Miracle-dz/UniFAR-FTUs huggingface dataset:
εpsolute: Efficiently Querying Databases While Providing Differential Privacy

As organizations struggle with processing vast amounts of information, outsourcing sensitive data to third parties becomes a necessity. To protect the data, various cryptographic techniques are used in outsourced database systems to ensure data privacy, while allowing efficient querying. A rich collection of attacks on such systems has emerged. Even with strong cryptography, just communication volume or access pattern is enough for an adversary to succeed. In this work we present a model for differentially private outsourced database system and a concrete construction, εpsolute, that provably conceals the aforementioned leakages, while remaining efficient and scalable. In our solution, differential privacy is preserved at the record level even against an untrusted server that controls data and queries. εpsolute combines Oblivious RAM and differentially private sanitizers to create a generic and efficient construction. We go further and present a set of improvements to bring the solution to efficiency and practicality necessary for real-world adoption. We describe the way to parallelize the operations, minimize the amount of noise, and reduce the number of network requests, while preserving the privacy guarantees. We have run an extensive set of experiments, dozens of servers processing up to 10 million records, and compiled a detailed result analysis proving the efficiency and scalability of our solution. While providing strong security and privacy guarantees we are less than an order of magnitude slower than range query execution of a non-secure plain-text optimized RDBMS like MySQL and PostgreSQL.
DISTINGUISHING BIPOLAR DEPRESSION FROM MAJOR DEPRESSIVE DISORDER USING FNIRS AND DEEP NEURAL NETWORK

A variety of psychological scales are utilized at present as the most important basis for clinical diagnosis of mood disorders. An experienced psychiatrist assesses and diagnoses mood disorders based on clinical symptoms and relevant assessment scores. This symptom based clinical criterion is limited by the psychiatrist’s experience. In practice, it is difficult to distinguish the patients with bipolar disorder with depression episode (bipolar depression, BD) from those with major depressive disorder (MDD). The functional near-infrared spectroscopy (fNIRS) technology is commonly used to perceive the emotions of a human. It measures the hemodynamic parameters of the brain, which correlate with cerebral activation. Here, we propose a machine learning classification method based on deep neural network for the brain activations of mood disorders. Large time scale connectivity is determined using an attention long short term memory neural network and short-time feature information are considered using the InceptionTime neural network in this method. Our combined method is referred to as AttentionLSTM-InceptionTime (ALSTMIT). We collected fNIRS data of 36 MDD patients and 48 BD patients who were in the depressed state. All the patients were monitored by fNIRS during conducting the verbal fluency task (VFT). We trained the model with the ALSTMIT network. The algorithm can distinguish the two types of patients effectively: the average accuracy of classification on the test set can reach 96.2% stably. The classification can provide an objective diagnosis tool for clinicians, and this algorithm may be critical for the early detection and precise treatment for the patients with mood disorders.
Headspace and small-chamber studies of airborne diacetyl release from selected food flavoring mixtures: activity coefficients and air modeling implications

Laboratory studies were conducted to evaluate airborne release of diacetyl from selected mixtures simulating butter flavorings added to foods. The test materials included diacetyl (97% purity); 0.015%, 0.15%, 1.5%, and 3.0% diacetyl in a water/propylene glycol mixture; 1.5% diacetyl in deionized water or soybean oil; and 3% or 6% diacetyl in a commercial steam distillate from milk fermentation known as “butter starter distillate. ” Diacetyl was quantified by gas chromatography with flame ionization detection. Expected concentration-dependent emission patterns based on liquid diacetyl content were demonstrated, but were significantly altered by mixture composition. Soybean oil and deionized water more readily released diacetyl when compared with starter distillate, propylene glycol solutions, and pure diacetyl. Measured diacetyl concentrations under static headspace and dynamic flow-chamber conditions were compared to estimated concentrations utilizing Raoult's law with published and fitted activity coefficient corrections for each mixture, indicating that published coefficients often understated the measured concentrations. It is concluded that headspace (static) and small-chamber (dynamic) measurements of airborne diacetyl provide data to assist in validating model-estimated airborne diacetyl concentrations by using mixture-specific activity coefficients. Implications of these empirical data for validating exposure estimates for diacetyl based on near-field/far-field modeling in workplace settings are discussed.
Acute surgery for a case of superior vermian arteriovenous malformation producing raised venous pressure coexisting with basilar-superior cerebellar artery aneurysm presenting subarachnoid hemorrhage; Case report

