Miracle-dz/UniFAR-FTUs huggingface dataset:
Radio Resource Management Optimization For Next Generation Wireless Networks

The prominent versatility of today’s mobile broadband services and the rapid advancements in the cellular phones industry have led to a tremendous expansion in the wireless market volume. Despite the continuous progress in the radio-access technologies to cope with that expansion, many challenges still remain that need to be addressed by both the research and industrial sectors. One of the many remaining challenges is the efficient allocation and management of wireless network resources when using the latest cellular radio technologies (e.g., 4G). The importance of the problem stems from the scarcity of the wireless spectral resources, the large number of users sharing these resources, the dynamic behavior of generated traffic, and the stochastic nature of wireless channels. These limitations are further tightened as the provider’s commitment to high quality-of-service (QoS) levels especially data rate, delay and delay jitter besides the system’s spectral and energy efficiencies. In this dissertation, we strive to solve this problem by presenting novel cross-layer resource allocation schemes to address the efficient utilization of available resources versus QoS challenges using various optimization techniques. The main objective of this dissertation is to propose a new predictive resource allocation methodology using an agile ray tracing (RT) channel prediction approach. It is divided into two parts. The first part deals with the theoretical and implementational aspects of the ray tracing prediction model, and its validation. In the second part, a novel RT-based scheduling system within the evolving cloud radio access network (C-RAN) architecture is proposed. The impact of the proposed model on addressing the long term evolution (LTE) network limitations is then rigorously investigated in the form of optimization problems. The main contributions of this dissertation encompass the design of several heuristic solutions based on our novel RT-based scheduling model, developed to meet the aforementioned objectives while considering the co-existing limitations in the context of LTE networks. Both analytical and numerical methods are used within this thesis framework. Theoretical results are validated with numerical simulations. The obtained results demonstrate the effectiveness of our proposed solutions to meet the objectives subject to limitations and constraints compared to other published works.
Problem Statement for SIP/SIMPLE

As more experience of deploying SIMPLE based presence systems is accumulated, it seems that deploying a large SIMPLE presence system is actually a very hard task. The document analyzes several aspects of SIMPLE based presence system deployment and shows that difficulties around the amount of messages (network), state management (memory) and processing load (cpu). Although this document is a problem statement document and not a BCP document, several possible optimizations and directions are listed at the end of the document in addition to an initial set of requirements for what should be the characteristic of the solution to the problem stated in the document This document is intended to be used by the SIMPLE WG in order to work on possible solutions that will make the deployment of a presence server more reasonable task. Note that the document does not try to compare the SIP based presence server to other types of presence servers but only analyzes the SIP based presence server.
Homeland Violence and Diaspora Insecurity: An Analysis of Israel and American Jewry

Abstract Jews and Jewish institutions have suffered the majority of reported religion-motivated hate crimes in the United States for nearly two decades. According to the Federal Bureau of Investigation (FBI), in 2014 the 609 reported anti-Semitic incidents made up 59% of all religious bias hate crimes alone. Rates of reported anti-Semitic hate crimes vary considerably over the course of a year. Yet, little scholarly attention has been given to what factors cause reported anti-Semitic hate crimes to fluctuate so substantially in the United States. This paper hypothesizes that violent Israeli military engagements are critical in explaining weekly surges of reported anti-Semitic hate crimes. Utilizing FBI hate crime data from 2001 to 2014 and fixed effects negative binomial regression models, consistent findings underscore that violent Israeli military engagements significantly increase the likelihood of a state reporting anti-Semitic hate crime. Most dramatically, their occurrence increases the likelihood of reported hate crime intimidating individuals or characterized as violent by nearly 35%. This paper underscores that homeland perpetrated violence can directly impact the security of diaspora communities.
Predicting Students’ Academic Performances – A Learning Analytics Approach using Multiple Linear Regression

Learning Analytics is an area of Information Systems research that integrates data analytics and data mining techniques with the aim of enhancing knowledge management and learning delivery in education management.The current research proposes a framework to administer prediction of Students Academic Performance using Learning Analytics techniques. The research illustrates how this model is used effectively on secondary data collected from the Department of Computer Science, University of Jos, Nigeria.Multiple Linear Regression was used with the aid of the Statistical Package for Social Sciences (SPSS) analysis tool. Statistical Hypothesis testing was then used to validate the model with a 5% level of significance.
LISP Alternative Topology (LISP+ALT)

This document describes a method of building an alternative, logical topology for managing Endpoint Identifier to Routing Locator mappings using the Locator/ID Separation Protocol. The logical network is built as an overlay on the public Internet using existing technologies and tools, specifically the Border Gateway Protocol and the Generic Routing Encapsulation. An important design goal for LISP+ALT is to allow for the relatively easy deployment of an efficient mapping system while minimizing changes to existing hardware and software.
Attachment disorders diagnosed by community practitioners: a replication and extension.

