Miracle-dz/UniFAR-FTUs huggingface dataset:
Location Extraction from Social Networks with Commodity Software and Online Data

Location is prevalent in most applications nowadays, and is considered a first class citizen in social networks. Locational information is of great significance since it can be used to map information from the online back to the physical world, to contextualize information, or to provide localized recommendations through Location-Based Services (LBS), and can be extended to trajectories and itineraries by adding a time-dimension. Despite all that, location extraction in social networks usually relies on GPS-enabled devices, that provide accurate geodetic coordinates. Nevertheless, most users provide general textual information about their surroundings, such as the city they live in, county or state (or equivalents), without using a GPS-enabled device, which is still valuable information for a number of applications. In this paper, we tackle the problem of extracting location information, usually referred to as geocoding, from additional user-provided content. Instead of using sophisticated and complex algorithms, which are common in online map services but require heavy development and tuning, we rely on software and data which are available online and public ally accessible. We discuss the particularities of geocoding in online social networks and present a simple, lightweight, yet efficient approach for location extraction in such a setting. We finally evaluate our approach experimentally on a large corpus of Twitter users.
The Performance Potential of Trace-based Dynamic Optimization

Dynamic optimization can apply powerful optimizations to h ot execution paths that span traditional boundaries such as branches and calls, including calls to dynamic libra ries. Because of this opportunity, it has gained academic and industrial attention. In this paper, we examine dynamic optimization in an overhea d-free environment to ascertain its optimization potential. Specifically, we explore an ideal implementatio n with zero overhead and ask: what is the performance potential of trace-based dynamic optimization? Using a met hodology consisting of a trace selector, optimizer, and trace-driven simulation framework, we provide insights on this potential. We begin by demonstrating the potential of dynamically-app lied classical optimizations as a function of trace length. Dynamic optimizers identify hot execution paths be tter than static mechanisms and, as a side-effect, are able to create longer optimization regions. In this analysi s, we quantify contributing performance factors and identify optimization hindrances and their cost. We perfor m cross-ISA analysis of SPARC and x86, demonstrate significant differences between the two, and explain the bas is for the differences. We also examine performance, trace cache size, and instruction stream coverage effects f or ideal and real-world trace selectors and estimate the remaining optimization potential available.
Cognitive Characteristics and Design Creativity: An Experimental Study

The objective of this research is to identify the relations between various cognitive characteristics and design creativity so that such relations could be exploited as a guide for design education. In this paper, an experimental study toward these goals is presented where various cognitive characteristics and abilities were evaluated for three groups of students whose exposure and education in design varies. Based on the experiment, constructive perception ability that combines perception and conception and basic ability in visual reasoning composed of visual analysis, synthesis and representation in iterative nature are equally related with creative design ability. However, the correlation between constructive perception and visual reasoning has not been identified in spite of some common aspects of the two. © 2005 ASME
SPATIAL RESOLUTION ENHANCEMENT VIA HYBRID ESTIMATION APPROACHES

Since the beamforming concept was first introduced to acoustic engineers, the acoustic phase array has become one of the most popular techniques used to identify noise sources. Generally speaking, the spatial resolution of the acoustic camera is proportional to the number of microphones and array size. The larger the array, the better the spatial resolution. In order to achieve better spatial resolution for a given microphone array structure, recently the so-called CLEAN method was proposed. However, if the noise sources are close to each other, then the non-parametric peak estimation in CLEAN method will fail to distinguish different sources. To overcome this shortness, this paper proposes to apply MUSIC and ESPRIT parametric algorithms for initial peak detection before performing CLEAN. As real world experiments indicate, the resulting hybrid approach can significantly improve the spatial resolution obtained by the original CLEAN method
Chemical kinetics and dynamics of plasma-assisted pyrolysis of assorted non-nuclear waste

Waste management is an important issue in both developed and developing countries nowadays. Today’s society uses, and quickly discards, a large volume of an increasingly diverse range of polymeric materials. Organic wastes, such as used rubber and plastic are among the waste materials that represent problematic wastes on one hand and valuable potential as secondary raw materials on the other hand (Huang and Tang, 2007). Most conventional chemical methods cannot deal with Municipal Solid Waste (MSW) due to the diversity of its composition, which range from simple thermoplastics to complex thermosets and composites (Guddeti et al., 2000). Nowadays MSW also contains outdated computer parts and electronic items. Printed Circuit Boards (PCB) are a major part of the waste from electrical and electronic equipment (WEEE); utilization, recycling, and final disposal of these products have become important issues (Hung-Lung et al., 2007).
Predicting the worst-case voltage violation in a 3D power network

