OpenRaiser/Intern-Atlas huggingface dataset:
Secure Information Flow Typing in LUSTRE
Venue: arXiv cs.PL 2022
Year: 2022

Synchronous reactive data flow is a paradigm that provides a high-level abstract programming model for embedded and cyber-physical systems, including the locally synchronous components of IoT systems. Security in such systems is severely compromised due to low-level programming, ill-defined interfaces and inattention to security classification of data. By incorporating a Denning-style lattice-based secure information flow framework into a synchronous reactive data flow language, we provide a framework in which correct-and-secure-by-construction implementations for such systems may be specified and derived. In particular, we propose an extension of the Lustre programming framework with a security type system. The novelty of our type system lies in a symbolic formulation of constraints over security type variables, in particular the treatment of node calls, which allows us to reason about secure flow with respect to any security class lattice. The main theorem is the soundness of our type system with respect to the co-inductive operational semantics of Lustre, which we prove by showing that well-typed programs exhibit non-interference. Rather than tackle the full language, we first prove the non-interference result for a well-behaved sub-language called "Normalised Lustre" (NLustre), for which our type system is far simpler. We then show that Bourke et al.'s semantics-preserving "normalisation" transformations from Lustre to NLustre are security-preserving as well. This preservation of security types by the normalisation transformations is a property akin to "subject reduction" but at the level of compiler transformations. The main result that well-security-typed Lustre programs are non-interfering follows from a reduction to our earlier result of non-interference for NLustre via the semantics-preservation (of Bourke et al.) and type preservation results.
Low-Density Spreading Design Based on an Algebraic Scheme for NOMA Systems
Venue: arXiv cs.IT 2022
Year: 2022

NOMA) technique based on an algebraic design is studied. We propose an improved low-density spreading (LDS) sequence design based on projective geometry. In terms of its bit error rate (BER) performance, our proposed improved LDS code set outperforms the existing LDS designs over the frequency nonselective Rayleigh fading and additive white Gaussian noise (AWGN) channels. We demonstrated that achieving the best BER depends on the minimum distance.
AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection
Venue: arXiv cs.LG 2022
Year: 2022

Automating machine learning has achieved remarkable technological developments in recent years, and building an automated machine learning pipeline is now an essential task. The model ensemble is the technique of combining multiple models to get a better and more robust model. However, existing automated machine learning tends to be simplistic in handling the model ensemble, where the ensemble strategy is fixed, such as stacked generalization. There have been many techniques on different ensemble methods, especially ensemble selection, and the fixed ensemble strategy limits the upper limit of the model's performance. In this article, we present a novel framework for automated machine learning. Our framework incorporates advances in dynamic ensemble selection, and to our best knowledge, our approach is the first in the field of AutoML to search and optimize ensemble strategies. In the comparison experiments, our method outperforms the state-of-the-art automated machine learning frameworks with the same CPU time in 42 classification datasets from the OpenML platform. Ablation experiments on our framework validate the effectiveness of our proposed method.
Turath-150K: Image Database of Arab Heritage
Venue: arXiv cs.CV 2022
Year: 2022

Large-scale image databases remain largely biased towards objects and activities encountered in a select few cultures. This absence of culturally-diverse images, which we refer to as the hidden tail, limits the applicability of pre-trained neural networks and inadvertently excludes researchers from under-represented regions. To begin remedying this issue, we curate Turath-150K, a database of images of the Arab world that reflect objects, activities, and scenarios commonly found there. In the process, we introduce three benchmark databases, Turath Standard, Art, and UNESCO, specialised subsets of the Turath dataset. After demonstrating the limitations of existing networks pre-trained on ImageNet when deployed on such benchmarks, we train and evaluate several networks on the task of image classification. As a consequence of Turath, we hope to engage machine learning researchers in under-represented regions, and to inspire the release of additional culture-focused databases. The database can be accessed here: danikiyasseh.github.io/Turath.
Event structure semantics for multiparty sessions
Venue: arXiv cs.LO 2022
Year: 2022

We propose an interpretation of multiparty sessions as "flow event structures", which allows concurrency between communications within a session to be explicitly represented. We show that this interpretation is equivalent, when the multiparty sessions can be described by global types, to an interpretation of global types as "prime event structures".
A new criterion for $\mathcal{M}, \mathcal{N}$-adhesivity, with an application to hierarchical graphs
Venue: arXiv cs.LO 2022
Year: 2022

