Miracle-dz/UniFAR-FTUs huggingface dataset:
1st International Doctoral Seminar in field of Geodesy, Geoinformatics and Geospace

In recent decades Kosovo has gone through various political and administrative systems. Hence, I’ve tried to focus my research in the attempts from different administration systems to improve land legislation framework in order to meet citizen’s needs. In addition, the study has been focused on the current administration system trying to emphasize land legislation weaknesses and suggestions for their improvements, because the challenges of the new-born country must be met. Given that the legal framework regarding real estates in Kosovo is consolidated, its functionality is still difficult. The subject has been studied from legal point of view and emphasizes the land legislation particularly the registration of different objects into the official state system. The expressed results explain that Kosovo, besides similar problems that other transitional countries in the region have, represents a “sui-generis” case. In the end, compilation of most essential suggestions is spread out which ought to be applied, thus insurance, protection and guarantee of immovable real properties related rights of citizens and others to have effect.
IT Software Project Management: Impact of Team Cohesiveness on Productivity and Performance

The efficiency of IT projects and, in particular of those that are software intensive, is determined by the productivity of the project’s team members. A large number and variety of researchers have proved that team cohesiveness has a major impact on productivity. This paper analyses the determinants of team cohesiveness, the impact of project goals and specific factors, e.g. team size, challenges or exclusivity, available to project managers for use in enhancing the desirable team effects. This paper also points out however, the inevitable dangers and the potentially negative influence, which a strongly coherent team may have on project team productivity. In summary, this paper will analyze the bottom-up relationship between all of the elements, from actions encouraging team cohesiveness up to software project performance. An awareness of this relationship led to the project management methodology of L-Timer™ presented at this conference.
60 Years in 100 Days: How COVID-19 Exposes the Lethality of Social Inequality

The pandemic outbreak of COVID-19 had begun in March of 2020 on a global scale. This outbreak has originated numerous questions for society and many challenges for public managers. The disease is worrying because it has a high propagation velocity, high lethality levels, and there is no cure. Some groups are considered more vulnerable due to pre-existing disease conditions, age-range, and living conditions. In Latin American countries, people live in different conditions than those who live in countries located in the North hemisphere, such as climatic conditions, less favorable socioeconomic conditions, different educational levels, inequality, precarious urban infrastructure, etc. These factors generate even more concern and uncertainty about the pandemic than in developed countries. This study aimed, although preliminarily, to identify areas of great socio-spatial vulnerability and susceptibility of infection of people over 60 years old of COVID-19 in Brasília, Brazil. In this research, publicly available data and information about the population characteristics and social aspects were used, all connected directly to the census sectors. With the support of Geographic Information System (GIS), a matrix was used to cross-check the data and, thereby, achieve the objective of identifying the most vulnerable sectors for people aged over 60 years old. The results point out to more than 400 census sectors classified as Extremely Vulnerable in Brasília and it should be the object of special attention for public managers to do specific health care actions.
THEORETICAL APPROACHES TO MANAGEMENT CONTROL SYSTEMS

This paper builds on conceptual frameworks developed in several prior works (Laughlin, 1995; Fleishman, Kalbers and Parker, 1996; Savall et al. 2001) by arguing that theoretical approaches to management control systems have been hampered by the idea that such systems can be externally imposed and intentially designed. Accounting research typically assumes that management control systems can be externally imposed in order to increase the efficiency and effectiveness of the organizations in which they exist. The weakness of this approach lies in its tendency to emphasize only economic measures of efficiency and effectiveness to the detriment of other measures. Alternatives to the mainstream approach have also been discussed in the literature. These alternatives focus more on social aspects of human interaction in organizations, including issues of power, conflict, domination and other aspects of organizational life. However, even these approaches to management control systems suffer from the assumption that such systems can be externally imposed either for good or evil purposes. The question of whether a management control system can be externally imposed is therefore more fully discussed in relation to Giddens’ theory of structuration and Savall et al’s theory of Socio-Economic Approach to Management.
A Study of Customer Satisfaction Business Customers Model

Most benefit looking for associations are at last kept running by cash and the specific retail stores are the same in that. In retail markets the customers are the hotspot for benefits and organizations trying to procure them are continually growing new techniques keeping in mind the end goal to get the aggressive edge that baits the customers in to their stores. At the point when a company offers products and services that satisfies the requirements of the customer and produces extra esteem, satisfaction and saw quality appropriately then the company has the best possibilities for progress. High customer satisfaction is one focused edge a company can have and one reason why it is such a critical piece of a company's methodology and has seen such a large number of researches and concentrates after some time is its association with gainfulness. The customers for the situation company X comprise of ordinary family unit purchasers and business conducts periodical studies that measure the satisfaction of the ordinary family unit buyers yet no research have yet been done to quantify the satisfaction of the business customers. This research will in this manner give new information to X and the aftereffects of this study can be valuable to th are somewhat happy with the services and products of X. The desires for the customers were likewise met great. The most noteworthy factor for the B2B customers to visit in X was impact to the expenses for the customers. Keywords-Growing New Techniques, Concentrates
A Survey On Approaches For Epileptic Seizure Detection And Prediction

