Miracle-dz/UniFAR-FTUs huggingface dataset:
NonHomogenous Anonymization Approach Using Association Rule Mining for Preserving Privacy

Statistics reveals that with the advancement of technology, data sharing has become crucial for the purpose of research and analysis. Many organizations share their data with third party venders to gain information regarding the relevant and meaningful patterns that are hidden among the vast amount of raw data. But as the data needs to be shared with the third party, maintaining privacy of the individual becomes a challenging task. Thus, it becomes critical to share data in such a way so that privacy of the individual records is not hampered. This requirement has escalated the demand of research in a relatively new field that is privacy preserving data mining. Many techniques has been studied and developed related to this field which are based on either central server or distributed server. Some of these techniques include K-Anonymity, I-diversity, cryptographic method, randomization etc. Based on k-anonymity framework, nonhomogenous approach of generalization has already been proposed earlier. This approach of nonhomogenous anonymization provides privacy to the identity of individual records but suffers high privacy risk factor. The objective of the present work is to extend the existing approach of nonhomogenous generalization using association rule mining. Experimental results show that proposed technique performs better than existing technique based on two evaluation parameters - i.e. data disclosure Risk factor and execution time.
Advancing the assessment of cultural orientation: A developmental and contextual framework of multiple psychological dimensions and social identities

Abstract This paper aims to advance the scientific understanding of the role of culture, particularly cultural orientation, in development and psychopathology. We advance a theoretical framework that conceptualizes cultural orientation as a developmental construct represented by multiple psychological dimensions and social identities, and influenced by the contexts in which individuals are embedded. This perspective suggests that cultural orientation changes within individuals over time as a function of their experiences with and memberships in multiple groups, including the mainstream and ethnic culture groups, as well as a function of their normative developmental changes (i.e., the development of cognitive, social, and emotional capabilities). In addition, this framework places the development of an ethnic culture social identity (e.g., an ethnic identity) and a mainstream culture social identity in broader developmental perspectives that recognize these as two of the many social identities that are simultaneously embedded within the individual's self-concept and that simultaneously influence one's cultural orientation. To support the successful integration of culture into the study of development and psychopathology, we describe how highly reliable and valid measures of cultural orientation, indexed by individuals’ social identities, are essential for generating a scientifically credible understanding of the role of cultural orientation in development and psychopathology. Further, we detail some best research practices associated with our developmental and contextual framework, and note some important considerations for researchers interested in studying cultural orientation, development, and psychopathology.
G-PY: A Game Playing AI to Simulate Real Time Close-Quarter Firefights Using 3D First-Person-Shooter Games

This paper is regarding the proposal of the use of virtual environments to create, test and improve complex algorithms that power battle-ready drones which are deployed in actual war environments. An overview of the drone's operation would include the use of extensive image recognition to identify and differentiate enemies and friendlies, the environment and the objectives, static and interactable objects, all possible through training, using thousands of samples. The navigation of the drone is made possible using a specialized area recognition, memorizing and tracing algorithm. The weapons system used is similar to that of an average human militant, for the purpose of training, testing and calibration, and it employs a ‘Flick Shot’ algorithm to maximize the hit probability even in processors with lower computation power. The damage and loss of property sustained during deployment of these drones during field work, and the difficulty in training them with dummies which never are a 100% imitation of the enemies' technology, and the cost incurred, all can be avoided by using the proposed model.
An Adaptive Weight Method for Image Retrieval Based Multi-Feature Fusion

With the rapid development of information storage technology and the spread of the Internet, large capacity image databases that contain different contents in the images are generated. It becomes imperative to establish an automatic and efficient image retrieval system. This paper proposes a novel adaptive weighting method based on entropy theory and relevance feedback. Firstly, we obtain single feature trust by relevance feedback (supervised) or entropy (unsupervised). Then, we construct a transfer matrix based on trust. Finally, based on the transfer matrix, we get the weight of single feature through several iterations. It has three outstanding advantages: (1) The retrieval system combines the performance of multiple features and has better retrieval accuracy and generalization ability than single feature retrieval system; (2) In each query, the weight of a single feature is updated dynamically with the query image, which makes the retrieval system make full use of the performance of several single features; (3) The method can be applied in two cases: supervised and unsupervised. The experimental results show that our method significantly outperforms the previous approaches. The top 20 retrieval accuracy is 97.09%, 92.85%, and 94.42% on the dataset of Wang, UC Merced Land Use, and RSSCN7, respectively. The Mean Average Precision is 88.45% on the dataset of Holidays.
Ectopic Transplastomic Expression of a Synthetic MatK Gene Leads to Cotyledon-Specific Leaf Variegation

