Miracle-dz/UniFAR-FTUs huggingface dataset:
Protecting cyber physical production systems using anomaly detection to enable self-adaptation

The industrial world is going through its fourth revolution also known as Industry 4.0. Modern industrial processes leverage advanced IT technologies to increase productivity and often combine multiple system concepts such as Internet of Things (IoT), Cyber Physical Systems (CPS) and Cloud Computing. Cyber Physical Production Systems (CPPS) are key enablers of this revolution. In CPPS, raw materials, machines, and operations are interconnected to form a sophisticated network. Protecting them against advanced cyber-threats is a priority concern for the future implementation of Industry 4.0 applications. Any impairment of such systems can lead, in fact, to catastrophic damages resulting in a substantial financial loss for governments, companies, as well as endanger the safety of the society. The need for high availability and reliability of these systems is therefore the pillar guiding our research. This paper proposes the adoption of anomaly detection as a method to support self-adaptation in CPPS and to ensure flexibility, reliability, and protection of industrial environments against modern cyber threats. An anomaly detection mechanism can be employed to monitor, and learn the normal behavior of an industrial system, and to generate alerts when the observed events indicate abnormal activities. On this concept we base our work, and we demonstrate how timely identifying critical security events can enable, through the self-adaptation (e.g., triggering automatic configuration changes), an efficient protection of the CPPS against advanced threats, and an effective containment of their effects.
The impact of different shift work schedules on the levels of anxiety and stress in workers in a petrochemicals company

This study evaluated anxiety and stress in workers under different shift work conditions. The sample comprised 239 workers, with an average age of 42.6, standard deviation = 5.7 years, divided into fixed daytime working (n=52) and different working shifts (n=187). Documentation: Free and informed consent form; ID's; State-Trait Anxiety Inventory; Lipp's Stress Symptom Inventory for Adults. We used the t-test for independent samples, ANOVA, Pearson's correlation and the two-sample Comparison of proportions Test. Results showed that shift workers had higher State-Trait Anxiety scores than fixed daytime workers (t=-4.994; p=0.0001; t=-2.816; p=0.005, respectively). Both samples exhibited stress, but there were no statistically significant differences between the groups (t=-1.052; p=0.294). Shift work schedules caused more situational and dispositional anxiety, but did not significantly increase stress levels when compared to fixed daytime working.
Large Graph Hashing with Spectral Rotation

Faced with the requirements of huge amounts of data processing nowadays, hashing techniques have attracted much attention due to their efficient storage and searching ability. Among these techniques, the ones based on spectral graph show remarkable performance as they could embed the data on a low-dimensional manifold and maintain the neighborhood structure via a non-linear spectral eigenmap. However, the spectral solution in real value of such methods may deviate from the discrete solution. The common practice is just performing a simple rounding operation to obtain the final binary codes, which could break constraints and even result in worse condition. In this paper, we propose to impose a so-called spectral rotation technique to the spectral hashing objective, which could transform the candidate solution into a new one that better approximates the discrete one. Moreover, the binary codes are obtained from the modified solution via minimizing the Euclidean distance, which could result in more semantical correlation within the manifold, where the constraints for codes are always held. We provide an efficient alternative algorithm to solve the above problems. And a manifold learning perceptive for motivating the proposed method is also shown. Extensive experiments are conducted on three large-scale benchmark datasets and the results show our method outperforms state-of-the-art hashing methods, especially the spectral graph ones.
Millennials and Their Parents: Implications of the New Young Adulthood for Midlife Adults

Abstract The period of young adulthood has transformed dramatically over the past few decades. Today, scholars refer to “emerging adulthood” and “transitions to adulthood” to describe adults in their 20s. Prolonged youth has brought concomitant prolonged parenthood. This article addresses 3 areas of change in parent/child ties, increased (a) contact between generations, (b) support from parents to grown children as well as coresidence and (c) affection between the generations. We apply the Multidimensional Intergenerational Support Model (MISM) to explain these changes, considering societal (e.g., economic, technological), cultural, family demographic (e.g., fertility, stepparenting), relationship, and psychological (normative beliefs, affection) factors. Several theoretical perspectives (e.g., life course theory, family systems theory) suggest that these changes may have implications for the midlife parents’ well-being. For example, parents may incur deleterious effects from (a) grown children’s problems or (b) their own normative beliefs that offspring should be independent. Parents may benefit via opportunities for generativity with young adult offspring. Furthermore, current patterns may affect future parental aging. As parents incur declines of late life, they may be able to turn to caregivers with whom they have intimate bonds. Alternately, parents may be less able to obtain such care due to demographic changes involving grown children raising their own children later or who have never fully launched. It is important to consider shifts in the nature of young adulthood to prepare for midlife parents’ future aging.
Feasibility of microRNA profiling in human inner ear perilymph

