Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Static and Contextualised Multilingual Embeddings

Static and contextual multilingual embeddings have complementary strengths. Static embeddings, while less expressive than contextual language models, can be more straightforwardly aligned across multiple languages. We combine the strengths of static and contextual models to improve multilingual representations. We extract static embeddings for 40 languages from XLM-R, validate those embeddings with cross-lingual word retrieval, and then align them using VecMap. This results in high-quality, highly multilingual static embeddings. Then we apply a novel continued pre-training approach to XLM-R, leveraging the high quality alignment of our static embeddings to better align the representation space of XLM-R. We show positive results for multiple complex semantic tasks. We release the static embeddings and the continued pre-training code. Unlike most previous work, our continued pre-training approach does not require parallel text.
Students’ perception of e-Learning during the Covid Pandemic: a fresh evidence from United Arab Emirates (UAE)

To examine the role of online learning on student’s perceptions during the recent pandemic from the 364 participating respondents’ parents and students from (Grade 1 to 12). The respondents’ data were analyzed through structural equation modelling (SEM) and the study confirm that 4 out of 4 of exogenous factors examined impacted learning positively, and 3 out of 4 impacted motivation positively. Besides, both study implications and limitations are also discussed under this study.
Geometric Morphometric analysis of Alosa braschnikowi (Teleostei, Clupeidae) populations in the southern Caspian Sea

Morphological studies are strong and instrumental for determining discreteness of the similar species and extensively used to identify differences between fish populations. A total of 216 specimens of Alosa braschnikowi were randomly collected by beach seine from three fishing regions along the southern Caspian Sea coasts, including some regions of Miankaleh, Sari and Anzali, in the fishing season during 2018-2019. The extracted landmark-points (body shape data) were submitted to a generalized Procrustes analysis (GPA) to remove nonshape data in PAST software. In the present study, the size effect was removed successfully by procrustes action in PAST software. Principal component analysis (PCA) was performed to summarize the variation among the specimens as few dimensions as possible. As a complement to discriminant analysis, morphometric distances among the three localities were inferred to cluster analysis by adopting the Euclidean square distance as a measure of dissimilarity method as the clustering algorithm. The dendrogram derived from cluster analysis of Euclidean square distances showed that the three populations of A. braschnikowi were distinct from each other in terms of morphometric characters. The Wilks’ lambda tests of discriminant analysis indicated significant differences in morphometric characters of the three populations. The results of the present study demonstrated significant morphological differences between the three populations of A. braschnikowi. These differences in the three studied basins were mainly related to the characteristics of head and snout, body height, caudal peduncle, dorsal and anal fin base, which could be related to the hydraulic conditions and diet of the populations.
Analysis on Three-dimensional Transient Temperature Field of Induction Motor

A model for three-dimensional transient temperature field of induction motor was set up and the heat exchange between stator and rotor was simulated by using the concept of "equivalent coefficient of heat conductivity". The influence of temperature rise on the stator and rotor resistance was considered for thermal loss calculation.The influence of the end of stator and rotor on axial temperature distribution was also taken into account.Three-dimensional transient temperature field of the motor was simulated under rated load,temperature field with different loads was also computed;and some influence factors were analyzed.It was shown that the temperature of stator and rotor increase rapidly in the beginning,and then the rising trend slow down until reaching the stable state.In the whole process,the rotor temperature is higher than the stator temperature all the time,and the maximum temperature point is around the axial center of the rotor bar.The axial temperature difference of the rotor core is larger than that of the stator core while the radial temperature difference of the stator core is larger than that of the rotor core.The temperature of air gap decreases linearly with the increase of the thickness,and the thermal insulation effect of air gap is so strong that it makes the temperature difference of stator and rotor obvious.The motor temperature grows with the increase of load.There is no linear relation between load and temperature.The higher the wings of shell,the lower motor temperature is,whereas the descend tendency slows down;Using cast copper instead of cast aluminum as rotor bars and end rings can reduce the temperature of rotor effectively.
Tracking the Vegetative Compositional Changes 4 Years After the Rim Fire in Yosemite National Park

