Miracle-dz/UniFAR-FTUs huggingface dataset:
Replication and Automatic Updates of File Fragments in Cloud

Generally, in cloud computing the data is outsourced to controls of third party, which may lead to issue of securitys. The data in cloud may lost because of attacks by unknown users and nodes inside the cloud . Therefore, the data within in the cloud must be kept in secured state by using different techniques . In this paper, we propose division and replication of data within in the cloud to improve optimal performance and security . In the present work we separate a file into multiple fragment sand replicate fragments on different nodes in the cloud. Each node, should store fragment only once. Therefore, no information can be get by the attacker. The node storing the fragments within the cloud are separated using a method called T-colouring, which avoids the attacker to find the location of nodes that contain related fragments. The traditional cryptographic techniques for data security are not used in this work, so that computationally expensive methodologies are reduced. The more advanced feature in this work is an automatic update of file fragments on nodes whenever any changes are done by the third person on that particular node.
ANÁLISE COMPARATIVA DA VOLATILIDADE DAS AÇÕES DE EMPRESAS DO SETOR SUCROALCOOLEIRO COM O IBOVESPA

O presente trabalho tem como objetivo comparar a volatilidade dos retornos das acoes de empresas do setor sucroalcooleiro e compara-las ao IBOVESPA. As acoes que compoem o estudo sao as acoes do Grupo Cosan, do Grupo Sao Martinho e da Acucar Guarani. Tal analise e importante para que os gestores de portfolios, que tomam decisoes de como alocar os investimentos, conhecam o historico e o corrente relacionamento entre as volatilidades dos ativos. Os retornos foram calculados por meio da diferenca do logaritmo neperiano do preco de fechamento diario das acoes e do indice. As volatilidades foram calculadas por meio do desvio padrao mensal dos retornos diarios. Os resultados mostram que os retornos das acoes de empresas do setor sucroalcooleiro sao mais volateis que os retornos do IBOVESPA, na media, mas houve periodos em que uma das empresas apresentou volatilidade inferior ao indice.
8-Bit ALU Design using m-GDI Technique

In this paper, the design of an 8-bit Arithmetic Logic Unit (ALU) using Gate Diffusion Input (GDI) technique is proposed. Implementing the GDI technique in designing the ALU results in low power consumption and the number of transistors it requires is much less. Which result in reduced chip-area and power consumption - two of the most important parameters in digital VLSI design. In this design, 3T XOR is used in the full adder. Moreover, a novel 1-to-8 demultiplexer circuit has been used in the design as well. A considerable number of research papers are studied and compared various logic families and then finally designed an 8-bit ALU which can perform 8 different operations. The design is validated using the schematic editor-DHCH 3.5 and the simulation have been carried out using Xilinx ISE 14.7.
Software Deployment Strategy Based on Performance and Heterogeneity

In the current cyberspace field, the positions of offensive and defensive parties are extremely imbalanced. Aiming at the weakness of web virtual hosting in this paper, we combine software diversity technology to build a heterogeneous software variant library, and propose a software deployment strategy based on software performance and heterogeneity. Considering the attributes of the software and the structural characteristics of the network topology comprehensively, we implement the software deployment of each virtual site in the network topology. Our analysis shows that our deployment strategy can make the heterogeneity and performance of the entire network topology reach a high level, and further improve the security of complex network environment. Finally, we use the real-world network topologies to verify the feasibility of our method.
Transient increase in intracellular pH during Dictyostelium differentiation

