Miracle-dz/UniFAR-FTUs huggingface dataset:
[ANT]: A Machine Learning Approach for Building Performance Simulation: Methods and Development

In this paper, we represent an approach for combining machine learning (ML) techniques with building performance simulation by introducing four methods in which ML could be effectively involved in this field i.e. Classification, Regression, Clustering and Model selection . Rhino-3d-Grasshopper SDK was used to develop a new plugin for involving machine learning in design process using Python programming language and making use of scikit-learn module, that is, a python module which provides a general purpose high level language to nonspecialist user by integration of wide range supervised and unsupervised learning algorithms with high performance, ease of use and well documented features. ANT plugin provides a method to make use of these modules inside Rhino\Grasshopper to be handy to designers. This tool is open source and is released under BSD simplified license. This approach represents promising results regarding making use of data in automating building performance development and could be widely applied. Future studies include providing parallel computation facility using PyOpenCL module as well as computer vision integration using scikit-image.
Machine Learning: Object Recognition Web Platform

Machine Learning is the backbone and most essential ingredient for working in the field of Artificial Intelligence as well as working in advance technologies. Some of the models which we already using are RNN, SPPNET. In this research paper we will be getting to know about the New Neural Model called Renshaw Model with its advantages and the web platform which makes the life easier for the user to work and understand the neural networks through machine learning. Usage of Cloud services and as well the usage of API will also be defined. The web platform is developed to tackle the issue of complexity storage, and security issue. This platform is basically running in three different formats and according to the user professionality the execution takes place. Researchers in the field of artificial intelligence can contribute and change the dimensions of the execution by designing and executing their code in swagger.api. Machine Learning: Object Recognition Web Platform
An Analysis of Internet of Things (IoT): Novel Architectures, Modern Applications, Security Aspects and Future Scope with Latest Case Studies

—While we might be thinking, “one of these things is not like the others,” these are all examples of the Internet of Things (IoT). The Internet of Things (IoT) connects the physical and the cyber worlds. On these days, one of the main objectives of Internet is its own progression. The Internet of Things (IoT) is a pattern where everyday objects can be furnished with classifying, sensing, networking and processing potentials that will allow them to correspond over the Internet to accomplish some purpose. The future of Internet of Things are, transform the real world things into intelligent virtual things. The Internet of Things (IoT)in tends to unite everything in our world under general infrastructures. Every object will have an exclusive identifier and will be able to locate itself and connect to the Internet. Moreover, Radio Frequency Identification (RFID) techniques will be the base of Internet of Things (IoT). Eventually, IoT devices will be pervasive, context-aware and will allow ambient ability and enable knowledge growth in proficiently. This paper reports on the current state of research and the meaning of IoT is defined with its progression structure. Moreover, present study attends to IoT conception through organized review of scholarly research papers, and professional discussions with competent. We also discussed about Internet of Things (IoT) by probing the literature, recognizing current trends and relating challenges that threaten IoT transmission. Though, this paper will give good conception for the new researchers, who want to do research in this field of Internet of Things (IoT).
Assessing IT Students’ Intentions to Commit Unethical Actions

ABSTRACT With the increased volume of and access to electronically held assets, ethical behavior of information technology (IT) professionals is critical. The importance of ethical behavior in the workplace has emphasized the need for coverage of ethics in IT-related academic programs. To better understand how academic IT programs support the teaching of ethics, this study examines people’s desires and intentions to act ethically within specific IT-related contexts by developing a survey. These contexts, derived from industry codes of conduct, include intellectual property rights (IPR), software piracy (SWP), and privacy (PR). Hypotheses are developed and tested based on the Theory of Planned Behavior and dual process cognitive models. The results show that people’s intent to behave ethically with IT decreases their intentions to behave unethically with IPR and SWP and that people’s desire to behave ethically with IT decreases their intentions to behave unethically with IPR and PR.
User Modeling and Attention Support: Towards a Framework of Personalization Techniques

— This paper aims to elaborate on the role of user modelling for personalization and enhanced attention support. User modelling is an important element in the management of personal profiles and identity of users, but also a key element for providing adaptive features and personalized interaction. In this paper, we present personalization as the process consisting on the customization, and the adaptation of the interaction along the structure, the content, the modality, the presentation and the level of attention required. The paper surveys personalization techniques and provides concrete examples of personalized interaction. In particular, the paper focuses on the role of user modeling for enhanced, personalized user support within interactive applications. The key contribution of the paper is to propose a framework of personalization techniques and to identify new forms of personalization that aim at taking into account human cognitive capabilities and emotions.
A New PUF Based Lock and Key Solution for Secure In-Field Testing of Cryptographic Chips

