nils-herrmann/clean_scirepeval huggingface dataset:
NEAT for large-scale reinforcement learning through evolutionary feature learning and policy gradient search

NeuroEvolution of Augmenting Topology (NEAT) is one of the most successful algorithms for solving traditional reinforcement learning (RL) tasks such as pole-balancing. However, the algorithm faces serious challenges while tackling problems with large state spaces, particularly the Atari game playing tasks. This is due to the major flaw that NEAT aims at evolving a single neural network (NN) that must be able to simultaneously extract high-level state features and select action outputs. However such complicated NNs cannot be easily evolved directly through NEAT. To address this issue, we propose a new reinforcement learning scheme based on NEAT with two key technical advancements: (1) a new three-stage learning scheme is introduced to clearly separate feature learning and policy learning to allow effective knowledge sharing and learning across multiple agents; (2) various policy gradient search algorithms can be seamlessly integrated with NEAT for training policy networks with deep structures to achieve effective and sample efficient RL. Experiments on several Atari games confirm that our new learning scheme can be more effective and has higher sample efficiency than NEAT and three state-of-the-art algorithms from the most recent RL literature.
Activity Recognition from Inertial Sensors with Convolutional Neural Networks

Human Activity Recognition is one of the attractive topics to develop smart interactive environment in which computing systems can understand human activities in natural context. Besides traditional approaches with visual data, inertial sensors in wearable devices provide a promising approach for human activity recognition. In this paper, we propose novel methods to recognize human activities from raw data captured from inertial sensors using convolutional neural networks with either 2D or 3D filters. We also take advantage of hand-crafted features to combine with learned features from Convolution-Pooling blocks to further improve accuracy for activity recognition. Experiments on UCI Human Activity Recognition dataset with six different activities demonstrate that our method can achieve 96.95%, higher than existing methods.
Replay Attack: A Prevalent Pattern of Fraudulent Online Transactions

The rapid advancement in the electronic commerce technology makes electronic transaction an indispensable part of our daily life. While, this way of transaction has always been facing security problems. Researchers persevere in looking for fraud transaction detection methodologies. A promising paradigm is to devise dedicated detectors for the typical patterns of fraudulent transactions. Unfortunately, this paradigm is really constrained by the lack of real electronic transaction data, especially real fraudulent samples. In this paper, by analyzing real B2C electronic transaction data provided by an Asian bank, from the perspective of transaction sequence, we discover a typical pattern of fraud transactions: Most of the fraud transactions are fast and repeated transactions between the same customer and the same vendor, and all the transaction amounts are nearly the same. We name this pattern Replay Attack. We prove the prominent existence of Replay Attack by comprehensive statistics, and we propose a novel fraud transaction detector, Replay Attack Killer (RAK). By experiment, we show that RAK can catch up to 92% fraud transactions in real time but only disturb less than 0.06% normal transactions.
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Recent region-based object detectors are usually built with separate classification and localization branches on top of shared feature extraction networks. In this paper, we analyze failure cases of state-ofthe-art detectors and observe that most hard false positives result from classification instead of localization. We conjecture that: (1) Shared feature representation is not optimal due to the mismatched goals of feature learning for classification and localization; (2) multi-task learning helps, yet optimization of the multi-task loss may result in sub-optimal for individual tasks; (3) large receptive field for different scales leads to redundant context information for small objects. We demonstrate the potential of detector classification power by a simple, effective, and widely-applicable Decoupled Classification Refinement (DCR) network. DCR samples hard false positives from the base classifier in Faster RCNN and trains a RCNN-styled strong classifier. Experiments show new stateof-the-art results on PASCAL VOC and COCO without any bells and whistles.
An Improved Cat Swarm Optimization Algorithm Based on Opposition-Based Learning and Cauchy Operator for Clustering

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new metaheuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.
A Survey on the Communication Protocols and Security in Cognitive Radio Networks

