Reviewerly/exHarmony huggingface dataset:
Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection

While Water Treatment Networks (WTNs) are critical infrastructures for local communities and public health, WTNs are vulnerable to cyber attacks. Effective detection of attacks can defend WTNs against discharging contaminated water, denying access, destroying equipment, and causing public fear. While there are extensive studies in WTNs attack detection, they only exploit the data characteristics partially to detect cyber attacks. After preliminary exploring the sensing data of WTNs, we find that integrating spatio-temporal knowledge, representation learning, and detection algorithms can improve attack detection accuracy. To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatio-temporal characteristics of cyber attacks in WTNs. In particular, we propose a spatio-temporal representation framework specially tailored to cyber attacks after separating the sensing data of WTNs into a sequence of time segments. This framework has two key components. The first component is a temporal embedding module to preserve temporal patterns within a time segment by projecting the time segment of a sensor into a temporal embedding vector. We then construct Spatio-Temporal Graphs (STGs), where a node is a sensor and an attribute is the temporal embedding vector of the sensor, to describe the state of the WTNs. The second component is a spatial embedding module, which learns the final fused embedding of the WTNs from STGs. In addition, we devise an improved one class-SVM model that utilizes a new designed pairwise kernel to detect cyber attacks. The devised pairwise kernel augments the distance between normal and attack patterns in the fused embedding space. Finally, we conducted extensive experimental evaluations with real-world data to demonstrate the effectiveness of our framework.
Research Guides: LSC 375: Social Media Analytics (Fall 2020): For Your Journal Article Reading

This guide provides links to selected resources from among library collections and services to support students in LSC 375.
One year of filtering before the Court of Justice of the European Union

One year of filtering before the Court of Justice of the European Union Antonella Gentile Antonella Gentile Email: antonella.gentile2@gmail.com Search for other works by this author on: Oxford Academic Google Scholar Journal of Intellectual Property Law & Practice, Volume 15, Issue 10, October 2020, Pages 807–810, https://doi.org/10.1093/jiplp/jpaa118 Published: 31 August 2020 Article history Received: 02 May 2020 Revision received: 19 June 2020 Accepted: 25 June 2020 Published: 31 August 2020
Wide Bezel Televisions Decrease Immersive Experiences

This study explored how telepresence could be affected by stimuli from reality that distracts people while they are watching television. The sample comprised of 36 undergraduate and graduate students from a university in South Korea (age range: 18–38 years, M = 22.61, and SD = 4.12). A between-subjects experimental design was employed with two types of viewing equipment (a television screen vs. a television screen with side screens that act as stimuli from reality) and two bezel widths (2 cm vs. 10 cm) to examine how each condition influenced the viewers’ perceived telepresence. The results revealed that participants’ perception of telepresence was not affected by the type of viewing equipment. However, the level of telepresence was affected by the bezel width: the thinner the bezel, the more telepresence felt by the viewers. These findings provide important insights that can guide the future designs of screen bezels for televisions and other devices in order to more effectively create immersive virtual worlds. Future studies are needed to examine the relationship between central vision and telepresence.
Effective Acceptance Strategy Using Cluster-Based Opponent Modeling in Multilateral Negotiation

Determining required conditions for accepting a bid has an important role in efficient negotiations. Conceding (aggressive) acceptance strategy of the agent against a conceding (an aggressive) opponent can lead to early and high utility agreement (losing the utility) especially in domains with discount factor. The literature is more focused on bilateral than multilateral negotiations, where they assume cost or utility function of the opponents or the historical data among the negotiation sessions are known to the agents. In this study, an effective acceptance strategy is proposed which only assumes the opponents’ rationality. The strategy extends the ParsAgent last moment concession strategy by developing an opponent model using k-means. The performance of the strategy is investigated against different combination of opponents consisting state-of-the-art strategies from Automated Negotiating Agents Competition (ANAC 2015 and 2016) in trilateral tournaments in different domains. The evaluation results indicate the superiority of proposed agent against the opponents in all domains, in average.
Component-Based Evaluation for Question Answering

Mitamura, Teruko Nyberg, EricThis chapter describes the component-based evaluation of automatic question answering (QA) systems, which was pioneered in the NTCIR-7 ACLIA challenge and has became a fundamental part of QA system development, especially for difficult real-world datasets which require a multi-strategy, multi-component approach. We summarize the history of component evaluation for QA and describe more recent work at Carnegie Mellon (on TREC Genomics, BioASQ, and LiveQA datasets) which has descended directly from our experiences in NTCIR.
Divisibility classes of qubit maps and singular Gaussian channels

