Miracle-dz/UniFAR-FTUs huggingface dataset:
On the trade-off between uncertainty and delay in UWB and 5 G localization

Location-aware technologies in combination with emerging wireless communication systems have revolutionized many aspects of our daily lives by means of applications within the commercial, public and military sectors. Ultra-wideband (UWB) and 5G stand out as emerging radio frequency (RF) based technologies that tackle the limitations of Global Positioning System solutions. The thrive in search for better accuracy involves improved ranging algorithms, higher transmission powers, network densification, larger bandwidths, and the use of cooperation among nodes in the network. However, practical implementations introduce communication related constraints. In this thesis, we study the trade-off between localization accuracy and communication constraints in terms of delay. This trade-off is investigated and quantified for two of the most rapidly growing RF technologies for high precision positioning: UWB and 5G. In UWB, we investigate the trade-off between medium access control (MAC) delay and accuracy based on a two-way-ranging and a spatial time division multiple access scheme. We quantify this relationship by deriving lower bounds on localization accuracy and MAC delay during the measurements phase, which is often neglected in the analyses. We find that the traditional means to improve accuracy such as increased number of anchors, increased communication range, and cooperation among nodes, come at a significant cost in terms of delay, which can be mitigated by means of techniques such as selective ranging and eavesdropping. We summarize and generalize our findings by characterizing the position error and delay lower bounds by deriving asymptotic scaling laws. These scaling laws are presented for dense noncooperative and cooperative networks in combination with delay mitigation techniques. Moreover, we introduce a delay/accuracy trade-off parameter, which can uniquely quantify the trade-off as a function of the agent and anchor density. Finally, we consider the problem of fast link scheduling and propose an optimization strategy to perform robust ranging scheduling with localization constraints. We propose two MAC-aware link selection heuristic approximation approaches which show similar performance as the optimal solution, but alleviate the problem complexity. In 5G, we analyze the interplay between communication and positioning within the initial access procedure between a transmitter and a receiver in a millimeter-wave multipleinput multiple-output system. We exploit the ability of the receiver to determine its location during the beam selection process and thus, improve the subsequent selection of beams within initial access. First, assuming that only the transmitter has beamforming capabilities, we propose an in-band position-aided transmitter beam selection protocol for scenarios with direct line-of-sight and scattering. Then, we extend the work and propose an in-band position-aided beam selection protocol where we also allow for the receiver to perform beamforming in scenarios with line-of-sight, reflected paths, and
Plastic hinge relocation in reinforced concrete beam–column joint using carbon fiber–reinforced polymer

Reinforced concrete buildings with moment-resisting frames comprising beam–column joints (without joint shear reinforcement) designed prior to introduction of seismic codes are shear deficient when subjected to seismic loading, thereby mostly fail in shear at the core of the beam–column joint. However, those designed to the new seismic codes may fail by flexural hinging at the interface of the beam–column joint due to the yielding of the beam reinforcement at the location of highest stress demand (usually the beam–column joint interface). The shear failure has been precluded through the provision of transverse reinforcement at the joint in new design and the use of carbon fiber–reinforced polymer retrofitting in old buildings. Plastic hinge formation at the interface of the beam–column joint is critical because of its penetration into the joint and its effect on bond deterioration. In this study, eight corner-external beam–column joint specimens of 1/3 scale of a typical moment-resisting frame, made without transverse reinforcement, were tested for monotonic and reversed cyclic test under displacement-controlled regime. The control specimens failed by flexural hinging at the beam–column joint interface. The experimental results have been validated using the finite element model. The specimens were retrofitted with unidirectional carbon fiber–reinforced polymer of different layers and different length. After retrofitting, the plastic hinge was relocated to the curtailment end of the carbon fiber–reinforced polymer. The relocation of the plastic hinge resulted in higher load capacity and ductility.
Construction of Secondary Single Segment Substitution Lines to Map QTLs on Grain Shape and Grain Weight in Rice (Oryza Sativa L.)

