nils-herrmann/clean_scirepeval huggingface dataset:
Gated Recurrent Convolution Neural Network for OCR

Optical Character Recognition (OCR) aims to recognize text in natural images. Inspired by a recently proposed model for general image classification, Recurrent Convolution Neural Network (RCNN), we propose a new architecture named Gated RCNN (GRCNN) for solving this problem. Its critical component, Gated Recurrent Convolution Layer (GRCL), is constructed by adding a gate to the Recurrent Convolution Layer (RCL), the critical component of RCNN. The gate controls the context modulation in RCL and balances the feed-forward information and the recurrent information. In addition, an efficient Bidirectional Long ShortTerm Memory (BLSTM) is built for sequence modeling. The GRCNN is combined with BLSTM to recognize text in natural images. The entire GRCNN-BLSTM model can be trained end-to-end. Experiments show that the proposed model outperforms existing methods on several benchmark datasets including the IIIT-5K, Street View Text (SVT) and ICDAR.
Are measures of self-esteem, neuroticism, locus of control, and generalized self-efficacy indicators of a common core construct?

The authors present results of 4 studies that seek to determine the discriminant and incremental validity of the 3 most widely studied traits in psychology-self-esteem, neuroticism, and locus of control-along with a 4th, closely related trait-generalized self-efficacy. Meta-analytic results indicated that measures of the 4 traits were strongly related. Results also demonstrated that a single factor explained the relationships among measures of the 4 traits. The 4 trait measures display relatively poor discriminant validity, and each accounted for little incremental variance in predicting external criteria relative to the higher order construct. In light of these results, the authors suggest that measures purporting to assess self-esteem, locus of control, neuroticism, and generalized self-efficacy may be markers of the same higher order concept.
Multi-floor Indoor Navigation with Geomagnetic Field Positioning and Ant Colony Optimization Algorithm

We here illustrate a new indoor navigation system. It is an outcome of creativity, which merges an imaginative scenario and new technologies. The system intends to guide a person in unknown building by relying on technologies which do not depend on infrastructures. The system includes two key components, namely positioning and path planning. Positioning is based on geomagnetic fields, and it overcomes the several limits of WIFI and Bluetooth, etc. Path planning is based on a new and optimized Ant Colony algorithm, called Ant Colony Optimization (ACO), which offers better performances than the classic A* algorithms. The paper illustrates the logic and the architecture of the system, and also presents experimental results.
Dependence of biodiesel fuel properties on the structure of fatty acid alkyl esters

Biodiesel, defined as the mono-alkyl esters of vegetable oils or animal fats, is an balternativeQ diesel fuel that is becoming accepted in a steadily growing number of countries around the world. Since the source of biodiesel varies with the location and other sources such as recycled oils are continuously gaining interest, it is important to possess data on how the various fatty acid profiles of the different sources can influence biodiesel fuel properties. The properties of the various individual fatty esters that comprise biodiesel determine the overall fuel properties of the biodiesel fuel. In turn, the properties of the various fatty esters are determined by the structural features of the fatty acid and the alcohol moieties that comprise a fatty ester. Structural features that influence the physical and fuel properties of a fatty ester molecule are chain length, degree of unsaturation, and branching of the chain. Important fuel properties of biodiesel that are influenced by the fatty acid profile and, in turn, by the structural features of the various fatty esters are cetane number and ultimately exhaust emissions, heat of combustion, cold flow, oxidative stability, viscosity, and lubricity.
Techniques for highly multiobjective optimisation: some nondominated points are better than others

The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much evidence of their worth in practical scenarios. An urgent emerging issue is that the favoured EMO algorithms scale poorly when problems have "many" (e.g. five or more) objectives. One of the chief reasons for this is believed to be that, in many-objective EMO search, populations are likely to be largely composed of nondominated solutions. In turn, this means that the commonly-used algorithms cannot distinguish between these for selective purposes. However, there are methods that can be used validly to rank points in a nondominated set, and may therefore usefully underpin selection in EMO search. Here we discuss and compare several such methods. Our main finding is that simple variants of the often-overlooked "Average Ranking" strategy usually outperform other methods tested, covering problems with 5-20 objectives and differing amounts of inter-objective correlation.
Design Considerations for MEMS-Scale Piezoelectric Mechanical Vibration Energy Harvesters

Design considerations for piezoelectric-based energy harvesters for MEMS-scale sensors are presented, including a review of past work. Harvested ambient vibration energy can satisfy power needs of advanced MEMS-scale autonomous sensors for numerous applications, e.g., structural health monitoring. Coupled 1-D and modal (beam structure) electromechanical models are presented to predict performance, especially power, from measured low-level ambient vibration sources. Models are validated by comparison to prior published results and tests of a MEMS-scale device. A non-optimized prototype low-level ambient MEMS harvester producing 30 μW/cm3 is designed and modeled. A MEMS fabrication process for the prototype device is presented based on past work.
Contexts for the Semantic Web

