Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of spatio-temporal prediction methods: a parking availability case study

In this paper, we present a comparative analysis of Statistical, Machine Learning and Deep Learning spatio-temporal models for parking occupancy prediction1. We evaluate such models on three public datasets, which are enriched by a set of hand-crafted features to take into account the temporal and spatial components when they are not natively handled by a model. Two approaches for this regression task are investigated: a univariate one and a multivariate one. In the former, we build a separate model for each parking lot. In the latter, a single model is used to predict the availability of all parking lots so as to learn the interactions and the co-movements among all time-series. All models exhibit similar performance. However, we highlight the higher effectiveness of gradient boosted methods when encompassing both temporal and spatial awareness in the feature space and of deep-learning models that take into account the spatial structure of the data.
Transpose-xen: virtualized mixed-criticality through dynamic allocation

Cloud systems continue to rise in popularity due to their ability to provide access to flexible, scalable systems to be shared among all their users. Several tasks can be executed simultaneously within a server, but have varying requirements for completion. While some jobs may have latency-critical quality-of-service (QoS) requirements, others may have stricter real-time constraints for maximum deadline misses. The introduction of hard real-time tasks, where zero deadline misses are acceptable, results in scheduling concurrent jobs becoming increasingly difficult. In this paper we propose Transpose-Xen, an adaptive hyper-visor scheduler capable of managing the scheduling of tasks of varying levels of criticality, including tasks with hard real-time constraints. Transpose-Xen is able to execute multiple jobs of varying criticality by finding similarities between the resource needs of each task despite potentially executing from separate VMs. Once grouped into these resource sub-pools, or ponds, our scheduler allocates the needed resources to ensure that each job is schedulable if possible. Transpose-Xen also leverages the use of virtual-deadlines, a scheduling algorithm that we use to prioritize higher-criticality tasks without completely starving lower-criticality tasks of resources. By profiling the impact of resource allocation on real-time tasks, different jobs of varying criticality levels can be scheduled concurrently - capable of satisfying both hard real-time constraints and satisfying up to 99% of all soft real-time deadlines.
Band selection techniques using discrete ranges and maxpooling operations for hyperspectral image pixel classification

Through this study, we intend to improve the utilization of hyperspectral high-capacity data feature information by reducing the dimension of neural network inputs in embedded systems. Through the number of bands clustered into subsets due to differences in maximum and minimum values for each band, the unique number of band selections and bands are determined for each data set by applying band selection algorithms in each subset. Unlike RGB images, hyperspectral images are used to identify objects collected as information on an electromagnetic spectrum, It is used for exploration of objects or materials because it has more information than general images. A band selection technique is used to reduce the amount of information on the hyperspectral image to be processed. Existing band selection techniques are statistics-based heuristic techniques[3, 8] that take a long time and often lack generality and universality. To compensate for this, in this paper, representative bands for each data set are selected and used for band selection through Discrete Range among quantization concepts. Through the experimental results, the proposed technique showed that the execution time was much faster than the existing band selection method, and the performance accuracy was similar to that of the original, even though the number of bands was reduced from 1/7 to 1/10.
Strategic selection of data sources for cyber attack detection in enterprise networks: a survey and approach

Cyber attacks leave traces in data sources, such as in log files, memory or data-streams. Detection systems utilize these data sources to detect the application of specific attack techniques. Attack techniques vary considerably in terms of their effectiveness, potential impact and application by threat actors. Data sources, on the other side, may contain traces of one or several attack techniques, and the effort to process their output may differ heavily. Therefore, it is obvious that not all data sources are of equal value for detection and organizations must carefully survey which sources shall be analyzed and what attack techniques need to be found. This paper introduces D3TECT, a process model that describes a procedure for dynamically ranking and selecting data sources suitable for detection. The novelty is that this model accounts for constraints in the selection process. For instance if a certain data source cannot be utilized in a specific setting, e.g., due to data privacy constraints, the discovery of the most important attack techniques are still ensured by the remaining data sources. Eventually, the D3TECT approach solves the challenge of strategically selecting data sources while accounting for their varying usefulness for attack detection. The model is tested with real data, utilizing the MITRE ATT&CK framework and numerous public cyber threat intelligence databases. The paper shows the ranking results and discusses their plausibility to validate D3TECT.
Performance Comparison of Microstrip Band-Reject Filters for Different Dielectric Materials

In this paper design of Microstrip band-reject filters have been presented for different dielectric materials on L-band applications. The various designs have also been compared with different dielectric constants and other performance parameters to check for viability and stability of designs on the considered frequency band. IE3D 14.1 simulation tool has been used for obtaining the Insertion loss and return loss performance of microstrip filter.
Toward replicated and asynchronous data streams for edge-cloud applications