BACKGROUND While considered a rare diagnosis, reactive attachment disorder (RAD) is simultaneously the subject of considerable debate. A recent report suggested that RAD is overdiagnosed in community settings and that conduct problems may be used to make a diagnosis of RAD (Woolgar & Baldock, Child and Adolescent Mental Health, 20, 2015, 34-40). This study seeks to replicate and extend these findings. METHOD Clinical assessment data from 100 consecutive admissions of maltreated foster and adopted children (ages 3-17) to a specialty treatment clinic in the United States were reviewed. Measures included semi-structured interviews of RAD and disinhibited social engagement disorder (DSED) symptoms and caregiver-report questionnaires of emotional problems, conduct problems, and the quality of the parent-child relationship. RESULTS Of the 100 cases reviewed, 39 presented with a diagnostic history of RAD, DSED, or 'attachment disorder'. Of these cases, three were diagnosed in-clinic with DSED; no cases met diagnostic criteria for RAD according to DSM-5 criteria. However, analyses found that those diagnosed with RAD by community-based clinicians were significantly more likely to display conduct problems and to be adopted (as opposed to in foster care). CONCLUSIONS These findings confirm those of Woolgar and Baldock (Child and Adolescent Mental Health, 20, 2015, 34-40). It appears that the diagnostic criteria of RAD are commonly being inaccurately applied in general community-based practice. Clarification of diagnostic criteria for RAD in recent revisions of diagnostic taxonomies, the accumulation of empirical data on RAD, and improved instrumentation are either poorly disseminated or inadequately implemented in community-based practice settings.
Using interpretative phenomenological analysis to study patient and family members' experiences of a mechanical ventilation weaning unit

This chapter sets out the use of interpretative phenomenological analysis to study lived experiences in healthcare and rehabilitation settings. The study focus is on patient and family members’ experiences of a mechanical ventilation weaning unit. The analysis highlighted participants’ perception of both themselves and the unit being different and special in a number of ways, particularly because the unit has the ability to choose who comes onto it, along with the difficulties surrounding the transition from unit to home. The process of transitioning from the unit back to home life was experienced as a difficult one as there were a range of barriers which impeded the discharge process. Although the present findings offer particular issues of consideration for specialist mechanical ventilation weaning units and ICUs, our description and application of IPA here serves an illustrative purpose for others interested in applying qualitative methods for similar purposes to other healthcare settings.
The Sensitivity of U.S. Inpayments and Outpayments to Real Exchange Rate Changes: Asymmetric Evidence From Africa

ABSTRACT A strand of the literature concentrates on assessing the impact of exchange rate changes on a country’s inpayments from and outpayments to its trading partners. Most studies have considered the U.S. experience with partners from OECD but not for those from Africa. We fill this gap by including 14 African partners in our study. We assess not only the symmetric effects of exchange rate changes on the U.S. inpayments from and outpayments to each African partner, but also the asymmetric effects which requires incorporating nonlinear adjustment of the exchange rate. While we found asymmetric effects in the trade with almost all partners in the short run, in a limited number of cases the short-run effects lasted into long-run asymmetric effects. All in all, our findings are partner specific, but they provide more support for using nonlinear models.
Optimized Route for Crowd Evacuation

An evacuation plan helps people move away from an area or a building. To achieve a fast evacuation, we present an algorithm to compute the optimal route for each local region. The idea is to reduce congestion and to maximize the number of evacuees arriving at exits in every time span. Our system considers the crowd distribution, exit locations, and corridor widths when determining the optimal routes. It also simulates crowd movements during the route optimization. To implement this idea, we expect that neighboring crowds who take different evacuation routes should arrive at respective exits nearly at the same time. If this is not the case, our system updates the routes of the slower crowds. Given that crowd simulation is non-linear, the optimal route is computed in an iterative manner. The process repeats until an optimal state is achieved. Experiment results demonstrate the feasibility of our evacuation route optimization.
Sequence Learning with Incremental Higher-Order Neural Networks

An incremental, higher-order, non-recurrent neural-network combines two properties found to be useful for sequence learning in neural-networks: higher-order connections and the incremental introduction of new units. The incremental, higher-order neural-network adds higher orders when needed by adding new units that dynamically modify connection weights. The new units modify the weights at the next time-step with information from the previous step. Since a theoretically unlimited number of units can be added to the network, information from the arbitrarily distant past can be brought to bear on each prediction. Temporal tasks can thereby be learned without the use of feedback, in contrast to recurrent neural-networks. Because there are no recurrent connections, training is simple and fast. Experiments have demonstrated speedups of two orders of magnitude over recurrent networks.
Vector Space Models for Scientific Document Summarization

