Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Models for EEG-based Rapid Serial Visual Presentation Event Classification

We consider deep learning (DL) for event classiﬁcation using electroencephalogram (EEG) measurements of brain activities. We proposed HDNN or hierarchical deep neural network, and CNN4EEG, a new convolution neural network (CNN). Both DL models are designed to improve the representation of spatial and local temporal correlations inherent in EEG data. These models were tested for image target prediction in a time-locked rapid serial visual presentation (RSVP) experiment. The performances were compared with the state-of-the-art RSVP classiﬁcation algorithms including HDCA and XDAWN and with other popular machine learning algorithms. The results show that global spatial local temporal CNN (CNN4EEG) achieved a 13% improvement over the best competing non-DL algorithm and a 9% improvement over canonical CNN for image processing, and a 6% over deep neural network (DNN). Our results suggest that the unique design of temporal and spatial ﬁlters in CNN4EEG can improve the representation of EEG data, hence the prediction performance.
Diagnostic accuracy of inflammatory back pain for axial spondyloarthritis in rheumatological care

Objective﻿ Inflammatory back pain (IBP), the key symptom of axial spondyloarthritis (axSpA), including ankylosing spondylitis, has been proposed as a screening test for patients presenting with chronic back pain in primary care. The diagnostic accuracy of IBP in the rheumatology setting is unknown. Methods Six rheumatology centres, representing secondary and tertiary rheumatology care, included routinely referred patients with consecutive chronic back pain with suspicion of axSpA. IBP (diagnostic test) was assessed in each centre by an independent (blinded) rheumatologist; a second (unblinded) rheumatologist made the diagnosis (axSpA or no-axSpA), which served as reference standard. Results Of 461 routinely referred patients, 403 received a final diagnosis. IBP was present in 67.3%, and 44.6% (180/403) were diagnosed as axSpA. The sensitivity of IBP according to various definitions (global judgement, Calin, Berlin, Assessment of SpondyloArthritis international Society criteria for IBP) was 74.4%–81.1 % and comparable to published figures, whereas the specificity was unexpectedly low (25.1%–43.9%). The resulting positive likelihood ratios (LR+) were 1.1–1.4 and without major differences between sets of IBP criteria. The presence of IBP according to various definitions increased the probability of axSpA by 2.5%–8.4% only (from 44.6% to 47.1%–53.0%). Conclusions The diagnostic utility of IBP in the rheumatology setting was smaller than expected. However, this was counterbalanced by a high prevalence of IBP among referred patients, demonstrating the effective usage of IBP in primary care as selection parameter for referral to rheumatology. Notably, this study illustrates potential shifts in specificity and LR+ of diagnostic tests if these tests are used to select patients for referral.
Content Analysis and the Measurement of Meaning: The Visualization of Frames in Collections of Messages

A step-to-step introduction is provided on how to generate a semantic map from a collection of messages (full texts, paragraphs or statements) using freely available software and/or SPSS for the relevant statistics and the visualization. The techniques are discussed in the various theoretical contexts of (i) linguistics (e.g., Latent Semantic Analysis), (ii) sociocybernetics and social systems theory (e.g., the communication of meaning), and (iii) communication studies (e.g., framing and agenda-setting). We distinguish between the communication of information in the network space (social network analysis) and the communication of meaning in the vector space. The vector space can be considered as the space in which the network of relations spans an architecture; words then are not only related, but also positioned. These positions are expected rather than observed and therefore one can communicate meaning.
CACTI: an enhanced cache access and cycle time model

In a clock having a synchronous motor and a chime that is struck once evey half hour by a biased hammer assembly that is prevented from striking the chime except when a tab on the hammer falls into a notch on a wheel driven by the minute shaft, a chime shutoff is mounted on the back cover of the clock for movement between a chime "on" and a chime "off" position.
The effect of aerobic exercises on peak expiratory flow rate and physical fitness index in female subjects

