Reviewerly/exHarmony huggingface dataset:
Distinguishing Attacks from Legitimate Authentication Traffic at Scale

Online guessing attacks against password servers can be hard to address.Approaches that throttle or block repeated guesses on an account (e.g., three strikes type lockout rules) can be effective against depth-first attacks, but are of little help against breadth-first attacks that spread guesses very widely.At large providers with tens, or hundreds, of millions of accounts breadth-first attacks offer a way to send millions or even billions of guesses without ever triggering the depth-first defenses.The absence of labels and non-stationarity of attack traffic make it challenging to apply machine learning techniques.We show how to accurately estimate the odds that an observation x indicates that a request is malicious.Our main assumptions are that successful malicious logins are a small fraction of the total, and that the distribution of x in the legitimate traffic is stationary, or very-slowly varying.From these we show how we can estimate the ratio of bad-to-good traffic among any set of requests; how we can then identify subsets of the request data that contain least (or even no) attack traffic; how these leastattacked subsets allow us to estimate the distribution of values of x over the legitimate data, and hence calculate the odds ratio.A sensitivity analysis shows that even when we fail to identify a subset with little attack traffic our odds ratio estimates are very robust.
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction

Providing explanations along with predictions is crucial in some text processing tasks. Therefore, we propose a new self-interpretable model that performs output prediction and simultaneously provides an explanation in terms of the presence of particular concepts in the input. To do so, our model's prediction relies solely on a low-dimensional binary representation of the input, where each feature denotes the presence or absence of concepts. The presence of a concept is decided from an excerpt i.e. a small sequence of consecutive words in the text. Relevant concepts for the prediction task at hand are automatically defined by our model, avoiding the need for concept-level annotations. To ease interpretability, we enforce that for each concept, the corresponding excerpts share similar semantics and are differentiable from each others. We experimentally demonstrate the relevance of our approach on text classification and multi-sentiment analysis tasks.
Development of a Prediction Model of Solar Irradiances Using LSTM for Use in Building Predictive Control

The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method, the LSTM (long term short term memory). Other than existing prediction models, the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center. The future cloudiness is generally announced with four categories and for three-hour intervals. In this work, a daily irradiance pattern is used as an input vector to the LSTM together with that cloudiness information. The proposed model showed an error of 5% for learning and 30% for prediction. This level of error has lower influence on the load prediction in typical building cases.
A Novel Imperialist Competitive Algorithm for Multithreshold Image Segmentation

Multithreshold image segmentation plays a very important role in computer vision and pattern recognition. However, the computational complexity of multithreshold image segmentation increases exponentially with the increasing number of thresholds. Thus, in this paper, a novel imperialist competitive algorithm is proposed to solve the multithreshold image segmentation problem. Firstly, a new strategy of revolution and assimilation is adopted to improve the search efficiency of the algorithm. Secondly, imperialist self-learning and reserve country set are introduced to enhance the search of outstanding individuals in the population. Combining with the reserve country set, a novel imperialist competition strategy is proposed to remove the poorer individuals and improve the overall quality of the population. Finally, the sensitivity of the algorithm parameters is analyzed. Ten standard test pictures are selected to test. The experimental results show that the novel imperialist competitive algorithm has faster convergence speed, higher quality, and higher stability in solving multithreshold segmentation problems than methods from literature.
RMMDI: A Novel Framework for Role Mining Based on the Multi-Domain Information

Role-based access control (RBAC) is widely adopted in network security management, and role mining technology has been extensively used to automatically generate user roles from datasets in a bottom-up way. However, almost all role mining methods discover the user roles from existing user-permission assignments, which neglect the dependency relationships between user permissions. To extend the ability of role mining technology, this paper proposes a novel role mining framework based on multi-domain information. The framework estimates the similarity between different permissions based on the fundamental information in the physical, network, and digital domains and attaches interdependent permissions to the same role. Three simulated network scenarios with different multi-domain configurations are used to validate the effectiveness of our method. The experimental results show that the method can not only capture the interdependent relationships between permissions, but also detect user roles and permissions more reasonably.
Protection of quantum coherence in an open V-type three-level atom through auxiliary atoms