The intracellular pH (pHi) of Dictyostelium discoideum amebae has been determined using the pH-dependent fluorescence of intracellularly trapped fluorescein (Thomas, J. A., R. N. Buschbaum, A. Zimiak, and E. Racker, Biochemistry, 18:2210-2218). The pHi of cells measured 45-60 min after initiation of differentiation was between 6.2 and 6.3. At approximately 2 h into differentiation cells underwent a transient intracellular alkalinization during which the pHi rose to 7.13 (+/- 0.3, n = 4), after which the pHi returned to approximately the original value (6.2-6.4). Cells that were removed from growth medium but were incubated in differentiation medium containing 3% dextrose did not exhibit this transient increase in pHi. The alkalinization event can also be prevented from occurring by differentiation in Na+-free solutions or by the addition of amiloride to sodium-containing buffer solutions, suggesting that the alkalinization is sodium dependent. When the alkalinization was prevented by amiloride treatment, cells did not progress normally into differentiation. This increase in pHi was initiated by the cells 2 h after removal from nutrient medium and it could be inhibited by several treatments that had been observed to delay the differentiation program, suggesting that it plays a major role in the initiation of the developmental program of this organism.
A Comparative Study of Early Detection of Parkinson's Disease using Machine Learning Techniques

Parkinson's Disease (PD) is considered a malison for mankind for several decades. Its detection with the help of an automated system is a subject undergoing intense study. This entails a need for incorporating a machine learning model for the early detection of PD. For discovering a full proof model, the cardinal prerequisite is to study the existing computational intelligent techniques in the field of research used for PD detection. Many existing models focus on singular modality or have a cursory analysis of multiple modalities. This encouraged us to provide a comparative literature study of four main modalities signifying major symptoms used for early detection of PD, namely, tremor at rest, bradykinesia, rigidity, and, voice impairment. State- of-the-art machine learning implementations namely Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-nearest neighbors (KNN), Stochastic Gradient Descent (SGD) and Gaussian Naive Bayes (GNB) are executed in these modalities with their respective datasets. Furthermore, ensemble approaches such as Random Forest Classifier (RF), Adaptive Boosting (AB) and Hard Voting (HV) are implemented. Our results are compared with those obtained with their respective researches. Among all the tests, applying Random Forest (RF) on Static Spiral Test (for detecting tremor) gave us the most significant result, i.e. the highest accuracy of 99.79%. This leads to the conclusion that the multimodal approach with the help of the ensemble method should be used to get better and accurate results.
Holographically Printed Freeform Mirror Array for Augmented Reality Near-Eye Display

We propose augmented reality (AR) near-eye display using a holographically printed freeform mirror array. A wide depth of field AR system is implemented by the retinal projection via a small mirror array. Since we use a holographic mirror array, more advantages over a traditional physical mirror array are achieved. Due to the see-through characteristics of the holographic optical element (HOE), the array structure does not obstruct the sight of an observer. Also, the holographic printing technique for the freeform mirror array enables a wide depth of field system without using a floating lens and compensates for the astigmatism of the HOE. The detailed design method of the freeform mirror array HOE is presented. Through the retinal image simulations and the experimental results, the feasibility of the proposed method is verified.
Artificial Neural-Network-Based Pre-Distortion for High Loss-Budget 60-km Long-Reach Passive Optical Network

High launch optical power can compensate for severe fading and power loss in long-reach passive optical networks (LR-PONs); however, it also aggravates nonlinear degradation, which necessitates the use of complex DSP-based nonlinear compensation techniques at optical network users (ONUs). DSP-related techniques also necessitate the use of additional hardware/software components by the receiver, which can greatly increase implementation costs and energy consumption, particularly when dealing with large-scale ONU deployment. This is the first study to propose artificial neural network (ANN)-based pre-distortion to eliminate the need for complex DSP at ONUs in a high-launch-power LR-PON, thereby permitting the use of a simplified architecture at the user end. In the first phase of the study, the proposed ANN-based pre-distortion scheme was implemented in a single-channel IMDD OFDM LR-PON, which achieved a data rate of >55 Gbps over 60-km transmission with a loss budget of 30 dB without the need for optical inline- or pre-amplification. In the second phase of experiments, the same scheme was applied to a 4-channel wavelength division multiplexing (WDM) OFDM LR-PON. Here, the proposed scheme achieved data rates of >200 Gbps using launch power of 18 dBm per lane, resulting in a loss budget of roughly 29 dB over 60-km single mode fiber transmission.
Outcome of multimodal MRI-guided intravenous thrombolysis in patients with stroke with unknown time of onset

