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The Boolean SATisfiability Problem (SAT) is an important intractable problem in computer science with numerous theoretical and industrial applications. With the growing availability of multi-core and multi-processor systems, parallel algorithms are increasingly important and useful. Yet, state-of-the-art parallel SAT solvers are mostly portfolio solvers that exchange learn’t clauses to achieve knowledge sharing. We describe some novel extensions to using Stalmarck’s method to create a parallel SAT solver. This includes two new knowledge sharing methods across solvers, t-BFD and c-BFD merging. We prove that t-BFD and c-BFD merging are distinct, and experimentally show that they occur significantly. In fact, t-BFD merging occurs more frequently at deeper levels of the problem decomposition tree. Within the problem subtree, t-BFDs merging gives unit clauses, and c-BFD merging gives clauses that are empirically short. Such short clauses are likely useful in simplifying the problem. Also, we introduce a more flexible problem decomposition method than naive splitting on N variables to form \(2^N\) subproblems. Based on the Dilemma Rule, it picks branching literals independently at every vertex in the decomposition tree. In addition, we engineered a method to avoid processor starvation in the parallelisation. We benchmarked a preliminary implementation of our modified solver against Glucose-Syrup 4.1 and show that certain configurations of restart and runtime budgets allow our solver to outperform Glucose-Syrup 4.1 on SAT instances. Furthermore, t-BFD merging significantly improved our solver’s performance on UNSAT benchmarks, suggesting that t-BFD and c-BFD merging are effective.

Stock market prediction has been a challenging task for machine due to time series analysis is needed. In recent years, deep neural networks have been widely applied in many financial time series tasks. Typically, deep neural networks require huge amount of data samples to train a good model. However, the data samples for stock market is limited which caused the networks prone to overfitting. In view of this, this paper leverages deep neural networks with ensemble learning to address this problem. We propose ensemble of Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and 1DConvNet with LSTM (Conv1DLSTM) to predict the stock market price, named EnsembleDNNs. The performance of the proposed EnsembleDNNs is evaluated with stock market of several companies. The experiment results show encouraging performance as compared to other baselines.

Sound waves are a form of energy produced by a vibrating object that travels through the medium that can be heard. Generally, the sound is used in human communication, music, alert, and so on. Furthermore, it also helps us to understand what are the events that occurring in the moment, and thereby, provide us hints to understand what is happening around us. This has prompt researchers to study on how humans understand what event is occurring based on the sound waves. In recent years, researchers also study on how to equip the machine with this ability, i.e. acoustic event detection. This study focuses on the acoustic event detection which leverage both frequency spectrogram technique and deep learning methods. Initially, a spectrogram image is generated from the acoustic data by using the frequency spectrogram technique. Then, the generated frequency spectrogram is fed into a pre-trained MobileNet model to extract robust features representations. In this work, 1 Dimensional Convolutional Neural Network (1D-CNN) is adopted to train a model for acoustic event detection. The feature representations are extracted from a pre-trained MobileNet. The proposed 1D-CNN consist of several alternatives of convolution and pooling layers. The last pooling layer is flattened and fed into a fully connected layer to classify the events. Dropout is employed to prevent overfitting. The proposed frequency spectrogram with pre-trained MobileNet and 1D-CNN is then evaluated with three datasets, which are Soundscapes1, Soundscapes2, and UrbanSound8k. From the experimental results, the proposed method obtained 81, 86, and 70 F1-score, for Soundscapes1, Soundscapes2, and UrbanSound8k, respectively.

Deciding on the most suitable university program to pursue after completing secondary education is a major step for school-leavers as it sets the tone of the career path they will embark on in the future. Undeniably, deciding on the university program is a sophisticated problem that involves a broad range of factors, such as geographic location, overall cost, campus security, personality, etc. Most of these factors, for instance, geographic location and campus security only have temporary impact on the school-leavers while they are attending to university. Nevertheless, the personality factor appears to be the long-term and the most crucial determinant of the success in their professional life. When choosing the university program, school-leavers often make the mistake of following friends' footsteps or adhering to parents' decisions, only to realize years later that it is not what suits their personality or what they want. Considering these challenges, this work studies on the Holland's Personality Test to examine the personality of school-leavers. There are six personality types in Holland's Personality Test, represented as RIASEC code or Holland code. On top of that, this work also compiles a comprehensive list of university programs with their corresponding Holland code. An expert system is then engineered to encode the information into the knowledge base. Upon taking the Holland Personality Test, the expert system will update the facts list based on the answers given. Subsequently, the inference engine activates the production rules if the conditions are fulfilled. Ultimately, the personality type and recommended university program are presented to the school-leavers.

