Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study to Compare the Performance of some Symmetric and Asymmetric Ciphers

In this paper we present empirical results obtained from Java implementation of Elliptic curve Cryptosystem (ECC) as an asymmetric block cipher algorithm and a set of symmetric block cipher algorithms namely Triple-Data Encryption Standard (T-DES), Advanced Encryption Standard (AES), and Blowfish. Performance evaluation based on CPU execution time is presented under WinXP and Linux. We used in our implementation Java programming language, Java Cryptography Architecture (JCA) and Java Cryptography Extension (JCE). The evaluation of the performance of these algorithms is done for secret key generation and encryption and decryption operations. Results indicated that ECC outperforms the other encryption/decryption algorithms considered in this study regarding the security strength, speed, and key size of ECC. Also, ECC’s performance advantage increases as security needs increases for newly emerging applications.
A Review on Condition Monitoring and Diagnostic Techniques of Rotating Electrical Machines

Electrical machines are critical components in industrial processes. A motor failure may yield an unexpected interruption at the industrial plant, with consequences in costs, product quality, and safety. To determine the conditions of each part of motor, various testing and monitoring methods have been developed. In this paper, a review on effective fault indicators and condition monitoring methods of rotating electrical machines has been accomplished. Fault detection methods divided to four groups: electrical, mechanical, chemical and thermal indicators. Some fault detection methods based on electrical symptoms like stator current, voltage, their combination or spectrum discussed in electrical group. In second branch, mechanical symptoms like torque, vibration and so on used for condition monitoring. Third group, chemical indicators, assigned to some chemical parameters of materials like oil characteristic or wear and debris in oil analysis. In last group, thermal symptoms in rotating electrical machines will be spoken. Between all methods, some of them are more known like vibration and some of them are recently added like motor current signature analysis (MCSA). Nowadays, combined methods and methods used artificial intelligence (AI) in condition monitoring are more popular. In every group, the fault detection method and the faults that can be detected have been mentioned. Mathematical equations of some new signal processing method have been discussed in literature presented in appendix. Review Article Physical Science International Journal, 4(3): 310-338, 2014 311
Error estimation of reduced-order modeling of high speed RLCG circuit

In this paper, we show that perturbations in pole valves in reduced-order modeling of RLCG circuits play a crucial role in the accuracy of time-domain simulation. We first derive a simple formula that expresses the error accumulation in the time domain as a function of the simulation time and perturbation of the pole values. Then, a posteriori error bound estimation of the poles generated by any modal-reduction method is provided by using a novel time-domain transformation on the state equations of the circuit. The error bounds are then employed to control the time-domain error in the reduced-order model, and to guarantee that the time-domain simulation is accurate.
Dissociative Disorders: Pathways to Diagnosis, Clinician Attitudes and Their Impact

Objectives: To study the attitudes and experiences of Australian clinicians with dissociative disorders and the paths to diagnosis and experiences of patients. Method: The attitudes of Australian clinicians to dissociative disorders and the experiences of patients were assessed by questionnaires. The clinicians were mental health specialists and a small number of general medical practitioners. The patients had all been diagnosed with a dissociative disorder. Results: Of the 250 clinicians, 21% reported experience with more than six cases on average of any one of the dissociative disorders, 38% with less than six, 42% with none; 55% regarded them as valid diagnoses, 35% dubiously valid and 10% invalid. Of the 55 patients, 76% reported delays in diagnosis (57%, <3 years and 25%, <10 years) with adverse consequences in 64%; 80% had experienced sceptical or antagonistic attitudes from clinicians, rated as destructive by 48%. They were disabled (60% rated as <50% impaired) and were heavy consumers of health services (48% hospitalized, 68% <5 times). There was considerable comorbidity including moderate or severe depression (96%), self-harm (68%), suicide attempts (69%), panic disorder (53%), eating disorders (75%), substance abuse (25%), poor physical health (44%), major interpersonal (70%) and sexual problems (90%). Patients rated individual psychotherapy as the most helpful treatment (90%) but medications, such as antidepressants, were also valued (60%). Conclusions: Although over half of the responding Australian clinicians thought that dissociative disorders were valid, the rest were dubious about their validity with 10% believing them to be invalid. Only 21% had considerable experience with the disorders. These findings may relate to some of the difficulties perceived by patients, which included delays in diagnosis, suboptimal treatment and negative experiences with clinicians.
Integrity and Privacy Preserving Data Aggregation Algorithm for WSNs

