Miracle-dz/UniFAR-FTUs huggingface dataset:
Forensic Analysis of Industrial Control Systems

Industrial Control Systems (ICS) contribute to our safety and convenience every day, yet remain unseen and unnoticed. From oil refineries to traffic lights, from the elevators we ride to the electric power plants that keep our lights on, they provide the control and monitoring for our essential services. ICS have served reliably for decades, but a changing technological environment is exposing them to risks they were not designed to handle. Internet connectivity, vulnerability assessment tools, and attacks by criminal and nation-state organizations are part of this changing picture. Along with this higher-risk environment comes the certainty that some of our ICS will be compromised. In order to prevent recurring attacks, security professionals must be able to discover where the compromise originated, how it was carried out, and, if possible, who was responsible. Many types of ICS run on proprietary hardware, so commonly accepted forensic techniques must be adapted for use in an ICS environment. In order to detect a compromise, baseline configurations should be documented. Networks should be monitored for unauthorized access and activity. In addition, a response plan should be in place to maintain service and streamline recovery. Techniques for forensic analysis were adapted and tested on live ICS, resulting in recommendations for successful detection and recovery after an incident. With adequate preparation and the appropriate response planning and execution, it is possible to successfully perform a forensic analysis for an ICS compromise. Forensic Analysis of Industrial Control Systems 2 Lew Folkerth, lew.folkerth@rfirst.org
Chapter 13 Exploiting multilingual lexical resources to predict MWE compositionality

Semantic idiomaticity is the extent to which the meaning of a multiword expression (MWE) cannot be predicted from the meanings of its component words. Much work in natural language processing on semantic idiomaticity has focused on compositionality prediction, wherein a binary or continuous-valued compositionality score is predicted for an MWE as a whole, or its individual component words. One source of information for making compositionality predictions is the translation of an MWE into other languages. This chapter extends two previously-presented studies – Salehi & Cook (2013) and Salehi et al. (2014) – that propose methods for predicting compositionality that exploit translation information provided by multilingual lexical resources, and that are applicable to many kinds of MWEs in a wide range of languages. These methods make use of distributional similarity of an MWE and its component words under translation into many languages, as well as string similarity measures applied to definitions of translations of an MWE and its component words. We evaluate these methods over English noun compounds, English verb-particle constructions, and German noun compounds. We show that the estimation of compositionality is improved when using translations into multiple languages, as compared to simply using distributional similarity in the source language. We further find that string similarity complements distributional similarity.
Reconciling corpus data and elicitation data in FLT

Because they tap into basically different things, corpora and elicitation tests may at times provide diverging results. In this paper, we will show that, far from being opposed to each other, corpus data and elicitation data should be seen as complementary and that their reconciliation can actually prove useful in a field such as Foreign Language Teaching (FLT). Two case studies will illustrate this. In the first one, we will use data coming from a learner corpus and from fill-in and evaluation exercises to investigate learners’ knowledge of make-collocations. We will see that only the combination of corpus and elicitation data can give the full picture, i.e. performance and competence, and that an analysis relying on just one source of language data therefore runs the risk of being biased. In the second case study, we continue to investigate the differences between competence and performance through a comparison of the existential structure in elicited data from native speakers and in a spoken corpus. The divergences between the prototype effects displayed by the elicited data and the frequency effects found in the corpus data are discussed in the light of Foreign Language Teaching, and it is argued that prototypes should be taught first and that structures should be revisited in a cyclical fashion as proficiency increases, with all extensions of a structure being eventually introduced to students.
CD 98-mediated Adhesive Signaling Enables the Establishment and Propagation of Acute Myelogenous Leukemia

Jeevisha Bajaj1,2,3,4,†, Takaaki Konuma1,2,3,4,†, Nikki K. Lytle1,2,3,4, Hyog Young Kwon1,2,3,4, Jailal N. Ablack4, Joseph M. Cantor4, David Rizzieri5, Charles Chuah6, Vivian G. Oehler7, Elizabeth H. Broome3,8, Edward D. Ball3,9, Edward H. van der Horst10, Mark H. Ginsberg4,*, and Tannishtha Reya1,2,3,4,*,§ 1Department of Pharmacology, University of California San Diego School of Medicine, La Jolla, CA 92093, USA
In vitro evaluation of microleakage in Class V restorations after cavity preparation with high-speed diamond bur , ultrasonic tip or laser