Object recognition has spurred much attention in recent years. The fact that computers are now able to detect and recognize objects has made Artificial Intelligence field, especially machine learning grow very rapidly. The proposed framework uses Deep Convolutional Neural Network (DCNN) that is based on ResNet 50 architecture. Due to the limited computational resources to train the whole model, the ResNet model is imitated and the pre-trained weights are imported. Thereafter, the last few layers of the model are trained on three datasets that have been acquired online. This process is called fine-tuning a pre-trained model. It is one of the most common approaches in building a DCNN architecture. The dataset that was used to evaluate the performance of the model are ETHZ-FOOD101, UECFOOD100 and UECFOOD256. The parameter setting and results of the proposed method are also presented in this paper.

Introduction: Trimethoprim-sulfamethoxazole (TMP-SMX) is a commonly used antibiotic. We present a case of severe hyponatremia and Type 4 renal tubular acidosis (functional hypoaldosteronism) in a patient treated with TMP-SMX. Clinical Case: A 62 year old gentleman with hypertension, dyslipidemia and a surgically repaired abdominal aortic aneurysm developed an aortic graft infection. He was admitted to hospital for acute right lower limb ischemia with embolic phenomena, and underwent surgical graft explantation. He required multiple courses of antibiotics post operatively. He was initially referred to Endocrinology for severe hyponatremia, deemed likely to be from a salt losing nephropathy secondary to polymyxin. Thyroid function and morning cortisol levels were normal. He was managed with intravenous hypertonic saline and oral salt tablets. The hyponatraemia resolved a week after polymyxin was stopped. Intravenous TMP-SMX was commenced the next day at 240 mg BD. A week later, the hyponatremia recurred, with concomitant hyperkalemia and a normal anion gap metabolic acidosis. The serum sodium was 126 mmol/L (reference interval (RI) 135-145) and the serum osmolality 275 mmol/kg (RI 275- 305). Urine studies showed a high urinary sodium (154 mmol/L) and osmolality (481 mmol/kg), consistent with renal salt wasting. The serum potassium rose to a peak of 6.1 mmol/L (RI 3.5 - 5.0), with a normal anion gap metabolic acidosis (bicarbonate 17 mmol/L (RI 21 – 31)). A paired urine pH of 8 pointed to an inability to acidify the urine. Given the clinical course and laboratory investigations, the diagnosis of TMP-associated hyponatremia and Type 4 RTA was made. Oral resonium was started to correct hyperkalemia, with a combination of oral sodium chloride and sodium bicarbonate used to treat the hyponatremia and metabolic acidosis. Fludrocortisone was not used given the concerns of causing hypertension in a patient with a diseased aortic graft. The dose of TMP-SMX was gradually reduced with improvement of the acid-base and electrolyte abnormalities, lending weight to our diagnosis. After the dose of the TMP-SMX was reduced to 80 mg BD, the hyperkalemia and metabolic acidosis resolved. The oral sodium chloride and sodium bicarbonate were gradually tailed off and stopped after cessation of the TMP-SMX. Clinical Lesson: Trimethoprim blocks the epithelial sodium channel (ENaC) of the principal cells in the terminal portion of the nephron, similar to potassium sparing diuretics like amiloride and triampterene. The resulting hyponatremia, hyperkalemia and metabolic acidosis can be life threatening. Therefore, monitoring of electrolytes and acid base status is important, particularly in susceptible patients or in those where a high dose of trimethoprim is required.

In this paper, we propose a hand tracking method which was inspired by the notion of the four dukkha: birth, aging, sickness and death (BASD) in Buddhism. Based on this philosophy, we formalize the hand tracking problem in the BASD framework, and apply it to hand track hand gestures in isolated sign language videos. The proposed BASD method is a novel nature-inspired computational intelligence method which is able to handle complex real-world tracking problem. The proposed BASD framework operates in a manner similar to a standard state-space model, but maintains multiple hypotheses and integrates hypothesis update and propagation mechanisms that resemble the effect of BASD. The survival of the hypothesis relies upon the strength, aging and sickness of existing hypotheses, and new hypotheses are birthed by the fittest pairs of parent hypotheses. These properties resolve the sample impoverishment problem of the particle filter. The estimated hand trajectories show promising results for the American sign language.

