nils-herrmann/clean_scirepeval huggingface dataset:
OpenSample : A Low-Latency , Sampling-Based Measurement Platform for SDN

In this paper we propose, implement and evaluate OpenSample: a low-latency, sampling-based network measurement platform targeted at building faster control loops for software-defined networks. OpenSample leverages sFlow packet sampling to provide near–real-time measurements of both network load and individual flows. While OpenSample is useful in any context, it is particularly useful in an SDN environment where a network controller can quickly take action based on the data it provides. Using sampling for network monitoring allows OpenSample to have a 100 millisecond control loop rather than the 1–5 second control loop of prior polling-based approaches. We implement OpenSample in the Floodlight OpenFlow controller and evaluate it both in simulation and on a testbed comprised of commodity switches. When used to inform traffic engineering, OpenSample provides up to a 150% throughput improvement over both static equal-cost multi-path routing and a polling-based solution with a one second control loop.
TagAssist: Automatic Tag Suggestion for Blog Posts

In this paper, we describe a system called TagAssist that provides tag suggestions for new blog posts by utilizing existing tagged posts. The system is able to increase the quality of suggested tags by performing lossless compression over existing tag data. In addition, the system employs a set of metrics to evaluate the quality of a potential tag suggestion. Coupled with the ability for users to manually add tags, TagAssist can ease the burden of tagging and increase the utility of retrieval and browsing systems built on top of tagging data.
Stereo-Based Autonomous Navigation and Obstacle Avoidance*

This paper presents a stereo vision-based autonomous navigation system using a GPS and a modified version of the VFH algorithm. In order to obtain a high-accuracy disparity map and meet the time constraints of the real time navigation system, this work proposes the use of a semi-global stereo method. By not suffering the same issues of the regularly used local stereo methods, the employed stereo technique enables the generation of a highly dense, efficient, and accurate disparity map. Obstacles are detected using a method that checks for relative slopes and heights differences. Experimental tests using an electric vehicle in an urban environment were performed to validate the proposed approach.
On Activation Functions for Complex-Valued Neural Networks - Existence of Energy Functions

Recently models of neural networks that can directly deal with complex numbers, complex-valued neural networks, have been proposed and several studies on their abilities of information processing have been done. One of the important factors to characterize behavior of a complex-valued neural network is its activation function which is a nonlinear complex function. This paper discusses the properties of activation functions from the standpoint of existence of an energy function for complex-valued neural networks. Two classes of complex functions which are widely used as activation functions in the models of complex-valued neural networks are considered. We investigate the properties of activation functions which assure existence of energy functions and discuss about how to find out complex functions which satisfy the properties.
Survey of microphthalmia in Japan

To report the current status of patients with microphthalmia based on a cross-sectional survey of patient hospital visits. A questionnaire was sent to the departments of ophthalmology in 1,151 major Japanese hospitals to survey the following: the number of patients with microphthalmia who visited the outpatient clinics between January 2008 and December 2009; gender; age; family history; associated ocular anomalies; complications and systemic diseases; surgical treatment; vision and management. A retrospective quantitative registry of 1,254 microphthalmic eyes (851 patients) from 454 hospitals (39.4%) was compiled. Of the patients for whom data were available, 50% ranged in age from 0 to 9 years. The major ocular findings were nanophthalmos, coloboma, and vitreoretinal malformations. Ocular complications frequently developed, including cataracts, glaucoma, and retinal detachment. Surgery was performed in 21.4% of all cases, and systemic diseases were present in 31% of all cases. The vision associated with microphthalmia exceeded 0.1 in about 30% of the eyes. Glasses and low vision aids were used by 21.6% of patients. Patients with microphthalmia often have ocular and systemic anomalies. Early assessment and preservation of vision and long-term complication management are needed.
Automatic Number Plate Recognition System

Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their license number. The objective is to design an efficient automatic authorized vehicle identification system by using the Indian vehicle number plate. In this paper we discus different methodology for number plate localization, character segmentation & recognition of the number plate. The system is mainly applicable for non standard Indian number plates by recognizing single & double line number plates under different varying illumination condition and works on multilingual, multicolour number plates according to Indian condition.  ANPR, Localization, Segmentation, Artificial Neural Network, Optical Character Recognition
DeepEmo: Learning and Enriching Pattern-Based Emotion Representations

