-herrmann/clean_scirepeval huggingface dataset:
Role of neuroinflammation in neurodegenerative diseases (Review).

Neurodegeneration is a phenomenon that occurs in the central nervous system through the hallmarks associating the loss of neuronal structure and function. Neurodegeneration is observed after viral insult and mostly in various so-called 'neurodegenerative diseases', generally observed in the elderly, such as Alzheimer's disease, multiple sclerosis, Parkinson's disease and amyotrophic lateral sclerosis that negatively affect mental and physical functioning. Causative agents of neurodegeneration have yet to be identified. However, recent data have identified the inflammatory process as being closely linked with multiple neurodegenerative pathways, which are associated with depression, a consequence of neurodegenerative disease. Accordingly, pro‑inflammatory cytokines are important in the pathophysiology of depression and dementia. These data suggest that the role of neuroinflammation in neurodegeneration must be fully elucidated, since pro‑inflammatory agents, which are the causative effects of neuroinflammation, occur widely, particularly in the elderly in whom inflammatory mechanisms are linked to the pathogenesis of functional and mental impairments. In this review, we investigated the role played by the inflammatory process in neurodegenerative diseases.
Phase Retrieval Using Alternating Minimization

Phase retrieval problems involve solving linear equations, but with missing sign (or phase, for complex numbers) information. More than four decades after it was first proposed, the seminal error reduction algorithm of Gerchberg and Saxton and Fienup is still the popular choice for solving many variants of this problem. The algorithm is based on alternating minimization; i.e., it alternates between estimating the missing phase information, and the candidate solution. Despite its wide usage in practice, no global convergence guarantees for this algorithm are known. In this paper, we show that a (resampling) variant of this approach converges geometrically to the solution of one such problem-finding a vector x from y, A, where y = |ATx| and |z| denotes a vector of element-wise magnitudes of z-under the assumption that A is Gaussian. Empirically, we demonstrate that alternating minimization performs similar to recently proposed convex techniques for this problem (which are based on “lifting” to a convex matrix problem) in sample complexity and robustness to noise. However, it is much more efficient and can scale to large problems. Analytically, for a resampling version of alternating minimization, we show geometric convergence to the solution, and sample complexity that is off by log factors from obvious lower bounds. We also establish close to optimal scaling for the case when the unknown vector is sparse. Our work represents the first theoretical guarantee for alternating minimization (albeit with resampling) for any variant of phase retrieval problems in the non-convex setting.
DP-EM: Differentially Private Expectation Maximization

The iterative nature of the expectation maximization (EM) algorithm presents a challenge for privacy-preserving estimation, as each iteration increases the amount of noise needed. We propose a practical private EM algorithm that overcomes this challenge using two innovations: (1) a novel moment perturbation formulation for differentially private EM (DP-EM), and (2) the use of two recently developed composition methods to bound the privacy “cost” of multiple EM iterations: the moments accountant (MA) and zero-mean concentrated differential privacy (zCDP). Both MA and zCDP bound the moment generating function of the privacy loss random variable and achieve a refined tail bound, which effectively decrease the amount of additive noise. We present empirical results showing the benefits of our approach, as well as similar performance between these two composition methods in the DP-EM setting for Gaussian mixture models. Our approach can be readily extended to many iterative learning algorithms, opening up various exciting future directions.
Drowsiness Detection Using Facial Expression Features

This paper presents the method of detecting driver’s drowsiness level from the facial expression. The motivation for this research is to realize the novel safety system which can detect the driver’s slight drowsiness and keep the driver awake while driving. The brain wave is commonly used as the drowsiness index. However, it is not suitable for the in-vehicle system since it is measured with sensors worn over the head. We precisely investigated the relationship between the change of brain wave and other drowsiness indices that can be measured without any contact; PERCLOS, heart rate, lane deviation, and facial expression. We found that the facial expression index had the highest linear correlation with the brain wave. Therefore, we selected the facial expression as the drowsiness-detection index and automated the drowsiness detection from the facial expression. Three problems need to be solved for automation; (1) how to de ne the features of drowsy expression, (2) how to capture the features from the driver’s video-recorded facial image, and (3) how to estimate the driver’s drowsiness index from the features. First, we found that frontalis muscle, zygomaticus major muscle, and masseter muscle activated with increase of drowsiness in more than 75 percents of participants. According to the result, we determined the coordinates data of points on eyebrows, eyelids, and mouth as the features of drowsiness expression. Second, we calculated the 3D coordinates data of the features by image processing with Active Appearance Model (AAM). Third, we applied k-Nearest-Neighbor method to classify the driver’s drowsiness level. Eleven participants’ data of the features and the drowsiness level estimated by trained observers were used as the training data. We achieved the classi cation of the drivers’ drowsiness in a driving simulator into 6 levels. The average Root Mean Square Errors (RMSE) among 12 participants was less than 1.0 level.
Support-vector networks

Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Exploiting Equitable Partitions for Efficient Block Triangularization

In graph theory a partition of the vertex set of a graph is called equitable if for all pairs of cells all vertices in one cell have an equal number of neighbours in the other cell. Considering the implications for the adjacency matrix one may generalize that concept as a block partition of a complex square matrix s.t. each block has constant row sum. It is well known that replacing each block by its row sum yields a smaller matrix whose multiset of eigenvalues is contained in the initial spectrum. We generalize this approach to weighted row sums and rectangular matrices and derive an efficient unitary transformation which approximately block triangularizes a matrix w.r.t. an arbitrary partition. Singular values and Hermiticity (if present) are preserved. The approximation is exact in the equitable case and the error can be bounded in terms of unitarily invariant matrix norms.
Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors

Detection and tracking of humans in video streams is important for many applications. We present an approach to automatically detect and track multiple, possibly partially occluded humans in a walking or standing pose from a single camera, which may be stationary or moving. A human body is represented as an assembly of body parts. Part detectors are learned by boosting a number of weak classifiers which are based on edgelet features. Responses of part detectors are combined to form a joint likelihood model that includes an analysis of possible occlusions. The combined detection responses and the part detection responses provide the observations used for tracking. Trajectory initialization and termination are both automatic and rely on the confidences computed from the detection responses. An object is tracked by data association and meanshift methods. Our system can track humans with both inter-object and scene occlusions with static or non-static backgrounds. Evaluation results on a number of images and videos and comparisons with some previous methods are given.
Diagnosis of Motor Faults Using Sound Signature Analysis

The objective of this paper is to present recent developments in the field of Induction motor fault signature analysis with particular regard to Sound signature analysis of induction motor of fan. The different types of fan faults that can be identified from the sound signature analysis  are, for example, rotor faults, bearing faults, unbalances wings etc. Corresponding to the above-mentioned faults, many types of machine fault signature analysis techniques  have been proposed for motor faults detection and diagnosis. These techniques include vibration monitoring, motor current signature analysis (MCSA) [3–6], electromagnetic field monitoring [7], chemical analysis, temperature measurability [8, 9], infrared measurement, acoustic noise analysis , and partial discharge measurement [11, 12]. Among these methods, vibration analysis, current analysis and Sound signature analysis are the most popular due to their easy measurability, high accuracy, and reliability.
Design of FIR filter for burst mode demodulator of satellite Receiver

This paper proposes a satellite receiver filter design using FIR digital filtering technique. We present various design methods like windowing, least squares and equiripple for satellite burst demodulator application and compare their performance. Various designs of FIR filter are compared from the view point of hardware complexity, frequency response characteristics and implementation strategies. The filter is designed for band pass of the frequency range of 100 MHz to 500 MHz suitable for the entire bandwidth of satellite transponder. The burst mode detector requires narrow passband to increase SNR for preamble portion. When acquisition phase is complete, the bandpass is increased to full bandwidth of the signal.
Motion Estimation from Image and Inertial Measurements Motion Estimation from Image and Inertial Measurements

Cameras and inertial sensors are each good candidates for autonomous vehicle navigation, modeling from video, and other applications that require six-degrees-of-freedom motion estimation. However, these sensors are also good candidates to be deployed together, since each can be used to resolve the ambiguities in estimated motion that result from using the other modality alone. In this paper, we consider the specific problem of estimating sensor motion and other unknowns from image, gyro, and accelerometer measurements, in environments without known fiducials. This paper targets applications where external positions references such as global positioning are not available, and focuses on the use of small and inexpensive inertial sensors, for applications where weight and cost requirements preclude the use of precision inertial navigation systems. We present two algorithms for estimating sensor motion from image and inertial measurements. The first algorithm is a batch method, which produces estimates of the sensor motion, scene structure, and other unknowns using measurements from the entire observation sequence simultaneously. The second algorithm recovers sensor motion, scene structure, and other parameters recursively, and is suitable for use with long or “infinite” sequences, in which no feature
Hot topic detection in local areas using Twitter and Wikipedia

