Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Student Understanding of Programming Concepts: The Case of a Scenario Based Approach to Teaching Programming Online

Higher education institutions have adopted the online, blended, as well as the student-centered teaching pedagogies in recent years. This study investigated how to improve the teaching of a programming module online, by introducing an intervention. An exploratory study was conducted, where a discussion forum was used to engage with undergraduate students registered for a programming module. The students were requested to list the programming aspects that they struggled to understand on the module. Custom study content was developed to focus and expand on the topics suggested by students. The compiled study content was in the form of scenario-based discussions of each concept. This research followed a qualitative case study with observations and content analysis methods to collect and analyse the data. The intervention showed some merit because the student pass rate significantly improved after the implementation of the intervention.
Data Analysis and Knowledge Discovery in Web Recruitment—Based on Big Data Related Jobs

This paper mines and analyzes the post information of the online recruitment data, and discovers the knowledge in large-scale web data, so as to achieve the precise connection between professional job demand and supply. First, internet crawler technology is adopted to acquire data. Second, the authors digitalize polymorphic data and conduct Chinese word segmentation, stop word filtering and other operations on data records. Third, the cosine similarity is used to measure the similarity of the vector, and the K-means++ is used for post clustering. Then, latent dirichlet allocation and apriori are used for post correlation analysis. Last, the authors use autoen-coder to achieve job matching recommendation.
Explainable Image Caption Generator Using Attention and Bayesian Inference

Image captioning is the task of generating textual descriptions of a given image, requiring techniques of computer vision and natural language processing. Recent models have utilized deep learning techniques to this task to gain performance improvement. However, these models can neither distinguish more important objects than others in a given image, nor explain the reasons why specific words have been selected when generating captions. To overcome these limitations, this paper proposes an explainable image captioning model, which generates a caption by indicating specific objects in a given image and providing visual explanation using them. The model has been evaluated with datasets such as MSCOCO, Flickr8K, and Flickr30K, and some qualitative results are presented to show the effectiveness of the proposed model.
Evaluating Compiler IR-Level Selective Instruction Duplication with Realistic Hardware Errors

Hardware faults (i.e., soft errors) are projected to increase in modern HPC systems. The faults often lead to error propagation in programs and result in silent data corruptions (SDCs), seriously compromising system reliability. Selective instruction duplication, a widely used software-based error detector, has been shown to be effective in detecting SDCs with low performance overhead. In the past, researchers have relied on compiler intermediate representation (IR) for program reliability analysis and code transformation in selective instruction duplication. However, they assumed that the IR-based analysis and protection are representative under realistic fault models (i.e., faults originated at lower hardware layers). Unfortunately, the assumptions have not been fully validated, leading to questions about the accuracy and efficiency of the protection since IR is a higher level of abstraction and far away from hardware layers. In this paper, we verify the assumption by injecting realistic hardware faults to programs that are guided and protected by IR-based selective instruction duplication. We find that the protection yields high SDC coverage with low performance overhead even under realistic fault models, albeit a small amount of such faults escaping the detector. Our observations confirm that IR-based selective instruction duplication is a cost-effective method to protect programs from soft errors.
Explaining Machine Learning-Based Classifications of In-Vivo Gastral Images

This paper proposes an explainable machine learning tool that can potentially be used for decision support in medical image analysis scenarios. For a decision-support system it is important to be able to reverse-engineer the impact of features on the final decision outcome. In the medical domain, such functionality is typically required to allow applying machine learning to clinical decision making. In this paper, we present initial experiments that have been performed on in-vivo gastral images obtained from capsule endoscopy. Quantitative analysis has been performed to evaluate the utility of the proposed method. Convolutional neural networks have been used for training the validating of the image data set to provide the bleeding classifications. The visual explanations have been provided in the images to help health professionals trust the black box predictions. While the paper focuses on the in-vivo gastral image use case, most findings are generalizable.
Hyperspectral Image Analysis for Writer Identification using Deep Learning

Handwriting is a behavioral characteristic of human beings that is one of the common idiosyncrasies utilized for litigation purposes. Writer identification is commonly used for forensic examination of questioned and specimen documents. Recent advancements in imaging and machine learning technologies have empowered the development of automated, intelligent and robust writer identification methods. Most of the existing methods based on human defined features and color imaging have limited performance in terms of accuracy and robustness. However, rich spectral information content obtained from hyperspectral imaging (HSI) and suitable spatio-spectral features extracted using deep learning can significantly enhance the performance of writer identification in terms of accuracy and robustness. In this paper, we propose a novel writer identification method in which spectral responses of text pixels in a hyperspectral document image are extracted and are fed to a Convolutional Neural Network (CNN) for writer classification. Different CNN architectures, hyperparameters, spatio-spectral formats, train-test ratios and inks are used to evaluate the performance of the proposed system on the UWA Writing Inks Hyperspectral Images (WIHSI) database and to select the most suitable set of parameters for writer identification. The findings of this work have opened a new arena in forensic document analysis for writer identification using HSI and deep learning.
Identifying Influential Users on Twitter's Trendy Hashtags Using Association Rule Learning

