Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy‐efficient multihop routing in WSN using the hybrid optimization algorithm

The technical growth in the field of the wireless sensor networks (WSNs) has resulted in the process of collecting and forwarding the massive data between the nodes, which was a major challenge to the WSNs as it is associated with greater energy loss and delay. This resulted in the establishment of a routing protocol for the optimal selection of the multipath to progress the routing in WSNs. This paper proposes an energy‐efficient routing in WSNs using the hybrid optimization algorithm, cat–salp swarm algorithm (C‐SSA), which chooses the optimal hops in progressing the routing. Initially, the cluster heads (CHs) are selected using the low‐energy adaptive clustering hierarchy (LEACH) protocol that minimizes the traffic in the network. The CHs are engaged in the multihop routing, and the selection of the optimal paths is based on the proposed hybrid optimization, which chooses the optimal hops based on the energy constraints, such as energy, delay, intercluster distance, intracluster distance, link lifetime, delay, and distance. The simulation results prove that the proposed routing protocol acquired minimal delay of 0.3165 with 50 nodes and two hops, maximal energy of 0.1521 with 50 nodes and three hops, maximal number of the alive nodes as 39 with 100 nodes and two hops, and average throughput of 0.9379 with 100 nodes and three hops.
Feature Based Domain Adaptation for Neural Network Language Models with Factorised Hidden Layers

SUMMARY Language models are a key technology in various tasks, such as, speech recognition and machine translation. They are usually used on texts covering various domains and as a result domain adaptation has been a long ongoing challenge in language model research. With the rising popularity of neural network based language models, many methods have been proposed in recent years. These methods can be separated into two categories: model based and feature based adaptation methods. Feature based domain adaptation has compared to model based domain adaptation the advantage that it does not require domain labels in the corpus. Most existing feature based adaptation methods are based on bias adaptation. We propose a novel feature based domain adaptation technique using hidden layer factorisation. This method is fundamentally di ﬀ erent from existing methods because we use the domain features to calculate a linear combination of linear layers. These linear layers can capture domain speciﬁc information and information common to di ﬀ erent domains. In the experiments, we compare our proposed method with existing adaptation methods. The compared adaptation techniques are based on two di ﬀ erent ideas, that is, bias based adaptation and gating of hidden units. All language models in our comparison use state-of-the-art long short-term memory based recurrent neural networks. We demonstrate the e ﬀ ectiveness of the proposed method with perplexity results for the well-known Penn Treebank and speech recognition results for a corpus of TED talks.
Selective Offloading by Exploiting ARIMA-BP for Energy Optimization in Mobile Edge Computing Networks

Mobile Edge Computing (MEC) is an innovative technique, which can provide cloud-computing near mobile devices on the edge of networks. Based on the MEC architecture, this paper proposes an ARIMA-BP-based Selective Offloading (ABSO) strategy, which minimizes the energy consumption of mobile devices while meeting the delay requirements. In ABSO, we exploit an ARIMA-BP model for estimating computation capacity of the edge cloud, and then design a Selective Offloading Algorithm for obtaining offloading strategy. Simulation results reveal that the ABSO can apparently decrease the energy consumption of mobile devices in comparison with other offloading methods.
Computation of Compact Distributions of Discrete Elements

In our daily lives, many plane patterns can actually be regarded as a compact distribution of a number of elements with certain shapes, like the classic pattern mosaic. In order to synthesize this kind of pattern, the basic problem is, with given graphics elements with certain shapes, to distribute a large number of these elements within a plane region in a possibly random and compact way. It is not easy to achieve this because it not only involves complicated adjacency calculations, but also is closely related to the shape of the elements. This paper attempts to propose an approach that can effectively and quickly synthesize compact distributions of elements of a variety of shapes. The primary idea is that with the seed points and distribution region given as premise, the generation of the Centroidal Voronoi Tesselation (CVT) of this region by iterative relaxation and the CVT will partition the distribution area into small regions of Voronoi, with each region representing the space of an element, to achieve a compact distribution of all the elements. In the generation process of Voronoi diagram, we adopt various distance metrics to control the shape of the generated Voronoi regions, and finally achieve the compact element distributions of different shapes. Additionally, approaches are introduced to control the sizes and directions of the Voronoi regions to generate element distributions with size and direction variations during the Voronoi diagram generation process to enrich the effect of compact element distributions. Moreover, to increase the synthesis efficiency, the time-consuming Voronoi diagram generation process was converted into a graphical rendering process, thus increasing the speed of the synthesis process. This paper is an exploration of elements compact distribution and also carries application value in the fields like mosaic pattern synthesis.
Quadrature-carrier amplitude phase shift keying