Chloroplasts (and other plastids) harbor their own genetic material, with a bacterial-like gene-expression systems. Chloroplast RNA metabolism is complex and is predominantly mediated by nuclear-encoded RNA-binding proteins. In addition to these nuclear factors, the chloroplast-encoded intron maturase MatK has been suggested to perform as a splicing factor for a subset of chloroplast introns. MatK is essential for plant cell survival in tobacco, and thus null mutants have not yet been isolated. We therefore attempted to over-express MatK from a neutral site in the chloroplast, placing it under the control of a theophylline-inducible riboswitch. This ectopic insertion of MatK lead to a variegated cotyledons phenotype. The addition of the inducer theophylline exacerbated the phenotype in a concentration-dependent manner. The extent of variegation was further modulated by light, sucrose and spectinomycin, suggesting that the function of MatK is intertwined with photosynthesis and plastid translation. Inhibiting translation in the transplastomic lines has a profound effect on the accumulation of several chloroplast mRNAs, including the accumulation of an RNA antisense to rpl33, a gene coding for an essential chloroplast ribosomal protein. Our study further supports the idea that MatK expression needs to be tightly regulated to prevent detrimental effects and establishes another link between leaf variegation and chloroplast translation.
Design Methodology of the Aircraft Power Generation System

The paper presents a multidisciplinary methodology for designing a power generation system for an aircraft. The presented design sequence corresponds to the proposed methodology; the design of the aircraft power generation system was created. Based on this project, a prototype of the power generation system was implemented; stand tests showed good convergence (an inaccuracy of no more than 3%) with the calculation results. During the design, we solved a number of optimization problems that made it possible to obtain an optimal power generation system in all, rather than components with optimal characteristics, as is usually done. The proposed methodology can be used to create power supply systems for aircraft with different voltage levels, including 270 VDC and 200 VAC. Developed power generation system will be used for future aircraft and helicopters, as it turned out to be 25-40% better in weight and efficiency than serial solutions. In addition, suggested power generation system has a higher reliability than industrial systems.
Cognitive Flexibility Predicts PTSD Symptoms: Observational and Interventional Studies

Introduction: Post-Traumatic Stress Disorder (PTSD) is a prevalent, severe and tenacious psychopathological consequence of traumatic events. Neurobehavioral mechanisms underlying PTSD pathogenesis have been identified, and may serve as risk-resilience factors during the early aftermath of trauma exposure. Longitudinally documenting the neurobehavioral dimensions of early responses to trauma may help characterize survivors at risk and inform mechanism-based interventions. We present two independent longitudinal studies that repeatedly probed clinical symptoms and neurocognitive domains in recent trauma survivors. We hypothesized that better neurocognitive functioning shortly after trauma will be associated with less severe PTSD symptoms a year later, and that an early neurocognitive intervention will improve cognitive functioning and reduce PTSD symptoms. Methods: Participants in both studies were adult survivors of traumatic events admitted to two general hospitals’ emergency departments (EDs) in Israel. The studies used identical clinical and neurocognitive tools, which included assessment of PTSD symptoms and diagnosis, and a battery of neurocognitive tests. The first study evaluated 181 trauma-exposed individuals one-, six-, and 14 months following trauma exposure. The second study evaluated 97 trauma survivors 1 month after trauma exposure, randomly allocated to 30 days of web-based neurocognitive intervention (n = 50) or control tasks (n = 47), and re-evaluated all subjects three- and 6 months after trauma exposure. Results: In the first study, individuals with better cognitive flexibility at 1 month post-trauma showed significantly less severe PTSD symptoms after 13 months (p = 0.002). In the second study, the neurocognitive training group showed more improvement in cognitive flexibility post-intervention (p = 0.019), and lower PTSD symptoms 6 months post-trauma (p = 0.017), compared with controls. Intervention- induced improvement in cognitive flexibility positively correlated with clinical improvement (p = 0.002). Discussion: Cognitive flexibility, shortly after trauma exposure, emerged as a significant predictor of PTSD symptom severity. It was also ameliorated by a neurocognitive intervention and associated with a better treatment outcome. These findings support further research into the implementation of mechanism-driven neurocognitive preventive interventions for PTSD.
Geometry Preserving Sampling Method based on Spectral Decomposition for 3D Registration

In the context of 3D mapping, larger and larger point clouds are acquired with LIDAR sensors. The Iterative Closest Point (ICP) algorithm is used to align these point clouds. However, its complexity is directly dependent of the number of points to process. Several strategies exist to address this problem by reducing the number of points. However, they tend to underperform with non-uniform density, large sensor noise, spurious measurements, and large-scale point clouds, which is the case in mobile robotics. This paper presents a novel sampling algorithm for registration in ICP algorithm based on spectral decomposition analysis and called Spectral Decomposition Filter (SpDF). It preserves geometric information along the topology of point clouds and is able to scale to large environments with non-uniform density. The effectiveness of our method is validated and illustrated by quantitative and qualitative experiments on various environments.
Automated Static Analysis and Classification of Android Malware using Permission and API Calls Models

