OpenRaiser/Intern-Atlas huggingface dataset:
Encoding Compositionality in Classical Planning Solutions
Venue: arXiv cs.AI 2021
Year: 2021

Classical AI planners provide solutions to planning problems in the form of long and opaque text outputs. To aid in the understanding transferability of planning solutions, it is necessary to have a rich and comprehensible representation for both human and computers beyond the current line-by-line text notation. In particular, it is desirable to encode the trace of literals throughout the plan to capture the dependencies between actions selected. The approach of this paper is to view the actions as maps between literals and the selected plan as a composition of those maps. The mathematical theory, called category theory, provides the relevant structures for capturing maps, their compositions, and maps between compositions. We employ this theory to propose an algorithm agnostic, model-based representation for domains, problems, and plans expressed in the commonly used planning description language, PDDL. This category theoretic representation is accompanied by a graphical syntax in addition to a linear notation, similar to algebraic expressions, that can be used to infer literals used at every step of the plan. This provides the appropriate constructive abstraction and facilitates comprehension for human operators. In this paper, we demonstrate this on a plan within the Blocksworld domain.
Automatic Seizure Detection Using the Pulse Transit Time
Venue: arXiv cs.CV 2021
Year: 2021

Documentation of epileptic seizures plays an essential role in planning medical therapy. Solutions for automated epileptic seizure detection can help improve the current problem of incomplete and erroneous manual documentation of epileptic seizures. In recent years, a number of wearable sensors have been tested for this purpose. However, detecting seizures with subtle symptoms remains difficult and current solutions tend to have a high false alarm rate. Seizures can also affect the patient's arterial blood pressure, which has not yet been studied for detection with sensors. The pulse transit time (PTT) provides a noninvasive estimate of arterial blood pressure. It can be obtained by using to two sensors, which are measuring the time differences between arrivals of the pulse waves. Due to separated time chips a clock drift emerges, which is strongly influencing the PTT. In this work, we present an algorithm which responds to alterations in the PTT, considering the clock drift and enabling the noninvasive monitoring of blood pressure alterations using separated sensors. Furthermore we investigated whether seizures can be detected using the PTT. Our results indicate that using the algorithm, it is possible to detect seizures with a Random Forest. Using the PTT along with other signals in a multimodal approach, the detection of seizures with subtle symptoms could thereby be improved.
A QUIC(K) Way Through Your Firewall?
Venue: arXiv cs.CR 2021
Year: 2021

The QUIC protocol is a new approach to combine encryption and transport layer stream abstraction into one protocol to lower latency and improve security. However, the decision to encrypt transport layer functionality may limit the capabilities of firewalls to protect networks. To identify these limitations we created a test environment and analyzed generated QUIC traffic from the viewpoint of a middlebox. This paper shows that QUIC indeed exposes traditional stateful firewalls to UDP hole punching bypass attacks. On the contrary we show the robustness against censorship of QUIC through the encrypted transport layer design and analyze the capabilities to re-gain stateful tracking capabilities by deep packet inspection of the few exposed QUIC header fields.
The Impact of Working From Home on the Success of Scrum Projects: A Multi-Method Study
Venue: arXiv cs.SE 2021
Year: 2021

The number of companies opting for remote working has been increasing over the years, and Agile methodologies, such as Scrum, were adapted to mitigate the challenges caused by the distributed teams. However, the COVID-19 pandemic imposed a fully working from home context, which has never existed before. This paper investigation a two-phased Multi-Method study. In the first phase, we uncover how working from home impacted Scrum practitioners through a qualitative survey. Then, in the second phase, we propose a theoretical model that we test and generalize using Partial Least Squares - Structural Equation Modeling (PLS-SEM) through a sample study of 200 software engineers who worked from home within Scrum projects. From assessing our model, we can conclude that all the latent variables are reliable and all the hypotheses are significant. Moreover, we performed an Importance-Performance Map Analysis (IPMA), highlighting the benefits of the home working environment and the use of Scrum for project success. We emphasize the importance of supporting the three innate psychological needs of autonomy, competence, and relatedness in the home working environment. We conclude that the home working environment and the use of Scrum both contribute to project success, with Scrum acting as a mediator.
Normal Sequences with Non-Maximal Automatic Complexity
Venue: arXiv cs.FL 2021
Year: 2021