The Rim Fire burned through 257,314 acres of the Stanisluas National Forest and Yosemite National Park, California from August 15 to October 23, 2013. In 2017, I measured total plant canopy cover, plant species richness and plant composition of areas of high and low burn sites. The total understory plant canopy cover was significantly higher in areas of high severity (p-value: 0.000278). Overall, there was high richness of non-native forb and graminoids species at higher severity sites but the cover was unexpectedly low at <3% across all severities. There were also no significant differences in exotic forb and graminoid cover between high and low burn severities sites. Areas of high severity burns did have significantly higher shrub cover compared to low severity burn sites (p-value: 0.00000343). Indicator species analysis showed the indicators of high severity sites were deerbrush (Ceanothus integerrimus) and cheatgrass (Bromus tectorum). In addition, NMDS analysis similarly identified deerbrush, whitethorn, cheatgrass and rat-tail as the vegetative differences between the high severity burned sites and low severity burned sites. Although shrub cover increases in high severity sites, any long-term impacts of high severity fires on the plant community requires continued monitoring of the sites.
Effects of E-Government Policy on the Management of Healthcare Systems

E-government began by addressing the challenges of new technologies by delivering e-services to its citizens and has since evolved to include a growing number of areas, such as citizen communication, macroeconomic projections, budget management, and e-healthcare services. E-government is known as the use of information technology to provide administrative services, communication transactions, information exchange, integration of various electronic systems, and autonomous services between the government and citizens, the government and business environment, and the government and government. This paper discusses the role of E-government policy in healthcare crises during COVID 19. Data collected from 435 employees in the tourism industry of Iraq was used to verify the abovementioned relationships via SPSS macro. The results indicate that E-government policy has a significant effect on healthcare crises; job insecurity negatively predicted healthcare crises. The results revealed that social support moderated the relationship between E-government policy and job insecurity. Results of the study contributed to the theory within this study by demonstrating that employees who enjoy a high level of social support show less job insecurity than those with a low level of social support.
The effect of different drilling protocols on bone healing around dental implants

Materials and Methods: 20 patients with kennedy class III modification 1 edentulous areas were recruited for this study and divided into two groups; Control group received implants with conventional osteotomy preparation following standard drilling protocol (speed= 1200 rpm) with torque of 30N and sufficient saline irrigation, while Study group received implants with osteotomy preparation following a low speed drilling protocol (speed < 200 rpm) without irrigation and a torque of 50 N. Cone Beam CT was used to evaluate bone height and density around the implants at 3, 6 and 12 months after insertion. Implant primary stability was evaluated using Ostell by Resonance Frequency Analysis.
SOLUTION OF UNIT COMMITMENT PROBLEM FOR OPTIMAL POWER FLOW FOR IEEE 26 BUS SYSTEM BY DYNAMIC PROGRAMMING METHOD

This paper presents a Dynamic programming based an algorithm to solve the Unit Commitment problem accounting voltage stability consideration and inequality constraints. In the present electricity environment, where power demands are in its peak, it has become very difficult for operators to fulfil the demand. There are many conventional and evolutionary programming techniques used for solving the unit commitment (UC) problem. Dynamic programming is conventional algorithm used to solve the deterministic problem. The developed algorithm has been implemented on IEEE-14 bus system. The results obtained from this technique was validated with the available techniques and outcome found satisfactory. The commitment in such a way that total cost of generation is
OWG Operators that Maintain Reciprocity Property in the Aggregation of Multiplicative Preference Relations

In multicriteria decision making (MCDM) problems, multiplicative preference relations are widely used to represent assessments on the set of alternatives with respect to all criteria. Multiplicative preference relations are usually assumed to be reciprocal. In [6] we present a decision model to solve a MCDM problem under multiplicative preference relations based on two steps: i) aggregation of the individual relations in a collective one and ii) explotation of the collective relation. However, it is well known that reciprocity is not generally preserved after aggregation is carried out.
NEW LOGIN AUTHENTICATION ALGORITHM USING DISTANCE BASED ANALYSIS THROUGH DIGITAL IMAGES

: Security is a typical marvel that exists in relatively every application. Number of techniques has been concocted to manage security issues. In any case, the issue is as yet open and requires huge research. In this proposition endeavors have been made to give a more effective approach to security. The past work catches the single casing and attempts to verify the client based on the casing caught. Consequently the likelihood of bungle is more. My work is center around to give a strategy which will be more exact when contrasted with existing security frameworks. In any case, the proposed plot works superior to the current frameworks. In our proposed thesis, we have exhibited a creative approach of putting away the password by catching the video as the password. Keeping in mind the end goal to additionally improve the security we have encrypted the casings caught utilizing the AES calculation which are decrypted amid the login procedure. Based on try it has been demonstrated that the proposed technique is more effective and the targets determined were accomplished. When contrasted with the customary login security, our proposed idea is considerably more secure and productive. And furthermore difficult to trap and figure the right password. In proposed work, reenacted the distance based comparison of the images which is speedier when contrasted with the base approach of the image comparison.
Nationalizing Truth: Digital Practices and Influences of State-Affiliated Media in a Time of Global Pandemic and Geopolitical Decoupling