Hearing loss is common and caused by a wide range of molecular and cellular pathologies. Current diagnosis of hearing loss depends on a combination of physiologic testing, patient history, and in some cases genetic testing. Currently, no biopsy or equivalent procedure exists to diagnose inner ear disorders. MicroRNAs (miRNA) are short ribonucleic acids that regulate a variety of cellular processes. They have been found to be reliable markers for a variety of disease processes. In particular, a variety of miRNAs that are markers for neurodegenerative disease have been identified in cerebrospinal fluid. The aim of this study was to determine whether miRNAs could be identified in human perilymph potentially leading to the development of biomarkers for inner ear disease. Prospective sampling of human perilymph and its analysis were carried out. Patients undergoing surgery in which the inner ear is opened as part of the procedure (cochlear implantation, stapedectomy, labyrinthectomy) were recruited. A total of 2–5 &mgr;l of perilymph was collected and analyzed using Affymetrix GeneChip miRNA 4.0 microarrays. MiRNA common to all sampling approaches were selected. Analysis of miRNAs was carried out by evaluating miRNA targets in a cochlear transcriptome library using the Ingenuity Pathway Analysis software package. MiRNAs could be isolated from the perilymph of all patients. Evaluation of miRNAs shows the presence of miRNA populations that are predicted to interact with genes expressed in the inner ear. Additional analysis of miRNA populations shows that perilymph miRNAs could be linked to pathways associated with hearing disorders. Sampling of human perilymph is feasible and can potentially identify miRNAs associated with hearing disorders.
A Continuum Model for the Effect of Dynamic Recrystallization on the Stress–Strain Response

Austenitic Stainless Steels and High-Strength Low-Alloy (HSLA) steels show significant dynamic recovery and dynamic recrystallization (DRX) during hot forming. In order to design optimal and safe hot-formed products, a good understanding and constitutive description of the material behavior is vital. A new continuum model is presented and validated on a wide range of deformation conditions including high strain rate deformation. The model is presented in rate form to allow for the prediction of material behavior in transient process conditions. The proposed model is capable of accurately describing the stress–strain behavior of AISI 316LN in hot forming conditions, also the high strain rate DRX-induced softening observed during hot torsion of HSLA is accurately predicted. It is shown that the increase in recrystallization rate at high strain rates observed in experiments can be captured by including the elastic energy due to the dynamic stress in the driving pressure for recrystallization. Furthermore, the predicted resulting grain sizes follow the power-law dependence with steady state stress that is often reported in literature and the evolution during hot deformation shows the expected trend.
Rate pressure product and the components of heart rate and systolic blood pressure in hospitalized heart failure patients with preserved ejection fraction: Insights from ASCEND‐HF

Heart rate and systolic blood pressure (SBP) are prognostic markers in heart failure (HF) with reduced ejection fraction (HFrEF). Their combination in rate pressure product (RPP) as well as their role in heart failure with preserved ejection fraction (HFpEF) remains unclear.
Discriminative Semi-Supervised Dictionary Learning with Entropy Regularization for Pattern Classification

Dictionary learning has played an important role in the success of sparse representation, which triggers the rapid developments of unsupervised and supervised dictionary learning methods. However, in most practical applications, there are usually quite limited labeled training samples while it is relatively easy to acquire abundant unlabeled training samples. Thus semi-supervised dictionary learning that aims to effectively explore the discrimination of unlabeled training data has attracted much attention of researchers. Although various regularizations have been introduced in the prevailing semi-supervised dictionary learning, how to design an effective unified model of dictionary learning and unlabeled-data class estimating and how to well explore the discrimination in the labeled and unlabeled data are still open. In this paper, we propose a novel discriminative semi-supervised dictionary learning model (DSSDL) by introducing discriminative representation, an identical coding of unlabeled data to the coding of testing data final classification, and an entropy regularization term. The coding strategy of unlabeled data can not only avoid the affect of its incorrect class estimation, but also make the learned discrimination be well exploited in the final classification. The introduced regularization of entropy can avoid overemphasizing on some uncertain estimated classes for unlabeled samples. Apart from the enhanced discrimination in the learned dictionary by the discriminative representation, an extended dictionary is used to mainly explore the discrimination embedded in the unlabeled data. Extensive experiments on face recognition, digit recognition and texture classification show the effectiveness of the proposed method.
Association of Polygenic Risk Scores for Multiple Cancers in a Phenome-wide Study: Results from The Michigan Genomics Initiative

Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether Polygenic Risk Scores (PRS) for common cancers are associated with multiple phenotypes in a Phenome-wide Association Study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI GWAS catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. Patients with at least one cancer diagnosis constituted 13,490 patients. PRSs exhibited strong association for several cancer traits they were designed for including female breast cancer, prostate cancer and melanomas of skin. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate primary trait-mediated PRS associations from associations that arise through shared genetic risk profiles, the idea of “exclusion PRS PheWAS” was introduced and uncovered phenome-wide significant associations between a lower risk for hypothyroidism in patients with high thyroid cancer PRS and a higher risk for actinic keratosis in patients with high squamous cell carcinoma PRS after removing all cases of the primary cancer trait. This is the first PheWAS study using PRS instead of single variant.
Tissue protein synthesis and nucleic acid concentrations in steers treated with somatotropin

The effect of injection with bovine somatotropin (bST) on the fractional rate of protein synthesis (FSR) in tissues of beef steers was studied using a continuous infusion of jl-'4Cjleucine. Minimum and maximum FSR were calculated from free leucine specific radioactivity (SRA) in plasma or tissue homogenate respectively. Tissue nucleic acid concentrations were also quantified. Tissue samples were obtained from several muscles, sections of the small intestine and liver. In response to bST, both minimum and maximum FSR increased in muscle but not liver or intestinal tissues. Absolute synthesis rate increased in several muscles and small intestine tissues. Treatment with bST increased the relative SRA of protein-bound leucine in muscles compared with liver ; increased the amount of protein synthesis per unit empty body-weight (EBW) in most muscles ; and increased weight of small intestine relative to EBW, suggesting a differential response between liver and the other tissues measured. Compositional changes in response to bST occurred only in muscles. DNA concentration increased while protein:DNA decreased in the gastrocnemius muscle and RNA: DNA increased in the longissimus dorsi. The maximum percentage contribution of tissue protein synthesis to whole-body protein synthesis was 12.6, 25.7 and 20.5, and 13.0, 29.4 and 25.8 for liver, muscle, and small intestine in placebo-treated and bSTinjected steers respectively.
Multimodal interaction for data visualization

Multimodal interaction offers many potential benefits for data visualization. It can help people stay in the flow of their visual analysis and presentation, with the strengths of one interaction modality offsetting the weaknesses of others. Furthermore, multimodal interaction offers strong promise for leveraging data visualization on diverse display hardware including mobile, AR/VR, and large displays. However, prior research on visualization and interaction techniques has mostly explored a single input modality such as mouse, touch, pen, or more recently, natural language. The unique challenges and opportunities of synergistic multimodal interaction for data visualization have yet to be investigated. This workshop will bring together researchers with expertise in visualization, interaction design, and natural user interfaces. We aim to build a community of researchers focusing on multimodal interaction for data visualization, explore opportunities and challenges in our research, and establish an agenda for multimodal interaction research specifically for data visualization.
A Secure and Efficient Way of Accessing Encrypted Cloud Databases Using Adaptive Encryption Scheme

: Now-a-days cloud computing is showing consistent growth in the field of computing. Users can utilize these services on pay-per-use basis. Database-as-a-service is a one type cloud computing service. DBAAS service provides users with database access without need for setting up physical configuration or installing software. But main drawback of DBAAS is, when data is exchanged in cloud, there exists the problem of disclosure of privacy. The idea is to build privacy preserving storage model where data sharing services can update and control the access and limit the usage of their shared data. Preserving privacy is an important issue for cloud computing and it needs to be considered at every phase of design. This paper proposes a metadata based data segregation and storage methodology along with an encryption technique to provide additional security. Here additional security is provided by SQL aware encryption scheme or adaptive encryption scheme. This would serve as a helping note in the progress of strengthening the privacy preserving approaches in cloud computing. This paper also compares both encryption schemes.
Overview of MPLNET Version 3 Cloud Detection 1 2

2 JASPER R. LEWIS 3 Joint Center for Earth Systems Technology, 4 University of Maryland Baltimore County, Baltimore, Maryland 5 JAMES R. CAMPBELL 6 Naval Research Laboratory, Monterey, California 7 ELLSWORTH J. WELTON 8 NASA Goddard Space Flight Center, Greenbelt, Maryland 9 SEBASTIAN A. STEWART 10 Science System and Applications, Inc., Lanham, Maryland 11 PHILLIP C. HAFTINGS 12 Science System and Applications, Inc., Lanham, Maryland 13
Open-Universe Weighted Model Counting