Background: Superior vermian subtype of arteriovenous malformation (AVM) coexisting with proximal feeder aneurysm on basilar-superior cerebellar artery (BA-SCA) junction is an extremely rare situation. We experienced a case of this rare entity presenting with subarachnoid hemorrhage (SAH), and herein, introduce the outline and clinical features of this experience together with the actual surgical video. Case Description: A 54-year-old man SAH patient with severe headache, disturbance of consciousness, and left oculomotor palsy was urgently admitted to our hospital. Imaging examination demonstrated superior vermian AVM with BA-SCA aneurysm, and both lesions were treated through two different approaches (left pterional craniotomy in conjunction with zygomectomy, and left posterior interhemispheric occipital transtentorial approach) in acute phase of SAH. Both lesions were completely disappeared postoperatively and the patient’s postoperative course was favorable, without symptomatic cerebral vasospasm. Although slight oculomotor palsy remained, the patient recovered well and was transferred to a rehabilitation hospital for further improvement. Conclusion: In the cases of AVM coexisting with proximal feeder aneurysm, presenting with SAH, disorders of intracranial venous return associated with an AVM can be a vital hindrance to managing cerebral vasospasm; therefore, treating both lesions in the acute phase may lead to good outcomes.
A Patient-Level Meta-analysis

ANTIDEPRESSANT MEDICATION (ADM) represents the current standard of treatment for major depressive disorder (MDD). Antidepressant medication has been shown to be superior to placebo in thousands of controlled clinical trials over the past 5 decades. The extent to which ADM outperforms placebo (which controls for nonpharmacological aspects of ADM) can be used to index the “true” pharmacological effect of ADM in clinical settings. The randomized, double-blind, placebo-controlled trial is the gold standard for testing treatment efficacy and affords the opportunity to identify patient characteristics that predict differential pharmacological response. Baseline symptom severity is one dimension that may affect treatment outcome. Kirsch et al and Khan et al presented independent meta-analyses of randomized placebo-controlled trials based on data from the Food and Drug Administration (FDA) clinical trial database. Using mean scores and standard deviations on the Hamilton Depression Rating Scale (HDRS) from each study, they examined the effect of baseline symptom severity on the relative efficacy of ADM vs placebo. Kirsch et al found that as the mean baseline HDRS score increased, the magnitude of HDRS change decreased for placebo but remained unchanged for ADM. Khan et al did not find a significant relationship between baseline scores and symptom change for the Author Affiliations: Departments of Psychology (Mr Fournier and Dr DeRubeis) and Psychiatry (Dr Amsterdam), University of Pennsylvania, Philadelphia; Departments of Psychology (Dr Hollon) and Psychiatry (Dr Shelton), Vanderbilt University, Nashville, Tennessee; Department of Psychology, University of Colorado at Boulder (Dr Dimidjian); and Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque (Dr Fawcett). Corresponding Author: Jay C. Fournier, MA, Department of Psychology, University of Pennsylvania, 3720 Walnut St, Philadelphia, PA 19104 (jcf@sas.upenn .edu). Context Antidepressant medications represent the best established treatment for major depressive disorder, but there is little evidence that they have a specific pharmacological effect relative to pill placebo for patients with less severe depression.
Character strengths as protective factors against engagement in sexting in adolescence

