Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilizing Cyber Threat Hunting Techniques to Find Ransomware Attacks: A Survey of the State of the Art

Ransomware is one of the most harmful types of cyber attacks that cause major concerns on a global scale. It makes the victims’ resources unusable by encrypting data or locking systems to extort ransom payments. Ransomware has variant families that continue to evolve. Moreover. cybercriminals use advanced techniques to develop ransomware, making it harder for anti-malware detection systems to detect them. Ransomware solutions need the capabilities of timely and effective detection and response to discover uncommon behavior before losing sensitive data. Cyber threat hunting (CTH) is a novel proactive malware detection approach that includes cyber threat intelligence (CTI) methods and data analysis methods. However, most present CTH solutions depend on internal data sources and reactive techniques to detect unusual activities. An effective CTI technique is required to obtain knowledge from external data sources and combine it with internal sources to enhance the hunting capabilities. Then, using the optimal data analysis technique is needed for the CTH approach to obtain valuable insights into abnormal patterns in running activities in the early stages. In this study, we investigate using a practical CTI approach and different CTH models. Subsequently, we discussed ransomware research directions to detect known and unknown ransomware attacks. Also, we discussed the available ransomware datasets used in present ransomware studies.
Nonlinear ordinary and partial differential equations on unbounded domains

Solutions are shown to exist for a variety of differential equations. Both ordinary and partial differential equations are considered, with specified initial conditions, boundary conditions, or simultaneous initial and boundary conditions. A key feature of the these problems is a condition at infinity; it is demanded that solutions decay towards zero as the temporal variable becomes arbitrarily large. This feature removes from the problem a certain compactness property, which precludes the use of traditional methods which employ the Leray-Schauder topological degree. This difficulty is overcome by use of a much newer theory of topological degree, developed by Fitzpatrick, Pejsachowicz and Rabier in 1992, and later developed further by Pejsachowicz and Rabier in 1998. This degree theory requires several properties in lieu of compactness. It is shown that these properties are available in a wide range of problems, and that there is a practical way to verify this fact in specific cases. Specific examples are given.
Bivariate splits and consistent split criteria in dichotomous classification trees

We extend the recursive partitioning approach to classifier learning to use more complex splits at each decision node. To do this a new split criterion is derived. Efficient algorithms for finding optimal splits under the new criterion for optimal linear, rectangular, and corner splits are presented. These allow decision trees to model non-orthogonal structure and result in decision trees that often perform better in terms of reclassification accuracy than traditional methods, as well as being significantly smaller. Second, we discuss the lack of consistency of traditional split criteria such as entropy and Gini. These criteria do no optimize accuracy, whereas predictive accuracy is the most important metric by which trees are measured and is the only criterion which leads to consistent estimates of thresholds. A modification to the standard tree growing algorithm that ensures consistent trees is proposed and is shown to give smaller trees with better performance on a number of datasets. We show that traditional split criteria might miss the optimal consistent split by large amounts and under certain conditions can generate trees of unbounded size.
A Hybrid Linguistic and Knowledge-Based Analysis Approach for Fake News Detection on Social Media

The rapid development of different social media and content-sharing platforms has been largely exploited to spread misinformation and fake news that make people believing in harmful stories, which allow to influence public opinion, and could cause panic and chaos among population. Thus, fake news detection has become an important research topic, aiming at flagging a specific content as fake or legitimate. The fake news detection solutions can be divided into three main categories: content-based, social context-based, and knowledge-based approaches. In this paper, we propose a novel hybrid fake news detection system that combines linguistic and knowledge-based approaches and inherits their advantages, by employing two different sets of features: (1) linguistic features (i.e., title, number of words, reading ease, lexical diversity,and sentiment), and (2) a novel set of knowledge-based features, called fact-verification features that comprise three types of information namely, (i) reputation of the website where the news is published, (ii) coverage, i.e., number of sources that published the news, and (iii) fact-check, i.e., opinion of well-known fact-checking websites about the news, i.e., true or false. The proposed system only employs eight features, which is less than most of the state-of-the-art approaches. Also, the evaluation results on a fake news dataset show that the proposed system employing both types of features can reach an accuracy of 94.4%, which is better compared to that obtained from separately employing linguistic features (i.e., accuracy=89.4% ) and fact-verification features (i.e., accuracy=81.2%).
Poor governance and massive unemployment in Nigeria: as causes of brain drain in the Buhari administration (2015-2020)

