Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Closed-loop Learning Analytics Scheme in a First Year Experience Course

Identifying non-thriving students and intervening to boost them are two processes that recent literature suggests should be more tightly integrated. We perform this integration over six semesters in a First Year Experience (FYE) course with the aim of boosting student success, by using an integrated closed-loop learning analytics scheme that consists of multiple steps broken into three main phases, as follows: Architecting for Collection (steps: design, build, capture), Analyzing for Action (steps: identify, notify, boost), and Assessing for Improvement (steps: evaluate, report). We close the loop by allowing later steps to inform earlier ones in real-time during a semester and iteratively year to year, thereby improving the course from data-driven insights. This process depends on the purposeful design of an integrated learning environment that facilitates data collection, storage, and analysis. Methods for evaluating the effectiveness of our analytics-based student interventions show that our criterion for identifying non-thriving students was satisfactory and that non-thriving students demonstrated more substantial changes from mid-term to final course grades than already-thriving students. Lastly, we make a case for using early performance in the FYE as an indicator of overall performance and retention of first-year students.
A Class of In-Place Linear Transformations Possessing the Cache-Oblivious Property

In-place linear transformations allow input to be overwritten with the output of the transformation. This paper presents a family of in-place linear transformations based on block lower/upper (LU) decompositions, of which the known transformation via LU decomposition is a special case, and shows that the proposed family includes a class of transformations that possesses the cache-oblivious property. Furthermore, the number of cache misses and approaches for non-square matrices is discussed. The simulation shows that the proposed in-place linear transformation is faster than the existing transformation via LU decompositions.
Early Detection of Kidney Disease Using ECG Signals Through Machine Learning Based Modelling

This paper introduces the idea of detecting the presence of kidney disease through machine learning based classification modelling, by processing the patient’s ECG signal. Recent studies and ongoing researches have showed that patients undergoing kidney problems start developing cardiac problems- scientifically known as the Cardio Renal Syndrome (CRS) which can lead to a sudden cardiac arrest in the last stages of their disease. Since cardio-vascular diseases and the chronic kidney disease is inter-related, this model can be used for patients undergoing cardio-vascular problems to determine whether their kidneys have been effected or not. If the Chronic Kidney Disease (CKD) can be diagnosed at an earlier stage, it may give the patient some time to help reverse the disease or at least slow its progression by taking necessary medical steps. For this model, digitized ECG data was collected from open access databases such as PTB (for kidney patients) and Fantasia (for healthy people) from Physionet Database (www.physionet.org) and the model was later validated using different data from the same online database. The validation process gave satisfactory results, as the model could successfully classify the users from being healthy or a kidney patient. In our study, we found an accuracy level of 97.6% which was the highest using both features QT and RR interval, in comparison to the accuracy that was found when either one of the features was used.
Estimating Sea State Using a Low Cost Buoy

Wave buoys are widely used to collect ocean data for sea state estimate. several applications such as forecast of weather conditions. To achieve that goal, the industry standard buoys are usually large and expansive as the CAMERI buoy, recently placed at the Haifa bay area. In this paper, we present a newly designed small-size, low-cost autonomous wave buoy equipped with inertial sensors and the ability to $\log$ their measurements for offline sea state estimation. A comparison of the estimated sea-state conditions is made between the proposed low-cost compact buoy to the high-end CAMERI buoy using sea experiments conducted at the Haifa bay area. It is to shown that a low-cost buoy can serve as a local, short-term deployable data collection unit for various applications.
Solving Graph Problems via Potential Maximal Cliques

The BT algorithm of Bouchitté and Todinca based on enumerating potential maximal cliques, originally proposed for the treewidth and minimum fill-in problems, yields improved exact exponential-time algorithms for various graph optimization problems related to optimal triangulations. While the BT algorithm has received significant attention in terms of theoretical analysis, less attention has been paid on engineering efficient implementations of the algorithm for different problems and thereby on empirical studies on its effectiveness in practice. In this work, we provide an experimental evaluation of an implementation of the BT algorithm, based on our second-place winning entry in the 2nd Parameterized Algorithms and Computational Experiments Challenge (PACE 2017), extended to several related graph problems: treewidth, minimum fill-in, generalized and fractional hypertreewidth, and the total table size problem. Instead of focusing on problem-specific optimization of BT for a particular problem, our focus in this work is on studying the applicability of BT more generally to a range of problems. Based on the results, we conclude that an efficient implementation of the BT algorithm yields an empirically competitive approach to each of the considered problems when compared to available implementations of alternative problem-specific algorithmic approaches.
Harnessing Machine Learning for interpersonal physical alignment