El sexting es una nueva forma de interacción sexual en adolescentes que ha sido asociada tanto a problemas psicológicos como comportamientos de riesgo sexual. Aunque varios estudios han examinado la relación entre sexting y rasgos de personalidad, es posible que la asociación con fortalezas del carácter no haya sido explorada. El objetivo de este estudio fue analizar cuales fortalezas de carácter podrían funcionar como factores protectores contra la participación en el sexting, controlando otras variables que pueden influenciar en esta relación. Los adolescentes cumplimentaron cuestionarios sobre recursos tecnológicos, sexting activo y fortalezas de carácter. Los resultados de la regresión lineal mostraron que el sexting activo estuvo positivamente relacionado con las fortalezas de curiosidad y humor y negativamente relacionado con justicia y autenticidad. Estos resultados sugieren que programas escolares o familiares que tienen como objetivo la prevención del sexting deben incluir actividades específicas que fomenten el uso positivo de la curiosidad y el humor y que busquen promover la honestidad, el respeto, la justicia y la responsabilidad en el uso de la tecnología para las relaciones interpersonales. Sexting is a new kind of sexual interaction among adolescents that has been linked to both psychological problems and sexual risk behaviors. Although studies have examined the relationship between sexting and personality traits, its possible association with character strengths remains unexplored. The aim of this study was to analyze which character strengths may function as protective factors against engagement in sexting, controlling for other variables that might influence this relationship. Adolescents completed questionnaires about technological resources, active sexting, and character strengths. The results from hierarchical regression showed that active sexting was positively related to the strengths of curiosity and humor and negatively related to fairness and authenticity. These findings suggest that school- or family-based programs aimed at preventing sexting should include specific activities that encourage a positive use of curiosity and humor and which seek to promote honesty, respect, fairness, and responsibility in the use of technology for interpersonal relationships
Content Based Image Retrieval System for Lung Cancer Detection Using Neural Network and Circular Hough Transform

Computer Aided Detection (CAD) systems that automatic detection and localize lung nodules in CT scans. A major problem in this system is a large number of false positives because of no provision for comparison of the predicted output. This paper recommends a new system with a combination of CBIR and neural network to full fill the gap in the area of early detection of lung cancer. From the preprocessed CT scan image, the system identifies whether it contains nodules using Circular Hough Transform and classifies into benign or malignant nodule using Probabilistic Neural Network. Then, it searched for the most identical pictures and retrieved it from the database. From the retrieved image, it is easy to identify the present cancer stage of the patient. Experiments have done based on both LIDC database and the locally collected database. The performance evaluation of the system is done by using both. The experimental results show that the present study easily differentiates benign and malignant nodules with an efficiency of 97 % accuracy on LIDC dataset, 95 % accuracy on Local dataset and similar images are retrieved with its present stage from the available database with a higher precision and recall rate.
Hybridization of Finite Element-Boundary Element Methods using an adaptive absorbing boundary condition for vibro-acoustic underwater noise simulations

Sound propagation from industrial activities in underwater or estimation of the target strength of waterside security systems are considered immensely important by many scientists for both regulators of development projects or military aspects. This paper presents a comparison of numerical methods used to model large scale acoustic coupled fluid structure interaction underwater problems. Concerning the mechanical behavior of the structure, it is absolutely essential during the computation of the modal basis to take into account the added mass effect of the heavy fluid, the water, around the structure. In that work, the added mass matrix is evaluated by a Boundary Element Method and the modal basis is computed by a substructuring algorithm to deal with both large number of degrees of freedom and modes. On the acoustic point of view, this article presents an efficient way to deal with very complex and large scale underwater target. This method is competing against standard Perfectly Match Layers (PML), Infinite Element Method (IEM), standard Boundary Element Method (BEM) and more recently the coupled MultiLevel Fast Multipole Method (MLFMM). Reducing considerably both computational time and RAM requirement keeping a very good accuracy, this approach hybridizes advantages of FEM, BEM and MLFMM methods through a domain decomposition technic using an Adaptive Absorbing Boundary Condition (AABC). Some numerical results are presented to present the capabilities of that approach on academic cases but also on more industrial applications.
EFFECT OF STREPTOCOCCUS MUTANS BACTERIA CAUSING DISEASES OF TOOTH DECAY AND GINGIVITIS IN MALE AND FEMALE PATIENTS