Amplitude phase shift keying (APSK) is the emerging modulation scheme used in deep satellite communications and known to have a high spectral and energy efficiency among other two-dimensional (2-D) M-ary schemes. Besides, it provides a robust transmission in non-linear wireless channels and therefore it has been adopted and employed in the state-of-the-art digital video broadcasting standards DVB.S2/S2X. However, APSK is still away from utilising all available space dimensions such as its other counter-parts, where this potential gap provides the motivation to develop spectrally a more efficient modulation scheme. In this respect, a novel digital modulation scheme called quadrature-carrier APSK (QC-APSK) was proposed. QC-APSK uses two orthogonal carriers to convey ordinary APSK modulated signals to increase the spectral efficiency via doubling the transmission rate. It can be regarded as two orthogonal 2-D constellations which occupy 4-D signal space. This study covers the theoretical analysis of the proposed approach and further considers the effect of pulse shaping. The proposed scheme improves spectral efficiency by one-third and achieves better error performance against conventional APSK. Simulation results show that the obtained bit error rate and symbol error rate performances comply with the derived theoretical background which validates the effective use of the proposed modulation scheme.
Can We Predict the Change in Code in a Software Product Line Project?

Software programs are always prone to change for several reasons. In a software product line, the change is more often as many software units are carried from one release to another. Also, other new files are added to the reused files. In this work, we explore the possibility of building a model that can predict files with a high chance of experiencing the change from one release to another. Knowing the files that are likely to face a change is vital because it will help to improve the planning, managing resources, and reducing the cost. This also helps to improve the software process, which should lead to better software quality. Also, we explore how different learners perform in this context, and if the learning improves as the software evolved. Predicting change from a release to the next release was successful using logistic regression, J48, and random forest with accuracy and precision scored between 72% to 100%, recall scored between 74% to 100%, and F-score scored between 80% to 100%. We also found that there was no clear evidence regarding if the prediction performance will ever improve as the project evolved.
Surface Conditions That Constrain Alkane Oxidation on Perovskites

The crystal structure of perovskites can incorporate a wide variety of cations, which makes this class of materials so interesting for studies of links between solid-state chemistry and catalysis. Perovskites are known as typical total combustion catalysts in hydrocarbon oxidation reactions. The fundamental question that we investigate here is whether surface modifications of perovskites can lead to the formation of valuable reaction products in alkane oxidation. We studied the effect of segregated two-dimensional surface nanostructures on selectivity to propene in the oxidative dehydrogenation of propane. Manganese-based perovskites AMnO3 (A = La, Sm) were prepared by combustion and hydrothermal synthesis. Bulk and surface structures were investigated by X-ray diffraction, temperature-programmed reduction, aberration-corrected scanning transmission electron microscopy (STEM), multiwavelength Raman, and ambient-pressure X-ray photoelectron spectroscopy (AP-XPS) in combination with near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. Surface oxygen species responsible for C–H activation were distinguished by AP-XPS on the basis of a rigorous in situ analysis of the O 1s spectra recorded under a broad range of reaction conditions. Signals at 529.2, 530.1, 530.9, 531.2, and 531.8 eV were attributed to lattice O, defect-affected O, surface O, oxygen in carbonates, and hydroxyl groups, respectively. Operando AP-XPS revealed critical surface features, which occur under catalyst operation. The catalyst performance depends on the synthesis technique and the reaction conditions. In presence of a two-dimensional MnOx surface phase, addition of steam to the feed resulted in an increase in selectivity to the partial oxidation product propene to practically relevant values. The selectivity increase is related to the presence of Mn in a low oxidation state (2+/3+), an increased concentration of hydroxyl groups, and a higher abundance of adsorbed activated oxygen species on the catalyst surface. The surface analysis of a working catalyst highlights the importance of the termination layer of polycrystalline perovskites as a genuine property implemented by catalyst preparation. Such a termination layer controls the chemical properties and reactivity of perovskites. The information provides input for the development of realistic models that can be used by theory to predict functional properties.
Sparse representation based multi-frame image super-resolution reconstruction using adaptive weighted features

This study presents a novel sparse-representation based multi-frame super-resolution (SR) technique to reconstruct a high-resolution (HR) frame from multiple noisy low-resolution (LR) frames by using registration in sub-pixel accuracy and adaptive weighted feature operators. First, the registration of multiple frames in sub-pixel level and the mapping of pixels from LR frames to HR grid puts more information into the reconstructed image with respect to the conventional sparse representation based single image SR technique. This improves the overall resolution of the output image. Second, the introduction of adaptive weighted feature operators in the reconstruction process has significantly improved the robustness of the algorithm to noisy input frames. Hence, from the outputs, it can be seen that the proposed method outperforms the recent techniques in terms of noise robustness even in the higher noise level in the input image. The performance of the proposed algorithm is evaluated and quantified through a set of well-defined quality metrics and compared with some recently developed techniques. The results of the proposed technique confirm the claims of the authors.
A Heuristic Approach for a Real-World Electric Vehicle Routing Problem