Objective Intravenous tissue plasminogen activator (tPA) is the standard therapy for patients with acute ischaemic stroke (AIS) within 4.5 hours of onset. Recent trials have expanded the endovascular treatment window to 24 hours. We investigated the efficacy and safety of using multimodal MRI to guide intravenous tPA treatment for patients with AIS of unknown time of onset (UTO). Methods Data on patients with AIS with UTO and within 4.5 hours of onset were reviewed. Data elements collected and analysed included: demographics, National Institutes of Health Stroke Scale (NIHSS) score at baseline and 2 hours, 24 hours, 7 days after thrombolysis and before discharge, the modified Rankin Scale (mRS) score at 3 months after discharge, imaging findings and any adverse event. Results Forty-two patients with UTO and 62 in control group treated within 4.5 hours of onset were treated with intravenous tPA. The NIHSS scores after thrombolysis and/or before discharge in UTO group were significantly improved compared with the baseline (p<0.05). Between the two groups, no significant differences in NIHSS score were observed (p>0.05). Utilising the non-inferiority test, to compare mRS scores (0–2) at 3 months between the two groups, the difference was 5.2% (92% CI, OR 0.196). Patients in the UTO group had mRS scores of 0-2, which were non-inferior to the control group. Their incidence of adverse events was similar. Conclusions Utilising multimodal MRI to guide intravenous only thrombolysis for patients with AIS with UTO was safe and effective. In those patients with AIS between 6 and 24 hours of time of onset but without large arterial occlusion, intravenous thrombolysis could be considered an option.
Extended Worst-Case OSNR Searching Algorithm for Optical Network-on-Chip Using a Semi-Greedy Heuristic With Adaptive Scan Range

The advances in silicon photonics technology have facilitated the realization of optical network-on-chips (ONoCs) to cope with the physical limitations of metal interconnections in traditional CMOS integrated circuits. As the performance and reliability of optical links are adversely affected by insertion losses and crosstalk, which inevitably occur during the propagation of optical signals, optimizing the power of a laser source requires a sophisticated analysis of the optical signal-to-noise ratio (OSNR). Calculating the worst-case OSNR for all possible communication links in an ONoC is an NP-hard problem even under the assumption of a single-wavelength laser source. Moreover, when expanding the design space by accommodating wavelength-division multiplexing (WDM), semiconductor optical amplifier (SOA), diverse topologies, and the associated router architectures, the computational complexity becomes excessive. Therefore, in this study, we propose an extended worst-case OSNR search algorithm (EWOSA) that significantly reduces the computational burden through a preprocessing algorithm that reduces the number of candidate paths when the combined effect of the insertion loss and crosstalk on the OSNR is considered. Simulation results demonstrate that the EWOSA can identify approximately 0.18 dB lower worst-case OSNR than the existing formal worst-case analysis method in $8\times 8$ mesh-based ONoC, and this improvement in OSNR accuracy becomes more apparent (up to 4.81 dB) when SOAs are deployed in ONoCs. Furthermore, the EWOSA can be used for OSNR optimization, owing to its rapid analysis speed and generality.
Provisioning behaviour of macaroni penguins Eudyptes chrysolophus

Understanding how parental expenditure reflects food availability and influences reproductive output is a key part of studies of breeding performance. Provisioning behaviour is an important aspect of parental expenditure. We show that Macaroni Penguins Eudyptes chrysolophus have clear sex-specific differences in provisioning behaviour. Females provision chicks throughout rearing and at higher rates than males, which only participate in the later stages. Female provisioning is consistent throughout chick-rearing and appears to relate to a threshold rate governing whether or not chicks survive. The additional expenditure by males (but not females) during the creche period influenced chick growth and fledging mass of survivors. We suggest a very simple model to account for these sex-specific differences and effects. Interannual variation in parental expenditure resulted in differences in reproductive output between years. Years of lowest expenditure resulted in lowest growth rate of chicks. Sex-specific differences in provisioning were similar among years, however, with a consistent proportion of expenditure by males.
LoRaWAN Class B Multicast Scalability