This work presents a novel way to determine interpersonal physical synchrony state by inspecting hands’ postures obtained from a unique 3D depth camera device named Leap-Motion Controller. Several ML methods are utilized such as SVM, shallow feed-forward ANN and XGBoot. We show that even a simple ANN can outperform XgBoost in simple classification tasks.
Domain Wall Motion Control for Racetrack Memory Applications

Increasing demand for large capacity data storage can only be fulfilled by hard disk drives (HDDs) and to some extent by solid-state drives (SSDs). However, HDDs are favorable in many applications, as they are approximately 5–10 times cheaper than SSDs. Attempts are being made to increase the capacity of HDDs by technologies such as heat-assisted magnetic recording and microwave assisted magnetic recording. However, increasing the capacity has been a slow process and there are limitations in achieving areal density above 10 Tbpsi. Thus, the introduction of new technologies is important for attaining high capacity. In this scenario, domain wall (DW) memory is a potential candidate, but there are still many unsolved issues. One of these is ensuring controlled and reliable motion of DWs along the nanowire. In this paper, we provide an overview of existing technologies and our attempts to control DW motion. Many methods of fabricating pinning centers have been proposed and demonstrated. These methods can mainly be categorized as 1) geometrical and 2) non-geometrical methods. In the first part, we review the geometrical approach to pin DWs. Later, we provide an overview of our approaches to create pinning centers using non-geometrical means. Non-geometrical approach provides more advantages as it provides a variety of choices to tailor the properties. In particular, this approach suits scalability.
SINGLE CHANNEL JOINT SPEECH DEREVERBERATION AND DENOISING USING DEEP PRIORS

Single channel speech de-reverberation and de-noising is a challenging problem, since directional information is not available in a single channel when compared to multi-channel approaches. Several deep neural network (DNN) based solutions have been proposed in the recent past to solve this problem. These solutions are sequential and de-reverberate the signal after denoising. Additionally these solutions have not utilized the maximum a posteriori (MAP) method which requires the knowledge of the prior. In this work a MAP method is proposed to solve the speech de-reverberation and de-noising problem jointly. A half quadratic splitting (HQS) method is used to solve the joint MAP problem in a DNN framework by splitting it into two minimization problems. The deep prior is modeled using a latent variable and obtained using an iterative method. The performance of the proposed method is illustrated using subjective and objective measures. Experiments on continuous speech recognition are also used to demonstrate the significance of this method.
Graph-Based Skill Acquisition For Reinforcement Learning

In machine learning, Reinforcement Learning (RL) is an important tool for creating intelligent agents that learn solely through experience. One particular subarea within the RL domain that has received great attention is how to define macro-actions, which are temporal abstractions composed of a sequence of primitive actions. This subarea, loosely called skill acquisition, has been under development for several years and has led to better results in a diversity of RL problems. Among the many skill acquisition approaches, graph-based methods have received considerable attention. This survey presents an overview of graph-based skill acquisition methods for RL. We cover a diversity of these approaches and discuss how they evolved throughout the years. Finally, we also discuss the current challenges and open issues in the area of graph-based skill acquisition for RL.
Constructing Concurrent Data Structures on FPGA with Channels

The performance of High-Level Synthesis (HLS) applications with irregular data structures is limited by its imperative programming paradigm like C/C++. In this paper, we show that constructing concurrent data structures with channels, a programming construct derived from CSP (communicating sequential processes) paradigm, is an effective approach to improve the performance of these applications. We evaluate concurrent data structure for FPGA by synthesizing a K-means clustering algorithm on the Intel HARP2 platform. A fully pipelined KMC processing element can be synthesized from OpenCL with the help of a SPSC (single-producer-single-consumer) queue and stack built from channels, achieving 15.2x speedup over a sequential baseline. The number of processing element can be scaled up by leveraging a MPMC (multiple-producer-multiple-consumer) stack with work distribution for dynamic load balance. Evaluation shows that an additional 3.5x speedup can be achieved when 4 processing element is instantiated. These results show that the concurrent data structure built with channels has great potential for improving the parallelism of HLS applications. We hope that our study will stimulate further research into the potential of channel-based HLS.
Hyperspectral Image Classification With Squeeze Multibias Network

