Reviewerly/exHarmony huggingface dataset:
Update to the South African Stratigraphic Code and Guidelines for Standardised Lithostratigraphic/Lithodemic Descriptions

Summary The South African Committee for Stratigraphy (SACS) is the official body in South Africa which regulates the nomenclature and definition of lithostratigraphic and lithodemic rock units, unconsolidated deposits and soils. The robust framework set in the existing stratigraphic code and guide for publication by the Council for Geoscience (CGS) have for many years provided leadership in this field, but there are a number of aspects in need of reconsideration. One example is the non-compliance of our nomenclature for high-grade supracrustal rocks with international (and South African) stratigraphic codes and another is manuscript format changes necessitated by publishing SACS descriptions in the SAJG. Any stratigraphic code is a living document that should be able to accommodate changing views and circumstances. This review provides a consensus opinion from experts on how to deal with changing needs.
Weighted-Features Construction as a Bi-level Problem

Feature selection and construction are important pre-processing techniques in machine learning and data mining. They may allow not only dimensionality reduction but also classifier accuracy and efficiency improvement. Feature selection aims at selecting relevant features from the original feature set, which could be less informative to achieve good performance. Feature construction may work well as it creates new highlevel features, but these features do not have the same degree of importance, which makes the use of weighted-features construction a very challenging topic. In this paper, we propose a bi-level evolutionary approach for efficient feature selection and simultaneous feature construction and feature weighting, called Bi-level Weighted-Features Construction (BWFC). The basic idea of our BWFC is to exploit the bi-level model for performing feature selection and weighted-features construction with the aim of finding an optimal subset of features combinations. Our approach has been assessed on six high-dimensional datasets and compared against three existing approaches, using three different classifiers for accuracy evaluation. Experimental results show that our proposed algorithm gives competitive and better results with respect to the state-of-the-art algorithms.
Semantic Concept Extraction for Eyebrow Shapes via AFS Clustering

In this paper, a revised directional triangle-area curve representation method (DTAR) is proposed to address the problem of eyebrow semantic shape characterization via curve representation. First, 11 or 12 DTAR values are selected to describe eyebrows via considering the eyebrow corner information roughly, and then the corresponding DTAR curves are acquired via the cubic spline interpolation based on these selected points. Second, a descriptor of the landmarks is developed to represent selected reference eyebrows, and the corresponding DTAR curves are obtained for the selected reference eyebrows. Lastly, a similarity notion based on AFS is introduced via measuring the membership degrees of each eyebrow shape similar to the given reference shapes, and then one can describe each eyebrow shape by using two given reference eyebrow shapes via computing the membership degrees representing the relative similarities. To illustrate the effectiveness of the proposed approach, we use the AR and BJUT databases for experiments to demonstrate the consistency comparison with human perceptions. The experimental results show that the extracted semantic notions of eyebrow shapes obtained by the proposed approach are much better than those by only utilizing 11 DTAR values or 12 DTAR values directly in terms of the consistency with human perceptions.
Using Node-Based Resilience Clustering to Predict and Analyze Medical Data

This paper examines the use of a machine learning technique called Node-Based Resilience Clustering, or NBR-Clust, for the purpose of predicting attributes of medical data and analyzing the composition of specific subgroups. We perform unsupervised clustering on the large, well-known REGARDS database for the target categories of current smokers and deaths and find that the clusters are somewhat predictive of the targets, with a precision similar to the well-known Louvain method and the newer Leiden algorithm. We then use the NBR-Clust, Louvain and Leiden methods to cluster the individual populations and describe the composition of the categorizations. NBR-Clust performs best at clustering the current smokers and also outperforms the other methods at determining the composition of the deaths. Although the Leiden method works very well, NBR-Clust creates clusters with greater purity. Results demonstrate that node-based resilience clustering is potentially a useful tool both for predicting and describing target populations.
Synthesis of a DNF formula from a sample of strings using Ehrenfeucht–Fraïssé games

In order to define a DNF version of first-order sentences over strings in which atomic sentences represent substring properties of strings, we use results of the Ehrenfeucht–Fraïssé game over strings. Then, given a sample of strings and the number of disjunctive clauses, we investigate the problem of finding a DNF formula that is consistent with the sample. We show that this problem is NP-complete, and we solve it by a translation into Boolean satisfiability. We also present an extension of this problem that is robust concerning noisy samples. We solve the generalized version by a codification into the maximum satisfiability problem. As first-order logic over strings defines exactly the class of locally threshold testable (LTT) languages, our results can be useful in the grammatical inference framework when the goal is to find a model of a LTT language from a sample of strings.
$$\mathcal {B}$$ SIEM-IoT: A Blockchain-Based and Distributed SIEM for the Internet of Things