This paper proposed an integrity and privacy preserving data aggregation algorithm for WSNs, which is called IPPDA. First, it attached a group of congruent numbers to the sensing data in order to execute integrity checking operated by sink node using Chinese remainder theorem (CRT); then it computed the hash function-based message authentication codes with time and key as the parameters to satisfy data freshness; finally, it adopted a homomorphic encryption scheme to provide privacy preserving. The simulation results show that IPPDA can effectively preserve data privacy, check data integrity, satisfy data freshness, and get accurate data aggregation results while having less computation and communication cost than iCPDA and iPDA.
Poor health, physical workload and occupational social class as determinants of health-related job loss: results from a prospective cohort study in the UK

Objectives The aims of the present study were to assess the association and interactions of physical workload and poor health with health-related job loss (HRJL) among older workers, and the association and interactions of occupational social class and poor health with HRJL. Methods Data were used from an existing prospective cohort study, Health and Employment after Fifty, where employed or self-employed workers aged 50–64 years (n=4909) were followed-up between 2014 and 2016. Associations between potential determinants (self-perceived health status, physical workload and occupational social class) and 2-year HRJL were examined by Cox regression analyses. To study whether physical workload or occupational social class moderates the influence of poor health on HRJL, additive and multiplicative interactions were calculated. Results Older workers with poor self-perceived health status had increased risk of HRJL during the 2-year follow-up period (men: HR 2.57 (95%CI: 1.68 to 3.92); women: HR 3.26 (95%CI: 2.33 to 4.55)). Furthermore, men with high physical workload were at increased risk for HRJL (HR 1.63 (95%CI: 1.09 to 2.43)). No significant interactions (p<0.05) were identified between poor health and high physical workload, nor between poor health and lower occupational social class. Conclusion Our study indicates that older workers in poor health, and older workers with a physically demanding job, are at increased risk of HRJL. Having a physically demanding job or working in routine/manual occupations does not moderate the association between poor health and HRJL.
Improving Letter and Word Recognition Using a Multisensory Approach

ii Chapter One: Introduction/Problem Statement/Rationale . 1 Definition of Terms . 4 Chapter Two: Literature Review . 6 History . 6 Learning Styles . 8 Multiple Intelligence Theory . 10 Memory . 1 1 Chapter Three: Methodology . •. 13 Participants and Setting . 13 Measures . 13 Procedure . 14 Letter and Word Recognition Assessments . 16 Interview Questions . 16 Observations . 17 Data Analysis . 17 Chapter Four: Results . 19 Letter and Word Recognition Assessments . 19 Interviews . 2 1 Observations . 22 Chapter Five: Discussion . 23 Implications of Research . 24 Limitations . 25 References . : . 27 Appendix . 29 Interview Questions . 29 Apple Apple AAA Alphabet Chart. 30
Towards a Seamless Provision of Multimedia Course Material

This paper describes the functionality and user-interface issues associated with the design of an automated system that provides a visually based online index to multimedia information. This generic software has been used to provide 'one-stop'access to a substantial corpus of teaching material required by students undertaking one module of an engineering degree. In particular the paper addresses the metaphorical access to and provision of teaching material, non-linear access to that material, user-interface issues that interfere with the learning process, and the multifarious nature of the material needed for such courses. It concludes that there is a need for a hypermedia system that presents an integrated view of the material with graceful navigational dynamics. It should enable the student to organize interrelationships between that material according to need, while retaining the 'published' structure.
Constructing a Representation of a Melody: Transforming Melodic Segments into Reduced Pitch Patterns Operated on by Modifiers

