nils-herrmann/clean_scirepeval huggingface dataset:
Functional programming with structured graphs

This paper presents a new functional programming model for graph structures called structured graphs. Structured graphs extend conventional algebraic datatypes with explicit definition and manipulation of cycles and/or sharing, and offer a practical and convenient way to program graphs in functional programming languages like Haskell. The representation of sharing and cycles (edges) employs recursive binders and uses an encoding inspired by parametric higher-order abstract syntax. Unlike traditional approaches based on mutable references or node/edge lists, well-formedness of the graph structure is ensured statically and reasoning can be done with standard functional programming techniques. Since the binding structure is generic, we can define many useful generic combinators for manipulating structured graphs. We give applications and show how to reason about structured graphs.
On Modelling in Memetics

The field of memetics is characterised and two types of memetic model analysed: the a priori model and the ‘black-box’ model. These are used to motivate a picture of scientific modelling, where by chains of models are built from abstract models at the ‘top’ down to data models derived from measurement. These models are linked to each other by validation (where the structure of a model comes from another) andverification(where the results of a model are checked against another). The role of formal models in clearly establishing these links are discussed.This picture is applied to the process of modelling in memetics. The paper argues that stronger selection are required to enable the field to evolve. In particular it argues that validation and verification constitute a minimum such set for memetic models.
Software-defined wireless mesh networks: architecture and traffic orchestration

SDN has been envisioned as the next generation network paradigm by decoupling control plane and data plane, such that network management and optimization can be conducted in a centralized manner using global network information. In this article, we study how to apply SDN concept to a wireless mesh network that has been widely adopted by various applications. We first propose a novel architecture of SD-WMNs, and identify several critical challenges. Since wireless spectrum is a scarce resource that is shared by both data and control traffic in SD-WMNs, we propose three spectrum allocation and scheduling algorithms, namely FB-NS, NFB-NS, and NFB-S that orchestrate both control and data traffic. Finally, performance is evaluated via extensive simulations.
Plasmodium vivax segmentation using modified fuzzy divergence

This paper aims at introducing a new approach to Plasmodium vivax (P. vivax) detection from Leishman stained thin blood film. This scheme follows retrospective study design protocol where patients were selected at random in the clinic. The scheme consists of two main stages - firstly artefacts reduction, and secondly fuzzy divergence based segmentation of P. vivax infected region(s) from erythrocytes. Here, malaria parasite segmentation is done using divergence based threshold selection. Fuzzy approach is chosen to minimize ambiguity inherent in the microscopic images. Divergence algorithm is derived from Cauchy membership function to overcome the drawbacks in comparison with other well known membership functions.
Perspectives on Infrastructure for Crowdsourcing

Human computation has gained a lot of interest lately as a paradigm for solving problems that computers can‟t do yet. Crowdsourcing marketplaces are showing that is possible to get tasks completed in time with a modest budget in a wide range of applications. However, the infrastructure that is currently available for supporting crowdsourcing tasks is very limited. There is non-trivial extra work that the experimenter has to do to get successful results. Consequently, the developer tends to spend more time dealing with the complexity and operational aspects of the task than on its design. Given the mix of different subareas of computer science that are potentially involved, defining and designing such platform presents a number of interesting problems. In this paper, I outline the rise of human computation, summarize the limitations of a popular platform, and present a perspective on the topic that can be viewed as a starting point to debate requirements for this new research area.
Applying connectionist modal logics to distributed knowledge representation problems

Neural-Symbolic Systems concern the integration of the symbolic and connectionist paradigms of Artificial Intelligence. Distributed knowledge representation is traditionally seen under a symbolic perspective. In this paper, we show how neural networks can represent distributed symbolic knowledge, acting as multi-agent systems with learning capability (a key feature of neural networks). We apply the framework of Connectionist Modal Logics to well-known testbeds for distributed knowledge representation formalisms, namely the muddy children and the wise men puzzles. Finally, we sketch a full solution to these problems by extending our approach to deal with knowledge evolution over time.
Black Box Testing: Techniques for Functional Testing of Software and Systems

Peter Anderson Rochester Institute of Technology 1 Lomb Memorial Dr. Rochester, NY 14623 pga@cs.rit.edu Each technique receives similar coverage, including a description of its new and prerequisite vocabulary, its model, the bug assumptlons that underlie its effectiveness, typical applications, examples (almost all based on standard IRS tax forms), caveats and limitations, automation and tools for the technique, a self-evaluation quiz, and exercises. Common threads in the presentation show the role
Identification of Adopted Pali Words in Myanmar Text