Within the framework of open quantum systems, we analytically study the dynamics of a V-type three-level atom interacting with a dissipative reservoir in the presence of N − 1 auxiliary and similar atoms. We demonstrate how the initial coherence of the V-type three-level atom can be protected by inserting more auxiliary atoms into the reservoir and manipulating the spontaneously generated interference between two possible transitions of the atoms. Furthermore, we show that as the number of auxiliary atoms becomes sufficiently large, the initial coherence of the considered V-type three-level atom is preserved to a great extent in the dissipative reservoir.
Inferring Art Preferences from Gaze Exploration in a Museum

This paper is a first step towards identifying the links between the characteristics of gaze behaviour and visitor preferences in a museum. In the long term, the real-time analysis of visitors' gaze should allow a fine estimation of their interest for the different artworks exhibited and should replace the fastidious and time-consuming elicitation of preferences commonly used in traditional recommender systems. To study these links, we carried out a user study at the Nancy Museum of Fine Arts in the North-East of France. This pilot study involved 13 volunteers who had the opportunity to freely explore the museum and contemplate hundreds of artworks for more than 50 minutes on average in May 2018. We were able to analyze millions of fixation points so as to find correlations between the number of fixation points per painting, the time spent looking at a painting, and whether or not this painting is appreciated. We plan to extend this study to 100 visitors in the coming months.
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization

Recent developments on large-scale distributed machine learning applications, e.g., deep neural networks, benefit enormously from the advances in distributed non-convex optimization techniques, e.g., distributed Stochastic Gradient Descent (SGD). A series of recent works study the linear speedup property of distributed SGD variants with reduced communication. The linear speedup property enable us to scale out the computing capability by adding more computing nodes into our system. The reduced communication complexity is desirable since communication overhead is often the performance bottleneck in distributed systems. Recently, momentum methods are more and more widely adopted in training machine learning models and can often converge faster and generalize better. For example, many practitioners use distributed SGD with momentum to train deep neural networks with big data. However, it remains unclear whether any distributed momentum SGD possesses the same linear speedup property as distributed SGD and has reduced communication complexity. This paper fills the gap by considering a distributed communication efficient momentum SGD method and proving its linear speedup property.
An Approach for Arabic Diacritization

Modern Standard Arabic (MSA) contains optional diacritical marks (diacritics, in Arabic harakat), which became less used in Arabic books, newspapers and other written media. Diacritics are very important for readability and understandability of texts. Their absence causes critical problems that add to the lexical, morphological and semantic ambiguities. In this paper, we present an automatic diacritization system of the Arabic language, using Hidden Markov Models with the Viterbi's algorithm, based on probabilities based on learning on diacritized Arabic texts. The corpus used was mostly composed of religious texts. Our results were satisfactory, achieving a precision of up to 80% at the word level.
Optimizing the Cost of Executing Mixed Interactive and Batch Workloads on Transient VMs

Container Orchestration Platforms (COPs), such as Kubernetes, are increasingly used to manage large-scale clusters by automating resource allocation between applications encapsulated in containers. Increasingly, the resources underlying COPs are virtual machines (VMs) dynamically acquired from cloud platforms. COPs may choose from many different types of VMs offered by cloud platforms, which differ in their cost, performance, and availability. While transient VMs cost significantly less than on-demand VMs, platforms may revoke them at any time, causing them to become unavailable. While transient VMs' price is attractive, their unreliability is a problem for COPs designed to support mixed workloads composed of, not only delay-tolerant batch jobs, but also long-lived interactive services with high availability requirements.
Gated CRF Loss for Weakly Supervised Semantic Image Segmentation

State-of-the-art approaches for semantic segmentation rely on deep convolutional neural networks trained on fully annotated datasets, that have been shown to be notoriously expensive to collect, both in terms of time and money. To remedy this situation, weakly supervised methods leverage other forms of supervision that require substantially less annotation effort, but they typically present an inability to predict precise object boundaries due to approximate nature of the supervisory signals in those regions. While great progress has been made in improving the performance, many of these weakly supervised methods are highly tailored to their own specific settings. This raises challenges in reusing algorithms and making steady progress. In this paper, we intentionally avoid such practices when tackling weakly supervised semantic segmentation. In particular, we train standard neural networks with partial cross-entropy loss function for the labeled pixels and our proposed Gated CRF loss for the unlabeled pixels. The Gated CRF loss is designed to deliver several important assets: 1) it enables flexibility in the kernel construction to mask out influence from undesired pixel positions; 2) it offloads learning contextual relations to CNN and concentrates on semantic boundaries; 3) it does not rely on high-dimensional filtering and thus has a simple implementation. Throughout the paper we present the advantages of the loss function, analyze several aspects of weakly supervised training, and show that our `purist' approach achieves state-of-the-art performance for both click-based and scribble-based annotations.
Exploring, Defining, &amp; Advancing Community-Driven Design for Social Impact

