nils-herrmann/clean_scirepeval huggingface dataset:
Content-based retrieval of logo and trademarks in unconstrained color image databases using Color Edge Gradient Co-occurrence Histograms

In this paper, we present an algorithm that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme on compound color objects, for the retrieval of logos and trademarks in unconstrained color image databases. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, as compared to the simple color pixel difference classification of edges seen with the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Color Edge Gradient Co-occurrence Histogram (CEGCH). We also introduce a color quantization method based in the hue–saturation–value (HSV) color space, illustrating that it is a more suitable scheme of quantization for image retrieval, compared to the color quantization scheme introduced with the CECH. Experimental results demonstrate that the CEGCH and the HSV color quantization scheme is insensitive to scaling, rotation, and partial deformations, and outperforms the use of the CECH in image retrieval, with higher precision and recall. We also perform experiments on a closely related algorithm based on the Color Co-occurrence Histogram (CCH) and demonstrate that our algorithm is also superior in comparison, with higher precision and recall.
Are Faculty Members Ready? Individual Factors Affecting Knowledge Management Readiness in Universities

Knowledge Management (KM) provides a systematic process to help in the creation, transfer and use of knowledge across the university, leading to increased productivity. While KM has been successfully used elsewhere, universities have been late in adopting it. Before a university can initiate KM, it needs to determine if it is ready for KM or not. Through a web-based survey sent to 1263 faculty members from 59 accredited Library and Information Science programs in universities across North America, this study investigated the e®ect of individual factors of trust, knowledge self-e±cacy, collegiality, openness to change and reciprocity on individual readiness to participate in a KM initiative, and the degree to which this a®ects perceived organisational readiness to adopt KM. 157 valid responses were received. Using structural equation modeling, the study found that apart from trust, all other factors positively a®ected individual readiness, which was found to a®ect organisational readiness. Findings should help universities identify opportunities and barriers before they can adopt KM. It should be a useful contribution to the KM literature, especially in the university context.
Combining Gradient Boosting Machines with Collective Inference to Predict Continuous Values

Gradient boosting of regression trees is a competitive procedure for learning predictive models of continuous data that fits the data with an additive non-parametric model. The classic version of gradient boosting assumes that the data is independent and identically distributed. However, relational data with interdependent, linked instances is now common and the dependencies in such data can be exploited to improve predictive performance. Collective inference is one approach to exploit relational correlation patterns and significantly reduce classification error. However, much of the work on collective learning and inference has focused on discrete prediction tasks rather than continuous. In this work, we investigate how to combine these two paradigms together to improve regression in relational domains. Specifically, we propose a boosting algorithm for learning a collective inference model that predicts a continuous target variable. In the algorithm, we learn a basic relational model, collectively infer the target values, and then iteratively learn relational models to predict the residuals. We evaluate our proposed algorithm on a real network dataset and show that it outperforms alternative boosting methods. However, our investigation also revealed that the relational features interact together to produce better predictions.
Self-efficacy: toward a unifying theory of behavioral change.

The present article presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from four principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. The more dependable the experiential sources, the greater are the changes in perceived selfefficacy. A number of factors are identified as influencing the cognitive processing of efficacy information arising from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. Possible directions for further research are discussed.
A new word sense similarity measure in wordnet

Recognizing similarities between words is a basic element of computational linguistics and artificial intelligence applications. This paper presents a new approach for measuring semantic similarity between words via concepts. Our proposed measure is a hybrid system based on using a new Information content metric and edge counting-based tuning function. In proposed system, hierarchical structure is used to present information content instead of text corpus and our result will be improved by edge counting-based tuning function. The result of the system is evaluated against human similarity ratings demonstration and shows significant improvement in compare with traditional similarity measures.
A BiCMOS Ultra-Wideband 3.1&#8211;10.6-GHz Front-End

This paper presents a direct-conversion receiver for FCC-compliant ultra-wideband (UWB) Gaussian-shaped pulses that are transmitted in one of fourteen 500-MHz-wide channels within the 3.1-10.6-GHz band. The receiver is fabricated in 0.18-mum SiGe BiCMOS. The packaged chip consists of an unmatched wideband low-noise amplifier (LNA), filter, phase-splitter, 5-GHz ISM band switchable notch filter, 3.1-10.6-GHz local oscillator (LO) amplifiers, mixers, and baseband channel-select filters/buffers. The required quadrature single-ended LO signals are generated externally. The average conversion gain and input P1dB are 32 dB and -41 dBm, respectively. The unmatched LNA provides a system noise figure of 3.3 to 5 dB over the entire band. The chip draws 30 mA from 1.8 V. To verify the unmatched LNA's performance in a complete system, wireless testing of the front-end embedded in a full receiver at 100 Mbps reveals a 10-3 bit-error rate (BER) at -80 dBm sensitivity. The notch filter suppresses out-of-band interferers and reduces the effects of intermodulation products that appear in the baseband. BER improvements of an order of magnitude and greater are demonstrated with the filter
Encountering stronger password requirements: user attitudes and behaviors