LoRaWAN has emerged as a popular IoT communications technology. It comes with three classes of operation: A, B, and C. Although many IoT use-cases, like Firmware-over-the-Air updates, require multicast, Class A cannot be used for that purpose. Class C can, but consumes a lot of energy. This leaves Class B. In this paper, we investigate Class B multicast and its scalability properties. Issues like multicast member capacity, beacon blocking, and beacon collisions are highlighted, and several approaches to mitigate them are proposed: (1) “Ping-Slot Relaying,” to allow for more multicast members, (2) a scheduling approach indicating when to best send multicast packets, and (3) “Dynamic Region Formation” to coordinate the sending of beacons over multiple gateways. The proposed solutions do not require any modifications to the LoRaWAN protocol.
Reversible Image Secret Sharing

In reversible image secret sharing (RISS), the cover image can be recovered to some degree, and a share can be comprehensible rather than noise-like. Reversible cover images play an important role in law enforcement and medical diagnosis. The comprehensible share can not only reduce the suspicion of attackers but also improve the management efficiency of shares. In this paper, we first provide a formal definition of RISS. Then, we propose an RISS algorithm for a $(k,n)$ -threshold based on the principle of the Chinese remainder theorem-based ISS (CRTISS). In the proposed RISS, the secret image is losslessly decoded by a modular operation, and the original cover image is recovered by a binarization operation, both of which are just simple operations. Theoretical analyses and experiments are provided to validate the proposed definition and algorithm.
A Lightweight Network-based Android Malware Detection System

Over the last years, mobile devices became target of thousands of malicious applications. Since then, several works have proposed and evaluated highly accurate machine-learning malware detection schemes. However, these schemes are hardly used in production, either because of their resource-intensive nature for deployment in mobile devices or due to high false alarm rates. This paper proposes a lightweight malware detection system by means of network behavior analysis. Our system relies on lightweight machine-learning techniques to monitor network behavior of suspicious applications. To evaluate our proposal, we construct a realistic and up-to-date network traffic dataset made of 359 goodware and malware applications. The evaluation results show that our proposal is able to detect new malware variants with accuracy near 90% and false-positive rates below 3% using only 14 features inferred directly from the TCP/IP packet header. In addition, when deployed in a Samsung Galaxy S9 +, our technique consumes on average less than 5% of CPU, even in network peaks of 90 Mb/s.
Analysing Android App Privacy Using Classification Algorithm

The interface permits the client to scan for a subjective utility on the Play Store; the authorizations posting and the protection arrangement are then routinely recovered, on all events imaginable. The client has then the capability of choosing an interesting authorization, and a posting of pertinent sentences are separated with the guide of the privateer's inclusion and introduced to them, alongside a right depiction of the consent itself. Such an interface allows the client to rapidly assess the security-related dangers of an Android application, by utilizing featuring the pertinent segments of the privateer's inclusion and by introducing helpful data about shrewd authorizations. A novel procedure is proposed for the assessment of privateer's protection approaches with regards to Android applications. The gadget actualized widely facilitates the way toward understanding the security ramifications of placing in 1/3 birthday celebration applications and it has just been checked in a situation to feature troubling examples of uses. The gadget is created in light of expandability, and correspondingly inclines in the strategy can without trouble be worked in to broaden the unwavering quality and adequacy. Likewise, if your application handles non-open or delicate individual information, it would be ideal if you also allude to the extra necessities in the “Individual and Sensitive Information” territory underneath. These Google Play necessities are notwithstanding any prerequisites endorsed by method for material security or data assurance laws. It has been proposed that, an individual who needs to perform the establishment and utilize any 1/3 festival application doesn't perceive the significance and which methods for the consents mentioned by method for an application, and along these lines sincerely gives all the authorizations as a final product of which unsafe applications furthermore get set up and work their malevolent leisure activity in the rear of the scene.
A Reinforcement Learning-Based Routing Protocol for Clustered EV-VANET