Like other African countries of the developing world, Nigeria underperforms due to a lack of state capacity to deal with the present-day complexities of governance, thereby leading to poor governance, massive unemployment, and brain drain in the Buhari administration. Poor governance is the absence of good governance. Close observation of governance in Nigeria indicates a poor attempt at democratic consideration and unstable politics thereby leading to a battered economy, impoverishment of citizens, corruption, mismanagement of public funds, infrastructure decay leading to massive unemployment which results in human capital flight, a barrier to Nigeria’s quest for consolidating democracy, As a result of visionless leaders, selfish, nepotic, mediocrates, tribalistic and opportunist in the political space of the country. This paper examines the electionary campaign promises of the Buhari administration on security for all citizens, bumper economy, and anti-corruption, with the analysis of secondary data, it examines the relationship between poor governance, massive unemployment, and brain drain in Nigeria from 2015 to 2020, adopting the relative deprivation of frustration-aggression theory to x-ray the concepts of poor governance and massive unemployment, resulting to human capital flight. This study has identified poor governance and massive unemployment leading to brain drain in this administration and made valuable recommendations.
Elastic waves in anisotropic media

We shall consider some basic topics in wave propagation in anisotropic elastic solids. We rst treat the theory of plane waves in uniform media, which introduces several fundamental concepts such as pulse shape, polarization, slowness, and the slowness surface S. With these concepts at our disposal we consider brie y the interaction of plane waves with plane interfaces in uniform media and then go on to develop the fundamental solution, or Green's tensor, for a uniform whole space. We nd that the wave front set is a conical hypersurface in space-time whose intersection with the hyperplane t = 1is a surface reciprocal to S, the group velocity surface. Finally we develop ray theory for a smoothly varying inhomogeneous medium using for variety the equations of motion in the rst-order symmetric hyperbolic form. En route we discuss energy ux, energy density, and energy conservation, and show that the ray velocity is the same as the group velocity with which the energy ows. In general this is not normal to the wavefronts. The amplitude of the leading term in the ray series varies in such a way as to conserve energy ux along narrow tubes of rays. We illustrate some geometrical entities graphically for a cubic medium and for a transversely isotropic medium.
Caring for people with dementia in their own homes: homecare workers' experiences of tolerating and mitigating risk

Little is known about risk management in homecare for people with dementia. We aimed to gain an understanding of the ways in which homecare workers assess and manage risk whilst caring for people with dementia in their own homes. We conducted a qualitative interview study with 17 homecare workers assisting people with dementia with their personal care. Interviews were face-to-face, semi-structured, recorded and transcribed verbatim. Analysis was inductive and thematic. A key theme of risk was identified, with three main sources: the client as a source of risk to the homecare worker, the clients' home and behaviours as a risk to the client, and the wider health and social care system as a risk to both clients and homecare workers. Three interrelated aspects of risk were found to influence homecare workers' decision making and actions: homecare workers' perception of the level of risk, their perceived ability to control the risk and their tolerability of risk. The higher the perceived risk, the stronger the action taken by the worker or agency to mitigate it and the greater the impact on the client. To support effective development of this workforce there is a need to devise training that incorporates the use of tacit knowledge and experiential learning. Risk management policies for homecare should acknowledge and utilise the expertise, experiences and values of homecare workers.
Learning Propositional Logic From Scratch

We present a computational model for developing intelligent agents that are able to reason in multiple symbolic domains. The agents have deductive and inductive reasoning abilities. The deductive mechanism is based on a simple cognitive model with bounded cognitive resources. The main learning mechanism is a formalization of Occam’s razor. Agents constructed in our model can learn generalized knowledge from concrete examples. For example, an agent can learn elementary arithmetic and propositional logic, and then compute correct answers to previously unseen questions such as “what is 27*9? ” and “is P∨(P→Q) a tautology? ”. We illustrate the learning process in the case of propositional logic, where an agent first learns the syntax and then some basic logical laws. The performance of this agent beats average human scores in terms of accuracy when tested on tautologies used in an experiment reported in [16].
Design of Remote Real-Time Monitoring and Control Management System for Smart Home Equipment Based on Wireless Multihop Sensor Network

With the intensification of the pace of modern urban life and the increase of work pressure, people have higher requirements for the safety and comfort of home. However, the rapid development of Internet of Things technology, wireless communication technology, embedded technology, and data fusion technology has made this desire possible, thus making intelligent and safe. Using wireless sensor network, intelligent home system can realize the real-time collection of object location and state information, remote operation of home equipment, as well as the monitoring of the information of personnel activities, etc. However, how to efficiently analyze and integrate these information to be applied to the intelligent home system is one of the problems that need to be solved; further study of the smart home system of personal behavior identifies fire monitoring and remote monitoring of mining applications. Such information collection and integration of technology and method are given based on wireless sensor network transfer of key technologies; intelligent home security monitoring system based on Bayesian networks is proposed and a card. According to the theoretical fire monitoring method, and based on the Bayesian belief network personal behavior identification model, this paper provides the theory and method for the remote safety monitoring and control in the intelligent home environment. In this paper, based on the multisensor data fusion of smart home security, real-time monitoring alarm platform is built. Through the multisensor detection module for the home fire gas human infrared monitoring, the detected data is transmitted to the data fusion center for processing; processed results are uploaded to the cloud through the Internet. When abnormal conditions occur, users can log in to the mobile phone client to view abnormal alarm results.
PENERAPAN METODE YOLO OBJECT DETECTION V1 TERHADAP PROSES PENDETEKSIAN JENIS KENDARAAN DI PARKIRAN