Vast number of computing devices, both in the cloud and at the edge, are continuously generating and consuming large amount of data. Some applications require the data to be always accessible, even when the devices are occasionally offline. We can make the data available by replicating and storing the generated data and query results locally at the devices. The data and results are kept updated when the devices are connected. The challenge, though, is to ensure that the generated data and query results are consistent. We present an approach to replicated and asynchronous data streams that is built on two well-established techniques: data provenance semirings and Conflict-Free Replicated Data Types (CRDTs). Our approach guarantees that the replicated and asynchronously updated data and query results are eventually consistent.
Leveraging n-gram neural embeddings to improve deep learning DGA detection

Several families of malware are based on the need to establish a connection with a Command and Control (C&C) server. In addition, to avoid detection, these servers "hide" behind domain names that are periodically changed according to a specific Domain Generation Algorithm (DGA). Hence, the malware that has infected a particular host uses the same DGA to make DNS queries in order to establish a connection with the C&C server. The identification of "malicious" domain names used in DNS queries is therefore crucial for their detection. For this purpose, various machine learning techniques have been used, in particular, recently, deep learning techniques have proved especially effective. However, to get good results, these techniques require very large and labelled training datasets. Nevertheless, the construction of such datasets, decidedly with regard to the collection of malicious domain names, is a very difficult and non-scalable task. In this paper, therefore, we explore the possibility of exploiting unsupervised character n-gram embeddings to improve the performance of a Deep Learning DGA classifier. Embeddings are trained using a large dataset of benign names, opening up the possibility of using a small classifier training dataset requiring a small number of malicious names. A series of experiments, which use the same embedding for classifiers trained with datasets of increasing size, are then presented. These experiments show how the embedding is particularly effective for classifiers trained with small datasets having a small number of malicious names.
Semi-supervised learning for image alignment in teach and repeat navigation

Visual teach and repeat navigation (VT&R) is a framework that enables mobile robots to traverse previously learned paths. In principle, it relies on computer vision techniques that can compare the camera's current view to a model based on the images captured during the teaching phase. However, these techniques are usually not robust enough when significant changes occur in the environment between the teach and repeat phases. In this paper, we show that contrastive learning methods can learn how the environment changes and improve the robustness of a VT&R framework. We apply a fully convolutional Siamese network to register the images of the teaching and repeat phases. Their horizontal displacement between the images is then used in a visual servoing manner to keep the robot on the intended trajectory. The experiments performed on several datasets containing seasonal variations indicate that our method outperforms state-of-the-art algorithms tailored to the purpose of registering images captured in different seasons.
HYGAR: a hybrid genetic algorithm for software architecture recovery

Genetic algorithms have been used for clustering modules of a software system in line with the modularity principle. The goal of these algorithms is to recover an architectural view in the form of a modular structural decomposition of the system. We discuss design decisions and variations in existing genetic algorithms devised for this purpose. We introduce HYGAR, a novel hybrid variant of existing algorithms. We apply HYGAR for software architecture recovery of 5 real systems and compare its effectiveness with respect to a baseline and a state-of-the-art hybrid algorithm. Results show that HYGAR outperforms these algorithms in maximizing the modularity of the obtained clustering.
pi-Lisco: parallel and incremental stream-based point-cloud clustering

Point-cloud clustering is a key task in applications like autonomous vehicles and digital twins, where rotating LiDAR sensors commonly generate point-cloud measurements in data streams. The state-of-the-art algorithms, Lisco and its parallel equivalent P-Lisco, define a single-pass distance-based clustering. However, while outperforming other batch-based techniques, they cannot incrementally cluster point-clouds from consecutive LiDAR rotations, as they cannot exploit result-similarity between rotations. The simplicity of Lisco, along with the potential of improvements through utilization of computational overlaps, form the motivation of a more challenging objective studied here. We propose Parallel and Incremental Lisco (pi-Lisco), which, with a simple yet efficient approach, clusters LiDAR data in streaming sliding windows, reusing the results from overlapping portions of the data, thus, enabling single-window (i.e., in-place) processing. Moreover, pi-Lisco employs efficient work-sharing among threads, facilitated by the ScaleGate data structure, and embeds a customised version of the STINGER concurrent data structure. Through an orchestration of these key ideas, pi-Lisco is able to lead to significant performance improvements. We complement with an evaluation of pi-Lisco, using the Ford Campus real-world extensive data-set, showing (i) the computational benefits from incrementally processing the consecutive point-clouds; and (ii) the fact that pi-Lisco' parallelization leads to continuously increasing sustainable rates with increasing number of threads, shifting the saturation point of the baseline.
Enhanced configurable snapshot: snapshot-based fast booting on NAND flash with lifetime control

