nils-herrmann/clean_scirepeval huggingface dataset:
MACHINE TRANSLATION The process of translating comprises in its essence the whole secret of human understanding and social communication

The process of translating comprises in its essence the whole secret of human understanding and social communication. This chapter introduces techniques for machine translation (MT), the use of MACHINE TRANSLATION MT computers to automate some or all of the process of translating from one language to another. Translation, in its full generality, is a difficult, fascinating, and intensely human endeavor, as rich as any other area of human creativity. Consider the following passage from the end of Chapter 45 of the 18th-century novel The Story of the Stone, also called Dream of the Red Chamber, by Cao Xue Qin (Cao, 1792), transcribed in the Mandarin dialect: Fig. 24.1 shows the English translation of this passage by David Hawkes, in sentences labeled E 1-E 4. For ease of reading, instead of giving the Chinese, we have shown the English glosses of each Chinese word IN SMALL CAPS. Words in blue are Chinese words not translated into English, or English words not in the Chinese. We have shown alignment lines between words that roughly correspond in the two languages. Consider some of the issues involved in this translation. First, the English and Chinese texts are very different structurally and lexically. The four English sentences (notice the periods in blue) correspond to one long Chinese sentence. The word order of the two texts is very different, as we can see by the many crossed alignment lines  The English has many more words than the Chinese, as we can see by the large number of English words marked in blue. Many of these differences are caused by structural differences between the two languages. For example, because Chinese rarely marks verbal aspect or tense; the English translation has additional words like as, turned to, and had begun, and Hawkes had to decide to translate Chinese tou as penetrated, rather than say was penetrating or had penetrated. Chinese has less articles than English, explaining the large number of blue thes. Chinese also uses far fewer pronouns than English, so Hawkes had to insert she and her in many places into the
Deep Learning on Graphs: A Survey

Deep learning has been shown successful in a number of domains, ranging from acoustics, images to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, a significant amount of research efforts have been devoted to this area, greatly advancing graph analyzing techniques. In this survey, we comprehensively review different kinds of deep learning methods applied to graphs. We divide existing methods into three main categories: semi-supervised methods including Graph Neural Networks and Graph Convolutional Networks, unsupervised methods including Graph Autoencoders, and recent advancements including Graph Recurrent Neural Networks and Graph Reinforcement Learning. We then provide a comprehensive overview of these methods in a systematic manner following their history of developments. We also analyze the differences of these methods and how to composite different architectures. Finally, we briefly outline their applications and discuss potential future directions.
Partial Fingerprint Matching through Region-Based Similarity

Despite advances in fingerprint matching, partial/incomplete/fragmentary fingerprint recognition remains a challenging task. While miniaturization of fingerprint scanners limits the capture of only part of the fingerprint, there is also special interest in processing latent fingerprints which are likely to be partial and of low quality. Partial fingerprints do not include all the structures available in a full fingerprint, hence a suitable matching technique which is independent of specific fingerprint features is required. Common fingerprint recognition methods are based on fingerprint minutiae which do not perform well when applied to low quality images and might not even be suitable for partial fingerprint recognition. To overcome this drawback, in this research, a region-based fingerprint recognition method is proposed in which the fingerprints are compared in a pixel- wise manner by computing their correlation coefficient. Therefore, all the attributes of the fingerprint contribute in the matching decision. Such a technique is promising to accurately recognise a partial fingerprint as well as a full fingerprint compared to the minutiae-based fingerprint recognition methods.The proposed method is based on simple but effective metrics that has been defined to compute local similarities which is then combined into a global score such that it is less affected by distribution skew of the local similarities. Extensive experiments over Fingerprint Verification Competition (FVC) data set proves the superiority of the proposed method compared to other techniques in literature.
End-to-End Simulation of 5G mmWave Networks

Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation (5G) cellular systems. While there has been considerable progress in understanding the mmWave physical layer, innovations will be required at all layers of the protocol stack, in both the access and the core network. Discrete-event network simulation is essential for end-to-end, cross-layer research and development. This paper provides a tutorial on a recently developed full-stack mmWave module integrated into the widely used open-source ns–3 simulator. The module includes a number of detailed statistical channel models as well as the ability to incorporate real measurements or ray-tracing data. The physical and medium access control layers are modular and highly customizable, making it easy to integrate algorithms or compare orthogonal frequency division multiplexing numerologies, for example. The module is interfaced with the core network of the ns–3 Long Term Evolution (LTE) module for full-stack simulations of end-to-end connectivity, and advanced architectural features, such as dual-connectivity, are also available. To facilitate the understanding of the module, and verify its correct functioning, we provide several examples that show the performance of the custom mmWave stack as well as custom congestion control algorithms designed specifically for efficient utilization of the mmWave channel.
Comment Abuse Classification with Deep Learning

Social media platforms, online news commenting spaces, and many other public forum of the Internet have become increasingly known for issues of abusive behavior such as cyberbullying and personal attacks. However, determining whether or not a comment or post should be ”flagged” is difficult and time-consuming, and many platforms are still searching for more efficient moderation solutions. Automating the process of identifying abuse in comments would not only save websites time but would also increase user safety and improve discussions online. In this paper, we use Wikipedia talk page data in order to train deep learning models to flag comments. Comments are classified as personal attacks or not personal attacks, and ratings were determined by Crowdflower workers. We tested three models: a recurrent neural network (RNN) with a long-short term memory cell (LSTM) and word embeddings, a convolutional neural network (CNN) with word embeddings, and a CNN with character embeddings. Our models improve upon previous results from non-deep learning machine-learning models, and we find that a CNN with character-level embeddings reaches the highest performance.
Sophisticated Honeypot mechanism - the autonomous hybrid solution for enhancing computer system security

Computer system security is one of the most popular and the fastest evolving Information Technology (IT) areas. Protection of information access, availability and data integrity represents the basic security characteristics desired on information sources. Any disruption of these properties would result in system intrusion and the related security risk. Advanced decoy based technology called Honeypot has a huge potential for the security community and can achieve several goals of other security technologies, which makes it almost universal. Paper is devoted to sophisticated hybrid Honeypot with autonomous feature that allows to, based on the collected system parameters, adapt to the system of deployment. By its presence Honeypot attracts attacker by simulating vulnerabilities and poor security. After initiation of interaction Honeypot will record all attacker activities and after data analysis allows improving security in computer systems.
Voluntary facial action generates emotion-specific autonomic nervous system activity.

Four experiments were conducted to determine whether voluntarily produced emotional facial configurations are associated with differentiated patterns of autonomic activity, and if so, how this might be mediated. Subjects received muscle-by-muscle instructions and coaching to produce facial configurations for anger, disgust, fear, happiness, sadness, and surprise while heart rate, skin conductance, finger temperature, and somatic activity were monitored. Results indicated that voluntary facial activity produced significant levels of subjective experience of the associated emotion, and that autonomic distinctions among emotions: (a) were found both between negative and positive emotions and among negative emotions, (b) were consistent between group and individual subjects' data, (c) were found in both male and female subjects, (d) were found in both specialized (actors, scientists) and nonspecialized populations, (e) were stronger when the voluntary facial configurations most closely resembled actual emotional expressions, and (f) were stronger when experience of the associated emotion was reported. The capacity of voluntary facial activity to generate emotion-specific autonomic activity: (a) did not require subjects to see facial expressions (either in a mirror or on an experimenter's face), and (b) could not be explained by differences in the difficulty of making the expressions or by differences in concomitant somatic activity.
Model compression

Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits their use in applications where test sets are large (e.g. Google), where storage space is at a premium (e.g. PDAs), and where computational power is limited (e.g. hea-ring aids). We present a method for "compressing" large, complex ensembles into smaller, faster models, usually without significant loss in performance.
Improving co-authorship network structures by combining multiple data sources: evidence from Italian academic statisticians

The aim of the present contribution is to merge bibliographic data for members of a bounded scientific community in order to derive a complete unified archive, with top-international and nationally oriented production, as a new basis to carry out network analysis on a unified co-authorship network. A two-step procedure is used to deal with the identification of duplicate records and the author name disambiguation. Specifically, for the second step we strongly drew inspiration from a well-established unsupervised disambiguation method proposed in the literature following a network-based approach and requiring a restricted set of record attributes. Evidences from Italian academic statisticians were provided by merging data from three bibliographic archives. Non-negligible differences were observed in network results in the comparison of disambiguated and not disambiguated data sets, especially in network measures at individual level.
Saliency, attention, and visual search: an information theoretic approach.

