Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Malware, Malicious URLs and Virus Using Machine Learning and Signature Matching

Nowadays most of our data is stored on an electronic device. The risk of that device getting infected by Viruses, Malware, Worms, Trojan, Ransomware, or any unwanted invader has increased a lot these days. This is mainly because of easy access to the internet. Viruses and malware have evolved over time so identification of these files has become difficult. Not only by viruses and malware your device can be attacked by a click on forged URLs. Our proposed solution for this problem uses machine learning techniques and signature matching techniques. The main aim of our solution is to identify the malicious programs/URLs and act upon them. The core idea in identifying the malware is selecting the key features from the Portable Executable file headers using these features we trained a random forest model. This RF model will be used for scanning a file and determining if that file is malicious or not. For identification of the virus, we are using the signature matching technique which is used to match the MD5 hash of the file with the virus signature database containing the MD5 hash of the identified viruses and their families. To distinguish between benign and illegitimate URLs there is a logistic regression model used. The regression model uses a tokenizer for feature extraction from the URL that is to be classified. The tokenizer separates all the domains, sub-domains and separates the URLs on every ‘/’. Then a TfidfVectorizer (Term Frequency – Inverse Document Frequency) is used to convert the text into a weighted value. These values are used to predict if the URL is safe to visit or not. On the integration of all three modules, the final application will provide full system protection against malicious software.
A Privacy-Enhanced and Personalized Safe Route Planner with Crowdsourced Data and Computation

We introduce a novel safe route planning problem and develop an efficient solution to ensure the travelers’ safety on roads. Though few research attempts have been made in this regard, all of them assume that people share their sensitive travel experiences with a centralized entity for finding the safest routes, which is not ideal in practice for privacy reasons. Furthermore, existing works formulate the safe route planning query in ways that do not meet a traveler’s need for safe travel on roads. Our approach finds the safest routes within a user-specified distance threshold based on the personalized travel experience of the knowledgeable crowd without involving any centralized computation. We develop a privacy preserving model to quantify the travel experience of a user into personalized safety scores. Our algorithms for finding the safest route further enhance user privacy by minimizing the exposure of personalized safety scores with others. We implement a working prototype of our solution on the Android platform. Extensive experiments using real datasets show that our approach finds the safest route in seconds with 50% less exposure of personalized safety scores.
An IoT mutual authentication scheme based on PUF and blockchain

Security is one of the major challenges of the Internet of Things (IoT). In an IoT network, data are processed and exchanged without human intervention. Because of this autonomy, objects must authenticate each other as well as to ensure the integrity of the exchanged data. An efficient authentication scheme allows to protect the network against several attacks. Several IoT authentication schemes have already been proposed but they are mostly ineffective and sometimes have limitations. This work proposes a new mutual authentication scheme for IoT based on digital signatures, the Physical Unclonable Functions (PUF) and the blockchain technology. The Global Assessment and analyses show that our new protocol gives more resistance to different types of attacks, and that it also provides a better performance in terms of computing load although it requires fewer storage resources.
DeepSight: Land Use and Land Cover Classification Using Satellite Images

Remote Sensing data is constantly on the rise with launches of various satellites around the world, generating a huge amount of data. This data is raw i.e. it lacks semantics. Due to the lack of semantics, this data is untapped. To fully utilize this data, we propose a classification method based on deep learning, deployed as a web service- DeepSight. DeepSight uses a convolutional neural network- SpectrumNet to effectively classify land use and land cover. After training the model over 27,000 images, an accuracy of 96.3% is achieved for the testing set whereas the validation set gives us an accuracy of 95.1%. Thus, the results show a fairly high accuracy rate of classifying the multi-spectral images and this can be further used by multiple domains requiring Remote Sensing data semantics relating to land use and land cover.
Improving the Relevance of a Web Navigation Recommender System Using Categorization of Users' Experience

We propose a method for a recommender system for generating web-navigation suggestions. The purpose of this system is to assist its users by providing them suggestions for possible desired next steps whenever they get stuck in using any software. We are able to achieve this goal by leveraging the principal of “crowd-sourcing”. Specifically, we leverage the crowd's knowledge under the assumption that there are cohesive groups of experienced and novice users. Therefore, we present an algorithm that measures the right heuristics in order to classify users by their experience, and then relates these users with association rules of web-navigation derived from frequent patterns mining. In this paper we introduce our method, compare it with other current solutions in the field, outline the proposed algorithm, and present an experiment which serves as our proof-of-concept.
Overview of Application Layer Protocol of Internet of Things