A central theme of the semantic web is that programs should be able to easily aggregate data from different sources. Unfortunately, even if two sites provide their data using the same data model and vocabulary, subtle differences in their use of terms and in the assumptions they make pose challenges for aggregation. Experiences with the TAP project reveal some of the phenomena that pose obstacles to a simplistic model of aggregation. Similar experiences have been reported by AI projects such as Cyc, which has lead to the development and use of various context mechanisms. In this paper we report on some of the problems with aggregating independently published data and propose a context mechanism to handle some of these problems. We briefly survey the context mechanisms developed in in AI and contrast them with the requirements of a context mechanism for the semantic web. Finally, we present a context mechanism for the semantic web that is adequate to handle the aggregation tasks, yet simple from both computational and model theoretic perspectives.
Iris recognition based on human interpretable features

The human iris is used for human recognition in various applications. However, deployment of iris recognition in forensic applications has not been reported. A primary reason is the lack of human-friendly techniques for iris comparison. The usage of iris recognition can be increased by visualizing the similarity between irises. Scientist Shen proposed a human-in-the-loop method for detecting and matching iris crypts. Thus with the help of this, we proposed a new approach for automatic detection and matching of crypts. This detection method is able to capture iris crypts of various sizes. This matching scheme is designed to handle potential topological changes in the detection of the same crypt in different images. In particular, this approach achieves over 22% higher rank one hit rate in the identification, and over 51% lower equal error rate in verification. In addition, the benefit of this approach on Multi-enrollment is experimentally demonstrated.
Feature Selection with Attributes Clustering by Maximal Information Coefficient

Feature selection is usually a separate procedure which can not benefit from result of the data exploration. In this paper, we propose a unsupervised feature selection method which could reuse a specific data exploration result. Furthermore, our algorithm follows the idea of clustering attributes and combines two state-of-the-art data analyzing methods, that’s maximal information coefficient and affinity propagation. Classification problems with different classifiers were tested to validation our method and others. Data experiments result exhibits our unsupervised algorithm is comparable with classical feature selection methods and even outperforms some supervised learning algorithms. Data simulation with one credit dataset of our own from a bank of China shows the capability of our method for real world application.
hdbscan: Hierarchical density based clustering

Hierarchical Density-Based Spatial Clustering of Applications with Noise (Campello, Moulavi, and Sander 2013), (Campello et al. 2015). Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. The library also includes support for Robust Single Linkage clustering (Chaudhuri et al. 2014), (Chaudhuri and Dasgupta 2010), GLOSH outlier detection (Campello et al. 2015), and tools for visualizing and exploring cluster structures. Finally support for prediction and soft clustering is also available.
Multi-objective Moth Flame Optimization

Mirjalili in 2015, proposed a new nature-inspired meta-heuristic Moth Flame Optimization (MFO). It is inspired by the characteristics of a moth in the dark night to either fly straight towards the moon or fly in a spiral path to arrive at a nearby artificial light source. It aims to reach a brighter destination which is treated as a global solution for an optimization problem. In this paper, the original MFO is suitably modified to handle multi-objective optimization problems termed as MOMFO. Typically concepts like the introduction of archive grid, coordinate based distance for sorting, non-dominance of solutions make the proposed approach different from the original single objective MFO. The performance of proposed MOMFO is demonstrated on six benchmark mathematical function optimization problems regarding superior accuracy and lower computational time achieved compared to Non-dominated sorting genetic algorithm-II (NSGA-II) and Multi-objective particle swarm optimization (MOPSO).
Detection of splicing forgery using wavelet decomposition

Authenticity of an image is an important issue in many social areas such as Journalism, Forensic investigation, Criminal investigation and Security services etc. and digital images can be easily manipulated with the help of sophisticated photo editing software and high-resolution digital cameras. So there is a requirement for the implementation of new powerful and efficient algorithms for forgery detection of a tampered images. The splicing is the common forgery in which two images are combine and make a single composite and the duplicated region is retouched by performing operations like edge blurring to get the appearance of the authentic image. In this paper, we have proposed a new computationally efficient algorithm for splicing (copy-create) forgery detection of an image using block matching method. The proposed method achieve an accuracy of 87.75% within a small processing time by modeling the threshold.
A 13.5-mW 10-Gb/s 4-PAM Serial Link Transmitter in 0.13-  $\mu\hbox{m}$ CMOS Technology

