Miracle-dz/UniFAR-FTUs huggingface dataset:
ATAM: Arabic Traffic Analysis Model for Twitter

Harvesting Twitter for insight and meaning in what is called sentiment analysis (SA) is a major trend stemming from computational linguistics and AI. Industry and academia are interested in maximizing efficiency while mining text to attain the most currently available data and crowdsourcing opinions. In this study, we present the ATAM model for traffic analysis using the data available on Twitter. The model comprises five components that start with data streaming and collection and ends with the road incident prediction through classification. The classification of data is done using a lexicon-based method. The predicted classes are as follows: safe, needs attention, dangerous, and neutral. The data were collected for three months in the city of Riyadh, Saudi Arabia. The model was applied on 10k tweets with an overall accuracy of the model classifying all four classes of 82%.
ACCEPTANCE LEVELS OF WELD DEFECTS FOR FATIGUE SERVICE

This paper has attempted to show how rationally based acceptance standards for weld defects operating under fatigue loading conditions have been derived, based on information gathered together from research carried out worldwide. Based on this information, a Drait British Standard on the derivation of acceptance levels of weld defects has now been written. The validity of the concept of defect acceptance based on fitness for purpose has been confirmed in a recent investigation of defective joints operating in the low cycle regime and at elevated temperatures. Methods of adjusting the quality band system to account for defects operating in aggressive environments have been proposed. One point should be made clear to those who regard the defect levels given in the acceptance proposals and in this paper as being totally unacceptable because they seem to indicate a reduction in quality. Welding costs will escalate as NDT of unsophisticated structures becomes more widespread because more and more unnecessary repairs will be made. In operating a business for profit, fitness for purpose is surely a more logical axiom than quality regardless of cost.
Normal-weight obese syndrome : early inflammation ? 1 – 3

Background: In obese subjects, the adipose mass represents an important source of proinflammatory cytokines. We have identified a new syndrome—the normal-weight obese (NWO) syndrome—in women with normal weight and body mass index but whose fat mass is 30% of their total body weight and whose risk of developing obesity-related diseases is likely increased. Objective: The aim of the present study was to verify the hypothesis that NWO women are characterized by early inflammation, related to body fat mass, and that their plasma proinflammatory cytokine concentrations are greater than those of nonobese women. Design: Twenty NWO, 20 preobese-obese, and 20 healthy (nonobese), age-matched white Italian women were studied. Anthropometric variables and plasma concentrations of proinflammatory cytokines and cardiovascular disease (CVD) risk factors were measured and compared between groups. Results: Plasma values and body-composition measures were significantly different between the preobese-obese and nonobese women. No significant differences in body weight, laboratory values, or CVD risk factors were found between the NWO and nonobese groups. Compared with concentrations in the NWO women, plasma concentrations of interleukin (IL)-1 , IL-1 , IL-6, IL-8, and TNFwere significantly lower in the nonobese group and were significantly greater in the preobese-obese group. IL-6 and TNFconcentrations were related to fat mass distribution in the NWO women. Conclusions: The proinflammatory cytokines could be regarded as significant prognostic indicators of the risk of obesity, CVD, and the metabolic syndrome in NWO women. Am J Clin Nutr 2007;85:
Implementation of Lean Manufacturing to Reduce Waste in the Maintenance Section in National Automotive Sub Companies of Indonesia

Diamond Motorindo is a company that produces four-wheeled vehicles, one of which is a Multi-Purpose Vehicle (MPV) type. In the MPV manufacturing process, the quality problem that is currently a trend is the AC Not Fill defect. Based on company data for the period March 2019 ~ February 2020, the defect continues to increase. The defect occurred due to the failure of filling the AC gas into the vehicle by the AC Filling Machine. This study aims to determine the OEE value of the AC filling machine is, the factors that cause machine failure that causes defects and can obtain improvement efforts to improve quality. The steps taken in this study are first to calculate the respective values of availability, performance, and rate of quality from the AC Filling Machine to find which variable has the lowest value with the OEE method. After obtaining the variable with the lowest value, the next step is to solve the problem of low variables by using the DMAIC method. After analyzing it, it can be concluded that the average OEE AC Filling Machine value during March 2019 ~ February 2020 is around 96%. This value is still below the target set by the company, which is 98%. What prevents the OEE value target from being achieved is the machine's low rate of quality. The contributing factors are humans, methods, machines/tools, and materials.
Solution structure and NMR characterization of the binding to methylated histone tails of the plant homeodomain finger of the tumour suppressor ING 4

Plant homeodomain (PHD) fingers are frequently present in proteins involved in chromatin remodelling, and some of them bind to histones. The family of proteins inhibitors of growth (ING) contains a PHD finger that bind to histone-3 trimethylated at lysine 4, and those of ING1 and ING2 also act as nuclear phosphoinositide receptors. We have determined the structure of ING4 PHD, and characterised its binding to phosphoinositides and histone methylated tails. In contrast to ING2, ING4 is not a phosphoinositide receptor and binds with similar affinity to the different methylation states of histone-3 at lysine 4. 2006 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
Imaging of lignin and cellulose in hardwood using Fourier transform infrared microscopy - comparison of two methods.