We present two projects concerning the main part of my PhD work. In the first one we study quantum channels, which are the most general operations mapping quantum states into quantum states, from the point of view of their divisibility properties. We introduced tools to test if a given quantum channel can be implemented by a process described by a Lindblad master equation. This in turn defines channels that can be divided in such a way that they form a one-parameter semigroup, thus introducing the most restricted studied divisibility type of this work. Using our results, together with the study of other types of divisibility that can be found in the literature, we characterized the space of qubit quantum channels. We found interesting results connecting the concept of entanglement-breaking channel and infinitesimal divisibility. Additionally we proved that infinitely divisible channels are equivalent to the ones that are implementable by one-parameter semigroups, opening this question for more general channel spaces. In the second project we study the functional forms of one-mode Gaussian quantum channels in the position state representation, beyond Gaussian functional forms. We perform a black-box characterization using complete positivity and trace preserving conditions, and report the existence of two subsets that do not have a functional Gaussian form. The study covers as particular limit the case of singular channels, thus connecting our results with the known classification scheme based on canonical forms. Our full characterization of Gaussian channels without Gaussian functional form is completed by showing how Gaussian states are transformed under these operations, and by deriving the conditions for the existence of master equations for the non-singular cases.
Enhanced characterization of the yield behavior of sheet metal at torsional load using digital image correlation methods

Abstract This work describes an approach for strain determination at the “in-plane” torsional test using digital image correlation (DIC) without brushing a statistical pattern on the specimen. It is well known that the in-plane torsion test represents a appropriate test method for material characterization of sheet metal in terms of yielding and kinematic hardening (Wagner et al. in Application of the in-plane torsion test in an industrial environment—recent advances and remaining challenges. In: Conference Paper of the 12th Forming Technology Forum, Herrsching, Germany, 2019). However, the gained measurement data do not allow the exact reading of material models for direct use in numerical analysis. The determination of shear values represents thereby the main challenge. Besides well-established methods for stress–strain analysis mentioned in this contribution, the applicability of DIC methods for the direct reading of distortion on the specimen has been investigated. Therefore, a torsion test rig has been developed and torsion tests using specimen out of mild- and high-strength steel have been conducted. The in-plane situation allows the distortion tracking of single points on the specimen by using a 2D image correlation software. The needed pictures were taken by using a common digital lens reflex camera. It has been shown that the resolution of the inherent specimen pattern is sufficient for the use of common 2D image correlation software. Torsional test results in the form of computed yield curves of mild- and advanced high-strength steels are aimed to be presented in this contribution.
Content Analysis of TechnologyᆞHome Economics and General Engineering Curriculum for Subject Integrated Engineering Career Education in High School

The purpose of this study is to analyze the engineering-related contents in the curriculum and build the contents system of engineering for subjects integrated engineering career education in high school. To accomplish this goal, TechnologyㆍHome Economics and General Engineering curriculums of high school were selected for analysis, and analysis standards were developed. The results are as follows. First, the contents for engineering career education within Technology · Home Economics and General Engineering curriculum corresponded to two of the four areas of career education, ‘Understanding the Work and the Occupational World’ and ‘Exploring the Career’. Second, when comparing the areas of ‘Understanding the Changing World of Work’, contents about the role of engineering for a sustainable society, technical standards, inventions, and start-ups were only in Technology · Home Economics. Third, contents for the area of ‘Forming Heathy Occupational Consciousness’ existed only in General Engineering. Fourth, when comparing the area of ‘Exploring Education Opportunities’, the engineering design experience was in both TechnologyㆍHome Economics and General Engineering. However the understanding of engineering design was only in Technology · Home Economics, and the development of engineering thinking was only in General Engineering. Fifth, contents for the area of ‘Job Information Search’ were found only in General Engineering. These results suggest that it is necessary to use a mixture of General Engineering and Technology · Home Economics contents for engineering career education in high school. However both ‘Self-Understanding and Social Competency Development’ and ‘Career Design and Preparation’ domains of career education were lack in both Technology · Home Economics and General Engineering, so these parts should be considered for further studies.
Let MT simplify and speed up your Alignment for TM creation.

