Miracle-dz/UniFAR-FTUs huggingface dataset:
From Raw Data to Social Systems - Separating the Signal from the Noise in Smartphone Sensor Measurements

Digital tools for communication and information exchange are now deeply ingrained in our everyday routines. This fact, however, does not mean that our lives have fully migrated to the digital domain. Even though the Internet makes it possible to survive without ever leaving the confines of our bedrooms, we still choose to meet our friends in person or to travel through physical, rather than virtual, space. There is a richness to personal contact and direct experience that has not yet been replaced by digital services. Until this shift happens, we continue to analyze and investigate our offline lives in the pursuit for deepening our understanding of humans and societies. The digital traces, which we leave behind with every online activity, are relatively easy to collect. On the other hand, capturing our offline behaviors remains a challenge. Scientists often rely on data that approximates only one facet of our lives. For example, mobile operator records reveal who we call, but not who me meet. An alternative approach is to derive proxies of certain behaviors from smartphone sensor readings. Copenhagen Networks Study (CNS) employs this method, among others, to build the largest dataset of the kind available to academics. From a bird’s eye view, the thesis shows a path from collecting raw smartphone data for CNS, through extracting increasingly meaningul information, to gaining novel insights into human behavior. Step by step, the goal is to turn a cryptic collection of hardware identifiers and received signal strengths into a detailed record of people’s mobility, person-to-person interactions, and social ties. The methods for location and interaction sensing that I propose in the thesis constitute a more privacy-aware alternative to currently employed approaches. At the same time, the findings presented in this work emphasize the fragility of our privacy: the data we today consider as safe to share, might prove to carry unexpected and rich information about our lives tomorrow.
Wind turbine control and model predictive control for uncertain systems

This paper describes a nonlinear control law for controlling variable speed wind turbines using feedback linearization. The novel aspect of the control law is its ability to decouple the effect of wind fluctuations. Furthermore, the transformation to feedback linearizable coordinates is chosen intelligently so that the majority of the system structure is invariant under the transformation. Consequently the physical interpretation is preserved. The method assumes that the effective wind speed and acceleration are estimated from measurements on the wind turbine. The performance of the control is compared to that of a LQG controller using a specific wind turbine and wind model.
MEDAN INTERNATIONAL CONFERENCE ON ADVANCE PHARMACEUTICAL SCIENCES 2014, NOVEMBER 3-4 Adherence to Prescribed Medications in Patients with Hypertension in Puskesmas Medan Oleh:

Introduction: Poor adherence to medications in management of patients with hypertension contributes to increased morbidity and mortality. Purpose: This study aimed to analyze adherence to medications in management of patients with hypertension. Method: This study was undertaken by distribution a questionnaire to patients with hypertension (n=86) for one-month period in four Puskesmas in Medan (Puskesmas is an Indonesian functional health organization that provides primary health care for people that reside in a certain area). Inclusion criteria were patients diagnosed with hypertension. Data collected include gender, age, education, the administered antihypertensive drugs, and adherence to medications. Characteristics of the patients were analyzed using descriptive statistics in SPSS program version 19. Adherence to medication was analyzed applying Morisky scaling method (8 = good, 6-< 8 = fair, < 6 = poor). Result: Characteristics of the patients: male, 25.6%, female, 74.4%, mean age, 62.03 with standard deviation of 10.23 years. Education of the patients: Primary school, 32.6%; Junior high school, 20.9%; Senior high school, 36.0%; University, 10.5%. The patients’ adherence to medication was poor (scale = 3.67). Gender had no impact on the patients’ medication adherence, p=0.39. Also, education had no impact on the patients’ medication adherence, p=0.2. Conclusion: Adherence to medications among patients with hypertension was still poor. In the future, adherence problems must be highlighted to improve the patients’ outcomes.
Small RNA metabolism and function in Arabidopsis

The transcriptome of plants includes diverse species of 21‐24 nucleotide small RNAs, which are composed of microRNAs (miRNAs) and endogenous small interfering RNAs (siRNAs). miRNAs regulate target mRNAs through transcript cleavage or translational inhibition and play crucial roles in various developmental and physiological processes. Endogenous siRNAs trigger and maintain DNA and histone methylation leading to heterochromatin formation.
Analysing Product Utilization by Islamic Retail Banks: The Case of Bahrain Islamic Bank and Kuwait Finance House-Bahrain