Myanmar language has been significantly influenced by Pali language due to the practice of Buddhism and study of Buddhist literature in Myanmar. As a result, Pali words have been widely adopted and used in Myanmar language. This study presents an algorithm for identifying Myanmar-adopted Pali words in Myanmar text. The system employs a combination of rule-based syllable segmentation and a dictionary-based longest matching method. A program was developed and trained on a corpus containing 8,895 sentences. It recognized 579 unique Pali words. The accuracy of the system was tested on a different corpus containing 3,641 sentences and the system correctly identified 279 unique Pali words, achieving a Precision of 97.59%, Recall of 99.04% and F-measure of 98.31%. Usages of Pali words are inevitable in Myanmar text and the results of this study will improve many NLP tasks of Myanmar language such as spelling checking, text categorization and text-to-speech synthesis etc.
Manufacturing and finite element assessment of a novel pressure reducing insole for Diabetic Neuropathic patients

Diabetes is one of the metabolic diseases. Uncontrolled diabetes can lead to diabetic foot ulcers and if it was not treated would lead to amputation. Foot ulcers can be prevented by using suitable insoles which are made of appropriate material and geometrically designed by constituent layers. In this study, single-layer and three-layer insoles have been compared during static and dynamic loading. The selected materials were silicone gel (SG), plastazote foam (PLZ), polyfoam (PF) and ethyl vinyl acetate foam (EVA). Four single-layer and 18 combinations of three-layer insoles were selected. Materials behaviors were determined by using a uniaxial pressure test. The description of stress and strain is obtained by using the model of three dimensional nonlinear Finite Element Method (FEM). Then samples were tested by using commercially available plantar pressure measurement system. The FEM results showed that the SG and PLZ insoles are more appropriate compared to single-layer insoles. The combinations of PLZ, SG and EVA (from top to bottom) are recognized as the best between three-layer insoles. Also the best three-layer insole is more effective in promoting a favourable stress and strain distribution than single-layer insoles, especially in dynamic mode. According to simulation results, three-layer insole decreases stress concentration by 9 %. Also experimental tests showed that using three-layer insole decreases plantar pressure by 63 % compared to barefoot condition bare foot.
ABNORMALITY DETECTION IN LUMBAR DISCS FROM CLINICAL MR IMAG ES WITH A PROBABILISTIC MODEL

Detection of abnormal discs from clinical T2weighted MR scans. This aids the radiologist as well as subsequent CAD methods in focusing only on abnormal discs for further diagnosis. Furthermore, it gives a degree of con fidence about the abnormality of the intervertebral discs th at helps the radiologist in making his decision.  We propose a probabilistic model for detection of abnormality of intervertebral discs . We use three features to label abnormal discs that includes appearance, location, and context. We model the abnormal disc appearance with a Gaussian model, the location with a 2D Gaussian model, and the context with a Gaussian model for the distance between abnormal discs. We use clinical T2-weighted MR volume for each case and inference on the middle slide of each volume. These MR scans are specific for the lumbar area. The ground truth is provided by our collaborating radiologist.  We achieve over 91% abnormality detection accuracy in a cross-validation experiment with 80 clinical cases. The experiment runs ten rounds, in every round 30 cases are randomly left out for testing and the rest are used for training.  We achieve high accuracy for detection of abnormal discs using our proposed model that incorporates disc appearance, location, and context. We show that our proposed model is extensible for subsequent diagnosis task specific to each intervertebral disc abnormality such as des iccation, stenosis, and herniation.
Sentence entailment in compositional distributional semantics

Distributional semantic models provide vector representations for words by gathering co-occurrence frequencies from corpora of text. Compositional distributional models extend these from words to phrases and sentences. In categorical compositional distributional semantics, phrase and sentence representations are functions of their grammatical structure and representations of the words therein. In this setting, grammatical structures are formalised by morphisms of a compact closed category and meanings of words are formalised by objects of the same category. These can be instantiated in the form of vectors or density matrices. This paper concerns the applications of this model to phrase and sentence level entailment. We argue that entropy-based distances of vectors and density matrices provide a good candidate to measure word-level entailment, show the advantage of density matrices over vectors for word level entailments, and prove that these distances extend compositionally from words to phrases and sentences. We exemplify our theoretical constructions on real data and a toy entailment dataset and provide preliminary experimental evidence.
Case Study: Explaining Diabetic Retinopathy Detection Deep CNNs via Integrated Gradients