As microblog services become increasingly popular, spatial-temporal text data has increased explosively. Many studies have proposed methods to spatially and temporally analyze an event, indicated by the text data. These studies have aimed a extracting the period and the location in which a specified topic frequently occurs. In this paper, we focus on a system that detects hot topic in a local area and during a particular period. There can be a variation in the words used even though the posts are essentially about the same hot topic. We propose a classification method that mitigates the variation of posted words related to the same topic.
Automated production system simulations using commercial off-the-shelf simulation tools

A multi-year research project focused on a global aerospace company's design-to-production transition, and in particular how to answer production-related questions much earlier in a program's design cycle than is possible today. A fundamental difficulty is that the time and expertise required to formulate appropriate analysis models prevents their routine use, especially in new program development. The project's goal was to reduce these requirements, and by late 2014 a methodology had been developed for on-demand analysis generation to answer routine questions about production systems. A pilot project was conducted in 2015 to demonstrate efficacy, that an implementation of the methodology could in fact reduce by at least an order of magnitude the time required to answer a frequently-asked question, in a repeatable way while specification of the products, their process plans, planned facilities, and available resources were frequently changing. This paper summarizes the methodology, its pilot project implementation, and preliminary results.
A two-step convolutional neural network approach for semantic role labeling

Semantic role labeling (SRL) is a well known task in Natural Language Processing, consisting of identifying and labeling verbal arguments. It has been widely studied in English, but scarcely explored in other languages. In this paper, we employ a two-step convolutional neural architecture to label semantic arguments in Brazilian Portuguese texts, and avoid the use of external NLP tools. We achieve an F1 score of 62.2, which, although considerably lower than the state-of-the-art for English, seems promising considering the available resources. Also, dividing the process into two easier subtasks makes it more feasible to further improve performance through semi-supervised learning. Our system is available online and ready to be used out of the box to label new texts.
Trustworthy scrum: Development of secure software with scrum

Software development process models focus on ordering and combination of phases to develop the intended software product within time and cost estimates. However, commonness of software vulnerabilities in the fielded systems show that there is a need for more stringent software development process that focuses on improved security demands. Meanwhile, there are some reports that demonstrate the efficiency of existing security enhanced conventional processes and success of agile projects over conventional waterfall projects. Based on this finding and the demand for secure software, we propose a security enhanced Scrum model (Trustworthy Scrum) by taking advantages of both security activities and Scrum framework which has fast adaptation and iterative cycle. While enhancing Scrum with security activities, we try to retain agile and security disciplines by considering that conventional security approach conflicts with agile methodologies.
Silver conductive features on flexible substrates from a thermally accelerated chain reaction at low sintering temperatures.

In this study, a simple and effective silver ink formulation was developed to generate silver tracks with high electrical conductivity on flexible substrates at low sintering temperatures. Diethanolamine (DEA), a self-oxidizing compound at moderate temperatures, was mixed with a silver ammonia solution to form a clear and stable solution. After inkjet-printed or pen-written on plastic sheets, DEA in the silver ink decomposes at temperatures higher than 50 °C and generates formaldehyde, which reacts spontaneously with silver ammonia ions to form silver thin films. The electrical conductivity of the inkjet-printed silver films can be 26% of the bulk silver after heating at 75 °C for 20 min and show great adhesion on plastic sheets.
Performance Analysis of Touch-Interaction Behavior for Active Smartphone Authentication

The increasing use of touchscreen smartphones to access sensitive and privacy data has given rise to the need of secure and usable authentication technique. Smartphone users have their own unique behavioral characteristics when performing touch operations. These personal characteristics are reflected on different rhythm, strength, and angle preferences of touch-interaction behavior. This paper investigates the reliability and applicability on the usage of users' touch-interaction behavior for active authentication on smartphones. For each common type of touch operations, both static and dynamic features are extracted and analyzed for fine-grained characterization of users' touch behavior. Classification techniques (nearest neighbor, neural network, support vector machine, and random forest) are applied to the feature space for performing the task of active authentication. Analyses are conducted using data from around 134 900 touch operations of 71 participants in real-world scenarios, and the authentication performance is evaluated across various types of touch operations, varying operation lengths, different application tasks, and different application scenarios. The extensive experimental results are included to show that touch-interaction behavior exhibits sufficient discriminability and stability among smartphone users for active authentication, and achieves equal-error rates between 1.72% and 9.01% for different types of touch operations with the operation length of 11; the authentication accuracies improve when having long observation or small timespan between the training and testing phases, and express more reliably and stably in a specific task than in the free task. We also discuss a number of avenues for additional research that we believe are necessary to advance the state-of-the-art in this area.
AMiner: Toward Understanding Big Scholar Data

