Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Preserving Keyword Search over Cloud Data

- With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. We present a scheme that discusses secure rank based keyword search over an encrypted cloud data. The data that has to be outsourced is encrypted using symmetric encryption algorithm for data confidentiality. The index file of the keyword set that has to be searched is outsourced to the local trusted server where the keyword set that is generated from the data files is also stored. This is done so that any un-trusted server cannot learn about the data with the help of the index formed. The files are listed based on the certain relevance criteria. User requests for the required files to the un-trusted server. The parameters required for ranking is got from the data stored while indexing. Based on the ranking, the files are retrieved from the un-trusted server and displayed to the user. The proposed system can be extended to support Boolean search and Fuzzy keyword search techniques.
Retrieval algorithm for the column CO2 mixing ratio from pulsed multi-wavelength lidar measurements

Abstract. The retrieval algorithm for CO2 column mixing ratio from measurements of a pulsed multi-wavelength integrated path differential absorption (IPDA) lidar is described. The lidar samples the shape of the 1572.33 nm CO2 absorption line at multiple wavelengths. The algorithm uses a least-squares fit between the CO2 line shape computed from a layered atmosphere model and that sampled by the lidar. In addition to the column-average CO2 dry-air mole fraction (XCO2), several other parameters are also solved simultaneously from the fit. These include the Doppler shift at the received laser signal wavelength, the product of the surface reflectivity and atmospheric transmission, and a linear trend in the lidar receiver's spectral response. The algorithm can also be used to solve for the average water vapor mixing ratio, which produces a secondary absorption in the wings of the CO2 absorption line under humid conditions. The least-squares fit is linearized about the expected XCO2 value, which allows the use of a standard linear least-squares fitting method and software tools. The standard deviation of the retrieved XCO2 is obtained from the covariance matrix of the fit. The averaging kernel is also provided similarly to that used for passive trace-gas column measurements. Examples are presented of using the algorithm to retrieve XCO2 from measurements of the NASA Goddard airborne CO2 Sounder lidar that were made at constant altitude and during spiral-down profile maneuvers.
Cryptanalysis of Merkle-Hellman cipher using ant colony optimization

<p class="Abstract"><span id="docs-internal-guid-d3fe8e21-7fff-17fc-df0e-00893428243c"><span>The Merkle-Hellman (MH) cryptosystem is one of the earliest public key cryptosystems, which is introduced by Ralph Merkle and Martin Hellman in 1978 based on an NP-hard problem, known as the subset-sum problem. Furthermore, ant colony optimization (ACO) is one of the most nature-inspired meta-heuristic optimization, which simulates the social behaviour of ant colonies. ACO has demonstrated excellent performance in solving a wide variety of complex problems. In this paper, we present a novel ant colony optimization (ACO) based attack for cryptanalysis of MH cipher algorithm, where two different search techniques are used. Moreover, experimental study is included, showing the effectiveness of the proposed attacking scheme. The results show that ACO based attack is more suitable than many other algorithms like genetic algorithm (GA) and particle swarm optimization (PSO). </span></span></p>
Facial Expression Recognition System Using Neural Network based on LBP Features

: A novel expression recognition scheme is proposed to identify seven dissimilar facial emotions by using JAFFE (Japanese Female Expressions) database. Three different approaches of feature selection and extraction have been used for generation of optimal feature vector. Here, 2D-DCT (Two-Dimensional Discrete Cosine Transforms), uniform rotation-invariant LBP (Local Binary Patterns) and HOG (Histogram of Oriented Gradients) coefficients are employed in addition to image statistics parameters with a view to design a hybrid feature vector. The single hidden layer feed forward neural network has been used as a classifier in order to classify and also to recognize different emotions from frontal facial images. Three learning algorithms such as resilient back propagation, scaled conjugate gradient and gradient descent algorithm with momentum and adaptive learning rate have been compared. Major contribution of this research is in the design of an optimal feature vector comprising of uniform LBP based hybrid feature vector for encoding subtle texture information of images and also in the development of a simple neural network classifier containing a single hidden layer with carefully chosen number of neurons based on the experiments. The proposed neural network classifier trained with gradient descent algorithm with momentum and adaptive learning rate delivers the maximum average facial expression recognition accuracy of 97.2% along with the least number of connection weights and biases.
Chronic Extremity Joint Pain-Suspected Inflammatory Arthritis