This study was conducted to investigate the effect of Streptococcus mutans on tooth health in both genders. 189 samples of patients with tooth decay and gingivitis of different ages and from both sexes who visited the specialized dental centers in Diwaniyah for the period from October 2019 to February 2020, were collected. Streptococcus mutans was isolated from biofilms, which are the most frequent and common pathogen for tooth decay and gingivitis. The results showed that the Gram-positive isolates are the largest percentage of 76.5% (153) bacterial isolates. Each of the S. mutan included 55 isolates (35.94%), S. pyogens 36 isolates (23.52%), and S. salivarus 10 isolates (6.53%). It was followed by staphylococcus, from which 52 isolates belonging to two bacterial species were Staphylococcus aureus (33 isolates) and Staph epidermidis (19 isolates). As for the Gram-negative bacterial species, it accounted for 18%, with 36 isolates distributed by (19, 8, 3, 5, 1) for E. coli, Klebseilla pneumoniae, Pseudomonas aerugenosa, Proteus spp, Citrobacter spp. The results showed a difference in the incidence of dental caries and gingivitis for males and females. The number of injured females was 101, at a rate of (53.5%), which is the largest, compared to the injured males 88 (46.5%), which is the lowest.
Feature-based initialization for Monocular Direct Visual Odometry

Direct Sparse Odometry (DSO) [1] is a state-of-art approach that proposes direct and sparse solution to the monocular-based Visual Odometry (VO). In this work we have looked into its initialization part and investigated a possibility to incorporate methods from indirect VO solutions into its framework. In particular, we implemented an initialization module that utilizes features and computes relative camera transformation based on their correspondences analogous to ORB-SLAM work [2]. We further extended the solution by augmenting the extracted features and estimating the depth map based on the probabilistic approach formulated in [3]. A lot of attention was addressed to the optimization and map refinement by means of bundle adjustment and outlier filtering. Evaluations based on EuRoC [4], KITTI [5] and CARLA [6] image sequences have shown that the feature-based initialization outperforms the original approach proposed by DSO authors and increases overall robustness and accuracy.
Response of ornamental plants, study sites and their interaction to various pollutants in respect of secondary metabolites, enzymes and proline content

The present investigation was carried out during the year 2020 at four study sites. Corresponding to biochemical traits, peroxidase activity recorded was maximum in OP-13 (87.99 ΔOD min-1 g-1 FW) among plants, S3 (28.74 ΔOD min-1 g-1 FW) among sites. None of the OP X S interactions had significant effect on this trait. Highest catalase activity of 63.48 µmol H2O2 min.-1 g-1 FW was observed in OP-15 among plants, S3 (27.98 µmol H2O2 min-1 g-1 FW) among sites. This attribute had no significant influence among interaction treatments. OP-4 had maximum phenol content of 25.34 mg 100g-1 among plants, S3 (6.87 mg 100g-1) among sites and OP-4 X S3 (26.08 mg 100g-1) in interactins. Flavonoid content observed to be higher in OP-4 (1.86 mg 100 g-1) among plants, S3 (1.47 mg 100 g-1) among sites and OP-1 X S3 (2.50 mg100 g-1) among interaction combinations. Significantly higher proline content was recorded in OP-3 (158.76 μg g-1) among plants, S3 (37.94 μg g-1) among areas and OP-3 X S3 (183.00 μg g-1) among interactions. Malondialdehyde content recorded was highest in OP-15 (3.66 nmol MDA g-1) among species, S3 (2.02 nmol MDA g-1) among sites. OP X S combinations showed non significant influence on MDA content.
Brain Tumor Detection using Neural Network