Adhesive categories provide an abstract framework for the algebraic approach to rewriting theory, where many general results can be recast and uniformly proved. However, checking that a model satisfies the adhesivity properties is sometimes far from immediate. In this paper we present a new criterion giving a sufficient condition for $\mathcal{M}, \mathcal{N}$-adhesivity, a generalisation of the original notion of adhesivity. We apply it to several existing categories, and in particular to hierarchical graphs, a formalism that is notoriously difficult to fit in the mould of algebraic approaches to rewriting and for which various alternative definitions float around.
Towards a secure API client generator for IoT devices
Venue: arXiv cs.NI 2022
Year: 2022

Given the success of IoT platforms, more developers and companies want to include the technology in their portfolio. However, in the case of single board microcontrollers, the support for networking operations is not ideal, and different IoT platforms allow access to the networking submodule via different libraries and system calls, leading to a steeper learning curve. Code generators for API clients can enhance productivity, but they tend to generate universal purpose code, and on the other hand the networking primitives of IoT devices are platform specific, especially when security mechanisms such as Transport Layer Security are part of the picture. This paper presents \texttt{cpp-tiny-client}, an API client generator developed as a plugin for the OpenAPI Generator project, which can tailor the generated code based on the IoT platform specified by the user. Our work allows to generate correct code for API clients for IoT devices, and thus can empower a developer with more productivity and a faster time-to-market for its own applications. By combining together mainstream technologies only, \texttt{cpp-tiny-client} offers a gentle learning curve. Moreover, experiments show that the generated code has a reasonable footprint, at least with respect to the IoT devices that were used in the validation of the work. The code related to this work is available through the OpenAPI Generator project~\cite{OpenAPIGenerator}. This technical report is an extension of~\cite{acmsac22}, and it integrates the information presented at the ACM SAC 2022 conference.
Service-Based Drone Delivery
Venue: arXiv cs.RO 2022
Year: 2022

Service delivery is set to experience a major paradigm shift with fast advances in drone technologies coupled with higher expectations from customers and increased competition. We propose a novel service-oriented approach to enable the ubiquitous delivery of packages in a drone-operated skyway network. We discuss the benefits, framework and architecture, contemporary approaches, open challenges and future visioned directions of service-based drone deliveries.
Applications of Gaussian Mutation for Self Adaptation in Evolutionary Genetic Algorithms
Venue: arXiv cs.NE 2022
Year: 2022

In recent years, optimization problems have become increasingly more prevalent due to the need for more powerful computational methods. With the more recent advent of technology such as artificial intelligence, new metaheuristics are needed that enhance the capabilities of classical algorithms. More recently, researchers have been looking at Charles Darwin's theory of natural selection and evolution as a means of enhancing current approaches using machine learning. In 1960, the first genetic algorithm was developed by John H. Holland and his student. We explore the mathematical intuition of the genetic algorithm in developing systems capable of evolving using Gaussian mutation, as well as its implications in solving optimization problems.
Algorithm-Level Confidentiality for Average Consensus on Time-Varying Directed Graphs
Venue: arXiv cs.MA 2022
Year: 2022

Average consensus plays a key role in distributed networks, with applications ranging from time synchronization, information fusion, load balancing, to decentralized control. Existing average consensus algorithms require individual agents to exchange explicit state values with their neighbors, which leads to the undesirable disclosure of sensitive information in the state. In this paper, we propose a novel average consensus algorithm for time-varying directed graphs that can protect the confidentiality of a participating agent against other participating agents. The algorithm injects randomness in interaction to obfuscate information on the algorithm-level and can ensure information-theoretic privacy without the assistance of any trusted third party or data aggregator. By leveraging the inherent robustness of consensus dynamics against random variations in interaction, our proposed algorithm can also guarantee the accuracy of average consensus. The algorithm is distinctly different from differential-privacy based average consensus approaches which enable confidentiality through compromising accuracy in obtained consensus value. Numerical simulations confirm the effectiveness and efficiency of our proposed approach.
Rough analysis of computation trees
Venue: arXiv cs.CC 2022
Year: 2022

This paper deals with computation trees over an arbitrary structure consisting of a set along with collections of functions and predicates that are defined on it. It is devoted to the comparative analysis of three parameters of problems with $n$ input variables over this structure: the complexity of a problem description, the minimum complexity of a computation tree solving this problem deterministically, and the minimum complexity of a computation tree solving this problem nondeterministically. Rough classification of relationships among these parameters is considered and all possible seven types of these relations are enumerated. The changes of relation types with the growth of the number $n$ of input variables are studied.
On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations
Venue: arXiv cs.CL 2022
Year: 2022

