Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Adult Learning Principles as a Framework for Learning ICT Skills Needed for Research Projects

Students in higher institutions need to carry out research projects. The focus of this paper explores a model to help students learn ICT skills needed for research projects. Generally students go through the ‘long and hard route’ to learn and use ICT resources because they do not know how to do it. The paper explores the Adult Learning Theories as a model to develop the contents of an ICT information resource. Adult learning requires building on prior learning, using methods that treat learners with respect, and recognizing that people have different learning styles and have a variety of responsibilities and time commitments. The optimal role of the adult learner in the learning situation is that of a self-directed, self-motivated manager of personal learning who collaborates as an active participant in the learning process and who takes responsibility for learning. The modules developed were for Computer Operations, Internet, Microsoft Word®, Microsoft Excel® and PowerPoint®. Forty-four students who volunteered evaluated the information resource. Based on the evaluation and comments and question from the students, the design of the information resource can be adjudged highly successful. All the functional requirements were met in the ICT information resource design. Students from three (3) out of the four (4) faculties rated their satisfaction of the functionality of the system at 100%. The students highly regarded the perceived usefulness of the ICT information resource. Eight out of the 9 items related to perceived potential usefulness were rated at over 90%, while only one item was rated at 88.6%. Over 90% of the students agreed that the system has been designed with an understanding of their ICT skills need. In 9 out of the 10 items related to task technology fit, 85% of the students agreed to the Task Technology Fit. It is therefore expected that with the high level of their satisfaction with the perceived potential usefulness, and Task Technology Fit, the ICT information resource will be accepted, adopted and used by the students.
Effect of COX-2 Inhibitors Selectivity on Lipid Profile in Hyperlipidemic and Normolipidemic Type 2 Diabetics

Development of NSAIDS based on inhibiting cyclooxygenase activity. However, the different physiological consequences arrised by appearance of new drugs with different selectivity to COX-2 enzyme upon their administration with their relevant affects on some cardiovascular risk factors. To study the potential effects of relatively diclofenac and highly specific celecoxib COX-2 inhibitors on lipid profile and serum C-reactive protein in type 2 diabetes, whom have hyperlipidemia to be compared by their effects with normolipidemic patients. A total number of 34 type 2 diabetics (14 normolipidemics and 20 hyperlipidemics) treated with either diclofenac 100mg/day or celecoxib 200mg/day for eight weeks. Analysis of results indicated that diclofenac increased serum triglycerides (TG) whereas; celecoxib group exerted a significant reduction in total cholesterol (TC), triglycerides (TG) levels in hyperlipidemic patients. Normolipidemic diabetics showed a significant elevation in serum total cholesterol, triglycerides and low density lipoprotein-cholesterol (LDL) with significant reduction in high density lipoprotein-cholesterol (HDL) in those treated with diclofenac , whilst those treated with celecoxib exhibited no modification of serum lipids. The results of the present study indicated that the net effect of treatment of hyperlipidemic type 2 diabetics by diclofenac was mostly qualitative as indicated by elevated TG/HDL ratio, to be a marker of atherogenic- small dense LDL particles in diabetics, whereas celecoxib exerted no such effect in this group but produced a beneficial reduction in LDL/HDL ratio. Meanwhile, diclofenac in normolipidemic diabetics exert a significant qualitative and quantitative modulation of their serum lipid components presented by net elevation in both LDL/HDL and TG/HDL ratios. As a conclusion the administration of relatively selective COX-2 inhibitors ( diclofenac ) to normolipidemic type 2 diabetics could adversely affect lipid metabolism by producing undesirable qualitative as well as , quantitative changes in serum lipid components, more than that observed in the hyperlipidemic diabetics. Key Words: Diabetes, lipid profile, cox-2 selectivity
Fabrication and Characterisation of Low-noise Monolithic Mode-locked Lasers

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A Workload Prediction Based Controller for Energy-Efficient Resource Allocation in Virtualized Data Centers