Large quantities of multilingual legal documents are waiting to be regularly aligned and used for future translations. For reasons of time, effort and cost, manual alignment is not an option. Automatically aligned segments are suitable for concordance search but are unreliable for fuzzy search and pretranslation. MT-based alignment could be the key to improving the results.
Knowledge Efficient Deep Learning for Natural Language Processing

Deep learning has become the workhorse for a wide range of natural language processing applications. But much of the success of deep learning relies on annotated examples. Annotation is time-consuming and expensive to produce at scale. Here we are interested in methods for reducing the required quantity of annotated data -- by making the learning methods more knowledge efficient so as to make them more applicable in low annotation (low resource) settings. There are various classical approaches to making the models more knowledge efficient such as multi-task learning, transfer learning, weakly supervised and unsupervised learning etc. This thesis focuses on adapting such classical methods to modern deep learning models and algorithms. This thesis describes four works aimed at making machine learning models more knowledge efficient. First, we propose a knowledge rich deep learning model (KRDL) as a unifying learning framework for incorporating prior knowledge into deep models. In particular, we apply KRDL built on Markov logic networks to denoise weak supervision. Second, we apply a KRDL model to assist the machine reading models to find the correct evidence sentences that can support their decision. Third, we investigate the knowledge transfer techniques in multilingual setting, where we proposed a method that can improve pre-trained multilingual BERT based on the bilingual dictionary. Fourth, we present an episodic memory network for language modelling, in which we encode the large external knowledge for the pre-trained GPT.
Fractional Pseudorandom Generators from the $k$th Fourier Level.

In recent work by [CHHL19, CHLT19], the authors exhibit a flexible construction of pseudorandom generators for classes of that satisfy $L_1$ Fourier bounds. [CHHL19] show that if a class satisfies such tail bounds at all levels, this implies a PRG through their random walk framework that composes together fractional PRGs that polarize quickly to the Boolean hypercube. [CHLT19] show by derandomizing the analysis of [RT19] that just level-two Fourier bounds suffice to construct a PRG but naturally obtain exponentially worse dependence on the error in the seed length compared to [CHHL19]. Moreover, this derandomization relies on simulating nearly independent Gaussians for the fractional PRG, which necessitates the polynomial dependence on $1/\epsilon$ in each fractional step. In this work, we attempt to bridge the gap between these two results. We partially answer an open question by [CHLT19] that nearly interpolates between them. We show that if one has bounds up to the level-$k$ $L_1$ Fourier mass of a closely related class of functions, where $k>2$, one can obtain improved seed length, the degree to which is determined by how high $k$ can be taken. Our analysis shows that for error $\epsilon=1/\text{poly}(n)$, one needs control at just level $O(\log n)$ to recover the seed length of [CHHL19], without assumptions on the entire tail. We avoid this by providing a simple, alternate analysis of their fractional PRG that instead relies on Taylor's theorem and $p$-biased Fourier analysis to avoid assumptions on the weights of the higher-order terms. This further allows us to show that this framework can handle the class of low-degree polynomials over $\mathbb{F}_2$, with slightly worse dependence than the state-of-the-art. We hope that this alternate analysis will be fruitful in improving the understanding of this new and powerful framework.
AuScope Geothermal Demonstrators - Latrobe Valley, Victoria, Australia

AuScope aims to establish world-class research infrastructure to enable the characterisation of structure and evolution for the Australian continent. AuScope's Simulation, Analysis and Modelling capability makes it possible to draw together geological data-products and analysis software, for the purpose of developing quality scenario models. This allows researchers to test geological hypotheses and to answer large-scale “what-if ” questions relevant to Australia’s geological resources.This 2010 demonstrator is an example what-if scenario. The model below shows the geothermal potential of Victoria's Latrobe Valley given the best available data. It shows the insulating effects of the coal and geothermal characteristics pioneered by Professor Mike Sandiford's research group, and thus building confidence in deep data-acquisition targeting.This activity has provided Geoscience Victoria with the opportunity to explore new engagements with the community using its value-added data products. The demonstrator embeds a 3D model into the pdf, to enable interaction.
Propiciando encuentros de aprendizaje y compartiendo experiencias que enseñan

In this course we have decided to work in an integrated way on didactic unit 1 of the subject of Didactics of Cultural Heritage in the Grade of Infant Education and theme 1 of the subject Didactics of Social Sciences in the Grade of Primary Education.The integration is motivated by two main reasons: the first is to provide meeting spaces and exchange between teachers of different disciplines and look for possible confluences; the second is to create coordination experiences between students and teachers who put them in the background of a professional future in schools.We will see in this work the complexity of the proposal and the results where an integrated learning of the two vertebral concepts of the proposal has not been achieved but an enrichment of the ideas of the students by sharing the chosen problems.
Temporal Information Access

Yoshioka, Masaharu Joho, HideoThis chapter introduces the research background and details of temporal information accessTemporal information access tasks in the NTCIR. The GeoTime task was the first attempt to evaluate temporal information retrievalTemporal information retrieval as an extension of an information-retrieval-for-question-answering task. Temporalia was a task to investigate the role of temporal factors in a search.
Look into Facial Expression Domain Adaptation: Adversarial Graph Learning and A Fair Evaluation Benchmark.

