Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of sheep colostrum via laser induced fluorescence and chemometrics

In the present work sheep colostrum and milk at different milking times postpartum were qualitatively evaluated using laser-induced fluorescence (LIF). First and second milking samples showed higher fluorescence intensity compared with the subsequent milking samples. Such increase in the fluorescence intensity in first and second milking samples can be interpreted in view of the presence of high levels of immunoglobulins (IgG) and lactoferrin in the colostrum. The LIF results have been confirmed by the quantitative evaluation of the IgG in the same samples adopting the enzyme-linked immunosorbent assay (ELISA) as validation technique and a very good agreement has been obtained. Principal component analysis (PCA) of the obtained data led to the conclusion that the fluorescence band from 475 nm to 560 nm is strongly connected to changes during milking time from colostrum to milk. The fluorescence band is linked to changes in the concentration of proteins (IgG, lactoferrin) from colostrum to milk. According to the presented results, LIF spectroscopy can be used as a reliable, accurate, and fast method for real time and in situ evaluation of sheep colostrum. LIF coupled with the chemometric analysis of the data can be utilized in designing feeding strategies for newly born lambs.
Accountant: Protection of Data Integrity and Identification of Malicious Nodes in In-network Data Processing

Data integrity in distributed data sensing and processing platforms or middlewares is an important issue, especially if those platforms are open to anyone. To leverage the resources of participating nodes and to enhance the scalability, nodes can be included in the data processing, e.g., in the aggregation of results. In an open system, it is also likely that some participating nodes are malicious and lie about their sensed values or about the results of data processed by them. Current approaches that preserve data integrity for in-network processing require expensive cryptographic operations. With Accountant we propose a new approach, which requires significantly less computation at the expense of slightly more signalling overhead. Furthermore, our approach cannot only preserve data integrity, but also allows to identify malicious nodes. For that, Accountant uses multiple inner node-disjoint trees for data dissemination and hash trees for preserving the data integrity. We compare it to existing solutions, showing that with only minor additional messaging overhead, Accountant can protect the data integrity and can identify attackers at the same time.
Software Defect Analysis: A Review

— The primary aim of the successful software development organizations is to provide reliable software. An extension in the field of software testing process has ensured better scope, however a few sections of a software framework are observed to be more helpless to defects than others, and discovery of these can significantly elevate the designers to convey exclusive expectation software. The software business spends more cash on finding and settling bugs than for some other known cost driver. This ought not be the situation. Different methods can be utilized to guarantee the nature of software. Defect Prevention is a crucial undertaking in any software venture. Defect Prevention includes examining defects that were experienced because of the inaccurate prerequisites, defects in outline and distinctive kinds of codes. Insufficient defect counteractive action and deficient pre-test defect expulsion are emphatically corresponded with failure to gauge defect evacuation effectiveness. Defect Detection procedure is utilized as a part of a considerable lot of the software ventures to recognize the defects, report such defects and break down them in order to refine the item and improve it. The defect archive will be a contribution for defect analysis. In Defect Analysis procedure, investigating the defect prompts distinguishing its underlying driver and to discover an answer for the defect along these lines keeping it from proliferating into encourage improvement stages. Arrangement of defects into classes and finding the conceivable reason are parts of defect analysis. Reckoning and counteractive action of defects before their event is the primary point of Defect Prediction strategy. In this paper, we have checked on Software Defect Prevention by its analysis and prediction and furthermore featured the models of defect prediction examines over the most recent fifty years. These strategies help in early recognition of defects, counteracting them to spread to next improvement stages and repressing the occurrence of comparative defects in future activities.
A COMPARISON OF MARGINAL LIKELIHOOD BASED AND APPROXIMATE POINT OPTIMAL TESTS FOR RANDOM REGRESSION COEFFICIENTS IN THE PRESENCE OF AUTOCORRELAT ION Shahidur Rahman and Maxwell L. King

With respect to testing linear regression disturbances, two methods of test construction have recently been found to work well. These are traditional asymptotic tests based on the marginal likelihood or equivalently the likelihood of the maximal invariant and point optimal or approximate point optimal (APO) tests. The former approach has been found to work well for testing for random regression coefficients in the presence of autocorrelated errors. This paper constructs APO invariant (APOI) tests for this testing problem and extends the previous Monte Carlo study to include APOI tests. We conclude that for this testing problem, the extra work required to apply APOI tests hardly seems worthwhile, particularly for larger sample sizes.
University of Southern Denmark Prolonged stimulation of a brainstem raphe region attenuates experimental autoimmune encephalomyelitis