In this paper we compare the performance of three approaches for estimating the latent weights of terms for scientific document summarization, given the document and a set of citing documents. The first approach is a termfrequency (TF) vector space method utilizing a nonnegative matrix factorization (NNMF) for dimensionality reduction. The other two are language modeling approaches for predicting the term distributions of human-generated summaries. The language model we build exploits the key sections of the document and a set of citing sentences derived from auxiliary documents that cite the document of interest. The parameters of the model may be set via a minimization of the Jensen-Shannon (JS) divergence. We use the OCCAMS algorithm (Optimal Combinatorial Covering Algorithm for Multi-document Summarization) to select a set of sentences that maximizes the term-coverage score while minimizing redundancy. The results are evaluated with standard ROUGE metrics, and the performance of the resulting methods achieve ROUGE scores exceeding those of the average human summarizer.
Investigation and Implementation of Square Type Patch Based Algorithm on Texture Synthesis of Image Inpainting Technique for Medical Images

In this paper, we have investigated and implemented “Square Type Patch Based Algorithm of Texture Synthesis” for Medical Images. The algorithm is designed to unravel the texture synthesis dilemma of Image Inpainting Technique. It proposes to overlay together patches of an input image in order to construct the output image. The uniqueness of the algorithm is in allowing neighboring patches to meet on boundaries that are not purely straight lines. We will exclusively confer the parameters that organize the performance of the algorithm and the investigation's outcome that we obtained when implementing. We have used Different Types of Human Cells Images as Medical Images and implemented results over them. Human Cells have repetitive structure. So, we have taken Hybrid Stem Cells, Nerve Cells, Stem Cells and Endothelial Cells as Medical Images and generated the texture synthesized results of the same.
DO FATWAS HAMPER MUSLIM WOMEN’S SOCIO-ECONOMIC PARTICIPATION IN MALAYSIA?

A number of studies show that, in Malaysia, women continue to make significant contributions to the country’s economic and social development. Moreover, women’s access to health and education has increased rapidly over the years. However, some studies claim that socio-economic discrimination against Muslim women persists. Although several factors contribute to this discrimination, fatwas (a legal opinion of Muslim jurists on Muslim affairs) are accused of discriminating against Muslim women in Malaysia such as prohibiting the appointment of female judges in shariah courts, employment of women for night shifts i.e. in the electronic factories, police force, and hospitals. Women’s socio-economic participation is important as it empowers them and creates gender equality. This paper reviews 16 fatwas on five topics relevant to women’s socio-economic participation in Malaysia issued by 11 state muftis (who issue fatwa) during the period of 1970 to 2012 in order to realize whether they hamper their participation. Using a qualitative content analysis approach, this paper concludes that fatwas had neither affected their socio-economic participation nor shaped their life negatively. This paper is hoped to positively shape the society’s impression of fatwas and help Muslim women in Malaysia abide to Islamic teachings by realizing the good impacts of fatwas issued. Keywords: Fatwa, Muslim, women, socio-economic participation, Malaysia. Cite as: Mohammad Monawer, A. T. , Grine, F. , Abdullah, M. F. , & Mohd Nor, M. R. (2019). Do fatwas hamper Muslim women’s socio-economic participation in Malaysia? Journal of Nusantara Studies, 4(1), 232-245. http://dx.doi. org/10.24200/jonus.vol4iss1pp232-245
Geometric reasoning planning in the context of Human-Robot Interaction

In the last few years, the Human robot interaction (HRI) field has been in the spotlight of the robotics community. One aspect of this field is making robots act in the presence of humans, while keeping them safe and comfortable. In order to achieve this, a robot needs to plan its actions while explicitly taking into account the humans and adapt its plans to their whereabouts, capacities and preferences. The first part of this thesis is about human-robot handover: where, when and how to perform them? Depending on the human preferences, it may be better, or not, to share the handover effort between him and the robot, while in other cases, a unique handover might not be enough to achieve the goal (bringing the object to a target agent) and a sequence of handovers might be needed. In any case, during the handover, a number of cues should be used by both protagonists involved in one handover. One of the most used cue is the gaze. When the giver reaches out with his arm, he should look at the object, and when the motion is finished, he should look at the receiver's face to facilitate the transfer. The handover can be considered as a basic action in a bigger plan. The second part of this thesis reports about a formalization of these kind of basic actions" and more complex ones by the use of conditions, search spaces and restraints. It also reports about a framework and different algorithms used to solve and compute these actions based on their description. The last part of the thesis shows how the previously cited framework can fit in with a higher level planner (such as a task planner) and a method to combine a symbolic and geometric planner. The task planner uses external calls to the geometric planner to assess the feasibility of the current task, and in case of success, retrieve the state of the world provided by the geometric reasoner and use it to continue the planning. This part also shows different extensions enabling a faster search. Some of these extensions are \Geometric checks" where we test the infeasibility of multiple actions at once, \constraints" where adding constraints at the symbolic level can drive the geometric search, and \cost driven search" where the symbolic planner uses information form the geometric one to prune out over costly plans.
RFID anti-collision technique: coherent collision