Background: A widely acknowledged fact is that people with more physical activity inclined to possess greater degree of fitness and physical activity can enhance cardiorespiratory health. Aims and Objective: To study the effect of aerobic exercises on peak expiratory flow rate (PEFR), body mass index (BMI), and physical fitness index (PFI) in apparently healthy female subjects. Materials and Methods: This study was conducted after a clearance from institutional ethical committee on 25 apparently healthy female subjects in the age group of 19–35 years who had voluntarily enrolled in a local health club, under the supervision of an expert trainer. The subjects were divided into two groups depending upon the aerobic exercise regimen they followed. They were assessed for height, weight, BMI, body surface area (BSA), PEFR, and PFI by Harvard step test at three different time intervals: 0 week, 6 weeks, and 10 weeks. The mean values of all the parameters were compared and evaluated. Result: There were significant changes (p < 0.001) in all the parameters while comparing with the baseline values at the three time intervals; an increase in PEFR, fall in BMI, and rise in PFI was seen. While comparing the values between the two groups, no significant difference could be found. Conclusion: Any form of aerobic exercise proves to be beneficial if followed consistently. Both the groups experienced an improvement in PEFR, BMI, and PFI, but labeling as which aerobic regimen was better could not be done.
INVESTIGATING A BEHAVIOUR ANALYSIS-BASED EARLY WARNING SYSTEM TO IDENTIFY BOTNETS USING MACHINE LEARNING ALGORITHMS

xiii List of Abbreviations Used . xiv Acknowledgements . xvi Chapter
Effects of stretching exercise during strength training in prepubertal, pubertal and adolescent boys

Abstract The purpose of the study was to investigate the effect of stretching exercises on lower extremity range of motion (ROM) in prepubertal, pubertal and adolescent boys. We also aimed to examine the initial ROM among the three age groups for both sides of the body. A total of 110 boys aged 10, 13 and 16 years old were divided into two equal groups (training group A and control group B), each group including three age subgroups. Group A participated in school physical activities and an interval strength training programme (12 weeks, 50 min/day, 3 days/week) at high intensity (80–85% of max) and at light intensity (30–40% of max). High-intensity workload was performed on a cycle ergometer for 4 min, alternating with light intensity workload for 8 min including stretching exercises. Group B participated only in the school physical activities, 2–3 times/week. ROM was determined during hip flexion, extension and abduction and knee flexion for right and left sides, using a flexometer and a goniometer. A mixed within- and between-subjects analysis of variance (ANOVA) with repeated measures revealed that initial ROM in hip joint flexion and extension for the left side were similar at all three ages. ROM in hip joint abduction and knee joint flexion was significantly greater in 10-year-old boys than 16-year-olds boys ( P P
Graph theoretical analysis of the Dynamic Lines of Collaboration model for disruption response

Ferialdy, Arfinandi. M.S.I.E., Purdue University, August 2016. Graph Theoretical Analysis of The Dynamic Lines of Collaboration Model for Disruption Response. Major Professor: Shimon Y. Nof. The Dynamic Lines of Collaboration (DLOC) model was developed to address the Networkto-Network (N2N) service challenge found in e-Work networks with pervasive connectivity. A variant of the N2N service challenge found in emerging Cyber-Physical Infrastructures (CPI) networks is the collaborative disruption response (CDR) operation under cascading failures. The DLOC model has been validated as an appropriate modelling tool to aid the design of disruption responders in CPIs by eliciting the dynamic relation among the service team when handling service requests from clients in the CPI network. The DLOC model for CDR operation is conceptually an abstraction of the CPI network into two interdependent networks of client and service networks. No preliminary design guidelines have been devised for DLOC-CDR from a network perspective using graph properties. Previous results of graph theoretical analysis for network behaviors under disruption may also not apply to DLOC-CDR due to the intrinsic nature of the N2N service challenge. Previous research works in DLOC-CDR have also not taken into consideration in protecting vulnerable CPI network elements which can cause system collapse by a single failure. Based on these observations, this research is guided by the following
Validation of the Digiwalker™ pedometer for measuring physical activity in young children.