Text classification is a fundamental Natural Language Processing task that has a wide variety of applications, where deep learning approaches have produced state-of-the-art results. While these models have been heavily criticized for their black-box nature, their robustness to slight perturbations in input text has been a matter of concern. In this work, we carry out a data-focused study evaluating the impact of systematic practical perturbations on the performance of the deep learning based text classification models like CNN, LSTM, and BERT-based algorithms. The perturbations are induced by the addition and removal of unwanted tokens like punctuation and stop-words that are minimally associated with the final performance of the model. We show that these deep learning approaches including BERT are sensitive to such legitimate input perturbations on four standard benchmark datasets SST2, TREC-6, BBC News, and tweet_eval. We observe that BERT is more susceptible to the removal of tokens as compared to the addition of tokens. Moreover, LSTM is slightly more sensitive to input perturbations as compared to CNN based model. The work also serves as a practical guide to assessing the impact of discrepancies in train-test conditions on the final performance of models.
A Bound on the Minimal Field Size of LRCs, and Cyclic MR Codes That Attain It
Venue: arXiv cs.IT 2022
Year: 2022

We prove a new lower bound on the field size of locally repairable codes (LRCs). Additionally, we construct maximally recoverable (MR) codes which are cyclic. While a known construction for MR codes has the same parameters, it produces non-cyclic codes. Furthermore, we prove both necessary conditions and sufficient conditions that specify when the known non-cyclic MR codes may be permuted to become cyclic, thus proving our construction produces cyclic MR codes with new parameters. Furthermore, using our new bound on the field size, we show that the new cyclic MR codes have optimal field size in certain cases. Other known LRCs are also shown to have optimal field size in certain cases.
Theory and Practice of Quality Assurance for Machine Learning Systems An Experiment Driven Approach
Venue: arXiv cs.LG 2022
Year: 2022

The crafting of machine learning (ML) based systems requires statistical control throughout its life cycle. Careful quantification of business requirements and identification of key factors that impact the business requirements reduces the risk of a project failure. The quantification of business requirements results in the definition of random variables representing the system key performance indicators that need to be analyzed through statistical experiments. In addition, available data for training and experiment results impact the design of the system. Once the system is developed, it is tested and continually monitored to ensure it meets its business requirements. This is done through the continued application of statistical experiments to analyze and control the key performance indicators. This book teaches the art of crafting and developing ML based systems. It advocates an "experiment first" approach stressing the need to define statistical experiments from the beginning of the project life cycle. It also discusses in detail how to apply statistical control on the ML based system throughout its lifecycle.
Semi-Supervised Graph Attention Networks for Event Representation Learning
Venue: arXiv cs.LG 2022
Year: 2022

Event analysis from news and social networks is very useful for a wide range of social studies and real-world applications. Recently, event graphs have been explored to model event datasets and their complex relationships, where events are vertices connected to other vertices representing locations, people's names, dates, and various other event metadata. Graph representation learning methods are promising for extracting latent features from event graphs to enable the use of different classification algorithms. However, existing methods fail to meet essential requirements for event graphs, such as (i) dealing with semi-supervised graph embedding to take advantage of some labeled events, (ii) automatically determining the importance of the relationships between event vertices and their metadata vertices, as well as (iii) dealing with the graph heterogeneity. This paper presents GNEE (GAT Neural Event Embeddings), a method that combines Graph Attention Networks and Graph Regularization. First, an event graph regularization is proposed to ensure that all graph vertices receive event features, thereby mitigating the graph heterogeneity drawback. Second, semi-supervised graph embedding with self-attention mechanism considers existing labeled events, as well as learns the importance of relationships in the event graph during the representation learning process. A statistical analysis of experimental results with five real-world event graphs and six graph embedding methods shows that our GNEE outperforms state-of-the-art semi-supervised graph embedding methods.
Succinct Differentiation of Disparate Boosting Ensemble Learning Methods for Prognostication of Polycystic Ovary Syndrome Diagnosis
Venue: arXiv cs.LG 2022
Year: 2022

Prognostication of medical problems using the clinical data by leveraging the Machine Learning techniques with stellar precision is one of the most important real world challenges at the present time. Considering the medical problem of Polycystic Ovary Syndrome also known as PCOS is an emerging problem in women aged from 15 to 49. Diagnosing this disorder by using various Boosting Ensemble Methods is something we have presented in this paper. A detailed and compendious differentiation between Adaptive Boost, Gradient Boosting Machine, XGBoost and CatBoost with their respective performance metrics highlighting the hidden anomalies in the data and its effects on the result is something we have presented in this paper. Metrics like Confusion Matrix, Precision, Recall, F1 Score, FPR, RoC Curve and AUC have been used in this paper.
Cascaded Asymmetric Local Pattern: A Novel Descriptor for Unconstrained Facial Image Recognition and Retrieval
Venue: arXiv cs.CV 2022
Year: 2022