The paper at hand proposes $$\mathcal {B}$$ SIEM-IoT, a Security Information and Event Management solution (SIEM) for the Internet of Things (IoT) relying on blockchain to store and access security events. The security events included in the blockchain are contributed by a number of IoT sentinels in charge of protecting a group of IoT devices. A key feature here is that the blockchain guarantees a secure registry of security events. Additionally, the proposal permits SIEM functional components to be assigned to different miners servers composing a resilient and distributed SIEM. Our proposal is implemented using Ethereum and validated through different use cases and experiments.
Information Models: Creating and Preserving Value in Volatile Cloud Resources

Volatile resources are surplus cloud resources not consumed by high priority foreground (reserved/on-demand) load. These resources are exploited by a growing number of users. Today, cloud operators provide no statistical characterization of volatile resources. We consider how releasing such statistics could improve user value by studying Amazon's 608 EC2 Spot Instance types. Results show that as little as two parameters such as (average, 90pctile) can increase user value by 30%. These results are robust over four-fifths (475 of 608) of instance types. Beyond competitive concerns, cloud operators are reluctant to share volatile resource statistics because they might be considered a service-level agreement (SLA), and thus constrain their ability to serve foreground load. We show that clever resource management can allay such concerns. We study two plausible classes of foreground load changes, showing one class where such a concern is indeed valid and another where it is not. We design two online resource management algorithms that detect foreground load variation and adapt to maintain a statistical SLA. The algorithms not only improve the ability to maintain guarantees and user value but also improve user experience, reducing job failures by 50%. These results apply to the Stable and Transition classes of instance types, which account for nearly all of the instance types (577 of 608).
Contributors’ impact on a FOSS project’s quality

Engaging contributors in a Free Open Source Software (FOSS) project can be challenging. Finding an appropriate task to start with is a common entrance barrier for newcomers. Poor code quality contributes to difficulties in the onboarding process and limits contributor satisfaction in general. In turn, dissatisfied developers tend to exacerbate problems with system integrity. Poorly designed systems are difficult to maintain and extend. Users can often directly experience these issues as instabilities in system behavior. Thus code quality is a key issue for users and contributors in FOSS. We present a case study on the interactions between code quality and contributor experience in the real-world FOSS project Catrobat. We describe the implications of a refactoring process in terms of code metrics and benefits for developers and users.
Measurement of the flatness or the shapes of mirrors by one-shot projection of structured light dot pattern

No existing methods could measure the 3D shapes of mirrors accurately enough for inverse engineering. Consequently, manufacturing highly accurate mirrors remains challenging in space optics. For mirror flatness measurement, the best accuracy state-of-the-art methods have achieved so far is nanometer. In this paper, we propose an approach for mirror measurement with a single shot projection of structured light dot pattern. The proposed approach is not only capable of measuring the mirror flatness with femtometre height resolution (10−13 mm), but also capable of measuring the 3D shapes of mirrors more accurately than many state of the art methods. This approach first computes the projected dot pattern in the world coordinate system and gets the measured points. Then, it computes the dot pattern in the ideal coordinate system and gets the true points. After the system is re-calibrated with the true points, the flatness or the 3D shapes of the mirrors could be measured robustly. Experimental results showed that the proposed approach is significantly more accurate than state-of-the-art methods in measuring the flat mirror or mirror flatness with femtometre height resolution under laboratory conditions and it also the potential to achieve similar accuracy in measuring the 3D shapes of mirrors. Thus, it might benefit the manufacturing industry of space optics significantly in the future..
The Evaluation of Thai Poem's Content Consistency using Siamese Network

Many research describes Textual Entailment model for compare pair of the sentence but two sentences in term of the poem content consistency are not the same. The content consistency is very important for storytelling in Thai poem composing. In this article, we propose the model and result of The evaluation of Thai poem's content consistency using The Siamese Network 3 models comprise 1) Merge Vector Model 2) Siamese Absolute Different Model and 3) Siamese Dot Vector Model compare with the Basic CNN model. The training data is Thai poem 14,173 pair (batt) and validation data is Thai poem 3,544 pair. All models learn by apply one shot learning technic. The accuracy of Siamese Absolute Different Model near 100%. The macro average of F1-score shows 99.27%. The Area Under Curve shows 0.997 near the perfect value.
Principles of genetic algorithm