A proposal for saliency computation within the visual cortex is put forth based on the premise that localized saliency computation serves to maximize information sampled from one's environment. The model is built entirely on computational constraints but nevertheless results in an architecture with cells and connectivity reminiscent of that appearing in the visual cortex. It is demonstrated that a variety of visual search behaviors appear as emergent properties of the model and therefore basic principles of coding and information transmission. Experimental results demonstrate greater efficacy in predicting fixation patterns across two different data sets as compared with competing models.
Evolutionary Generation of Whole Test Suites

Recent advances in software testing allow automatic derivation of tests that reach almost any desired point in the source code. There is, however, a fundamental problem with the general idea of targeting one distinct test coverage goal at a time: Coverage goals are neither independent of each other, nor is test generation for any particular coverage goal guaranteed to succeed. We present EvoSuite, a search-based approach that optimizes whole test suites towards satisfying a coverage criterion, rather than generating distinct test cases directed towards distinct coverage goals. Evaluated on five open source libraries and an industrial case study, we show that EvoSuite achieves up to 18 times the coverage of a traditional approach targeting single branches, with up to 44% smaller test suites.
Current bone substitutes for implant dentistry.

Alveolar ridge augmentation is essential for success in implant therapy and depends on the biological performance of bone graft materials. This literature review aims to comprehensively explain the clinically relevant capabilities and limitations of currently available bone substitutes for bone augmentation in light of biomaterial science.



The biological performance of calcium phosphate-based bone substitutes was categorized according to space-making capability, biocompatibility, bioabsorption, and volume maintenance over time. Each category was reviewed based on clinical studies, preclinical animal studies, and in vitro studies.



Currently available bone substitutes provide only osteoconduction as a scaffold but not osteoinduction. Particle size, sensitivity to enzymatic or chemical dissolution, and mechanical properties affect the space-making capability of bone substitutes. The nature of collagen fibers, particulate size, and release of calcium ions influence the biocompatibility of bone substitutes. Bioabsorption of bone substitutes is determined by water solubility (chemical composition) and acid resistance (integrity of apatite structure). Bioabsorption of remnant bone substitute material and volume maintenance of the augmented bone are inversely related.



It is necessary to improve the biocompatibility of currently available bone substitutes and to strike an appropriate balance between bioabsorption and volume maintenance to achieve ideal bone remodeling.
Detection of GAN-Generated Fake Images over Social Networks

The diffusion of fake images and videos on social networks is a fast growing problem. Commercial media editing tools allow anyone to remove, add, or clone people and objects, to generate fake images. Many techniques have been proposed to detect such conventional fakes, but new attacks emerge by the day. Image-to-image translation, based on generative adversarial networks (GANs), appears as one of the most dangerous, as it allows one to modify context and semantics of images in a very realistic way. In this paper, we study the performance of several image forgery detectors against image-to-image translation, both in ideal conditions, and in the presence of compression, routinely performed upon uploading on social networks. The study, carried out on a dataset of 36302 images, shows that detection accuracies up to 95% can be achieved by both conventional and deep learning detectors, but only the latter keep providing a high accuracy, up to 89%, on compressed data.
IMPROVING SEQ2SEQ CONVERSATION MODELS

We study reinforcement learning of chat-bots with recurrent neural network architectures when the rewards are noisy and expensive to obtain. For instance, a chat-bot used in automated customer service support can be scored by quality assurance agents, but this process can be expensive, time consuming and noisy. Previous reinforcement learning work for natural language processing uses onpolicy updates and/or is designed for on-line learning settings. We demonstrate empirically that such strategies are not appropriate for this setting and develop an off-policy batch policy gradient method (BPG). We demonstrate the efficacy of our method via a series of synthetic experiments and an Amazon Mechanical Turk experiment on a restaurant recommendations dataset.
Are Word Embedding-based Features Useful for Sarcasm Detection?