• Jéssika Barcellos Giuriato Department of Restorative Dentistry, School of Dentistry, University of São Paulo, São Paulo, SP, Brazil • Patricia Moreira Freitas Special Laboratory of Lasers in Dentistry (LELO), Department of Restorative Dentistry, School of Dentistry, University of São Paulo, São Paulo, SP, Brazil • Denis Yudi Nagase Department of Restorative Dentistry, School of Dentistry, University of São Paulo, São Paulo, SP, Brazil • Margareth Oda Department of Restorative Dentistry, School of Dentistry, University of São Paulo, São Paulo, SP, Brazil
Performance comparison of known ICA algorithms to a wavelet-ICA merger

These signals are however contaminated with artifacts which must be removed to have pure EEG signals. These artifacts can be removed by Independent Component Analysis (ICA). In this paper we studied the performance of three ICA algorithms (FastICA, JADE, and Radical) as well as our newly developed ICA technique. Comparing these ICA algorithms, it is observed that our new techniques perform as well as these algorithms at denoising EEG signals.
A Novel Shard-Based Approach for Asynchronous Many-Task Models for In Situ Analysis

We present the current status of our work towards a scalable, asynchronous many-task, in situ statistical analysis engine using the Legion runtime system, expanding upon earlier work, that was limited to a prototype implementation with a proxy mini-application as a surrogate for a full-scale scientific simulation code. In contrast, we have more recently integrated our in situ analysis engines with S3D, a full-size scientific application, and conducted numerical tests therewith on the largest computational platform currently available for DOE science applications. The goal of this article is thus to describe the SPMD-Legion methodology we devised in this context, and compare the data aggregation technique deployed herein to the approach taken within our previous work.
SIGIR 2018 Workshop on Learning from Limited or Noisy Data for Information Retrieval

In recent years, machine learning approaches, and in particular deep neural networks, have yielded significant improvements on several natural language processing and computer vision tasks; however, such breakthroughs have not yet been observed in the area of information retrieval. Besides the complexity of IR tasks, such as understanding the user's information needs, a main reason is the lack of high-quality and/or large-scale training data for many IR tasks. This necessitates studying how to design and train machine learning algorithms where there is no large-scale or high-quality data in hand. Therefore, considering the quick progress in development of machine learning models, this is an ideal time for a workshop that especially focuses on learning in such an important and challenging setting for IR tasks. The goal of this workshop is to bring together researchers from industry---where data is plentiful but noisy---with researchers from academia---where data is sparse but clean to discuss solutions to these related problems.
Deep Domain Adaptation Based on Multi-layer Joint Kernelized Distance

Domain adaptation refers to the learning scenario where a model learned from the source data is applied on the target data which have the same categories but different distributions. In information retrieval, there exist application scenarios like cross domain recommendation characterized similarly. In this paper, by utilizing deep features extracted from the deep networks, we proposed to compute the multi-layer joint kernelized mean distance between the k th target data predicted as the i th category and all the source data of the j th category $d_ij ^k$. Then, target data $T_m$ that are most likely to belong to the i th category can be found by calculating the relative distance $d_ii ^k/\sum_j d_ij ^k$. By iteratively adding $T_m$ to the training data, the finetuned deep model can adapt on the target data progressively. Our results demonstrate that the proposed method can achieve a better performance compared to a number of state-of-the-art methods.
Enriching Taxonomies With Functional Domain Knowledge

The rising need to harvest domain specific knowledge in several applications is largely limited by the ability to dynamically grow structured knowledge representations, due to the increasing emergence of new concepts and their semantic relationships with existing ones. Such enrichment of existing hierarchical knowledge sources with new information to better model the "changing world" presents two-fold challenges: (1) Detection of previously unknown entities or concepts, and (2) Insertion of the new concepts into the knowledge structure, respecting the semantic integrity of the created relationships. To this end we propose a novel framework, ETF, to enrich large-scale, generic taxonomies with new concepts from resources such as news and research publications. Our approach learns a high-dimensional embedding for the existing concepts of the taxonomy, as well as for the new concepts. During the insertion of a new concept, this embedding is used to identify semantically similar neighborhoods within the existing taxonomy. The potential parent-child relationships linking the new concepts to the existing ones are then predicted using a set of semantic and graph features. Extensive evaluation of ETF on large, real-world taxonomies of Wikipedia and WordNet showcase more than 5% F1-score improvements compared to state-of-the-art baselines. We further demonstrate that ETF can accurately categorize newly emerging concepts and question-answer pairs across different domains.
A review of introductory programming research 2003–2017

