Reviewerly/exHarmony huggingface dataset:
Design Thinking for Class Imbalance Problems Using Compound Techniques

One of most commonly occurring phenomena in ML methods is that of class imbalance, wherein one class dominates the entire class distribution in terms of frequency. Over a period of time, many methods have been proposed to deal with the issue of class imbalance. Primarily, these methods to address class imbalance can be done through either one of the combinations of the following: This paper analyzes the design options across both the available options in combination and provides guidance on suitable Algorithms to be used for a minority class scenario in conjunction with the right sampling technique to improve the accuracy of prediction. While the results have been obtained with an objective to optimize F1 score, the paper also analyzes the pattern of Precision and Recall values with respect to each of the algorithms under various sampling techniques. Later, the paper also explores a few loss functions for tree-based algorithms and their corresponding variations to validation measures. We use the open-source Credit Card Fraud dataset, hosted on Kaggle [1]. We will use F1 metric as the model evaluation criteria, as it is generally suited to class imbalance problems and captures trade-off between precision and recall [2]. We do report our insights on precision and recall as well in this study. We also explore a more semi-supervised approach using Autoencoders and evaluate performance with other (more traditional) Machine Learning methods. The credit card fraud dataset used here contains transactions made with credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in 2 days, where there are 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) accounts for only 0.172% of all transactions.
ENT 600 : 6 Blade Mondo Voice Ceiling Fan / Hanisah Ajizan

Panasonic Corporation is a company that produces various types of home appliances and electrical products. Konosuke Matshushita founded Matshushita Electric Home Appliances Manufacturing Jobs in 1918. One of the company was located in Selangor, Malaysia which is Panasonic Malaysia Sdn Bhd. One of the items this company makes is the Panasonic 6 Blade Mondo Ceiling Fan. This fan allows users to provide customers in the house with satisfaction and comfort. This fan is very beneficial to customers because when the hot weather arrives in Malaysia, Malaysia has a warm and humid climate during the year, it can decrease the warmth in the home. The objective to demonstrate the creation process of 6 Blade Mondo Voice Ceiling Fan, including idea generation, idea filtering, ceiling fan design and its functionality, and to find out whether the readable ceiling fan will be released on the market as a commodity. We also conduct design tests to define and make changes to the proposed product before using CTC analysis in order to achieve these targets. Not just that, by doing Test Marketing research, we still strive to identify the consumer need. In addition, the method used in this study is questionnaire form from Google form and the respondents in this survey are mainly household with various employment backgrounds aged 25-45 years and above. In conclusion, we have so many feedback from the people in this process. However, we more focus on adding voice sensor to ceiling fan. Then, we create two questionnaires for a user to responses. The questionnaires are the concept testing and marketing testing. From this survey, we can learn more about what the user wants and what user don't like to improve in our product. Beside that, we want to know how customer rate our products before we make the products. Not just that, we can learn many ways to get the best outcome because we launch the product as we do in the new product development process.
Multi-Level Contrastive Learning for Few-Shot Problems

Many few-shot learning approaches depend on learning an encoder that can produce discriminative representations. Contrastive learning is one of the most effective representation learning methods. We explore contrastive learning to train an encoder to generate distinguishable and informative representations. Most current applications of contrastive learning only train a single representation for the downstream task. Rather than following this straightforward scheme, we propose a multi-level contrastive learning approach for few-shot learning problems. Specifically, contrastive losses are applied at different layers of an encoder to learn multiple representations from the encoder. Afterward, an ensemble is constructed to take advantage of the multiple representations. We evaluated the proposed method on two few-shot learning benchmark datasets. Our model achieved the new state-of-the-art results for both datasets, comparing to previous regular, ensemble, and contrastive learning (single representation) based approaches.
Geometric Data Augmentation Based on Feature Map Ensemble

Deep convolutional networks have become the mainstream in computer vision applications. Although CNNs have been successful in many computer vision tasks, it is not free from drawbacks. The performance of CNN is dramatically degraded by geometric transformation, such as large rotations. In this paper, we propose a novel CNN architecture that can improve the robustness against geometric transformations without modifying the existing backbones of their CNNs. The key is to enclose the existing backbone with a geometric transformation (and the corresponding reverse transformation) and a feature map ensemble. The proposed method can inherit the strengths of existing CNNs that have been presented so far. Furthermore, the proposed method can be employed in combination with state-of-the-art data augmentation algorithms to improve their performance. We demonstrate the effectiveness of the proposed method using standard datasets such as CIFAR, CUB-200, and Mnist-rot-12k.
Cambridge Analytica: A Property-Based Solution