A cognitive radio (CR) is a radio that can change it s transmission parameters based on the perceived avai lability of the spectrum bands in its operating environment. CRs supp ort dynamic spectrum access and can facilitate a second ary unlicensed user to efficiently utilize the availabl e underutilized spectrum allocated to the primary licensed users. A cognitive radio network (CRN) is composed of both the secondary users with CR-enabled radios and the primary users whose r adios need not be CR-enabled. Most of the active research condu cte in the area of CRNs has been so far focused on spectrum sens ing, allocation and sharing. There is no comprehensive r e iew paper available on the strategies for medium access contr ol (MAC), routing and transport layer protocols, and the appr o riate representative solutions for CRNs. In this paper, we provide an exhaustive analysis of the various techniques/mecha nisms that have been proposed in the literature for communicat ion protocols (at the MAC, routing and transport layers), in the c ontext of a CRN, as well as discuss in detail several security at tacks that could be launched on CRNs and the countermeasure solu tions that have been proposed to avoid or mitigate them. This paper would serve as a good comprehensive review and anal ysis of the strategies for routing and transport protocols and security issues for CRNs as well as would lay a strong foundation for s meone to further delve onto any particular aspect in grea ter depth.
Quality of Service Based Routing: A Performance Perspective

Recent studies provide evidence that Quality of Service (QoS) routing can provide increased network utilization compared to routing that is not sensitive to QoS requirements of traffic. However, there are still strong concerns about the increased cost of QoS routing, both in terms of more complex and frequent computations and increased routing protocol overhead. The main goals of this paper are to study these two cost components, and propose solutions that achieve good routing performance with reduced processing cost. First, we identify the parameters that determine the protocol traffic overhead, namely (a) policy for triggering updates, (b) sensitivity of this policy, and (c) clamp down timers that limit the rate of updates. Using simulation, we study the relative significance of these factors and investigate the relationship between routing performance and the amount of update traffic. In addition, we explore a range of design options to reduce the processing cost of QoS routing algorithms, and study their effect on routing performance. Based on the conclusions of these studies, we develop extensions to the basic QoS routing, that can achieve good routing performance with limited update generation rates. The paper also addresses the impact on the results of a number of secondary factors such as topology, high level admission control, and characteristics of network traffic.
Shape Matching: Similarity Measures and Algorithms

Shape matching is an important ingredient in shape retrieval, recognition and classification, alignment and registration, and approximation and simplification. This paper treats various aspects that are needed to solve shape matching problems: choosing the precise problem, selecting the properties of the similarity measure that are needed for the problem, choosing the specific similarity measure, and constructing the algorithm to compute the similarity. The focus is on methods that lie close to the field of computational geometry.
Home automation design using 6LoWPAN wireless sensor networks

Wireless sensor and actuator networks (WS&ANs) are a new technology based on networks of small radio-enabled embedded devices that are being deployed in areas such as environmental monitoring, vehicle tracking, building management, body monitoring and other applications. Power sources for network nodes are often limited, which imposes restrictions on hardware resources and their use by the underlying embedded software. We propose a new wireless sensor network architecture that is especially designed for the task of home automation. Our system relies on a low power WS&AN that employs energy harvesting techniques to maximize node lifetime and an embedded residential gateway that offers user interaction and secure connectivity to the outside world. The advantages of our system are its scalability, low power, self sufficiency and versatility.
Interleave division multiple-access

This paper provides a comprehensive study of interleave-division multiple-access (IDMA) systems. The IDMA receiver principles for different modulation and channel conditions are outlined. A semi-analytical technique is developed based on the density evolution technique to estimate the bit-error-rate (BER) of the system. It provides a fast and relatively accurate method to predict the performance of the IDMA scheme. With simple convolutional/repetition codes, overall throughputs of 3 bits/chip with one receive antenna and 6 bits/chip with two receive antennas are observed for IDMA systems involving as many as about 100 users.
Simultaneous HPLC determination of caffeine, theobromine, and theophylline in food, drinks, and herbal products.

A rapid and selective high-performance liquid chromatographic (HPLC) method is developed for the separation and determination of caffeine, theobromine, and theophylline. The chromatography is performed on a Zorbax Eclipse XDB-C8 column (4.6x150 mm i.d., 5-microm particle size) at 25 degrees C, with a mobile phase of water-THF (0.1% THF in water, pH 8)-acetonitrile (90:10, v/v). The flow rate is 0.8 mL/min, and detection is by UV at 273 nm. This method permits the simultaneous determination of caffeine, theobromine, and theophylline in food, drinks, and herbal products with detection limits of 0.07-0.2 mg/L and recoveries of 100.20-100.42%. Correlation coefficients, for the calibration curves in the linear range of 0.2-100 mg/L, are greater than 0.9999 for all compounds. The within- and between-day precision is determined for both retention times and peak area. The data suggests that the proposed HPLC method can be used for routine quality control of food, drinks, and herbal products.
Broad Learning for Healthcare