* See Last Page for Key New 2016 Jacobson Page 1 Reference Study Type Patients/ Events Study Objective (Purpose of Study) Study Results Study Quality 1. Mattar M, Salonen D, Inman RD. Imaging of spondyloarthropathies. Rheum Dis Clin North Am. 2013;39(3):645-667. Review/OtherDx N/A To focus on the pattern of spinal involvement in the axial skeleton, initially speaking about the relevant anatomy of the spine and sacroiliac joints. Then described are the imaging modalities most commonly used today, in addition to the standard imaging protocols for diagnosing and monitoring disease progression Different types of spondyloarthropathies demonstrate different imaging characteristics that are important to identify to reach the correct diagnosis. 4
An Investigative Study on Minimising Security Attacks using Deep learning in a Cyber Physical System

-Irregularity identification is essential to guarantee the security of cyber-physical systems (CPS). Be that as it may, because of the expanding intricacy of CPSs and more modern assaults, traditional inconsistency recognition strategies can't be straightforwardly applied to defeat such issues, which additionally need area explicit information and handle the developing volume of information. Profound learning-based peculiarity discovery techniques have been proposed to accomplish solo recognition in the period of CPS huge information. In this paper, we audit best aspects of these deep learning strategies in CPSs. We propose a scientific categorization as far as the sort of peculiarities, techniques, and usage and assessment measurements to comprehend the fundamental properties of current strategies. Further, we use this scientific categorization to recognize and feature new qualities and plans in every CPS area. We sum up top notch of openly accessible datasets for preparing and assessment. We additionally talk about our discoveries, the restrictions of existing investigations, and potential headings to improve security in CPS using deep learning techniques.
Effects of medicinal plants mixture on growth performance, nutrient digestibility, blood profiles, and fecal microbiota in growing pigs

Background and Aim: Alternative natural materials to antibiotics for improving digestive health and growth performance are needed due to strengthening regulations related to the use of antibiotic growth promoters. The study aimed to evaluate the effects of medicinal plants mixture (60% Bidens pilosa L., 15% Urena lobata L., 15% Pseuderanthemum palatiferum, 5% Ramulus cinnamomi, and 5% Star anise) as alternative growth promotors on animal health, nutrient digestibility, blood parameters, and growth performance of growing pigs. Materials and Methods: The study was conducted, from April 2020 to June 2020, at a private pig production farm located in Cam Giang district Hai Duong Province, Vietnam. Forty-eight 10-week-old crossbred (♂Duroc×♀ [Landrace×Yorkshire]) pigs, average initial body weight 30.3±1.42 kg, were randomly allocated to four dietary groups, three replicate pens per experimental group, with 4 pigs/pen. For 7 weeks, the pigs were fed a basal diet supplemented with the mixture at levels of 0, 20, 40, and 60 g/kg of feed. Results: Final body weight, average daily gain, average daily feed intake, and feed conversion ratio, as well as apparent total tract digestibility of dry matter, organic matter, crude protein, ether extract, and gross energy were not significantly influenced by the diets (p>0.05). Inclusion of the plant mixture decreased significantly red blood cell count, blood cholesterol, urea nitrogen, and low-density lipoprotein (LDL) concentrations (p<0.05) compared with the control diet. No diet effect was observed on fecal Escherichia coli, Salmonella spp. , Clostridium spp. , and total bacteria counts. Conclusion: The incorporation of the plant mixture into the diet of growing pigs reduced serum cholesterol, LDL, and urea concentrations with no adverse effect on performance and nutrient digestibility.
IMPACTS OF POSITIVE PARENTING INTERVENTION ON STRESS MANAGEMENT OF MOTHERS HAVING CHILDREN WITH INTELLECTUAL DISABILITY IN BIRHAN LEHETSANAT REHABILITATION CENTER, ADDIS ABABA

Research evidence indicates that parents of children with intellectual disabilities are exposed to a host of stressful life events while caring for their children with intellectual disabilities. The purpose of this study was then to determine the effectiveness of the Positive Parenting Program (Triple-P) in enabling mothers to manage stress related to parenting a child with intellectual disability (ID). Additionally, the study also aimed at assessing the impact of Triple-P in improving the relationship between a mother and a child with an intellectual disability. Quasi-experimental Design (with Pretest-Posttest Control Group approach) was used to test the effectiveness of the intervention. Parental Stress Scale (PSS) and Parent-Child Relationship Scale (PCRS) were administered to 28 mothers of children with intellectual disabilities before and after the intervention to gather data on the level of stress, conflict, closeness, dependency, and positive relationship. An equal number (n=14) of mothers were randomly assigned to the experimental and control groups. The experimental group participated in 8 sessions of Tipple-P, but the control group did not. Descriptive statistics, independent sample t-test, matched sample t-test, and Multivariate Analysis of Covariance (MANCOVA) were used to analyze the data. Findings revealed that mothers in the treatment group showed a significant decrease in stress levels compared to the comparison group. Furthermore, mothers in the experimental group demonstrated statistically significant improvement in their relationship with the intellectually disabled children compared to the control group. However, closeness and dependency were the only two main contributors to the variance in the relationship and their contribution was statistically significant. In conclusion, Triple-P was found to be effective in improving the mother-child relationship and managing the stress of mothers of children with intellectual disabilities.
IS BITCOIN A SAFE HAVEN? A STUDY ON THE FACTORS THAT AFFECT BITCOIN PRICES