Having its total assets to be estimated at USD 2.05 trillion as of 2017, the Islamic financial industry seems to have experienced a tremendous growth within a short span of time. The Kingdom of Bahrain has arguably been considered as one of the global Islamic financial hubs. Yet, a review of literature suggests that contemporary Islamic finance researches that use this jurisdiction as a case study seems to be minimal, indicating an existence of an imperative research gap. The purpose of this research was to partially fill this gap by analysing the main Islamic financial products utilized in the Islamic retail banking industry in the Kingdom of Bahrain, including the reasons for its utilization. This research qualitatively and empirically analysed the utilization of Islamic financial products by two Islamic retail banks in the Kingdom of Bahrain, namely Bahrain Islamic Bank and Kuwait Finance House-Bahrain. The findings indicate that murābaha, tawarruq, and ijāra muntahia bil tamlik are the main Islamic financial products utilized for financing purposes, while mudhāraba is the main concept utilized for the establishment of savings/investment accounts.
Self-Supervised Learning of Interpretable Keypoints From Unlabelled Videos

We propose a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses. Video frames differ primarily in the pose of the objects they contain, so our method distils the pose information by analyzing the differences between frames. The distillation uses a new dual representation of the geometry of objects as a set of 2D keypoints, and as a pictorial representation, i.e. a skeleton image. This has three benefits: (1) it provides a tight 'geometric bottleneck' which disentangles pose from appearance, (2) it can leverage powerful image-to-image translation networks to map between photometry and geometry, and (3) it allows to incorporate empirical pose priors in the learning process. The pose priors are obtained from unpaired data, such as from a different dataset or modality such as mocap, such that no annotated image is ever used in learning the pose recognition network. In standard benchmarks for pose recognition for humans and faces, our method achieves state-of-the-art performance among methods that do not require any labelled images for training. Project page: http://www.robots. ox.ac. uk/~vgg/research/unsupervised_pose/
Matrix Algebra Problems with Applications

I-1. Factoring a matrix into the product of two matrices is the basis of many methods in multivariate analysis. Choleski’s method involves factoring a matrix into the product of two triangular matrices, one of which has 0s above the diagonal, the other below the diagonal. Fill in the ? entries in the following matrix equations: Hint: Let each ? be an unknown letter; solve the equations. (a) [ 5 10 2 7 ]
Consequences of FTA Withdrawal: Evidence from EU Enlargement

Political opposition to trade agreements has been on the rise, suggesting that some existing agreements may be severed in the future. But so far very little is known about the potential e ects of withdrawal from trade agreements, because economic integration has been largely a “one–way street” in recent decades. This paper studies a notable exception: Estonia withdrew from its FTA with Ukraine in the course of its EU accession. Through a triple di erence–in– di erences strategy, I estimate that Ukrainian imports to fell by more 20%. The result is robust to di erent speci cations and obtains in an environment where preference margins were low. This suggests that loss of FTAs can be very costly for trade.
Comparative evaluation of performance-boosting tools for Python

– The Python programming language has a number of advantages, such as simple and clear syntax, concise and readable code, and open source implementation with a lot of extensions available, that makes it a great tool for teaching programming to students. Unfortunately, Python, as a very high level interpreted programming language, is relatively slow, which becomes a nuisance when executing computationally intensive programs. There is, however, a number of tools aimed at speeding-up execution of programs written in Python, such as Just-in-Time compilers and automatic translators to statically compiled programming languages. In this paper a comparative evaluation of such tools is done with a focus on the attained performance boost.
Empirical Tests of the Gradual Learning Algorithm

The Gradual Learning Algorithm (Boersma 1997) is a constraint-ranking algorithm for learning optimality-theoretic grammars. The purpose of this article is to assess the capabilities of the Gradual Learning Algorithm, particularly in comparison with the Constraint Demotion algorithm of Tesar and Smolensky (1993, 1996, 1998, 2000), which initiated the learnability research program for Optimality Theory. We argue that the Gradual Learning Algorithm has a number of special advantages: it can learn free variation, deal effectively with noisy learning data, and account for gradient well-formedness judgments. The case studies we examine involve Ilokano reduplication and metathesis, Finnish genitive plurals, and the distribution of English light and dark /l/.
Context-aware security and secure context-awareness in ubiquitous computing environments

Context-awareness emerges as an important element of future wireless systems. In particular, concepts like ambient intelligence and ubiquitous computing rely on context information in order to personalize services provided to their target users. However, security implications of employing context-awareness in computing systems are not well understood. Security challenges in context-aware systems include integrity, confidentiality and availability of context information, as well as target user’s privacy. Another interesting and open question is to what extent availability of additional context information could be used in order to optimise and reconfigure security-related services.
Accurate Anonymization of Surgical Video using Semi-Supervised Learning