Multiple sclerosis (MS), a neuroinflammatory disease, has few treatment options, none entirely adequate. We studied whether prolonged electrical stimulation of a hindbrain region (the nucleus raphe magnus) can attenuate experimental autoimmune encephalomyelitis, a murine model of MS induced by MOG35-55 injection. Eight days after symptoms emerged, a wireless electrical stimulator with a connectorless protruding microelectrode was implanted cranially, and daily intermittent stimulation of awake, unrestrained mice began immediately. The thoracic spinal cord was analyzed for changes in histology (on day 29) and gene expression (on day 37), with a focus on myelination and cytokine production. Controls, with inactive implants, showed a phase of disease exacerbation on days 19–25 that stimulation for >16 days eliminated. Prolonged stimulation also reduced infiltrating immune cells and increased numbers of myelinated axons. It additionally lowered gene expression for some pro-inflammatory cytokines (interferon gamma and tumor necrosis factor) and for platelet-derived growth factor receptor alpha, a marker of oligodendrocyte precursors, while raising it for myelin basic protein. Restorative treatments for MS might profitably consider ways to stimulate the raphe magnus, directly or via its inputs, or to emulate its serotonergic and peptidergic output.
The Effect of Spin-Orbit Interaction On Structural and Electronic Properties of ScIr2

The structural and electronic properties of face-centred cubic ScIr2 compound is investigated by using a generalised gradient approximation scheme of density functional theory with and without spin-orbit interaction. The structural results show that the spin-orbit interaction has a negligible effect for the crystallizing of ScIr2 compound. The Fermi surface calculations suggest considerable nesting along Γ-X direction that could affect the vibrational properties.
Blending the Past and Present of Automatic Image Annotation

Real world images depict varying scenes, actions and multiple objects interacting with each other. We consider the fundamental Computer Vision problem of image annotation, where an image needs to be automatically tagged with a set of discrete labels that best describe its semantics. As more and more digital images become available, image annotation can help in the automatic archival and retrieval of large image collections. Being at the heart of image understanding, image annotation can also assist in other visual learning tasks, such as image captioning, scene recognition, multi-object recognition, etc. With the advent of deep neural networks, recent research has achieved ground-breaking results in single-label image classification. However, for images representing the real world, containing different objects in varying scales and viewpoints, modelling the semantic relationship between images and all of their associated labels continues to remain a challenging problem. Additional challenges are posed from class-imbalance, incomplete labelling, label-ambiguity and several other issues that are commonly observed in the image annotation datasets. In this thesis, we study the image annotation task from two aspects. First, we bring to attention some of the core issues in the image annotation domain related to dataset properties and evaluation metrics that inherently affect the annotation performance of existing approaches to a significant extent. To examine these key aspects, we evaluate ten benchmark image annotation techniques on five popular datasets using the same baseline features, and perform thorough empirical analyses. With novel experiments, we explore possible reasons behind variations in per-label versus per-image evaluation criteria and discuss when each one of these should be used. We investigate dataset specific biases and propose new quantitative measures to quantify the degree of image and label diversity in a dataset, that can also be useful in developing new image annotation datasets. We believe the conclusions derived in this analysis would be helpful in making systematic advancements in this domain. Second, we attempt to address the annotation task by a CNN-RNN framework that jointly models label dependencies in an image while annotating it. We base our approach on the premise that labels corresponding to different visual concepts in an image share rich semantic relationships among them (e.g., “sky” is related to “cloud”). We follow recent works that have explored the CNN-RNN style models due to RNN’s capacity to model higher-order dependencies, but are limited in their approach to train the RNN in a pre-defined label order sequence. We overcome this limitation and propose a new method to learn multiple label prediction paths. We evaluate our proposed method on a number of popular and relevant datasets and achieve superior results compared to existing CNN-RNN based approaches. We also discuss the scope of the CNN-RNN framework in the context of image annotation.
WHY WE SHOULD CARE ABOUT EXCHANGE RELATIONSHIPS WITH COWORKERS AS WELL AS SUPERVISORS: BOTH FELLOW EMPLOYEES AND THE ORGANIZATION BENEFIT