In this paper we propose a heuristic approach to static analysis of Android applications based on matching suspicious applications with the predefined malware models. Static models are built from Android capabilities and Android Framework API call chains used by the application. All of the analysis steps and model construction are fully automated. Therefore, the method can be easily deployed as one of the automated checks provided by mobile application marketplaces or other interested organizations. Using the proposed method we analyzed the Drebin and ISCX malware collections in order to find possible relationships and dependencies between samples in collections, and a large fraction of Google Play apps collected between 2013 and 2016 representing benign data. Analysis results show that a combination of relatively simple static features represented by permissions and API call chains is enough to perform binary classification between malware and benign apps, and even find the corresponding malware family, with an appropriate false positive rate of about 3% (less than 1% in case of filtering adware). Malware collections exploration results show that Android malware rarely uses obfuscation or encryption techniques to make static analysis more difficult, which is quite the opposite of what we see in the case of the 'Wintel' endpoint platform family. We also provide the experiment-based comparison with the previously proposed state-of-the-art Android malware detection method adagio.
Improvement of TF-IDF Algorithm Based on Knowledge Graph

The TF-IDF algorithm is commonly used for text information retrieval and data mining. The traditional TF-IDF algorithm does not consider the domain characteristics of the article, and does not consider the distribution ratio. Currently, the solution proposed by many scholars only solves the problems of distribution ratio and the like, and does not solve the problem that the domain keywords have unreasonable weights. The problem has led to the use of domain-specific applications where relevant keywords in some areas have not been given appropriate weights. This paper proposes an improved method based on domain knowledge graph. This method will mainly consider the application of the legal field, and use the legal knowledge graph to make improvements to the TF-IDF algorithm, so as to achieve the reasonable weight assigned to the domain-related keywords in text feature extraction. Experiments show that this method can effectively improving the accuracy of the extraction.
Knowledge Aggregation via Good-Enough Model Spaces

In many practical applications, the training data for a machine learning task is partitioned across multiple learners. In many such cases, aggregating all the data onto a single device for model training may not be possible for privacy and communications bandwidth reasons. Existing approaches for learning global models in these settings iteratively aggregate local updates from each learner. These approaches can require many rounds of communication between learners and may produce suboptimal results when the data distribution is extremely non-i.i. d. In this work, we present Good-Enough Model Spaces (GEMS), a framework for learning a global satisficing (i.e. "good-enough") model by considering the space of local learners satisficing models. Our approach does not require sharing of data between learners, makes no assumptions on the distribution of data across learners, and requires minimal communication between learners. We present methods for learning both shallow and deep models within this framework and discuss how hold-out data can be used for post-learning fine-tuning. We find our approaches perform comparably to non-distributed models on experiments in image recognition and sentiment analysis.
Laboratory monitoring of P2Y12 inhibitors: communication from the SSC of the ISTH

A. L . FREL INGER I I I , *† C. GACHET ,‡ A. D . MUMFORD,§ P . NORIS , ¶ D. MEZZANO,** P . HARR ISON†† and P . GRESELE‡‡ FOR THE SUBCOMMITTEE ON PLATELET PHYS IOLOGY *Center for Platelet Research Studies, Division of Hematology/Oncology, Boston Children’s Hospital, Dana-Farber/Boston Children’s Cancer and Blood Disorders Center; †Harvard Medical School, Boston, MA, USA; ‡UMR-S1255 INSERM, Universit e de Strasbourg, Etablissement Franc ais du Sang-Grand Est, Strasbourg, France; §School of Clinical Sciences, University of Bristol, Bristol, UK; ¶Department of Internal Medicine, University of Pavia-IRCCS Policlinico San Matteo Foundation, Pavia, Italy; **School of Medicine, P. Universidad Cat olica de Chile, Santiago, Chile; ††Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; and ‡‡Department of Medicine, University of Perugia, Perugia, Italy
Multilevel 24-Sided Polygonal Voltage-Space-Vector Structure Generation for an IM Drive Using a Single DC Source

Conventional two-level as well as multilevel voltage-source-inverter topologies with a hexagonal voltage-space vector structure generate low-order harmonics in the machine terminal voltages when operated beyond the linear modulation range in variable speed drive application. Progressing from hexagonal to a higher sided polygonal voltage-space-vector structure (VSVS) suppresses significant low-order harmonics throughout the modulation range. In addition, the linear modulation range will also be enhanced very near to the base speed. In this paper, for the first time, the generation of a multilevel 24-sided VSVS using a single dc source for induction-motor drives is proposed. A cascaded topology with a dc-source-fed flying-capacitor inverter and two low-voltage capacitor-fed H-bridge cells forms the power circuit. The features and advantages of the proposed scheme are validated through experimental results and discussions.
Progressing super-enhancer landscape during mammary differentiation controls tissue-specific gene regulation