The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. Besides, IoT has now a wide range of life applications such as industry, transportation, logistics, healthcare, smart environment, as well as personal, social gaming robot, and city information. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper will give an overview of the current popular Internet of Things application layer protocols, including CoAP, MQTT, XMPP, DDS and AMQP. In addition, it will compare between commonly IoT communication protocols, with an emphasis on the main features and behaviors of various metrics of power security coding method transport layer protocol and other features. This comparison aims at presenting guidelines for the researchers to be able to select the right protocol for different applications.
Analysis of Novel Dual-PM Vernier Machines

In this paper, a novel dual-PM vernier machine (dual-PM VM) is proposed. Stator PMs are magnetized tangentially and set between the stator teeth, while a consequent-pole PM rotor is equipped with radially-magnetized PMs. With the dual-PM topology, the main magnetic field can be increased by forcing the flux-leakage across the stator slots into the stator teeth. Based on the global optimizations, it is found that compared with an existing single-PM inset-type flux-reversal machine (single-PM IFRM), the proposed dual-PM VM can produce 18.1% larger torque with an optimal PM volume and 10.7% larger torque with the same PM volume, respectively. To further improve the torque density, the influences of stator geometric features such as unequal tooth, iron bridge, and slot-opening are studied. It is found that dual-PM VM with unequal teeth, no iron bridge, and no slot-opening can exhibit the largest torque density within the same geometric restrictions.
CrowdRL: An End-to-End Reinforcement Learning Framework for Data Labelling

Data labelling is very important in many database and machine learning applications. Traditional methods rely on humans (workers or experts) to acquire labels. However, the human cost is rather expensive for a large dataset. Active learning based methods only label a small set of data with large uncertainty, train a model on these labelled data, and use the trained model to label the remainder unlabelled data. However they have two limitations. First, they cannot judiciously select appropriate data (task selection) and assign the tasks to proper humans (task assignment). Moreover, they independently process task selection and task assignment, which cannot capture the correlation between them. Second, they simply infer the truth of a task based on the answers from humans and the trained model (truth inference) by independently modeling humans and models. In other words, they ignore the correlation between them (the labelled data may have noise caused by humans with biases, and the model trained by the noisy labels may bring additional biases), and thus lead to poor inference results.To address these limitations, in this paper, we propose CrowdRL, an end-to-end reinforcement learning (RL) based framework for data labelling. To the best of our knowledge, CrowdRL is the first RL framework designed for the data labelling workflow by seamlessly integrating task selection, task assignment and truth inference together. CrowdRL fully utilizes the power of heterogeneous annotators (experts and crowdsourcing workers) and machine learning models together to infer the truth, which highly improves the quality of data labelling. CrowdRL uses RL to model task assignment and task selection, and designs an agent to judiciously assign tasks to appropriate workers. CrowdRL jointly models the answers of workers, experts and models, and designs a joint inference model to infer the truths. Experimental results on real datasets show that CrowdRL outperforms state-of-the-art approaches with the same (even fewer) monetary cost while achieving 5%-20% higher accuracy.
Design and Simulation of SOA based Optical NAND gate for photonics FPGA

The requirement of larger bandwidths is increasing in the field of telecommunication so all optical processing is expected to be as the right solution in the future. In all the present optical circuits semiconductor optical amplifier (SOA) is the main component. The optical devices have many applications such as wavelength converters, bit comparators, optical logic gates and several optical digital signal processing. Many distinct combinational circuits are implemented using universal gate that is Optical NAND without requiring other gates. The photonics based digital devices resolves the need for speed and low power in a Very Large Scale Integration (VLSI) designs. Optical NAND gate is a basic element in the photonic FPGA. In this dissertation work, a concept of developing the optical NAND logic device has been explained. Two different wavelengths of light with same or different values of amplitude are used in the optical NAND gate design. Nonlinear characteristic of semiconductor optical amplifier (SOA) that is four wave mixing (FWM) is utilized to design and simulate SOA based optical NAND gate. The parameters of semiconductor optical amplifier and Band pass filter are also analyzed to find high output power.
Assessment of Lng Fire Scenarios on Board of Lng-fuelled Ships