Vehicular Ad hoc Network for electric vehicles (EV-VANET) is an information and communication network composed of electric vehicles (EVs), charging stations and power grid. Due to the mobility and sparse distribution of electric vehicles, there exist many problems such as unstable link connection and routing void, so the reliability of communication between nodes cannot be guaranteed. In this paper, we propose a reinforcement learning-based routing protocol for clustered EV-V ANET (RLRC), where the network is divided into some clusters by employing the improved K-Harmonic Means (KHM) algorithm to improve the stability of cluster structure. RLRC uses reinforcement learning (RL) to calculate the Q-Value and evaluate the future reward of a decision, in which the available bandwidth and relative EVs movement are taken into account to improve the reliability and efficiency of the route. The simulation results show the effectiveness of the proposed protocol.
IMPLICATIONS OF TREE MICROHABITAT PREFERENCES OF COMPSILURA CONCINNATA (DIPTERA: TACHINIDAE) FOR ITS EFFECTIVENESS AS A GYPSY MOTH PARASITOID

Abstract Tethered gypsy moth larvae, Lymantria dispar (L.) (Lepidoptera: Lymantriidae), placed in various tree microhabitats were most heavily parasitized by Compsilura concinnata (Meigen) (Diptera: Tachinidae) near the ground especially on leaves. In laboratory experiments, hosts were parasitized primarily between 6:00 AM and 6:00 PM (E.S.T.), with little parasitism at night. These data suggest that in the field, this tachinid will most efficiently attack young gypsy moth caterpillars, as instars 1 to 3 are the only ones consistently on or near foliage during daylight. C. concinnata females successfully parasitized young hosts, but larval development on 2nd instars was twice as long as that on 4th instars. Thus, only one generation of parasitoids on gypsy moths is likely per year. Results indicate that methods for improving the effectiveness of this parasitoid should emphasize increasing adult numbers early in the season. Résumé L'incidence la plus élevée du parasitisme de Compsilura concinnata (Meigen) (Diptera : Tachinidae) sur des chenilles de la spongieuse, Lymantria dispar (L.) (Lepidoptera : Lymantriidae) attachées et placées dans divers microhabitats sur des arbres, a été observée près du sol spécialement sur les feuilles. Lors d'expériences en laboratoire, des hôtes furent parasités surtout entre 0600 h et 1800 h (HNE), peu de parasitisme ayant été observé la nuit. Ces données indiquent que sur le terrain, l'attaque de cette tachinide aura le plus d'impact sur les jeunes chenilles, car les stades 1 à 3 sont les seuls à se trouver régulièrement sur ou près du feuillage durant le jour. Les femelles de C. concinnata ont réussi à parasiter de jeunes hôtes, mais le développement larvaire sur des stades deux s'est avéré 2 fois plus long que sur des stades quatre. Ainsi, il ne devrait y avoir qu'une génération du parasitoïde par an. Les résultats indiquent que les tentatives d'améliorer l'efficacité de ce parasitoïde devraient être concentrées sur l'augmentation du nombre d'adultes tôt en saison.
Rectangular Microstrip Patch Antenna Miniaturization using improvised Genetic Algorithm

This paper presents the antenna miniaturization at 2.4 GHz (desired frequency) using genetic algorithm. The microstrip patch antenna resonating at higher frequency is designed conventionally and the structure of the rectangular patch is modified through the algorithm in order to shift the resonant frequency while keeping the physical volume of the antenna constant. Genetic Algorithm has been used to shift the resonance frequency, but the resultant frequency is always random. Here the resultant frequency (2.4 GHz) is fixed with the help of a proper fitness function. Genetic algorithm was applied on patch surface of conventional 3 GHz and 4 GHz antennas and the rate of miniaturization is 24.4% and 45.8% respectively.