Bitcoin and other digital currencies are financial assets with high volatility, which calls for an investigation of the factors that influence their prices and thus has led to a debate on whether they are reliable investment instruments or diversification tools. The present study aims to explore the impact upon Bitcoin prices of commodities such as gold and oil, the S&P 500 index, and the volatility index and financial stress index, which represent the financial risk environment. To this purpose, we analyze this relationship using the Autoregressive Distributed Lag (ARDL) approach based on the monthly data from the 2010-2021 period. The results suggest that both in the long and short run, gold price per ounce does not have a statistically significant effect on Bitcoin price. On the other hand, an increase in crude oil prices has a negative impact on Bitcoin price in the short run, with no significant effect in the long run. The S&P 500 stock market index positively affects the Bitcoin price both in the short and long run. In addition, our analysis results also demonstrate that developments indicating increased risk in the long run tend to reduce Bitcoin returns.
The Relationship between the Care Burden, Anxiety, and Social Support Levels of Mothers of Children with Cancer

RESULTS The mean age of the mothers was 37.46±9.67 old years and the duration of cancer diagnosis was 10.57±12.59 months. The primary caregivers of 83.7% of children were their mothers. It was determined that 76.7% of mothers had a change in their social life after their children diagnosis. There was a positive correlation between the mothers’ Beck Anxiety Scale scores and the ZBI scores (r=0.508; p=0.01). There was a statistically significant difference between the status of changes in mothers social life and Beck Anxiety Scale (t=4,454; p<0.001) and the ZBI scores (t=3.259; p=0.003). The status of mothers receiving support in household chores (t=0.864; p=0.018) affected their mean scores of ZBI scores.
Harvesting the Web for building Large-scale Argumentation Graphs

Knowledge graphs have been used successfully in the development of many artificial intelligence systems. In computational argumentation, the emergent area in natural language processing, a new argumentation graph has been constructed by Al-Khatib et al. [2020]. The graph models argumentation knowledge as an ‘effect’ relation (edge) between concepts (nodes). The effect relation has two categories: positive and negative. Al-Khatib et al. [2020] proposes an approach to acquire the knowledge automatically applying machine learning models. This method takes a claim as input and outputs whether the claim has an ‘effect’ relation, the relation category, and concepts. However, the mentioned approach shows some shortcomings, such as dealing only with the claim inputs, that diminish the applicability of constructing a large argumentation knowledge graphs. To overcome these shortcomings and construct a large argumentation knowledge graph, we introduce a new approach to mine effect relations between concepts. The new approach, different than Al-Khatib et al. [2020], covers more complex input types and employs the recent advances in text classification. Our implementation includes two main steps: building a new dataset utilizing the paradigm of distance supervision, and developing neural-based classifiers applying an active learning technique to filter the dataset and improve the classification effectiveness. The results of the evaluation experiments show that our approach helps the classifiers to learn new patterns and be able to handle sentences with complex structure.
Screening of Convergence Disorders and Accommodation Disorders among School Children in Gorakhpur

Accommodative insufficiency (AI) and convergence insufficiency (CI) play a major role in a reading insufficiency.CI is a binocular vision problem but it is not a strabismus. It is generally associated with symptoms such as eyestrain, blurry vision, double vision, headache and reading related problems. AI is a condition that affects the ability to maintain near vision focus for a prolonged time. The purpose of this study was to estimate the frequency of NBV (normal binocular vision), convergence insufficiency and accommodative insufficiency in children 4 th to 9 th school years in Gorakhpur U.P (India). A comprehensive eye examination and binocular vision assessment, integrating accommodative and convergence parameters, were used to analyse the visual condition. Of the 415 students who completed the eye examination and binocular vision testing. All students in the group of schools of the rural were invited to attend. 415 children were assessed from a total of 450 students (response rate of 92%). fifteen were excluded because they did not meet the inclusion criteria. The assessment was completed by 400 children, 175 females and 225 males. We found significant differences in expected values from the current clinical criteria for near point of convergence (NPC) with penlight, distance and near horizontal phorias, accommodative amplitudes, monocular and binocular accommodative facility (normal distribution p < 0.001). The study will also provide insight into the differences in binocular vision parameters between ethnicities, and the minimum battery of tests needed to detect binocular vision anomalies in a community setting. European Journal of Molecular & Clinical Medicine ISSN 2515-8260 Volume 07, Issue 10, 202
The Borel Distribution: Approximation and Concentration