Online Social Networks invaded every home in the world and thus the analysis of such networks became a challenging case of study. Identifying influential users on online social networks is one of the most interesting fields of study in social network analysis. These users might have impact on other users on a network yielding a change in the whole network graph. This paper studies the problem of identifying influential users on some trendy hashtags on Twitter. In order to identify these influential users, data reflecting real tweets from the top trendy hashtags on Twitter have been collected between December 2015 and March 2016. In this paper, Association Rule Learning has been employed to identify influential users in these trendy hashtags. Moreover, these identified influential users were ranked based on different Influence Measures in order to study the effect of these measures on studying the evolution of the hashtags. The results of this study indicate the effectiveness of identifying influential users using Association Rule Learning and identifying the most effective Influence Measures.
OriNet: Robust 3-D Orientation Estimation With a Single Particular IMU

Estimating the robot's heading is a crucial requirement in odometry systems which are attempting to estimate the movement trajectory of a robot. Small errors in the orientation estimation result in a significant difference between the estimated and real trajectory, and failure of the odometry system. The odometry problem becomes much more complicated for micro flying robots since they cannot carry massive sensors. In this manner, they should benefit from the small size and low-cost sensors, such as IMU, to solve the odometry problem, and industries always look for such solutions. However, IMU suffers from bias and measurement noise, which makes the problem of position and orientation estimation challenging to be solved by a single IMU. While there are numerous studies on the fusion of IMU with other sensors, this study illustrates the power of the first deep learning framework for estimating the full 3D orientation of the flying robots (as yaw, pitch, and roll in quaternion coordinates) accurately with the presence of a single IMU. A particular IMU should be utilized during the training and testing of the proposed system. Besides, a method based on the Genetic Algorithm is introduced to measure the IMU bias in each execution. The results show that the proposed method improved the flying robots’ ability to estimate their orientation displacement by approximately 80% with the presence of a single particular IMU. The proposed approach also outperforms existing solutions that utilize a monocular camera and IMU simultaneously by approximately 30%.
Using OFDMA for MU-MIMO User Selection in 802.11ax-Based Wi-Fi Networks

Two innovative multi-user (MU) communication mechanisms, multi-user multi-input multi-output (MU-MIMO) and orthogonal frequency division multiple access (OFDMA), have recently attracted much attention as key technologies to boost the capabilities of 802.11 Wi-Fi systems. The method of MU transmission in Wi-Fi systems still suffers from severe problems with channel state information (CSI) feedback overhead, however, and this precludes obtaining much higher MU-MIMO gains through user selection. Although in 802.11ax-based Wi-Fi systems the methods used for uplink OFDMA transmission and downlink MU-MIMO transmission bear a great deal of resemblance to each other, in the sense that both are initiated by the AP, this similarity in protocol is not appropriately utilized for close collaboration between the two technologies. This paper proposes MUSE, a novel multi-user transmission scheme for 802.11ax networks. By exploiting uplink OFDMA transmissions for MU-MIMO user selection, MUSE takes advantage of both OFDMA and MU-MIMO functionalities. In particular, the AP estimates and gathers multiple CSI values from the uplink OFDMA frame, and this channel information is then used to find the optimal downlink MU-MIMO receiver group to maximize the system utility. To realize MUSE, a new OFDMA resource allocation algorithm and frame structure are developed. Extensive MATLAB simulation results show that MUSE significantly improves the network throughput, even in dense network scenarios, and also works effectively in co-existence with legacy nodes.
A New Assessment Method of the Pilot Stress Using ECG Signals During Complex Special Flight Operation