Seiring dengan perkembangan transportasi maka jenis kendaraan semakin banyak dan di butuhkan suatu sistem yang dapat mengklasifikasi jenis kendaraan tersebut. Penelitian ini berfokus pada deteksi objek berupa jenis kendaraan. Tujuan penelitian ini mendeteksi jenis kendaraan di parkiran dengan menggunakan sistem pendeteksian untuk memberikan keakuratan dalam mengklasifikasi kendaraan sesuai dengan jenisnya seperti motor, mobil, truck dan bus. Sistem klasifikasi dalam penelitian ini menggunakan teknik pengolahan citra digital sebagai cara untuk menghasilkan suatu citra baru yang telah di proses dan diolah dengan komputer. Metode penelitian menggunakan Metode YOLO Object Detection yang memiliki model sangat sederhana dan kemampuan sangat cepat dalam mengidentifikasi objek dengan format jpg berjumlah 15 citra uji coba yang terdiri dari 4 jenis kendaraan yaitu mobil, motor, truck dan bus dan 15 citra uji coba tersebut akan dilatih untuk menghasilkan model baru citra pendeteksian. Hasil pengujian pendeteksian jenis kendaraan berdasarkan Metode YOLO Object Detection diperoleh presentase tingkat akurasi sebesar 98,667%, total akurasi jumlah kendaraan terdeteksi sebanyak 64 objek kendaraan dari total 66 objek kendaraan kendaraan yang terdapat pada 15 sampel , dimana ada 1 sampel hanya memberi akurasi 82 % ,dan rata-rata waktu komputasi untuk mendeteksi objek sebesar 3,067 detik.
Meteorological parameters study and temperature forecasting in selected stations in Sub-Sahara Africa using MERRA-2 data

The study and forecast of climatic phenomena have progressed over time, but the huge knowledge with information gathered has aided in comprehending and anticipating weather changes. The goal of this research was to look at some specific meteorological characteristics and forecast air temperature over a number of stations in Osun State, Nigeria. Monthly rainfall, relative humidity, air temperature, air pressure, wind speed and direction for four stations were gathered from the HelioClim website archives and used in this study. For each variable, descriptive statistics were calculated. The normality of the data was determined using the Shapiro-Wilks test. The data was tested for stationarity using the Augmented Dickey Fuller (ADF) test, and different types of Autoregressive Integrated Moving Average (ARIMA) models were fitted. According to the statistics, the highest average temperature was observed in March (𝑇=23.90𝐶). The months of June, July, August, and September had the highest mean relative humidity (𝑅𝐻=88.4%), pressure (𝑃=987ℎ𝑃𝑎), wind speed (𝑊𝑆=1.8𝑚/𝑠), wind direction (𝑊𝐷=225.10), and rainfall (𝑅𝐹=413𝑚𝑚). In January, the coefficient of variation (COV) for temperature was larger than in other months. In June, January, and December, relative humidity (RH), air pressure, rainfall, and wind speed and direction were all higher. The air temperature stationarity was tested using the Augmented Dickey Fuller (ADF) test, which revealed (p>0.05) but became stationary after the first difference (p<0.05). The temperature effect follows the same trend in ARIMA (1, 1, 1) forecasts for 2021 and 2022. As a result, air temperature modeling and forecasting are difficult tasks for any monthly time series. It is advised that all-time series models for any investigated location be considered, as well as meteorological conditions, in order to select the most appropriate model.
Competitive Swarm Improved Invasive Weed Optimization-Based Secret Sharing Scheme for Visual Cryptography

Abstract Visual cryptography is a cryptographic method that encrypts visual facts and the information decrypted appears as an original visual image. Here, the confidential data is shared by dividing the visual details into multiple shares such that the encryption and the decryption are performed for reconstructing the original image back by stacking the splitted shares. However, the data sharing process lacks adequate security protection. To overcome the issues, this paper presents a robust visual cryptography-based data sharing scheme, named Competitive Swarm Improved Invasive Weed Optimization (CSIIWO) for sharing multiple data. Initially, the grayscale input image is shared between several parties, such that the shared image is encrypted and decrypted for retrieving the original image back. However, the cryptographic key generation is done using the devised CSIIWO algorithm such that the cryptographic keys are generated for the data sharing process to maintain confidentiality among multiple parties. Moreover, the devised method achieved the maximum Peak signal-to-noise ratio (PSNR) of 40.749 dB, minimum conditional privacy loss of 0.508, and maximum entropy of 7.987, respectively using Lena image. Furthermore, the proposed scheme can protect the multiple data sharing information and has numerous advantages compared with the previous mechanisms.
Considering the role of social media: #BlackLivesMatter as a pedagogical intervention to decolonise curriculum