Text-based passwords are still the most commonly used authentication mechanism in information systems. We took advantage of a unique opportunity presented by a significant change in the Carnegie Mellon University (CMU) computing services password policy that required users to change their passwords. Through our survey of 470 CMU computer users, we collected data about behaviors and practices related to the use and creation of passwords. We also captured users' opinions about the new, stronger policy requirements. Our analysis shows that, although most of the users were annoyed by the need to create a complex password, they believe that they are now more secure. Furthermore, we perform an entropy analysis and discuss how our findings relate to NIST recommendations for creating a password policy. We also examine how users answer specific questions related to their passwords. Our results can be helpful in designing better password policies that consider not only technical aspects of specific policy rules, but also users' behavior in response to those rules.
A Faster Scrabble Move Generation Algorithm

Appel and Jacobson1 presented a fast algorithm for generating every possible move in a given position in the game of Scrabble using a DAWG, a finite automaton derived from the trie of a large lexicon. This paper presents a faster algorithm that uses a GADDAG, a finite automaton that avoids the non-deterministic prefix generation of the DAWG algorithm by encoding a bidirectional path starting from each letter of each word in the lexicon. For a typical lexicon, the GADDAG is nearly five times larger than the DAWG, but generates moves more than twice as fast. This time/space trade-off is justified not only by the decreasing cost of computer memory, but also by the extensive use of move-generation in the analysis of board positions used by Gordon in the probabilistic search for the most appropriate play in a given position within realistic time constraints.
Automatic Text Categorization of Mathematical Word Problems

This paper describes a novel application of text categorization for mathematical word problems, namely Multiplicative Compare and Equal Group problems. The empirical results and analysis show that common text processing techniques such as stopword removal and stemming should be selectively used. It is highly beneficial not to remove stopwords and not to do stemming. Part of speech tagging should also be used to distinguish words in discriminative parts of speech from the non-discriminative parts of speech which not only fail to help but even mislead the categorization decision for mathematical word problems. An SVM classifier with these selectively used text processing techniques outperforms an SVM classifier with a default setting of text processing techniques (i.e. stopword removal and stemming). Furthermore, a probabilistic meta classifier is proposed to combine the weighted results of two SVM classifiers with different word problem representations generated by different text preprocessing techniques. The empirical results show that the probabilistic meta classifier further improves the categorization accuracy.
Arches: a Framework for Modeling Complex Terrains

In this paper, we present a framework for representing complex terrains with such features as overhangs, arches and caves and including different materials such as sand and rocks. Our hybrid model combines a volumetric discrete data structure that stores the different materials and an implicit representation for sculpting and reconstructing the surface of the terrain. Complex scenes can be edited and sculpted interactively with high level tools. We also propose an original rock generation technique that enables us to automatically generate complex rocky sceneries with piles of rocks without any computationally demanding physically-based simulation.
Deficits for Semantics and the Irregular Past Tense: A Causal Relationship?

The regular and irregular past tense has become a focus for recent debates about the structure of the language processing system, asking whether language functions are subserved by different neural and functional mechanisms or whether all processes can be accommodated within a single unified system. A critical claim of leading single mechanism accounts is that the relationship between an irregular stem and its past tense form is primarily semantic and not morphological in nature. This predicts an obligatory relationship between semantic performance and access to the irregular past tense, such that a semantic deficit necessarily leads to impairments on the irregulars. We tested this claim in a series of studies probing the comprehension and production of regular and irregular past tense forms in four semantic dementia patients, all of whom had profound semantic deficits. In two elicitation tasks and one auditory priming study, we found that three out of the four patients did not have a deficit for the irregular past tense, in spite of their semantic deficits. This argues against the view that the relationship between irregular past tense forms and their stems is primarily semantic, and more generally against the single system claim that morphological structure can be captured solely based on phonological and semantic relationships.
Genetic Algorithm Solution of the TSP Avoiding Special Crossover and Mutation