This study examines the cultural elements in Teaching Chinese as a Second Language (TCSL) based on six high-level of National Primary School’s Chinese Language (BCSK) textbooks, which are textbooks of BCSK KBSR (Primary School Integrated Curriculum) Standard four to Standard six and textbooks of BCSK KSSR (Primary School Standard Curriculum) Standard four to Standard six in order to investigate the changes of cultural elements content and describe the features of cultural elements in this two sets of high-level BCSK textbooks. Researcher has used library research methods and information technology methods to collect data and then use quantitative and qualitative analysis methods to calculate cultural elements in high-level BCSK textbooks by referenced to the criteria of classification of cultural elements which designed by researcher based on previous studies as well as the references of TCSL experts. The results showed that (1) high level BCSK KSSR textbooks are found that had more cultural elements for the total number of 622 compared to 522 in high-level BCSK KBSR textbook with the increasing of 19.16% and (2) high-level BCSK KSSR textbooks can be said to be more ideal to fostering communicative competence as the cultural elements in this set of BCSK textbooks are more geared towards communicative cultural elements with a record rate of 46.30% compared to 39.27% only in high-level BCSK KBSR textbooks.

Hand gesture recognition (HGR) serves as a fundamental way of communication and interaction for human being. While HGR can be applied in human computer interaction (HCI) to facilitate user interaction, it can also be utilized for bridging the language barrier. For instance, HGR can be utilized to recognize sign language, which is a visual language represented by hand gestures and used by the deaf and mute all over the world as a primary way of communication. Hand-crafted approach for vision-based HGR typically involves multiple stages of specialized processing, such as hand-crafted feature extraction methods, which are usually designed to deal with particular challenges specifically. Hence, the effectiveness of the system and its ability to deal with varied challenges across multiple datasets are heavily reliant on the methods being utilized. In contrast, deep learning approach such as convolutional neural network (CNN), adapts to varied challenges via supervised learning. However, attaining satisfactory generalization on unseen data is not only dependent on the architecture of the CNN, but also dependent on the quantity and variety of the training data. Therefore, a customized network architecture dubbed as enhanced densely connected convolutional neural network (EDenseNet) is proposed for vision-based hand gesture recognition. The modified transition layer in EDenseNet further strengthens feature propagation, by utilizing bottleneck layer to propagate the features being reused to all the feature maps in a bottleneck manner, and the following Conv layer smooths out the unwanted features. Differences between EDenseNet and DenseNet are discerned, and its performance gains are scrutinized in the ablation study. Furthermore, numerous data augmentation techniques are utilized to attenuate the effect of data scarcity, by increasing the quantity of training data, and enriching its variety to further improve generalization. Experiments have been carried out on multiple datasets, namely one NUS hand gesture dataset and two American Sign Language (ASL) datasets. The proposed EDenseNet obtains 98.50% average accuracy without augmented data, and 99.64% average accuracy with augmented data, outperforming other deep learning driven instances in both settings, with and without augmented data.

This article examines electoral malapportionment by illuminating the relationship between malapportionment level and democracy. Although a seminal study rejects this relationship, we argue that a logical and empirically significant relationship exists, which is curvilinear and is based on a framework focusing on incumbent politicians' incentives and the constraints they face regarding malapportionment. Malapportionment is lowest in established democracies and electoral authoritarian regimes with an overwhelmingly strong incumbent; it is relatively high in new democracies and authoritarian regimes with robust opposition forces. The seminal study's null finding is due to the mismatch between theoretical mechanisms and choice of democracy indices. Employing an original cross-national dataset, we conduct regression analyses; the results support our claims. Furthermore, on controlling the degree of democracy, the single-member district system's effects become insignificant. Australia, Belarus, the Gambia, Japan, Malaysia, Tunisia, and the United States illustrate the political logic underlying curvilinear relations at democracy's various levels.

Patients presenting with a painful swollen leg are not infrequently encountered at the emergency department and can pose a diagnostic dilemma for attending physicians. The potential causes of leg pain and swelling include trauma, infection, inflammation, and neurogenic, vascular, and iatrogenic conditions; with magnetic resonance imaging (MRI) being an important tool in evaluation. We describe the MRI features of various conditions causing painful swollen legs. We also discuss the differential diagnosis and the useful clinical and laboratory findings that radiologists should be aware of, in order to arrive at an accurate diagnosis.

Gait has unique advantage at a distance when other biometrics cannot be used since they are at too low resolution or obscured, as commonly observed in visual surveillance systems. This paper provides a survey of the technical advancements in vision-based gait recognition. A wide range of publications are discussed in this survey embracing different perspectives of the research in this area, including gait feature extraction, classification schemes and standard gait databases. There are two major groups of the state-of-the-art techniques in characterizing gait: Model-based and motion-free. The model-based approach obtains a set of body or motion parameters via human body or motion modeling. The model-free approach, on the other hand, derives a description of the motion without assuming any model. Each major category is further organized into several subcategories based on the nature of gait representation. In addition, some widely used classification schemes and benchmark databases for evaluating performance are also discussed.