To address the problem of data inconsistencies among different facial expression recognition (FER) datasets, many cross-domain FER methods (CD-FERs) have been extensively devised in recent years. Although each declares to achieve superior performance, fair comparisons are lacking due to the inconsistent choices of the source/target datasets and feature extractors. In this work, we first analyze the performance effect caused by these inconsistent choices, and then re-implement some well-performing CD-FER and recently published domain adaptation algorithms. We ensure that all these algorithms adopt the same source datasets and feature extractors for fair CD-FER evaluations. We find that most of the current leading algorithms use adversarial learning to learn holistic domain-invariant features to mitigate domain shifts. However, these algorithms ignore local features, which are more transferable across different datasets and carry more detailed content for fine-grained adaptation. To address these issues, we integrate graph representation propagation with adversarial learning for cross-domain holistic-local feature co-adaptation by developing a novel adversarial graph representation adaptation (AGRA) framework. Specifically, it first builds two graphs to correlate holistic and local regions within each domain and across different domains, respectively. Then, it extracts holistic-local features from the input image and uses learnable per-class statistical distributions to initialize the corresponding graph nodes. Finally, two stacked graph convolution networks (GCNs) are adopted to propagate holistic-local features within each domain to explore their interaction and across different domains for holistic-local feature co-adaptation. We conduct extensive and fair evaluations on several popular benchmarks and show that the proposed AGRA framework outperforms previous state-of-the-art methods.
The Sound of Trust: Towards Modelling Computational Trust using Voice-only Cues at Zero-Acquaintance

Trust is essential in many interdependent human relationships.Trustworthiness is measured via the effectiveness of the relationships involving human perception.The decision to trust others is often made quickly (even at zero acquaintance).Previous research has shown the significance of voice in perceived trustworthiness.However, the listeners' characteristics were not considered.A system has yet to be produced that can quantitatively predict the degree of trustworthiness in a voice.This research aims to investigate the relationship between trustworthiness and different vocal features while considering the listener's physical characteristics, towards modelling a computational trust model.This study attempts to predict the degree of trustworthiness in voice by using an Artificial Neural Network (ANN) model.A set of 30 audio clips of white males were obtained, acoustically analyzed and then distributed to a large group of untrained Malaysian respondents who rated their degree of trust in the speakers of each audio clip on a scale of 0 to 10.The ANOVA test showed a statistically significant difference of trust ratings across different types and intensities of emotion, duration of audio clip, average fundamental frequencies, speech rates, articulation rates, average loudness, ethnicity of listener and ages of listener (p <.01).The findings conclude that Malaysians tend to trust white males who talk faster and longer, speak louder, have an f0 between 132.03Hz & 149.52Hz, and show a neutral emotion or rather stoic (arousal<.325).Results suggest that Indians are the most trusting Malaysian ethnic group, followed by Bumiputera from East Malaysia and then followed by Malays.Chinese are the least trusting Malaysian ethnic group.The data was fed into an ANN model to be evaluated, which yielded a perfect percentage accuracy (100%) in degree of trustworthiness 39.70% of the time.Given a threshold of two-point deviation, the ANN had a prediction accuracy of 76.86%.
Enhancement of Single Document Text Summarization using Reinforcement Learning with Non-Deterministic Rewards

A text summarization system generates short and brief summaries of original document for given user queries.The machine generated summaries uses information retrieval techniques for searching relevant answers from large corpus.This research article proposes a novel framework for generating machine generated summaries using reinforcement learning techniques with Non-deterministic reward function.Experiments have exemplified with ROUGE evaluation metrics with DUC 2001, 20newsgroup data.Evaluation results of proposed system with hypothesis of automatic summarization from given datasets prove that statistically significant improvement for answering complex questions with f-actual vs. critical values.
Design of Remote-Control Multi-view Monitor for Vehicle-Mounted Mobile Phone Based on IoT

In order to improve the deficiency of traditional vehicle mounted monitor and reduce the incidence of traffic accidents, a remote control multiple view monitor for the vehicle mounted mobile phone which is based on the technology of Internet of things is designed. The monitor includes the circuit board module, the camera module and the lift rod module. The monitor controls the operation of the multiple steering gears through the embedded circuit, and then controls the rotation of the lifting rod as well as the cloud deck which carries the camera; the router is used to generate the wireless signals which connect the mobile phone, and the mobile phone is used to send instructions and receive the collected signals. The monitor can realize the lifting and rotation of the camera through the remote control with the mobile phone, implementing acquisition of the scenes in multiple directions. With this monitor, it is possible to carry out the auxiliary observations while the vehicle is moving forward, backing up or perform the side parking or in the case that there is any high obstacle in front of the vehicle, and this will be helpful in avoid traffic accidents in an effective manner. This monitor can also be used for the household monitoring, the commercial monitoring as well as the monitoring in the public security system and the traffic system.