Ordinary representations of permutations in Genetic Algorithms (GA) is handicapped with producing offspring which are not permutations at all. The conventional solution for crossover and mutation operations of permutations is to device ‘special’ operators. Unfortunately these operators suffer from violating the nature of crossover. Namely, considering the gene positions on the chromosome, these methods do not allow n-point crossover techniques which are known to favour building-block formations. In this work, an inversion sequence is proposed as the representation of a permutation. This sequence allows repetitive values and hence is robust under ordinary (n-point) crossover. There is a one-to-one mapping from ordinary permutation representation to the inversion sequence representation. The proposed method is used for solving TSPs and is compared to the well known PMX special crossover method. It is observed that this method outperforms PMX in convergence rate by a factor which can be as high as 11.1 times, on a cost of obtaining slightly worse solutions on average.
New generation 6.5 kV SiC power MOSFET

Following the recent advances in the production of 3.3 kV and 10 kV SiC power MOSFETs, Wolfspeed launched an effort to develop a new generation 6.5 kV SiC power MOSFET to fill the medium-to high-voltage fast switching device void in applications such as rail traction and mediumvoltage motor drives. The characteristics of the new generation 6.5 kV SiC power MOSFET rated at a drain current of 30 A, are presented for the first time. The device shows a typical RDS, on < 90 mΩ and Vth of 3.6 V (at ID=1 mA) measured at room temperature. Our 6.5 kV SiC power MOSFET includes a useable body diode with low Vf and reverse-recovery charge at operating temperature. The use of the body diode as a freewheeling device is demonstrated, eliminating the need for an external anti-parallel freewheeling SiC JBS diode, hence, reducing size and cost of a system. The effects of changing the 6.5 kV SiC power MOSFET gate runner design on the internal on-chip gate resistance and the MOSFET switching capability are presented.
Real-time driver's eye state detection

Driver's eye state is a key factor for identifying the driver's visual attention. This paper proposes a real-time system to detect the driver's eye state. The system combines several basic image processing techniques which can achieve better performance than other single arithmetic. And we contrive different methods to detect the eye state in the daytime and at night. Firstly, the system combines the skin-color matching & vertical projection in the daytime and applies the intensity adjusting & vertical projection at night to detect and track the face region, and then validates it by geometric symmetry of face; secondly, we apply the horizontal projection to detect the eye position and validate it by some facial features; Lastly, a new complexity function is employed to judge the eye state, and the frequency of eye closure is registered. The method in our paper can detect whether the eyes are close more efficient than many other methods and it is more real-time with less computation. The experiment results show that this method is a robust and real-time one.
Pustulobullous variant of SDRIFE (symmetrical drug-related intertriginous and flexural exanthema).

We report on a 64-year-old patient from Sri Lanka who presented to the emergency room with a 24-hour history of pruritic, erythematous patches – with occasional pustules and blisters – in the groins, axillae, and antecubital fossae. The patient reported no pain or general symptoms. Six days earlier, he had started taking cefuroxime (500 mg BID) for the first time. The antibiotic had been prescribed following temporary ureteral stent placement due to nephrolithiasis. He denied any other medical or dermatological conditions, and no other drugs had been taken. At initial presentation, he exhibited sharply demarcated, symmetrically disseminated, dark-red patches in the groins, axillae, and antecubital fossae. Some lesions showed pustules and blisters (Figure 1a–c). The physical exam was otherwise unremarkable; in particular, there was no fever. Histology revealed a superficial perivascular, spongiform dermatitis with lymphocytes, eosinophils, and neutrophils. Focally, necrotic keratinocytes and neutrophils were noted in the epidermis. Overall, the findings were consistent with a drug reaction . Apart from slightly elevated CRP levels (5.7 mg/dL, normal range: < 0.5) and mild thrombocytopenia (111,000, normal range: 166,000–308,000), laboratory tests were within normal limits. The detection of Escherichia coli in a swab taken from a pustule in the inguinal region was thought to represent contamination; the pathological lab results were considered to be consistent with an inflammatory response to nephrolithiasis. Taking the history, clinical symptoms, and histological findings into account, the patient was diagnosed with a pustulobullous variant of SDRIFE (symmetrical drug-related intertriginous and flexural exanthema). Since 2004, the acronym SDRIFE has been used for the condition previously known as baboon syndrome. First reported in 1984, the term baboon syndrome referred to the acute development of erythematous lesions in the gluteal area following contact with mercury from broken fever thermometers [1, 2]. In particular, the new term SDRIFE takes into account that systemic drugs, too, may induce flexural skin lesions without prior sensitization [3–6]. In the present case, the development of skin lesions six days after the intake of cefuroxime suggests first exposure to the drug. The diagnosis of SDRIFE includes five clinical criteria that are summarized in Table 1. Atypical disease courses with pustules, papules, and blisters have also been described . The onset of SDRIFE is independent of patient age, and occurs a few hours or even up to eight days after the administration/application of the triggering factor. Apart from systemic antibiotics – in particular β-lactam antibiotics – corticosteroids, psychopharmaceuticals, biologics, and many other drug classes are
Classification of Ulcerative Colitis Severity in Colonoscopy Videos using CNN

