Miracle-dz/UniFAR-FTUs huggingface dataset:
An Entity-Based Fine-Grained Geolocalization of User Generated Short Text

Recently, the fine-grained geolocalization of user-generated short text (UGST), which can benefit many location-based applications, has been attracting the attention of academica. The semantic information in UGST is seldom introduced in most existing work, which reduces the effectiveness of existing methods. To address this issue, we propose an entity-based fine-grained geolocalization of UGST, which consists of following steps. (1) We employ location-based social network to model the coupling between entities and locations, which can introduce much semantic information. (2) We extract entities from non-geotagged UGST, and discards this UGST if it has not location-related entities. Otherwise, (3) we utilize the built coupling model to rank the candidate locations for this UGST, and then select top <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> locations as the result. The experiments demonstrate that our method shows marked improvement on <inline-formula> <tex-math notation="LaTeX">$Accuracy\text{@}1km$ </tex-math></inline-formula> and <italic>average error distance</italic> compared to the state-of-the-art FRV, WMV and LW methods.
A 192-pW Voltage Reference Generating Bandgap– $V_{\text{th}}$ With Process and Temperature Dependence Compensation

This article presents a methodology to design a circuit to compensate for process skew by exploiting an inherent dimension-dependent effect of process skew on change in the threshold voltage. We design a voltage reference circuit with a hybrid architecture of bandgap reference (BGR) and CMOS reference, which generates a nominal voltage level of (bandgap - threshold). By compensating the process skew of the threshold term with the proposed dimension-induced effect as well as the temperature dependence, the circuit achieves the simultaneous benefits of BGR and CMOS references. For verification, the circuit was fabricated in three wafers of a 0.18- $\mu \text{m}$ CMOS including extreme slow and fast corners. With an active area of 0.0045 mm2, it consumes 192 pW at room temperature. Measurement from 45 chips (15 chips per wafer) shows untrimmed process/voltage/temperature variations of 0.53%, 0.020%/V, and 33 ppm/°C, respectively.
A Learning-Based Personalized Driver Model Using Bounded Generalized Gaussian Mixture Models

Individual driver's driving behavior plays a pivotal role in personalized driver assistance systems. Gaussian mixture models (GMM) have been widely used to fit driving data, but unsuitable for capturing the data with a long-tailed distribution. Though the generalized GMM (GGMM) could overcome this fitting issue to some extent, it still cannot handle naturalistic data which is generally bounded. This paper presents a learning-based personalized driver model that can handle non-Gaussian and bounded naturalistic driving data. To this end, we develop a BGGMM-HMM framework to model driver behavior by integrating a hidden Markov model (HMM) in a bounded GGMM (BGGMM), which synthetically includes GMM and GGMM as special cases. Further, we design an associated iterative learning algorithm to estimate the model parameters. Naturalistic car-following driving data from eight drivers are used to demonstrate the effectiveness of BGGMM-HMM. Experimental results show that the personalized driver model of BGGMM-HMM that leverages the non-Gaussian and bounded support of driving data can improve model accuracy from 23$ \sim$30% over traditional GMM-based models.
Joint User Association and Beamforming Design for Millimeter Wave UDN With Wireless Backhaul

As millimeter wave (mmWave) beamforming (BF) system can increase the areal capacity in ultra-dense network (UDN), a joint design of user association and hybrid BF is challenging due to the high feedback overhead and complicated interference management where many communication nodes use large antenna arrays. In addition, the wireless backhaul should be incorporated because it allows a flexible design, while the wired backhaul is difficult to be installed as the number of small base stations increases. To integrate the wireless backhaul and access, the time resource partitioning between the backhaul and access transmissions is needed, which affects the network performance combined with the user association and BF design. In this regard, this paper deals with the joint problem of the time resource allocation, user association, and BF design to maximize the weighted sum rate. Then a two-stage design approach is proposed based on the limited channel feedback to reduce the complexity and overhead. Simulation results show that the proposed limited feedback hybrid BF scheme outperforms the baseline schemes. In addition, we find that the maximum spatial multiplexing gain can be achieved if the number of antennas of each node increases with an order of larger than the square of the number of RF chains. At the same time, the multi-node diversity gain can be obtained in mmWave UDN, which depends on the path loss exponent of line-of-sight.
LLVM-IR based Decompilation

Jeon, Ilsoo. M.S.C.E. , Department of Computer Science and Engineering, Wright State University, 2019. LLVM-IR based Decompilation. Decompilation is a process of transforming an executable program into a source-like high-level language code, which plays an important role in malware analysis, and vulnerability detection. In this thesis, we design and implement the middle end of a decompiler framework, focusing on Low Level Language properties reduction using the optimization techniques, propagation and elimination. An open-source software tool, dagger, is used to translate binary code to LLVM (Low Level Virtual Machine) Intermediate Representation code. We perform data flow analysis and control flow analysis on the LLVM format code to generate high-level code using a Functional Programming Langauge (FPL), Haskell. The result code generated by our decompiler framework is compared with the sample source code to verify the correctness of the decompiler framework.
Chilling Effects of Profiling Activities: Mapping the Issues