Large labeled datasets are an important precondition for deep learning models to achieve state-of-the-art results in computer vision tasks. In the medical imaging domain, privacy concerns have limited the rate of adoption of artiﬁcial intelligence methodologies into clinical practice. To alleviate such concerns, and increase comfort levels while sharing and storing surgical video data, we propose a high accuracy method for rapid removal and anonymization of non-relevant surgery segments. Training a deep model to detect out-of-body and non-relevant segments in surgical videos requires suitable labeling. Since annotating surgical videos with per-second relevancy labeling is a tedious task, our proposed framework initiates the learning process from a weakly labeled dataset and iteratively applies Semi-Supervised Learning (SSL) to re-annotate the training data samples. Evaluating our model, on an independent test set, shows a mean detection accuracy of above 97% after several training-annotating iterations. Since our ﬁnal goal is achieving non-relevant segments anonymization, we evaluate our ability to detect these segments at a high demanding recall of 97%, which leads to a precision of 83.5%. We believe this approach can be applied to similar related medical problems, in which only a coarse set of relevancy labels exists, currently limiting the possibility for supervision training.
Classic Rational Bubbles in U.K. Housing Markets

In Giglio, Maggiori and Stroebel (2016), we propose and implement a new test for classic rational bubbles. Such bubbles derive their value from each agent’s rational expectation of being able to resell the bubble claims to the next agent. Backward induction ensures that classic rational bubbles can only exist on infinite-maturity assets. Our empirical exercise shows that infinite-maturity claims and 999-year claims for otherwise identical housing assets trade at the same price, and thus rules out the presence of classic rational bubbles. Domeij and Ellingsen (DE) informally propose an alternative equilibrium of a bubble that they claim is consistent with our empirical findings. DE’s bubble relies on information frictions such that market participants are unaware of the bubble. Our paper clearly excluded this type of bubble from the scope of our test, and DE’s note thus has no implications for the validity of our test. Instead, DE’s bubble simply represents one of many possible examples of bubbles on which our test was explicitly silent.
Evolutionary Patterns and Processes in the Desert-Adapted Fern Genus Myriopteris (Pteridaceae)

iv List of Tables . viiii List of Figures . viii Acknowledgements . ix Introduction . 2 I. Deciphering the origins of apomictic polyploids in the Cheilanthes yavapensis complex (Pteridaceae) . 5 II. Toward a monophyletic Cheilanthes: the revision and recircumscription of Myriopteris (Pteridaceae) . 28 III. Patterns of diversification in the xeric-adapted fern genus, Myriopteris (Pteridaceae) . 54 IV. Investigating the origin of genetic variation in apomictic triploid Myriopteris lindheimeri . 94 V. Further Contributions. 122 Appendices . 124 Literature Cited . 141 Biography . 155
A Toolbox for RFID Protocol Analysis

Many RFID tags and contact less smart cards use proprietary security mechanisms for authentication and confidentiality. There are several examples in the literature showing that once these mechanisms have been reverse engineered, their security turns out to be unsatisfactory. Since the use of these tags is quickly expanding to access control and ticketing systems, it is important to independently assess their security. In this paper, we propose three tools for the analysis of RFID protocols. These tools facilitate message eavesdropping and emulation of both tags and readers. The tools focus on high frequency tags but one of them also supports low frequency. These tools are fully programable and allow for quick prototyping, testing and debugging of new RFID protocols. All the software, firmware and hardware we have developed that is described here is open source and open design.
What Influences the Willingness of Chinese WeChat Users to Forward Food-Safety Rumors?

With the rapid development of social media, the management of online rumors on food safety has become an important issue. The precondition of effectively preventing and treating food safety problems lies in discovering the factors that influence the spreading of rumors. In this exploratory study, we investigate the factors that influence the willingness of Chinese WeChat users to forward food-safety rumors from the perspective of rumor audience. We first construct a theoretical model and put forward certain hypotheses relating variables such as rumor-content credibility, source credibility, and audience’s quality of rational criticism that influences the willingness to forward food-safety rumors. The empirical results show that WeChat food-safety rumors are characterized by obvious narrative features, dependence on professional sources, and highly realistic resonance. Stronger altruistic motive, content credibility, and source credibility lead to stronger audience willingness to forward rumors. However, the government and other authorities are generally viewed as ineffectively coping with the spread of such online rumors. This study’s findings suggest several ways to improve their counterrumor efforts on social media.
Comparison Between Ultrasound- and Bronchoscopy-guided Percutaneous Dilational Tracheostomy in Critically Ill Patients

To compare the clinical efficacy, safety, and prognosis between fiberoptic bronchoscopy-assisted percutaneous dilatation tracheostomy and real-time ultrasound-guided percutaneous dilatation tracheostomy, and evaluate their clinical application value for further clinical use. From January, 1, 2018, to November 1, 2019, 64 newly admitted patients in our hospital undergoing percutaneous dilatation tracheostomy were included in the study. They were divided into two groups, the random fiber bronchoscopy-assisted percutaneous dilatation tracheostomy group (control group) and the modified bedside ultrasound-guided percutaneous dilatation tracheostomy group (study group), according to the random number table method, with 32 patients in each group. Subsequently, the intraoperative and postoperative complications and clinical prognostic indicators of the two groups were compared. There were statistically significant differences between the two groups in the number of punctures, puncture time, and incidence of air sac leakage (P 0.05). Real-time ultrasound-guided percutaneous dilatation tracheostomy can reduce the number of punctures in the air sac with no further complications and clinical prognosis.
Ocelot (Leopardus pardalis).