Activity measurement using a uniaxial pedometer was validated against behavioral observation using the Children’s Activity Rating Scale (CARS) in 30 three- to four-year-old children in a nursery school setting. Correlations were calculated for individual children, whereas the relationship for the total group was investigated using multilevel linear regression. The mean counts for boys and girls for the Digiwalker™ were 66.8 (± 64.0) and 47.4 (± 61.3; p < .01) steps per 3 minutes, respectively, whereas the mean CARS scores for boys and girls were 1.8 (± 0.6) and 1.6 (± 0.6; p < .01), respectively. Within-child correlations for CARS versus Digiwalker counts ranged from 0.64 to 0.95 with a median value of 0.86, whereas the multilevel analysis provided strong evidence of a relationship between CARS and Digiwalker (all p < .001). Data from the current study show that gender differences in physical levels exist in very young children and support the utility of the Digiwalker pedometer for assessing physical act.
Preservation of hippocampal ultrastructural constituents with Nigella sativa oil treatment to rats challenged by global cerebral oligemia

The oil extract of Nigella sativa seeds (NSO) was found to preserve viability of cerebellar neurons in vitro. Recent in vivo experimental study proved that NSO daily oral treatment was able to prevent memory and learning deterioration in a murine model of mild global cerebral ischemia (MGCI) that was created with permanent bilateral occlusion of the common carotid arteries (2VO) in rats. The current study’s object ive was to assess the effect of NSO treatment on pyramidal and other neighboring neurons within CA1 hippocampal subfield of rats with MGCI that was achieved through 2VO procedure. The assessment was ultrastructural using transmission electron microscopy (TEM). Nine rats were equally divided into three groups: sham control, untreated 2VO and NSO treated group (2VO with daily oral NSO treatment). By the end of the 10th postoperative week coronal sections of the hippocampus were collected and processed for transmission electron microscopy (TEM). The neurodegenerative changes observed in pyramidal cells, astrocytes, endothelial basement membrane and axoplasms of CA1 hippocampal region of 2VO group were completely absent in sham operated control group. Meanwhile, NSO treated groups were almost free of ultrastructural neurodegenerative changes as those detected in untreated 2VO group. This adds another supporting evidence of the promising neuroprotective activity provided by NSO treatment to prevent age related neurodegenerative diseases especially those associated with chronic global cerebral hypoperfusion such as Alzheimer’s disease.
Statistical models for the analysis of short user-generated documents

In recent years short user-generated documents have been gaining popularity on the Internet and attention in the research communities. This kind of documents are generated by users of the various online services: platforms for instant messaging communication, for real-time status posting, for discussing and for writing reviews. Each of these services allows users to generate written texts with particular properties and which might require specific algorithms for being analysed. In this dissertation we are presenting our work which aims at analysing this kind of documents. We conducted qualitative and quantitative studies to identify the properties that might allow for characterising them. We compared the properties of these documents with the properties of standard documents employed in the literature, such as newspaper articles, and defined a set of characteristics that are distinctive of the documents generated online. We also observed two classes within the online user-generated documents: the conversational documents and those involving group discussions. We later focused on the class of conversational documents, that are short and spontaneous. We created a novel collection of real conversational documents retrieved online (e.g. Internet Relay Chat) and distributed it as part of an international competition (PAN @ CLEF’12). The competition was about author characterisation, which is one of the possible studies of authorship attribution documented in the literature. Another field of study is authorship identification, that became our main topic of research. We approached the authorship identification problem in its closed-class variant. For each problem we employed documents from the collection we released and from a collection of Twitter messages, as representative of conversational or short user-generated documents. We proved the unsuitability of standard authorship identification techniques for conversational documents and proposed novel methods capable of reaching better accuracy rates. As opposed to standard methods that worked well only for few authors, the proposed technique allowed for reaching significant results even for hundreds of users.
Multi-temporal comparisons of ERS-1 and JERS-1 SAR data for land applications