Fourier transform infrared microscopy combined with a focal plane array detector was used to illustrate the distribution of cellulose and lignin in wood of European beech (Fagus sylvatica L.). Two independent methods were used for data analysis: (a) an integration; and (b) a correlation method. In method (a), the typical range of wave-numbers for cellulose (1390-1350 cm) or lignin (1530-1490 cm) was integrated in each spectrum and the distribution of the measured intensities was recorded. For the correlation method (b), reference Fourier transform infrared spectra for isolated beech lignin and cellulose were generated. The spectra obtained with the focal plane array detector in beech wood were correlated with reference spectra for cellulose or lignin, respectively illustrating the tightness of the correlation in each measuring point in the wood cross-section. Both methods gave similar results, but the integration method yielded higher resolution than the correlation method. Since structural properties of wood such as wall thickness and lumen width affected the results, the optimum method needs to be evaluated for each species separately.
Variational phase recovering without phase unwrapping in phase-shifting interferometry

ABSTRACT We present a variational method for recovering the phase term from the information obtained from phase-shifting methods. First we introduce the new method based on a variational approach and then describe the numerical solution of the proposed cost function, which results in a simple algorithm. Numerical experiments with both synthetic and real fringe patterns show the accuracy and simplicity of the resulting algorithm.
Quasi-Orthogonal Maps for Dynamic Language Recognition

Although recurrent neural networks can be trained to recognize formal languages from positive and negative examples, it is di cult for them to learn large and complex data sets due to the geometry of their state space, which tends to push activations into the corners and dilute the e ect of di erentials backpropagated through multiple levels of recurrence. We propose a new framework for dynamical recognizers in which the state space is the surface of an n-sphere and the maps are either orthogonal or quasi-orthogonal.
Evaluation of security and performance of clustering in the Bitcoin network, with the aim of improving the consistency of the Blockchain

Bitcoin is a digital currency based on a peer-to-peer network to propagate and verify transactions. Bitcoin is gaining wider adoption than any previous crypto-currency and many well-known businesses have begun accepting bitcoins as means of financial payments. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about the underlying physical topology can cause a delay overhead in information propagation which makes the system vulnerable to double spend attacks due to inconsistencies in the blockchain. Aiming at alleviating the propagation delay problem, this thesis evaluates the concept of network clustering in tackling the propagation delay problem in the Bitcoin network throughout introducing a proximity-aware extensions to the current Bitcoin protocol, named Locality Based Clustering (LBC), Ping Time Based Clustering (BCBPT), Super Node Based Clustering (BCBSN), and Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocols, that are based on how clusters are formulated and nodes define their membership, is to improve the information propagation delay in the Bitcoin network. The proximity of connectivity in the Bitcoin network is increased in the LBC and BCBPT protocol by grouping Bitcoin nodes based on different criteria, physical location in LBC protocol and link latencies between nodes in the BCBPT. In the BCBSN protocol, geographical connectivity increases as well as the number of hops between nodes decreases through assigning one node to be a cluster head that is responsible for maintaining the cluster. Whereas, MNBC incorporates master node technology and proximity-awareness into the existing Bitcoin protocol with the aim of creating fully connected clusters based on physical Internet proximity. We show, through simulations, that the proposed approaches define better clustering structures that optimize the transaction propagation delay over the Bitcoin protocol. However, MNBC is more effective at reducing the transaction propagation delay compared to the BCBPT, LBC, and BCBSN. On the other hand, this thesis evaluates the resistance of the Bitcoin network and the proposed approaches against the partitioning attack. Even though the Bitcoin network is more resistant against partition attacks than the proposed approaches, more resources need to be spent to split the network in the proposed approaches especially with a higher number of nodes.
Development of the video stream object detection algorithm (VSODA) with tracking