Feature description is one of the most frequently studied areas in the expert systems and machine learning. Effective encoding of the images is an essential requirement for accurate matching. These encoding schemes play a significant role in recognition and retrieval systems. Facial recognition systems should be effective enough to accurately recognize individuals under intrinsic and extrinsic variations of the system. The templates or descriptors used in these systems encode spatial relationships of the pixels in the local neighbourhood of an image. Features encoded using these hand crafted descriptors should be robust against variations such as; illumination, background, poses, and expressions. In this paper a novel hand crafted cascaded asymmetric local pattern (CALP) is proposed for retrieval and recognition facial image. The proposed descriptor uniquely encodes relationship amongst the neighbouring pixels in horizontal and vertical directions. The proposed encoding scheme has optimum feature length and shows significant improvement in accuracy under environmental and physiological changes in a facial image. State of the art hand crafted descriptors namely; LBP, LDGP, CSLBP, SLBP and CSLTP are compared with the proposed descriptor on most challenging datasets namely; Caltech-face, LFW, and CASIA-face-v5. Result analysis shows that, the proposed descriptor outperforms state of the art under uncontrolled variations in expressions, background, pose and illumination.
On Automating Triangle Constructions in Absolute and Hyperbolic Geometry
Venue: arXiv cs.CG 2022
Year: 2022

We describe first steps towards a system for automated triangle constructions in absolute and hyperbolic geometry. We discuss key differences between constructions in Euclidean, absolute and hyperbolic geometry, compile a list of primitive constructions and lemmas used for constructions in absolute and hyperbolic geometry, build an automated system for solving construction problems and test it on a corpus of triangle-construction problems. We also provide an online compendium containing construction descriptions and illustrations.
A Method for the Automated Discovery of Angle Theorems
Venue: arXiv cs.SC 2022
Year: 2022

The Naive Angle Method, used by Geometry Expressions for solving problems which involve only angle constraints, represents a geometrical configuration as a sparse linear system. Linear systems with the same underlying matrix structure underpin a number of different geometrical theorems. We use a graph theoretical approach to define a generalization of the matrix structure.
SMART: a Technology Readiness Methodology in the Frame of the NIS Directive
Venue: arXiv cs.CY 2022
Year: 2022

An ever shorter technology lifecycle engendered the need for assessing new technologies w.r.t. their market readiness. Knowing the Technology readiness level (TRL) of a given target technology proved to be useful to mitigate risks such as cost overrun, product roll out delays, or early launch failures. Originally developed for space programmes by NASA, TRL became a de facto standard among technology and manufacturing companies and even among research funding agencies. However, while TRL assessments provide a systematic evaluation process resulting in meaningful metric, they are one dimensional: they only answer the question if a technology can go into production. Hence they leave an inherent gap, i.e., if a technology fulfils requirements with a certain quality. This gap becomes intolerable when this metric is applied software such as technological cybersecurity measures. With legislation such as the General Data Protection Regulation4 (GDPR) and the Network and Information Systems Directive5 (NIS-D) making reference to state of the art when requiring appropriate protection measures, software designers are faced with the question how to measure if a technology is suitable to use. We argue that there is a potential mismatch of legal aim and technological reality which not only leads to a risk of non-compliance, but also might lead to weaker protected systems than possible. In that regard, we aim to address the gaps identified with existing Technology Readiness Assessment (TRA)s and aim to overcome these by developing standardised method which is suitable for assessing software w.r.t. its market readiness and quality (in sum maturity).
Deterministic Service Function Chaining over Beyond 5G Edge Fabric
Venue: arXiv cs.NI 2022
Year: 2022

Along with the increasing demand for latencysensitive services and applications, Deterministic Network (DetNet) concept has been recently proposed to investigate deterministic latency assurance for services featured with bounded latency requirements in 5G edge networks. The Network Function Virtualization (NFV) technology enables Internet Service Providers (ISPs) to flexibly place Virtual Network Functions (VNFs) achieving performance and cost benefits. Then, Service Function Chains (SFC) are formed by steering traffic through a series of VNF instances in a predefined order. Moreover, the required network resources and placement of VNF instances along SFC should be optimized to meet the deterministic latency requirements. Therefore, it is significant for ISPs to determine an optimal SFC deployment strategy to ensure network performance while improving the network revenue. In this paper, we jointly investigate the resource allocation and SFC placement in 5G edge networks for deterministic latency assurance. We formulate this problem as a mathematic programming model with the objective of maximizing the overall network profit for ISP. Furthermore, a novel Deterministic SFC deployment (Det-SFCD) algorithm is proposed to efficiently embed SFC requests with deterministic latency assurance. The performance evaluation results show that the proposed algorithm can provide better performance in terms of SFC request acceptance rate, network cost reduction, and network resource efficiency compared with benchmark strategy.