nils-herrmann/clean_scirepeval huggingface dataset:
Three-Port Triple-Half-Bridge Bidirectional Converter With Zero-Voltage Switching

A three-port triple-half-bridge bidirectional dc-dc converter topology is proposed in this paper. The topology comprises a high-frequency three-winding transformer and three half-bridges, one of which is a boost half-bridge interfacing a power port with a wide operating voltage. The three half-bridges are coupled by the transformer, thereby providing galvanic isolation for all the power ports. The converter is controlled by phase shift, which achieves the primary power flow control, in combination with pulsewidth modulation (PWM). Because of the particular structure of the boost half-bridge, voltage variations at the port can be compensated for by operating the boost half-bridge, together with the other two half-bridges, at an appropriate duty cycle to keep a constant voltage across the half-bridge. The resulting waveforms applied to the transformer windings are asymmetrical due to the automatic volt-seconds balancing of the half-bridges. With the PWM control it is possible to reduce the rms loss and to extend the zero-voltage switching operating range to the entire phase shift region. A fuel cell and supercapacitor generation system is presented as an embodiment of the proposed multiport topology. The theoretical considerations are verified by simulation and with experimental results from a 1 kW prototype.
Analysis and Evolution of Journaling File Systems

We develop and apply two new methods for analyzing file system behavior and evaluating file system changes. First, semantic block-level analysis (SBA) combines knowledge of on-disk data structures with a trace of disk traffic to infer file syste m behavior; in contrast to standard benchmarking approaches, S BA enables users to understand why the file system behaves as it does. Second, semantic trace playback (STP) enables traces of disk traffic to be easily modified to represent changes in the fi le system implementation; in contrast to directly modifying t he file system, STP enables users to rapidly gauge the benefits of new policies. We use SBA to analyze Linux ext3, ReiserFS, JFS, and Windows NTFS; in the process, we uncover many strengths and weaknesses of these journaling file systems. We also appl y STP to evaluate several modifications to ext3, demonstratin g the benefits of various optimizations without incurring the cos ts of a real implementation.
COMPARATIVE EVALUATION OF EXPERIMENTAL APPROACHES TO THE STUDY OF HABITAT FRAGMENTATION EFFECTS

Ecologists have used a variety of comparative mensurative and manipulative experimental approaches to study the biological consequences of habitat fragmentation. In this paper, we evaluate the merits of the two major approaches and offer guidelines for selecting a design. Manipulative experiments rigorously assess fragmentation effects by comparing preand post-treatment conditions. Yet they are often constrained by a number of practical limitations, such as the difficulty in implementing large-scale treatments and the impracticality of measuring the long-term (decades to centuries) responses to the imposed treatments. Comparative mensurative studies generally involve substituting space for time, and without pre-treatment control, can be constrained by variability in ecological characteristics among different landscapes. These confounding effects can seriously limit the strength of inferences. Depending on the scale of the study system and how ‘‘landscape’’ is defined, both approaches may be limited by the difficulty of replicating at the landscape scale. Overall, both mensurative and manipulative approaches have merit and can contribute to the body of knowledge on fragmentation. However, from our review of 134 fragmentation studies published recently in three major ecological journals, it is evident that most manipulative and mensurative fragmentation experiments have not provided clear insights into the ecological mechanisms and effects of habitat fragmentation. We discuss the reasons for this and conclude with recommendations for improving the design and implementation of fragmentation experiments.
Context-aware Frame-Semantic Role Labeling

Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations

Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more structured graphical representations. This work explores the possibility of learning generic latent relational graphs that capture dependencies between pairs of data units (e.g., words or pixels) from large-scale unlabeled data and transferring the graphs to downstream tasks. Our proposed transfer learning framework improves performance on various tasks including question answering, natural language inference, sentiment analysis, and image classification. We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden units), or embedding-free units such as image pixels.
Brand Concepts as Representations of Human Values : Do Cultural Congruity and Compatibility Between Values Matter ?