This paper proposes an efficient method to predict the worst case of voltage violation by multi-domain clock gating in a three-dimensional (3D) on-chip power network considering leakage current. We first describe the 3D Power Distribution Network (PDN) structure which includes on-chip inductance and through-silicon-vias (TSV). The analysis flow using a superposition technique will be introduced later on. Then, we propose a general model to identify the worst-case gating pattern and the maximum variation area with arbitrary leakage current. For low power wireless chips, we introduce another simplified model, which treats the leakage to be a DC current. We formulate these two models with integer linear programming (ILP). The ILP based method is significantly faster than a conventional method based on enumeration. The experimental results also show that the noise contributed by leakage current is not negligible.
Matrix Completion for Multi-label Image Classification

Recently, image categorization has been an active research topic due to the urgent need to retrieve and browse digital images via semantic keywords. This paper formulates image categorization as a multi-label classification problem using recent advances in matrix completion. Under this setting, classification of testing data is posed as a problem of completing unknown label entries on a data matrix that concatenates training and testing features with training labels. We propose two convex algorithms for matrix completion based on a Rank Minimization criterion specifically tailored to visual data, and prove its convergence properties. A major advantage of our approach w.r. t. standard discriminative classification methods for image categorization is its robustness to outliers, background noise and partial occlusions both in the feature and label space. Experimental validation on several datasets shows how our method outperforms state-of-the-art algorithms, while effectively capturing semantic concepts of classes.
Advantage of Minimally Invasive Lateral Approach Relative to Conventional Deltopectoral Approach for Treatment of Proximal Humerus Fractures

Background Despite the wide application of open reduction and internal fixation with locking plates for the treatment of proximal humeral fractures, the surgical invasive approach remains controversial. This study aimed to evaluate the pros and cons of the minimally invasive lateral approach for the treatment of proximal humeral fracture (PHF) in comparison with the deltopectoral approach. Material/Methods All patients who sustained a PHF and received open reduction and internal fixation (ORIF) surgery with locking plate through either minimally invasive subacromial approach or conventional deltopectoral approach between January 2008 and February 2012 were retrospectively analyzed. Patients were divided into the conventional group and min-group according to the surgical incision. Surgery-related information, postoperative radiography, complications, and shoulder functional measurement scores in a 2-year follow-up were collected and evaluated. Results Ninety-one patients meeting the inclusion criteria were included in this study. We observed a significant difference in both surgery time (81.8±18.3 vs. 91.0±18.4) (p=0.021) and blood loss (172±54.2 vs. 205±73.6) (p=0.016) between the min-group and conventional group. Compared to the conventional group, the min-group had significantly better Constant-Murley score and DASH score at early follow-up (p<0.05) and higher patients satisfaction rate (8.1±1.1 vs. 7.6±1.2) (p= 0.019). The multiple linear regression analysis indicated that age, PHF types, surgical groups, surgery time, and blood loss have significant effect on the activity of affected shoulder in both abduction and forward flexion (p<0.05) except for gender factor. While larger range of movement of the affected shoulder, mainly in the 2-part and 3-part fractures, was observed in the min-group, the conventional group obtained better movement in the 4-part fractures. Conclusions The minimally invasive lateral approach is the optimal alternative for the treatment of Neer’s type 2 and 3 proximal humerus fractures.
6.892 Computing on Encrypted Data Lecture 4

In the previous lecture we constructed a LWE-based secret-key encryption scheme that is somewhat homomorphic. More precisely, the scheme supports evaluation of log n depth circuits for every < 1 after using the Dimension Reduction technique that we also introduced in the previous lecture. Building upon this starting point, we will introduce two more techniques in this lecture. The first technique is called Modulus Reduction[2] and can improve the scheme to support evaluation of O(n ) depth circuits for every < 1, which results in a leveled FHE scheme, i.e. a FHE scheme that can evaluate L depth circuit for every L. The second technique called Bootstrapping [4] is a general transformation from a sufficiently strong HE scheme to a FHE scheme that can evaluate arbitrary circuits of polynomial size.
Detection and Prediction of Resource-Exhaustion Vulnerabilities

Systems connected to the Internet are highly susceptible to denial-of-service attacks that can compromise service availability, causing damage to customers and providers. Due to errors in the design or coding phases, particular client-server interactions can be made to consume much more resources than necessary easing the success of this kind of attack.To address this issue we propose a new methodology for the detection and identification of local resource-exhaustion vulnerabilities. The methodology also gives a prediction on the necessary effort to exploit a specific vulnerability, useful to support decisions regarding the configuration of a system, in order to sustain a certain attack magnitude.The methodology was implemented in a tool called PREDATOR that is able to automatically generate malicious traffic and to perform post-processing analysis to build accurate resource usage projections on a given target server.The validity of the approach was demonstrated with several synthetic programs and well-known DNS servers.
Glucuronidation of the Antiretroviral Drug Efavirenz by UGT2B7 and an in Vitro Investigation of Drug-Drug Interaction with Zidovudine