The 2018 Cambridge Analytica scandal exposed the concern that current privacy regulation is not sufficient to regulate data. In response, this paper proposes a property-based solution, arguing that only by recognizing the property right toward data, a more comprehensive regulatory system can be established. Concerning the current development of the information society, property rights should be granted toward Data Analytical Products and databases. Thus, as a new data regulation covering the two new types of data property will be constructed, the data-driven economy can be regulated and consumer privacy can be protected.
CoCo: Online Mixed-Integer Control via Supervised Learning

Many robotics problems, from robot motion planning to object manipulation, can be modeled as mixed-integer convex programs (MICPs). However, state-of-the-art algorithms are still unable to solve MICPs for control problems quickly enough for online use and existing heuristics can typically only find suboptimal solutions that might degrade robot performance. In this work, we turn to data-driven methods and present the Combinatorial Offline, Convex Online (CoCo) algorithm for quickly finding high quality solutions for MICPs. CoCo consists of a two-stage approach. In the offline phase, we train a neural network classifier that maps the problem parameters to a (logical strategy), which we define as the discrete arguments and relaxed big-M constraints associated with the optimal solution for that problem. Online, the classifier is applied to select a candidate logical strategy given new problem parameters; applying this logical strategy allows us to solve the original MICP as a convex optimization problem. We show through numerical experiments how CoCo finds near optimal solutions to MICPs arising in robot planning and control with 1 to 2 orders of magnitude solution speedup compared to other data-driven approaches and solvers.
IOT BASED GLOBAL NAVIGATION SATELLITE SYSTEM (GNSS) GROUND MONITORING SYSTEM USING CLOUD

These days, the Internet of Things is a quickly developing innovation that grants to includeadvanced gadgets into a system. Utilizing the Internet of things innovation to gather facts from an assortment ofInternet associated GNSS beneficiaries gives an interesting chance to get continuous data about the uncommonand Fleeting movement of ionospheric attributes with excessive desires. The capacity to make a thick sensorarrangement is accomplished through the utilization of modest single-recurrence GNSS beneficiaries dependenton the Arduino innovation. This methodology can be actualized to acquire ongoing information on the all-out totalelectron content of the ionosphere. The assured information of the ionosphere deferral to radio signal of Globalnavigation for system satellite /Global positioning system satellite TV for PC and the estimation of theionospheric TEC are accomplished straightforwardly within the Global navigation for system satellitebeneficiary. The consequences are transmitted over a remote correspondence medium through Web to the cloudserver, in which maps of the TEC to the ionosphere are developed. Now are proposed the new Architecture, byusing this architecture we can directly store the data into the cloud. By using this architecture, we can provideremote monitoring.
Self-Supervised Learning of Depth and Ego-Motion from Video by  Alternative Training and Geometric Constraints from 3D to 2D

Self-supervised learning of depth and ego-motion from unlabeled monocular video has acquired promising results and drawn extensive attention. Most existing methods jointly train the depth and pose networks by photometric consistency of adjacent frames based on the principle of structure-from-motion (SFM). However, the coupling relationship of the depth and pose networks seriously influences the learning performance, and the re-projection relations is sensitive to scale ambiguity, especially for pose learning. In this paper, we aim to improve the depth-pose learning performance without the auxiliary tasks and address the above issues by alternative training each task and incorporating the epipolar geometric constraints into the Iterative Closest Point (ICP) based point clouds match process. Distinct from jointly training the depth and pose networks, our key idea is to better utilize the mutual dependency of these two tasks by alternatively training each network with respective losses while fixing the other. We also design a log-scale 3D structural consistency loss to put more emphasis on the smaller depth values during training. To makes the optimization easier, we further incorporate the epipolar geometry into the ICP based learning process for pose learning. Extensive experiments on various benchmarks datasets indicate the superiority of our algorithm over the state-of-the-art self-supervised methods.
Generating Diverse Descriptions from Semantic Graphs

Text generation from semantic graphs is traditionally performed with deterministic methods, which generate a unique description given an input graph. However, the generation problem admits a range of acceptable textual outputs, exhibiting lexical, syntactic and semantic variation. To address this disconnect, we present two main contributions. First, we propose a stochastic graph-to-text model, incorporating a latent variable in an encoder-decoder model, and its use in an ensemble. Second, to assess the diversity of the generated sentences, we propose a new automatic evaluation metric which jointly evaluates output diversity and quality in a multi-reference setting. We evaluate the models on WebNLG datasets in English and Russian, and show an ensemble of stochastic models produces diverse sets of generated sentences, while retaining similar quality to state-of-the-art models.
Decoding Error Probability of the Random Matrix Ensemble over the Erasure Channel

