nils-herrmann/clean_scirepeval huggingface dataset:
SDN-based security services using interface to network security functions

This paper proposes a framework for security services using Software-Defined Networking (SDN) and Interface to Network Security Functions (I2NSF). It specifies requirements for such a framework for security services based on network virtualization. It describes two representative security systems, such as (i) centralized firewall system and (ii) DDoS-attack mitigation system. For each service, this paper discusses the limitations of existing systems and presents a possible SDN-based system to protect network resources by controlling suspicious and dangerous network traffic.
Functional Distributional Semantics

Learning Linguistically Informed Representations from a Precisely Annotated Corpus The aim of distributional semantics is to design computational techniques that can automatically learn the meanings of words from a body of text. The twin challenges are: how do we represent meaning, and how do we learn these representations? The current state of the art is to represent meanings as vectors – but vectors do not correspond to any traditional notion of meaning. In particular, there is no way to talk about truth, a crucial concept in logic and formal semantics. In this thesis, I develop a framework for distributional semantics which answers this challenge. The meaning of a word is not represented as a vector, but as a function, mapping entities (objects in the world) to probabilities of truth (the probability that the word is true of the entity). Such a function can be interpreted both in the machine learning sense of a classifier, and in the formal semantic sense of a truth-conditional function. This simultaneously allows both the use of machine learning techniques to exploit large datasets, and also the use of formal semantic techniques to manipulate the learnt representations. I define a probabilistic graphical model, which incorporates a probabilistic generalisation of model theory (allowing a strong connection with formal semantics), and which generates semantic dependency graphs (allowing it to be trained on a corpus). This graphical model provides a natural way to model logical inference, semantic composition, and context-dependent meanings, where Bayesian inference plays a crucial role. I demonstrate the feasibility of this approach by training a model on WikiWoods, a parsed version of the English Wikipedia, and evaluating it on three tasks. The results indicate that the model can learn information not captured by vector space models. Guy Edward Toh Emerson
Intra-sentential Zero Anaphora Resolution using Subject Sharing Recognition

In this work, we improve the performance of intra-sentential zero anaphora resolution in Japanese using a novel method of recognizing subject sharing relations. In Japanese, a large portion of intrasentential zero anaphora can be regarded as subject sharing relations between predicates, that is, the subject of some predicate is also the unrealized subject of other predicates. We develop an accurate recognizer of subject sharing relations for pairs of predicates in a single sentence, and then construct a subject shared predicate network, which is a set of predicates that are linked by the subject sharing relations recognized by our recognizer. We finally combine our zero anaphora resolution method exploiting the subject shared predicate network and a state-ofthe-art ILP-based zero anaphora resolution method. Our combined method achieved a significant improvement over the the ILPbased method alone on intra-sentential zero anaphora resolution in Japanese. To the best of our knowledge, this is the first work to explicitly use an independent subject sharing recognizer in zero anaphora resolution.
Knowledge Discovery and Data Mining Applications in the Healthcare Industry : A Comprehensive Study

The healthcare industry is one of the most attractive domains to realize the actionable knowledge discovery objectives. This chapter studies recent researches on knowledge discovery and data mining applications in the healthcare industry and proposes a new classification of these applications. Studies show that knowledge discovery and data mining applications in the healthcare industry can be classified to three major classes, namely patient view, market view, and system view. Patient view includes papers that performed pure data mining on healthcare industry data. Market view includes papers that saw the patients as customers. System view includes papers that developed a decision support system. The goal of this classification is identifying research opportunities and gaps for researchers interested in this context.
Single-layer slotted post-wall waveguide array with compact feed-line structures for 77 GHz automotive radar

A single-layer antenna composed of four subarrays and compact low-loss feed-line structures connecting to an RFmodule for 77 GHz automotive radar systems is presented. This antenna is based on a slotted post-wall waveguide array, which is integrated in a ceramic-filled PTFE substrate. The antenna gain is higher than 16 dBi relative to the maximum available directivity of 20.4 dBi at 76.5 GHz. The proposed compact feedline structures achieve the low transmission loss of less than 3.2 dB.
Evaluation of drug release kinetics from ibuprofen matrix tablets using HPMC.