The site chosen for this investigation is Flevoland in the Netherlands. The terrain is very uniform and mostly covered by bare soil surfaces, crops, and forests. ERS-1 SAR precision images in PRI format and JERS-2 SAR data version 2.1 collected during 1993 are used as well as ground data measured by local investigators at the time of the overflight. Due to the excellent stability of the spaceborne satellites, it is possible to make multi-temporal observations over the site. The technique is used to improve the classification of the different crops. Moreover, taking into account that the SAR data are sensitive to temporal changes in surface roughness and soil moisture, the variations of these two parameters are investigated over a period spanning several months. In order to retrieve geo- and bio-physical parameters from microwave remote sensing data, a very detailed description of the interaction of the electromagnetic waves with the earth terrain being imaged is required. In the case of SAR images, both in situ data and radar data are needed in order to validate direct models, i.e. predict the backscattering coefficient as a function of the soil parameters, and retrieval algorithms to derive bio- and geophysical parameters from the SAR data. Some of the latest direct models for rough surfaces are tested and their domain of validity for ERS-1 data is investigated. <<ETX>>
Analisis Tekstur untuk Klasifikasi Motif Kain (Studi Kasus Kain Tenun Nusa Tenggara Timur)

Abstrak Indonesia memiliki banyak kekayaan budaya dalam bentuk kain tradisional, salah satunya kain tenun dari Nusa Tenggara Timur (NTT). Kain tenun dari tiap etnik di NTT memiliki cirikhas motif masing-masing yang merupakan manifestasi kehidupan sehari-hari, kebudayaan dan kepercayaan masyarakat setempat. Di mata pemerhati kain tenun NTT, asal kain tenun dapat diketahui dari motifnya. Tidak semua orang dapat membedakan asal daerah dari motif kain tenun tertentu dikarenakan sulitnya mendefinisikan karakteristik motif kain tenun suatu daerah dan beragamnya motif kain tenun yang ada dan komposisi warna yang beragam pula. Analisis tekstur adalah teknik analisis citra berdasarkan anggapan bahwa citra dibentuk oleh variasi intensitas piksel, baik citra keabuan maupun warna. Motif kain tenun terbentuk dari variasi intensitas warna sehingga dapat dipandang sebagai tekstur berwarna dari kain tenun. Penelitian ini bertujuan untuk mengetahui diantara pendekatan analisis tekstur menggunakan Gray Level Co-occurrence Matrix (GLCM) yang dikombinasikan dengan momen warna dan pendekatan analisis tekstur menggunakan Color Co-occurrence Matrix (CCM), metode manakah yang memberikan hasil lebih baik untuk klasifikasi motif kain tenun NTT. Hasil penelitian menunjukkan bahwa untuk klasifikasi motif kain tenun NTT, pendekatan analisis tekstur menggunakan metode CCM memberikan hasil lebih baik dibandingkan pendekatan analisis tekstur menggunakan GLCM yang dikombinasikan dengan momen warna. Kata kunci —klasifikasi citra, GLCM, CCM, momen warna, motif kain tenun NTT Abstract Indonesia have many culture in the form of traditional fabrics, one of them is woven fabric from Nusa Tenggara Timur (NTT). Each NTT ethnic has motif characteristic which ismanifestation of daily life, culture and the faith of local people. For a NTT woven fabric observer, the origin of a woven fabric can be known from the motif. But its difficult to recognising the origin of a woven fabrics because it is hard to define the characteristics of woven fabric motif from a region and wide variety of existing woven fabric motifs and also color composition. Texture analysis is image analysis technique based on assumption that an image formed by the variation of pixels intensity, both gray and color image. Woven fabric motif formed by the variation of color intensity that can be seen as color texture of the woven fabric. This study aims to determine between texture analysis using GLCM combined with color moment and texture analysis using CCM, which method gives better results for the NTT woven fabric motif classification. The results showed that for the NTT woven fabric motif classification, texture analysis using CCM gives better results than the texture analysis using GLCM combined with color moment. Keywords — image classification, GLCM , CCM , color moment, NTT woven fabric motif
HCI Dilemmas for Context-Aware Support in Intelligence Analysis

