Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy efficient planning and operation models for wireless cellular networks

Prospective demands of next-generation wireless networks are ambitious and will require cellular networks to support 1000 times higher data rates and 10 times lower round-trip latency. While this data deluge is a natural outcome of the increasing number of mobile devices with data hungry applications and the internet of things (IoT), the low latency demand is required by the future interactive applications such as ”tactile internet”, virtual and enhanced reality, and online internet gaming, etc. The motivation behind this thesis is to meet the increasing quality of service (QoS) demands in wireless communications and reduce the global carbon footprint at the same time. To achieve these goals, energy efficient planning and operations models for wireless cellular networks are proposed and analyzed. Firstly, a solution based on the overlay cognitive radio (CR) along with cooperative relaying is proposed to reduce the effect of the scarcity problem of the radio spectrum. In overlay technique, the primary users (PUs) cooperate with cognitive users (CUs) for mutual benefits. The achievable cognitive rate of two-way relaying (TWR) system assisted by multiple antennas is proposed. Compared to traditional relaying where the transmission to exchange two different messages between two sources takes place in four time slots, using TWR, the required number of transmission slots reduces to two slots. In the first slot, both sources transmit their signals simultaneously to the relay. Then, during the second slot the relay broadcasts its signal to the sources. Using an overlay CR technique, the CUs are allowed to allocate part of the PUs’ spectrum to perform their cognitive transmission. In return, acting as amplify-and-forward (AF) TWR, the CUs are exploited to support PUs to reach their target data rates over the remaining bandwidth. A meta-heuristic approach based on particle swarm optimization algorithm is proposed to find a near optimal resource allocation in addition to the relay amplification matrix gains. Then, we investigate a multiple relay selection scheme for energy harvesting (EH)-based
MAGIC Highlights

MAGIC has been exploring the sky at Very High Energy gamma-rays (50 GeV - 50 TeV) since 2004, operating first with a single telescope and from 2009 with two telescopes in stereoscopic mode. MAGIC has carried out a observational program involving fundamental physics and astrophysics topics. In this paper we present some of the most important results obtained by MAGIC, with a special emphasis in the most recent ones.
Mapping Marine Ferrous Targets Using the SeaQuest Gradiometer System

The purpose of the EMFM system is to measure the relatively small electromagnetic signatures of unmanned undersea vehicles, so selection of a magnetically quiet location was crucial to the success of the project. The area was deemed likely to contain quantities of scrap metal from historical and industrial activities (such as the nearby torpedo testing range) that could interfere with the operation of the EMFM system. The objective of the survey was to locate the geographic position and depth of burial of all ferro-metallic objects within the survey area that would generate anomalies greater than one nanoTesla (nT) and gradients greater than 3 nT/m at a distance of less than 25 m. With that information, the optimum locations for the EMFM System sensors could then be determined.
JoSEL : A Job Specification and Execution Language for Model Checking

Model checking tools and techniques are being applied for verification of concurrent systems by users having different skills and background. This ranges from formal methods experts with detailed knowledge of the inner workings of the tools over students learning about model checking techniques to engineers that are mostly interested in applying the technology as a black-box. This paper proposes JoSEL, a visual language for specification of executable model checking jobs. JoSEL makes it possible to work at different levels of abstraction when interacting with model checking tools and thereby support the different kinds of users in a coherent manner. A verification job in JoSEL consists of tasks, ports, and connections describing the models to be verified, the behavioural properties to be checked, and the model checking techniques to be applied. A job can then be mapped onto an underlying model checking tool for execution. We introduce the syntax of JoSEL, informally define its semantics, and describe how it has been realised in the ASAP model
A 128-BIT SECRET KEY GENERATION USING UNIQUE ECG BIO-SIGNAL FOR MEDICAL DATA CRYPTOGRAPHY IN LIGHTWEIGHT WIRELESS BODY AREA NETWORKS

In wireless Body Area Sensor Networks, the communication with the In-body Medical sensor node placed inside the patient must be crypto graphed to protect it. Existing wireless sensor network modes are not suitable for Wireless Body Area sensor Networks (WBAN) as these are resource constrained devices. Securing the implanted device in WBAN is essential for preserving not only the privacy of patient data, but also for ensuring safety of healthcare delivery. This paper presents a secret key generation scheme from the ECG signal parameters and allows device authentication. The proposed model allows the doctor to generate secret key from the ECG signal parameter of the patient without using the battery power of IBS node and providing authentication for both nodes without any initialization or pre-deployment of secret key. Simply we can deploy the sensor in a WBAN and make them to communicate securely (a plug and play model). In our analysis, result and compared with other key generation protocols. Showing that proposed method is a viable key generation and key agreement model for WBAN.
Earp ’ s High Noon ? Rejoinder to Pitt