This paper examines Automatic Complexity, a complexity notion introduced by Shallit and Wang in 2001. We demonstrate that there exists a normal sequence $T$ such that $I(T) = 0$ and $S(T) \leq 1/2$, where $I(T)$ and $S(T)$ are the lower and upper automatic complexity rates of $T$ respectively. We furthermore show that there exists a Champernowne sequence $C$, i.e. a sequence formed by concatenating all strings of length $1$ followed by concatenating all strings of length $2$ and so on, such that $S(C) \leq 2/3$.
Generating Gender Augmented Data for NLP
Venue: arXiv cs.CL 2021
Year: 2021

Gender bias is a frequent occurrence in NLP-based applications, especially pronounced in gender-inflected languages. Bias can appear through associations of certain adjectives and animate nouns with the natural gender of referents, but also due to unbalanced grammatical gender frequencies of inflected words. This type of bias becomes more evident in generating conversational utterances where gender is not specified within the sentence, because most current NLP applications still work on a sentence-level context. As a step towards more inclusive NLP, this paper proposes an automatic and generalisable rewriting approach for short conversational sentences. The rewriting method can be applied to sentences that, without extra-sentential context, have multiple equivalent alternatives in terms of gender. The method can be applied both for creating gender balanced outputs as well as for creating gender balanced training data. The proposed approach is based on a neural machine translation (NMT) system trained to 'translate' from one gender alternative to another. Both the automatic and manual analysis of the approach show promising results for automatic generation of gender alternatives for conversational sentences in Spanish.
Recent Advances in Energy Efficient Resource Management Techniques in Cloud Computing Environments
Venue: arXiv cs.DC 2021
Year: 2021

Nowadays cloud computing adoption as a form of hosted application and services is widespread due to decreasing costs of hardware, software, and maintenance. Cloud enables access to a shared pool of virtual resources hosted in large energy-hungry data centers for diverse information and communication services with dynamic workloads. The huge energy consumption of cloud data centers results in high electricity bills as well as emission of a large amount of carbon dioxide gas. Needless to say, efficient resource management in cloud environments has become one of the most important priorities of cloud providers and consequently has increased the interest of researchers to propose novel energy saving solutions. This chapter presents a scientific and taxonomic survey of recent energy efficient cloud resource management' solutions in cloud environments. The main objective of this study is to propose a novel complete taxonomy for energy-efficient cloud resource management solutions, review recent research advancements in this area, classify the existing techniques based on our proposed taxonomy, and open up new research directions. Besides, it reviews and surveys the literature in the range of 2015 through 2021 in the subject of energy-efficient cloud resource management techniques and maps them to its proposed taxonomy, which unveils novel research directions and facilitates the conduction of future researches.
A Deep Generative Artificial Intelligence system to decipher species coexistence patterns
Venue: arXiv cs.LG 2021
Year: 2021

1. Deciphering coexistence patterns is a current challenge to understanding diversity maintenance, especially in rich communities where the complexity of these patterns is magnified through indirect interactions that prevent their approximation with classical experimental approaches. 2. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to decipher species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. 3. The GAN accurately reproduces the species composition of real patches as well as the affinity of plant species to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high order interactions tend to suppress the positive effects of low order interactions. Finally, by reconstructing successional trajectories we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. 4. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.
Finite Model Property and Bisimulation for LFD
Venue: arXiv cs.LO 2021
Year: 2021

Recently, Baltag and van Benthem arXiv:2103.14946 [cs.LO] introduced a new decidable logic of functional dependence (LFD) with local dependence formulas and dependence quantifiers. The language is interpreted over dependence models, which are pairs of first-order structures with a set of available variable assignments, also called a team. The team associated with a dependence model can be seen as a labelled transition system over which LFD becomes a modal logic, where the dependence quantifiers become modalities and local dependence formulas are treated as special atoms. In this paper, we introduce appropriate notions of bisimulation characterizing LFD (and some related logics) as a fragment of first order logic (FOL), and show it is equivalent to a notion of bisimulation along more standard lines proposed in arXiv:2102.10368 [cs.LO], yet more efficient for bisimilarity-checking. Our main result is that LFD has the finite model property (FMP), by a new application of Herwig's theorem on extending partial isomorphisms.
Deterministic Identification Over Poisson Channels
Venue: arXiv cs.IT 2021
Year: 2021