To develop a non-polluting and sustainable city, urban administrators encourage logistics companies to use electric vehicles instead of conventional (i.e., fuel-based) vehicles for transportation services. However, electric energy-based limitations pose a new challenge in designing reasonable visiting routes that are essential for the daily operations of companies. Therefore, this paper investigates a real-world electric vehicle routing problem (VRP) raised by a logistics company. The problem combines the features of the capacitated VRP, the VRP with time windows, the heterogeneous fleet VRP, the multi-trip VRP, and the electric VRP with charging stations. To solve such a complicated problem, a heuristic approach based on the adaptive large neighborhood search (ALNS) and integer programming is proposed in this paper. Specifically, a charging station adjustment heuristic and a departure time adjustment heuristic are devised to decrease the total operational cost. Furthermore, the best solution obtained by the ALNS is improved by integer programming. Twenty instances generated from real-world data were used to validate the effectiveness of the proposed algorithm. The results demonstrate that using our algorithm can save 7.52% of operational cost.
Detecting new, informative propositions in social media

The ever growing quantity of online text produced makes it increasingly challenging to find new important or useful information. This is especially so when topics of potential interest are not known a-priori, such as in “breaking news stories”. This thesis examines techniques for detecting the emergence of new, interesting information in Social Media. It sets the investigation in the context of a hypothetical knowledge discovery and acquisition system, and addresses two objectives. The first objective addressed is the detection of new topics. The second is filtering of non-informative text from Social Media. A rolling time-slicing approach is proposed for discovery, in which daily frequencies of nouns, named entities, and multiword expressions are compared to their expected daily frequencies, as estimated from previous days using a Poisson model. Trending features, those showing a significant surge in use, in Social Media are potentially interesting. Features that have not shown a similar recent surge in News are selected as indicative of new information. It is demonstrated that surges in nouns and news entities can be detected that predict corresponding surges in mainstream news. Co-occurring trending features are used to create clusters of potentially topic-related documents. Those formed from co-occurrences of named entities are shown to be the most topically coherent. Machine learning based filtering models are proposed for finding informative text in Social Media. News/Non-News and Dialogue Act models are explored using the News annotated Redites corpus of Twitter messages. A simple 5-act Dialogue scheme, used to annotate a small sample thereof, is presented. For both News/Non-News and Informative/Non-Informative classification tasks, using non-lexical message features produces more discriminative and robust classification models than using message terms alone. The combination of all investigated features yield the most accurate models.
EIMO-ESOLSR: energy efficient and security-based model for OLSR routing protocol in mobile ad-hoc network

In numerous areas mobile ad hoc network (MANET) has plenteous mobile nodes that are allowed to communicate independently. In optimised link state routing protocols (OLSR), multi-point relays (MPR) are unique nodes that are chosen by different nodes to relay their traffic information which may prompt to MPR nodes high energy consumption. However, misbehaving MPR nodes tend to protect their energy by dropping different nodes packets as opposed to sending them. This prompts to huge energy loss and debasement of execution in many existing energy efficient MPR selection plans. In this work, an energy and security aware routing model is introduced for MANET including an enhanced intellects-masses optimiser (EIMO). At first, by methods of MPR selection the route discovery is advanced. In this approach, parameters, for example, available bandwidth, queue occupancy, and lifetime are considered as willingness nodes and misbehaving probability, power factor and forwarding behavior are considered as Composite Eligibility Index (CEI). Contrasted with other energy models this approach has less energy and secured. The accomplished simulation upshot exhibits that the EIMO-energy-efficient and secure optimised link state routing (ESOLSR) outperforms other state of art with respect to the performance metrics like energy consumption, total remaining time, average network lifetime, and a variance of energy.
Pilot decontamination in massive multiuser MIMO systems based on low-rank matrix approximation