Ulcerative Colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. The therapeutic goals of UC are to first induce and then maintain disease remission. However, it is very difficult to evaluate the severity of UC objectively because of non-uniform nature of symptoms and large variations in their patterns. To address this, we already developed an approach using the image textures in our previous work. But, we found that it could not handle larger number of variations in their patterns. In this paper, we propose a different approach using CNN (Convolutional Neural Network) to measure and classify objectively the severity of UC presented in optical colonoscopy video frames. We call the proposed approach using CNN as Ulcerative Colitis Severity CNN (UCS-CNN) which utilizes endoscopic domain knowledge and convolutional neural network to classify different UC severity of colonoscopy images. The experimental results show that the proposed UCS-CNN can evaluate the severity of UC reasonably.
High-Efficiency Charge Pumps for Low-Power On-Chip Applications

This paper proposes charge pumps with improved power efficiency suitable for low-power on-chip applications. Undesired charge transfer, which has a direction opposite to that of the intended current flow, presents a significant source of power loss in charge pumps. The proposed charge pump circuit utilizes charge transfer switches with a complementary branch scheme to significantly reduce undesired charge transfer, thereby improving power efficiency and increasing output voltage effectively. An optimized gate control strategy is applied to further decrease the power loss caused by undesired charge transfer. Simulations of 8-stage charge pumps in a  $0.13~\mu \text{m}$  standard CMOS technology show that for an input supply voltage of 1.2 V, the proposed charge pump circuit reaches a power efficiency of 58.72% with an output voltage of 7.45 V, when delivering 5-mA load current, and is able to maintain a power efficiency of around 50% and an output voltage of over 5 V as the load current increases to 10 mA. Compared with the other charge pump circuits, the simulation results demonstrate better performance of proposed charge pump circuits in terms of both voltage pumping gain and power efficiency.
Recurrent networks with attention and convolutional networks for sentence representation and classification

In this paper, we propose a bi-attention, a multi-layer attention and an attention mechanism and convolution neural network based text representation and classification model (ACNN). The bi-attention have two attention mechanism to learn two context vectors, forward RNN with attention to learn forward context vector c⃗ $\overrightarrow {\mathbf {c}}$ and backward RNN with attention to learn backward context vector c⃖ $\overleftarrow {\mathbf {c}}$, and then concatenation c⃗ $\overrightarrow {\mathbf {c}}$ and c⃖ $\overleftarrow {\mathbf {c}}$ to get context vector c. The multi-layer attention is the stack of the bi-attention. In the ACNN, the context vector c is obtained by the bi-attention, then the convolution operation is performed on the context vector c, and the max-pooling operation is used to reduce the dimension. After max-pooling operation the text is converted to low-dimensional sentence vector m. Finally, the Softmax classifier be used for text classification. We test our model on 8 benchmarks text classification datasets, and our model achieved a better or the same performance compare with the state-of-the-art methods.
4D Generic Video Object Proposals

Many high-level video understanding methods require input in the form of object proposals. Currently, such proposals are predominantly generated with the help of networks that were trained for detecting and segmenting a set of known object classes, which limits their applicability to cases where all objects of interest are represented in the training set. This is a restriction for automotive scenarios, where unknown objects can frequently occur. We propose an approach that can reliably extract spatio-temporal object proposals for both known and unknown object categories from stereo video. Our 4D Generic Video Tubes (4D-GVT) method leverages motion cues, stereo data, and object instance segmentation to compute a compact set of video-object proposals that precisely localizes object candidates and their contours in 3D space and time. We show that given only a small amount of labeled data, our 4D-GVT proposal generator generalizes well to real-world scenarios, in which unknown categories appear. It outperforms other approaches that try to detect as many objects as possible by increasing the number of classes in the training set to several thousand.