A convolutional neural network (CNN) has recently demonstrated its outstanding capability for the classification of hyperspectral images (HSIs). Typical CNN-based methods usually adopt image patches as inputs to the network. However, a fixed-size image patch in HSI with complex spatial contexts may contain multiple ground objects of different classes, which will deteriorate the classification performance of the CNN. In addition, traditional convolutional layers adopted in the CNN have a huge amount of parameters needed to be tuned, which will cause high computational cost. To address the above-mentioned issues, a novel squeeze multibias network (SMBN) is proposed for HSI classification. Specifically, the proposed SMBN first introduces the multibias module (MBM), which incorporates multibias into the rectified linear unit layers. The MBM can decouple the feature maps of input patches into multiple response maps (corresponding to different ground objects) and adaptively select the meaningful maps for classification. Furthermore, the proposed SMBN replaces the traditional convolutional layer with a squeeze convolution module, which can greatly reduce the number of parameters in the network, thus saving the running time, while still maintaining high classification accuracy. Experimental results on three real HSIs demonstrate the superiority of the proposed SMBN method over several state-of-the-art classification approaches.
Human Muscle Progenitor Cells Overexpressing Neurotrophic Factors Improve Neuronal Regeneration in a Sciatic Nerve Injury Mouse Model

The peripheral nervous system has an intrinsic ability to regenerate after injury. However, this process is slow, incomplete, and often accompanied by disturbing motor and sensory consequences. Sciatic nerve injury (SNI), which is the most common model for studying peripheral nerve injury, is characterized by damage to both motor and sensory fibers. The main goal of this study is to examine the feasibility of administration of human muscle progenitor cells (hMPCs) overexpressing neurotrophic factor (NTF) genes, known to protect peripheral neurons and enhance axon regeneration and functional recovery, to ameliorate motoric and sensory deficits in SNI mouse model. To this end, hMPCs were isolated from a human muscle biopsy, and manipulated to ectopically express brain-derived neurotrophic factor (BDNF), glial-cell-line-derived neurotrophic factor (GDNF), vascular endothelial growth factor (VEGF), and insulin-like growth factor (IGF-1). These hMPC-NTF were transplanted into the gastrocnemius muscle of mice after SNI, and motor and sensory functions of the mice were assessed using the CatWalk XT system and the hot plate test. ELISA analysis showed that genetically manipulated hMPC-NTF express significant amounts of BDNF, GDNF, VEGF, or IGF-1. Transplantation of 3 × 106 hMPC-NTF was shown to improve motor function and gait pattern in mice following SNI surgery, as indicated by the CatWalk XT system 7 days post-surgery. Moreover, using the hot-plate test, performed 6 days after surgery, the treated mice showed less sensory deficits, indicating a palliative effect of the treatment. ELISA analysis following transplantation demonstrated increased NTF expression levels in the gastrocnemius muscle of the treated mice, reinforcing the hypothesis that the observed positive effect was due to the transplantation of the genetically manipulated hMPC-NTF. These results show that genetically modified hMPC can alleviate both motoric and sensory deficits of SNI. The use of hMPC-NTF demonstrates the feasibility of a treatment paradigm, which may lead to rapid, high-quality healing of damaged peripheral nerves due to administration of hMPC. Our approach suggests a possible clinical application for the treatment of peripheral nerve injury.
In Situ Study of Radiation Stability and Associated Conduction Mechanisms of Nb-Doped TiO2/p-Si Heterojunction Diode Under Swift Heavy Ion Irradiation

In situ current–voltage characteristics of Nb-doped TiO<sub>2</sub>/p-Si-based heterojunction diode have been studied under dense electronic excitations of 84-MeV Si<sup>6+</sup> ions. The diode parameters such as ideality factor (<inline-formula> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula>), barrier height (<inline-formula> <tex-math notation="LaTeX">$\phi _{B}$ </tex-math></inline-formula>), reverse saturation current (<inline-formula> <tex-math notation="LaTeX">${J}_{s}$ </tex-math></inline-formula>), and series resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{s}$ </tex-math></inline-formula>) are found to be a strong function of ion irradiation fluence. The observed anomalies of fluence dependence of barrier height and ideality factor are explained in terms of irradiation-induced created defects complexes, modification of interface states and structural properties of Nb-doped TiO<sub>2</sub> (NTO) layer. Several mechanisms such as barrier height inhomogeneity, donor defects-induced enchantment in n-NTO layer conductivity, and various current conduction mechanisms involved at different voltage ranges are discussed as a function of fluence with the help of constructed energy band diagram. Such in situ studies on n-NTO/p-Si heterojunction diode under radiation harsh environment are very appropriate for the better understanding of heterojunction interface properties and make it suitable for use in aerospace industry and nuclear reactors.
Comprehensive Analysis of Cardiogenic Vibrations for Automated Detection of Atrial Fibrillation Using Smartphone Mechanocardiograms