We develop the tools necessary to use Stein’s method for approximation by a Borel distribution, which we illustrate by considering the approximation of the number of customers served in the busy period of an M/G/1 queue. We further derive concentration inequalities for the Borel distribution. Both these sets of results are based on a representation for the size-biased version of a Borel random variable.
Correcting Sociodemographic Selection Biases for Population Prediction from Social Media

Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population - a "selection bias". Within the social sciences, such a bias is typically addressed with restratification techniques, where observations are reweighted according to how under- or over-sampled their socio-demographic groups are. Yet, restratifaction is rarely evaluated for improving prediction. In this two-part study, we first evaluate standard, "out-of-the-box" restratification techniques, finding they provide no improvement and often even degraded prediction accuracies across four tasks of esimating U.S. county population health statistics from Twitter. The core reasons for degraded performance seem to be tied to their reliance on either sparse or shrunken estimates of each population's socio-demographics. In the second part of our study, we develop and evaluate Robust Poststratification, which consists of three methods to address these problems: (1) estimator redistribution to account for shrinking, as well as (2) adaptive binning and (3) informed smoothing to handle sparse socio-demographic estimates. We show that each of these methods leads to significant improvement in prediction accuracies over the standard restratification approaches. Taken together, Robust Poststratification enables state-of-the-art prediction accuracies, yielding a 53.0% increase in variance explained (R 2) in the case of surveyed life satisfaction, and a 17.8% average increase across all tasks.
Reasoning about Recursive Quantum Programs

Most modern (classical) programming languages support recursion. Recursion has also been successfully applied to the design of several quantum algorithms and introduced in a couple of quantum programming languages. So, it can be expected that recursion will become one of the fundamental paradigms of quantum programming. Several program logics have been developed for verification of quantum while-programs. However, there are as yet no general methods for reasoning about (mutual) recursive procedures and ancilla quantum data structure in quantum computing (with measurement). We fill the gap in this paper by proposing a parameterized quantum assertion logic and, based on which, designing a quantum Hoare logic for verifying parameterized recursive quantum programs with ancilla data and probabilistic control. The quantum Hoare logic can be used to prove partial, total, and even probabilistic correctness (by reducing to total correctness) of those quantum programs. In particular, two counterexamples for illustrating incompleteness of non-parameterized assertions in verifying recursive procedures, and, one counterexample for showing the failure of reasoning with exact probabilities based on partial correctness, are constructed. The effectiveness of our logic is shown by three main examples -- recursive quantum Markov chain (with probabilistic control), fixed-point Grover's search, and recursive quantum Fourier sampling.
TriPoll: Computing Surveys of Triangles in Massive-Scale Temporal Graphs with Metadata

Understanding the higher-order interactions within network data is a key objective of network science. Surveys of metadata triangles (or patterned 3-cycles in metadata-enriched graphs) are often of interest in this pursuit. In this work, we develop TriPoll, a prototype distributed HPC system capable of surveying triangles in massive graphs containing metadata on their edges and vertices. We contrast our approach with much of the prior effort on triangle analysis, which often focuses on simple triangle counting, usually in simple graphs with no metadata. We assess the scalability of TriPoll when surveying triangles involving metadata on real and synthetic graphs with up to hundreds of billions of edges. We utilize communication-reducing optimizations to demonstrate a triangle counting task on a 224 billion edge web graph in approximately half of the time of competing approaches, while additionally supporting metadata-aware capabilities.
Learning from Successful and Failed Demonstrations via Optimization

Learning from Demonstration (LfD) is a popular approach that allows humans to teach robots new skills by showing the correct way(s) of performing the desired skill. Human-provided demonstrations, however, are not always optimal and the teacher usually addresses this issue by discarding or replacing sub-optimal (noisy or faulty) demonstrations. We propose a novel LfD representation that learns from both successful and failed demonstrations of a skill. Our approach encodes the two subsets of captured demonstrations (labeled by the teacher) into a statistical skill model, constructs a set of quadratic costs, and finds an optimal reproduction of the skill under novel problem conditions (i.e. constraints). The optimal reproduction balances convergence towards successful examples and divergence from failed examples. We evaluate our approach through several 2D and 3D experiments in real-world using a UR5e manipulator arm and also show that it can reproduce a skill from only failed demonstrations. The benefits of exploiting both failed and successful demonstrations are shown through comparison with two existing LfD approaches. We also compare our approach against an existing skill refinement method and show its capabilities in a multi-coordinate setting.