This study explores Facebook-based state media accounts from various geopolitical players and focuses on three practices—content volume, intermedia agendasetting/following, and coordinated sharing through networks of Facebook pages, groups, and verified public profiles. Findings suggest that Russian and Chinese state media are more active in content production than their global peers, yet with limited reach. Chinese state media stand out as both agenda-setters and followers: They inject distinct agendas into the global news flows while closely following agendas first covered by other global outlets. State media from all types of geopolitical players engage in inauthentic coordinated sharing, but with notable differences in the ideological composition of the mobilized Facebook networks: The Chinese coordinated-sharing network is homegrown and limited; the Russian network consists of right-leaning and counter-mainstream political groups worldwide, while the coordinated-sharing networks mobilized by the state media in the Middle East, Venezuela, and Western liberal democracies are left-leaning and human-rights focused.
USER AUTHENTICATION BY USING BIOMETRICS & GRAPHICAL PASSWORDS BY IMPROVISING CONVENTIONAL METHODS USING SHA-256 HASH ALGORITHM WITH RANDOMIZED KEY SUPPORT

: Data security alludes to defensive processed protection estimates that are connected to avoid unapproved access to PCs, databases and sites. Information security in addition shields knowledge from uncleanness. Information security may be a basic a part of IT for associations of every size and kind. Information security is otherwise known as knowledge security (IS) or computer security. Instances of Information security advancements incorporate reinforcements, knowledge meshing and information wipeout. A key information security innovation live is coding, wherever processed knowledge, programming/equipment, and onerous drives are disorganized and afterward rendered unintelligible to unapproved users and programmers. one among the foremost commonly practiced methods for rehearsing knowledge security is that the utilization of authentication. With authentication, users should produce a secret word, code, biometric knowledge, or another form of information to ascertain temperament before access to a framework or knowledge is conceded. The proposed work includes the idea to stack the finger print/picture of the client, the dataset for the finger print is taken for the finger print reenactment of the enlisted clients. The client when snap on the heap photograph catch, pop will seem to choose the area where lives the record comparing to the finger print[1]. At that point the SHA 256 calculation will be included for the age of the hash code which is identified with the fingerprint and the a few pictures are additionally given the alternative of clicking over the pictures, here the selected snaps on every one of the pictures are records and will create the secret phrase in relationship with the hash of the photograph. the created OTP will further raise the degree of security. The outcome examination when contrasted with the base work, by utilizing the different on the web and disconnected instruments of registering the secret word quality, demonstrates that the bit quality is nearly expanded in excess of multiple times the base work and furthermore the entropy for the secret word or OTP which is produced is expanded to the extensive sum. The result of comparison is quite effective and promising towards the security.
IMPLEMENTING LEFTMOST PARSING TECHNIQUES FOR FINITE STATE AUTOMATA

: Implementation of a new compiler usually requires making frequent adjustments to grammar definitions. An incremental technique for updating the parser tables after a minor change to the grammar could potentially save much computational effort. More importantly, debugging a grammar is made easier if the grammar is re-checked for correctness after each small change to the grammar. The basic design philosophy of an incremental parser generator, and incremental algorithms for LR(0), SLR(1) and LALR(1) parser generation are discussed in this paper. Some of these algorithms have been incorporated into an implementation of an incremental LALR(1) parser generator.
Masters Thesis: Switched LQR control

This thesis studies the Switched Linear Quadratic Regulator (SLQR) problem, over a hybrid (continuous and discrete) dynamical model known as "switched system". The problem is defined as computing the optimal continuous and discrete switching control to minimise a quadratic cost function that weights the states and the continuous controls. The original SLQR problem does not handle constraints on states, continuous or discrete controls, and there is no probabilistic behaviour. This thesis focuses on the discrete dynamics in a SLQR problem. The first part of the thesis describes the SLQR problem with discrete constraints, whereas the second part is dedicated to probabilistic switching behaviour. The problem with discrete constraints is described as finding the optimal hybrid switching policy that minimises a quadratic cost function, weighting states and continuous controls, without violating the discrete constraints. The problem with probabilistic switches is defined as finding the optimal hybrid switching policy that minimises an expected value of a quadratic cost function, weighting states and continuous controls. For the SLQR problem with discrete constraints a general relaxation framework is developed to simplify the representations of the value functions and the corresponding control strategies. It is shown that the closed loop performance of the obtained solution with the relaxation framework can be made arbitrarily close to the optimal solution. For the SLQR problem with probabilistic switches it is shown that a relaxation framework can only be developed when there are no discrete constraints involved. Finally, the thesis concludes with a few case studies to illustrate how the optimal hybrid control sequence is computed. Master of Science Thesis J.B. Veneman
A REVIEW PAPER ON AUTOMATED ATTENDANCE MANAGEMENT SYSTEM USING FACE RECOGNITION ALGORITHMS