Scan-based side-channel attacks have become a new threat to cryptographic chips. Existing countermeasures require a secret test key to unlock the scan chain before in-field testing is allowed. However, test key disclosure poses tremendous risks to multiple crypto chips that share a common test key. We address this open problem of in-field testing by leveraging physical unclonable function (PUF) to make the derived test key unique to each chip. The PUF's response is invoked only once and hardened into a one-time programmable pad. The PUF response required by the designer to derive a test key of each crypto chip can only be recovered at the time of locking the scan chains without directly reading it out. The manufacturer can test the chip normally with no test time penalty before the passed chips are locked. The proposed solution is analyzed to be secure against all known scan-based side-channel attacks and the overhead incurred for the added security is negligibly small.
Development of generalized unified power flow controller (GUPFC) detailed model with a comparison with multi-UPFC

Abstract Unified power flow controller (UPFC) is a unique type FACTS device which is able to regulate power system parameters such as active and reactive power along with voltage magnitude and phase angle. In restructured power system, use of multi-machine systems increasing exponentially. In such systems, there is a need of control of parameters of more than one line. In such situation, multiple Unified power flow controllers (M-UPFCs) is used to achieve these power system requirements. A modified form of UPFC which is known as generalized unified power flow controller (GUPFC) is also used to control the power system parameters for more than one bus or even a subsystem. This paper envelops a innovative approach based on voltage source converter is used for the designing of detailed model of GUPFC in MATLAB/Simulink. A detailed comparison is done between a detailed GUPFC model and M-UPFC for power flow control (active power, reactive power and voltage). The simulation results the superiority of GUPFC over M-UPFC. This paper states first time designing of the detailed model of GUPFC in MATLAB/Simulink.
Distributed Online Voltage Control in Active Distribution Networks Considering PV Curtailment

In this paper, we propose a distributed online voltage control algorithm for distribution networks with multiple photovoltaic (PV) systems based on dual-ascent method. Conventional distributed algorithms implement voltage control only when the algorithms converge. However, our proposed algorithm is able to carry out voltage control immediately. In particular, we derive a closed-form solution for PV controllers to locally update the active and reactive power set points aiming at minimizing the total loss and maintaining bus voltages within the acceptable ranges. The optimality is guaranteed and the convergence is established analytically. Moreover, our proposed algorithm only requires the information exchange between neighboring PV systems, thus reducing communication complexity. Finally, numerical tests on IEEE 37-bus distribution system verify the effectiveness and robustness of our proposed algorithm.
Designing Navigation and Obstacle Avoidance System Combining Smart Car And Quadrotor Aircraft

The navigation and obstacle avoidance system combining smart car and quadrotor aircraft takes MK60FX512VLQ15 microcontroller as the intelligent car control unit and STM32F767IGT as the image processing unit of flying platform. This system first collects image information through the infrared camera equipped in the quadrotor aircraft, and sends it to the processing unit for extraction and processing. Then this system transmits the information to the smart car master through the NRF2.4G wireless module and integrates it with the smart car’s own gyroscope data. In this way, the system is able to tell the relative angle of the smart car with the target according to the result, obtain the operating environment and state of the car body through its own ultrasonic ranging, encoder and other modules, and adjust the car’s speed and direction so as to avoid obstacles. The system is smooth in operation, flexible in adjusting speed and direction, and is fully capable of avoiding obstacles.
Evaluation of 3D slicer as a medical virtual reality visualization platform

