nils-herrmann/clean_scirepeval huggingface dataset:
Hierarchical Clustering Algorithms for Document Datasets

Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, clustering algorithms that build meaningful hierarchies out of large document collections are ideal tools for their interactive visualization and exploration as they provide data-views that are consistent, predictable, and at different levels of granularity. This paper focuses on document clustering algorithms that build such hierarchical solutions and (i) presents a comprehensive study of partitional and agglomerative algorithms that use different criterion functions and merging schemes, and (ii) presents a new class of clustering algorithms called constrained agglomerative algorithms, which combine features from both partitional and agglomerative approaches that allows them to reduce the early-stage errors made by agglomerative methods and hence improve the quality of clustering solutions. The experimental evaluation shows that, contrary to the common belief, partitional algorithms always lead to better solutions than agglomerative algorithms; making them ideal for clustering large document collections due to not only their relatively low computational requirements, but also higher clustering quality. Furthermore, the constrained agglomerative methods consistently lead to better solutions than agglomerative methods alone and for many cases they outperform partitional methods, as well.
DC-DC Converter Design for Battery-Operated Systems

Abstrurt This paper describes performance, design and optimization of de-dc converters for energy limited, battery operated systems. Variable-frequency operation is used to achieve voltage regulation and high efficiency for an extremely wide range of load currents. An experimental 15W, 3.3V buck converter has been constructed to demonstrate design and optimization techniques. The converter employs synchronous rectification to reduce conduction losses, and discontinuous, variable-frequency, current-mode control with optimum peak current to maximize efficiency for a wide range o f loads. Applications include portable computers, hand-held instruments, and telecommunications.
Maneuver-Based Trajectory Planning for Highly Autonomous Vehicles on Real Road With Traffic and Driver Interaction

This paper presents the design and first test on a simulator of a vehicle trajectory-planning algorithm that adapts to traffic on a lane-structured infrastructure such as highways. The proposed algorithm is designed to run on a fail-safe embedded environment with low computational power, such as an engine control unit, to be implementable in commercial vehicles of the near future. The target platform has a clock frequency of less than 150 MHz, 150 kB RAM of memory, and a 3-MB program memory. The trajectory planning is performed by a two-step algorithm. The first step defines the feasible maneuvers with respect to the environment, aiming at minimizing the risk of a collision. The output of this step is a target group of maneuvers in the longitudinal direction (accelerating or decelerating), in the lateral direction (changing lanes), and in the combination of both directions. The second step is a more detailed evaluation of several possible trajectories within these maneuvers. The trajectories are optimized to additional performance indicators such as travel time, traffic rules, consumption, and comfort. The output of this module is a trajectory in the vehicle frame that represents the recommended vehicle state (position, heading, speed, and acceleration) for the following seconds.
Frequency-based anomaly detection for the automotive CAN bus

The modern automobile is controlled by networked computers. The security of these networks was historically of little concern, but researchers have in recent years demonstrated their many vulnerabilities to attack. As part of a defence against these attacks, we evaluate an anomaly detector for the automotive controller area network (CAN) bus. The majority of attacks are based on inserting extra packets onto the network. But most normal packets arrive at a strict frequency. This motivates an anomaly detector that compares current and historical packet timing. We present an algorithm that measures inter-packet timing over a sliding window. The average times are compared to historical averages to yield an anomaly signal. We evaluate this approach over a range of insertion frequencies and demonstrate the limits of its effectiveness. We also show how a similar measure of the data contents of packets is not effective for identifying anomalies. Finally we show how a one-class support vector machine can use the same information to detect anomalies with high confidence.
On the exploitation of a high-throughput SHA-256 FPGA design for HMAC

High-throughput and area-efficient designs of hash functions and corresponding mechanisms for Message Authentication Codes (MACs) are in high demand due to new security protocols that have arisen and call for security services in every transmitted data packet. For instance, IPv6 incorporates the IPSec protocol for secure data transmission. However, the IPSec's performance bottleneck is the HMAC mechanism which is responsible for authenticating the transmitted data. HMAC's performance bottleneck in its turn is the underlying hash function. In this article a high-throughput and small-size SHA-256 hash function FPGA design and the corresponding HMAC FPGA design is presented. Advanced optimization techniques have been deployed leading to a SHA-256 hashing core which performs more than 30% better, compared to the next better design. This improvement is achieved both in terms of throughput as well as in terms of throughput/area cost factor. It is the first reported SHA-256 hashing core that exceeds 11Gbps (after place and route in Xilinx Virtex 6 board).
Dynamic Locomotion Gaits of a Compliantly Actuated Quadruped With SLIP-Like Articulated Legs Embodied in the Mechanical Design