An integrative model of social exchange relationships was used to investigate whether coworker exchange relationships, in addition to leader-member exchange (LMX), contribute to outcomes beneficial for fellow employees and their organization. Data from university alumni showed both LMX and coworker support significantly related to perceived organizational support (POS). As predicted, LMX exhibited a stronger relationship with POS than coworker support. Furthermore, POS mediated relationships of both perceived coworker support and LMX with supervisor ratings of employees’ restraint from behaviors detracting from productivity. Only coworker support related to supervisor ratings of altruistic behaviors. Thus, perceived coworker support may play an important role in contributing to positive attitudes about the organization, help given coworkers and restraint from behaviors that detract from productivity.
Space Vector PWM Algorithm for Diode Clamped Multi-level Inverters using Fractal Structure

-In this paper a space vector pulse width modulation algorithm for diode clamped multilevel inverter fed induction motor using the fractal structure has been proposed and applied for three-level and five-level inverters. In this method, fractal structure is used to represent the space vector of multilevel inverters. The switching sequence is determined without using look up tables, so the memory of the controller can be saved. The 1switching times of voltage vectors are calculated at the same manner as two-level SVPWM. It is easy to implement the triangularisation algorithm, which is used to locate the tip of the reference voltage vector. Thus, the proposed method reduces the execution time and complexity of multi-level SVPWM. This method can be extended to n-level inverter also. Based on above method, the simulation is carried out for three-level and five-level inverter fed induction motor and results are presented and analyzed. The obtained total harmonic distortions for three-level and five-level inverters are 5.70% and 3.61% respectively.
Controllable Caustic Animation Using Vector Fields

In movie production, lighting is commonly used to redirect attention or to set the mood in a scene. The detailed editing of complex lighting phenomena, however, is as tedious as it is important, especially with dynamic lights or when light is a relevant story element. In this paper, we propose a new method to create caustic animations, which are controllable through constraints drawn by the user. Our method blends caustics into a speciﬁed target image by treating photons as particles that move in a divergence-free ﬂuid, an irrotational vector ﬁeld or a linear combination of the two. Once described as a ﬂow, additional user constraints are easily added, e.g., to direct the ﬂow, create boundaries or add synthetic turbulence, which offers new ways to redirect and control light. The corresponding vector ﬁeld is computed by ﬁtting a stream function and a scalar potential per time step, for which constraints are described in a quadratic energy that we minimize as a linear least squares problem. Finally, photons are placed at their new positions back into the scene and are rendered with progressive photon mapping.
Beating LM-cut with LM-cut: Efﬁcient Cutting and Practical Tie Breaking for the Precondition Choice Function

LM-cut is one of the most popular heuristics in optimal planning that computes strong admissible estimates of the perfect delete relaxation heuristic h + . The heuristic iteratively computes disjunctive action landmarks for the current state, reducing their action costs until no more landmarks with remaining action costs can be found. These landmarks are generated by ﬁnding cuts in the justiﬁcation graph, which depends on a precondition choice function mapping each action to its most expensive precondition according to h max . This precondition is not necessarily unique, yet the performance of the heuristic heavily depends on this choice. We introduce and analyze several new tie breaking strategies for the precondition choice function, and evaluate their effectiveness on the IPC benchmarks. Furthermore, we suggest a modiﬁcation to the computation of the cut, which leads to a signiﬁcant speed up of the LM-cut computation.
Sight to Sound: An End-to-End Approach for Visual Piano Transcription

Automatic music transcription has primarily focused on transcribing audio to a symbolic music representation (e.g. MIDI or sheet music). However, audio-only approaches often struggle with polyphonic instruments and background noise. In contrast, visual information (e.g. a video of an instrument being played) does not have such ambiguities. In this work, we address the problem of transcribing piano music from visual data alone. We propose an end-to-end deep learning framework that learns to automatically predict note onset events given a video of a person playing the piano. From this, we are able to transcribe the played music in the form of MIDI data. We find that our approach is surprisingly effective in a variety of complex situations, particularly those in which music transcription from audio alone is impossible. We also show that combining audio and video data can improve the transcription obtained from each modality alone.
A Survey on Linguistic-Based and User-Based Recommending Posts Using Two-Level Clustering Methods