This paper makes a simple increment to state-ofthe-art in sarcasm detection research. Existing approaches are unable to capture subtle forms of context incongruity which lies at the heart of sarcasm. We explore if prior work can be enhanced using semantic similarity/discordance between word embeddings. We augment word embedding-based features to four feature sets reported in the past. We also experiment with four types of word embeddings. We observe an improvement in sarcasm detection, irrespective of the word embedding used or the original feature set to which our features are augmented. For example, this augmentation results in an improvement in F-score of around 4% for three out of these four feature sets, and a minor degradation in case of the fourth, when Word2Vec embeddings are used. Finally, a comparison of the four embeddings shows that Word2Vec and dependency weight-based features outperform LSA and GloVe, in terms of their benefit to sarcasm detection.
Anomaly Detection on Graph Time Series

In this paper, we use variational recurrent neural network to investigate the anomaly detection problem on graph time series. The temporal correlation is modeled by the combination of recurrent neural network (RNN) and variational inference (VI), while the spatial information is captured by the graph convolutional network. In order to incorporate external factors, we use feature extractor to augment the transition of latent variables, which can learn the influence of external factors. With the target function as accumulative ELBO, it is easy to extend this model to on-line method. The experimental study on traffic flow data shows the detection capability of the proposed method.
Oxford Nanopore MinION Sequencing and Genome Assembly

The revolution of genome sequencing is continuing after the successful second-generation sequencing (SGS) technology. The third-generation sequencing (TGS) technology, led by Pacific Biosciences (PacBio), is progressing rapidly, moving from a technology once only capable of providing data for small genome analysis, or for performing targeted screening, to one that promises high quality de novo assembly and structural variation detection for human-sized genomes. In 2014, the MinION, the first commercial sequencer using nanopore technology, was released by Oxford Nanopore Technologies (ONT). MinION identifies DNA bases by measuring the changes in electrical conductivity generated as DNA strands pass through a biological pore. Its portability, affordability, and speed in data production makes it suitable for real-time applications, the release of the long read sequencer MinION has thus generated much excitement and interest in the genomics community. While de novo genome assemblies can be cheaply produced from SGS data, assembly continuity is often relatively poor, due to the limited ability of short reads to handle long repeats. Assembly quality can be greatly improved by using TGS long reads, since repetitive regions can be easily expanded into using longer sequencing lengths, despite having higher error rates at the base level. The potential of nanopore sequencing has been demonstrated by various studies in genome surveillance at locations where rapid and reliable sequencing is needed, but where resources are limited.
Retrofitting Word Vectors of MeSH Terms to Improve Semantic Similarity Measures

Estimation of the semantic relatedness between biomedical concepts has utility for many informatics applications. Automated methods fall into two broad categories: methods based on distributional statistics drawn from text corpora, and methods based on the structure of existing knowledge resources. In the former case, taxonomic structure is disregarded. In the latter, semantically relevant empirical information is not considered. In this paper, we present a method that retrofits the context vector representation of MeSH terms by using additional linkage information from UMLS/MeSH hierarchy such that linked concepts have similar vector representations. We evaluated the method relative to previously published physician and coder’s ratings on sets of MeSH terms. Our experimental results demonstrate that the retrofitted word vector measures obtain a higher correlation with physician judgments. The results also demonstrate a clear improvement on the correlation with experts’ ratings from the retrofitted vector representation in comparison to the vector representation without retrofitting.
2PFPCE: Two-Phase Filter Pruning Based on Conditional Entropy

Deep Convolutional Neural Networks (CNNs) offer remarkable performance of classifications and regressions in many high-dimensional problems and have been widely utilized in real-word cognitive applications. However, high computational cost of CNNs greatly hinder their deployment in resource-constrained applications, real-time systems and edge computing platforms. To overcome this challenge, we propose a novel filter-pruning framework, two-phase filter pruning based on conditional entropy, namely 2PFPCE, to compress the CNN models and reduce the inference time with marginal performance degradation. In our proposed method, we formulate filter pruning process as an optimization problem and propose a novel filter selection criteria measured by conditional entropy. Based on the assumption that the representation of neurons shall be evenly distributed, we also develop a maximum-entropy filter freeze technique that can reduce over fitting. Two filter pruning strategies – global and layer-wise strategies, are compared. Our experiment result shows that combining these two strategies can achieve a higher neural network compression ratio than applying only one of them under the same accuracy drop threshold. Twophase pruning, that is, combining both global and layer-wise strategies, achieves ∼ 10× FLOPs reduction and 46% inference time reduction on VGG-16, with 2% accuracy drop.