Mark Pitt (Pitt, 2012a MPa; see also Pitt 2012b – MPb, jointly MP) does indeed find many problems with our paper (“High Noon for Microfinance Impact Evaluations”, Duvendack and Palmer-Jones, 2012a DPJ), and provides what he terms a replication of our replication of Chemin (2008 Chemin) and Pitt and Khandker (1998 PnK). MP also makes a number of comments of a combative and belligerent nature. While owning up to many, but not all, of the problems identified in DPJ, we find significant problems with MP’s replication and show that it provides little support for PnK’s headline finding that microfinance benefits the poor especially when provided through women. MP’s derogatory texts are not conducive to understanding the issues involved and coming to reflective appreciation of what can be learnt from the various studies. These replicatory exchanges, together with those with Matthieu Chemin (DPJb), have provided significant evidence on the lack of validity of Chemin, cast further doubt on the conclusions of PnK, and do not contradict DPJ’s conclusion that PnK suffers from a weak research design which does not appear to be remediated by the use of complex analytical methods. Introduction Mark Pitt’ reply (MPa) to our paper “High Noon” (Duvendack and Palmer-Jones, 2012a DPJ) is the original a version of that published in JDS (Pitt, 2012b MPb) after editing by one of the JDS editors. In this paper we provide a more expansive rejoinder than that published in JDS (DPJc). We reply to the “strident” (Hamermesh, 2007) as well as the substantive issues raised by MP, including MP’s application of propensity score matching. We accept we made many errors in our paper, but many of them derive from Chemin and others from the poor state of the PnK data. We show that propensity score matching (PSM), when “properly“ applied to replicate PnK may “indicate positive and highly significant results of women’s participation in microfinance in every one of the matching estimators”, but when subjected to sensitivity analysis they are highly vulnerable to unobserved confounding variables, which are highly likely to be present i.e. we cannot assume ignorability or unconfoundedness (Caliendo and Kopeinig, 2008). This vulnerability, together with the theoretical reasons (de Aghion and Morduch, 2005) and ethnographic evidence (Fernando, 1997) for expecting such confounding unobservables to be present, would seem to suggest that these results do not provide robust support for the headline findings of PnK. We provide a rather more convincing PSM analysis of the PnK data 1 School of International Development, University of East Anglia, Norwich, NR4 7TJ, UK. r.palmerjones@uea.ac. uk; m.duvendack@uea.ac. uk 2 Not RPJ. The review process of DPJ and the various replies was handled entirely independently by one of the other JDS managing editors. 3 By sensitivity analysis we mean the application of Rosenbaum bounds; since this currently can only be done for n nearest neighbour matching our conclusion applies to PSM results obtained with this method, although we have no reason to believe that they are not generally applicable. See further below.
Deep Semi-Supervised Embeddings for Dynamic Targeted Anomaly Detection

Dynamic targeted anomaly detection classification problems can fool traditional machine learning models. While it is relatively simple to delineate between average members of the majority class and the target class, it is tremendously difficult to differentiate between anomalous members of the majority class and the target class. We propose a neural network architecture that is able to tackle this problem in its entirety, by acknowledging and attending to each level of the classification hierarchy. We propose chaining a deep semisupervised autoencoder with a supervised neural network, whose inputs are filtered latent space representations generated by the semi-supervised encoder. Our model is able to intelligently delineate both between conventional members of the majority class and the target class, and further, between anomalous members of the majority class and the target class. By tackling these interdependent hierarchically, and with deep architectures, we boast both greater control over the classification task and richer model expressiveness overall. Finally, we demonstrate the modularity of the proposed architecture, as allowing for parametrization of each component with LSTMs and CNNs.
RGBD Camera Effects

With the advent of RGBD cameras, like the Kinect sensor, we gain a powerful tool for image processing. The nature of the captured data (i.e. colored image and depth data) could be combined to realize tasks that before were very difficult. In this paper, we will discuss some camera effects done using RGBD images. These effects will be direct, such as image filtering and stereo image creation, or indirect, such as visualization using head tracking. Keywords-RGBD Image; Camera effects; Filtering; Head Tracking; Stereo Image
Estimation of affect scores accounting for user responsiveness

