Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing Statistics from Private Data

In several domains, privacy presents a significant obstacle to scientific and analytic research, and limits the economic, social, health and scholastic benefits that could be derived from such research. These concerns stem from the need for privacy about personally identifiable information (PII), commercial intellectual property, and other types of information. For example, businesses, researchers, and policymakers may benefit by analyzing aggregate information about markets, but individual companies may not be willing to reveal information about risks, strategies, and weaknesses that could be exploited by competitors. Extracting valuable utility from the new “big data” economy demands new privacy technologies to overcome barriers that impede sensitive data from being aggregated and analyzed. Secure multiparty computation (MPC) is a collection of cryptographic technologies that can be used to effectively cope with some of these obstacles, and provide a new means of allowing researchers to coordinate and analyze sensitive data collections, obviating the need for data-owners to share the underlying data sets with other researchers or with each other. This paper outlines the findings that were made during interdisciplinary workshops that examined potential applications of MPC to data in the social and health sciences. The primary goals of this work are to describe the computational needs of these disciplines and to develop a specific roadmap for selecting efficient algorithms and protocols that can be used as a starting point for interdisciplinary projects between cryptographers and data scientists.
Perceived value of interactive digital textbook and adaptive learning: Implications on student learning effectiveness

Abstract The authors studied the perceived value of an interactive digital textbook LearnSmart (McGraw-Hill Education, New York, NY) and examined its implications on adaptive learning and student learning effectiveness. Constructionism and constructivism provided the theoretical foundation. Data were collected from an American public university over three semesters. Multiple regression analysis results show that LearnSmart improves students’ perceived competency and their satisfaction with LearnSmart, thus increasing their perceived value of using LearnSmart. In addition, perceived value of LearnSmart varies across different course delivery formats and devices, while perceived challenge has no influence on perceived value. Pedagogical implications and future research directions are discussed.
Fully automatic segmentation and measurement of the fetal femur

Ultrasound (US) images are necessary in obstetrics because they provide the most important clinical parameters for fetal health assessment during the second and third trimesters: head circumference, biparietal diameter, abdominal circumference and femur length. These fetometric indices are helpful for gestational age and fetal weight estimation; they are also helpful for obstetricians to diagnose fetal development abnormalities. However, these indices are obtained manually, which provokes high intra and interobserver variability and lack of repeatability. A fully automatic method to segment and measure femur’s length is presented in this paper. The proposed methodology incorporates texture information and introduces a novel curvature analysis to adequately detect the femur. It consists on pre–processing US images with an anisotropic diffusion filter, followed by morphological operations and thresholding to isolate femur–candidate regions. A normalized metric composed of intensity, length, centroid position and entropy is assigned to each region in order to select the most probable candidate to be femur. This selected region is afterwards thinned to a one–pixel line, whose curvature is analyzed with an angle threshold criterion to accurately locate femur’s extrema. The method was tested on 64 US images (20 taken on the second and 44 on the third trimester of pregnancy); a correlation coefficient of 0.984 and an error of 1.016±2.764 mm were achieved between expert–obtained manual measures and automatically calculated indices. Results are consistent, outperform those reported previously by other authors and show a high correlation with measures obtained by experts; therefore, the developed method is suitable to be adapted for clinical use.
Interactive solar panel simulation tool - from GHI to PV output

The production and integration of solar panels is steadily growing in New Zealand and worldwide. The most common way to install solar panels in New Zealand is flat on top of the most north-facing roof. This method is used as it is space efficient, reduces mounting costs and will capture most of the sun’s movement throughout the day. New Zealand homes typically have a small roof pitch. This means an installed solar panel will have a low tilt, allowing for best input in summer. However, this may not be the best result for individuals, as they may want a different outcome such as: maximum energy output in winter, maximum yearly output or a balanced output throughout each month. This paper describes a solar panel tool, which has been developed to enable users to explore interactively the impact on electricity production based on the orientation of the panels.
Expediting arithmetic automaticity: Do inefficient computation methods induce spontaneous testing effects?