A 13.5-mW 10-Gb/s four-level pulse-amplitude modulation (4-PAM) serial link transmitter is presented. To improve the power efficiency, a voltage-mode 4-PAM driver is proposed. It consists of voltage-scaled pull-up and pull-down networks, instead of conventional current switching networks. Not employing a tail current source, the proposed 4-PAM driver achieves the higher output voltage swing and lower power dissipation than conventional 4-PAM drivers. As a result, the proposed 4-PAM transmitter implemented in a 0.13-μm CMOS process achieved 10-Gb/s data rate with only 13.5-mW power dissipation.
Anatomical evaluation of the modified posterolateral approach for posterolateral tibial plateau fracture

The study was undertaken to evaluate the efficacy and safety of a posterolateral reversed L-shaped knee joint incision for treating the posterolateral tibial plateau fracture. Knee specimens from eight fresh, frozen adult corpses were dissected bilaterally using a posterolateral reversed L-shaped approach. During the dissection, the exposure range was observed, and important parameters of anatomical structure were measured, including the parameters of common peroneal nerve (CPN) to ameliorate the incision and the distances between bifurcation of main vessels and the tibial articular surface to clear risk awareness. The posterolateral aspect of the tibial plateau from the proximal tibiofibular joint to the tibial insertion of the posterior cruciate ligament was exposed completely. There was no additional damage to other vital structures and no evidence of fibular osteotomy or posterolateral corner complex injury. The mean length of the exposed CPN was 56.48 mm. The CPN sloped at a mean angle of 14.7° toward the axis of the fibula. It surrounded the neck of the fibula an average of 42.18 mm from the joint line. The mean distance between the opening of the interosseous membrane and the joint line was 48.78 mm. The divergence of the fibular artery from the posterior tibial artery was on average 76.46 mm from articular surface. This study confirmed that posterolateral reversed L-shaped approach could meet the requirements of anatomical reduction and buttress fixation for posterolateral tibial plateau fracture. Exposure of the CPN can be minimized or even avoided by modifying the skin incision. Care is needed to dissect distally and deep through the approach as vital vascular bifurcations are concentrated in this region. Placement of a posterior buttressing plate carries a high vascular risk when the plate is implanted beneath these vessels.
Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems

We are living in a world which is undergoing profound changes brought about by rapid advances in science and technology. Among such changes, the most visible are those that relate to what is popularly referred to as the information revolution. The artifacts of this revolution are all around us: the e-mail, the world wide web, the cellular phone; the fax; and the desktop computer, among many others. Linked to the information revolution is another revolution — the intelligent systems revolution. The manifestations of this revolution are not as obvious as those of the information revolution because they involve, for the most part, not new products but higher MIQ (Machine IQ) of existing systems, products and devices. Among the familiar examples are smart appliances, smart cameras, smart robots and smart software for browsing, diagnosis, fraud detection and quality control. The information and intelligent systems revolutions are in a symbiotic relationship. Intelligence requires information and vice-versa. The confluence of intelligent systems and information systems leads to intelligent information systems. In this sense, the union of information systems, intelligent systems and intelligent information systems constitutes what might be referred to as information/intelligent systems, or I/IS for short. In my perception, in coming years, the design, construction and utilization of information/intelligent systems will become the primary focus of science and technology, and I/IS systems will become a dominant presence in our daily lives. When we take a closer look at information/intelligent systems what we see is the increasingly important role of soft computing (SC) in their conception, design and utilization. Basically, soft computing is an association of computing methodologies which includes as its principal members fuzzy
Whistleblowing in Organizations : An Examination of Correlates of Whistleblowing Intentions , Actions , and Retaliation

Whistleblowing on organizational wrongdoing is becoming increasingly prevalent. What aspects of the person, the context, and the transgression relate to whistleblowing intentions and to actual whistleblowing on corporate wrongdoing? Which aspects relate to retaliation against whistleblowers? Can we draw conclusions about the whistleblowing process by assessing whistleblowing intentions? Meta-analytic examination of 193 correlations obtained from 26 samples (N = 18,781) reveals differences in the correlates of whistleblowing intentions and actions. Stronger relationships were found between personal, contextual, and wrongdoing characteristics and whistleblowing intent than with actual whistleblowing. Retaliation might best be predicted using contextual variables. Implications for research and practice are discussed.
Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles

We consider the problem of recommending comment-worthy articles such as news and blog-posts. An article is defined to be comment-worthy for a particular user if that user is interested to leave a comment on it. We note that recommending comment-worthy articles calls for elicitation of commenting-interests of the user from the content of both the articles and the past comments made by users. We thus propose to develop content-driven user profiles to elicit these latent interests of users in commenting and use them to recommend articles for future commenting. The difficulty of modeling comment content and the varied nature of users' commenting interests make the problem technically challenging. The problem of recommending comment-worthy articles is resolved by leveraging article and comment content through topic modeling and the co-commenting pattern of users through collaborative filtering, combined within a novel hierarchical Bayesian modeling approach. Our solution, Collaborative Correspondence Topic Models (CCTM), generates user profiles which are leveraged to provide a personalized ranking of comment-worthy articles for each user. Through these content-driven user profiles, CCTM effectively handle the ubiquitous problem of cold-start without relying on additional meta-data. The inference problem for the model is intractable with no off-the-shelf solution and we develop an efficient Monte Carlo EM algorithm. CCTM is evaluated on three real world data-sets, crawled from two blogs, ArsTechnica (AT) Gadgets (102,087 comments) and AT-Science (71,640 comments), and a news site, DailyMail (33,500 comments). We show average improvement of 14% (warm-start) and 18% (cold-start) in AUC, and 80% (warm-start) and 250% (cold-start) in Hit-Rank@5, over state of the art.
Clustering Large Data Sets with Mixed Numeric and Categorical Values

Efficient partitioning of large data sets into homogenous clusters is a fundamental problem in data mining. The standard hierarchical clustering methods provide no solution for this problem due to their computational inefficiency. The k-means based methods are promising for their efficiency in processing large data sets. However, their use is often limited to numeric data. In this paper we present a k-prototypes algorithm which is based on the k-means paradigm but removes the numeric data limitation whilst preserving its efficiency. In the algorithm, objects are clustered against k prototypes. A method is developed to dynamically update the k prototypes in order to maximise the intra cluster similarity of objects. When applied to numeric data the algorithm is identical to the kmeans. To assist interpretation of clusters we use decision tree induction algorithms to create rules for clusters. These rules, together with other statistics about clusters, can assist data miners to understand and identify interesting clusters.
Implementation and control of grid connected AC-DC-AC power converter for variable speed wind energy conversion system

30 kW electrical power conversion system is developed for a variable speed wind turbine system. In the wind energy conversion system (WECS) a synchronous generator converts the mechanical energy into electrical energy. As the voltage and frequency of generator output vary along the wind speed change, a DC-DC boosting chopper is utilized to maintain constant DC link voltage. The input DC current is regulated to follow the optimized current reference for maximum power point operation of turbine system. Line side PWM inverter supply currents into the utility line by regulating the DC link voltage. The active power is controlled by q-axis current whereas the reactive power can be controlled by d-axis current. The phase angle of utility voltage is detected using software PLL (phased locked loop) in d-q synchronous reference frame. Proposed scheme gives a low cost and high quality power conversion solution for variable speed WECS.
Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation

With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these applications to user-specific needs or preferences. Despite their increasing popularity, in general, recommender systems suffer from data sparsity and cold-start problems. To alleviate these issues, in recent years, there has been an upsurge of interest in exploiting social information such as trust relations among users along with the rating data to improve the performance of recommender systems. The main motivation for exploiting trust information in the recommendation process stems from the observation that the ideas we are exposed to and the choices we make are significantly influenced by our social context. However, in large user communities, in addition to trust relations, distrust relations also exist between users. For instance, in Epinions, the concepts of personal “web of trust” and personal “block list” allow users to categorize their friends based on the quality of reviews into trusted and distrusted friends, respectively. Hence, it will be interesting to incorporate this new source of information in recommendation as well. In contrast to the incorporation of trust information in recommendation which is thriving, the potential of explicitly incorporating distrust relations is almost unexplored. In this article, we propose a matrix factorization-based model for recommendation in social rating networks that properly incorporates both trust and distrust relationships aiming to improve the quality of recommendations and mitigate the data sparsity and cold-start users issues. Through experiments on the Epinions dataset, we show that our new algorithm outperforms its standard trust-enhanced or distrust-enhanced counterparts with respect to accuracy, thereby demonstrating the positive effect that incorporation of explicit distrust information can have on recommender systems.
An optimal vector-network-analyzer calibration algorithm

We present an iterative algorithm for calibrating vector network analyzers based on orthogonal distance regression. The algorithm features a robust, yet efficient, search algorithm, an error analysis that includes both random and systematic errors, a full covariance matrix relating calibration and measurement errors, 95% coverage factors, and an easy-to-use user interface that supports a wide variety of calibration standards. We also discuss evidence that the algorithm outperforms theMultiCal software package in the presence of measurement errors and accurately estimates the uncertainty of its results.