The most common technology used in targeted advertising is facial recognition and vehicle recognition. Even though there are existing systems serving for the targeting purposes, most propose limited functionalities and the system performance is normally unknown. This paper presents an intelligent targeted advertising system with multiple functionalities, namely facial recognition for gender and age, vehicle recognition, and multiple object detection. The main purpose is to improve the effectiveness of outdoor advertising through biometrics approaches and machine learning technology. Machine learning algorithms are implemented for higher recognition accuracy and hence achieved better targeted advertising effect.

Algebraic Normal Form (ANF) and Conjunctive Normal Form (CNF) are commonly used to encode problems in Boolean algebra. ANFs are typically solved via Gr"obner basis algorithms, often using more memory than is feasible; while CNFs are solved using SAT solvers, which cannot exploit the algebra of polynomials naturally. We propose a paradigm that bridges between ANF and CNF solving techniques: the techniques are applied in an iterative manner to emph{learn facts} to augment the original problems. Experiments on over 1,100 benchmarks arising from four different applications domains demonstrate that learnt facts can significantly improve runtime and enable more benchmarks to be solved., To Appear in Proceedings of DATE 2019

A normalized histogram of optical flow as a hand representation of the sign language.Block-based histogram provides spatial information and local translation invariant.Block-based histogram of optical flow enables sign language length invariance. In this paper, we propose a block-based histogram of optical flow (BHOF) to generate hand representation in sign language recognition. Optical flow of the sign language video is computed in a region centered around the location of the detected hand position. The hand patches of optical flow are segmented into M spatial blocks, where each block is a cuboid of a segment of a frame across the entire sign gesture video. The histogram of each block is then computed and normalized by its sum. The feature vector of all blocks are then concatenated as the BHOF sign gesture representation. The proposed method provides a compact scale-invariant representation of the sign language. Furthermore, block-based histogram encodes spatial information and provides local translation invariance in the extracted optical flow. Additionally, the proposed BHOF also introduces sign language length invariancy into its representation, and thereby, produce promising recognition rate in signer independent problems.

A fusion of median and mode filtering for better background model.A serial particle filter that can better detect and track the object of interest.A novel covariance matrix feature for isolated sign language representation. As is widely recognized, sign language recognition is a very challenging visual recognition problem. In this paper, we propose a feature covariance matrix based serial particle filter for isolated sign language recognition. At the preprocessing stage, the fusion of the median and mode filters is employed to extract the foreground and thereby enhances hand detection. We propose to serially track the hands of the signer, as opposed to tracking both hands at the same time, to reduce the misdirection of target objects. Subsequently, the region around the tracked hands is extracted to generate the feature covariance matrix as a compact representation of the tracked hand gesture, and thereby reduce the dimensionality of the features. In addition, the proposed feature covariance matrix is able to adapt to new signs due to its ability to integrate multiple correlated features in a natural way, without any retraining process. The experimental results show that the hand trajectories as obtained through the proposed serial hand tracking are closer to the ground truth. The sign gesture recognition based on the proposed methods yields a 87.33% recognition rate for the American Sign Language. The proposed hand tracking and feature extraction methodology is an important milestone in the development of expert systems designed for sign language recognition, such as automated sign language translation systems.

Many longitudinal studies have collected serial body core temperature (T c) data to understand thermal work strain of workers under various environmental and operational heat stress environments. This provides the opportunity for the development of mathematical models to analyse and forecast temporal T c changes across populations of subjects. Such models can reduce the need for invasive methods that continuously measure T c. This current work sought to develop a nonlinear mixed effects modelling framework to delineate the dynamic changes of T c and its association with a set of covariates of interest (e.g. heart rate, chest skin temperature), and the structure of the variability of T c in various longitudinal studies. Data to train and evaluate the model were derived from two laboratory investigations involving male soldiers who participated in either a 12 (N = 18) or 15 km (N = 16) foot march with varied clothing, load and heat acclimatisation status. Model qualification was conducted using nonparametric bootstrap and cross validation procedures. For cross validation, the trajectory of a new subject's T c was simulated via Bayesian maximum a posteriori estimation when using only the baseline T c or using the baseline T c as well as measured T c at the end of every work (march) phase. The final model described T c versus time profiles using a parametric function with its main parameters modelled as a sigmoid hyperbolic function of the load and/or chest skin temperature. Overall, T c predictions corresponded well with the measured data (root mean square deviation: 0.16 °C), and compared favourably with those provided by two recently published Kalman filter models.