In this report, we applied integrated gradients to explaining a neural network for diabetic retinopathy detection. The integrated gradient is an attribution method which measures the contributions of input to the quantity of interest. We explored some new ways for applying this method such as explaining intermediate layers, filtering out unimportant units by their attribution value and generating contrary samples. Moreover, the visualization results extend the use of diabetic retinopathy detection model from merely predicting to assisting finding potential lesions.
NL-InSAR: Nonlocal Interferogram Estimation

Interferometric synthetic aperture radar (SAR) data provide reflectivity, interferometric phase, and coherence images, which are paramount to scene interpretation or low-level processing tasks such as segmentation and 3-D reconstruction. These images are estimated in practice from a Hermitian product on local windows. These windows lead to biases and resolution losses due to the local heterogeneity caused by edges and textures. This paper proposes a nonlocal approach for the joint estimation of the reflectivity, the interferometric phase, and the coherence images from an interferometric pair of coregistered single-look complex (SLC) SAR images. Nonlocal techniques are known to efficiently reduce noise while preserving structures by performing the weighted averaging of similar pixels. Two pixels are considered similar if the surrounding image patches are “resembling.” Patch similarity is usually defined as the Euclidean distance between the vectors of graylevels. In this paper, a statistically grounded patch-similarity criterion suitable to SLC images is derived. A weighted maximum likelihood estimation of the SAR interferogram is then computed with weights derived in a data-driven way. Weights are defined from the intensity and interferometric phase and are iteratively refined based both on the similarity between noisy patches and on the similarity of patches from the previous estimate. The efficiency of this new interferogram construction technique is illustrated both qualitatively and quantitatively on synthetic and true data.
Improving Text Simplification Language Modeling Using Unsimplified Text Data

In this paper we examine language modeling for text simplification. Unlike some text-to-text translation tasks, text simplification is a monolingual translation task allowing for text in both the input and output domain to be used for training the language model. We explore the relationship between normal English and simplified English and compare language models trained on varying amounts of text from each. We evaluate the models intrinsically with perplexity and extrinsically on the lexical simplification task from SemEval 2012. We find that a combined model using both simplified and normal English data achieves a 23% improvement in perplexity and a 24% improvement on the lexical simplification task over a model trained only on simple data. Post-hoc analysis shows that the additional unsimplified data provides better coverage for unseen and rare n-grams.
Autonomous Farming and Surveillance Agribot in Adjacent Boundary

This research strives to implement the agricultural farming robot which can move instinctively, involuntarily for ploughing in different depths depending upon soil, seeding and irrigation in adjacent field. This research also contributes to enhance functionality of agribot in the field for surveillance. The agribot is equipped with a camera and sends the data to pc through Wi-Fi network. The robot has servo motors for ploughing, distributing the seeds and irrigation into the field. It works with ultrasonic sensor and IR sensor. Ultrasonic sensor is for avoiding the obstacles in the field, IR sensor is for sensing the path and field of adjacent boundary. Arduino Uno controller acts as heart and brain of the system, it makes fast, accurate, autonomous movement. This Research features a agribot that uses Wi-Fi 802.11G along with TCP/IP protocol. IP address and Video is received in laptop for further processing. This research intends to reduce human efforts, increase profit and provide an intelligent aid to the farmers.
Improving the Transcription of Academic Lectures for Information Retrieval

Recording university lectures through lecture capture systems is increasingly common, generating large amounts of audio and video data. Transcribing recordings greatly enhances their usefulness by making them easy to search. However, the number of recordings accumulates rapidly, rendering manual transcription impractical. Automatic transcription, on the other hand, suffers from low levels of accuracy, partly due to the special language of academic disciplines, which standard language models do not cover. This paper looks into the use of Wikipedia to dynamically adapt language models for scholarly speech. We propose Ranked Word Correct Rate as a new metric better aligned with the goals of improving transcript search ability and specialist word recognition. The study shows that, while overall transcription accuracy may remain low, targeted language modelling can substantially improve search ability, an important goal in its own right.
A high gain multi-port bidirectional non-isolated DC-DC converter for renewable integration

A topology for bidirectional non-isolated multiport DC-DC converter is proposed in this paper. The energy generation from the renewable sources such as wind and solar aie intermittent in nature. A battery port is mandatory for this purpose. Hence, a high gain multiport bidirectional converter for DC micro-grid application with high conversion efficiency, reduced size, and low cost is proposed. The bidirectional power flow capability through a feedback back path to charge a battery from the load capacitor is also provided. Therefore, the battery is not only charged from the input source but also from the load capacitor. The number of outputs are independent from each other and the battery is charged from the second output. Steady state operation and theoretical analysis of the converter proposed is elaborated. To validate the performance and theoretical concepts, it is simulated in MATLAB/Simulink software. The results of simulation verify the appropriate performance of proposed topology.
An Overview of Vulnerabilities of Voice Controlled Systems