Performance of interaction robots can be improved significantly, if they can adapt the services to the varying intrinsic, i.e. cognitive or emotional, state of users. Especially robots for dementia or autism threaphy may profit from such adaptations [1, 2, 3]. Estimation of user’s state can be obtained by observing the individual user’s behavioral or biological responses. Such observations enable tailoring the content to specific users. On the other hand, it is also possible to apply direct analysis of the stimuli in order to design a broad-gauge scenario for the general tendency of anticipated response. In this respect, this study contrasts these the user specific and broad-gauge approaches for estimating users’ state and provides a discussion on bounding performance rates. To that end, we expose human subjects to emotionally stimulating visual content over a prolonged period and try to build a model of the relation between the stimulus intensity and decline in user’s responsiveness. We contrast this approach to direct estimation of emotional response, and particularly arousal, from the stimuli.
Characterizing the Effects of Rapid LTE Deployment: A Data-Driven Analysis

The deployment of LTE, while old news to developed countries, is ongoing in developing countries with some starting LTE deployments only a year ago. LTE is essential for developing countries where broadband access is mostly available through wireless connection. The process of LTE deployment is costly and time consuming. Network operators attempt to minimize both overheads. With the current challenges facing network operators to acquire new sites, 4G deployment always depends on reusing the already existing 2G/3G macro layer. In this paper, we study the approach of rapid 4G deployment by analyzing data from a major network operator in a developing country. In particular, we look at network measurements from a single large cluster right after LTE deployment. We analyze the impact of the deployment approach on user throughput and find that LTE throughput could be improved further for the majority of LTE users in more than 50% of the studied cells by improving either interference or coverage. We find that this is mainly because the LTE system is more sensitive to interference when compared to 3G. Finally, we show a data-driven approach to detect the affected cells and mitigate the issue through physical optimization, this approach balances employed LTE transition best practices with cost efficiency and rapid deployment.
SEISMIC AND TSUNAMI HAZARD IN THE SHORELINES OF KHARK ISLAND ; PERSIAN GULF , IRAN

The most conservative scenarios are determined in this study for possible earthquakes within the Khark zone (Persian Gulf) based on experimental relations between the fault length, magnitude and displacement, which are parameters for determining tsunamigenic sources. Subsequently, the maximum height of tsunami waves are calculated based on the specifications of the seismic source and its distant from the shore, as well as the coastal slope. A zoning map of tsunami hazard is finally presented. The maximum calculated wave height is 3.5 m. No serious hazard is posed to the structure by sources 1 and 3 due to the shallow depths at these sites. The maximum potential wave height are 30 and 60 cm, respectively. A far distance source produces waves heights of almost 1.5 m along the shore lines of Khark Island, despite. This is due to the expected magnitude of this source as well as its location in the deep waters of the Oman Sea.
Named Entity Recognition in Digitized Historical Texts

The thesis at hand presents an approach at automatically recognizing Named Entities in legal documents written in late medieval and early modern variants of German and French. It describes the transformation of the digitized texts into a structured XML format, while exemplifying how resources for tokenization and OCR-processing can be adapted and applied to this end. Named Entities are extracted by exploiting indices of place and person names, and subsequently detected in the texts via approximate string matching techniques. The resulting pre-annotated texts are then tagged with a ready-made Named Entity Recognition tool intended for the modern language. Ultimately, by training and testing own Machine Learning models, the thesis aims at illustrating the main possibilities and limitations characteristic of historical data.
Chapter 1 Systemic Inflammatory Response in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma induces systemic inflammatory response (SIR), which can be assessed either by ratios between blood cell counts (neutrophil to lymphocyte ratio, NLR; platelet to lymphocyte ratio, PLR) or concentrations of acute phase proteins, clotting factors and albumins. These tests are biologically justified by multiple events including bone marrow activation, development of immune-suppressing immature myeloid cells, generation of pre-metastatic niches and neutrophil extracellular trap formation from externalised DNA network in bidirectional association with platelet activation. Despite biological complexity, clinical assessment of SIR is widely available, patient-friendly and economically feasible. In this chapter, we present a review on NLR, PLR, Glasgow prognostic score and fibrinogen, recently reported to have a prognostic role regarding overall survival, cancer/progression free and cancer-specific survival in early and advanced pancreatic ductal adenocarcinoma. Practical consequences abound, including preference for surgical or combined, active or sparing treatment, as well as prediction of non-resectability or chemotherapy response. In this chapter, we also scrutinise the main controversies including different cut-off levels, hypothetic correlation with tumour burden and morphology, negative findings and discussions on the best marker. Future developments should include elaboration of complex scores as will be described here.
Influence of fever on surgical intervention of parapneumonic empyema