The object tracking is one of the most important task in video analysis. Many methods have been proposed such as TLD (Tracking, Learning, Detection), Meanshift and MIL but they show good accuracy in laboratory cases, not in real ones, where the accuracy is defined as a numerical difference between computed object coordinates and the real ones. One of the reasons is lack of information about tracked object and environment changes. If a method has the prior information about tracked object, then it will be able to perform with higher accuracy. Some of the newest object tracking methods such as GOTURN use trained CNN (convolutional neural network) and have better accuracy because of knowledge about how the tracked object looks like in different situations such as light intensity changes and tracked object’s rotations. If we use only a classification algorithm (classifier) then it can find an object that was in training set with high probability. But if its appearance is changing it will be lost when deviation will be higher than trust limit. Then it is important to have parts of prior and posterior information about tracked object. The prior information is given by detector (CNN) and posterior information – by tracking algorithm (TLD). One of the biggest detector problems is high computational complexity in terms of operations’ number and one of the solutions is to use the classifier in parallel with the tracker. In future work we are going to use different sensors, not only RGB camera, but RGBD camera, which may improve accuracy due to higher amount of information.
Temporal-numeric planning with control parameters

Over the last decade, significant progress has been made in task planning to explore temporal and numerical features of the real world. Interaction between time and metric fluents and temporal coordination problems have been well-addressed by innovative approaches in planning and scheduling. A standardised language, PDDL, has enabled modelling the conceptual models and benchmarking the work of numerous researchers since it was released. Although PDDL is an expressive modelling language, a significant limitation is imposed on the structure of actions: the parameters of actions are restricted to values from finite (in fact, explicitly enumerated) domains. There is one exception to this, introduced in PDDL2.1, which is that durative actions may have durations that are chosen (possibly subject to explicit constraints in the action models) by the planner. A motivation for this limitation is that it ensures that the set of grounded actions is finite and, ignoring duration, the branching factor of action choices at a state is therefore finite. Although the duration parameter can make this choice infinite, very few planners support this possibility, but restrict themselves to durative actions with fixed durations. In this thesis we motivate a proposed extension to PDDL to allow actions with infinite domain parameters, which we call control parameters. We illustrate reasons for using this modelling feature and then describe a planning approach that can handle domain models that exploit it, implemented in a new planner, called POPCORN. We propose an extension to a delete-relaxation heuristic, called the Temporal Relaxed Planning Graph, to tackle problems with control parameters; especially in problems with producer-consumer relationship of actions. We show that this approach scales to solve interesting problems. We apply this work in a task and motion planning scenario to show that our work has a great potential of re-inventing the way task planners are used in robot navigation.
Recognition of Partial Textual Entailment for Indian Social Media Text

Textual entailment (TE) is a unidirectional relationship between two expressions where the meaning of one expression called Hypothesis (H), infers from the other expression called Text (T). The definition of TE is rigid in a sense that if the H entails from T but lacks minor information or have some additional information, then the pair treated as non-entailed. In such cases we could not measure the relatedness of a T-H pair. Partial textual entailment (PTE) is a possible solution of this problem which defines partial entailment relation between a T-H pair. PTE relationship can plays an important role in different Natural Language Processing (NLP) applications like text summarization and question-answering system by reducing redundant information. In this paper we investigate the idea of PTE for Indian social media text (SMT). We developed a PTE annotated corpus for Bengali tweets and proposed a Sequential Minimal Optimization (SMO) based PTE recognition approach. We also evaluated our proposed approach through experiment results.
Using technology to address the problems of malnutrition and dehydration in older adults

Malnutrition and dehydration of important problems for older adults even in developed countries such as the UK and both have many serious health consequences. This programme of research developed an app, MyHealthyLiving, to support older adults in monitoring their intake of liquids and fruit and vegetables. I followed a user-centred design lifecycle and conducted six studies, starting with focus groups to understand the older users’ needs and wishes, through expert and user evaluations, to a two week field study of the use of the app by 15 older people. Using the data from the user evaluations, a new set of evidence-based heuristics for the development and evaluation of tablet apps for older people was also produced.
Integrative Approach to Reveal Cell Type Specificity and Gene Candidates for Psoriatic Arthritis Outside the MHC