In this paper we approach the problem of data centers' high energy consumption by proposing a prediction based controller for optimal and energy efficient allocation of the hardware computing resources to the incoming workload. The controller analyzes the incoming workload virtual tasks' requests for hardware computing resources and classifies them in predefined tasks classes. For each class, the tasks' arrival rates are computed and the next period workload is predicted using a neural network. An optimal green data center situation is determined, in which all the predicted virtual tasks are accommodated and the hardware computing resources utilization is minimized. By comparing the current and predicted data center situations, the controller decides and constructs an adaptation action plan to accommodate the incoming workload in an energy efficient manner. The controller implementation is tested on a real test case data center and significant energy savings are reported.
An augmented human computer interaction system by effective use of eye blink pattern

The process of eye blinking is not only for biological need of eye but it also carries significant gesture information may be termed with sound maxim “Language of eye”. In this paper it is proposed to utilize the controlled gesture using blink patterns for effective communication using automated identification and interpretation of eye gesture. The traditional human-computer interfaces demand good manual agility and refined motor control, which may be absent or unpredictable for people with certain disabilities. Here it is proposed a robust, accurate algorithm with relevant hardware setup to detect eyes and measure the duration of blinks to interpret them in real time to control a computer or computer interfaced devices. The complete system is divided into two primary modules. The first one is to detect voluntary eye blink and second module to trigger an onscreen soft agent that interprets the blink into proper mouse movement with associated mouse action sequence. This is a low cost proposal and prototypes designed to work with web camera interfaced to a standard PC.
Efficacy and safety of rituximab in the treatment of eosinophilic granulomatosis with polyangiitis

Introduction Eosinophilic granulomatosis with polyangiitis (EGPA) is a subset of antineutrophil cytoplasmic antibodies (ANCA) associated vasculitis with distinct pathophysiological mechanisms, clinical features and treatment responses. Rituximab is a licensed therapy for granulomatosis with polyangiitis and microscopic polyangiitis but there is limited experience of rituximab in EGPA. Methods EGPA patients from a tertiary centre who received rituximab for mostly refractory EGPA or in whom cyclophosphamide was contra indicated were studied. A standardised dataset was collected at time of initial treatment and every 3 months for 24 months. Response was defined as a Birmingham Vasculitis Activity Score (BVAS) of 0 and partial response as ≥50% reduction in BVAS from baseline. Remission was defined as a BVAS of 0 on prednisolone dose ≤5 mg. Results Sixty-nine patients (44 female) received rituximab between 2003 and 2017. Improvement (response and partial response) was observed in 76.8% of patients at 6 months, 82.8% at 12 months and in 93.2% by 24 months, while relapses occurred in 54% by 24 months, with asthma being the most frequent manifestation. The median BVAS decreased from 6 at baseline to 1 at 6 months, and 0 at 12 and 24 months. Prednisolone dose (mg/day, median) decreased from 12.5 to 7, 7.5 and 5 at 6, 12 and 24 months, respectively. ANCA positive patients had a longer asthma/ear, nose and throat (ENT) relapse-free survival time and a shorter time to remission. Discussion Rituximab demonstrated some efficacy in EGPA and led to a reduction in prednisolone requirement, but asthma and ENT relapse rates were high despite continued treatment. The ANCA positive subset appeared to have a more sustained response on isolated asthma/ENT exacerbations.
Learning Joint Gait Representation via Quintuplet Loss Minimization

Gait recognition is an important biometric method popularly used in video surveillance, where the task is to identify people at a distance by their walking patterns from video sequences. Most of the current successful approaches for gait recognition either use a pair of gait images to form a cross-gait representation or rely on a single gait image for unique-gait representation. These two types of representations emperically complement one another. In this paper, we propose a new Joint Unique-gait and Cross-gait Network (JUCNet), to combine the advantages of unique-gait representation with that of cross-gait representation, leading to an significantly improved performance. Another key contribution of this paper is a novel quintuplet loss function, which simultaneously increases the inter-class differences by pushing representations extracted from different subjects apart and decreases the intra-class variations by pulling representations extracted from the same subject together. Experiments show that our method achieves the state-of-the-art performance tested on standard benchmark datasets, demonstrating its superiority over existing methods.
Specificity of Anti-Citrullinated Protein Antibodies in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disease of unknown etiology. The majority of individuals with RA are positive for the disease-specific anti-citrullinated protein antibodies (ACPAs). These antibodies are primarily of cross-reactive nature, hence, the true autoantigen to ACPA remains unidentified. In this study, we analyzed the reactivity of RA sera to several post-translationally modified epitopes, in order to further characterize the specific nature of ACPAs by immunoassays. Substituting citrulline with other amino acids, e.g., D-citrulline, homo-citrulline and methyl-arginine illustrated that ACPAs are utmost specific for citrullinated targets. Collectively, these findings support that ACPAs and citrullinated targets are specific for RA, making citrulline-containing peptide targets the most effective assays for detection of ACPAs.
Neural correlates of conscious visual perception lie outside the visual system: evidence from the double-drift illusion