Atrial fibrillation (AFib) is the most common sustained heart arrhythmia and is characterized by irregular and excessively frequent ventricular contractions. Early diagnosis of AFib is a key step in the prevention of stroke and heart failure. In this paper, we present a comprehensive time–frequency pattern analysis approach for automated detection of AFib from smartphone-derived seismocardiography (SCG) and gyrocardiography (GCG) signals. We sought to assess the diagnostic performance of a smartphone mechanocardiogram (MCG) by considering joint SCG–GCG recordings from 435 subjects including 190 AFib and 245 sinus rhythm cases. A fully automated AFib detection algorithm consisting of various signal processing and multidisciplinary feature engineering techniques was developed and evaluated through a large set of cross-validation (CV) data including 300 (AFib = 150) cardiac patients. The trained model was further tested on an unseen set of recordings including 135 (AFib = 40) subjects considered as cross-database (CD). The experimental results showed accuracy, sensitivity, and specificity of approximately 97%, 99%, and 95% for the CV study and up to 95%, 93%, and 97% for the CD test, respectively. The F1 scores were 97% and 96% for the CV and CD, respectively. A positive predictive value of approximately 95% and 92% was obtained, respectively, for the validation and test sets suggesting high reproducibility and repeatability for mobile AFib detection. Moreover, the kappa coefficient of the method was 0.94 indicating a near-perfect agreement in rhythm classification between the smartphone algorithm and visual interpretation of telemetry recordings. The results support the feasibility of self-monitoring via easy-to-use and accessible MCGs.
A Method of Driver’s Eyes Closure and Yawning Detection for Drowsiness Analysis by Infrared Camera

A challenge of research in area of the driver drowsiness detection is to detect the drowsiness in low light condition. In this paper, we proposed a method to detect driver’s eyes closure and yawning for drowsiness analysis by infrared camera. This method consists of four steps, namely, face detection, eye detection, mouth detection, and eyes closure and yawning detection. 3,760 images were used to test the performance of the proposed method. The accuracy rate of eyes closure detection, and yawning detection were 98%, and 92.5%, respectively. The experimental resulted show that the proposed method performed effectively. The advantage of this work is that this method can detect eye closure and yawning in low light condition.
Adaptive Seabed Characterization With Hierarchical Bayesian Modeling of SAS Imagery

Seabed characterization has utility for numerous applications that seek to explore and interact with the seafloor, ranging from coastal habitat monitoring and subbottom profiling to man-made object detection. In this paper, we characterize seabeds based on the texture patterns within synthetic aperture SONAR (SAS) images constructed from high-frequency side-scan sonar. Features are measured from the SAS images (e.g., lacunarity, an established texture feature coding method, and a circularly shifted histogram of oriented gradients). Based on these SAS image features, we perform unsupervised clustering with a hierarchical Bayesian model, which creates categories of seabed textures. Our clustering algorithm is a new variant of the hierarchical Dirichlet process that is both adaptive to changes in seabeds and processes batches of SAS imagery in an online fashion to learn new seabed types as they are encountered. This allows observations to be clustered, as each batch is processed rather than only after all data have been collected. The model’s performance of seabed characterization by SAS image texture is demonstrated in the overall range and internal consistency of textures specific to each learned cluster with data across a variety of sites.
Dynamic contrast enhanced ultrasound imaging; The effect of imaging modes and parameter settings for a microvascular phantom

Dynamic contrast enhanced ultrasound (DCE-US) imaging has the potential to provide quantitative information which is sensitive to tumour perfusion, an indicator for tumour response to radiotherapy. To increase the reproducibility of time-intensity curve (TIC) characteristics, we are developing a 3 $D$ DCE-US imaging system. There are, however, many choices to be made in system design, such as whether to use plane wave (PWI) or focused imaging (FI), and the values to use for parameters such as focal depth (FD), F-number (F#), mechanical index (MI) and number of angles (NA) (for PWI). We evaluated the effect of such choices on TICs (we refer to time-amplitude curve (TAC) here), using a microvascular flow phantom containing $\sim 100, 000$ parallel microtubes, each $200\mu \text{m}$ in diameter. DCE-US 2D images were obtained using a Vantage (Verasonics Inc.) and a pulse-inversion algorithm. 800 frames were recorded at 10 Hz for PWI and FI. All measurements were repeated 3 times, injecting 0.4 ml of contrast agent (Sonozoid) and changing one parameter at a time, using the values: FD = 20, 40 mm; F# = 2, 4; MI = 0.11, 0.15, 0.25; NA = 3, 7, 11. For a large region of interest which included the periphery of the phantom, TACs were sharp and single-peaked for F2 but broader and double-peaked for F4, consistent with F4 averaging over a greater focal volume than F2. Choosing a smaller more central ROI reduced the effect but did not eliminate it completely. Placing the focus deeper than the center reduced the TAC amplitude due to attenuation but also resulted in a flatter TAC. PWI amplitude was greatest for 3 angles with evidence that this may be due to side lobe artefacts added to the contrast signal. TAC characteristics are thus expected to be highly sensitive to imaging parameters. This should be considered in longitudinal studies.