RFID technology has enabled great advances in the asset tracking industry. Shipping, warehouse inventory, and storefront security are just a few examples where the application of RFID systems has increased efficiency and lowered cost. A major area of research at the present time is concerned with optimal ways of reading multiple ID tags using a single reader as efficiently as possible. These methods are called “Anticollision” protocols, and they all seek to somehow arbitrate how a multitude of ID tags and a reader negotiate the process of reading all of the ID tags. As the name implies, nearly all of the methods seek to detect and avoid collisions between ID tags, therefore reading one ID tag at a time. This thesis seeks to present another, novel approach to solving the same problem. The proposed method seeks not to avoid collisions, but rather to orchestrate them in a manner that allows overlapping transmission of the ID tags while identifying all of the ID tags that take part in the transmission. This method will provide a highly time-efficient collision management protocol.
Research on Online Sports Metadata Extraction System based on Video Processing Technology

Sports video metadata extraction system based on the content of basic goal use an automated or semi-automated interactive means to obtain video data as complete features and attributes for efficient retrieval mechanism. For fast access to video information needed, sports video ornamental create conditions. Firstly, video-based layered metadata description model, we discuss the structure of the video processing technology, and an increase in the time domain and airspace video object motion information on this basis. Low-level visual features for video and high-level semantic features presents a particular field of video information for video implicit hierarchical division method. Video automated visual feature extraction, semantic feature places marked attracted achieve human-computer interaction. Focus on the sports information descriptors and visual content descriptors, descriptor structure video. Video data based on hierarchical structure model and video features standard video content description model.
Fueling Climate (In)Action: How organizations engage in hegemonization to avoid transformational action on climate change

This study examines how organizations avoid the urgent need for transformational action on climate change by engaging in a hegemonization process. To show how this unfolds, we draw from Laclau and Mouffe’s discourse theory, focusing on the case of BP and its engagement with the climate change debate from 1990 to 2015. Our study takes a longitudinal approach to illustrate how BP defended its core business of producing and selling fossil fuel products by enacting three sequential hegemonization strategies. These included: adopting new signifiers; building ‘win-win’ relationships; and adapting nodal points. In doing so, we demonstrate how hegemonic construction enables organizations to both incorporate and evade various types of stakeholder critique, which, we argue, reproduces business-as-usual. Our study contributes to organization studies literature on hegemony by highlighting how the construction of hegemony operates accumulatively over an extended period of time. We also contribute more broadly to conversations around political contests and the natural environment by illustrating how the lack of effective climate responses is shaped by temporal dynamics.
The construction of an asymmetric biplot for multivariate data sets

A scatterplot is a useful statistical tool for representing samples as points and variables by means of coordinate axes. A form of scatterplot that can be used for multivariate data is the biplot. In this paper, the fundamental idea behind the biplot is discussed, before its construction. An illustration using an 11-dimensional multivariate data set concerning the relationships between chemical measurements and sensory descriptors is presented.
Review on Human Re-identification with Multiple Cameras

Human re-identification is the core task in most surveillance systems and it is aimed at matching human pairs from different non-overlapping cameras. There are several challenging issues that need to be overcome to achieve reidentification, such as overcoming the variations in viewpoint, pose, image resolution, illumination and occlusion. In this study, we review existing works in human re-identification task. Advantages and limitations of recent works are discussed. At the end, this paper suggests some future research directions for human re-identification.
An Expert System using Conditional Rules

In this paper, fuzzy condition rules are studied to improve video format conversion. The propose method consists of three parts: (1) system consideration, (2) image quality assessment, and (3) adjusting parameters. By adopting hedges in membership function, we apply additional effects for fuzzy operations. In addition, by providing condition rules for four fuzzy sets ‘fast’, ‘slow’, ‘homogeneity’, and ‘heterogeneity’, we could improve restored images. The hedges are applied in each fuzzy set depending on the input values, which provides additional effect. Experimental results show that the proposed method outperforms two benchmark methods.