When perception differs from the physical stimulus, as it does for visual illusions and binocular rivalry, the opportunity arises to localize where perception emerges in the visual processing hierarchy. Representations prior to that stage differ from the eventual conscious percept even though they provide input to it. Here we investigate where and how a remarkable misperception of position emerges in the brain. This “double-drift” illusion causes a dramatic mismatch between retinal and perceived location, producing a perceived path that can differ from its physical path by 45° or more [1]. The deviations in the perceived trajectory can accumulate over at least a second [1] whereas other motion-induced position shifts accumulate over only 80 to 100 ms before saturating [2]. Using fMRI and multivariate pattern analysis, we find that the illusory path does not share activity patterns with a matched physical path in any early visual areas. In contrast, a whole-brain searchlight analysis reveals a shared representation in more anterior regions of the brain. These higher-order areas would have the longer time constants required to accumulate the small moment-to-moment position offsets that presumably originate in early visual cortices, and then transform these sensory inputs into a final conscious percept. The dissociation between perception and the activity in early sensory cortex suggests that perceived position does not emerge in what is traditionally regarded as the visual system but emerges instead at a much higher level.
Application Processing for Smart phone Devices in Mobile Cloud Computing

Mobile Cloud Computing (MCC) is a combination of Cloud computing and mobile networks. It is a technique or model in which mobile applications are built, powered and hosted using cloud computing technology. Users expect to run computational intensive applications on Smart Mobile Devices (SMDs) in the same way as powerful primary or mainframe computers. However, SMDs are still low potential computing devices, which are constrained by CPU potentials, memory capacity and battery life time. MCC provides practical solution to reduce this incapacitation by extending the services and resources of computational clouds to SMDs on demand basis. The current offloading algorithms offload computational intensive applications to remote servers by employing different cloud models. A challenging aspect of such algorithms is the establishment of distributed application processing platform at runtime which requires additional computing resources on SMDs. This paper describes Distributed Application Processing Approach for SMDs in MCC Environment. The purpose of this paper is to highlight issues and challenges to the existing algorithms in developing, implementing and executing computational intensive mobile applications within MCC environment. It proposes taxonomy of current Application Processing Approaches, reviews current offloading frameworks by using thematic taxonomy and analyzes the implications and critical aspects of current offloading frameworks.
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition

In skeleton-based action recognition, graph convolutional networks (GCNs), which model the human body skeletons as spatiotemporal graphs, have achieved remarkable performance. However, in existing GCN-based methods, the topology of the graph is set manually, and it is fixed over all layers and input samples. This may not be optimal for the hierarchical GCN and diverse samples in action recognition tasks. In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods. In this work, we propose a novel two-stream adaptive graph convolutional network (2s-AGCN) for skeleton-based action recognition. The topology of the graph in our model can be either uniformly or individually learned by the BP algorithm in an end-to-end manner. This data-driven method increases the flexibility of the model for graph construction and brings more generality to adapt to various data samples. Moreover, a two-stream framework is proposed to model both the first-order and the second-order information simultaneously, which shows notable improvement for the recognition accuracy. Extensive experiments on the two large-scale datasets, NTU-RGBD and Kinetics-Skeleton, demonstrate that the performance of our model exceeds the state-of-the-art with a significant margin.
Data structures, algorithms and architectures for efficient regular expression evaluation