We recently conducted a large association analysis to compare the genetic profiles between patients with psoriatic arthritis (PsA) and cutaneous-only psoriasis (PsC). Despite including over 7,000 genotyped patients, only the MHC achieved genome-wide significance. In this study, we hypothesized that appropriate epigenomic elements (H3K27ac marks for active enhancers) can guide us to reveal valuable information about the loci with suggestive evidence of association. Our aim is to investigate these loci and explore how they may lead to the development of PsA. We evaluated this potential by investigating the genes connected with these loci from the perspective of pharmacogenomics and gene expression. We illustrated that markers with suggestive evidence of association outside the MHC region are enriched in H3K27ac marks for osteoblast and chondrogenic differentiated cells; using pharmacogenomics resources, we showed that genes near these markers are targeted by existing drugs used to treat psoriatic arthritis. Significantly, six of the ten suggestive significant loci overlapping the regulatory elements encompass genes differentially expressed (FDR < 5%) in differentiated osteoblasts, including genes participating in the Wnt signaling such as RUNX1, FUT8, and CTNNAL1. Our approach shows that epigenomic information can be used as cost-effective approach to enhance the inferences for GWAS results, especially in situations when few genome-wide significant loci are available. Our results also point the way to more directed investigations comparing the genetics of PsA and PsC.
Therapeutic Implication of SOCS1 Modulation in the Treatment of Autoimmunity and Cancer

The suppressor of cytokine signaling (SOCS) family of intracellular proteins has a vital role in the regulation of the immune system and resolution of inflammatory cascades. SOCS1, also called STAT-induced STAT inhibitor (SSI) or JAK-binding protein (JAB), is a member of the SOCS family with actions ranging from immune modulation to cell cycle regulation. Knockout of SOCS1 leads to perinatal lethality in mice and increased vulnerability to cancer, while several SNPs associated with the SOCS1 gene have been implicated in human inflammation-mediated diseases. In this review, we describe the mechanism of action of SOCS1 and its potential therapeutic role in the prevention and treatment of autoimmunity and cancer. We also provide a brief outline of the other JAK inhibitors, both FDA-approved and under investigation.
Accumbal Adenosine A2A Receptors Enhance Cognitive Flexibility by Facilitating Strategy Shifting

The deficits of cognitive flexibility (including attentional set-shifting and reversal learning) concomitant with dysfunction of the striatum are observed in several neuropsychiatric disorders. Rodent and human studies have identified the striatum [particularly the dorsomedial striatum (DMS) and nucleus accumbens (NAc)] as the critical locus for control of cognitive flexibility, but the effective neuromodulator and pharmacological control of cognitive flexibility remains to be determined. The adenosine A2A receptors (A2ARs) are highly enriched in the striatopallidal neurons where they integrate dopamine and glutamate signals to modulate several cognitive behaviors, but their contribution to cognitive flexibility control is unclear. In this study, by coupling an automated operant cognitive flexibility task with striatal subregional knockdown (KD) of the A2AR via the Cre-loxP strategy, we demonstrated that NAc A2AR KD improved cognitive flexibility with enhanced attentional set-shifting and reversal learning by decreasing regressive and perseverative errors, respectively. This facilitation was not attributed to mnemonic process or motor activity as NAc A2AR KD did not affect the visual discrimination, lever-pressing acquisition, and locomotor activity, but was associated with increased attention and motivation as evident by the progressive ratio test (PRT). In contrast to NAc A2ARs, DMS A2ARs KD neither affected visual discrimination nor improved set-shifting nor reversal learning, but promoted the effort-related motivation. Thus, NAc and DMS A2ARs exert dissociable controls of cognitive flexibility with NAc A2ARs KD selectively enhancing cognitive flexibility by facilitating strategy shifting with increased motivation/attention.
How Cognitive Models of Human Body Experience Might Push Robotics

In the last decades, cognitive models of multisensory integration in human beings have been developed and applied to model human body experience. Recent research indicates that Bayesian and connectionist models might push developments in various branches of robotics: assistive robotic devices might adapt to their human users aiming at increased device embodiment, e.g., in prosthetics, and humanoid robots could be endowed with human-like capabilities regarding their surrounding space, e.g., by keeping safe or socially appropriate distances to other agents. In this perspective paper, we review cognitive models that aim to approximate the process of human sensorimotor behavior generation, discuss their challenges and potentials in robotics, and give an overview of existing approaches. While model accuracy is still subject to improvement, human-inspired cognitive models support the understanding of how the modulating factors of human body experience are blended. Implementing the resulting insights in adaptive and learning control algorithms could help to taylor assistive devices to their user's individual body experience. Humanoid robots who develop their own body schema could consider this body knowledge in control and learn to optimize their physical interaction with humans and their environment. Cognitive body experience models should be improved in accuracy and online capabilities to achieve these ambitious goals, which would foster human-centered directions in various fields of robotics.