With the continuous improvement in the man-machine-designed fighter aircraft, the pilot training intensity has increased. Complex special operation of the fighter aircraft is greatly affected by flight conditions and human control reliability, which imposes a great burden on the pilot psychophysiologically. To understand the intensity of the stress on the pilot, which is a crucial parameter, accurate evaluation of the changes in the stress intensity of the pilot during the flight is very important. This study aimed to analyze the stress intensity of pilots by collecting electrocardiogram (ECG) signals during the long flight involving 11 pilot trainees. First, an improved soft-threshold denoising method for the ECG data and an improved differential threshold method were used to process the first-order difference and the threshold value to the filtered signal to determine the R wave. Then, the multiscale analysis method was used to fuse the area of the heart rate curve of the pilots. The method of flight training and the division of the stress stage were derived. Further, the functional relationship of the stress intensity with the frequency of training was constructed. All these effectively improved the performance of the flight operation training and helped make a reasonable training plan. This was of great practical significance to the measurement and evaluation of the stress state of the individuals in dangerous jobs, such as special flight operations, high-altitude skydiving, bungee jumping, etc.
Registration Based Data Augmentation for Multiple Sclerosis Lesion Segmentation

Deep learning has shown outstanding performance on various computer vision tasks such as image segmentation. To take advantage of deep learning in image segmentation, one would need a huge amount of annotated data since deep learning models are data-intensive. One of the main challenges of using deep learning methods in the medical domain is the shortage of available annotated data. To tackle this problem, in this paper, we propose a registration based framework for augmenting multiple sclerosis datasets. In this framework, by registering images of two different patients, we create a new image, which smoothly adds lesions from the first patient into a brain image, structured like the second patient. Due to their nature, multiple sclerosis lesions vary in shape, size, location and number of occurrence, thus registering images of two different subjects, will create a realistic image. The proposed method is capable of introducing diversity to data distribution, which other traditional augmentation methods do not offer. To check the effectiveness of our proposed method, we compare the performance of 3D-Unet on different augmented and non-augmented datasets. Experimental results indicate that the best performance is achieved when combining both the proposed method with traditional augmentation techniques.
Auction Game Based Phone-to-Phone Electricity Trading via Wireless Energy Transfer

Smartphone has become an indispensable part of our life; however, due to the limited battery life and higher power consumption of emerging interactive multimedia applications, charging has become a disturbing question. Compared with carrying a portable mobile power supply or finding sockets, energy sharing among smartphones via wireless charging can be a more convenient way to address this issue. To encourage the energy sharing market, this paper proposes an auction game based peer-to-peer electricity trading model for smartphones via wireless energy trading to prolong the smartphone lifetime. The problem of electricity pricing and the distribution of electricity among smartphones are solved by an iterative double auction mechanism. Each buyer and seller bid prices and the controller iteratively implement a distributed energy allocation algorithm to maximize the social welfare as well as the individual’s interest. Numerical results indicate that that the double auction mechanism can guarantee the rationality of pricing and energy allocation, and maximize the satisfaction.
Compact Four-Element MIMO Antenna System for 5G Laptops

This paper presents a compact dual-band four-element multiple-input multiple-output (MIMO) antenna system for the fifth-generation (5G) laptops with a large display-to-body ratio. The four antenna elements, each being a three-dimensional inverted-F antenna (IFA) of the same structure and size, are constructed on the same T-cross-sectioned substrate, the vertical part of which is embedded in the $1\times64$ mm2 clearance area created from the upper edge of the display ground plane. They are configured into two dual-antenna units. Within each unit, the two IFAs are arranged to be skew-symmetric with respect to the display ground plane, and their short-circuit points are brought close to each other to achieve the so-called short-circuit decoupling. In addition, the two units are mirror imaged of each other with a small gap in between and are connected by two decoupling chip inductors to form a high-isolation four-element MIMO antenna system. In the operating bands of 3300–3600 and 4800–5000 MHz required by 5G, the measured isolation is larger than 10 dB, and the measured antenna efficiency is beyond 35 %, both being large enough for practical applications. In particular, the proposed MIMO antenna system has a profile height of only 1.4 mm, which is very small, making it suitable for laptops with a large display-to-body ratio.
Indexing and Search of Order-Preserving Submatrix for Gene Expression Data

Bicluster pattern discovery plays a key role in analysis of gene expression data. One vital model of bicluster mining is Order-Preserving SubMatrix (OPSM), which finds similar tendency of some genes on some conditions. Most of the OPSM discovery methods are batch mining techniques and not suitable for low latency data query. To make data analysis efficient and effective, in this paper, we first propose a prefix-tree based indexing method pfTree, then give an optimization technique pIndex that employs row and column header tables to search the positive, negative and time-delayed OPSMs. Meanwhile, we present an online sharing query technique to accelerate the frequent searches. Finally, we conduct extensive experiments and compare our methods with the existing approaches. Experimental results demonstrate the efficiency and effectiveness of the proposed methods.
Adversarial Text Generation for Google's Perspective API