: The face is identity of person. Human face recognition is an important branch of biometric verification and has widely used in many applications such as video monitor system, human computer interaction and network security. A large number of face recognition algorithms have been developed in last decades all having respective advantages and disadvantages. Some of the algorithms improve the efficiency of face recognition, under different varying illumination, pose variation and expression conditions for face images. The different authors have described novel approaches for face recognition. In this paper an attempt is made to review a wide range of methods used for face recognition.
Detection of PCB Based on Digital Image Processing

In order to solve the problem of high-cost,low efficiency of traditional detection methods for PCB,this paper presented an automatic optical inspection method based on image processing technology and designed the detection system between the standard image and the being tested image.For PCB production defects such as short circuit,open circuit,hollow,the unreasonable line width,some efficient measures such as finding the number of connected regions,calculating euler number,finding area of defects was used to detect these defects and the whole process of the algorithm was given.The result of experiment indicates that this method can accurately detect defects in a PCB and achieve the purpose of automatic real-time detection.
Study on Recognition for Armored Vehicle Based on Wavelet Moment and SVM

The theory that the wavelet moment could be applied to solve the problem of recognizing armored vehicles under complex ground background is analyzed.True gray images of certain tank and infantry fighting vehicle are acquired,the features of wavelet moment are extracted,and classification and recognition are carried out by using Support Vector Machine(SVM). Performance test experiments are made,the results show that: the features of wavelet moments of the normalization images are stable;the characteristics of wavelet moment have better adaptation to noise and partial-cover,and the recognition rate is more stable;the method of SVM has better capabilities of classification and recognition.
MOBILE SENSOR NODES BASED ENERGY EFFICIENT LEACH PROTOCOL FOR WSN SYSTEMS (MEE-LEACH)

: In today’s scenario automation abruptly changes our lifestyle. Fundamentally, automation is the result of sensors that plays a great role in deciding the behavior of the system. Wireless sensor networks are one kind of system used for collecting various parameters from its vicinity. Wireless sensor networks consists many sensor nodes which consist a CPU (Central Processing Unit), Memory Unit, Transceiver unit and Power Unit. The major challenge while designing any sensor network is the optimized use of power unit as this results in the enhancement of lifetime of the network. Many Clustering based routing algorithms have already been developed for optimizing usage of energy of sensor node. In clustering based algorithms the major issue is the selection of the cluster head that is responsible for data transmission towards the base station. In this paper, we will discuss a Mobile sensor nodes based Energy Efficient Low Energy Adaptive Clustering Hierarchy Protocol (MEE-LEACH) which is based on clustering mechanism. In this protocol, Cluster Heads (CHs) are elected in such a manner that results in prolongation of stability period and network lifetime. Clustering has been performed on the current energy level of the sensor node which means a threshold has been set for each node to be a CH. In addition to this, some Mobile Sensor Nodes (MSNs) are also deployed in the network which will replace that node which become dead immediately. The results for the proposed protocol had compared with LEACH, O-LEACH in terms of Network Lifetime and Energy Consumption.
ARTIFACT REDUCTION CONVOLUTIONAL NEURAL NETWORK BASED OBJECT IDENTIFICATION AND CLASSIFICATION FOR HIGH RESOLUTION IMAGES

: No two camera sensors are created equal, especially when it comes to DSLRs and camera phones. Every model captures pixels with its own quirks and problems, and some are more apparent than others. The artifacts reduction done through convolution neural network will give timely and accurate classification for the process of disaster rescue and mammography in medical field. However it is very difficult to capture the outline of different objects at the pixel level, Deep learning methods are required for the further classification. By using this concept we can accurately classify the image patterns with high resolution. High level representation which is extracted from ARCNN frame work has been systematically investigated over different configuration.