The non-nucleoside reverse transcriptase inhibitor efavirenz (EFV) is directly conjugated by the UDP-glucuronosyltransferase (UGT) pathway to form EFV-N-glucuronide (EFV-G), but the enzyme(s) involved has not yet been identified. The glucuronidation of EFV was screened with UGT1A and UGT2B enzymes expressed in a heterologous system, and UGT2B7 was shown to be the only reactive enzyme. The apparent Km value of UGT2B7 (21 μM) is similar to the value observed for human liver microsomes (24 μM), whereas the variant allozyme UGT2B7*2 (Tyr268) displayed similar kinetic parameters. Because 3′-azido-3′-deoxythymidine (AZT), one of the most current nucleotide reverse transcriptase inhibitors prescribed in combination with EFV, is also conjugated by UGT2B7, the potential metabolic interaction between EFV and AZT has been studied using human liver microsomes. Glucuronidation of both drugs was inhibited by one another, in a concentration-dependent manner. At Km values (25 and 1000 μM for EFV and AZT, respectively), EFV inhibited AZT glucuronidation by 47%, whereas AZT inhibited EFV glucuronidation by 23%. With a Ki value of 17 μM for AZT-glucuronide formation, EFV appears to be one of the most selective and potent competitive inhibitor of AZT glucuronidation in vitro. Moreover, assuming that concentrations of EFV achieved in plasma (Cmax = 12.9 μM) are in a range similar to its Ki value, it was estimated that EFV could produce a theoretical 43% inhibition of AZT glucuronidation in vivo. We conclude that UGT2B7 has a major role in EFV glucuronidation and that EFV could potentially interfere with the hepatic glucuronidation of AZT.
An optimal underwater image enhancement based on fuzzy gray world algorithm and Bacterial Foraging algorithm

Underwater images suffer from non uniform contrast and poor visibility due to bad illumination and color cast in deep water. Such images have a hazy and color diminished appearance making underwater studies a difficult task. Researches in last decades performed color correction, assuming that underwater images have bluish color cast which is not always true. In this paper, a new image enhancement approach is proposed which modifies the gray world algorithm by finding the color cast using fuzzy logic and then removing the color cast by optimizing the correction method using Bacterial Foraging Optimisation (BFO). Proposed approach is adaptive in nature as it finds the intensity of color cast instead of assuming it which improves the quality of underwater images. Computed results have enhanced visual details, contrast and color performance.
Design of switched reluctance motor competitive to 60 kW IPMSM in third generation hybrid electric vehicle

In this paper, a design of a switched reluctance motor (SRM) having competitive torque, power, speed range and efficiency of the interior permanent magnet synchronous motor (IPMSM) employed in the 2009 Toyota Prius has been investigated. The maximum torque of 207 Nm is needed up to the knee speed of 2768 r/min. The output power of 60 kW is required in the speed range from 2768 r/min to 13900 r/min. It is shown that the above requirements can be satisfied with a designed SRM, although, the current density and the weight are slightly increased. At the high speed, simulation results show that the output of the designed SRM is found to be greatly enhanced with respect to that in the IPMSM.
Qualitative Reasoning and the Web Ontology Language

The desire to share and reuse knowledge has led to the establishment of a new knowledge representation language: the Web Ontology Language (OWL). The design choices made in OWL are properly documented, but their implications for AI research are not yet clear. This thesis explores the expressiveness of OWL by formalising the vocabulary and models used in qualitative reasoning (QR), and the applicability of OWL reasoners to solve QR problems. In addition, a parser has been developed to export the QR knowledge representation to an OWL representation. To formalise the OWL definitions of the QR vocabulary and models existing OWL patterns were used as much as possible. However, some new patterns had to be developed in order to cope with the QR vocabulary and models. Furthermore, some conceptual patterns which are impossible to define in OWL are revealed.
A Java Framework for Seamless Sequential, Multi-threaded, and Distributed Programming

Due to its platform-independent execution model, its support for networking, multithreading and mobile code, Java has given hope that easy Internetwide high-performance network computing was at hand. Numerous attempts have then been made at providing a framework for the development of such metacomputing applications. Unfortunately, none of them addresses seamless cross-paradigm computing, i-e the execution of the same application on a multiprocessor shared-memory machine as well as on a network of workstations, or on any combination of both. In this article we rst identify four requirements for the development of such metacomputing frameworks. We then introduce Java//, a 100% Java library that provides transparent remote objects as well as asynchronous calls and high-level synchronization mechanisms. We also present the metaobject protocol Java// is built on and give some performance gures.
A Distributed Time-Footprint Spectrum Allocation Strategy for Cognitive Radio Networks