As the DIS community increasingly seeks to address social impact issues, it becomes important to examine the assumptions behind our methods to increase the likelihood of positive effects and reduce negative unintended consequences. The purpose of this workshop is to engage the design community in exploring, defining, and, if deemed valuable, advancing community-driven design. We invite DIS members to submit 1-page responses to this concept of community-driven design. Our hope is that a research agenda can emerge from this workshop for the DIS community.
ANALISIS DATA HIDROLOGI SUNGAI AIR BENGKULU MENGGUNAKAN METODE STATISTIK

Bengkulu municipality as a constituent element of the Air Bengkulu watershed with 51,500 of hectares area bypassed by the stream of Air Bengkulu River which empties into the Teluk Segara District, municipal of Bengkulu. Air Bengkulu River suffered flood at least twice a year of frequency as result of the increase in water discharge (Q) in the rainy season. The purpose of this research is to calculate the amount of peak river discharge of Air Bengkulu and mappingthe distribution of inundation water that may occur in the city of Bengkulu. Analysis of the mean daily maximum rainfall area was conducted by Thiessen Polygon using 15 last years of rainfall datas. Calculation of peak discharge using rational methods for different return period plans Analysis of Rainfall of flood modeling simulation is done by using HEC-RAS 4.1.0 and flood inundation mapping is done by using Hec-GeoRAS 4.3.1 flood modeling. The result of rainfall plan calculations was qualified by Gumbel type 1 method. The results of the Air Bengkulu river peak discharge quantify for return period 5, 10, 25, 50 and 100 years respectively 339.66 m3/sec; 470.38m3/sec; 520.59m3/sec; 557.83m3/sec; 594.79m3/sec; 631.62m3/sec. Results of generated mapping showed the areas affected by flood inundation in Bengkulu City namely Pasar Bengkulu village, Kampung Klawi, Rawa Makmur, Suka Merindu, Tanjung Agung,Tanjung Jaya, and Semarang. The depth value of inundation mapping for every affected village of floodwaters are vary, but in the range of 0 - 110 cm.
Improving Background Based Conversation with Context-aware Knowledge Pre-selection

Background Based Conversations (BBCs) have been developed to make dialogue systems generate more informative and natural responses by leveraging background knowledge. Existing methods for BBCs can be grouped into two categories: extraction-based methods and generation-based methods. The former extract spans frombackground material as responses that are not necessarily natural. The latter generate responses thatare natural but not necessarily effective in leveraging background knowledge. In this paper, we focus on generation-based methods and propose a model, namely Context-aware Knowledge Pre-selection (CaKe), which introduces a pre-selection process that uses dynamic bi-directional attention to improve knowledge selection by using the utterance history context as prior information to select the most relevant background material. Experimental results show that our model is superior to current state-of-the-art baselines, indicating that it benefits from the pre-selection process, thus improving in-formativeness and fluency.
"My Way of Telling a Story": Persona based Grounded Story Generation.

Visual storytelling is the task of generating stories based on a sequence of images. Inspired by the recent works in neural generation focusing on controlling the form of text, this paper explores the idea of generating these stories in different personas. However, one of the main challenges of performing this task is the lack of a dataset of visual stories in different personas. Having said that, there are independent datasets for both visual storytelling and annotated sentences for various persona. In this paper we describe an approach to overcome this by getting labelled persona data from a different task and leveraging those annotations to perform persona based story generation. We inspect various ways of incorporating personality in both the encoder and the decoder representations to steer the generation in the target direction. To this end, we propose five models which are incremental extensions to the baseline model to perform the task at hand. In our experiments we use five different personas to guide the generation process. We find that the models based on our hypotheses perform better at capturing words while generating stories in the target persona.
Real-time simulation of flux qubits used for quantum annealing