Decarbonization represent one of the main challenges of the maritime transport sector for the near future. As recent international environmental regulations have set more stringent emission limits, the use of liquefied natural gas (LNG) as alternative ship fuel has proven to be a viable and less-polluting solution, compared to conventional oil-based fuels. However, LNG is a highly flammable substance and safety aspects need to be assessed thoroughly, especially for its use on board passenger ships.Two different gas engine concepts are typically used for ship propulsion: Low-Pressure Dual Fuel (LPDF) and High-Pressure Dual Fuel (HPDF) engines. Regardless of the gas engine technology, the fuel gas supply system process equipment is located inside a specific enclosed space, the fuel preparation room (FPR), that can be sited below deck. Given this background, this study aims to investigate the consequences of LNG pool fires occurring inside a confined space, assessing the influence of different operating conditions. Credible loss of containment events were identified to define the characteristics of LNG pools. Furthermore, LNG pool fires were simulated using the fire dynamic simulator (FDS) to estimate the radiation heat flux received by the process equipment installed inside the FPR and to assess the possibility of experiencing accident escalation on board. To evaluate the effect of forced mechanical ventilation of the FPR, two different cases were modelled: one assuming the standard functioning of the ventilation system, while the other one considered a halted air supply inside the FPR with a working exhaust system only. The outcomes of this study provide useful data for the consequence estimation of small-scale LNG pool fires occurring inside enclosed spaces, also addressing the possibility of accident escalation on board LNG-fuelled ships.
An Analysis and Modeling of the Class-E Inverter for ZVS/ZVDS at Any Duty Ratio with High Input Ripple Current

This paper presents an analysis and modeling of the class-E inverter for ZVS/ZVDS execution at any duty ratio. The methodology is to determine the input current to the inverter analytically under the assumption that it always remains positive. The latter is ensured by proper selection of the input inductance such that the inverter always operates either in (1) the border condition mode or in (2) the continuous conduction mode regardless of the input ripple. Using this input current and applying the boundary conditions, the required input capacitance for the ZVS/ZVDS execution is determined at a specified input/output voltage, output power and load. The analysis shows that the ZVS/ZVDS can be achieved while the input capacitance is selected appropriately. A comparison between the analytical and simulation results is also formulated involving the proposed and other existing models. The simulation results that are provided at different duty ratios demonstrate that they are in a better agreement with the proposed analytical model regardless of the input inductance and the state of input ripple current. The analytical modeling is facilitated by using MAPLE®.
Antioxidant Activities of Eggplant (Solanum melongena) Powder with Different Drying Methods and Addition Levels to Pork Sausages

Abstract The objective of this study was to evaluate antioxidant activities of eggplant (EP) powder with different drying methods and addition levels to pork sausages to improve product quality. Antioxidant activities of EP with different drying methods, particle sizes, and solvents of extraction were determined. Freeze dried (FD) EP extracted with 100% ethanol had higher 2,2-diphenyl-1picrylhydrazyl radical-scavenging activities (DPPH-RSA) and total phenolic content (TPC) values than other drying methods. FD500 had the highest iron chelating ability (ICA) value. Oven-dried (OD) EP at 60°C had the highest reducing power. Dried EP was added to sausages of six groups: control without EP, reference added with ascorbic acid, O1 and O2 added with 0.25% and 0.5% OD EP, respectively, and F1 and F2 added with 0.25% and 0.5% FD EP, respectively. Pork sausages added with O2 had the lowest TBARS and TPC values. These values increased during storage. Purge loss (%), lightness (L*), and redness (a*) values of F2 were lower than those of other groups, whereas sausages containing F2 had the highest yellowness (b*). pH values of sausages added with EP were increased regardless of the level of EP added. Hardness values of F2 were higher. However, there were no significant differences in other textural characteristics. Sausages added with EP had higher moisture and protein contents (%), but lower fat contents (%). These results indicate that EP powder could be used to retard lipid oxidation and inhibit microbial counts during storage time.
ARANGE Deliverable D2.1 Improved and tested forest models for case study regions

Forest models are valuable tools that permit us to simulate forest dynamics at high accuracy and detail at different spatio-temporal scales. This is important to evaluate the spatial interdependencies of ecosystem goods and services. A wide range of state-of-the-art models of forest dynamics have been developed by the modeling partners of the ARANGE consortium. The set of forest models available in ARANGE includes both stand-level and landscape-level approaches, i.e. PICUS [BOKU]; ForClim and LandClim [ETHZ]; SAMSARA/CAPSIS [IRSTEA]; SILVA-SI/WEKA [UL]; Heureka [SLU]; SIBYLA [NFC]; MOSES [BOKU] and BIOME-BGC [BOKU]. In the first project year, model development and adaptation of the models to the ARANGE Case Study Areas (CSAs) has taken place; some models were applied preliminarily in several ARANGE CSAs, while others were tested mainly in one CSA. Most of the models were improved in terms of their structure and behavior with respect to a range of features. Overall, the forest models available in ARANGE have been tested for a wide range of conditions in the CSAs, and they have been set up so as to be ready for model application in the CSAs under specific site conditions, using measured stand data for model initialization. The next steps will be to define the management regimes so as to be able to simulate realistic forest structure data. <D2.1 Improved and testes models for case study regions> www.arange-project.eu 3 TABLE OF CONTENTS
Synergistic Effect of High Intensity Ultrasound, Antimicrobial Compounds, and UV-A Light on Microbial Quality of Dairy Products