In this paper, we present a gait recognition method using convolutional features and histograms of temporal gradients. The method comprises three stages, namely the convolutional stage, temporal gradient stage and classification stage. In the convolutional stage, the video frames are convolved with a set of pre-learned filters. This is followed by the temporal gradient stage. In this stage, the gradient of each convolved frame in time axis is calculated. Unlike histograms of oriented gradients that accumulate the gradients in the spatial domain, the proposed histogram of temporal gradients encodes the gradients in the spatial and temporal domain. The histogram of temporal gradients captures the gradient patterns of every pixel over the temporal axis throughout the video sequence. By doing so, the feature encodes both spatial and temporal information in the gait cycle. Finally, in the classification stage, a majority voting classification with Euclidean distance is performed for gait recognition. Experimental results show that the proposed method outperforms the state-of-the-art methods.

The magnetic fields produced by an electromagnet depends on the amount of current and voltage. A single current source can produce multi-field magnetic fields, the position of the solenoid connected to the electric current source, where there are multi connectors that can be switched based on the composition of the helical coil and from the data obtained that this can proved. This research was carried out in a helical and design of copper plates forming solenoid windings, each part of the partition plate numbered 10, 15, 20, and 30 connected to electric currents of 1.5, 3, and 4.5 Ampere and the switch functioned to flow electric current to the partition based on the number of electromagnet plates. The experimental results obtained that the number of plates can produce multi magnetic field with a single current source with a range 0 to 8 mT which is measured using Gaussmeter. The benefits of this research will have impact on physical and engineering research related to the use of electromagnets that are portable and concise but with a single electric current source with multi magnetic fields, besides the next research has the potential to be developed in order to produce high magnetic fields.

This paper presents an Android application aimed to ease the task of Chief Invigilators and fellow invigilators during an examination conduct in a university environment. The Chief Invigilator delegates tasks to fellow invigilators at the examination venue before the start of the examination including collecting attendance slips and monitoring for candidates' misconducts. After the examination ends, the invigilators need to ensure all answer scripts are submitted by the attendees of the examination. The Anrid application provides functionalities to facilitate task delegation among invigilators and improve efficiency of post examination tasks such as ensuring correct number of answer scripts are collected based on the right number of candidates.

Visual representation is crucial for a visual tracking method's performances. Conventionally, visual representations adopted in visual tracking rely on hand-crafted computer vision descriptors. These descriptors were developed generically without considering tracking-specific information. In this paper, we propose to learn complex-valued invariant representations from tracked sequential image patches, via strong temporal slowness constraint and stacked convolutional autoencoders. The deep slow local representations are learned offline on unlabeled data and transferred to the observational model of our proposed tracker. The proposed observational model retains old training samples to alleviate drift, and collect negative samples which are coherent with target's motion pattern for better discriminative tracking. With the learned representation and online training samples, a logistic regression classifier is adopted to distinguish target from background, and retrained online to adapt to appearance changes. Subsequently, the observational model is integrated into a particle filter framework to peform visual tracking. Experimental results on various challenging benchmark sequences demonstrate that the proposed tracker performs favourably against several state-of-the-art trackers., Pattern Recognition (Elsevier), 2015

This paper presents a gait recognition method using the combination of motion history image (MHI), binarized statistical image features (BSIF) and histograms of oriented gradients (HOG). The method first encodes the motion pattern and direction of the gait cycle in motion history image. Subsequently, performing convolution on the motion history image using pre-learnt filters as kernel, binarized statistical image features are generated by summing the convolution output images. Histograms of oriented gradients are then computed on binarized statistical image features. Gait signature of a gait cycle is attained by accumulating all the HOG descriptors. Experimental result shows that the proposed method performs promisingly in gait recognition.

This chapter explains the reasons behind classification of elections in Malaysia by examining the following six areas: the election system, election laws and regulations, campaign environment, campaign strategies of major political parties, possible changes in campaign style and possible future changes. There are many laws and regulations that have been enacted to ensure a clean and honest electoral campaign including restrictions on bribery, treating, undue influence and election expenses. Malaysia is unique among the countries in Southeast Asia. Malaysia has held eleven post independence general elections. Malaysia inherited the first-past-the-post (FPTP) single member constituencies (SMC) system from its colonial masters, the British. For historical reasons, general elections in Malaysia occur for all parliament seats in Peninsular Malaysia, Sabah and Sarawak and also for the state seats in Peninsular Malaysia. The ethnic composition of the new seats added and its effect on the ethnic composition of existing seats can determine the distribution of electoral power among different ethnic groups in Malaysia.