PURPOSE: There is a lack of open-source or free virtual reality (VR) software that can be utilized for research by medical professionals and researchers. We propose the design and implementation of such software. We also aim to assess the feasibility of using VR as a modality for navigating 3D visualizations of medical scenes. METHODS: To achieve our goal, we added VR capabilities to the open-source medical image analysis and visualization platform, 3D Slicer. We designed the VR extension by basing the software architecture on VTK’s vtkRenderingOpenVR software module. We extended this module by adding features such as full interactivity between 3D Slicer and the VR extension during VR use, variable volume rendering quality based on user headset motion etc. Furthermore, the VR extension was tested in a feasibility study in which participants were asked to complete specific tasks using bot the conventional mouse-monitor and VR method. For this experiment, we used 3D Slicer to create two virtual settings, each having an associated task. Participants were asked to maneuver the virtual settings using two approaches, the conventional method, using mouse and monitor, and VR using the head-mounted-display and controllers. The main outcome measure was total time to complete the task. RESULTS: We developed a VR extension to 3D Slicer—SlicerVirtualReality (SlicerVR). Additionally, from the experiment we conducted we found that when comparing mean completion times, participants, when using VR, were able to complete the first task 3 minutes and 28 seconds quicker than the mouse and monitor method (4 minutes and 24 seconds vs. 7 minutes and 52 seconds, respectively); and the second task 1 minute and 20 seconds quicker (2 minutes and 37 seconds, vs. 3 minutes and 57 seconds, respectively). CONCLUSION: We augmented the 3D Slicer platform with virtual reality capabilities. Experiments results show a considerable improvement in time required to navigate and complete tasks within complex virtual scenes compared to the traditional mouse and monitor method.
Improving splenomegaly segmentation by learning from heterogeneous multi-source labels

Splenomegaly segmentation on computed tomography (CT) abdomen anatomical scans is essential for identifying spleen biomarkers and has applications for quantitative assessment in patients with liver and spleen disease. Deep convolutional neural network automated segmentation has shown promising performance for splenomegaly segmentation. However, manual labeling of abdominal structures is resource intensive, so the labeled abdominal imaging data are rare resources despite their essential role in algorithm training. Hence, the number of annotated labels (e.g., spleen only) are typically limited with a single study. However, with the development of data sharing techniques, more and more publicly available labeled cohorts are available from different resources. A key new challenging is to co-learn from the multi-source data, even with different numbers of labeled abdominal organs in each study. Thus, it is appealing to design a co-learning strategy to train a deep network from heterogeneously labeled scans. In this paper, we propose a new deep convolutional neural network (DCNN) based method that integrates heterogeneous multi-resource labeled cohorts for splenomegaly segmentation. To enable the proposed approach, a novel loss function is introduced based on the Dice similarity coefficient to adaptively learn multi-organ information from different resources. Three cohorts were employed in our experiments, the first cohort (98 CT scans) has only splenomegaly labels, while the second training cohort (100 CT scans) has 15 distinct anatomical labels with normal spleens. A separate, independent cohort consisting of 19 splenomegaly CT scans with labeled spleen was used as testing cohort. The proposed method achieved the highest median Dice similarity coefficient value (0.94), which is superior (p-value<0.01 against each other method) to the baselines of multi-atlas segmentation (0.86), SS-Net segmentation with only spleen labels (0.90) and U-Net segmentation with multi-organ training (0.91). Our approach for adapting the loss function and training structure is not specific to the abdominal context and may be beneficial in other situations where datasets with varied label sets are available.
MADARS: A Method of Multi-Attributes Generalized Randomization Privacy Preserving

With the extending in the domain of data mining application, the research of the privacy preserving in data mining based on k-anonymity becomes more and more concerned. The key point of the research is to protect data while it’s still effective in data mining. However, these methods result in too much information loss, and Multi-dimension bucketization does not generalize quasi-identifiers, which make the anonymized data easy to suffer from linking attack. To overcome these drawbacks, we put forward the thought of data classification processing, named MADARS. MADARS first deal with identifier attributes based on MA-Datafly and then randomized multi-sensitive attributes. Experiment results show that compared with the widely used algorithm Incognito the method can greatly improve the efficiency of the privacy protection while reduce the less personal information and the effectiveness of data is greatly increased. This work is supported by National Natural Science Foundation of China (No.51277023).
Survey on Handwritten Digit Recognition using Machine Learning

— Machine learning and deep learning plays an important role in computer technology and artificial intelligence. With the use of deep learning and machine learning, human effort can be reduce in recognizing, learning, predictions and many more areas. This paper presents recognizing the handwritten digits (0 to 9) from the famous MNIST dataset, comparing classifiers like KNN, PSVM, NN and convolution neural network on basis of performance, accuracy, time, sensitivity, positive productivity, and specificity with using different parameters with the classifiers.
Privacy-Preserving Online Task Allocation in Edge-Computing-Enabled Massive Crowdsensing