Having a quick boot time has become a major issue in many consumer electronics to improve a user experience. Although instant on/off with persistent memory claims less than 1 second booting, the maturity of the technology is still in progress and the price has not reached a realistic level. Thus, snapshot-based booting with NAND flash is an attractive solution in the current consumer industry. Unfortunately, the snapshot-based booting technique yields extra writes to underlying storage devices such as NAND flash memory when saving the snapshot image. Naively adopting this technique on NAND flash memory, therefore, will bring reliability issues due to the limited lifetime of NAND flash memory. In this paper, we propose a new solution, Enhanced Configurable Snapshot (ECS), a snapshot-based fast booting technique on NAND flash memory, specially designed for improving its lifetime. Also, we present various techniques to improve the booting time. Experimental results show that ECS provides a 2.5x - 126.3x longer lifetime and 1.5x faster booting time on average than conventional snapshot-based booting on Linux kernel.
Detecting return-oriented programming on firmware-only embedded devices using hardware performance counters

Return-oriented programming (ROP) relies on in-memory code sequences ending in return instructions to chain together arbitrary malware. ROP is one of the most dangerous security exploits because, if wittingly crafted, it can be used to wreak havoc on the system, network, and nodes connected to it. It is not surprising that ROP has been studied on architectures such as x86 and ARM, mostly with an operating system (OS). Xtensa is one of the most popular industry standards for digital signal processors and it is present in many resource-constrained firmware-based embedded WiFi home automation devices, which operate by reading instructions directly from flash memory. Despite leveraging no real OS, Xtensa is not immune to ROP, and there have been reports of buffer overflow vulnerability exploitations leading to ROP in Xtensa. Therefore, we present the first detection of ROP, and its variant Jump-oriented programming (JOP), in a firmware-only environment using hardware performance counters (HPCs). Our approach discerns the variations in the HPC micro-architectural events triggered by ROP attacks and benign program execution. We implemented attack scenarios using instrumented programs and exploits that perform tasks similar to those in a known microprocessor benchmark programs. We recorded micro-architectural events to train a machine learning binary classifier. The learned model identifies relevant HPCs, which could serve as predictors of ROP/JOP execution even in embedded firmware-only configurations, where features atypical to conventional processors, like instruction memory and data memory, are available. Our evaluation results indicate a high precision, recall, and accuracy of the classifier predictions.
Energy-efficient fog computing-enabled data transmission protocol in tactile internet-based applications

Sensor nodes are one of the basic elements in the Tactile Internet-based fog computing architecture. They provide a huge amount of data to the network due to the widespread real-world applications that use these types of wireless devices. This huge number of data, transmitted by the sensor devices to the fog gateway and then to the cloud, leads to high communication costs, increased power consumption, and high latency at the fog gateway. These challenges would be considered as a hurdle in the Tactile Internet-based fog system. To tackle these challenges, this paper proposes an Energy-efficient Fog Computing-enabled Data Transmission (EFoCoD) protocol in Tactile Internet-based Applications. The protocol works on sensor devices level in the Tactile Internet-based fog computing architecture. EFoCoD protocol executes a Lightweight Data Redundancy Elimination (LiDaRE) Algorithm at the sensor level to reduce the collected data before transferring them to the smart fog gateway. To study the performance of the EFoCoD, it was compared to its counterpart protocols in the literature just like ATP and PFF. Simulation results show that EFoCoD outperforms these protocols in terms of energy consumption, transmitted data, and data accuracy.
IA-BERT: context-aware sarcasm detection by incorporating incongruity attention layer for feature extraction

Sarcasm as a form of figurative language has been widely used to implicitly convey an offensive opinion. While preliminary researches have constantly tried to identify the sarcasm lying in a text directly from tokens within the text, it is insufficient because sarcasm does not have specific vocabularies as in polarized sentences. Especially in threads or discussions, sarcasm can be identified after getting the context information from previous replies or discussions. To this end, we propose IA-BERT, a model architecture that considers contextual information to identify incongruity features that lie in sarcastic texts. IA-BERT is embedded with a feature attention layer that combines features extracted from the response alone and interactive features obtained from the context and the response. The model leverages BERT pretrained embedding and yields a performance improvement from the standard fine-tuned BERT classifier. IA-BERT also outperforms the sophisticated architecture of LCF-BERT in the accuracy and F1-score.
Bootstrapped learning for car detection in planar lidars