Deterministic identification (DI) for the discrete-time Poisson channel, subject to an average and a peak power constraint, is considered. It is established that the code size scales as $2^{(n\log n)R}$, where $n$ and $R$ are the block length and coding rate, respectively. The authors have recently shown a similar property for Gaussian channels [1]. Lower and upper bounds on the DI capacity of the Poisson channel are developed in this scale. Those imply that the DI capacity is infinite in the exponential scale, regardless of the dark current, i.e., the channel noise parameter.
Secure Charging and Payment System for Electric Land Vehicles with Authentication Protocol
Venue: arXiv cs.CR 2021
Year: 2021

It is obvious that fossil fuels are a limited resource and will be replaced by other energy sources in the future considering economic and en-vironmental problems. Electricity comes to the forefront among the sources that are candidates to replace fossil fuels. In the near future, electric land, air and sea vehicles will start to take more place in daily life. For this reason, systems for the charging systems of these devices and post-charge payments have been developed. There is no general standard on this issue yet. In this study, a charge and payment system, which is safe against known cyber-attacks for use in electric land ve-hicles, and which prioritizes privacy, is proposed. A system has been proposed to verify each other wired or wirelessly with an authentication protocol, where the data communication is encrypted, and the payment transactions are performed securely and invoiced to the vehicle owners.
Querying Linked Data: how to ensure user's quality requirements
Venue: arXiv cs.DB 2021
Year: 2021

In the distributed and dynamic framework of the Web, data quality is a big challenge. The Linked Open Data (LOD) provides an enormous amount of data, the quality of which is difficult to control. Quality is intrinsically a matter of usage, so consumers need ways to specify quality rules that make sense for their use, in order to get only data conforming to these rules. We propose a user-side query framework equipped with a checker of constraints and confidence levels on data resulting from LOD providers\' query evaluations. We detail its theoretical foundations and we provide experimental results showing that the check additional cost is reasonable and that integrating the constraints in the queries further improves it significantly.
A model of systems with modes and mode transitions
Venue: arXiv cs.LO 2021
Year: 2021

We propose a method of classifying the operation of a system into finitely many modes. Each mode has its own objectives for the system's behaviour and its own mathematical models and algorithms designed to accomplish its objectives. A central problem is deciding when to transition from one mode to some other mode, a decision that may be contested and involve partial or inconsistent information or evidence. We model formally the concept of modes for a system and derive a family of data types for analysing mode transitions. The data types are simplicial complexes, both abstract and realised in euclidean space $\mathbb{R}^{n}$. In the data type, a mode is represented by a simplex. Each state of a system can be evaluated relative to different modes by mapping it into one or more simplices. This calibration measures the extent to which distinct modes are appropriate for the state and can decide on a transition. We explain this methodology based on modes, introduce the mathematical ideas about simplicial objects we need and use them to build a theoretical framework for modes and mode transitions. To illustrate the general model in some detail, we work though a case study of an autonomous racing car.
Predictive models for wind speed using artificial intelligence and copula
Venue: arXiv cs.LG 2021
Year: 2021

Electricity generation from burning fossil fuels is one of the major contributors to global warming. Renewable energy sources are a viable alternative to produce electrical energy and to reduce the emission from the power industry. These energy sources are the building blocks of green energy, which all have different characteristics. Their availabilities are also diverse, depending on geographical locations and other parameters. Low implementation cost and distributed availability all over the world uplifts their popularity exponentially. Therefore, it has unlocked opportunities for consumers to produce electricity locally and use it on-site, which reduces dependency on centralized utility companies. The research considers two main objectives: the prediction of wind speed that simplifies wind farm planning and feasibility study. Secondly, the need to understand the dependency structure of the wind speeds of multiple distant locations. To address the first objective, twelve artificial intelligence algorithms were used for wind speed prediction from collected meteorological parameters. The model performances were compared to determine the wind speed prediction accuracy. The results show a deep learning approach, long short-term memory (LSTM) outperforms other models with the highest accuracy of 97.8%. For dependency, a multivariate cumulative distribution function, Copula, was used to find the joint distribution of two or more distant location wind speeds, followed by a case study. We found that the appropriate copula family and the parameters vary based on the distance in between. For the case study, Joe-Frank (BB8) copula shows an efficient joint distribution fit for a wind speed pair with a standard error of 0.0094. Finally, some insights about the uncertainty aspects of wind speed dependency were addressed.
Adaptive Machine Learning for Time-Varying Systems: Low Dimensional Latent Space Tuning
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a recently developed method of adaptive ML for time-varying systems. Our approach is to map very high (N>100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N~2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly high parameter systems and to track their evolution in real time based on feedback without massive new data sets for re-training.
'CADSketchNet' -- An Annotated Sketch dataset for 3D CAD Model Retrieval with Deep Neural Networks
Venue: arXiv cs.CV 2021
Year: 2021