The most important algorithm for oil spill detection is a genetic algorithm (GA). Therefore, it is important to understand in detail how the GA works to implement it for oil spill detection in the next chapters. This chapter delivers foundational knowledge regarding the GA. In fact, the tiniest element of a GA is called a gene, which denotes a piece of information in the dilemma province. A succession of genes, known as a chromosome, characterizes one promising elucidation to the problem. Indeed, an individual gene in the chromosome signifies one constituent of the elucidation array. So how can we use this approach to detect an oil spill, as we’ll see in the next chapter?
Search Personalization Based on Social-Network-Based Interestedness Measures

Personalized search methods try to tailor the search results to the user’s needs and preferences. A popular source of information for personalization is social networks of users. In this paper, we aim at proposing a social-network-based personalized information retrieval (PIR) method that: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i)</i> is holistic (not just considering some local neighborhood of the searcher); <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii)</i> is efficient in term of computational/storage cost; <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii)</i> its personalization component has a probabilistic basis. To the best of our knowledge, no such method exists. We propose an interestedness measure and several variations of it for quantifying the interestedness of each user in another user based on a social network of users. The general idea is to try to mimic a searcher’s behavior in the real world to estimate the interestedness. The measures are then exploited to personalize the retrieval results. We evaluate the resulting PIR methods and compare them in terms of retrieval effectiveness and computational/storage cost. We also compare them with some baseline methods. In summary, our analyses suggest that retrieval based on at least one of the measures performs well in terms of both retrieval effectiveness and computational/storage cost.
Mechanised Assessment of Complex Natural-Language Arguments Using Expressive Logic Combinations

We present and illustrate an approach to combining logics based on shallow semantical embeddings, a technique that harnesses the high expressive power of classical higher-order logic (HOL) as a meta-language in order to embed the syntax and semantics of some object logic. This approach allows us to reuse existing (higher-order) automated reasoning infrastructure for seamlessly combining and reasoning with different non-classical logics (modal, deontic, intensional, epistemic, etc.). In particular, the work presented here illustrates the utilisation of the Isabelle proof assistant for the representation and assessment of linguistically complex arguments. We illustratively combine a dyadic deontic logic (also featuring alethic modalities) enhanced with higher-order quantifiers and a 2D-semantics drawing on Kaplan’s logic of indexicals.
A vocabulary recommendation method for spatiotemporal data discovery based on Bayesian network and ontologies

In the research field of spatiotemporal data discovery, how to utilize the semantic characteristics of spatiotemporal datasets is an important topic. This paper presented a content-based recommendation method, and applied Bayesian networks and ontologies into the vocabulary recommendation process for spatiotemporal data discovery. The source data of this research was from the MUDROD (Mining and Utilizing Dataset Relevancy from Oceanographic Datasets) search platform. From the historical search log, major keywords were extracted and organized according to ontologies in a hierarchical structure. Using the search history, the posterior probability between each subclass and their super class in the ontologies was calculated, indicating a recommendation likelihood. We created a Bayesian network model for inference based on ontologies. This model can address the following two objectives: (1) Given one class in the ontology, the model can judge which class has the biggest likelihood to be selected for recommendation. 2) Based on the search history of a user, the Bayesian network model can judge which class has the biggest probability to be recommended. Comparison experimentation with existing system and evaluation experimentation with expert knowledge show that this method is specifically helpful for spatiotemporal data discovery.
TT-Net: Topic Transfer-Based Neural Network for Conversational Reading Comprehension

Conversational machine reading comprehension (MRC) is a new question answering task, which is more challenging compared to traditional single-turn MRC since it requires a better understanding of conversation history. In this paper, a novel neural network model for conversational reading comprehension, namely TT-Net, is proposed, which is capable of capturing topic transfer features using temporal convolutional network (TCN) in the dialog. The TT-Block packaged by the BiLSTM, TCN and Self-attention mechanism is presented to extract topic transfer features between questions. Our model is evaluated on the CoQA benchmark dataset compared with several baseline models including the strong baseline model named FlowQA. The results show that the model outperforms the baseline models: BiDAF++ by 7.6% and FlowQA by 0.7%, especially in children's story domain our model promotes FlowQA's performance by 3.9%, which indicates that the TT-Net contributes to a decent promotion for conversational reading comprehension.
Is Your Vote Overheard? A New Scalable Side-Channel Attack Against Paper Voting

In an ongoing discussion comparing the security properties of electronic and paper voting, decreased privacy is often presented as an argument against remote Internet voting. We contribute to this discussion by presenting a side-channel attack against the physical environment of traditional paper-based elections. More precisely, we build a device based on an Arduino development board and cheap electret microphones, capable of triangulating the locations of marks made on wooden tables with high precision. In the best configuration, we are able to determine the correct cell having dimensions 4×5 cm with more than 90% accuracy. This will allow breaching privacy of ballot sheet designs that rely on the voter marking her choice(s) between a potentially high number of candidates printed on one large sheet. We complement our attack with a study on various aspects of deployment of facial recognition. This gives rise to the setup where the attacker installs cameras in the polling stations, aiming at automated detection of people leaving the voting booths. Combining the two approaches, we will have a completely automated (and hence relatively well scalable) attack against the privacy of paper-based voting.
An Evidential Clustering for Collaborative Filtering Based on Users’ Preferences