We propose a graph-based mechanism to extract rich-emotion bearing patterns, which fosters a deeper analysis of online emotional expressions, from a corpus. The patterns are then enriched with word embeddings and evaluated through several emotion recognition tasks. Moreover, we conduct analysis on the emotion-oriented patterns to demonstrate its applicability and to explore its properties. Our experimental results demonstrate that the proposed techniques outperform most stateof-the-art emotion recognition techniques.
Personalization as a service: the architecture and a case study

Cloud computing has become a hot topic in the IT industry. Great efforts have been made to establish cloud computing platforms for enterprise users, mostly small businesses. However, there are few researches about the impact of cloud computing over individual users. In this paper we focus on how to provide personalized services for individual users in the cloud environment. We argue that a personalized cloud service shall compose of two parts. The client side program records user activities on personal de-vices such as PC. Besides that, the user model is also computed on the client side to avoid server overhead. The cloud side program fetches the user model periodically and adjusts its results accordingly. We build a personalized cloud data search engine prototype to prove our idea.
Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction

Translation-based methods for grammar correction that directly map noisy, ungrammatical text to their clean counterparts are able to correct a broad range of errors; however, such techniques are bottlenecked by the need for a large parallel corpus of noisy and clean sentence pairs. In this paper, we consider synthesizing parallel data by noising a clean monolingual corpus. While most previous approaches introduce perturbations using features computed from local context windows, we instead develop error generation processes using a neural sequence transduction model trained to translate clean examples to their noisy counterparts. Given a corpus of clean examples, we propose beam search noising procedures to synthesize additional noisy examples that human evaluators were nearly unable to discriminate from nonsynthesized examples. Surprisingly, when trained on additional data synthesized using our best-performing noising scheme, our model approaches the same performance as when trained on additional nonsynthesized data.
From value chain to value constellation: designing interactive strategy.

In today's fast-changing competitive environment, strategy is no longer a matter of positioning a fixed set of activities along that old industrial model, the value chain. Successful companies increasingly do not just add value, they reinvent it. The key strategic task is to reconfigure roles and relationships among a constellation of actors--suppliers, partners, customers--in order to mobilize the creation of value by new combinations of players. What is so different about this new logic of value? It breaks down the distinction between products and services and combines them into activity-based "offerings" from which customers can create value for themselves. But as potential offerings grow more complex, so do the relationships necessary to create them. As a result, a company's strategic task becomes the ongoing reconfiguration and integration of its competencies and customers. The authors provide three illustrations of these new rules of strategy. IKEA has blossomed into the world's largest retailer of home furnishings by redefining the relationships and organizational practices of the furniture business. Danish pharmacies and their national association have used the opportunity of health care reform to reconfigure their relationships with customers, doctors, hospitals, drug manufacturers, and with Danish and international health organizations to enlarge their role, competencies, and profits. French public-service concessionaires have mastered the art of conducting a creative dialogue between their customers--local governments in France and around the world--and a perpetually expanding set of infrastructure competencies.
Putting Things in Context: Community-specific Embedding Projections for Sentiment Analysis

Variation in language is ubiquitous, and is particularly evident in newer forms of writing such as social media. Fortunately, variation is not random, but is usually linked to social factors. By exploiting linguistic homophily — the tendency of socially linked individuals to use language similarly — it is possible to build models that are more robust to variation. In this paper, we focus on social network communities, which make it possible to generalize sociolinguistic properties from authors in the training set to authors in the test sets, without requiring demographic author metadata. We detect communities via standard graph clustering algorithms, and then exploit these communities by learning community-specific projections of word embeddings. These projections capture shifts in word meaning in different social groups; by modeling them, we are able to improve the overall accuracy of Twitter sentiment analysis by a significant margin over competitive prior work.
An Ontology-based Approach for Software Architecture Recommendations Full Paper

The design and development of sustainable software systems require software architects to consider a variety of architectural solutions and their trade-offs. With the frequent introduction of new architectural methods and software solutions, as well as, due to time-to-market constraints faced by software architects, considering even a subset of alternative architectural solutions during the decision-making process is a challenge. In this paper, we propose a recommendation system that automatically annotates architectural elements in software architecture documents and then proposes a) alternative architectural solutions for the annotated elements and b) concrete software solutions to realize an architectural design decision. These annotations and recommendations are derived from the knowledge captured in a publicly available cross-domain ontology. The evaluation of the recommendation system indicates that our approach can effectively support software architects to consider alternative architectural solutions while making architectural design decisions.
Bi-histogram equalization with a plateau limit for digital image enhancement