The Ocelot is a beautiful mediumsized spotted cat with body dimensions similar to the bobcat (30-41 inches long and 15-30 lbs). Its body coloration is variable; with the upper parts gray or buff with dark brown or black spots, small rings, blotches, and short bars. A key feature is the parallel stripes running down the nape of the neck. The under parts are white spotted with black. The Ocelot’s long tail is ringed or marked with dark bars on the upper surface. The backs of the rounded ears are black with a white central spot.
Competition Between Reinforcement Learning Methods in a Predator-Prey Grid World

Tabular and linear function approximation based variants of Monte Carlo, temporal difference, and eligibility trace based learning methods are compared in a simple predator-prey grid world from which the prey is able to escape. These methods are compared both in terms of how well they lead a prey agent to escape randomly moving predators, and in terms of how well they do in competition with each other when one agent controls the prey and each of the predators is controlled by a different type of agent. Results show that tabular methods, which must use a partial state representation due to the size of the full state space, actually do surprisingly well against linear function approximation methods, which can make use of a full state representation and generalize their behavior across states.
A Behavioral Model for Analysis and Intervention of Healthy Dietary Behavior

Proper diet is an important way to improve and maintain health, which involves the comprehensive matching of food (category, quantity) and individual (physical fitness, health). Behavior is the key point to achieve this matching. Without behavior change, all cognition and motivations can’t get any tangible health benefits. The aim of this study was to construct a model for analysis and intervention of healthy dietary behavior (HDB). Based on the Integrated Behavioral Model (IBM), Maslow’s hierarchy of needs and Satter’s hierarchy of food needs, this study abstracted the characteristics of longevity, specificity and uncertainty of Healthy Dietary Behavior, constructed a Healthy Dietary Behavioral Model (HDBM), divided 9 negative behaviors of healthy diet (NBHD), and put forward 9 behavior interventions, which could provide ideas for change and habit formation of HDB.
A hierarchical fusion framework to integrate random projectionbased classifiers: application in head and neck squamous carcinoma

: Ensemble methods achieves better performance than single classifier model. Classifier diversity and fusion architecture are equally important for building a successful multi-classifier system. In this study, we introduced random projection to obtain the required classifier diversity and then proposed a hierarchical framework, namely a novel hierarchical fusion integrating random projection diversified classifiers (HFRPC). The proposed hierarchical fusion scheme was validated on survival prediction of head and neck squamous carcinoma cancer (HNSCC). Experimental results have demonstrated the superiority of the proposed HFRPC framework over the base classifier member and the state-of-the-art benchmark ensemble methods, rendering it a potential tool to assist medical decision making in the practical clinical setting.
Scene Linking Annotation and Automatic Scene Characterization in TV Series

In the context of a large collection of multimedia documents, creating links between documents or scenes can help to organize the collection. For TV series, this organization can be achieved by means of narrative structure extraction through scene linking. Narrative characteristics such as speaking characters, entity mentions and theme can be used to characterize scenes. The linking of scenes can be between scenes inside an episode, between scenes in di ↵ erent episodes and/or in di ↵ erent seasons, since stories in TV series progress at di ↵ erent level of granularity. In this work, we have annotated the links between the scenes of the ﬁrst two seasons of the TV series Game of Thrones, using predeﬁned stories and sub stories. We have also automatically extracted the narrative characteristics of each scene. The dataset is composed by 444 scenes, involving 154 speaking character organized in 46 stories divided into 151 sub stories and 5 sub sub-stories.
Speed scaling in fork-join queues: a comparative study

Frequency scaling plays an important power-saving role in computer systems. In fork-join systems, dynamic adaptation of the server speeds can significantly reduce system power consumption while maintaining high throughput. In previous work, we studied a rate adaptation policy that dynamically chooses server speeds based on the difference in join-queue lengths, with each server knowing only its own join-queue length and that of one other server. In this work, we increase the information available to each server, and choose speeds based on the knowledge of the join-queue lengths of two other servers. We show that, under a specific canonical configuration of the service rates, the new system has exactly the same throughput and subtask dispersion as before, but with reduced power consumption. We use time-reversal analysis to derive the exact stationary performance of this new model under saturation conditions, and use simulation to study more general cases.