The REcommending Cases based on cONtext (RECON) system is a prototype adaptive technology designed to support intelligence analysis using dynamic load balancing and advanced human-machine synergy. RECON combines a brain-computer interface, machine learning, and simulation in order to create an innovative case-based recommendation capability. Several dilemmas emerge when designing joint cognitive systems endowed with an adaptive capacity. Herein, we critically discuss these dilemmas related to human modeling and human-computer interaction. Keywords-adaptive system; human-computer interaction; context awareness; case-based recommendation; brain-computer interface; information relevance; modeling.
From Cells to Cities

Since mathematical models came to be applied to problems of architectural and urban form, new concepts based on predicting large-scale structure from local rules have emerged through insights originating in computation and biology. The clearest of these are computer models based on cellular automata (CA) and their recent generalization in evolutionary biology and artificial life. Here we show how such models can be used to simulate urban growth and form, thus linking our exposition to the longer tradition of ideas in studies of built form emanating from the ‘Cambridge School’. We first review developments of CA in general and then in urban systems in particular. We propose a general class of CA models for urban simulation and illustrate two simple applications, the first a simulation of the development of the historical ‘cell’ city of Savannah, Georgia, the second, a generic hypothetical application. We then show how this generic model can be used to simulate the growth dynamics of a suburban area of a mid-sized North American city, thus illustrating how this approach provides insights into the way microprocesses lead to aggregate development patterns.
Modulation of immune responses on mucosal surfaces through vaccination and dietary intervention

MODULATION OF IMMUNE RESPONSES ON MUCOSAL SURFACES THROUGH VACCINATION AND DIETARY INTERVENTION Numerous pathogenic organisms enter the body at the mucosal surfaces and therefore the mucosal immune response must function as the first line of defense. The ability of the body to induce protective immune responses on the mucosal surfaces is a powerful strategy for the prevention of disease. Therefore, understanding the mechanism of induction associated with protection is critical if there is to be improvement in current treatments. In these studies, the use of vaccination and diet were investigated as potential strategies for the induction of potent immune responses on the mucosal surfaces. The principle of vaccination has been used successfully for centuries. However, there is still a great need for the development of vaccines against mucosal pathogens such HIV, TB, and newly emerging pathogens. The primary way to improve mucosal vaccination is through the use of a potent vaccine adjuvant. The first part of this project focuses on the use of cationic-liposome plasmid DNA complexes (CLDC) as a mucosal vaccine adjuvant for enhancing the immune response to both particulate and soluble antigens. In these studies, intranasal vaccination using CLDC resulted in a balanced humoral and cellular immune response capable of protecting against a lethal pulmonary
Web image annotation using two-step filtering on social tags

Web image annotation has become an important issue with exploding web images and the necessity of effective image search. The social tags have recently utilized at image annotation because they can reflect the user's tagging tendency, and reduce the semantic gap. However, an effective filtering procedure is required to extract the relevant tags since the user's subjectivity and noisy tags. In this paper, we propose a two-step filtering on social tags for image annotation. This method conducts the filtering and verification tasks by analyzing the tags of visual neighbor images using voting method and co-occurrence analysis. Our method consists of the following three steps: 1) the tag candidate set is founded by searching the visual neighbor images, 2) from a given tag candidate set, coarse filtering is conducted by tag grouping and voting technique, 3) the dense filtering is conducted by using similarity verification for coarse filtered candidate tag set. To evaluate the performance of our approach, we conduct the experiments on a social-tagged image dataset obtained from Flickr. We compare the annotation accuracy between the voting method and our proposed method. Our experimental results show that our method has an improvement in image annotation.
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization

On behalf of the organizing committee of the International Symposium on Code Generation and Optimization, it is our pleasure to welcome you to San Francisco.Many symposia benefit from a long history that helps keep them going year after year as new teams take over. CGO is a new symposium. As such, there was no history for us to work on when starting out. But we were by far not alone. The Steering Committee, in particular its chair C. J. Newburn, worked hard to give CGO a proper launching. Many Steering Committee members stepped up to the task and took on leadership roles in organizing CGO 2003.