To investigate how to support user communication sessions in cognitive radio (CR), the radio technology should jointly perform power control, scheduling, and flow routing, which are generated by a multi-hop wireless network. With the adjustments of migrating nodes to neighboring nodes, a bandwidth footprint tree (BFT) is defined in this paper. Based on reducing hot spots approach, the distributed protocol is proposed to address spectrum allocation problem and to maximize the discovery of spectrum opportunities. With the derived model, different scenario schemes are compared based on their influence on traffic effect and low load flows limitations. The possible benefit from growing trees iteratively is clarified. In addition, a Chebyshev sum metric is utilized for sensing/monitoring of spectrum availability. With a cross-layer design approach, more evenly distributed sessions among the networks are achieved. Experimental and simulation results are provided as verification of the analyses and solutions.
Coordination control of a free-flying manipulator and its base attitude to capture and detumble a noncooperative satellite

This paper focuses on the guidance of a robot manipulator to capture a tumbling satellite and then bring it to state of rest (detumbling). First, a coordination control for combined system of the space robot and the target satellite, which acts as the manipulator payload, is presented so that the robot tracks the optimal path while regulating the attitude of the chase vehicle to a desired value. Subsequently, two optimal trajectories for the pre- and post-capture phases are designed. In the pre-capturing phase, the manipulator maneuvers are optimized by minimizing a cost function which includes the time of travel and the weighted norms of the end-effector velocity and acceleration, subject to the constraint that the robot end-effector and a grapple fixture on the satellite arrive at the rendezvous point with the same velocity. In the post-grasping phase, the manipulator dumps the initial velocity of the tumbling satellite in minimum time subject to the constraint that the magnitude of the torque applied to the satellite remains below a safe value. Simulation and experimental results are appended.
Remote Safety Monitoring for Elderly Persons Based on Omni-Vision Analysis

Remote monitoring service for elderly persons is important as the aged populations in most developed countries continue growing. To monitor the safety and health of the elderly population, we propose a novel omni-directional vision sensor based system, which can detect and track object motion, recognize human posture, and analyze human behavior automatically. In this work, we have made the following contributions: (1) we develop a remote safety monitoring system which can provide real-time and automatic health care for the elderly persons and (2) we design a novel motion history or energy images based algorithm for motion object tracking. Our system can accurately and efficiently collect, analyze, and transfer elderly activity information and provide health care in real-time. Experimental results show that our technique can improve the data analysis efficiency by 58.5% for object tracking. Moreover, for the human posture recognition application, the success rate can reach 98.6% on average.
Progressive Shape-Distribution-Encoder for Learning 3D Shape Representation

Since there are complex geometric variations with 3D shapes, extracting efficient 3D shape features is one of the most challenging tasks in shape matching and retrieval. In this paper, we propose a deep shape descriptor by learning shape distributions at different diffusion time via a progressive shape-distribution-encoder (PSDE). First, we develop a shape distribution representation with the kernel density estimator to characterize the intrinsic geometry structures of 3D shapes. Then, we propose to learn a deep shape feature through an unsupervised PSDE. Specially, the unsupervised PSDE aims at modeling the complex non-linear transform of the estimated shape distributions between consecutive diffusion time. In order to characterize the intrinsic structures of 3D shapes more efficiently, we stack multiple PSDEs to form a network structure. Finally, we concatenate all neurons in the middle hidden layers of the unsupervised PSDE network to form an unsupervised shape descriptor for retrieval. Furthermore, by imposing an additional constraint on the outputs of all hidden layers, we propose a supervised PSDE to form a supervised shape descriptor. For each hidden layer, the similarity between a pair of outputs from the same class is as large as possible and the similarity between a pair of outputs from different classes is as small as possible. The proposed method is evaluated on three benchmark 3D shape data sets with large geometric variations, i.e., McGill, SHREC’10 ShapeGoogle, and SHREC’14 Human data sets, and the experimental results demonstrate the superiority of the proposed method to the existing approaches.
The Application of Fuzzy Taxonomy for Classifiyng Territorial Administration Units

Summary In the paper there are presented the problems of classijjing territorial units of the region. For the study, the Western Pomeranian region was chosen, which had been called into being in 1999. For classijication purposes, the algorithm and fuzzy taxonomy computer software were used. Both the attributes that describe territorial units and the classes are of fuzzy character. Practical results were obtained, in which territorial units (counties) had been divided into 4 classes. The obtained results are -of great importance when inventing a strategy for economic and ecological development of the region.