ABSTRACT Before achieving arithmetic automaticity (direct answer recall), learners self-compute answers or use calculators/tables. Mechanisms supporting arithmetic automaticity are of interest because automaticity predicts math achievement and employment outcomes. Future recall is enhanced when, during study, learners attempt to recall the to-be-learned information (“testing effect”). However, learners may not attempt retrieval when another strategy is available (computation, re-reading). We expected learners to more frequently attempt recall when the alternative strategy was inefficient. Participants learned novel arithmetic problems, and could recall answers (if able) or use a computation method, which varied by condition: (i) self-computation; (ii) calculator-computation; and (iii) using a calculator with scrambled keys. Final recall ability was highest after the condition with the most inefficient non-retrieval method (scrambled calculator). We assumed recall attempts preceded computation, but also considered parallel execution. As evidence against parallel retrieval, answer recall rarely interrupted computation (mental/calculator) after it began. Thus, learners should make pre-emptive recall attempts.
Visual speech recognition

Speech is the most natural means of communication for humans. Therefore, since the beginning of computers it has been a goal to interact with machines via speech. While there have been gradual improvements in this field over the decades, and with recent drastic progress more and more commercial software is available that allow voice commands, there are still many ways in which it can be improved. One way to do this is with visual speech information, more specifically, the visible articulations of the mouth. Based on the information contained in these articulations, visual speech recognition (VSR) transcribes an utterance from a video sequence. It thus helps extend speech recognition from audio-only to other scenarios such as silent or whispered speech (e.g. in cybersecurity), mouthings in sign language, as an additional modality in noisy audio scenarios for audio-visual automatic speech recognition, to better understand speech production and disorders, or by itself for human machine interaction and as a transcription method. In this thesis, we present and compare different ways to build systems for VSR: We start with the traditional hidden Markov models that have been used in the field for decades, especially in combination with handcrafted features. These are compared to models taking into account recent developments in the fields of computer vision and speech recognition through deep learning. While their superior performance is confirmed, certain limitations with respect to computing power for these systems are also discussed. This thesis also addresses multi-view processing and fusion, which is an important topic for many current applications. This is due to the fact that a single camera view often cannot provide enough flexibility with speakers moving in front of the camera. Technology companies are willing to integrate more cameras into their products, such as cars and mobile devices, due to lower hardware cost for both cameras and processing units, as well as the availability of higher processing power and high performance algorithms. Multi-camera and multi-view solutions are thus becoming more common, which means that algorithms can benefit from taking these into account. In this work we propose several methods of fusing the views of multiple cameras to improve the overall results. We can show that both, relying on deep learning-based approaches for feature extraction and sequence modelling, as well as taking into account the complementary information contained in several views, improves performance considerably. To further improve the results, it would be necessary to move from data recorded in a lab environment, to multi-view data in realistic scenarios. Furthermore, the findings and models could be
Automatically explaining literature based discoveries

Literature based discovery (LBD) identifies potentially related pairs of concepts that are not mentioned together in the same documents. The concept pairs may be identified via linking concepts that are mentioned in both sets of documents or via other statistical relatedness measures like latent semantic indexing. Unfortunately, the nature of the relationships are not identified so the importance and relevancy of the LBD pairs are not known. The primary objectives of this thesis are to identify candidate LBD related concepts and to determine if the natures of the relationship may be automatically explained using supervised machine learning classification. For example, in the benchmark LBD example of Raynaud’s phenomenon (A) being related to fish oil (C), candidate linking concepts are blood viscosity, platelet function and vascular reactivity. The linking concepts are referred to as Bs and, thus, create A-B-C LBD triples. The objectives of this work are to identify a training set of data that includes linking B terms, to identify the relationships between the A and B and the B and C pairs, and to apply supervised machine learning classification techniques to suggest relationship between the A to C concepts. In the Raynaud’s example, the suggestion would be that fish oil may treat Raynaud’s phenomenon. This work explores data representations suitable for applying classification techniques to explain the relationships. This work applies traditional classification evaluation methods on both classifier outcomes and data designs. Classifiers applied to the training data ultimately accurately predicted the A to C relationships over 70% of the time, while the chosen baselines only achieved approximately 30% accurately predicted relationships. The classifiers were then used on real LBD candidate pairs from an older set of MEDLINE abstracts found using statistical LBD. The predicted LBD explanations were validated against more recent literature which is a time-slice validation approach. To the best of my knowledge and research, relationship prediction techniques have not been applied to statistically related LBD candidate pairs to provide an explanation of how the A and C pairs are related. Additionally, applying time-slicing for validation of explained LBD candidates is also novel.
Comparing Real and Virtual Object Manipulation by Physiological Signals Analysis: A First Study