Over the last few years, a rapidly increasing number of Internet-of-Things (IoT) systems that adopt voice as the primary user input have emerged. These systems have been shown to be vulnerable to various types of voice spoofing attacks. However, how exactly these techniques differ or relate to each other has not been extensively studied. In this paper, we provide a survey of recent attack and defense techniques for voice controlled systems and propose a classification of these techniques. We also discuss the need for a universal defense strategy that protects a system from various types of attacks.
Enhancing Recommendation Diversity Through a Dual Recommendation Interface

e beyond-relevance objectives of recommender system are drawing more and more aention. For example, a diversity-enhanced interface has been shown to positively associate with overall levels of user satisfaction. However, lile is known about how a diversityenhanced interface can help users to accomplish various real-world tasks. In this paper, we present a visual diversity-enhanced interface that presents recommendations in a two-dimensional scaer plot. Our goal was to design a recommender system interface to explore the dierent relevance prospects of recommended items in parallel and to stress their diversity. A within-subject user study with real-life tasks was conducted to compare our visual interface to a standard ranked list interface. Our user study results show that the visual interface signicantly reduced exploration eorts required for explored tasks. Also, the users’ subjective evaluation shows signicant improvement on many user-centric metrics. We show that the users explored a diverse set of recommended items while experiencing an improvement in overall user satisfaction.
Emotion regulation for frustrating driving contexts

Driving is a challenging task because of the physical, attentional, and emotional demands. When drivers become frustrated by events their negative emotional state can escalate dangerously. This study examines behavioral and attitudinal effects of cognitively reframing frustrating events. Participants (N = 36) were asked to navigate a challenging driving course that included frustrating events such as long lights and being cut-off. Drivers were randomly assigned to three conditions. After encountering a frustrating event, drivers in a reappraisal-down condition heard voice prompts that reappraised the event in an effort to deflate negative reactions. Drivers in the second group, reappraisal-up, heard voice prompts that brought attention to the negative actions of vehicles and pedestrians. Drivers in a silent condition drove without hearing any voice prompts. Participants in the reappraisal-down condition had better driving behavior and reported less negative emotions than participants in the other conditions.
To prune , or not to prune : exploring the efficacy of pruning for model compression

Model pruning seeks to induce sparsity in a deep neural network’s various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports (Han et al., 2015a; Narang et al., 2017) prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size. This hints at the possibility that the baseline models in these experiments are perhaps severely over-parameterized at the outset and a viable alternative for model compression might be to simply reduce the number of hidden units while maintaining the model’s dense connection structure, exposing a similar trade-off in model size and accuracy. We investigate these two distinct paths for model compression within the context of energy-efficient inference in resource-constrained environments and propose a new gradual pruning technique that is simple and straightforward to apply across a variety of models/datasets with minimal tuning and can be seamlessly incorporated within the training process. We compare the accuracy of large, but pruned models (large-sparse) and their smaller, but dense (small-dense) counterparts with identical memory footprint. Across a broad range of neural network architectures (deep CNNs, stacked LSTM, and seq2seq LSTM models), we find large-sparse models to consistently outperform small-dense models and achieve up to 10x reduction in number of non-zero parameters with minimal loss in accuracy.
Excipient-drug interactions in parenteral formulations.

Excipients are added to parenteral formulations to enhance or maintain active ingredient solubility (solubilizers) and/or stability (buffers, antioxidants, chelating agents, cryo- and lyoprotectants). Excipients also are important in parenteral formulations to assure safety (antimicrobial preservatives), minimize pain and irritation upon injection (tonicity agents), and control or prolong drug delivery (polymers). These are all examples of positive or synergistic interactions between excipients and drugs. However, excipients may also produce negative effects such as loss of drug solubility, activity, and/or stability. This review article will highlight documented interactions, both synergistic and antagonistic, between excipients and drugs in parenteral formulations. The reader will gain better understanding and appreciation of the implications of adding formulation ingredients to parenteral drug products.
Query Expansion Techniques for Information Retrieval: a Survey

With the ever increasing size of web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge. To overcome this, query expansion (QE) plays a crucial role in improving the Internet searches, where the user’s initial query is reformulated to a new query by adding new meaningful terms with similar significance. QE – as part of information retrieval (IR) – has long attracted researchers’ attention. It has also become very influential in the field of personalized social document, Question Answering over Linked Data (QALD), and, Text Retrieval Conference (TREC) and REAL sets.