A broad review of research on the teaching and learning of programming was conducted by Robins et al. in 2003. Since this work there have been several reviews of research concerned with the teaching and learning of programming, in particular introductory programming. However, these reviews have focused on highly specific aspects, such as student misconceptions, teaching approaches, program comprehension, potentially seminal papers, research methods applied, automated feedback for exercises, competency-enhancing games, and program visualisation. While these aspects encompass a wide range of issues, they do not cover the full scope of research into novice programming. Some notable areas that have not been reviewed are assessment, academic integrity, and novice student attitudes to programming. There does not appear to have been a comprehensive review of research into introductory programming since that of Robins et al. It is therefore timely to conduct and present such a review in order to gain an understanding of the research focuses, to highlight advances in knowledge since 2003, and to indicate possible future directions for research. The working group will conduct a systematic literature review based on the guidelines proposed by Kitchenham et al. This research project is well suited to an ITiCSE working group as the synthesis and discussion of the literature will benefit from input from a variety of researchers drawn from different backgrounds and countries.
The Second Workshop on Knowledge Graphs and Semantics for Text Retrieval, Analysis, and Understanding (KG4IR)

Semantic technologies such as controlled vocabularies, thesauri, and knowledge graphs have been used throughout the history of information retrieval for a variety of tasks. Recent advances in knowledge acquisition, alignment, and utilization have given rise to a body of new approaches for utilizing knowledge graphs in text retrieval tasks and it is therefore time to consolidate the community efforts and study how such technologies can be employed in information retrieval systems in the most effective way. It is also time to start and deepen the dialogue between researchers and practitioners in order to ensure that breakthroughs, technologies, and algorithms in this space are widely disseminated. The goal of this workshop is to bring together and grow a community of researchers and practitioners who are interested in using, aligning, and constructing knowledge graphs and similar semantic resources for information retrieval applications.
A Test Collection for Evaluating Legal Case Law Search

Test collection based evaluation represents the standard of evalua- tion for information retrieval systems. Legal IR, more speci cally case law retrieval, has no such standard test collection for evalua- tion. In this paper, we present a test collection for use in evaluating case law search, being the retrieval of judicial decisions relevant to a particular legal question. The collection is made available at ielab.io/caselaw.
Improving computer-aided detection systems through advanced pattern recognition techniques

Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems dealing with breast cancer. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-imagelocation basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting in maxima misalignment and differences of maxima spread, are identified, and two solutions are proposed. The first solution, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second solution, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both alternatives can be applied independently or together. They are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval from 3.5 to 15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows estimating the quality of the mass candidate detector’s output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval from 3.5 to 15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity increases from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, it is believed that the strategies proposed in this study can render mass candidate detection approaches based on image location classification more robust to feature discrepancies and prove advantageous not only at the candidate detection level but also at subsequent steps of a CAD system.
Perspectives on Data and Practices

There is not something like big data research, but a variety of diverse big data research practices in different fields. They are based on different logics, rationalities, epistemological beliefs, types of data, and even different forms of objectivity as well as concepts of theory. Practices are specific regarding their temporalities and materialities. Furthermore, big data practices are not necessarily about doing research, but also about governance. To make these differences explicit is a step towards better collaboration in the many fields of the web sciences.
Multimedia: Computing, Communications and Applications