Regular expression matching is a crucial task in several networking applications, where packet payloads need to be inspected at line rate over large sets of complex patterns. Modern, payload-scanning firewalls and intrusion detection systems are perhaps the most notable applications that rely on this mechanism. When targeting memory-based architectures, in which the pattern data structure is stored in off-chip memory such as DRAM, the design of efficient regular expression matching engines is subject to two basic resource requirements: memory size and memory bandwidth. When designing Field Programmable Gate Array implementations, in which the pattern data structure is stored in on-chip memories or logic gates, the focus is on minimizing logic cell utilization and allowing high operational dock frequency. Keeping these requirements feasible is a challenge as pattern sets grow in size and expressiveness. In this work, we describe novel data structures and algorithms for high-speed regular expression evaluation and map the proposed schemes onto suitable parallel architectures. We aim to provide a comprehensive solution that is scalable with respect to both the size of the rule-set and the complexity of the individual rules. Our main contributions are the following. First, we propose an efficient and low complexity compression scheme for deterministic finite automata (DFAs), leading to a compression factor in excess of 98% across a set of representative data sets. Second, we identify and address problematic subpatterns, such as bounded and unbounded repetitions of wildcards and large character sets and back-references. Specifically, we propose a novel automaton with a limited memory footprint and a bounded memory bandwidth requirement that overcomes the limitations of pure DFA-based solutions. Third, we describe how to utilize our proposed techniques when targeting FPGA implementations. Finally, our tools for constructing and analyzing a range of alternative automata for regular-expression evaluation have been released as open-source, and are used actively by many researchers from around the world.
Term frequency-information content for focused crawling to predict relevant web pages.

With the rapid growth of the Web, finding desirable information on the Internet is a tedious and time consuming task. Focused crawlers are the golden keys to solve this issue through mining of the Web content. In this regard, a variety of methods have been devised and implemented. Many of these methods coming from information retrieval viewpoint are not biased towards more informative terms in multi-term topics (topics with more than one keyword). In this paper, by considering terms’ information contents, we propose Term Frequency-Information Content (TF-IC) method which assigns appropriate weight to each term in a multi-term topic. Through the conducted experiments, we compare our method with other methods such as Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Semantic Indexing (LSI). Experimental results show that our method outperforms those two methods by retrieving more relevant pages for multi-term topics.
Next Web Page Prediction by Utilization of Content and Web-Log Features with Markov Model

In prediction of next page several models are prepare which utilize different techniques. For this new technique different outcomes are obtain which have probability to predict the chance of the next page. So targeting the most probable page makes the very low precision. The basic component here is to collect different features of the web so that prediction is more effective then the single and unorganized one. For the optimization of the prediction it is require that all the feature of the website should utilize. In this paper, the content of web with Markov model is utilized for the prediction of next page. Result shows that by the use of Markov Model with web content and web log feature is better for prediction.
CasCP: Efficient and Secure Certificateless Authentication Scheme for Wireless Body Area Networks with Conditional Privacy-Preserving

As the aging population of society continues to intensify, the series of problems brought about by aging is becoming more and more serious. Because the health problem of the elderly brings many social problems, people have paid close attention to it. Fortunately, as a typical smart healthcare system, wireless body area networks (WBANs) present quit nice medical care for people, especially the aged. However, personal health information is very sensitive. But, the common communication channel is used in WBANs and any malicious entity can initiate a security attack on WBANs. To ensure secure communication and privacy-preserving which are the premise of the sound development of WBANs, an improved and efficient certificateless authentication scheme with conditional privacy-preserving is proposed in this paper on the basis of analyzing the most recent presented certificateless authentication scheme for WBANs. The proposed scheme also provides batch authentication to decrease authentication and communication cost. A rigid security proof demonstrates that our proposed scheme resists every type of security attack and can provide condition privacy-preserving. The performance analysis shows that our proposed scheme has some advantages in computation and communication cost.
Lip modeling for visual speech recognition

In this paper, we describe an algorithm for modeling the shape of the mouth, and extracting meaningful dimensions for use by automatic lipreading systems. One advantage of this technique lies in the ability to normalize the model to compensate for scale and rotation. An error function is defined which relates the model to the image, and minimization of the error yields the best fit model. This is similar to deformable templates, but we attempt to perform the minimization in closed form. Visual only recognition was performed with features extracted from the model, and the recognition system achieved 85% accuracy on a two word discrimination task. <<ETX>>