Users are often surrounded by a large variety of items. For this purpose, Recommender Systems (RSs) have emerged aiming to help and to guide users towards items of interest. Collaborative Filtering (CF) is among the most popular recommendation approaches, which seeks to pick out the most similar users to the active one in order to provide recommendations. In CF, clustering techniques can be used for grouping the most similar users into some clusters. Nonetheless, the impact of uncertainty involved throughout the clusters' assignments as well as the final predictions should also be considered. Therefore, in this paper, we propose a clustering approach for user-based CF based on the belief function theory. This theory, also referred to as evidence theory, is known for its strength and flexibility when dealing with uncertainty. In our approach, an evidential clustering process is performed to cluster users based on their preferences and predictions are then generated accordingly.
Vaccine:: Obfuscating Access Pattern Against File-Injection Attacks

Searchable Encryption can search over encrypted data without accessing data or queries in plaintext. It preserves privacy while queries are performed over data on an untrusted server. To ensure the efficiency of search, most Searchable Encryption schemes reveal access patterns, i.e., a server learns which encrypted files are retrieved for each query. Unfortunately, by collecting access patterns, a file-injection attack can completely compromise the query privacy offered by Searchable Encryption. In this paper, we propose a novel pre-encryption obfuscation mechanism, referred to as Vaccine, which can effectively protect searchable encrypted data against a file-injection attack. Specifically, the main idea of Vaccine is to introduce a self file-injection attack, which obfuscates access patterns obtained by an attacker and prevents this attacker from inferring correct queries in plaintext. In addition, by harnessing natural language processing techniques, Vaccine can effectively remove self-injected files from search results, and therefore introduce minimal tradeoffs. Our experimental results on a real-world dataset show that Vaccine can reduce an adversary's guessing probability from 1 to 3.7×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , which significantly promotes privacy protection. Furthermore, Vaccine introduces only 3.4% false negatives and no false positives in search results.
A Systematic Mapping Study of MMOG Backend Architectures

The advent of utility computing has revolutionized almost every sector of traditional software development. Especially commercial cloud computing services, pioneered by the likes of Amazon, Google and Microsoft, have provided an unprecedented opportunity for the fast and sustainable development of complex distributed systems. Nevertheless, existing models and tools aim primarily for systems where resource usage—by humans and bots alike—is logically and physically quite disperse resulting in a low likelihood of conflicting resource access. However, a number of resource-intensive applications, such as Massively Multiplayer Online Games (MMOGs) and large-scale simulations introduce a requirement for a very large common state with many actors accessing it simultaneously and thus a high likelihood of conflicting resource access. This paper presents a systematic mapping study of the state-of-the-art in software technology aiming explicitly to support the development of MMOGs, a class of large-scale, resource-intensive software systems. By examining the main focus of a diverse set of related publications, we identify a list of criteria that are important for MMOG development. Then, we categorize the selected studies based on the inferred criteria in order to compare their approach, unveil the challenges faced in each of them and reveal research trends that might be present. Finally we attempt to identify research directions which appear promising for enabling the use of standardized technology for this class of systems.
Using Intuition from Empirical Properties to Simplify Adversarial Training Defense

Due to the surprisingly good representation power of complex distributions, neural network (NN) classifiers are widely used in many tasks which include natural language processing, computer vision and cyber security. In recent works, people noticed the existence of adversarial examples. These adversarial examples break the NN classifiers' underlying assumption that the environment is attack free and can easily mislead fully trained NN classifier without noticeable changes. Among defensive methods, adversarial training is a popular choice. However, original adversarial training with single-step adversarial examples (Single-Adv) can not defend against iterative adversarial examples. Although adversarial training with iterative adversarial examples (Iter-Adv) can defend against iterative adversarial examples, it consumes too much computational power and hence is not scalable. In this paper, we analyze Iter-Adv techniques and identify two of their empirical properties. Based on these properties, we propose modifications which enhance Single-Adv to perform competitively as Iter-Adv. Through preliminary evaluation, we show that the proposed method enhances the test accuracy of state-of-the-art (SOTA) Single-Adv defensive method against iterative adversarial examples by up to 16.93% while reducing its training cost by 28.75%.