Foreword Preface 1.Introduction Branch-overlapping Aspects of Multimedia. Content. Global Structure. Multimedia Literature. 2. Multimedia: Media and Data Streams. Medium. Main Properties of a Multimedia System. Multimedia. Traditional Data Stream Characteristics. Data Streams Characteristics for Continuous Media. Information Units. 3. Sound/Audio. Basic Sound Concepts. Music. Speech. 4. Image and Graphics. Basic Concepts. Computer Image Processing. Comments. 5. Video and Animation. Basic Concepts. Television. Computer-based Animation. 6. Data Compression. Storage Space. Coding Requirements. Source, Entropy, and Hybrid Coding. Some Basic Compression Techniques. JPEG. H.261 (px64). MPEG. DVI. Comments. 7. Optical Storage. History. Basic Technology. Video Disks and Other WORMs. Compact Disk Digital Audio. Compact Disk Read Only Memory. CD-ROM Extended Architecture. Further CD-ROM-based Developments. Compact Disc Write Once. Compact Disk Magneto Optical. The Prospects of CD Technologies. 8. Computer Technology. Communication Architecture. Multimedia Workstations. Comments. 9. Multimedia Operating Systems. Introduction. Real-Time. Resource Management. Process Management. File Systems. Additional Operating System Issues. System Architecture. Concluding Remarks. 10. Networking Systems. Layers, Protocols, and Services. Networks. Local Area Networks (LANs). Metropolitan Area Networks (MANs). Wide Area Networks (WANs). Comments. 11. Multimedia Communication Systems. Application Subsystem. Transport Subsystem. Quality of Service and Resource Management. Comments. 12. Data Base Systems. Multimedia Database Management System. Characteristics of an MDBMS. Data Analysis. Data Structure. Operations on Data. Integration in a Database Model. Comments. 13. Documents, Hypertext and Hypermedia. Documents. Hypertext and Hypermedia. Document Architecture SGML. Document Architecture ODA. MHEG. 14. User Interface. General Design Issues. Current Work. Extension through Video and Audio. Video at the User Interface. Audio at the User Interface. User- friendliness as the Primary Goal. Comments. 15. Synchronization. Introduction. Notion of Synchronization. Presentation Requirements. A Reference Model for Multimedia Synchronization. Synchronization. Case Studies. Comments. Summary and Outlook. 16. Abstractions for Programming. Abstraction Levels. Libraries. System Software. Toolkits. Higher Programming Languages. Object-oriented Approaches. Comments. 17. Multimedia Applications. Introduction. Media Preparation. Media Composition. Media Integration. Media Communication. Media Consumption. Media Entertainment. Trends. 18. Future Directions. Where Are We Today? What Are the Next Steps? What Are the Multimedia Research Issues? Abbreviations. Bibliography. Index.
On the Volatility of Commercial Search Engines and its Impact on Information Retrieval Research

We studied the volatility of commercial search engines and reflected on its impact on research that uses them as basis of algorithmical techniques or for user studies. Search engine volatility refers to the fact that a query posed to a search engine at two different points in time returns different documents. By comparing search results retrieved every 2 days over a period of 64 days, we found that the considered commercial search engine API consistently presented volatile search results: it both retrieved new documents, and it ranked documents previously retrieved at different ranks throughout time. Moreover, not only results are volatile: we also found that the effectiveness of the search engine in answering a query is volatile. Our findings reaffirmed that results from commercial search engines are volatile and that care should be taken when using these as basis for researching new information retrieval techniques or performing user studies.
Ego-Centric Analysis of Supportive Networks

The way we think about ourselves has a direct influence on our emotional state and our mood. Consequently by changing the way we think we can positively influence our mood and how we respond to situations. Many studies have shown a robust relationship in which emotional support from others positively affects how we think about ourselves. Emotional support can come from many sources, such as family, friends, neighbors, and more recently we have seen the emergence of emotional support networks. In such networks users share their moods and receive emotional support from others, and the objective is to nurture supportive relationships and build a social support network. In this paper we present an ego-centric study of supportive networks to show how user mood evolves as the user ego-network is expanded. We considered different types of ego-networks induced by gender and psychological disorders. We found that the way user mood evolves strongly depends on the type of connections that are created. The behavior of users that show mood improvement is very distinct from the behavior of users that do not show mood improvement.
Combining static and dynamic variables in species distribution models under climate change

1. Methods used to predict shifts in species’ ranges because of climate change commonly involve species distribution (niche) modelling using climatic variables, future values of which are predicted for the next several decades by general circulation models. However, species’ distributions also depend on factors other than climate, such as land cover, land use and soil type. Changes in some of these factors, such as soil type, occur over geologic time and are thus imperceptible over the timescale of these types of projections. Other factors, such as land use and land cover, are expected to change over shorter timescales, but reliable projections are not available. Some important predictor variables, therefore, must be treated as unchanging, or static, whether because of the properties of the variable or out of necessity. The question of how best to combine dynamic variables predicted by climate models with static variables is not trivial and has been dealt with differently in studies to date. Alternative methods include using the static variables as masks, including them as independent explanatory variables in the model, or excluding them altogether.
Assessment of Unsupervised Standard Pattern Recognition Methods for Industrial Energy Time Series

Finding and extracting standard patterns in energy time series is very important to many real-world applications. Hence, there exists a multitude of pattern recognition algorithms with a majority of them being supervised ones. The advantage of supervision is that it can easily be checked if the algorithm is performing well or not. However, if no labels are available, an unsupervised pattern search is necessary. This search is faced with the challenge of how to measure success. Thus the question arises, when is a found pattern -- for example a motif or a mean cluster curve -- really describing the standard behaviour of a process and not just some kind of irrelevant behaviour? The present paper introduces a new method to assess two methods -- namely clustering and motif discovery -- in their quest to find standard profiles in energy time series data from industrial processes. Although both methods share the same aim, the results are incongruent. This finding has profound implications for real-world applications.