- Online social networks have produced bunch of online social groups where individuals can collaborate and exchange their ideas. The real problems that conflicts with the user security and convenience are confidentiality break, groups without inception, confusion created from various groups’ difficulty in managing groups. This can be overcome to an extent by an automated filtering method required to categorizing group members based on their responses. This paper proposes clustering of group posts on thematic, emotional, stylistic, sentimental and psycholinguistic methods and group members are categorized based on their responses to the posts belonging to different clustering methods. This categorization helps to stop irrelevant notifications to the users which are received from more than one groups, via recommending the posts which might interest the users. It also helps to identify the group members those violate group policies by posting negative posts. The post categorization increases the performance where there are large numbers of members in a social group by performing linguistic clustering. The contribution work is to implement location-aware personalized posts recommendation using user’s behavioral patterns and their geographic location. Another, important work is to implement text-to-speech system converting English text into speech using speech synthesis technique. The system gives user rating to the users who share posts depending on the cluster they belong to. The system provides read later post functionality to the users. The system also provides the users with hash-tag recommendations. The system also provides user with posts based on long - term and short - term analysis. The system has been tested on Twitter API group data where a significant solution to an unaddressed problem associated with social networking groups is offered.
Social Exchange of Motivated Beliefs

We use laboratory experiments to study whether biases in beliefs grow more severe when people socially exchange these beliefs with one another. We elicit subjects’ (naturally biased) beliefs about their relative performance in an intelligence quotient (IQ) test and allow them to update these beliefs in real time. Part of the way through the task we give each subject access to the beliefs of a counterpart who performed similarly on the test and allow them both to observe the evolution of one another’s beliefs. We find that subjects respond to one another’s beliefs in a highly asymmetric way, causing a severe amplification of subjects’ initial bias. We find no such patterns in response to objective public signals or in control treatments without social exchange or scope for motivated beliefs. We also provide evidence that the pattern is difficult to reconcile with Bayesianism and standard versions of confirmation bias. Overall, our results suggest that bias amplification is likely driven by “motivated assignment of accuracy” to others’ beliefs: subjects selectively attribute higher informational value to social signals that reinforce their motivation.
Model-based keypoint detection in Images with Radial Distortion

Keypoint detection and matching is of fundamental importance for many applications in computer and robot vision. The association of points across diﬀerent views is problematic because image features can undergo signiﬁcant changes in appearance. Unfortunately, state-of-the-art methods, like the Scale-Invariant Feature Transform (SIFT), are not resilient to the radial distortion that often arises in images acquired by cameras with micro-lenses and/or wide ﬁeld-of-view. This article proposes modiﬁcations to the SIFT algorithm that substantially improve the re-peatability of detection and eﬀectiveness of matching under radial distortion, while preserving the original invariance to scale and rotation. The scale-space representation of the image is obtained using adaptive ﬁltering that compensates the local distortion, and the keypoint description is carried after implicit image gradient correction. Unlike competing methods, our approach avoids image re-sampling (the processing is carried in the original image plane), it does not require accurate camera calibration (an approximate modeling of the distortion is suﬃcient), and it adds minimum computational overhead. Extensive experiments using both synthetic and real imagery show the superiority of our method in establishing point correspondence across images with radial distortion.
Arctic sea ice state estimation from thermodynamic PDEmodel

Recent rapid loss of the Arctic sea ice motivates the study of the Arctic sea ice thickness. Global climate model that describes the ice thickness evolution requires an accurate spatial temperature profile of the Arctic sea ice. However, measuring the complete temperature profile is not feasible within and throughout the Arctic icecap. Instead, measuring the ice thickness is doable with the acquisition of data from submarine and satellite devices. In this paper, we develop a backstepping observer algorithm to estimate the temperature profile for the Arctic sea ice model via available measurements of sea ice thickness and sea ice surface temperature. The observer is designed in a rigorous manner to drive the temperature profile estimation error to zero, for a salinity-free sea ice model. Moreover, the proposed observer is used to estimate the temperature profile of the original sea ice model with salinity via numerical simulation. The simulation results illustrate that our observer design achieves a successful estimation of the sea ice temperature profile in three days, which is ten times faster than the straightforward open-loop algorithm. © 2019 Elsevier Ltd. All rights reserved.
VIBRATION ANALYSIS OF SIMPLY SUPPORTED BEAM WITH VARYING CRACK DEPTH AND LOCATION BY USING ANSYS

Assistant Professor, Dept. of Civil Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India. ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract The existence of damage in a structure results in changes of global dynamic characteristics. Therefore, relatively simple vibration measurements of a structural response and extraction of information on natural frequencies, damping or mode shapes, make damage detection feasible. Presence of crack in a structure cause vibration in the system, thus an accurate and comprehensive investigation about vibration of cracked dynamic structures seems to be necessary. This paper deals with the damage assessment technique for the non-destructive detection and size estimation of cracks in a simply supported beam using finite element method of analysis by ANSYS programme. The purpose of the study is to present the results of experimental and numerical analyses of damage detection.