The aim of this study is to develop a once-daily sustained release matrix tablet of ibuprofen using hydroxypropyl methylcellulose (HPMC) as release controlling factor and to evaluate drug release parameters as per various release kinetic models. In order to achieve required sustained release profile tablets were directly compressed using Avicel pH 101 and Magnesium stearate. The formulated tablets were also characterized by physical and chemical parameters and results were found in acceptable limits. Different dissolution models were applied to drug release data in order to evaluate release mechanisms and kinetics. Criteria for selecting the most appropriate model was based on linearity (coefficient of correlation). The drug release data fit well to the Higuchi expression. Drug release mechanism was found as a complex mixture of diffusion, swelling and erosion.
A Retrospective Review from 2006 to 2011 of Lower Extremity Injuries in Badminton in New Zealand

To describe lower extremity injuries for badminton in New Zealand.  Lower limb badminton injuries that resulted in claims accepted by the national insurance company Accident Compensation Corporation (ACC) in New Zealand between 2006 and 2011 were reviewed.  The estimated national injury incidence for badminton injuries in New Zealand from 2006 to 2011 was 0.66%. There were 1909 lower limb badminton injury claims which cost NZ$2,014,337 (NZ$ value over 2006 to 2011). The age-bands frequently injured were 10–19 (22%), 40–49 (22%), 30–39 (14%) and 50–59 (13%) years. Sixty five percent of lower limb injuries were knee ligament sprains/tears. Males sustained more cruciate ligament sprains than females (75 vs. 39). Movements involving turning, changing direction, shifting weight, pivoting or twisting were responsible for 34% of lower extremity injuries.  The knee was most frequently OPEN ACCESS
Extracting hand grasp and motion for intent expression in mid-air shape deformation: A concrete and iterative exploration through a virtual pottery application

We describe the iterative design and evaluation of a geometric interaction technique for bare-hand mid-air virtual pottery. We model the shaping of a pot as a gradual and progressive convergence of the pot-profile to the shape of the user’s hand represented as a pointcloud (PCL). Our pottery-inspired application served as a platform for systematically revealing how users use their hands to express the intent of deformation during a pot shaping process. Our approach involved three stages: (a) clutching by proximal-attraction, (b) shaping by proximal-attraction, and (c) shaping by grasp+motion. The design and implementation of each stage was informed by user evaluations of the previous stage. Our work evidently demonstrates that it is possible to enable users to express their intent for shape deformation without the need for a fixed set of gestures for clutching and deforming a shape. We found that the expressive capability of hand articulation can be effectively harnessed for controllable shaping by organizing the deformation process in broad classes of intended operations such as pulling, pushing, and fairing. After minimal practice with the pottery application, users could figure out their own strategy for reaching, grasping, and deforming the pot. Users particularly enjoyed using day-to-day physical objects as tools for shaping pots.
Introducing LETOR 4.0 Datasets

LETOR is a package of benchmark data sets for research on LEarning TO Rank, which contains standard features, relevance judgments, data partitioning, evaluation tools, and several baselines. Version 1.0 was released in April 2007. Version 2.0 was released in Dec. 2007. Version 3.0 was released in Dec. 2008. This version, 4.0, was released in July 2009. Very different from previous versions (V3.0 is an update based on V2.0 and V2.0 is an update based on V1.0), LETOR4.0 is a totally new release. It uses the Gov2 web page collection (~25M pages) and two query sets from Million Query track of TREC 2007 and TREC 2008. We call the two query sets MQ2007 and MQ2008 for short. There are about 1700 queries in MQ2007 with labeled documents and about 800 queries in MQ2008 with labeled documents. If you have any questions or suggestions about the datasets, please kindly email us (letor@microsoft.com). Our goal is to make the dataset reliable and useful for the community.
Learning to Search in Long Documents Using Document Structure

Reading comprehension models are based on recurrent neural networks that sequentially process the document tokens. As interest turns to answering more complex questions over longer documents, sequential reading of large portions of text becomes a substantial bottleneck. Inspired by how humans use document structure, we propose a novel framework for reading comprehension. We represent documents as trees, and model an agent that learns to interleave quick navigation through the document tree with more expensive answer extraction. To encourage exploration of the document tree, we propose a new algorithm, based on Deep Q-Network (DQN), which strategically samples tree nodes at training time. Empirically we find our algorithm improves question answering performance compared to DQN and a strong information-retrieval (IR) baseline, and that ensembling our model with the IR baseline results in further gains in performance.
Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora

Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of scholarly authority to find the prominent authors but these authority indices do not differentiate authority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and topical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author’s influence on citations of a paper based on the topics of the cited papers, as well as the citing papers. We fit LTAI into four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various baselines, starting with the latent Dirichlet allocation, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, citations and authors of publications.
Machine Theory of Mind

Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans’ ability to represent the mental states of others, including their desires, beliefs, and intentions. We propose to train a machine to build such models too. We design a Theory of Mind neural network – a ToMnet – which uses meta-learning to build models of the agents it encounters, from observations of their behaviour alone. Through this process, it acquires a strong prior model for agents’ behaviour, as well as the ability to bootstrap to richer predictions about agents’ characteristics and mental states using only a small number of behavioural observations. We apply the ToMnet to agents behaving in simple gridworld environments, showing that it learns to model random, algorithmic, and deep reinforcement learning agents from varied populations, and that it passes classic ToM tasks such as the “SallyAnne” test (Wimmer & Perner, 1983; BaronCohen et al., 1985) of recognising that others can hold false beliefs about the world. We argue that this system – which autonomously learns how to model other agents in its world – is an important step forward for developing multi-agent AI systems, for building intermediating technology for machine-human interaction, and for advancing the progress on interpretable AI.  ncr@google.com.
How emotion shapes behavior: feedback, anticipation, and reflection, rather than direct causation.

Fear causes fleeing and thereby saves lives: this exemplifies a popular and common sense but increasingly untenable view that the direct causation of behavior is the primary function of emotion. Instead, the authors develop a theory of emotion as a feedback system whose influence on behavior is typically indirect. By providing feedback and stimulating retrospective appraisal of actions, conscious emotional states can promote learning and alter guidelines for future behavior. Behavior may also be chosen to pursue (or avoid) anticipated emotional outcomes. Rapid, automatic affective responses, in contrast to the full-blown conscious emotions, may inform cognition and behavioral choice and thereby help guide current behavior. The automatic affective responses may also remind the person of past emotional outcomes and provide useful guides as to what emotional outcomes may be anticipated in the present. To justify replacing the direct causation model with the feedback model, the authors review a large body of empirical findings.
A disciplined approach to neural network hyper-parameters: Part 1 - learning rate, batch size, momentum, and weight decay

Although deep learning has produced dazzling successes for applications of image, speech, and video processing in the past few years, most trainings are with suboptimal hyper-parameters, requiring unnecessarily long training times. Setting the hyper-parameters remains a black art that requires years of experience to acquire. This report proposes several efficient ways to set the hyper-parameters that significantly reduce training time and improves performance. Specifically, this report shows how to examine the training validation/test loss function for subtle clues of underfitting and overfitting and suggests guidelines for moving toward the optimal balance point. Then it discusses how to increase/decrease the learning rate/momentum to speed up training. Our experiments show that it is crucial to balance every manner of regularization for each dataset and architecture. Weight decay is used as a sample regularizer to show how its optimal value is tightly coupled with the learning rates and momentums.
Interaction Networks for Learning about Objects, Relations and Physics

Reasoning about objects, relations, and physics is central to human intelligence, and 1 a key goal of artificial intelligence. Here we introduce the interaction network, a 2 model which can reason about how objects in complex systems interact, supporting 3 dynamical predictions, as well as inferences about the abstract properties of the 4 system. Our model takes graphs as input, performs objectand relation-centric 5 reasoning in a way that is analogous to a simulation, and is implemented using 6 deep neural networks. We evaluate its ability to reason about several challenging 7 physical domains: n-body problems, rigid-body collision, and non-rigid dynamics. 8 Our results show it can be trained to accurately simulate the physical trajectories of 9 dozens of objects over thousands of time steps, estimate abstract quantities such 10 as energy, and generalize automatically to systems with different numbers and 11 configurations of objects and relations. Our interaction network implementation 12 is the first general-purpose, learnable physics engine, and a powerful general 13 framework for reasoning about object and relations in a wide variety of complex 14 real-world domains.
Cell-free DNA (cfDNA): Clinical Significance and Utility in Cancer Shaped By Emerging Technologies.