In this article, we provide an in-depth overview of the literature on chilling effects and corporate profiling and connect the two topics. We start by explaining how profiling, within an increasingly data-rich environment, creates substantial power asymmetries between users and platforms (and corporations more broadly). We stress the notion of inferences and the increasingly automatic nature of decision-making, based on user data, as essential aspects of profiling. We then discuss chilling effects in depth, connecting them to corporate profiling. In the article, we first stress the relationship and similarities between profiling and surveillance. Second, we illustrate chilling effects as a result of state and peer surveillance; we then show the interrelatedness of corporate and state profiling, and we finally spotlight the customization of behavior and behavioral manipulation as particularly outstanding issues in this discourse. We also explore the legal foundations of profiling through an in-depth analysis of European and US data protection law. We found that, while Europe has a clear regulatory framework in place for profiling, the US primarily relies on a patchwork of sector-specific or state laws. Besides, there is an attempt to regulate differential impacts of profiling, via anti-discrimination statutes, yet few policies focus on combating generalized, concrete harms of profiling, such as chilling effects. At the end of the article, we bring together the diverse strands of literature in concise propositions to guide future research on the connection between corporate profiling and chilling effects.
TelematicsTelematics Transport System Transport System

Abstract. The capability of a telematic vision system of estimating dimensions of vehicles is used for such tasks as vehicle classification or preselection of vehicles that violate local vehicle size limitations. Also in some European countries dimensions of heavy vehicles must obey some global regulations. Furthermore, vehicle size estimation allows us to determine the structure of traffic and can be very useful for advanced traffic flow control. Many existing Intelligent Transportation Systems consist of a large number of video cameras located in various places e.g., the ITS in Wrocław uses more than 1400 cameras. In this paper we propose a new method developed by the ArsNumerica Group and CyberTech Scientific Circle for the precise estimation of vehicle sizes using a single camera. The method does not require the entering of measurements such as the distance between lane lines or the height of the camera above the roadway. Only one vehicle’s dimensions are used for calibration. The proposed method is easy to implement and may be applied with the OpenCV library which is free both for academic and commercial use. The method is tested on real-world video streams. The obtained results are shown and analyzed in the paper.
A Hybrid Interface Recovery Method for Android Kernels Fuzzing

Android kernel fuzzing is a research area of interest specifically for detecting kernel vulnerabilities which may allow attackers to obtain the root privilege. The number of Android mobile phones is increasing rapidly with the explosive growth of Android kernel drivers. Interface aware fuzzing is an effective technique to test the security of kernel driver. Existing researches rely on static analysis with kernel source code. However, in fact, there exist millions of Android mobile phones without public accessible source code. In this paper, we propose a hybrid interface recovery method for fuzzing kernels which can recover kernel driver interface no matter the source code is available or not. In white box condition, we employ a dynamic interface recover method that can automatically and completely identify the interface knowledge. In black box condition, we use reverse engineering to extract the key interface information and use similarity computation to infer argument types. We evaluate our hybrid algorithm on on 12 Android smartphones from 9 vendors. Empirical experimental results show that our method can effectively recover interface argument lists and find Android kernel bugs. In total, 31 vulnerabilities are reported in white and black box conditions. The vulnerabilities were responsibly disclosed to affected vendors and 9 of the reported vulnerabilities have been already assigned CVEs.
Large Scale Community Discovery of Complex Networks Based On Approximate Optimization

Study of structural features associated with complex networks has its utility. In the process a key role is played by community. Online networks are showing unprecedented growth in complexity. In this context, the traditional approach used to discovery community is not sufficient to deal with large networks. Thus it leads to the problem of inability to discovery communities efficiently in the large scale networks. To overcome this problem, a Parallel Community Discovery (PCD) algorithm is proposed based on the concept of approximate optimization. The algorithm has two parts. They are known as mountain model and landslide algorithm. The former is meant for providing approximate selection of nodes that are used for representation in graph theory. It shows users and the data posted by them in the network. The landslide algorithm on the other hand updates the modularity from time to time incrementally based on the aforementioned technique named approximate optimization. Thus the proposed methodology helps in discovering communities in parallel. The process of clustering on the modularity plays important role in mountain model as it employs weight associated with each edge in the given network. The communities also exhibit relationship among them. However, they are simplified by the landslide algorithm thus helping in extraction of community structural features from large networks.
Learning Context Using Segment-Level LSTM for Neural Sequence Labeling