Epileptic seizure is a neurological disorder. Currently, approximately 50 million people are affected by epileptic seizures. Detection of epileptic seizure is very difficult as they vary from patient to patient. The electroencephalography (EEG) capture brain activity, these EEG signals are required to be analyzed by neurologists in order to detect seizures. Traditionally, neurologists perform visual inspection to detect the presence of epileptic seizures which is time taking. But accurate diagnosis is required to be done within time so that appropriate therapies can be initiated and the further complications can be reduced. Hence an automatic approach is required for the detection and prediction of epileptic seizures. Seizure prediction is important because if seizures are predicted at an early state, then they can be suppressed using electric stimulations. We first start with the different algorithms or approaches used to detect and predict seizures, followed by comparison study of different approaches used for seizure diagnosis, our proposed approach, and finally the conclusion.
Non-sorted circle occurrence

Non-sorted circles are common frost features in periglacial environments. They play an important role in the storage of soil carbon and vegetation, nutrient and hydrology pattern. Global climate change is expected to alter non-sorted circle occurrence and the activity, however the direction and magnitude of the changes is still unclear. The purpose of this study was to determine landscape characteristics that drive non-sorted circle occurrence and activity. First field work was conducted in Abisko, northern Sweden, and second analysis were done using a Digital Elevation Model (DEM). In the field, characteristics of non-sorted circles were measured and analysed in relation to local altitude and slope. Using a DEM, the occurrence and activity of non-sorted circles was studied with respect to different landscape properties. This study shows that non-sorted circle occurrence is highest above the tree line, but limited at altitudes above 1000 m, where slopes are high and fine materials and soil moisture are limited. On a larger scale, concave topography increases non-sorted circle occurrence, but within the valleys, small ridges are favourable positions for non-sorted circle formation. Hypothesized is that non-sorted circles are favoured by high soil moisture and presence of fine materials, which is found in valley positions. A higher snow depth and longer snow cover in the lowest positions limits non-sorted circle formation. In the study area, few active circles were present. Under the current conditions, soil moisture is found to be the most important driver for non-sorted circle activity. The predicted higher precipitation for the 21st century could potentially increase activity, but higher air temperature and the effect it has on vegetation and active layer depth will decrease activity.
Successive Collaborative SLAM: Towards Reliable Inertial Pedestrian Navigation

In emergency scenarios, such as a terrorist attack or a building on fire, it is desirable to track first responders in order to coordinate the operation. Pedestrian tracking methods solely based on inertial measurement units in indoor environments are candidates for such operations since they do not depend on pre-installed infrastructure. A very powerful indoor navigation method represents collaborative simultaneous localization and mapping (collaborative SLAM), where the learned maps of several users can be combined in order to help indoor positioning. In this paper, maps are estimated from several similar trajectories (multiple users) or one user wearing multiple sensors. They are combined successively in order to obtain a precise map and positioning. For reducing complexity, the trajectories are divided into small portions (sliding window technique) and are partly successively applied to the collaborative SLAM algorithm. We investigate successive combinations of the map portions of several pedestrians and analyze the resulting position accuracy. The results depend on several parameters, e.g., the number of users or sensors, the sensor drifts, the amount of revisited area, the number of iterations, and the windows size. We provide a discussion about the choice of the parameters. The results show that the mean position error can be reduced to ≈0.5 m when applying partly successive collaborative SLAM.
Strategy for Postoperative Nausea and Vomiting After Cesarean Section: A Literature Review