Synergistic Effect of High Intensity Ultrasound, Antimicrobial Compounds, and UV-A Light on Microbial Quality of Dairy Products By Bryce Rodney Hales, Master of Science Utah State University, 2021 Major Professor: Dr. Luis J. Bastarrachea Department: Nutrition, Dietetics, and Food Sciences The food industry and many researchers have been focusing on nonthermal methods of food preservation due to their ability to preserve food quality and decrease the amount of energy required to treat food products. Some of the nonthermal methods studied include UV light, high intensity ultrasound (HIU), and the addition of natural, FDA approved, antimicrobial compounds. In this study, UHT milk, inoculated with Escherichia coli K12 or Listeria innocua L2, was exposed to a sequential treatment of HIU, UV-A light, and the addition of either Nisin or ε-poly(lysine). Storage studies at either 4 °C or 25 °C for 24 h were performed on all statistically significant (P < 0.05) treatments. The sequential treatments proved to be effective providing 1 ‒ 4 log (CFU/mL) reductions using a total of 111 ± 2 kJ kg, which is ~ 38% less energy than standard, thermal pasteurization. The storage studies had varying results, with some treatments showing an increase in the microbial load while others continued to decline or had no detectable colonies. Changes in color and pH were also measured, with statistically significant changes (P < 0.05) in color and significant changes in pH for the treatment combination that had ε-poly(lysine).
Revisiting the Design of LSM-tree Based OLTP Storage Engine with Persistent Memory

The recent byte-addressable and large-capacity commercialized persistent memory (PM) is promising to drive database as a service (DBaaS) into unchartered territories. This paper investigates how to leverage PMs to revisit the conventional LSM-tree based OLTP storage engines designed for DRAM-SSD hierarchy for DBaaS instances. Specifically we (1) propose a light-weight PM allocator named Hal-loc customized for LSM-tree, (2) build a high-performance Semi-persistent Memtable utilizing the persistent in-memory writes of PM, (3) design a concurrent commit algorithm named Reorder Ring to aschieve log-free transaction processing for OLTP workloads and (4) present a Global Index as the new globally sorted persistent level with non-blocking in-memory compaction. The design of Reorder Ring and Semi-persistent Memtable achieves fast writes without synchronized logging overheads and achieves near instant recovery time. Moreover, the design of Semi-persistent Memtable and Global Index with in-memory compaction enables the byte-addressable persistent levels in PM, which significantly reduces the read and write amplification as well as the background compaction overheads. The overall evaluation shows that the performance of our proposal over PM-SSD hierarchy outperforms the baseline by up to 3.8x in YCSB benchmark and by 2x in TPC-C benchmark.
Effect of Technological Innovations on Customer Loyalty among Commercial Banks in Eldoret Town

: The purpose of the study was investigate the effect of technological innovations on customer loyalty among commercial banks in Eldoret town, Uasin Gishu County. The study objectives were to determine the effect of mobile banking, online banking, branch networking and Electronic Fund Transfer at point of sale on customer loyalty among commercial banks. The study was guided by Task Technology Fit (TTF) Theory. The research design adopted a descriptive survey design. The study was conducted on commercial banks within Eldoret town, Uasin Gishu County. of 483000 customers and 10 managers were captured where a sample size of 10 managers and 225 customers was drawn from the population where stratified, convenient and purposive sampling techniques were applied. The researcher used questionnaire and interview schedules as data collection instruments. The data collected in the study was analyzed by the use of descriptive statistics and inferential statistics. This includes the use of descriptive statistical methods to analyze data consisting of frequency, mean and standard deviation. The relationship between variables was done using multiple linear regression models. Tables were used to present the results. Based on the findings of the study, the study recommended among others commercial banks should regularly conduct system checks and environmental analysis ,they should embrace innovation towards the improvement of online banking services which ensures customer convenience and at the same time improving product quality, better service delivery, process improvement, efficient customer management practices which translates to effective customer satisfaction.Further they should widen their branch networking and improve the usage of financial innovations to maximize their gains and in the process improve their customer loyalty. The study suggests that that more studies should be carried on effects of branch networking on customer loyalty to develop more insight on consumers in order to help commercial banks put these factors into consideration as they develop products and also widen their branch network which rely on the internet as a key delivery platform