Many histogram equalization based methods have been introduced for the use in consumer electronics in recent years. Yet, many of these methods are relatively complicated to be implemented, and mostly require a high computational time. Furthermore, some of the methods require several predefined parameters from the user, which make the optimal results cannot be obtained automatically. Therefore, this paper presents bi-histogram equalization with a plateau level (BHEPL) as one of the options for the system that requires a short processing time image enhancement. First, BHEPL divides the input histogram into two independent sub-histograms. This is done in order to maintain the mean brightness. Then, these sub-histograms are clipped based on the calculated plateau value. By doing this, excessive enhancement can be avoided. Experimental results show that this method only requires 34.20 ms, in average, to process images of size 3648 × 2736 pixels (i.e. 10 Mega pixels images). The proposed method also gives better enhancement results as compared with some multi-sections mean brightness preserving histogram equalization methods.
Protecting privacy using the decentralized label model

Stronger protection is needed for the confidentiality and integrity of data, because programs containing untrusted code are the rule rather than the exception. Information flow control allows the enforcement of end-to-end security policies, but has been difficult to put into practice. This article describes the decentralized label model, a new label model for control of information flow in systems with mutual distrust and decentralized authority. The model improves on existing multilevel security models by allowing users to declassify information in a decentralized way, and by improving support for fine-grained data sharing. It supports static program analysis of information flow, so that programs can be certified to permit only acceptable information flows, while largely avoiding the overhead of run-time checking. The article introduces the language Jif, an extension to Java that provides static checking of information flow using the decentralized label model.
Detecting Text in Natural Image with Connectionist Text Proposal Network

We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image. The CTPN detects a text line in a sequence of fine-scale text proposals directly in convolutional feature maps. We develop a vertical anchor mechanism that jointly predicts location and text/non-text score of each fixed-width proposal, considerably improving localization accuracy. The sequential proposals are naturally connected by a recurrent neural network, which is seamlessly incorporated into the convolutional network, resulting in an end-to-end trainable model. This allows the CTPN to explore rich context information of image, making it powerful to detect extremely ambiguous text. The CTPN works reliably on multi-scale and multilanguage text without further post-processing, departing from previous bottom-up methods requiring multi-step post filtering. It achieves 0.88 and 0.61 F-measure on the ICDAR 2013 and 2015 benchmarks, surpassing recent results [8, 35] by a large margin. The CTPN is computationally efficient with 0.14s/image, by using the very deep VGG16 model . Online demo is available at: http://textdet.com/.
Selective separation of virgin and post-consumer polymers (PET and PVC) by flotation method.

More and more polymer wastes are generated by industry and householders today. Recycling is an important process to reduce the amount of waste resulting from human activities. Currently, recycling technologies use relatively homogeneous polymers because hand-sorting waste is costly. Many promising technologies are being investigated for separating mixed thermoplastics, but they are still uneconomical and unreliable. At present, most waste polymers cause serious environmental problems. Burning polymers for recycling is not practiced since poisonous gases are released during the burning process. Particularly, polyvinyl chloride (PVC) materials among waste polymers generate hazardous HCl gas, dioxins containing Cl, etc., which lead to air pollution and shorten the life of the incinerator. In addition, they make other polymers difficult to recycle. Both polyethylene terephthalate (PET) and PVC have densities of 1.30-1.35g /cm(3) and cannot be separated using conventional gravity separation techniques. For this reason, polymer recycling needs new techniques. Among these techniques, froth flotation, which is also used in mineral processing, can be useful because of its low cost and simplicity. The main objective of this research is to recycle PET and PVC selectively from post-consumer polymer wastes and virgin polymers by using froth flotation. According to the results, all PVC particles were floated with 98.8% efficiency in virgin polymer separation while PET particles were obtained with 99.7% purity and 57.0% efficiency in post-consumer polymer separation.
Efficient pavement crack detection and classification