meaning (overall M = 5.89) and significantly higher than with any of the other three abstract meanings (overall M = 2.05, all ps < .001). Procedure. Under a cover story of studying advertising slogans, participants saw one of the 22 target brands and thought about its abstract concept in memory. They were then presented, on a single screen, with four alternative slogans (in random order) for the target brand and were asked to rank the slogans, from 1 (“best”) to 4 (“worst”), in terms of how well the slogan fits the image of the target brand. Each slogan was intended to distinctively communicate the abstract meaning associated with one of the four high-levelmeaning associated with one of the four high-level brand value dimensions uncovered in the pilot study. After a series of filler tasks, participants indicated their attitude toward the brand on a seven-point scale (1 = “very unfavorable,” and 7 = “very favorable”). Ranking of the slogans. We conducted separate nonparametric Kruskal-Wallis tests on each country’s data to evaluate differences in the rank order for each of the four slogans among the four types of brand concepts. In all countries, the tests were significant (the United States: all 2(3, N = 539) ≥ 145.4, all ps < .001; China: all 2(3, N = 208) ≥ 52.8, all ps < .001; Canada: all 2(3, N = 380) ≥ 33.3, all ps < .001; Turkey: all 2(3, N = 380) ≥ 51.0, all ps < .001). We pooled the data from the four countries and conducted follow-up tests to evaluate pairwise differences in the rank order of each slogan among the four brand concepts, controlling for Type I error across tests using the Bonferroni approach. The results of these tests indicated that each slogan was ranked at the top in terms of favorability when it matched the brand concept (self-enhancement brand concept: Mself-enhancement slogan = 1.77; openness brand FIGURE 2 Structural Relations Among Value Dimensions from Multidimensional Scaling ( b = benevolence, t = tradition, c = conformity, sec = security S e l f E n h a n c e m e n t IN D VID U A L C O N C ER N S C O LL EC TI VE C O N C ER N S
Formal models for expert finding in enterprise corpora

Searching an organization's document repositories for experts provides a cost effective solution for the task of expert finding. We present two general strategies to expert searching given a document collection which are formalized using generative probabilistic models. The first of these directly models an expert's knowledge based on the documents that they are associated with, whilst the second locates documents on topic, and then finds the associated expert. Forming reliable associations is crucial to the performance of expert finding systems. Consequently, in our evaluation we compare the different approaches, exploring a variety of associations along with other operational parameters (such as topicality). Using the TREC Enterprise corpora, we show that the second strategy consistently outperforms the first. A comparison against other unsupervised techniques, reveals that our second model delivers excellent performance.
Scene Text Detection and Recognition: The Deep Learning Era

With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inevitable influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, methodology and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and the grand challenges still remained. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected and compiled in our Github repository: https://github.com/Jyouhou/SceneTextPapers.
An Approach for Weakly-Supervised Deep Information Retrieval

(1) Disregard pseudo-queries that do not retrieve their pseudo-relevant document in the top nrank. (2) Select the top nneg retrieved documents are negative training examples.  Generate mock interaction embeddings and filter training examples down to those the most nearly match a set of template query-document pairs (given a distance function). Since interaction embeddings specific to what a model “sees,” interaction filters are model-specific.
A Deep Encoder-Decoder Networks for Joint Deblurring and Super-Resolution

In this paper, we propose an end-to-end convolution neural network (CNN) to restore a clear high-resolution image from a severely blurry image. It's a highly ill-posed problem and brings tremendous challenges to state-of-art deblurring or super-resolution (SR) methods. A straightforward way to solve this problem is to concatenate two types of networks directly. However, experiments show that the concatenation of independent networks increases computation complexity instead of generating satisfying high-resolution images. Consequently, we focus on designing a single deep network to solve the deblurring and SR problems in parallel. Our method, called ED-DSRN, extends the traditional Super-Resolution network by adding a deblurring branch that shares the same feature maps extracted from an encoder-decoder module with the original SR branch. Extensive experiments show that our method produces remarkable deblurred and super-resolved images simultaneously with high efficiency.
Learning with Compositional Semantics as Structural Inference for Subsentential Sentiment Analysis

Determining the polarity of a sentimentbearing expression requires more than a simple bag-of-words approach. In particular, words or constituents within the expression can interact with each other to yield a particular overall polarity. In this paper, we view such subsentential interactions in light of compositional semantics, and present a novel learningbased approach that incorporates structural inference motivated by compositional semantics into the learning procedure. Our experiments show that (1) simple heuristics based on compositional semantics can perform better than learning-based methods that do not incorporate compositional semantics (accuracy of 89.7% vs. 89.1%), but (2) a method that integrates compositional semantics into learning performs better than all other alternatives (90.7%). We also find that “contentword negators”, not widely employed in previous work, play an important role in determining expression-level polarity. Finally, in contrast to conventional wisdom, we find that expression-level classification accuracy uniformly decreases as additional, potentially disambiguating, context is considered.
Effective targeting of solid tumors in patients with locally advanced cancers by radiolabeled pegylated liposomes.