This article introduces an approach that learns segment-level context for sequence labeling in natural language processing (NLP). Previous approaches limit their basic unit to a word for feature extraction because sequence labeling is a token-level task in which labels are annotated word-by-word. However, the text segment is an ultimate unit for labeling, and we are easily able to obtain segment information from annotated labels in a IOB/IOBES format. Most neural sequence labeling models expand their learning capacity by employing additional layers, such as a character-level layer, or jointly training NLP tasks with common knowledge. The architecture of our model is based on the charLSTM-BiLSTM-CRF model, and we extend the model with an additional segment-level layer called segLSTM. We therefore suggest a sequence labeling algorithm called charLSTM-BiLSTM-CRF-segLSTM$^{sLM}$ which employs an additional segment-level long short-term memory (LSTM) that trains features by learning adjacent context in a segment. We demonstrate the performance of our model on four sequence labeling datasets, namely, Peen Tree Bank, CoNLL 2000, CoNLL 2003, and OntoNotes 5.0. Experimental results show that our model performs better than state-of-the-art variants of BiLSTM-CRF. In particular, the proposed model enhances the performance of tasks for finding appropriate labels of multiple token segments.
Survey and comparison of different classification techniques for select appropriate classifier of image

In human visual system, visual object classification is easy and effortless but in computer vision systems it is extremely hard Because of the various images of different objects within a specific class may have together with the various viewing conditions had led to have a serious problem. If some images have noisy contents or it contains blurry data, thus it is very hard to classify these types of images. Images processing introduces several techniques which be able to classify the data, but if image is blurry or noisy so they can not able to give the acceptable results. In this survey discuss the main classification methods consider, Supervised learning and unsupervised learning. The major motivation of this survey is to gives a brief comparison among different images classification techniques and methods. Finally, it is determined method that more accurately if an image contains blurry or noisy data.
Historia de la Medicina Familiar en Uruguay

Family Medicine emerges in Uruguay as a result of complex social and historic processes. In 1987 the Ministry of Public Health created the first of 139 positions for Family Medicine Practitioners which still exist to this day. In 2002 the specialty of family and Community Medicine was established in the University, but the Residents and Postgraduate Physicians have not been granted their laboral insertion in the Sanitary System of Uruguay.
Study of factors associated with drop out after first visit to psychiatric clinic

Despite the availability of effective regimens of pharmacotherapy and psychotherapy, only a small fraction of patients with common mental disorders are adequately treated in a year. Dropping out of mental health services is very common. Identifying the extent and reasons for treatment dropout is a critical task. The present study was carried out at the Out Patient Department(OPD) of Psychiatry. The data of all new patients attending the clinic from January to December (1999) were scrutinized one year after the initial assessment (retrospective study). Study group included drop out patients after first visit to Psychiatry OPD (N=180) and control group comprised of 180 consecutive patients of same year who continued in treatment after first contact. The drop out patients were assessed for reasons for drop out. The highest number for drop out included patients who had improved and did not feel need for further treatment. Another three major reasons were migration and transfer, patient taking treatment from other hospital; stigmatization etc. Successful initiation and adherence to mental health treatments depend on patients' knowledge and awareness, clinician's timely treatment and advice, clinician's communication skills and less waiting period in OPD and simple treatment seeking procedures.
A Force/Position Hybrid Controller for Rehabilitation Robot

The growing ageing population in China poses a huge demand for rehabilitation care, which can be satisfied by the latest robot technology. Focusing on the motion system of a six degrees-of-freedom (DOF) robot, this paper explores the relationship between the force, torque, velocity and the postures of the end effector and joint. Drawing on robot control theories, the existing manipulator force/position hybrid controllers were reviewed, and a force/position hybrid controller was designed for path planning of rehabilitation robot. Then, the robot was modelled on the Robot Operating System (ROS), using the Unified Robot Description Format (URDF) file and the MoveIt! Setup Assistant. Finally, our controller was tested in the ROS virtual simulation environment. The results show that our controller can facilitate and optimize the design of the path of rehabilitation robot.
Gestures conversion to Arabic letters

Gestures is one of the best ways of communication between dumb and blind people depend on the expression of signs. In this paper we suggest an algorithm to recognizing hand gestures of Arabic latters to communicate between the dumb (through signs) and blind (hear the voice corresponding to sings). The proposed algorithm used the video of gesture from the dumb then convert the video into frames ( images) and calculate the distance to recognition the letters by using k-mean , k- medoid and artificial neural network, calculate the distance by using Euclidean distance and slop . There are sixteen features (8-features from Euclidean distance and 8-features from slop ). The results were (93.3% For k-mean),(93.1% for k-medoid ) and(92.9% for ANN). We create our data base (from 5- videos with 308 frames).
FUSION LEARNING OF DEPENDENT STUDIES BY CONFIDENCE DISTRIBUTION (CD): THEORY AND APPLICATIONS