The spring loaded inverted pendulum (SLIP) model has been extensively shown to be fundamental for legged locomotion. However, the way this low-order template model dynamics is anchored in high-dimensional articulated multibody systems describing compliantly actuated robots (and animals) is not obvious and has not been shown so far. In this letter, an articulated leg mechanism and a corresponding quadrupedal robot design are introduced, for which the natural oscillation dynamics is structurally equivalent to the SLIP. On the basis of this property, computationally simple and robust control methods are proposed, which implement the gaits of pronking, trotting, and dynamic walking in the real robotic system. Experiments with a compliantly actuated quadruped featuring only low-performance electrical drives validate the effectiveness of the proposed approach.
Complex PTSD, affect dysregulation, and borderline personality disorder

Complex PTSD (cPTSD) was formulated to include, in addition to the core PTSD symptoms, dysregulation in three psychobiological areas: (1) emotion processing, (2) self-organization (including bodily integrity), and (3) relational security. The overlap of diagnostic criteria for cPTSD and borderline personality disorder (BPD) raises questions about the scientific integrity and clinical utility of the cPTSD construct/diagnosis, as well as opportunities to achieve an increasingly nuanced understanding of the role of psychological trauma in BPD. We review clinical and scientific findings regarding comorbidity, clinical phenomenology and neurobiology of BPD, PTSD, and cPTSD, and the role of traumatic victimization (in general and specific to primary caregivers), dissociation, and affect dysregulation. Findings suggest that BPD may involve heterogeneity related to psychological trauma that includes, but extends beyond, comorbidity with PTSD and potentially involves childhood victimization-related dissociation and affect dysregulation consistent with cPTSD. Although BPD and cPTSD overlap substantially, it is unwarranted to conceptualize cPTSD either as a replacement for BPD, or simply as a sub-type of BPD. We conclude with implications for clinical practice and scientific research based on a better differentiated view of cPTSD, BPD and PTSD.
Identifying and retrieving distress images from road pavement surveys

This paper proposes a system capable of identifying and retrieving distress images from a road pavement survey image database. A database of images acquired during road pavement surface surveys along Portuguese roads is considered. Regions corresponding to cracks are detected over the acquired images, based on a subdivision of the images into a set of non-overlapping windows, which may be classified as containing cracks, or not. Crack detection results, represented by binary images where windows containing crack pixels are set to one, undergo a second classification stage to distinguish several crack types. This classification follows the structure proposed by the Portuguese Distresses Catalog, produced by the national entity in charge of road maintenance. Three crack types are identified at this stage: longitudinal cracks, transversal cracks and miscellaneous cracks. The experimental results, obtained by processing real survey imagery over Portuguese roads, present encouraging results for automating the process of identifying road distresses from images.
Detection and Recognition of Malaysian Special License Plate Based On SIFT Features

Automated car license plate recognition systems are developed and applied for purpose of facilitating the surveillance, law enforcement, access control and intelligent transportation monitoring with least human intervention. In this paper, an algorithm based on SIFT feature points clustering and matching is proposed to address the issue of recognizing Malaysian special plates. These special plates do not follow the normal standard car plates’ format as they may contain italic, cursive, connected and small letters. The algorithm is tested with 150 Malaysian special plate images under different environment and the promising experimental results demonstrate that the proposed algorithm is relatively robust. Keywords-license plate recognition, scale invariant feature transform (SIFT), feature extraction, homography, special license plate.
The Evolvable Mars Campaign - study status