The COVID-19 pandemic resulted in a drastic transformation to schooling for students throughout the world. During this period, a number of issues arose in our local, national and global communities, including the death of George Floyd and subsequent protests and rallies organised by #BlackLivesMatter. Living through and witnessing many social issues, coupled with the new and enduring pandemic, furthered our understandings of how young people were engaging with these topics without the structures of schools to support them. This article presents the results of a case study where youth aged 15–17 years shared their experiences and understandings about many social justice issues they were observing. The most significant learning around these issues for youth occurred informally through social media as opposed to in the classroom, reinforcing that schools are not ethical spaces from which to challenge institutional, structural and systemic barriers to justice. As such, this article discusses the potential for formal education to be transformed into an ethical and decolonising space to learn about and challenge injustice.
Development of an algorithm to convert mammographic images to appear as if acquired with different technique factors

Abstract. Purpose: We set out a fully developed algorithm for adapting mammography images to appear as if acquired using different technique factors by changing the signal and noise within the images. The algorithm accounts for difference between the absorption by the object being imaged and the imaging system. Approach: Images were acquired using a Hologic Selenia Dimensions x-ray unit for the validation, of three thicknesses of polymethyl methacrylate (PMMA) blocks with or without different thicknesses of PMMA contrast objects acquired for a range of technique factors. One set of images was then adapted to appear the same as a target image acquired with a higher or lower tube voltage and/or a different anode/filter combination. The average linearized pixel value, normalized noise power spectra (NNPS), and standard deviation of the flat field images and the contrast-to-noise ratio (CNR) of the contrast object images were calculated for the simulated and target images. A simulation study tested the algorithm on images created using a voxel breast phantom at different technique factors and the images compared using local signal level, variance, and power spectra. Results: The average pixel value, NNPS, and standard deviation for the simulated and target images were found to be within 9%. The CNRs of the simulated and target images were found to be within 5% of each other. The differences between the target and simulated images of the voxel phantom were similar to those of the natural variability. Conclusions: We demonstrated that images can be successfully adapted to appear as if acquired using different technique factors. Using this conversion algorithm, it may be possible to examine the effect of tube voltage and anode/filter combination on cancer detection using clinical images.
Modified valence aware dictionary for sentiment reasoning classifier for detection and classification of Covid‐19 related rumors from social media data streams

The perpetual increase in social media data due to social distancing policy in practice and the low cost of communication has acquired an interest in rumor detection in social media. During the togetherness of the globalized world in the pandemic situation of Covid‐19, social media applications drive excellent value to the analysts and journalists working in different domains. However, the unmoderated nature of social media users often leads to the spread of rumors due to a variety of opinions on government decisions, which becomes notoriously hard to detect. Also, the users may deny rumors as soon as they are debunked. This research extracts diffused information such as word cloud, hashtags, re‐tweets and so forth and uses it as features. These features are applied in the sliding windows of data streams to detect tweets that may be rumors by validating using credible news sources. To address the challenge of veracity, tweets are classified into rumors and nonrumors. A modified unsupervised VADER sentiment classifier is proposed to further classify rumors tweets into amalgamated, unauthoritative, or exaggerated. It is revealed from the results that the proposed classifier is quite efficient in finding and classifying rumorous tweets that arise in critical situations.
A Learned Bloom Filter-Assisted Scheme for Packet Classification in Software-Defined Networking

Traditional routing technologies based on a single IP address domain faces the challenge to meet the increasing demand for network services and security. Packet classification is a technique to differentiate multi-domain network traffic in a fine-grained manner using packet header fields. Packet classification requires to operate efficiently to avoid it becoming a bottleneck in the packet routing process. Tuple space search (TSS) used in SDN supports fast rule updates but low-speed packet classification. In this paper, we propose a learned Bloom filter (LBF)-based packet classification algorithm that combines LBF and TSS to promote classification speed by avoiding invalid hash table accesses. Specifically, LBF consists of multiple RNN models and one support Bloom filter (SBF), in which the learned models are trained with the positive and negative sets, and used as a pre-filter to identify the two sets. For the filter outcomes with a negative result from learned models, SBF is constructed to perform the second filtration. To ensure efficiency of RNN and SBF, we carefully select key features to be used in RNN and SBF, which can also maintain efficient search in the final stage of hash checking. Our experimental results show that the proposed algorithm saves more memory space than Tuple space pruning (TSP) given the same false positive rate. The proposed algorithm is competitive in terms of the number of memory accesses, while achieving almost one order of magnitude improvement on pre-processing time over NeuroCuts which is an advanced decision tree classifier.