OF THE DISSERTATION Fusion Learning of Dependent Studies by Confidence Distribution (CD): Theory and Applications by CHENGRUI LI Dissertation Director: Minge Xie This dissertation focuses on developing efficient Fusion Learning methodologies for combining information from non-independent sources using confidence distribution (CD). The sources hereby are broadly construed as different pieces of information extracted from possibly correlated datasets. This situation typically arises when multiple inferences are performed over different times, locations or experiment settings due to computational and statistical considerations, which encompasses a wide range of scientific and engineering applications (e.g. seismic monitoring and detection, computer experiments). In this dissertation, we develop a general framework to effectively and efficiently combine these correlated information using CD, and furthermore, explore the advantages of this framework in different problems that are of theoretical and practical interests. The first problem we address is related to analysis of big spatial data. We
Patients’ Satisfaction in Healthcare Services: A Comparative Analysis on Public and Private Hospitals in Bangladesh

This paper aims to compare the patients’ satisfaction in healthcare services provided by the public and private hospitals in Bangladesh. A sample of 280 patients was selected to collect the primary data through SERVQUAL model to compare the patients’ satisfaction in healthcare services. We have selected six (06) different hospitals comprising three public and three private hospitals. The questionnaire was based on SERVQUAL instrument consisting of 22 items representing five dimensions of service quality and considered as five different constructs which are Empathy (4 items), Tangibles (6 items), Assurance (6 items), Timeliness (3 items) and Responsiveness (3 items). In this study, service quality constructs has been developed based on patient perception about the service quality of the hospitals. Levene’s Test for Equality of Variance (an independent t-test) is performed to compare the service quality provided by the hospitals. Equal variances were assumed for each item. This Test provides mean difference, t-value, degrees of freedom (df) and their significance (p-value). The finding of the study shows that the public hospitals are performing better in providing quality services compared to private hospitals.
Técnicas Dialíticas na Insuficiência Renal Aguda

Acute Renal Failure (ARF) is an uncommon disease in paediatric age, and may occur associated to several clinical conditions. When the support measures are not enough to control ARF, substitution therapeutics become necessary. Objective: To analyse the indications and complications of the different dialysis modalities at the Intensive Care Unit and/ or Paediatrics Nephrology Department in Maria Pia Hospital. Patients and methods: The authors present a retrospective study of ARF patients who needed substitutive treatment of renal function between January 1990 and December 2004. We analysed the following: clinical and laboratorial data, indications and complications of dialysis and patients’ evolution. We was also analysed the evolution of the dialytic treatment in our hospital. Results: The study included 29 children with ARF treated with dialytic therapies, aged 1 day to 15 years. Eleven (37,9%) were 12 months or younger (6 newborns). Sepsis was the subjacent condition to ARF in 11 (37,9%) patients, the majority newborns. The uraemic haemolytic syndrome was the aetiology of ARF in 10 (34,5%) infants and children. The peritoneal dialysis was the most used dialytic modality (65.5%), followed by the intermittent haemodialysis (31%) and continuous venovenous haemodiafi ltration (3,4%). The dialytic therapy proved to be safe and effi cient, although carrying some inherent complications to the dialytic method, that were resolved with success. Mortality was 17,2%, which is a rather satisfactory result when compared with other series. The number of patients submitted to dialysis in our hospital has been increasing, through the years (16 patients out of 29 were treated after 1999), specially newborns and low birth weight newborns. Conclusion: We verifi ed an increasing use of dialytic techniques in patients with ARF, which are safe and effi cient. We emphasize the good results in newborn including the low birth weight. Peritoneal dialysis remains the most used technique in our hospital, however nowadays, we are investing in the continuous extracorporeal dialysis as we recognize the advantages of this method in critical patients. Key-words: Acute Renal Failure, dialysis, children Nascer e Crescer 2006; 15(2): 71-76
Need of 5G Wireless Technology in Internet of Things (IOT) as a Catalyst for Technical Revolution

: 5G technology is visualized as a complicated mixture of technological implementation to the next-generation telecommunication networks. In the past 10 years, the Internet of Things has transformed the universal computing with abundant applications built throughout with the usage of sensor devices. An enormous development is made in the past few years and is forecasted to increase in the forthcoming years with huge number of devices on an average of 6 or 7 devices for an individual by the year 2020. Gadget to gadget communication accompanied with intuitive data are presumed to slowly and efficiently change the working environment of industries. In fact, to establish IoT services at scale efficiently, 5Gs critical satisfaction is mandatory. These incorporate improved network speeds and ability, lowered communication latency and adaptable service delivery methods. The exposure and adoption of cloud computing to with expansion of devices is expected to held forth revolution. These revolutions excite us to form a survey on existing work, sketch new techniques and discover new implementations associated with IoT.