Precision oncology is predicated upon the ability to detect specific actionable genomic alterations and to monitor their adaptive evolution during treatment to counter resistance. Because of spatial and temporal heterogeneity and comorbidities associated with obtaining tumor tissues, especially in the case of metastatic disease, traditional methods for tumor sampling are impractical for this application. Known to be present in the blood of cancer patients for decades, cell-free DNA (cfDNA) is beginning to inform on tumor genetics, tumor burden, and mechanisms of progression and drug resistance. This substrate is amenable for inexpensive noninvasive testing and thus presents a viable approach to serial sampling for screening and monitoring tumor progression. The fragmentation, low yield, and variable admixture of normal DNA present formidable technical challenges for realization of this potential. This review summarizes the history of cfDNA discovery, its biological properties, and explores emerging technologies for clinically relevant sequence-based analysis of cfDNA in cancer patients. Molecular barcoding (or Unique Molecular Identifier, UMI)-based methods currently appear to offer an optimal balance between sensitivity, flexibility, and cost and constitute a promising approach for clinically relevant assays for near real-time monitoring of treatment-induced mutational adaptations to guide evidence-based precision oncology.
A Survey on Internet of Things From Industrial Market Perspective

The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as radio frequency identifications, sensors, and actuators, as well as other instruments and smart appliances that are becoming an integral component of the Internet. Over the last few years, we have seen a plethora of IoT solutions making their way into the industry marketplace. Context-aware communications and computing have played a critical role throughout the last few years of ubiquitous computing and are expected to play a significant role in the IoT paradigm as well. In this paper, we examine a variety of popular and innovative IoT solutions in terms of context-aware technology perspectives. More importantly, we evaluate these IoT solutions using a framework that we built around well-known context-aware computing theories. This survey is intended to serve as a guideline and a conceptual framework for context-aware product development and research in the IoT paradigm. It also provides a systematic exploration of existing IoT products in the marketplace and highlights a number of potentially significant research directions and trends.
Semantic Speech Retrieval With a Visually Grounded Model of Untranscribed Speech

There is a growing interest in models that can learn from unlabelled speech paired with visual context. This setting is relevant for low-resource speech processing, robotics, and human language acquisition research. Here, we study how a visually grounded speech model, trained on images of scenes paired with spoken captions, captures aspects of semantics. We use an external image tagger to generate soft text labels from images, which serve as targets for a neural model that maps untranscribed speech to semantic keyword labels. We introduce a newly collected data set of human semantic relevance judgements and an associated task, semantic speech retrieval, where the goal is to search for spoken utterances that are semantically relevant to a given text query. Without seeing any text, the model trained on parallel speech and images achieves a precision of almost 60% on its top ten semantic retrievals. Compared to a supervised model trained on transcriptions, our model matches human judgements better by some measures, especially in retrieving non-verbatim semantic matches. We perform an extensive analysis of the model and its resulting representations.
BinClone: Detecting Code Clones in Malware

To gain an in-depth understanding of the behaviour of a malware, reverse engineers have to disassemble the malware, analyze the resulting assembly code, and then archive the commented assembly code in a malware repository for future reference. In this paper, we have developed an assembly code clone detection system called BinClone to identify the code clone fragments from a collection of malware binaries with the following major contributions. First, we introduce two deterministic clone detection methods with the goals of improving the recall rate and facilitating malware analysis. Second, our methods allow malware analysts to discover both exact and inexact clones at different token normalization levels. Third, we evaluate our proposed clone detection methods on real-life malware binaries. To the best of our knowledge, this is the first work that studies the problem of assembly code clone detection for malware analysis.
Defect Reduction Strategies for the Manufacture of Contoured Laminates Using Vacuum Bag-Only Prepregs

Complex structures manufactured using low-pressure vacuum bag-only (VBO) prepreg processing are more susceptible to defects than flat laminates due to complex compaction conditions present at sharp corners. Consequently, effective defect mitigation strategies are required to produce structural parts. In this study, we investigated the relationships between laminate properties, processing conditions`, mold designs and part quality in order to develop science-based guidelines for the manufacture of complex parts. Generic laminates consisting of a central corner and two flanges were fabricated in a multi-part study that considered variation in corner angle and local curvature radius, the applied pressure during layup and cure, and the prepreg material and laminate thickness. The manufactured parts were analyzed in terms of microstructural fiber bed and resin distribution, thickness variation, and void content. The results indicated that defects observed in corner laminates were influenced by both mold design and processing conditions, and that optimal combinations of these factors can mitigate defects and improve quality.