With the preponderance of harassment and abuse, social media platforms and online discussion platforms seek to curb toxic comments. Google's Perspective aims to help platforms classify toxic comments. We have created a pipeline to modify toxic comments to evade Perspective. This pipeline uses existing adversarial machine learning attacks to find the optimal perturbation which will evade the model. Since these attacks typically target images, as opposed to discrete text data, we include a process to generate text candidates from perturbed features and select candidates to retain syntactic similarity. We demonstrated that using a model with just 10,000 queries, changing three words in each comment evades Perspective 25% of the time, suggesting that building a surrogate model may not require many queries and a more robust approach is needed to improve the toxic comment classifier accuracy.
Seven-Point Checklist with Convolutional Neural Networks for Melanoma Diagnosis

Reliable skin lesion detection is an important pre-requisite for melanoma and other skin diseases diagnosis. Existing melanoma assessment models consider either pattern analysis methods or seven-point checklist criteria to investigate skin lesion. However, automatic and accurate detection of the skin lesion and subsequently melanoma diagnosis remain an unresolved challenge. Furthermore, there is limitations in both approaches and a trade-off between the two assessment strategies. This paper proposes a pattern analysis method incorporated with seven-point checklist exploiting convolutional neural network for melanoma diagnosis where the lesion features are extracted automatically. The benefit of features learned automatically from the dermoscopic images through the stacked layers of convolution filters have been designed, realised and evaluated. Both clinical and dermoscopic images have been considered as input to the developed multiple-input convolutional neural networks (CNNs) where a separate feature extraction model is implemented for each image type. The features produced from both models are concatenated for interpretation and final lesion type prediction. The sum of the weight for predicated lesions which are calculated according seven-points check list criteria are then passed into a threshold model to decide whether the image normal or abnormal (melanoma or non-melanoma). Performance of the developed algorithm is assessed using a dataset of 2000 dermoscopic images. The initial results obtained from the proposed system show a convincing and promising ability for lesion detection and automated melanoma diagnosis from dermoscopy images.
Transmit Antenna Selection for Spatial Modulation OFDM Systems

Transmit antenna selection (TAS) is one of the effective techniques to improve the diversity of the spatial modulation (SM) system. In this paper, first, we considered the development of TAS algorithm based on the optimal Euclidean distance (EDAS) criterion in SM when orthogonal frequency division multiplex (OFDM) is also combined. Furthermore, a low-complexity TAS algorithm based on the idea of decremental antenna selection (D-AS) criterion is proposed. Compared to the optimal ED criterion, we show that the proposed low-complexity algorithm can achieve beneficial tradeoff between the performance and complexity in the SM-OFDM system.
DeepTLE: Learning Code-Level Features to Predict Code Performance before It Runs

With the continuous expansion of the software market and the updating of the maturity of the software development process, the performance requirements of software users are becoming increasingly prominent. Performance issues are essentially related to the source code. For solving the same problem, different programmers may write completely different "correct" code with the same functionality but have different performance. Most online judge system on programming make use of automated grading systems, usually rely on test results to quantify the correctness and performance for the submitted source code. However, traditional dynamic testing takes a lot of time, and the discovery of performance problems is usually after the fact even for those small scale programs. Therefore, we proposed DeepTLE which is used to effectively predict the performance of submitted source code before it runs. DeepTLE can automatically learn the semantic and structural features of the source code. In order to verify the effect of our approach, we applied it to the source code collected from the program competition website to predict if the source code would be time limit exceed or not without running its test cases. Experiment results show that our method can save 96% of the time cost compared to the dynamic testing, and the accuracy of the prediction reaches 82%.
Delay Minimization for Secure NOMA Mobile-Edge Computing

This paper considers an uplink non-orthogonal multiple access (NOMA)-based mobile-edge computing (MEC) network, in the presence of a malicious eavesdropper. The studied model, we investigate the task completion time minimization problem is studied while the secrecy rate is guaranteed. The aim is to minimize the maximum task latency for the uplink NOMA users, subject to the worst-case secrecy rate, the transmit power and secrecy outage probability constraints. Due to the non-convexity of the formulated problem, we cannot solve it directly. By analyzing the attributes of the problem, some significant insights are revealed to simplify the original formulated problem. Based on this result, a bisection searching (BSS) based algorithm is proposed to find the optimal solution of the transformed problem. Finally, numerical results are provided to demonstrate the effectiveness of our proposed scheme.