A broad spectrum of data from different modalities are generated in the healthcare domain every day, including scalar data (e.g., clinical measures collected at hospitals), tensor data (e.g., neuroimages analyzed by research institutes), graph data (e.g., brain connectivity networks), and sequence data (e.g., digital footprints recorded on smart sensors). Capability for modeling information from these heterogeneous data sources is potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Our works in this thesis attempt to facilitate healthcare applications in the setting of broad learning which focuses on fusing heterogeneous data sources for a variety of synergistic knowledge discovery and machine learning tasks. We are generally interested in computer-aided diagnosis, precision medicine, and mobile health by creating accurate user profiles which include important biomarkers, brain connectivity patterns, and latent representations. In particular, our works involve four different data mining problems with application to the healthcare domain: multi-view feature selection, subgraph pattern mining, brain network embedding, and multi-view sequence prediction.
Automatic Classification of Eating and Ruminating in Cattle Using a Collar Mounted Accelerometer

Within the past ten years, methods for automating the process of monitoring the behaviour of cattle have become increasingly important. Within the UK, there has been a steady decline in the number of milk producers and increased commercial pressures have forced increasing consolidation within dairy farming. As a result the average farm size has grown from around 90 to 160 cows. A direct consequence of these trends is that the farmers have less time to observe their herd and are increasingly reliant on technology to undertake this function, most readily underlined with the growth in the use of oestrus or ‘heat’ detection collars to assist in the optimisation of fertility. There is also a desire to derive additional information for collars that have to date been utilised solely to indicate the onset of heat The paper reports on the analysis of signatures obtained from an accelerometer based collar (Silent Herdsman) to identify both eating and rumination signatures, identified through a combination of frequency and statistical analysis. A range of post processing methods have been evaluated in order to determine the most appropriate for integration within a low power processor on the collar. Trials have been carried out using a rumination sensing halter to provide verification data. Analysis of this data over a period of several days, on a minute by minute basis has shown that it is possible to recover eating and rumination with sensitivity and positive predictive value greater than 85%.
A survey - data mining frameworks in credit card processing

During the last two decades, the credit card system has been widely used as a mechanism to drive the global economy to grow dramatically. A credit card provider has issued millions of credit cards to its customers. However, issuing credit cards to wrong customers can be a crucial factor of a financial crisis, e.g., the ones happened in 1997 and 2008. This paper presents a systematic analysis and a comprehensive review of data mining techniques and their applications in the credit card process which we divide into 4 main activities. We have studied research works which were published between 2007 and the first quarter of 2015 inclusively. Our work focuses on data mining techniques applied specifically in the credit card process, and this makes our review different from others' which emphasize much wider areas. As a result, this survey can be useful for any credit card provider to select an appropriate solution for their problem and, also, for researchers to have a comprehensive view of the literature in this area.
A re-examination of Montreal Cognitive Assessment (MoCA) cutoff scores.

The Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) is a cognitive screening tool that aims to differentiate healthy cognitive aging from Mild Cognitive Impairment (MCI). Several validation studies have been conducted on the MoCA, in a variety of clinical populations. Some studies have indicated that the originally suggested cutoff score of 26/30 leads to an inflated rate of false positives, particularly for those of older age and/or lower education. We conducted a systematic review and meta-analysis of the literature to determine the diagnostic accuracy of the MoCA for differentiating healthy cognitive aging from possible MCI.



Of the 304 studies identified, nine met inclusion criteria for the meta-analysis. These studies were assessed across a range of cutoff scores to determine the respective sensitivities, specificities, positive and negative predictive accuracies, likelihood ratios for positive and negative results, classification accuracies, and Youden indices.



Meta-analysis revealed a cutoff score of 23/30 yielded the best diagnostic accuracy across a range of parameters.



A MoCA cutoff score of 23, rather than the initially recommended score of 26, lowers the false positive rate and shows overall better diagnostic accuracy. We recommend the use of this cutoff score going forward.
Beyond Black and White : An Examination of Afrocentric Facial Features and Sex in Criminal Sentencing

This Thesis is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. For more information, please contact pdxscholar@pdx.edu. Recommended Citation Petersen, Amanda Mae, "Beyond Black and White: An Examination of Afrocentric Facial Features and Sex in Criminal Sentencing" (2014). Dissertations and Theses.
THEORY OF HUMAN CARING