Single Segment Substitution Lines (SSSLs) had already been demonstrated as a powerful tool on QTL analysis. However, numerous current SSSLs carried with long substitution segments. In this paper, 18 primary SSSLs were used in construction of secondary SSSLs, which were with shorter substitution segments. The 55 secondary SSSLs with an average size of 17.56 cm were achieved, which reduced markedly the size of primary substitution segments. Numerous overlapping substituted segments were detected to exist among these SSSLs, and then using these SSSLs QTLs on four traits of grain length, grain width, ratio of grain length to width, and 100-grain weight were mapped via overlapping substitution mapping. There were 12, 6, 13 and 15 QTLs to be primary identified on the four traits, involving in eight chromosomes, respectively. Their positions were located within given marker intervals with relative small lengths. About 10 QTL clusters were detected on the four traits, which might be responsible for the genetic correlations among grain shape and grain weight. The results improved greatly the precision of QTL mapping used by primary SSSLs, and provided credible information of QTLs and useful materials for further genetic analysis and pyramiding breeding on grain shape and grain weight in next research program.
Segmentation of Tweets for Multilingual Named Entity Recognition

Now-a-days Twitter is providing a way to collect and recognize user’s views about many private and public organizations. However, traditional applications suffer rigorously due to short nature of tweets. A novel framework called HybridSeg is presented which easily extract and well preserves the linguistic meaning or context information by dividing tweets into significant segments and finds the optimal segmentation of tweets based on the amount of stickiness score of individual segments. The stickiness score is calculated based on the likelihood of a segment being a English phrase (i.e. global linguistic context) and likelihood of a segment being phrase within the bunch of tweets(i.e. local linguistic context). This framework will iteratively learn the candidate segments based on pseudo feedback. This model is trained on a tweet data sets, it shows that the quality of tweet segmentation has improved by learning both on global and local context. The experimental results demonstrate that tweet segmentation model significantly benefits the downstream applications.
Algorithm for moving object tracking based on Kalman filter

Based on the situation that the classic Camshift algorithm could not effective solve the problems such as target was covered and high speed moving in the field of real-time video tracking,this paper proposed a improved moving object tracking algorithm that combinating Camshift algorithm and Kalman filter. Firstly,in order to improve tracking stability of Camshift, adjusted the places and sizes by twice searching and then,in order to ensure real-time tracking,implemented motion prediction in search window by Kalman filter on the base of Camshift algorithms. The experimental results show that the improved algorithm has better real-time and can solve the cover problem more effective.
CANONICAL QUANTIZATION AND THE RELATIVISTIC WAVE EQUATIONS Iwo Bialynicki-Birula

Relativistic wave equations describing massless particles (Maxwell equations, Weyl equation) have never been derived directly from classical physics by a straightforward quantization procedure. In this work, I show that there is a road that leads from the classical phase-space of a point particle in four dimensions to the relativistic wave equations. My approach is closely related to the twistor formalism of Penrose [1, 2, 3], but it offers a new interpretation within the standard formulation of quantum mechanics. The wave functions arising here may be subjected to all conformal transformations. In the special case of the space-time translations, one obtains a somewhat different version of the Penrose transform. The description of the states in terms of the Wigner function seems to be especially promising.
Classification Reasoning as a Basic Part of Machine Learning

We focus on the logical or symbolic supervised methods of machine learning. This mode of learning covers mining logical rules and dependencies from data: “if-then” rules, decision trees, functional, and associative dependencies. This learning is also used for extracting concept from data sets, constructing rough sets, hierarchical classification of objects, mining ontology from data, generating hypotheses, and some others (Kotsiantis, 2007). It has been proven in (Naidenova, 1996) that the tasks of mining all logical dependencies from data sets are reduced to approximation of a given classification (partitioning) on a given set of object descriptions. The search for best approximation of a given object classification leads to a concept of good classification (diagnostic) test firstly introduced in (Naidenova & Polegaeva, 1986). A good classification test has a dual nature. On the one hand, it makes up a logical expression in the form of implication, associative or functional dependency. On the other hand, it generates the partition of a training set of objects equivalent to a given classification (partitioning) of this set or the partition that is nearest to the given classification with respect to the inclusion relation between partitions (Cosmadakis et al., 1986). We consider two ways for giving classifications as it is shown in Figure 1: (1) by a target attribute KL or (2) by a value v of target attribute. The target attribute partitions a given set of examples into disjoint classes the number of which is equal to the number of values of this attribute. The target value of attribute partitions a given set of examples into two disjoint classes: the examples in description of which the target value appears (positive examples); all the other examples (negative examples). We are interested in solving the following tasks:
IL-17 expression in synovial fluid and synovial membrane in patients with knee osteoarthritis