Weighted model counting (WMC) has recently emerged as an effective and general approach to probabilistic inference, offering a computational framework for encoding a variety of formalisms, such as factor graphs and Bayesian networks.The advent of large-scale probabilistic knowledge bases has generated further interest in relational probabilistic representations, obtained by according weights to first-order formulas, whose semantics is given in terms of the ground theory, and solved by WMC. A fundamental limitation is that the domain of quantification, by construction and design, is assumed to be finite, which is at odds with areas such as vision and language understanding, where the existence of objects must be inferred from raw data. Dropping the finite-domain assumption has been known to improve the expressiveness of a first-order language for open-universe purposes, but these languages, so far, have eluded WMC approaches. In this paper, we revisit relational probabilistic models over an infinite domain, and establish a number of results that permit effective algorithms. We demonstrate this language on a number of examples, including a parameterized version of Pearl's Burglary-Earthquake-Alarm Bayesian network.
Spectral Clustering with Brainstorming Process for Multi-View Data

Clustering tasks often requires multiple views rather than a singleview to correctly reflect diverse characteristics of the cluster boundaries. The cluster boundaries estimated using a single view are incorrect in general, and those incorrect estimation should be compensated by helps of other views. If each viewis independent to other views, incorrect estimations will be mostly revised as the number of views grow. However, as the number of views grow, it is almost impossibleto avoid dependencies among views, and such dependencies often delude correct estimations. Thus, dependencies among views should be carefully considered in multi-view clustering. This paper proposes a new spectral clustering method to deal with multi-view data and dependencies among views. The proposed method is motivated by the brainstorming process. In the brainstorming process, an instance is regarded as an agenda to be discussed, while each view is considered as a brainstormer. Through the discussion step in the brainstorming process, a brainstormer iteratively suggests their opinions and accepts others’ different opinions. To compensate the biases caused by information sharing between brainstormers with dependent opinions, those having independent opinions are more encouraged to discuss together than those with dependent opinions. The conclusion step makes a compromise by merging or concatenating all opinions. The clustering is finally done after the conclusion. Experimental results in three tasks show the effectiveness of the proposed method comparing with ordinary single and multi-view spectral clusterings.
MoS2 Negative‐Capacitance Field‐Effect Transistors with Subthreshold Swing below the Physics Limit

The Boltzmann distribution of electrons induced fundamental barrier prevents subthreshold swing (SS) from less than 60 mV dec‐1 at room temperature, leading to high energy consumption of MOSFETs. Herein, it is demonstrated that an aggressive introduction of the negative capacitance (NC) effect of ferroelectrics can decisively break the fundamental limit governed by the “Boltzmann tyranny”. Such MoS2 negative‐capacitance field‐effect transistors (NC‐FETs) with self‐aligned top‐gated geometry demonstrated here pull down the SS value to 42.5 mV dec‐1, and simultaneously achieve superior performance of a transconductance of 45.5 μS μm and an on/off ratio of 4 × 106 with channel length less than 100 nm. Furthermore, the inserted HfO2 layer not only realizes a stable NC gate stack structure, but also prevents the ferroelectric P(VDF‐TrFE) from fatigue with robust stability. Notably, the fabricated MoS2 NC‐FETs are distinctly different from traditional MOSFETs. The on‐state current increases as the temperature decreases even down to 20 K, and the SS values exhibit nonlinear dependence with temperature due to the implementation of the ferroelectric gate stack. The NC‐FETs enable fundamental applications through overcoming the Boltzmann limit in nanoelectronics and open up an avenue to low‐power transistors needed for many exciting long‐endurance portable consumer products.
A pr 2 01 8 CONFORMAL C LASSIFICATION 1 conformalClassification : A Conformal Prediction R Package for Classification by Niharika Gauraha

The conformalClassification package implements Transductive Conformal Prediction (TCP) and Inductive Conformal Prediction (ICP) for classification problems. Conformal Prediction (CP) is a framework that complements the predictions of machine learning algorithms with reliable measures of confidence. TCP gives results with higher validity than ICP, however ICP is computationally faster than TCP. The package conformalClassification is built upon the random forest method, where votes of the random forest for each class are considered as the conformity scores for each data point. Although the main aim of the conformalClassification package is to generate CP errors (p-values) for classification problems, the package also implements various diagnostic measures such as deviation from validity, error rate, efficiency, observed fuzziness and calibration plots. In future releases, we plan to extend the package to use other machine learning algorithms, (e.g. support vector machines) for model fitting.