We present a proof-of-concept method for using bootstrapped learning for car detection in lidar scans using neural networks. We transfer knowledge from a traditional hand-engineered clustering and geometry-based detection technique to deep-learning-based methods. The geometry-based method automatically annotates laserscans from a vehicle travelling around a static car park over a long period of time. We use these annotations to automatically train the deep-learning neural network and evaluate and compare this method against the original geometrical method in various weather conditions. Furthermore, by using temporal filters, we can find situations where the original method was struggling or giving intermittent detections and still automatically annotate these frames and use them as part of the training process. Our evaluation indicates an increased detection accuracy and robustness as sensing conditions deteriorate compared to the method from which trained the neural network.
Nested trees for longitudinal classification

Longitudinal datasets contain repeated measurements of the same variables at different points in time. Longitudinal data mining algorithms aim to utilize such datasets to extract interesting knowledge and produce useful models. Many existing longitudinal classification methods either dismiss the longitudinal aspect of the data during model construction or produce complex models that are scarcely interpretable. We propose a new longitudinal classification algorithm based on decision trees, named Nested Trees. It utilizes a unique longitudinal model construction method that is fully aware of the longitudinal aspect of the predictive attributes (variables) and constructs tree nodes that make decisions based on a longitudinal attribute as a whole, considering measurements of that attribute across multiple time points. The algorithm was evaluated using 10 classification tasks based on the English Longitudinal Study of Ageing (ELSA) data.
Lightweight VANET authentication protocols

Security and privacy of vehicles, occupants of such vehicles, roadside infrastructure, and other entities that are part of Vehicular Ad hoc NETwork (VANET) cannot be overstated. Cryptography is commonly used to authenticate and to secure communication among VANET entities. As vehicles are mobile, it is essential for authentication protocols to be lightweight, quick, and with minimal number of passed messages. It is also necessary to ensure that these protocols are secure against attacks. However, extant authentication protocols are not necessarily lightweight and almost all of them are vulnerable to relay attacks. We propose secure and truly lightweight authentication protocols for the VANET environment.
RANC: reward-all nakamoto consensus

In this work we present Reward-All Nakamoto-Consensus (RANC), a Proof-of-Work cryptocurrency that resiliently rewards each miner with a number of coins that is directly proportional to its individual mining power, rather than to its relative share of the entire network's mining power as done in Bitcoin. Under this approach, the security of mining in RANC achieves near-perfect incentive compatibility, and near-zero censorship susceptibility, for adversarial mining shares up to 45%, but at the cost of regression in subversion-gain resilience. Moreover, mining rewards in RANC exhibit significantly lower variance for non-majority miners compared to NC, enabling dependable reward stability. Consequently, depending on the network transaction-fees, RANC improves miner's waiting time for rewards, and incentivizes forming mining pools smaller than required in Bitcoin for equal reward stability. A detailed specification of RANC is presented, along with an evaluation of the practicality and efficiency achieved by our prototype RANC implementation.
The Ubiquitous Role of New Social Media Channels and Innovative Mobile Services: Are Consumers Ready?

Over the past decade, the world of social media is evolving at warp speed. In light of this rapid evolution, investigating social media users behaviour is top of the agenda for many managers and marketing researchers today (Kaplanand Haenlein, 2010). Indeed, still there are several areas in which we believe social media channels such as social networking, e-commerce, s-commerce, and blogs will make the most trivial evolution in years to come. Given the background provided about social media platforms adoption and strategic usage, there are some interesting and relevant topics that are particularly relevant in research on consumer behaviour towards these geo-information platforms and await further empirical exploration. Firstly, considering the cross-cultural aspects in studying the LBSN adoption on mobile phones, in a relationship social context, is a key driver of the growth and success of mobile LBSN and is even crucial to telecommunication operators to implement these services. Secondly, recently s-commerce is considered as a new form of social media that has arisen which due to its social features seem to have a great power of influence on consumers’ purchasing decision-making process. The marketing area has thus evolved from a time where marketers had the power of influence to today where consumers have a greater power of influence on their peers (Jaffe, 2010). Finally, with the advent of 4G mobile devices and the mobile location based advertising, consumers’ preferences can be pre-identified and advertising content can therefore be delivered to consumers at the right time and at the right place with the right message (Chen and Hsieh, 2012). Therefore, it becomes necessary and reasonable to enhance our understanding about the role of mobile advertising in mediating consumers shopping experience, since we begin to find ourselves bringing our mobile devices everywhere we go.