Using tools developed in a recent work by Shen and the second author, in this paper we carry out an in-depth study on the average decoding error probability of the random matrix ensemble over the erasure channel under three decoding principles, namely unambiguous decoding, maximum likelihood decoding and list decoding. We obtain explicit formulas for the average decoding error probabilities of the random matrix ensemble under these three decoding principles and compute the error exponents. Moreover, for unambiguous decoding, we compute the variance of the decoding error probability of the random matrix ensemble and the error exponent of the variance, which imply a strong concentration result, that is, roughly speaking, the ratio of the decoding error probability of a random code in the ensemble and the average decoding error probability of the ensemble converges to 1 with high probability when the code length goes to infinity.
Semi-device-independent full randomness amplification based on energy bounds

Quantum Bell nonlocality allows for the design of protocols that amplify the randomness of public and arbitrarily biased Santha-Vazirani sources, a classically impossible task. Information-theoretical security in these protocols is certified in a device-independent manner, i.e. solely from the observed nonlocal statistics and without any assumption about the inner-workings of the intervening devices. On the other hand, if one is willing to trust on a complete quantum-mechanical description of a protocol's devices, the elementary scheme in which a qubit is alternatively measured in a pair of mutually unbiased bases is, straightforwardly, a protocol for randomness amplification. In this work, we study the unexplored middle ground. We prove that full randomness amplification can be achieved without requiring entanglement or a complete characterization of the intervening quantum states and measurements. Based on the energy-bounded framework introduced in [Van Himbeeck et al., Quantum 1, 33 (2017)], our prepare-and-measure protocol is able to amplify the randomness of any public Santha-Vazirani source, requiring the smallest number of inputs and outcomes possible and being secure against quantum adversaries.
SwissCovid in the Perspective of Its Goals

SwissCovid is the Swiss digital contact tracing app, which was deployed to help fighting against the COVID-19 pandemic. After a year of activity, it is high time to evaluate how effective it has been in its mission. At the highest peak, about 22% of the Swiss population was actively using SwissCovid. The activity of SwissCovid follows the curve on the number of COVID-19 cases. However, performances are rather poor. About 1% of the cases may have been discovered by SwissCovid and much less than 2% of SwissCovid alerts may have been useful, while SwissCovid generates 5% of the quarantines. The measure of the proximity of each encounter and its duration are also imprecise. It further comes with security and privacy issues: adversaries can inject false alerts for SwissCovid users and users can be tracked. On top of that, SwissCovid contributes to strengthen the monopoly of Apple and Google and to make users and their data captive of these giants. It also digs the digital divide. Contrarily to the original plan, the implementation is not open source and the law was twisted to fit the constraints by Apple and Google. Therefore, SwissCovid did not meet its goals.
Towards More Efficient Federated Learning with Better Optimization Objects

Federated Learning (FL) is a privacy-protected machine learning paradigm that allows model to be trained directly at the edge without uploading data. One of the biggest challenges faced by FL in practical applications is the heterogeneity of edge node data, which will slow down the convergence speed and degrade the performance of the model. For the above problems, a representative solution is to add additional constraints in the local training, such as FedProx, FedCurv and FedCL. However, the above algorithms still have room for improvement. We propose to use the aggregation of all models obtained in the past as new constraint target to further improve the performance of such algorithms. Experiments in various settings demonstrate that our method significantly improves the convergence speed and performance of the model.
Topology-based Whole-body Multi-organ CT Image Segmentation

In this work, we present a whole-body image segmentation algorithm that is completely unsupervised. It uses diverse topological features of intensity distributions over different organs to perform segmentations. We compared our results on Computed Tomography (CT) images, with that of traditional graph-cut segmentation algorithm that is semi-manual (for a given organ).
Image Super-Resolution Based on Residual Block Dense Connection

Image super-resolution models based on convolution neural networks are facing problems such as gradient disappearance, gradient explosion, and insufficient feature utilization. This paper proposes an image super-resolution model based on feature fusion of dense connection of residual blocks. The key contributions are as follows: (1) residual block mechanism, which can make full use of the hierarchical features extracted from the residual block to alleviate the shallow feature losing. (2) In order to extract more representative key features, the feature of each level extracted from residual blocks is input into subsequent residual blocks by dense connection mechanism. (3) local feature fusion is used in a single residual block, and global feature fusion is used in the tail of the model, so that the shallow key information can be transferred to the reconstruction layer as much as possible. Empirical experiment is deployed on four benchmark test sets (Set5, Set14, Urban100 and BSDS100), the results show that both the peak signal-to-noise ratio and structural similarity are improved. (Source code: https://github.com/brown-cats/SR_RFB ).
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector

Stereo-based 3D detection aims at detecting 3D object bounding boxes from stereo images using intermediate depth maps or implicit 3D geometry representations, which provides a low-cost solution for 3D perception. However, its performance is still inferior compared with LiDAR-based detection algorithms. To detect and localize accurate 3D bounding boxes, LiDAR-based models can encode accurate object boundaries and surface normal directions from LiDAR point clouds. However, the detection results of stereo-based detectors are easily affected by the erroneous depth features due to the limitation of stereo matching. To solve the problem, we propose LIGA-Stereo (LiDAR Geometry Aware Stereo Detector) to learn stereo-based 3D detectors under the guidance of high-level geometry-aware representations of LiDAR-based detection models. In addition, we found existing voxel-based stereo detectors failed to learn semantic features effectively from indirect 3D supervisions. We attach an auxiliary 2D detection head to provide direct 2D semantic supervisions. Experiment results show that the above two strategies improved the geometric and semantic representation capabilities. Compared with the state-of-the-art stereo detector, our method has improved the 3D detection performance of cars, pedestrians, cyclists by 10.44%, 5.69%, 5.97% mAP respectively on the official KITTI benchmark. The gap between stereo-based and LiDAR-based 3D detectors is further narrowed.
인성교육으로서 정보윤리교육의 핵심 주제 및 관련 교육과정 성취기준 탐색

The purpose of this study was to categorize various educational contents related to information ethics education from the perspective of personality education and to contribute to strengthening information ethics education by exploring relevant curriculum achievement criteria. Education related to information ethics should be strengthened as the fourth industrial revolution accelerates the information society and spreads the categories of the cyber world so that it is meaningless to distinguish the real world from the cyber world. However, information education in the current curriculum is mainly focused on improving informatization functions, and information ethics is mainly focused on elementary school or middle school information subjects, so only topics directly related to information life are covered. For this reason, in this work, we propose topic categorization that can explore information ethics education as personality education to cover human relationships in the cyber world and a wide range of ethical issues resulting from them. To this end, the “Value Relationship Expansion Act”, which was used as the 2015 revised ethics and curriculum, was intended to present “media usage habits”, “privacy protection”, “relationships and communication”, “copyright protection”, and “development of desirable digital culture” as a social and community relationship. In addition, in consideration of the physical environment in which the number of classes for information ethics education is significantly insufficient in the current curriculum, the curriculum achievement standards related to each key topic are proposed to realize practical information ethics education.
CAD Based Analysis for Feasibility of Design of an Amphibious Vehicle for Recreation

An amphibious vehicle is the vehicle that allows occupants to travel on both land and water. Since an amphibious vehicle must inherently possess both land and water capabilities, design of the vehicle should not compromise the requirements for a good boat as well as a good land vehicle. Although, amphibious vehicles were used for different purposes, still there is no mention of application of amphibious vehicle in recreational purposes such as beach riding, surfing on water and travel within Science Parks. Further, with depletion of fossil fuel and for all the negative effect on marine life. Hence, an attempt is made to design an electric amphibious vehicle for recreational purpose and utilize for travel within Science &amp; Technology Park. A conceptual design was worked out and it is matched with visual aspects of amphibian animal like duck. A CAD model was prepared with structural details. Structural strength was analyzed using ANSYS software. CFD analysis was conducted for calculating drag and surface shear stress and buoyancy was calculated. Thus it gives a practical approach to construction of an amphibious vehicle for recreational purpose. The results of the CAD analysis shows that the stress on the Frame was within acceptable limit and the buoyancy conditions are fulfilled. It also suggest for a maximum speed of 40kmph on water. As look and feel of the vehicle inspired from duck beak, it gives expression of amphibian characteristics along with sporty look. Thus, the conceptual vehicle is suitable for use on land and on water inside Science &amp; Technology park.
Edge-featured Graph Neural Architecture Search

Graph neural networks (GNNs) have been successfully applied to learning representation on graphs in many relational tasks. Recently, researchers study neural architecture search (NAS) to reduce the dependence of human expertise and explore better GNN architectures, but they over-emphasize entity features and ignore latent relation information concealed in the edges. To solve this problem, we incorporate edge features into graph search space and propose Edge-featured Graph Neural Architecture Search to find the optimal GNN architecture. Specifically, we design rich entity and edge updating operations to learn high-order representations, which convey more generic message passing mechanisms. Moreover, the architecture topology in our search space allows to explore complex feature dependence of both entities and edges, which can be efficiently optimized by differentiable search strategy. Experiments at three graph tasks on six datasets show EGNAS can search better GNNs with higher performance than current state-of-the-art human-designed and searched-based GNNs.