Objective: In the present study, we aimed to detect the levels of IL-17 in the serum, synovial fluid, and synovial membrane of patients with knee osteoarthritis (KOA) and investigate their association with the severity of articular cartilage damage and synovitis. Methods: According to the American College of Rheumatology criteria for osteoarthritis (OA) diagnosis, 46 cases diagnosed with knee OA (KOA group) were selected from patients that underwent arthroscopic surgery at our hospital from June 2012 to June 2014. Additional 46 cases with other knee joint diseases (non-KOA group) were included as controls. The IL-17 levels in synovial fluid and serum were assayed by ELISA, and those in the synovial membrane were assayed by immunohistochemistry. The severity of knee articular cartilage damage and synovitis-related pathological changes was evaluated by arthroscopy using the Outerbridge and Ayral scores, respectively. Results: IL-17 levels in the synovial fluid and synovial membranes of the KOA group were significantly higher than in the non-KOA group (P < 0.05). The levels of IL-17 in the synovial fluid and synovial membranes were positively correlated with the serum level of high-sensitivity C-reactive protein (HS-CRP), the Outerbridge score, and the Ayral score in the KOA group. Conclusion: IL-17 levels in the synovial fluid and synovial membrane are increased in KOA patients and are positively correlated with KOA severity.
Influence of single vacancy defect at varying length on electronic
 properties of zigzag graphene nanoribbons

Graphene, identified in 2004, is now an established two-dimensional (2D) material due to its outstanding physical and electronic characteristics namely its superior electrical conductivity. Graphene is a zero-gap material that has linear dispersion with electron-hole symmetry. As pristine sheet, it cannot be utilized in digital logic application without the induction of a band gap inside the band structure. In our work, the modeling and simulation of graphene nanoribbons (GNRs) are carried out to determine its electronics properties that are benchmarked with other published simulation data. A 4-Zigzag GNRs (4-ZGNRs) under different length are utilized. A single vacancy defects is introduced at various positions inside the atomic structure. The theoretical model is implemented based on single-neighbour tight binding technique coupled with a non-equilibrium Green’s function formalism. The single vacancy defects are represented by the elimination of tight binding energies in the Hamiltonian matrix. Subsequently, these matrix elements are utilized to compute dispersion relation and density of states (DOS) through Green’s function. It is found that single vacancy defects at different positions in 4-ZGNRs’ atomic structure under varying length has no significant impacts on the sub-band structure but these vacancies impact the DOS that are computed throught Green’s function approach.
TIMING BEHAVIOR VERSUS ADJUSTMENT TOWARDS THE TARGET : WHAT DETERMINES THE CAPITAL STRUCTURE DYNAMICS

The adjustment towards the target leverage and the timing of equity market are not necessarily two distinct behaviors as theoretically suggested. The firm choice to adjust towards the target leverage depends on the costs of being away from the target and the cost of adjustment toward that target. If the cost of deviation is high, adjustment becomes a first priority whatever the market conditions of the firm are. Lower cost of deviation opens the door for other aspects, especially timing the market, to be considered in the financing decision. Using GMM system estimators with the Malaysian data for the period of 1992-2009, this study find that Malaysian firms, on average, are adjusting their capital structure toward the target but at a slow rate. At the same time, firms consider timing of the market conditions as an important factor when making financing decisions. Comparing subgroups of firms that are likely to have different costs of deviation reveal that the speed of adjustment is higher and the timing role is lower for firms with high cost of deviation. Particularly, firms far above the target adjust at a high speed and do not consider timing the market. Firms far below or close to the target adjust slowly and take market valuation into account. Deviating from the target to the upper side is more costly because bankruptcy costs and agency costs of debt will intensify quickly as the firm deviates more above the target. Firms seem to be very sensitive to be significantly above the target. Once the firm is not far above the target it becomes less sensitive to the cost of deviation and more likely to consider other motives including timing the equity market. The finding of this study supports that firms consider risk of distress and possible conflict with debtors as leading factors in the financing decision. This result is consistent with both tradeoff and agency theories. Only when the firm is not in the risky area, it may consider other factors as timing the market. Timing is better seen as an additional factor in a broad tradeoff framework. A framework that interprets all the empirical findings is still lacking.
Infrared human action recognition using dense trajectories-based feature