In this talk, I will present a novel academic search and mining system, AMiner, the second generation of the ArnetMiner system. Different from traditional academic search systems that focus on document (paper) search, AMiner aims to provide a systematic modeling approach for researchers (authors), ultimately to gain a deep understanding of the big (heterogeneous) network formed by authors, papers they have published, and venues they published those papers. The system extracts researchers' profiles automatically from the Web and integrates the researcher profiles with publication papers after name disambiguation. For now, the system has collected a big scholar data with more than 130,000,000 researcher profiles and 100,000,000 papers from multiple publication databases. We also developed an approach named COSNET to connect AMiner with several professional social networks such as LinkedIn and VideoLectures, which significantly enriches the metadata of the scholarly data. Based on the integrated big scholar data, we devise a unified topic modeling approach for modeling the different entities (authors, papers, venues) simultaneously and provide a topic-level expertise search by leveraging the modeling results. In addition, AMiner offers a set of researcher-centered functions including social influence analysis, influence visualization, collaboration recommendation, relationship mining, similarity analysis and community evolution. The system has been put into operation since 2006 and has attracted more than 7,000,000 independent IP accesses from over 200 countries/regions.
Blending Aboriginal and Western healing methods to treat intergenerational trauma with substance use disorder in Aboriginal peoples who live in Northeastern Ontario, Canada

As with many Indigenous groups around the world, Aboriginal communities in Canada face significant challenges with trauma and substance use. The complexity of symptoms that accompany intergenerational trauma and substance use disorders represents major challenges in the treatment of both disorders. There appears to be an underutilization of substance use and mental health services, substantial client dropout rates, and an increase in HIV infections in Aboriginal communities in Canada. The aim of this paper is to explore and evaluate current literature on how traditional Aboriginal healing methods and the Western treatment model "Seeking Safety" could be blended to help Aboriginal peoples heal from intergenerational trauma and substance use disorders. A literature search was conducted using the keywords: intergenerational trauma, historical trauma, Seeking Safety, substance use, Two-Eyed Seeing, Aboriginal spirituality, and Aboriginal traditional healing. Through a literature review of Indigenous knowledge, most Indigenous scholars proposed that the wellness of an Aboriginal community can only be adequately measured from within an Indigenous knowledge framework that is holistic, inclusive, and respectful of the balance between the spiritual, emotional, physical, and social realms of life. Their findings indicate that treatment interventions must honour the historical context and history of Indigenous peoples. Furthermore, there appears to be strong evidence that strengthening cultural identity, community integration, and political empowerment can enhance and improve mental health and substance use disorders in Aboriginal populations. In addition, Seeking Safety was highlighted as a well-studied model with most populations, resulting in healing. The provided recommendations seek to improve the treatment and healing of Aboriginal peoples presenting with intergenerational trauma and addiction. Other recommendations include the input of qualitative and quantitative research as well as studies encouraging Aboriginal peoples to explore treatments that could specifically enhance health in their respective communities.
Evaluating and improving adaptive educational systems with learning curves

Personalised environments such as adaptive educational systems can be evaluated and compared using performance curves. Such summative studies are useful for determining whether or not new modifications enhance or degrade performance. Performance curves also have the potential to be utilised in formative studies that can shape adaptive model design at a much finer level of granularity. We describe the use of learning curves for evaluating personalised educational systems and outline some of the potential pitfalls and how they may be overcome. We then describe three studies in which we demonstrate how learning curves can be used to drive changes in the user model. First, we show how using learning curves for subsets of the domain model can yield insight into the appropriateness of the model’s structure. In the second study we use this method to experiment with model granularity. Finally, we use learning curves to analyse a large volume of user data to explore the feasibility of using them as a reliable method for fine-tuning a system’s model. The results of these experiments demonstrate the successful use of performance curves in formative studies of adaptive educational systems.