NASA is developing a long-term strategy for achieving extended human missions to Mars in support of the policies outlined in the 2010 NASA Authorization Act and National Space Policy. The Authorization Act states that “A long term objective for human exploration of space should be the eventual international exploration of Mars.” Echoing this is the National Space Policy, which directs that NASA should, “By 2025, begin crewed missions beyond the moon, including sending humans to an asteroid. By the mid-2030s, send humans to orbit Mars and return them safely to Earth.” Further defining this goal, NASA's 2014 Strategic Plan identifies that “Our long-term goal is to send humans to Mars. Over the next two decades, we will develop and demonstrate the technologies and capabilities needed to send humans to explore the red planet and safely return them to Earth.” To accomplish these goals, NASA is employing a capability-driven approach to its human spaceflight strategy. This approach is based on developing a suite of evolving capabilities that provide specific functions to solve exploration challenges. These capabilities can be leveraged and reused, enabling more complex operations over time and exploration of more distant solar system destinations. The Evolvable Mars Campaign is an ongoing study identifying potential exploration options leading to sustainable human exploration of Mars. This campaign will leverage existing activities, adapt to capability developments, scientific discovery, and ever-changing programmatic environments. The results of this study will not produce “The Plan” for sending humans to Mars, but instead develop potential human Mars exploration strategies to inform NASA management on key decision options and investment priorities. This paper provides a summary of the 2014 study activities and key findings to date.
Online Learning for Adversaries with Memory: Price of Past Mistakes

The framework of online learning with memory naturally captures learning problems with temporal effects, and was previously studied for the experts setting. In this work we extend the notion of learning with memory to the general Online Convex Optimization (OCO) framework, and present two algorithms that attain low regret. The first algorithm applies to Lipschitz continuous loss functions, obtaining optimal regret bounds for both convex and strongly convex losses. The second algorithm attains the optimal regret bounds and applies more broadly to convex losses without requiring Lipschitz continuity, yet is more complicated to implement. We complement the theoretical results with two applications: statistical arbitrage in finance, and multi-step ahead prediction in statistics.
LoRa-based localization systems for noisy outdoor environment

LoRa-based (Long Range Communication based) localization systems make use of RSSI (Received Signal Strength Indicator) to locate an object properly in an outdoor environment for IoT (Internet of Things) applications in smart environment and urban networking. The accuracy of localization is highly degraded, however, by noisy environments (e.g., electronic interference, blocking). To address this issue, this paper proposes two new localization algorithms to improve localization accuracy. Furthermore, the paper develops a winner selection strategy to select a winner from the traditional localization algorithm and the two new algorithms to minimize localization errors. Our simulations and real experiments found that the two new algorithms can significantly improve localization accuracy. Additionally, the winner selection strategy effectively selects a winner from the three algorithms to improve localization accuracy further. Using our proposed algorithms in some real experiments, the localization error is only a few meters in a real measurement field longer than 100 m.
Cluster Forests

With inspiration from Random Forests (RF) in the context of classification, a new clustering ensemble method—Cluster Forests (CF) is proposed. Geometrically, CF randomly probes a high-dimensional data cloud to obtain “good local clusterings” and then aggregates via spectral clustering to obtain cluster assignments for the whole dataset. The search for good local clusterings is guided by a cluster quality measure kappa. CF progressively improves each local clustering in a fashion that resembles the tree growth in RF. Empirical studies on several real-world datasets under two different performance metrics show that CF compares favorably to its competitors. Theoretical analysis reveals that the kappa measure makes it possible to grow the local clustering in a desirable way—it is “noiseresistant”. A closed-form expression is obtained for the mis-clustering rate of spectral clustering under a perturbation model, which yields new insights into some aspects of spectral clustering.
Exploring Various Knowledge in Relation Extraction

Extracting semantic relationships between entities is challenging. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using SVM. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. We also demonstrate how semantic information such as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types.
Relational duality: unsupervised extraction of semantic relations between entities on the web

Extracting semantic relations among entities is an important first step in various tasks in Web mining and natural language processing such as information extraction, relation detection, and social network mining. A relation can be expressed extensionally by stating all the instances of that relation or intensionally by defining all the paraphrases of that relation. For example, consider the ACQUISITION relation between two companies. An extensional definition of ACQUISITION contains all pairs of companies in which one company is acquired by another (e.g. (YouTube, Google) or (Powerset, Microsoft)). On the other hand we can intensionally define ACQUISITION as the relation described by lexical patterns such as X is acquired by Y, or Y purchased X, where X and Y denote two companies. We use this dual representation of semantic relations to propose a novel sequential co-clustering algorithm that can extract numerous relations efficiently from unlabeled data. We provide an efficient heuristic to find the parameters of the proposed coclustering algorithm. Using the clusters produced by the algorithm, we train an L1 regularized logistic regression model to identify the representative patterns that describe the relation expressed by each cluster. We evaluate the proposed method in three different tasks: measuring relational similarity between entity pairs, open information extraction (Open IE), and classifying relations in a social network system. Experiments conducted using a benchmark dataset show that the proposed method improves existing relational similarity measures. Moreover, the proposed method significantly outperforms the current state-of-the-art Open IE systems in terms of both precision and recall. The proposed method correctly classifies 53 relation types in an online social network containing 470; 671 nodes and 35; 652; 475 edges, thereby demonstrating its efficacy in real-world relation detection tasks.
Computational Thinking in Practice : How STEM Professionals Use CT in Their Work