Human action recognition(HAR)in videos is a challenging problem in computer vision and pattern recognition with wide applications.Most works on HAR have been visible-spectrum oriented.However,this paper uses thermal infrared imaging for HAR.In order to overcome the deficiency of single scale and individual representation method of action on HAR,a new recognition algorithm of human action using dense trajectories-based multi-feature fusion is presented.Our method consists of the following steps:The dense trajectories(DTs)of the input action video are obtained by using dense sampling;Three dense trajectories-based descriptors are constructed,namely histogram of oriented gradient(HOG),histogram of optical flow(HOF)and motion boundary histograms(MBH);The fusion feature is constructed by using the popular bag-of-features(BoF)representation of HOG,HOF and MBH,respectively.And from the pattern recognition perspective,action recognition can commonly be viewed as a multiclass classification problem.Consequently,ak-NNclassifier is employed to recognize the human action using the computed dense trajectories-based fusion features.The intensive experimental results show that the proposed method can achieve the correct recognition rate above 96.67% on the benchmark infrared action dataset of IADB.Interrelated analyses conclude that the proposed algorithm is effective and promising for visible and infrared human action recognition.
Electrical signals parameter estimation using adaptive filtering: A comparative study

This paper presents the implementation of adaptive LMS, RLS and Kalman filters, for real time estimation of magnitude and phase angle of distorted electrical signals. The filters performance is evaluated by means of the steady-state error, the settling time, among others, through simulations in MATLAB/Simulink. In addition, adaptive filters are implemented in the DS1104 dSPACE development board to evaluate its performance in real time. The simulation and experimental results are shown to validate the algorithms performance.
Evaluation of Cognitive Processes using Synthesized Words: Screening of Hearing and Global Speech Perception 

This study focuses on children’s cognitive capability within the framework of cognitive infocommunication. Speech processing works in quasi-parallel in time between hearing and speech comprehension. Hierarchical operations are decisive for elaboration of the speech signal. To test children’s speech processing quickly and reliably is of great importance both for language acquisition and for learning to read and write. Specific speech synthesis using sufficient, but not redundant spectral cues highlight hearing and global speech perception processes. 644 monolingual Hungarian children aged between 4 and 8 years participated in the study. 20 monosyllables were specially synthesized based on a set of pre-determined spectral values. Children were asked to repeat what they heard. The combination of speech synthesis as information and communication technology with the study of cognitive capabilities is a new direction in research and practice. Our results show that the great majority of children were confirmed to have good hearing (about 95%), while some children had a previously unknown hearing impairment. More than 30% of all children encountered speech perception deficit, despite good hearing. Digital technology including speech synthesis has reshaped both speech science and its cognitive connections to get closer to a proper interpretation of the mechanisms analyzed.
Expressive 2018 2 DToonShade : A stroke based toon shading system

We present 2DToonShade: a semi-automatic method for creating shades and self-shadows in cel animation. Besides producing attractive images, shades and shadows provide important visual cues about depth, shapes, movement and lighting of the scene. In conventional cel animation, shades and shadows are drawn by hand. As opposed to previous approaches, this method does not rely on a complex 3D reconstruction of the scene: its key advantages are simplicity and ease of use. The tool was designed to stay as close as possible to the natural 2D creative environment and therefore provides an intuitive and user-friendly interface. Our system creates shading based on hand-drawn objects or characters, given very limited guidance from the user. The method employs simple yet very efficient algorithms to create shading directly out of drawn strokes. We evaluate our system through a subjective user study and provide qualitative comparison of our method versus existing professional tools and recent state of the art. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
TOOL SUPPORT FOR CAPTURING THE ESSENCE OF A CONCERN IN SOURCE CODE

Command 2:execute 4 10:getDrawingEditor 4 DrawApplication 12:getUndoManager 3 StandardDrawingView 96:checkDamage 1 UndoManager 14:isUndoable 4 UndoableAdapter 84:isRedoable 3 ChangeAttributeCommand_UndoActivity 28:undo 1
Improving Search Engine Query Expansion Techniques with ILP

Query Expansion is the process in which a query is augmented so that it matches more documents, thus potentially increasing the number of relevant results. This is frequently done by spell correcting the query and adding synonyms, morphological variations or other type of relevant data. Query Expansion would, for example, expand ’automobile’ with ’car’ or ’car’ with ’cars’. Given a concrete set of queries and particular words in these, it is relatively simple to generate lists of candidates that would later reflect as a good or a bad alterations. However, generalizing from a ground truth set, which is a list of alterations considered to be good, to a model which allows the generation of good alterations to an unseen set of words is a challenging task. In the present work we provide such a model for the English language, discovered with Inductive Logic Programming (ILP). The model induced by ILP is a set of Prolog rules. An important aspect of having the model as rules in Prolog is that, instead of merely being able to classify a given pair 〈term, candidate〉 as good or bad, we are now able to generate good alterations for a given word, which is the main goal of this investigation.