Ongoing advancements in the fields of 3D modelling and digital archiving have led to an outburst in the amount of data stored digitally. Consequently, several retrieval systems have been developed depending on the type of data stored in these databases. However, unlike text data or images, performing a search for 3D models is non-trivial. Among 3D models, retrieving 3D Engineering/CAD models or mechanical components is even more challenging due to the presence of holes, volumetric features, presence of sharp edges etc., which make CAD a domain unto itself. The research work presented in this paper aims at developing a dataset suitable for building a retrieval system for 3D CAD models based on deep learning. 3D CAD models from the available CAD databases are collected, and a dataset of computer-generated sketch data, termed 'CADSketchNet', has been prepared. Additionally, hand-drawn sketches of the components are also added to CADSketchNet. Using the sketch images from this dataset, the paper also aims at evaluating the performance of various retrieval system or a search engine for 3D CAD models that accepts a sketch image as the input query. Many experimental models are constructed and tested on CADSketchNet. These experiments, along with the model architecture, choice of similarity metrics are reported along with the search results.
Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2021
Year: 2021

We introduce an unsupervised domain adaption (UDA) strategy that combines multiple image translations, ensemble learning and self-supervised learning in one coherent approach. We focus on one of the standard tasks of UDA in which a semantic segmentation model is trained on labeled synthetic data together with unlabeled real-world data, aiming to perform well on the latter. To exploit the advantage of using multiple image translations, we propose an ensemble learning approach, where three classifiers calculate their prediction by taking as input features of different image translations, making each classifier learn independently, with the purpose of combining their outputs by sparse Multinomial Logistic Regression. This regression layer known as meta-learner helps to reduce the bias during pseudo label generation when performing self-supervised learning and improves the generalizability of the model by taking into consideration the contribution of each classifier. We evaluate our method on the standard UDA benchmarks, i.e. adapting GTA V and Synthia to Cityscapes, and achieve state-of-the-art results in the mean intersection over union metric. Extensive ablation experiments are reported to highlight the advantageous properties of our proposed UDA strategy.
Tight running times for minimum $\ell_q$-norm load balancing: beyond exponential dependencies on $1/ε$
Venue: arXiv cs.CC 2021
Year: 2021

We consider a classical scheduling problem on $m$ identical machines. For an arbitrary constant $q>1$, the aim is to assign jobs to machines such that $\sum_{i=1}^m C_i^q$ is minimized, where $C_i$ is the total processing time of jobs assigned to machine $i$. It is well known that this problem is strongly NP-hard. Under mild assumptions, the running time of an $(1+ε)$-approximation algorithm for a strongly NP-hard problem cannot be polynomial on $1/ε$, unless $\text{P}=\text{NP}$. For most problems in the literature, this translates into algorithms with running time at least as large as $2^{Ω(1/\varepsilon)}+n^{O(1)}$. For the natural scheduling problem above, we establish the existence of an algorithm which violates this threshold. More precisely, we design a PTAS that runs in $2^{\tilde{O}(\sqrt{1/ε})}+n^{O(1)}$ time. This result is in sharp contrast to the closely related minimum makespan variant, where an exponential lower bound is known under the exponential time hypothesis (ETH). We complement our result with an essentially matching lower bound on the running time, showing that our algorithm is best-possible under ETH. The lower bound proof exploits new number-theoretical constructions for variants of progression-free sets, which might be of independent interest. Furthermore, we provide a fine-grained characterization on the running time of a PTAS for this problem depending on the relation between $ε$ and the number of machines $m$. More precisely, our lower bound only holds when $m=Θ(\sqrt{1/ε})$. Better algorithms, that go beyond the lower bound, exist for other values of $m$. In particular, there even exists an algorithm with running time polynomial in $1/ε$ if we restrict ourselves to instances with $m=Ω(1/ε\log^21/ε)$.