A reduced-pitch-pattern model for melodic processing is proposed. The model assumes that experienced listeners divide a melody into segments, derive a reduced pitch pattern from each segment, and then try to match each pattern to one of the prototypes stored in long-term memory. As a result, the melody is memorized accurately and quickly. This model and two competing models (the contour model and the harmonic progression model) make different predictions concerning what aspects of a melody would be preserved and what types of error in recall would appear. Recall data of a tonal melody of 12 measures in length were used to examine these predictions. The data were gathered from eight college music majors. Analysis of erroneous reproductions showed that reduced pitch patterns and harmonic progressions were preserved well and that errors predicted by the reduced-pitch-pattern model occurred more often than those predicted by the two competing models. It is concluded that the reduced-pitch-pattern model is the most tenable of the three. pieces of music that we hear in everyday life are composed of several building blocks; that is, musically meaningful segments, that are arranged in a structured and coherent way. Previous studies on expertise in chess, showing that formation of higher order units facilitates memory for the positions of pieces (e.g., Chi, 1978), suggest that in music
Balance between DNA‐binding affinity and specificity enables selective recognition of longer target sequences in vivo

Although genome‐editing enzymes such as TALEN and CRISPR/Cas9 are being widely used, they have an essential limitation in that their relatively high‐molecular weight makes them difficult to be delivered to cells. To develop a novel genome‐editing enzyme with a smaller molecular weight, we focused on the engrailed homeodomain (EHD). We designed and constructed proteins composed of two EHDs connected by a linker to increase sequence specificity. In bacterial one‐hybrid assays and electrophoresis mobility shift assay analyses, the created proteins exhibited good affinity for DNA sequences consisting of two tandemly aligned EHD target sequences. However, they also bound to individual EHD targets. To avoid binding to single target sites, we introduced amino acid mutations to reduce the protein–DNA affinity of each EHD monomer and successfully created a small protein with high specificity for tandem EHD target sequences.
3D model-based tracking of human actions from multiple image sequences

This paper proposes a new method for model-based tracking of human bodies in 3D motion from multiple views. The tracking is performed by estimating the pose increment of the body parts between successive images after fitting an articulated model to the human body in the initial frame. The pose increment can be obtained by solving a system of linear equations. The amount of calculation does not depend on the number of viewpoints. Experiments verify that the proposed method can avoid occlusion and visual degeneration from a particular viewpoint, and can obtain highly reliable estimations. © 1998 Scripta Technica, Syst Comp Jpn, 29(8): 48–56, 1998
A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning

Background Detection of pulmonary nodules is an important aspect of an automatic detection system. Incomputer-aided diagnosis (CAD) systems, the ability to detect pulmonary nodules is highly important, which plays an important role in the diagnosis and early treatment of lung cancer. Currently, the detection of pulmonary nodules depends mainly on doctor experience, which varies. This paper aims to address the challenge of pulmonary nodule detection more effectively. Methods A method for detecting pulmonary nodules based on an improved neural network is presented in this paper. Nodules are clusters of tissue with a diameter of 3 mm to 30 mm in the pulmonary parenchyma. Because pulmonary nodules are similar to other lung structures and have a low density, false positive nodules often occur. Thus, our team proposed an improved convolutional neural network (CNN) framework to detect nodules. First, a nonsharpening mask is used to enhance the nodules in computed tomography (CT) images; then, CT images of 512×512 pixels are segmented into smaller images of 96×96 pixels. Second, in the 96×96 pixel images which contain or exclude pulmonary nodules, the plaques corresponding to positive and negative samples are segmented. Third, CT images segmented into 96×96 pixels are down-sampled to 64×64 and 32×32 size respectively. Fourth, an improved fusion neural network structure is constructed that consists of three three-dimensional convolutional neural networks, designated as CNN-1, CNN-2, and CNN-3, to detect false positive pulmonary nodules. The networks’ input sizes are 32×32×32, 64×64×64, and 96×96×96 and include 5, 7, and 9 layers, respectively. Finally, we use the AdaBoost classifier to fuse the results of CNN-1, CNN-2, and CNN-3. We call this new neural network framework the Amalgamated-Convolutional Neural Network (A-CNN) and use it to detect pulmonary nodules. Findings Our team trained A-CNN using the LUNA16 and Ali Tianchi datasets and evaluated its performance using the LUNA16 dataset. We discarded nodules less than 5mm in diameter. When the average number of false positives per scan was 0.125 and 0.25, the sensitivity of A-CNN reached as high as 81.7% and 85.1%, respectively.
Integrative Genomic Characterization Identifies Molecular Subtypes of Lung Carcinoids.