Each year, millions of dollars are invested on road maintenance and reparation all over the world. In order to minimize costs, one of the main aspects is the early detection of those flaws. Different types of cracks require different types of repairs; therefore, not only a crack detection is required but a crack type classification. Also, the earlier the crack is detected, the cheaper the reparation is. Once the images are captured, several processes are applied in order to extract the main characteristics for emphasizing the cracks (logarithmic transformation, bilateral filter, Canny algorithm, and a morphological filter). After image preprocessing, a decision tree heuristic algorithm is applied to finally classify the image. This work obtained an average of 88% of success detecting cracks and an 80% of success detecting the type of the crack. It could be implemented in a vehicle traveling as fast as 130 kmh or 81 mph.
Exploring Semantic Properties of Sentence Embeddings

Neural vector representations are ubiquitous throughout all subfields of NLP. While word vectors have been studied in much detail, thus far only little light has been shed on the properties of sentence embeddings. In this paper, we assess to what extent prominent sentence embedding methods exhibit select semantic properties. We propose a framework that generate triplets of sentences to explore how changes in the syntactic structure or semantics of a given sentence affect the similarities obtained between their sentence embeddings.
Discovering patterns in categorical time series using IFS

The detection of patterns in categorical time series data is an important task in many fields of science. Several efficient algorithms for finding frequent sequential patterns have been proposed. An online-approach for sequential pattern analysis based on transforming the categorical alphabet to real vectors and generating fractals by an iterated function systems (IFS) is suggested. Sequential patterns can be analyzed with standard methods of cluster analysis using this approach. A version of the procedure allows detecting patterns visually.
Transport and fate of microplastic particles in wastewater treatment plants.

Municipal wastewater treatment plants (WWTPs) are frequently suspected as significant point sources or conduits of microplastics to the environment. To directly investigate these suspicions, effluent discharges from seven tertiary plants and one secondary plant in Southern California were studied. The study also looked at influent loads, particle size/type, conveyance, and removal at these wastewater treatment facilities. Over 0.189 million liters of effluent at each of the seven tertiary plants were filtered using an assembled stack of sieves with mesh sizes between 400 and 45 μm. Additionally, the surface of 28.4 million liters of final effluent at three tertiary plants was skimmed using a 125 μm filtering assembly. The results suggest that tertiary effluent is not a significant source of microplastics and that these plastic pollutants are effectively removed during the skimming and settling treatment processes. However, at a downstream secondary plant, an average of one micro-particle in every 1.14 thousand liters of final effluent was counted. The majority of microplastics identified in this study had a profile (color, shape, and size) similar to the blue polyethylene particles present in toothpaste formulations. Existing treatment processes were determined to be very effective for removal of microplastic contaminants entering typical municipal WWTPs.
A Survey on Recent Traffic Classification Techniques Using Machine Learning Methods

Recent research tends to apply machine learning techniques to classify network traffic using flow statistical features and IP packet payload. This survey paper looks into the use of Machine Learning (ML) algorithms for traffic classification in the period 2009 to 2013. It provides motivation for the application of ML techniques to network traffic classification, and reviews significant work in this area. The recent techniques focus on statistical features of network packets to provide security. A review of ML techniques has been done Keywords— Machine learning, Traffic classification, feature extraction, signature generation, cluster aggregation.
CRISP: a computational model of fixation durations in scene viewing.

Eye-movement control during scene viewing can be represented as a series of individual decisions about where and when to move the eyes. While substantial behavioral and computational research has been devoted to investigating the placement of fixations in scenes, relatively little is known about the mechanisms that control fixation durations. Here, we propose a computational model (CRISP) that accounts for saccade timing and programming and thus for variations in fixation durations in scene viewing. First, timing signals are modeled as continuous-time random walks. Second, difficulties at the level of visual and cognitive processing can inhibit and thus modulate saccade timing. Inhibition generates moment-by-moment changes in the random walk's transition rate and processing-related saccade cancellation. Third, saccade programming is completed in 2 stages: an initial, labile stage that is subject to cancellation and a subsequent, nonlabile stage. Several simulation studies tested the model's adequacy and generality. An initial simulation study explored the role of cognitive factors in scene viewing by examining how fixation durations differed under different viewing task instructions. Additional simulations investigated the degree to which fixation durations were under direct moment-to-moment control of the current visual scene. The present work further supports the conclusion that fixation durations, to a certain degree, reflect perceptual and cognitive activity in scene viewing. Computational model simulations contribute to an understanding of the underlying processes of gaze control.