The biodistribution and pharmacokinetics of (111)In-DTPA-labeled pegylated liposomes (IDLPL) were studied in 17 patients with locally advanced cancers. The patients received 65-107 MBq of IDLPL, and nuclear medicine whole body gamma camera imaging was used to study liposome biodistribution. The t(1/2beta) of IDLPL was 76.1 h. Positive tumor images were obtained in 15 of 17 studies (4 of 5 breast, 5 of 5 head and neck, 3 of 4 bronchus, 2 of 2 glioma, and 1 of 1 cervix cancer). The levels of tumor liposome uptake estimated from regions of interest on gamma camera images were approximately 0.5-3.5% of the injected dose at 72 h. The greatest levels of uptake were seen in the patients with head and neck cancers [33.0 +/- 15.8% ID/kg (percentage of injected dose/kg)]. The uptake in the lung tumors was at an intermediate level (18.3 +/- 5.7% ID/kg), and the breast cancers showed relatively low levels of uptake (5.3 +/- 2.6% ID/kg). These liposome uptake values mirrored the estimated tumor volumes of the various tumor types (36.2 +/- 18.0 cm3 for squamous cell cancer of the head and neck, 114.5 +/- 42.0 cm3 for lung tumors, and 234.7 +/- 101.4 cm3 for breast tumors). In addition, significant localization of the liposomes was seen in the tissues of the reticuloendothelial system (liver, spleen, and bone marrow). One patient with extensive mucocutaneous AIDS-related Kaposi sarcoma was also studied according to a modified protocol, and prominent deposition of the radiolabeled liposomes was demonstrated in these lesions. An additional two patients with resectable head and neck cancer received 26 MBq of IDLPL 48 h before undergoing surgical excision of their tumors. Samples of the tumor, adjacent normal mucosa, muscle, fat, skin, and salivary tissue were obtained at operation. The levels of tumor uptake were 8.8 and 15.9% ID/kg, respectively, with tumor uptake exceeding that in normal mucosa by a mean ratio of 2.3:1, in skin by 3.6:1, in salivary gland by 5.6:1, in muscle by 8.3:1, and in fat by 10.8:1. These data strongly support the development of pegylated liposomal agents for the treatment of solid tumors, particularly those of the head and neck.
Contemporary post-Humanism: teCHnologiCal and Human singularity

Posthumanism entails the idea of transcendence of the human being achieved through technology. The article begins by distinguishing perfection and change (or growth). It also attempts to show the anthropological premises of posthumanism itself and suggests that we can identify two roots: the liberal humanistic subject (autonomous and unrelated that simply realizes herself/himself through her/his own project) and the interpretation of thought as a computable process. Starting from these premises, many authors call for the loosening of the clear boundaries of one’s own subject in favour of blending with other beings. According to these theories, we should become post-human: if the human being is thought and thought is a computable process, whatever is able to process information broader and faster is better than the actual human being and has to be considered as the way towards the real completeness of the human being itself. The paper endeavours to discuss the adequacy of these premises highlighting the structural dependency of the human being, the role of the human body, the difference between thought and a computational process, the singularity of some useless and unexpected human acts. It also puts forward the need for axiological criteria to define growth as perfectionism.
Illegal, Inappropriate, And Unethical Behavior In An Information Technology Context: A Study To Explain Influences

A recent study by Banerjee et al. (1998) proposed and tested an information technology (IT) ethics model. They found that personal normative beliefs, organizational ethical climate, and organization-scenario were significant indicators of ethical behavioral intention. Moreover, they found that factors affecting ethical intention are situational and depend upon the ethical dilemma. Further research was suggested and recommended, among other things, replications with different samples. The present study furthers the development/validation of the IT ethical model by utilizing a large sample of students in the same organizational climate (a university).
Abandoning Objectives: Evolution Through the Search for Novelty Alone