Lung carcinoids (LCs) are rare and slow growing primary lung neuroendocrine tumors. We performed targeted exome sequencing, mRNA sequencing and DNA methylation array analysis on macro-dissected lung carcinoids. Recurrent mutations were enriched for genes involved in covalent histone modification/chromatin remodeling (34.5%; MEN1, ARID1A, KMT2C and KMT2A) as well as DNA repair (17.2%) pathways. Unsupervised clustering and principle component analysis on gene expression and DNA methylation profiles showed three robust molecular subtypes (LC1, LC2, LC3) with distinct clinical features. MEN1 gene mutations were found to be exclusively enriched in the LC2 subtype. LC1 and LC3 subtypes were predominately found at peripheral and endobronchial lung respectively. The LC3 subtype was diagnosed at a younger age than LC1 and LC2 subtypes. Immunohistochemical staining of two biomarkers, ASCL1 and S100, sufficiently stratified the three subtypes. This molecular classification of lung carcinoids into three subtypes may facilitate understanding of their molecular mechanisms and improve diagnosis and clinical management.
First responder tracking and visualization for command and control toolkit

In order for First Responder Command and Control personnel to visualize incidents at urban building locations, DHS sponsored a small business research program to develop a tool to visualize 3D building interiors and movement of First Responders on site. 21st Century Systems, Inc. (21CSI), has developed a toolkit called Hierarchical Grid Referenced Normalized Display (HiGRND). HiGRND utilizes three components to provide a full spectrum of visualization tools to the First Responder. First, HiGRND visualizes the structure in 3D. Utilities in the 3D environment allow the user to switch between views (2D floor plans, 3D spatial, evacuation routes, etc.) and manually edit fast changing environments. HiGRND accepts CAD drawings and 3D digital objects and renders these in the 3D space. Second, HiGRND has a First Responder tracker that uses the transponder signals from First Responders to locate them in the virtual space. We use the movements of the First Responder to map the interior of structures. Finally, HiGRND can turn 2D blueprints into 3D objects. The 3D extruder extracts walls, symbols, and text from scanned blueprints to create the 3D mesh of the building. HiGRND increases the situational awareness of First Responders and allows them to make better, faster decisions in critical urban situations.
High efficient methods of content-based 3D model retrieval