In multi-cell massive multiuser multi-input multi-output (MU-MIMO) systems, the channel estimation performance is degraded by the pilot contamination problem. This effect occurs when non-orthogonal pilot sequences are reused by other users in the adjacent cells. In this study, two channel estimation methods based on low-rank matrix approximation technique are proposed to mitigate pilot contamination problem in time division duplex multi-cell massive MU-MIMO systems. In the first method, the massive MU-MIMO channel estimation is formulated as the nuclear norm (NN) optimisation problem and solved by using a novel estimation algorithm proposed in this study. In the second method, the iterative weighted NN (IWNN) is proposed to improve the NN estimation performance. Consequently, the regularisation parameter of both optimisation methods is selected based on the cross-validation curve method. The simulation results show that both proposed methods outperform the traditional least square method in terms of the normalised mean square error. Moreover, the IWNN estimation method demonstrates substantial improvement over the NN estimation method in the presence of strong pilot contamination problem.
Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions

In recent years, there has been significant advancement in resource management mechanisms for cloud computing infrastructure performance in terms of cost, quality of service (QoS) and energy consumption. The emergence of the Internet of Things has led to the development of infrastructure that extends beyond centralised data centers from the cloud to the edge, the so-called cloud-to-thing continuum (C2T). This infrastructure is characterised by extreme heterogeneity, geographic distribution, and complexity, where the key performance indicators (KPIs) for the traditional model of cloud computing may no longer apply in the same way. Existing resource management mechanisms may not be suitable for such complex environments and therefore require thorough testing, validation and evaluation before even being considered for live system implementation. Similarly, previously discounted resource management proposals may be more relevant and worthy of revisiting. Simulation is a widely used technique in the development and evaluation of resource management mechanisms for cloud computing but is a relatively nascent research area for new C2T computing paradigms such as fog and edge computing. We present a methodical literature analysis of C2T resource management research using simulation software tools to assist researchers in identifying suitable methods, algorithms, and simulation approaches for future research. We analyse 35 research articles from a total collection of 317 journal articles published from January 2009 to March 2019. We present our descriptive and synthetic analysis from a variety of perspectives including resource management, C2T layer, and simulation.
Strategies of peptide identification using tandem Mass Spectrometry

Proteomics has made a massive advance and reached enough fullness previously reserved only for genomics. Therefore, it generates a flood of data that represents significant challenges for their interpretation. With the technological advances of mass spectrometers that allow the study of proteins in a large-scale, the analysis of complex protein samples becomes possible. The capacity to identify reliably all proteins present in a sample is a requirement to provide the suitable biologically interpretation. Mainly, two basic strategies are used for protein identification: Database searching strategy and De novo sequencing strategy. These two strategies differ in term of their ability and performance to control the false discovery rate (FDR). Here we review the achivements of mass spectrometry-based proteomics. We are shedding light on strategies of peptide/protein identification and the challenge of sensitivity and specificity, as they are the key factor to control the growth of false positive.
The need for deterministic virtualization in the industrial internet of things

Real-Time virtualization is commonly accepted to act as one of the key enablers of Fog Computing and the Industrial Internet of Things (IIoT). We motivate requirements which any hypervisor qualifying as a deterministic virtualization solution for IIoT should fulfill. We characterize existing work in the field of real-time virtualization to illustrate the trade-off between flexibility and deterministic execution. Furthermore, we indicate a lack of hypervisors that meet all of our requirements on deterministic virtualization. Our preliminary experimental results comparing the system latencies of ACRN, KVM, and Xen RTDS support our claim for the need of further investigation of deterministic virtualization.
Cracking the channel hopping sequences in IEEE 802.15.4e-based industrial TSCH networks

Industrial networks typically connect hundreds or thousands of sensors and actuators in industrial facilities, such as manufacturing plants, steel mills, and oil refineries. Although the typical industrial applications operate at low data rates, they pose unique challenges because of their critical demands for reliable and real-time communication in harsh industrial environments. IEEE 802.15. 4 based Wireless Sensor-Actuator Networks (WSANs) technology is appealing for use to construct industrial networks because it does not require wired infrastructure and can be manufactured inexpensively. Battery-powered wireless modules easily and inexpensively retrofit existing sensors and actuators in industrial facilities without running cables for communication and power. To address the stringent real-time and reliability requirements, WSANs made a set of unique design choices such as employing the Time-Synchronized Channel Hopping (TSCH) technology that distinguish themselves from traditional wireless sensor networks that require only best effort services. The function-based channel hopping used in TSCH simplifies the network operations at the cost of security. Our study shows that an attacker can reverse engineer the channel hopping sequences by silently observing the channel activities and put the network in danger of selective jamming attacks. To our knowledge, this paper represents the first systematic study that investigates the security vulnerability of TSCH channel hopping in IEEE 802.15. 4e under realistic traffic. In this paper, we demonstrate the process of cracking the TSCH channel sequences, present two case studies using publicly accessible TSCH implementations (developed for Orchestra and WirelessHART), and provide a set of insights.