The goal of this study is to bring current computational thinking in STEM educational efforts in line with the increasingly computational nature of STEM research practices. We conducted interviews with STEM practitioners in various fields to understand the nature of CT as it happens in authentic research settings and to revisit a first iteration of our definition of CT in form of a taxonomy. This exploration gives us insight into how scientists use computers in their work and help us identify what practices are important to include in high school STEM learning contexts. Our findings will inform the design of classroom activities to better prepare today’s students for the modern STEM landscape that awaits them.
The Generalized Ridge Trace Plot : Visualizing Bias and Precision Michael Friendly Psychology

In ridge regression and related shrinkage methods, the ridge trace plot, a plot of estimated coefficients against a shrinkage parameter, is a common graphical adjunct to help determine a favorable trade-off of bias against precision (inverse variance) of the estimates. However, standard unidimensional versions of this plot are ill-suited for this purpose because they show only bias directly and ignore the multidimensional nature of the problem. A generalized version of the ridge trace plot is introduced, showing covariance ellipsoids in parameter space, whose centers show bias and whose size and shape show variance and covariance, respectively, in relation to the criteria for which these methods were developed. These provide a direct visualization of both bias and precision. Even two-dimensional bivariate versions of this plot show interesting features not revealed in the standard univariate version. Low-rank versions of this plot, based on an orthogonal transformation of predictor space extend these ideas to larger numbers of predictor variables, by focusing on the dimensions in the space of predictors that are likely to be most informative about the nature of bias and precision. Two well-known datasets are used to illustrate these graphical methods. The genridge package for R implements computation and display.
Phase-Shifted Full Bridge DC-DC Converter with Energy Recovery Clamp and Reduced Circulating Current

This paper introduces a full bridge converter with reduced circulating current and a reduction of the switch conduction and turn-off losses achieved by an energy recovery secondary clamp circuit. The proposed clamp circuit allows the converter to operate with wide input or output voltage range with minimum voltage stress across the output diodes. The paper describes an experimental 25 kW (1500 V, 16.7 A) full bridge converter operating at 32 kHz switching frequency. Losses and efficiency of the experimental prototype compare favorably against the standard full bridge and previously proposed zero-voltage/zero-current switching configurations.
SOBER: statistical model-based bug localization

Automated localization of software bugs is one of the essential issues in debugging aids. Previous studies indicated that the evaluation history of program predicates may disclose important clues about underlying bugs. In this paper, we propose a new statistical model-based approach, called SOBER, which localizes software bugs without any prior knowledge of program semantics. Unlike existing statistical debugging approaches that select predicates correlated with program failures, SOBER models evaluation patterns of predicates in both correct and incorrect runs respectively and regards a predicate as bug-relevant if its evaluation pattern in incorrect runs differs significantly from that in correct ones. SOBER features a principled quantification of the pattern difference that measures the bug-relevance of program predicates.We systematically evaluated our approach under the same setting as previous studies. The result demonstrated the power of our approach in bug localization: SOBER can help programmers locate 68 out of 130 bugs in the Siemens suite when programmers are expected to examine no more than 10% of the code, whereas the best previously reported is 52 out of 130. Moreover, with the assistance of SOBER, we found two bugs in bc 1.06 (an arbitrary precision calculator on UNIX/Linux), one of which has never been reported before.
Fuzzy logic in control systems: fuzzy logic controller. I

During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of fuzzy set theory, especially in the realm of industrial processes which do not lead them selves to control by conventional methods because of a lack of quantitative data regarding the input-output relations. Fuzzy control is based on fuzzy logic a logical system which is much closer in spirit to human thinking and natural language than traditional logical systems. The Fuzzy Logic Controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy. This paper presents a survey of the FLC; describes a general methodology for constructing an FLC and assessing its per formance; and points to problems which need further research. In particular, our exposition includes a discussion of fuzzification and denazification stra tegies, the derivation of the database and fuzzy control rules, the definition of fuzzy implication, and an analysis of fuzzy reasoning mechanisms.