Post operative nausea and vomiting (PONV) as a common side effect occurs 20-30% in all patients and 70%-80% in high risk patients[1,2]. PONV is an unpleasant experience usually associated with poor satisfaction. The risk factors of PONV is commonly including female sex, nonsmoking, younger age and preoperative opioid use etc [2,3]. Cesarean Section (CS) as a way for childbirth, is popular worldwide. Liu et al. reported the CS rate by analyzing 160,278,075 live cesarean births had increased from 28.8% in 2008 and to 36.7% in 2018 in China [3]. The World Health Organization (WHO)reported that CS rate was up to 25.7% worldwide, higher above 40% in Latin America[4,5]. In the USA, the prevalence rate of CS was also raised from 5.5% in 1970 to 32.2% in 2014 [6,7]. CS estimated to take account of 7% of all surgical proceduresb worldwide [8]. Different from the virginal delivery, the patients with CS suffer stronger pain postoperatively. Traditionally, as first-line analgesia, more opioids are consumed after CS. Evidences showed the consumption of opioids in the United States increased fourfold from 1999 to 2015 [9]. The reason of PONV after CS has not been defined. It mostly focused on hypotension, opioid consumption, Peritoneal Cavity Saline Irrigation (PCSI) intraoperatively and so on. Hypotension which occurs commonly in cesarean section under spinal anesthesia, is associated with high incidence of nausea and vomiting intraoperative and early postoperative. The treatment of hypotension mostly focuses on fluid resuscitatation and vasopressor administration [10,11]. Keeping the maternal blood pressure decreased no more than 20% of baseline should reduce the incidence of nausea and vomiting intraoperative and early postoperative [12]. However, which vasopressor and how to administrate is better for preventing nausea and vomiting intraoperative and early postoperative still undefined.
Evolutive learning of walking gaits for Nao humanoid robot using the Gazebo simulator

—This contribution presents the work in progress for the automatic learning of walking gaits using an evolutionary algorithm. The humanoid movement is modelled as coupled waves of its limbs, which are coordinated by a Central Pattern Generator. The waves are deﬁned with a set of parameters. The values of parameter set deﬁne the space of possible walking gaits. The evolutionary algorithm searches in that space a good walking gait such that maximizes the robot advance speed, provides stability and minimizes lateral deviation. The evolutionary algorithm proposes parameter combinations which are tested in a Nao humanoid inside the Gazebo simulator. The developed Nao support in Gazebo software and the implementation of this gait evaluation on the simulated humanoid are presented.
Mast cells in infection and immunity

Mast cells are multifunctional cells with diversified roles in allergy, inflammation, angiogenesis, tissue-remodeling and during infection. There are two major subsets of mast cells, e.g. mucosal mast cells (MMCs) and connective tissue mast cells (CTMCs) that differ in their locations, granule contents and functions. The effector functions of these cells are mediated by soluble molecules released by them following activation. Mast cells respond effectively to various types of pathogens and exhibit different functions like protective/secretory and phagocytic response to bacteria, protection from sepsis, role in viral infection and antigen presentation and role in parasitic infestation. Mast cells also help in recruitment of inflammatory cells and play role in immune surveillance. Above all, mast cell acts as an effector arm of innate and acquired immunity. Thus, there are ample evidences of multi-faceted role of mast cells in host's immune response to pathogens.
Machine Learning based Energy Consumption Prediction of Appliances in a Low Energy House

— In reaction to the impacts of global warming, individuals are becoming increasingly conscious of their homes unchecked power usage, particularly the use of electrical energy for cooking, heating, refrigeration, dish-washing and drying. There is an increased concern about idle losses, expended by devices when not in use, which not only add to utility bills but also add to the waste of energy. Monitoring and controlling end-use electricity demand in residential buildings can have a significant impact on reducing peak demand and optimizing energy consumption that can be achieved in smart households with residential load control systems. This study benchmarked eight Machine learning based algorithms: Linear, Ridge and LASSO regression; Support Vector Machine; Multilayer Perceptron; Nearest Neighbor regression; Extra-Trees and XG-Boost on a pre-collected “appliance energy” data-set. The specified algorithms were benchmarked on error metric of: training and testing set R-squared statistic; MAE; RMSE and also training time. Data-preprocessing and visualization was done to yield insight into data used. Firstly, un-tuned version of the eight algorithms were benchmarked, then model tuning via Grid-Search was carried for five algorithms and finally the effects of inclusion of certain features and varying parameters was tabulated and graphed. The least scores, on the specified error metrics, were obtained by the regression algorithms. The best scores were obtained by Extra-Trees and XG-Boost, which belong to ensemble algorithms of which Extra-Trees obtained best variance explanation (R-squared) scores of 98.94% on training set and 60.21% on testing set along with least scores on above specified error metrics.
DPIFuzz: A Differential Fuzzing Framework to Detect DPI Elusion Strategies for QUIC