Brain Tumor is basically the unusual growth of some new cells found in the brain. This can happen in any area of the brain. Tumor are categorized by finding the origin of the cell which has tumor and if the cells are cancerous or not. Segmentation process is carried out to find if brain tumor exists or not, then the response of the patient to the tests performed is collected, different therapy sessions and also by creating models which has tumor growth in it. This one is different from the other types of tumor. Anyone can suffer from this disease. Primary tumors are basically Benign or Malignant. Here, we propose CNN (Convolutional Neural Network) based approach for improving accuracy. It also have capacity to detect certain features without any interaction from human beings. With the help of this model it classifies whether the MRI brain scan has tumor or not. There are other different algorithms, but this paper shows that CNN gives more accuracy than the rest. This model gives validation accuracy between 77%-85%. gives more precise and accurate results. CNN also let us to train large data sets and cross validate results, hence the most easy and reliable model to use.
Online processing of Mandarin relative clauses

A core question in language acquisition is whether children’s syntactic processing is experience-dependent and language-specific, or whether it is governed by abstract, universal syntactic machinery. We address this question by presenting corpus and on-line processing data from children learning Mandarin Chinese, a language that has been important in debates about the universality of parsing processes. The corpus data revealed that the two different relative clause constructions differentially prefer syntactic subjects and objects. In the experiment, 4-year-old children’s eye-movements were recorded as they listened to the two RC construction types (e.g., Can you pick up the pig that pushed the sheep? ). A permutation analysis showed that children’s ease of comprehension was closely aligned with the distributional frequencies, suggesting syntactic processing preferences are shaped by the input experience of these constructions.
Computational screening of phytocompounds from Moringa oleifera leaf as potential inhibitors of SARS-CoV-2 Mpro

Background: Coronavirus Disease (COVID-19), caused by novel SARS CoV-2 is rapidly spreading all over the World creating a global public health emergency at unprecedented levels. Till today, no effective treatments or vaccines against this global pandemic is reported and hence to identify lead compounds having potential action in controlling the spread the pandemic is a global concern. This study aimed at in silico screening of phytocompounds from M.oleiera leaf against novel SARS CoV-2 main protease (Mpro) through molecular docking. M.oleiera is an Indian medicinal plant as well as a vegetable, all parts of the plant is medicinally useful and is being used in many of the traditional and Ayurvedic medicinal preparations. Result: When the 19 compounds identified from M.oleifera leaf methanolic extract by Liquid Chromatography Mass Spectrometry (LCMS/MS) analysis and 5 FDA approved anti-viral drugs were screened in silico with SARS CoV-2 main protease (Mpro), the following compounds showed top interaction; apigenin-7-O-rutinoside (-8.8 kcal/mol), Mudanpioside (-8.3 kcal/mol), isoquercetin (-8 kcal/mol), isoquercitrin (-8 kcal/mol), quercetin (-7.8 kcal/mol) and dihydroquercetin (-7.8 kcal/mol). Anti-viral drugs: Raltegravir (-7.2 kcal/mol), Lopinavir-Ritonavir (-7.7 kcal/mol), maraviroc (-8.2 kcal/mol), Nelfinavir (-8.3 kcal/mol) and Tipranavir (-9.2 kcal/mol) also showed active interaction with Mpro. Preliminary phytochemical screening of methanol extract showed the presence of flavonoids, cardiac glycosides, phenols, coumarins, saponins, steroids and phytosteroids. In vitro antioxidant activity of methanolic extract of M.oleifera also showed greater activity, which would ameliorate the post-COVID secondary infection. Conclusion: Hence these compounds from M.oleifera, which are our diet based components, which can interact with the Mpro and curtail COVID-19 virus multiplication in the host cell.
Prediction of milling forces from an oblique cutting thermomechanical model, application to peripheral milling of Ti-6Al-4V alloy

In this work, a predictive machining theory, based on an analytical thermomechanical approach of oblique cutting, Moufki et al. [18, 19], is applied to the peripheral milling process. The material characteristics such as strain rate sensitivity, strain hardening and thermal softening are considered. In the primary shear zone, thermomechanical coupling and inertia effects are accounted for. The present analytical approach leads to a three dimensional cutting force model for end milling operations. Calculated and experimental results are compared for several operations: full immersion, up-milling and down-milling and for different cutting conditions. The proposed model appears as an interesting alternative to the mechanistic approach which requires many experimental tests to determine the milling cutting force coefficients. Nomenclature  angular velocity