Abstract The mammary luminal lineage relies on the common cytokine-sensing transcription factor STAT5 to establish super-enhancers during pregnancy and initiate a genetic program that activates milk production. As pups grow, the greatly increasing demand for milk requires progressive differentiation of mammary cells with advancing lactation. Here we investigate how persistent hormonal exposure during lactation shapes an evolving enhancer landscape and impacts the biology of mammary cells. Employing ChIP-seq, we uncover a changing transcription factor occupancy at mammary enhancers, suggesting that their activities evolve with advancing differentiation. Using mouse genetics, we demonstrate that the functions of individual enhancers within the Wap super-enhancer evolve as lactation progresses. Most profoundly, a seed enhancer, which is mandatory for the activation of the Wap super-enhancer during pregnancy, is not required during lactation, suggesting compensatory flexibility. Combinatorial deletions of structurally equivalent constituent enhancers demonstrated differentiation-specific compensatory activities during lactation. We also demonstrate that the Wap super-enhancer, which is built on STAT5 and other common transcription factors, retains its exquisite mammary specificity when placed into globally permissive chromatin, suggesting a limited role of chromatin in controlling cell specificity. Our studies unveil a previously unrecognized progressive enhancer landscape where structurally equivalent components serve unique and differentiation-specific functions.
Nonfilament-forming RecA dimer catalyzes homologous joint formation

Abstract Homologous recombination is essential to genome maintenance, and also to genome diversification. In virtually all organisms, homologous recombination depends on the RecA/Rad51-family recombinases, which catalyze ATP-dependent formation of homologous joints—critical intermediates in homologous recombination. RecA/Rad51 binds first to single-stranded (ss) DNA at a damaged site to form a spiral nucleoprotein filament, after which double-stranded (ds) DNA interacts with the filament to search for sequence homology and to form consecutive base pairs with ssDNA (‘pairing’). How sequence homology is recognized and what exact role filament formation plays remain unknown. We addressed the question of whether filament formation is a prerequisite for homologous joint formation. To this end we constructed a nonpolymerizing (np) head-to-tail-fused RecA dimer (npRecA dimer) and an npRecA monomer. The npRecA dimer bound to ssDNA, but did not form continuous filaments upon binding to DNA; it formed beads-on-string structures exclusively. Although its efficiency was lower, the npRecA dimer catalyzed the formation of D-loops (a type of homologous joint), whereas the npRecA monomer was completely defective. Thus, filament formation contributes to efficiency, but is not essential to sequence-homology recognition and pairing, for which a head-to-tail dimer form of RecA protomer is required and sufficient.
Biomechanical evaluation of four surgical scenarios of lumbar fusion with hyperlordotic interbody cage: A finite element study.

BACKGROUND Lumbar spinal fusion in the interbody space is augmented with interbody fusion cages to provide structural support while arthrodesis occurs. Subsidence is a serious complication of interbody fusion. However, the biomechanical influence of anterior longitudinal ligament (ALL) and pedicle screws on subsidence has not been fully understood. OBJECTIVE To investigate biomechanical effects of the hyperlordotic cages in different surgical conditions using finite element analysis. METHODS Four surgical finite element (FE) models were constructed by inserting 15 degree lordosis cage at the L3-L4 disc space. The four surgical conditions were ALL intact (M1), ALL resected (M2), ALL intact and bilateral pedicle screws (M3), and ALL resected and bilateral pedicle screws (M4). Follow loads were applied at the L2 vertebral body while the inferior surface of L5 was fixed. FEA was implemented to simulate the four motion modes and biomechanical properties of four fusion scenarios with hyperlordotic interbody cage were compared. RESULTS The range of motion (ROM) and facet joint force (FJF) at L3-L4 decreased significantly after fusion during all the motion modes. The cage stress and endplate stress at L3-L4 increased significantly after fusion during all the motion modes. The cage stress and endplate stress at L3-L4 for M3 and M4 were smaller than that for M1 and M2 during all the motion modes. The FJF at L3-L4 for M3 and M4 were smaller than that for M1 and M2 during extension, bending, and rotation. CONCLUSIONS ALL has little effect on the biomechanics after lumbar fusion with hyperlordotic interbody cage. The bilateral pedicle screws significantly decreased the stress in cage, stress in endplate at L3-L4, and lowered facet contact force except for flexion mode. The implication is that the supplemental bilateral pedicle screws are recommended whether or not the ALL is resected.