We propose a novel context-aware task allocation framework for mobile crowdsensing in the scenario of edge computing to enable the crowdsensing platform effectively and real-timely handle large-scale crowdsensing tasks in smart city. The task allocation performs in both cloud computing layer and edge computing layer. It aims to combine the merits of cloud and edge computing, i.e., diminishing communication latency while guaranteeing overall scheduling. The cloud layer evaluates the participants’ task-oriented reputation based on the participants’ background information, task context, and historical feedbacks (i.e., rewards) and sends the edge layer the most promising subset of participants. Then the edge layer communicates with the participants for the real-time information and makes optimization based on the task requirement (e.g., maximizing the sensing coverage under the constraint of the task budget). In the cloud layer, we propose a privacy-preserving and contextual online learning algorithm to manage the participants’ reputation. The algorithm can adapt the decision-making strategy based on previous performances of participants. In the edge layer, plenty of existing centralized task allocation strategies can be directly applied to optimize based on the participants’ real-time information. Theoretical analysis shows that our proposal achieves sublinear regret and differential privacy for both requesters and participants. Experiments results validate that our proposed algorithm supports increasing big dataset while striking a balance between the privacy-preserving level and the prediction accuracy.
Exploration of Deep Learning-based Multimodal Fusion for Semantic Road Scene Segmentation

Deep neural networks have been frequently used for semantic scene understanding in recent years. Effective and robust segmentation in outdoor scene is prerequisite for safe autonomous navigation of autonomous vehicles. In this paper, our aim is to find the best exploitation of different imaging modalities for road scene segmentation, as opposed to using a single RGB modality. We explore deep learning-based early and later fusion pattern for semantic segmentation, and propose a new multi-level feature fusion network. Given a pair of aligned multimodal images, the network can achieve faster convergence and incorporate more contextual information. In particular, we introduce the first-of-its-kind dataset, which contains aligned raw RGB images and polarimetric images, followed by manually labeled ground truth. The use of polarization cameras is a sensory augmentation that can significantly enhance the capabilities of image understanding, for the detection of highly reflective areas such as glasses and water. Experimental results suggest that our proposed multimodal fusion network outperforms unimodal networks and two typical fusion architectures.
Elimination of Boundary Effects at the Numerical Implementation of Continuous Wavelet Transform to Nonstationary Biomedical Signals

The main objective of the paper is to develop an algorithm improving the situation with boundary effects at the numerical implementation of continuous wavelet transform, what makes it possible to hold down important information in the study of many nonstationary biological signals. As a basis for the research, we propose the mathematical model of nonstationary signal (NS) with a complex amplitude variation in time. Such signals show peaks near the beginning and end of the observation period. The model leads to the analytical expression for continuous wavelet transform (CWT). Thus, we get to know how boundary effects relate to the finiteness in time of the mother wavelet function. To avoid boundary effects arising at the procedure of numerical wavelet transform, we develop the algorithm of signal time shift (STS). The results of analytical solution and numerical CWT calculation with and without the use of STS show the benefit of using STS in the processing of nonstationary signals. We have applied the technique to the study of nonstationary heart tachogram and discovered heart activity bursts in different spectral ranges, which appear and disappear at different time moments.
Enabling Authorized Encrypted Search for Multi-Authority Medical Databases

E-medical records are sensitive and should be stored in a medical database in encrypted form. However, simply encrypting these records will eliminate data utility and interoperability of the existing medical database system because encrypted records are no longer searchable. Moreover, multiple authorities could be involved in controlling and sharing the private medical records of clients. However, authorizing different clients to search and access records originating from multiple authorities in a secure and scalable manner is a nontrivial matter. To address the above issues, we propose an authorized searchable encryption scheme under a multi-authority setting. Specifically, our proposed scheme leverages the RSA function to enable each authority to limit the search capability of different clients based on clients’ privileges. To improve scalability, we utilize multi-authority attribute-based encryption to allow the authorization process to be performed only once even over policies from multiple authorities. We conduct rigorous security and cost analysis, and perform experimental evaluations to demonstrate that the proposed scheme introduces moderate overhead to existing searchable encryption schemes.
A Survey on Framework of Efficient keyword query suggestion on Geo-Location using document proximity

Keyword suggestion is most essential functions of the internet search engine. The principal trouble of a modern-day net search engine is that search queries are short and ambiguous. In this paper we discuss about some methodologies, which gives importance for a list of keywords which were related to query, geo location of the user and document submitted to a search engine. In this survey various techniques like Click Through Data, Query Clustering, Click-Through Bipartite, Suggestion Using Hitting Time and Prefix-Region Tree is discussed. None of the existing techniques used the geo-location which increases the performance and reduces the time complexity in resulting of keyword suggestions.