The Theory of Human Caring was developed between 1975-1979, while engaged in teaching at the University of Colorado; it emerged from my own views of nursing, combined and informed by my doctoral studies in educational-clinical and social psychology. It was my initial attempt to bring meaning and focus to nursing as an emerging discipline and distinct health profession with its own unique values, knowledge and practices, with its own ethic and mission to society. The work also was influenced by my involvement with an integrated academic nursing curriculum and efforts to find common meaning and order to nursing that transcended settings, populations, specialty, subspecialty areas, and so forth.
Deadlock Control of Automated Manufacturing Systems Based on Petri Nets—A Literature Review

Deadlocks are a rather undesirable situation in a highly automated flexible manufacturing system. Their occurrences often deteriorate the utilization of resources and may lead to catastrophic results in safety-critical systems. Graph theory, automata, and Petri nets are three important mathematical tools to handle deadlock problems in resource allocation systems. Particularly, Petri nets are considered as a popular formalism because of their inherent characteristics. They received much attention over the past decades to deal with deadlock problems, leading to a variety of deadlock-control policies. This study surveys the state-of-the-art deadlock-control strategies for automated manufacturing systems by reviewing the principles and techniques that are involved in preventing, avoiding, and detecting deadlocks. The focus is deadlock prevention due to its large and continuing stream of efforts. A control strategy is evaluated in terms of computational complexity, behavioral permissiveness, and structural complexity of its deadlock-free supervisor. This study provides readers with a conglomeration of the updated results in this area and facilitates engineers in finding a suitable approach for their industrial scenarios. Future research directions are finally discussed.
Mortality, mauling, and maiming by vicious dogs.

Maiming and death due to dog bites are uncommon but preventable tragedies. We postulated that patients admitted to a level I trauma center with dog bites would have severe injuries and that the gravest injuries would be those caused by pit bulls.



We reviewed the medical records of patients admitted to our level I trauma center with dog bites during a 15-year period. We determined the demographic characteristics of the patients, their outcomes, and the breed and characteristics of the dogs that caused the injuries.



Our Trauma and Emergency Surgery Services treated 228 patients with dog bite injuries; for 82 of those patients, the breed of dog involved was recorded (29 were injured by pit bulls). Compared with attacks by other breeds of dogs, attacks by pit bulls were associated with a higher median Injury Severity Scale score (4 vs. 1; P = 0.002), a higher risk of an admission Glasgow Coma Scale score of 8 or lower (17.2% vs. 0%; P = 0.006), higher median hospital charges ($10,500 vs. $7200; P = 0.003), and a higher risk of death (10.3% vs. 0%; P = 0.041).



Attacks by pit bulls are associated with higher morbidity rates, higher hospital charges, and a higher risk of death than are attacks by other breeds of dogs. Strict regulation of pit bulls may substantially reduce the US mortality rates related to dog bites.
SenSprout: inkjet-printed soil moisture and leaf wetness sensor

In this paper we show a low cost and environmentally friendly fabrication for an agricultural sensing application. An antenna, a soil moisture sensor, and a leaf wetness sensor are inkjet-printed on paper substrate. A microprocessor attached to the paper substrate is capable of detecting the capacitance change on the surface of the sensor, and report the data over the wireless communication interface. This sensing system is useful to optimize irrigation systems.
The three-dimensional structure of foot-and-mouth disease virus at 2.9 Å resolution

The structure of foot-and-mouth disease virus has been determined at close to atomic resolution by X-ray diffraction without experimental phase information. The virus shows similarities with other picornaviruses but also several unique features. The canyon or pit found in other picornaviruses is absent; this has important implications for cell attachment. The most immunogenic portion of the capsid, which acts as a potent peptide vaccine, forms a disordered protrusion on the virus surface.
Links between creativity and mental disorder.

A link between mental disorder and decreased ability is commonly assumed, but evidence to the contrary also exists. In reviewing any association between creativity and mental disorder, our aim is not only to update the literature but also to include an epidemiological and theoretical discussion of the topic. For literature retrieval, we used Medline, PsycINFO, and manual literature searches. Studies are numerous: most are empirical, many having methodological difficulties and variations in definitions and concepts. There is little consensus. However, some trends are apparent. We found 13 major case series (over 100 cases), case-control studies, or population-based studies, with valid, reliable measures of mental disorders. The results of all but one of these studies supported the association, at least when concerning particular groups of mental disorders; the findings were somewhat unclear in two studies. Most of the remainder that are not included in our more detailed examination also show a fragile association between creativity and mental disorder, but the link is not apparent for all groups of mental disorders or for all forms of creativity. In conclusion, evidence exists to support some form of association between creativity and mental disorder, but the direction of any causal link remains obscure.