The real-time flux dynamics of up to three superconducting quantum interference devices (SQUIDs) are studied by numerically solving the time-dependent Schr\"odinger equation. The numerical results are used to scrutinize the mapping of the flux degrees of freedom onto two-level systems (the qubits) as well as the performance of the intermediate SQUID as a tunable coupling element. It is shown that the qubit representation yields a good description of the flux dynamics during quantum annealing and the presence of the tunable coupling element does not have negative effects on the overall performance. Additionally, data obtained from a simulation of the dynamics of two-level systems during quantum annealing are compared to experimental data produced by the D-Wave 2000Q quantum annealer. The effects of finite temperature are incorporated in the simulation by coupling the qubit system to a bath of two-level systems. It is shown that an environment modeled as non-interacting two-level systems coupled to the qubits can produce data which matches the experimental data much better than the simulation data of the qubits without coupling to an environment and better than data obtained from a simulation of an environment modeled as interacting two-level systems coupling to the qubits.
IoT Security and Consumer Trust

The adoption and usage of IoT devices is on the rise, globally, with consumers already owning smart devices planning on purchasing more devices. Consumers are generally concerned about their personal information being leaked, but are not equipped with knowledge to help them purchase products with adequate security and privacy measures. Manufacturers focus on rushing more products to the market, and do not systematically communicate information about the security features devices possess that would need to be evaluated to assess their level of security.
Refining the PoinTER “human firewall” pentesting framework

Purpose Penetration tests have become a valuable tool in the cyber security defence strategy in terms of detecting vulnerabilities. Although penetration testing has traditionally focussed on technical aspects, the field has started to realise the importance of the human in the organisation, and the need to ensure that humans are resistant to cyberattacks. To achieve this, some organisations “pentest” their employees, testing their resilience and ability to detect and repel human-targeted attacks. In a previous paper, the authors reported on PoinTER (Prepare TEst Remediate), a human pentesting framework, tailored to the needs of SMEs. This paper aims to propose improvements to refine the framework. The improvements are based on a derived set of ethical principles that have been subjected to ethical scrutiny Design/methodology/approach The authors conducted a systematic literature review of academic research, a review of actual hacker techniques, industry recommendations and official body advice related to social engineering techniques. To meet the requirements to have an ethical human pentesting framework, the authors compiled a list of ethical principles from the research literature which they used to filter out techniques deemed unethical. Findings Drawing on social engineering techniques from academic research, reported by the hacker community, industry recommendations and official body advice and subjecting each technique to ethical inspection, using a comprehensive list of ethical principles, the authors propose the refined GDPR-compliant and privacy respecting PoinTER framework. The list of ethical principles, as suggested, could also inform ethical technical pentests. Originality/value Previous work has considered penetration testing humans, but few have produced a comprehensive framework such as PoinTER. PoinTER has been rigorously derived from multiple sources and ethically scrutinised through inspection, using a comprehensive list of ethical principles derived from the research literature.
A general framework for multivariate functional principal component analysis of amplitude and phase variation

Functional data typically contain amplitude and phase variation. In many data situations, phase variation is treated as a nuisance effect and is removed during preprocessing, although it may contain valuable information. In this note, we focus on joint principal component analysis (PCA) of amplitude and phase variation. As the space of warping functions has a complex geometric structure, one key element of the analysis is transforming the warping functions to . We present different transformation approaches and show how they fit into a general class of transformations. This allows us to compare their strengths and limitations. In the context of PCA, our results offer arguments in favour of the centred log‐ratio transformation. We further embed two existing approaches from the literature for joint PCA of amplitude and phase variation into the framework of multivariate functional PCA, where we study the properties of the estimators based on an appropriate metric. The approach is illustrated through an application from seismology.
Data Interpolating Prediction: Alternative Interpretation of Mixup

Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks. However, this strategy is not always effective due to the gap between augmented samples for training and original samples for testing. This gap may prevent a classifier from learning the optimal decision boundary and increase the generalization error. To overcome this problem, we propose an alternative framework called Data Interpolating Prediction (DIP). Unlike common data augmentations, we encapsulate the sample-mixing process in the hypothesis class of a classifier so that train and test samples are treated equally. We derive the generalization bound and show that DIP helps to reduce the original Rademacher complexity. Also, we empirically demonstrate that DIP can outperform existing Mixup.