Content-based 3D model retrieval is of great help to facilitate the reuse of existing designs and to inspire designers during conceptual design. However, there is still a gap to apply it in industry due to the low time efficiency. This paper presents two new methods with high efficiency to build a Content-based 3D model retrieval system. First, an improvement is made on the “Shape Distribution (D2)” algorithm, and a new algorithm named “Quick D2” is proposed. Four sample 3D mechanical models are used in an experiment to compare the time cost of the two algorithms. The result indicates that the time cost of Quick D2 is much lower than that of D2, while the descriptors extracted by the two algorithms are almost the same. Second, an expandable 3D model repository index method with high performance, namely, RBK index, is presented. On the basis of RBK index, the search space is pruned effectively during the search process, leading to a speed up of the whole system. The factors that influence the values of the key parameters of RBK index are discussed and an experimental method to find the optimal values of the key parameters is given. Finally, “3D Searcher”, a content-based 3D model retrieval system is developed. By using the methods proposed, the time cost for the system to respond one query online is reduced by 75% on average. The system has been implemented in a manufacturing enterprise, and practical query examples during a case of the automobile rear axle design are also shown. The research method presented shows a new research perspective and can effectively improve the content-based 3D model retrieval efficiency.
Random error effects in matched MOS capacitors and current sources

Explicit formulas are derived using statistical methods for the random errors affecting capacitance and current ratios in MOS integrated circuits. They give the dependence of each error source on the physical dimensions, the standard deviations of the fabrication parameters, the bias conditions, etc. Experimental results, obtained for both matched capacitors and matched current sources using a 3.5-/spl mu/m NMOS technology, confirmed the theoretical predictions. Random effects represent the ultimate limitation on the achievable accuracy of switched-capacitor filters, D/A converters, and other MOS analog integrated circuits. The results indicate that a 9-bit matching accuracy can be obtained for capacitors and an 8-bit accuracy for MOS current sources without difficulty if the systematic error sources are reduced using proper design and layout techniques.
NanoSIMS: Microscale Quantification of Biogeochemical Activity with Large-Scale Impacts.

One major objective of aquatic microbial ecology is to understand the distribution of microbial populations over space and time and in response to environmental factors. Perhaps more importantly, it is crucial to quantify how those microbial cells affect biogeochemical processes of interest, such as primary production, nitrogen cycling, or the breakdown of pollutants. One valuable approach to link microbial identity to activity is to carry out incubations with stable-isotope-labeled substrates and then quantify the isotope incorporation by individual microbial cells using nanoscale secondary ion mass spectrometry (NanoSIMS). This review summarizes recent efforts in this field, highlights novel methods, describes studies investigating rare metabolisms as well as widespread microbial activity, and hopes to provide a framework to increase the use and capabilities of NanoSIMS for microbial biogeochemical studies in the future. Expected final online publication date for the Annual Review of Marine Science Volume 12 is January 3, 2020. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
The weirdness of having a bunch of other minds like yours in the room: The lived experiences of mentalization-based therapy for borderline personality disorder.

OBJECTIVES Studies of lived experiences are important for improving treatment effectiveness, but most studies of mentalization-based therapy (MBT) are quantitative. This qualitative study aimed to better understand service users' lived experiences of MBT, including their experiences of change. DESIGN This is a qualitative study that used one-to-one semi-structured interviews. METHOD Semi-structured interviews were conducted with eight MBT service users recruited via four NHS trusts. Interviews were analysed using interpretative phenomenological analysis (IPA). FINDINGS Three superordinate themes were identified: being borderline, being in the group, and being on a journey. 'Experiences of diagnosis' and 'the group' are salient topics in the lived experiences of service users' during the MBT journey, as is the nature/type of 'change' that can create symptom reduction albeit alongside a negative felt experience. CONCLUSION Our research aligns with current thought regarding the complexity and challenges of treating BPD via psychotherapy and adds a further dimension, that of experiencing MBT and changes during therapy. The participants' experiences of BPD and of experiencing MBT are discussed. PRACTITIONER POINTS Therapists are observant of how each client gives meaning to their experience of diagnosis, the group, and change, particularly since the experience of recovery is not all positive. Service users' emerging and ongoing construction of their experience of diagnosis is closely monitored and additional appropriate strategies implemented where necessary. The impact of joining MBT, especially the group, becomes a process for formal regular review. Therapists undertake an in-depth exploration of service users' felt experiences to capture less quantifiable dimensions of change.