In evolutionary computation, the fitness function normally measures progress toward an objective in the search space, effectively acting as an objective function. Through deception, such objective functions may actually prevent the objective from being reached. While methods exist to mitigate deception, they leave the underlying pathology untreated: Objective functions themselves may actively misdirect search toward dead ends. This paper proposes an approach to circumventing deception that also yields a new perspective on open-ended evolution. Instead of either explicitly seeking an objective or modeling natural evolution to capture open-endedness, the idea is to simply search for behavioral novelty. Even in an objective-based problem, such novelty search ignores the objective. Because many points in the search space collapse to a single behavior, the search for novelty is often feasible. Furthermore, because there are only so many simple behaviors, the search for novelty leads to increasing complexity. By decoupling open-ended search from artificial life worlds, the search for novelty is applicable to real world problems. Counterintuitively, in the maze navigation and biped walking tasks in this paper, novelty search significantly outperforms objective-based search, suggesting the strange conclusion that some problems are best solved by methods that ignore the objective. The main lesson is the inherent limitation of the objective-based paradigm and the unexploited opportunity to guide search through other means.
Fault Monitoring of Wind Turbine Generator Brushes : A Data-Mining Approach

Components of wind turbines are subjected to asymmetric loads caused by variable wind conditions. Carbon brushes are critical components of the wind turbine generator. Adequately maintaining and detecting abnormalities in the carbon brushes early is essential for proper turbine performance. In this paper, data-mining algorithms are applied for early prediction of carbon brush faults. Predicting generator brush faults early enables timely maintenance or replacement of brushes. The results discussed in this paper are based on analyzing generator brush faults that occurred on 27 wind turbines. The datasets used to analyze faults were collected from the supervisory control and data acquisition (SCADA) systems installed at the wind turbines. Twenty-four data-mining models are constructed to predict faults up to 12 h before the actual fault occurs. To increase the prediction accuracy of the models discussed, a data balancing approach is used. Four data-mining algorithms were studied to evaluate the quality of the models for predicting generator brush faults. Among the selected data-mining algorithms, the boosting tree algorithm provided the best prediction results. Research limitations attributed to the available datasets are discussed.
Finding the Needle: A Study of the PE32 Rich Header and Respective Malware Triage

Performing triage of malicious samples is a critical step in security analysis and mitigation development. Unfortunately, the obfuscation and outright removal of information contained in samples makes this a monumentally challenging task. However, the widely used Portable Executable file format (PE32 ), a data structure used by the Windows OS to handle executable code, contains hidden information that can provide a security analyst with an upper hand. In this paper, we perform the first accurate assessment of the hidden PE32 field known as the Rich Header and describe how to extract the data that it clandestinely contains. We study 964,816 malware samples and demonstrate how the information contained in the Rich Header can be leveraged to perform rapid triage across millions of samples, including packed and obfuscated binaries. We first show how to quickly identify post-modified and obfuscated binaries through anomalies in the header. Next, we exhibit the Rich Header’s utility in triage by presenting a proof of concept similarity matching algorithm which is solely based on the contents of the Rich Header. With our algorithm we demonstrate how the contents of the Rich Header can be used to identify similar malware, different versions of malware, and when malware has been built under different build environment; revealing potentially distinct actors. Furthermore, we are able to perform these operations in near real-time, less than 6.73 ms on commodity hardware across our studied samples. In conclusion, we establish that this littlestudied header in the PE32 format is a valuable asset for security analysts and has a breadth of future potential.
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction

We propose, analyze, and test an alternating minimization algorithm for recovering images from blurry and noisy observations with total variation (TV) regularization. This algorithm arises from a new half-quadratic model applicable to not only the anisotropic but also the isotropic forms of TV discretizations. The per-iteration computational complexity of the algorithm is three fast Fourier transforms. We establish strong convergence properties for the algorithm including finite convergence for some variables and relatively fast exponential (or q-linear in optimization terminology) convergence for the others. Furthermore, we propose a continuation scheme to accelerate the practical convergence of the algorithm. Extensive numerical results show that our algorithm performs favorably in comparison to several state-of-the-art algorithms. In particular, it runs orders of magnitude faster than the lagged diffusivity algorithm for TV-based deblurring. Some extensions of our algorithm are also discussed.