Background: Fever is the most common symptom in parapneumonic empyema. However, few studies have investigated the influence of fever on surgical intervention of empyema. This study aimed to evaluate the influence of fever on surgical intervention of empyema. Methods: Patients were divided into two groups according to ear temperature with 38.0°C as a threshold. All patients initially received video-assisted thoracic surgery (VATS), and then open thoracotomy if necessary. Relative variables including between two groups were compared and the predictors of conversion to thoracotomy were analyzed by multivariate analysis. Results: 65.1% of patients presented with fever. Fever was associated with less preoperative chest tube drainage, shorter delay in operation, higher pH and glucose in pleural effusion and earlier stage (all P<0.001). Fever had no influence on the intra-operative and post-operative variables, but patients with fever showed lower rate of conversion (P<0.001). After stratification by conversion, patients with fever could benefit from VATS, particularly in blood loss and operative time. In multivariate analysis, delay in treatment was a predictor of conversion (odds ratio: 1.589, 95% confidence interval: 1.0362.438, P = 0.034); however, fever was a protective factor of conversion (odds ratio: 0.032, 95% confidence interval: 0.001-0.708, P = 0.029). Conclusions: Fever is associated with preoperative modality, delay in treatment, pH and glucose in pleural effusion and empyema stage. By contrast, fever is not an inverse predictor of surgery conversion. More fever patients will benefit from minimally invasive surgery.
International Review of Law and Economics o anti-bullying policies deter in-school bullying victimization ?

Despite the significant increase in the number of anti-bullying laws between 2000 and 2015, there is little evidence on whether such policies can decrease the amount of bullying that occurs on school grounds. In this paper, I evaluate the effectiveness of bullying laws on decreasing the share of students who experience in-school bullying victimization using a difference-in-difference framework. The school-level results show that schools in states with such laws had less reported school bullying incidents (up to 8.4%) compared to schools in states without anti-bullying laws, and these effects are much stronger in states where there is a specific clause in the law defining the term bullying. Falsification tests for other crime-related behaviors, on which the anti-bullying laws should not have an effect, corroborate a causal
Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks

Detection of adverse drug reactions in postapproval periods is a crucial challenge for pharmacology. Social media and electronic clinical reports are becoming increasingly popular as a source for obtaining health related information. In this work, we focus on extraction information of adverse drug reactions from various sources of biomedical textbased information, including biomedical literature and social media. We formulate the problem as a binary classification task and compare the performance of four state-of-the-art attention-based neural networks in terms of the F-measure. We show the effectiveness of these methods on four different benchmarks.
Eﬃcient Large-Scale Media Selection Optimization for Online Display Advertising: Web Appendix

Danaher et al. (2010) provides one state-of-the-art method for optimal budget allocation of Internet display ads. Danaher considers a traditional advertising setup, in which costs are fixed and a small number of websites are considered for advertising purposes. A basic premise of this method is that the number of web pages viewed by individuals at websites (denoted as a n by p matrix Z in our context) can be characterized by a multivariate negative binomial distribution (referred to as MNBD hereafter). In particular Danaher’s method models the full exposure distribution as follows:
Interpreting Self-Attention Weights

A key component of designing effective neural architectures is having an understanding of how the models and the data interact. Recent work in NLP has developed large-scale, pre-trained stacks that can be applied to downstream NLP tasks with fine-tuning, revolutionizing NLP in the same way pre-trained ImageNet revolutionized computer vision. Understanding these nascent pre-trained stacks will give us a better understanding of the limitations of these methods, enabling further improvements to the their capabilities. In this work, we analyze the behaviour of a Directional Self-Attention Network (DiSAN) model, pre-trained on the SNLI dataset, to understand the underlying linguistic structure it learns from the data. We propose a method for discovering latent structure encoded in the self-attention weights by measuring the KL divergence between attention distributions and our proposed dependency distributions. Quantitative and qualitative experiments show that DiSAN does in fact encode some degree of linguistic structure.
TSP tabu search heuristic algorithm for the split delivery vehicle routing problem

The split delivery vehicle routing problem (SDVRP) is a relaxed problem of the classical capacitated vehicle routing problem (CVRP), which allows that each customer can be visited for multiple times while the CVRP allows only once. And it is also an NP-hard problem. In this paper, a tabu search heuristic algorithm with TSP cutting and splitting based on a bi-level program model of SDVRP is proposed. The bi-level programming model redefined the objective function of the SDVRP. The whole cost of the solution can be seemed as the cost of a big TSP path and the increased cost of cutting and splitting. So, the algorithm includes three phases. First, a big TSP path including the distribution depot and all the customers is constructed. Then, the big TSP is cut and split. And in the third phase, the algorithm solves the traveling salesman problem within each route to improve the solution. Finally, a computational study shows that the proposed method can really solve the SDVRP effectively on the benchmark instances in the SDVRP literature.