Virtual reality aims at reproducing reality and simulating actions like object manipulation tasks. Despite abundant past research on designing 3D interaction devices and methods to achieve close-to-real manipulation in virtual environments, strong differences exist between real and virtual object manipulation. Past work that compared between real and virtual manipulation mainly focused on user performance only. In this paper, we propose using also physiological signals, namely electromyography (EMG), to better characterize these differences. A first experiment featuring a simple pick-and-place task on a real setup and in a CAVE system showed that participants’ muscular activity reveals a clearly different spectrum in the virtual environment compared to that in reality.
Defect Clustering-Aware Spare-TSV Allocation in 3-D ICs for Yield Enhancement

The manufacturing yield challenge of 3-D integrated circuit is one of the key obstacles in the industry adoption of 3-D integration based on through-silicon-vias (TSVs). The addition of spare TSVs to repair faulty functional TSVs (f-TSVs) is an effective method for yield and reliability enhancement, but this approach results in significant hardware cost and delay overhead. Most existing solutions are only suitable for a “dual-uniform” scenario in which both the placement and the defect probabilities of f-TSVs are assumed to be uniform. In this paper, we propose a design technique that is compatible with nonuniform TSV placement and it can repair faulty TSVs based on a realistic clustered defect-distribution model. The proposed solution is based on two consecutive stages, which utilize a greedy algorithm and an integer-linear-programming formulation, respectively. By considering the tradeoff between chip yield, hardware cost, and delay overhead, the proposed technique provides higher yield and reliability under a clustered defect distribution, and with minimum hardware cost and delay overhead, compared to the previous work.
Deep Learning and Visualization for Identifying Malware Families

The growing threat of malware is becoming more and more difficult to ignore. In this paper, a malware feature images generation method is used to combine the static analysis of malicious code with the methods of recurrent neural networks (RNN) and convolutional neural networks (CNN). By using an RNN, our method considers not only the original information of malware but also the ability to associate the original code with timing characteristics; furthermore, the process reduces the dependence on category labels of malware. Then, we use minhash to generate feature images from the fusion of the original codes and the predictive codes from the RNN. Finally, we train a CNN to classify feature images. When we trained very few samples (the proportion of the sample size of training dataset to validation dataset was 1:30), we obtained accuracy over 92 percent. When we adjust the proportion to 3:1, the accuracy exceeds 99.5 percent. As shown in confusion matrices, our method obtains a good result, where the worst false positive rate of all the malware families is 0.0147 and the average false positive rate is 0.0058.
A Model Predictive Control-Based Open-Circuit Fault Diagnosis and Tolerant Scheme of Three-Phase AC–DC Rectifiers

Three-phase ac–dc rectifiers are extensively employed in critical power conversion applications. This paper proposes an open-circuit fault diagnosis and tolerant scheme based on model predictive control (MPC) to enhance their reliability. The proposed fault diagnosis method is directly implemented by checking the mismatch signal between the applied switching state and the estimated one. The switching state error is independent of the voltage or current signals. Therefore, it is applicable to different voltage rating and loading scenarios. As long as the faulty switch conducts current, the open-circuit fault can be isolated very fast within several sampling periods. By introducing the healthy leg variables, a generalized model including both normal and faulty operations is proposed and the fault-tolerant scheme can be integrated into the MPC scheme for normal operations. With the proposed generalized model and fault-tolerant scheme, the rectifier can autonomously recover from open-circuit faults and the continuous operation of three-phase ac–dc rectifiers is maintained. The proposed fault diagnosis and tolerant scheme are applicable under balanced and unbalanced grid conditions. Experimental results demonstrate the effectiveness of the proposed integrated fault diagnosis and fault-tolerant scheme.
IMPROVING FEATURE MAPS IN EARLY LAYERS OF CONVOLUTIONAL NEURAL NETWORKS USING OTSU METHOD