QUIC is an emerging transport protocol that has the potential to replace TCP in the near future. As such, QUIC will become an important target for Deep Packet Inspection (DPI). Reliable DPI is essential, e.g., for corporate environments, to monitor traffic entering and leaving their networks. However, elusion strategies threaten the validity of DPI systems, as they allow attackers to carefully design traffic to fool and thus evade on-path DPI systems. While such elusion strategies for TCP are well documented, it is unclear if attackers will be able to elude QUIC-based DPI systems. In this paper, we systematically explore elusion methodologies for QUIC. To this end, we present DPIFuzz: a differential fuzzing framework which can automatically detect strategies to elude stateful DPI systems for QUIC. We use DPIFuzz to generate and mutate QUIC streams in order to compare (and find differences in) the server-side interpretations of five popular open-source QUIC implementations. We show that DPIFuzz successfully reveals DPI elusion strategies, such as using packets with duplicate packet numbers or exploiting the diverging handling of overlapping stream offsets by QUIC implementations. DPIFuzz additionally finds four security-critical vulnerabilities in these QUIC implementations.
A Survey on Frequent Pattern Rules Techniques for Privacy Preserving mining

-I nformation sharing among the organizations is a general movement in a few zones like business advancement and showcasing. As portion of the sensitive rules that should be kept private might be revealed and such revelation of sensitive patterns may impacts the benefits of the organization that possess the information. Consequently the principles which are sensitive must be covered before sharing the information. In this paper to provide secure data sharing sensitive rules are perturbed first which was found various techniques are discussed. Here techniques of privacy data mining was also detailed.
Intrinsic Probing through Dimension Selection

Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks. Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it. In this paper, we draw a distinction between intrinsic probing, which examines how linguistic information is structured within a representation, and the extrinsic probing popular in prior work, which only argues for the presence of such information by showing that it can be successfully extracted. To enable intrinsic probing, we propose a novel framework based on a decomposable multivariate Gaussian probe that allows us to determine whether the linguistic information in word embeddings is dispersed or focal. We then probe fastText and BERT for various morphosyntactic attributes across 36 languages. We find that most attributes are reliably encoded by only a few neurons, with fastText concentrating its linguistic structure more than BERT.
Localized and Distributed $\mathcal{H}_{2}$ State Feedback Control

Distributed linear control design is crucial for large-scale cyber-physical systems. It is generally desirable to both impose information exchange (communication) constraints on the distributed controller, and to limit the propagation of disturbances to a local region without cascading to the global network (localization). Recently proposed System Level Synthesis (SLS) theory provides a framework where such communication and localization requirements can be tractably incorporated in controller design and implementation. In this work, we derive a solution to the localized and distributed H2 state feedback control problem without resorting to Finite Impulse Response (FIR) approximation. Our proposed synthesis algorithm allows a column-wise decomposition of the resulting convex program, and is therefore scalable to arbitrary large-scale networks. We demonstrate superior cost performance and computation time of the proposed procedure over previous methods via numerical simulation.
Assessing Automated Machine Learning service to detect COVID-19 from X-Ray and CT images: A Real-time Smartphone Application case study

The purpose of our study was to evaluate Microsoft Cognitive Service to detect COVID19 induced pneumonia and ordinary viral or bacterial infection in Lung using X-Ray and CT scan images. We have used Datasets from a recognized and trusted source to build our model. The primary objective is a Smartphone based on device real-time inference system. In this case, the model would run by a mobile device’s System on Chip (SoC) and will not require an internet connection for inference with zero latency. This system would be particularly suitable for rural areas of developing countries where internet connection is poor or not available. The secondary solution would be a web portal running the inference through REST API from Custom Vision. Now, given the nature of The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), which causes respiratory disease as a novel one, the majority of the radiologists are not acquainted enough to detect the virus-related changes from the X-Ray. Moreover, the morphology of COVID-19 and common Pneumonia are hard to differentiate from X-Ray alone without the patient's symptoms by a radiologist. Here, AI comes into play with the role of an expert assistant. It is much faster and efficient to train a machine over thousands of labeled training data to observe and detect subtle differences between various X-Ray images to train its Artificial Neural Network and classify them quickly which is otherwise not possible by a human eye. A Radiologist can use the app to primarily identify the X-Ray in question and combine it with his/her medical expertise along with the patient's case history before in conjunction with tests like RT PCR/Antibody.
PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation

Pre-trained Transformers have enabled impressive breakthroughs in generating long and fluent text, yet their outputs are often "rambling" without coherently arranged content. In this work, we present a novel content-controlled text generation framework, PAIR, with planning and iterative refinement, which is built upon a large model, BART. We first adapt the BERT model to automatically construct the content plans, consisting of keyphrase assignments and their corresponding sentence-level positions. The BART model is employed for generation without modifying its structure. We then propose a refinement algorithm to gradually enhance the generation quality within the sequence-to-sequence framework. Evaluation with automatic metrics shows that adding planning consistently improves the generation quality on three distinct domains, with an average of 20 BLEU points and 12 METEOR points improvements. In addition, human judges rate our system outputs to be more relevant and coherent than comparisons without planning.