A novel deep architecture Thresholding Convolution Neural Network (ThCNN) progresses in this paper; Which is a simple and effective method to regularizing features map in the early layers of Convolution Neural Network(CNN). One of the issues identified with deep learning is the features in early layers that robustness and discriminativeness. In this paper, we compute the optimal global threshold to determine the features that are passed to the next layers. We then evaluate ThCNN on an MNIST dataset comparing it CNN by applying multiple trained models. It yield decent accuracy compared to traditional CNN. It gives a 99.5% accuracy compared to 99.3% for traditional CNN.
Optimizing the Self-Organizing Team Size Using a Genetic Algorithm in Agile Practices

Abstract In agile software processes, the issue of team size is an important one. In this work we look at how to find the optimal, or near optimal, self-organizing team size using a genetic algorithm (GA) which considers team communication efforts. Communication, authority, roles, and learning are the team’s performance characteristics. The GA has been developed according to performance characteristics. A survey was used to evaluate the communication weight factors, which were qualitatively assessed and used in the algorithm’s objective function. The GA experiments were performed in different stages: each stage results were tested and compared with the previous results. The results show that self-organizing teams of sizes ranged from five to nine members scored more. The model can be improved by adding other team characteristics, i.e. software development efforts and costs.
The specific activity of proteins involved in iron metabolism depends on compensation of type 2 diabetes mellitus

Objective. We aimed to analyze the alterations of activity of iron metabolism members i.e. ceruloplasmin (Cp) and transferrin (Tf), in relation to the percentage of glycated hemoglobin. The latter is one of biochemical criteria of chronic hyperglycemia compensation in case of type 2 diabetes mellitus. Materials and methods. Concentration and activity of Cp and Tf, concentration of iron, copper and lipoprotein cholesterol were measured by biochemical methods in blood serum samples obtained from healthy donors and patients with type 2 diabetes mellitus divided in three groups according to glycated hemoglobin level. Results. The significant decrease in serum copper, ferroxidase activity of Cp and iron-binding capacity of Tf, as well as an increase of Tf concentration, in groups with compensated and uncompensated type 2 diabetes mellitus was found. Conclusion. Our data demonstrate a statistical link between the degree of type 2 diabetes mellitus compensation and alteration of iron metabolism members’ activity. Thus, an increase of hyperglycemia is associated with a decrease of both Cp ferroxidase activity and the degree of Tf saturation with iron. These alterations may explain the efficiency of treatment with iron chelators of such type 2 diabetes mellitus complications as trophic ulcers. The said disease condition is directly connected with the changes in iron efflux.
An Improved Strategy for Modular Multilevel Converter at Low Speed Operation

One of the main problems of the Modular Multilevel Converter (MMC) for ship electric propulsion applications is the significant magnitude of capacitor voltage ripple at low speed operation. The commonly used solution is harmonic injection. In this paper, the injection rules are explored through the dynamic relationship of MMC in a comprehensive way, and the harmonic characteristics of injection current are derived. Furthermore, the circulating current controller is modified according to the harmonics of injected current with changing frequency, which reduces the ripple of capacitor voltage effectively in a wide speed range. Simulation results are shown to demonstrate the effectiveness of the proposed method.
Motion Planning using Positively Invariant Sets on a Small-Scale Autonomous Vehicle

Self-driving technology has the opportunity to increase safety in automotive transportation by reducing the impact of human error. Motion planning is a key component of an autonomous system, responsible for providing reference trajectories and paths that the vehicle should follow. This thesis studies a motion-planning algorithm based on positively invariant sets. The focus is on design, implementation, and evaluation of the algorithm on a small-scale ground-vehicle robot platform. By incorporating a gain-scheduling approach into the motion planner, guaranteed safe reference trajectories, capable of navigating the vehicle in a dynamic environment of static and moving obstacles, can be computed for time-varying velocities. This thesis also deals with sensor-fusion aspects for autonomous vehicles. Through a localization system based on an Extended Kalman Filter (EKF), reliable and robust state estimates can be obtained from inertial sensor data, without the use of an external positioning system. It is shown that the motion-planning algorithm together with the localization system is capable of performing safe overtaking maneuvers for time-varying velocities. Simpler urban driving scenarios involving traffic signs and intersections are used to illustrate the ability of the proposed motion-planning algorithm to also handle more complex driving scenarios. By using laser scans from Light Detection and Ranging (LIDAR) equipment, it is shown that obstacles can be detected and avoided in real-life driving experiments.