Reviewerly/exHarmony huggingface dataset:
Framework for the Retrieval of Social Media and Instant Messaging Evidence from Volatile Memory

The human society today has had to be confronted by the threat posed by criminals who are taking the advantages of social media and instant-messaging apps to conduct their criminal activities. In literature, there are many approaches for forensic acquisition and analysis of these apps. However, most of the approaches mainly focus on non-volatile storage media. Meanwhile, social media and instant-messaging platforms do not usually store information relating to social transactions in non-volatile bases. Hence, in this paper, we propose a framework for volatile memory forensics that is essential for the ready retrieval of evidence. Our approach is based on the retrieval of social media and instant-messaging evidence through the use of string search mechanism extended by the usage of regular expressions and the functionality of match groups. Our approach has been tested and proved to be effective with different social media apps on two popular operating systems: Windows and Mac.
A Secured Private-Cloud Computing System

The advent of internet and rapid growth of digital media has vastly increased concerns of cloud computing as source of data and information storage ever than before. It allows users to setup their own private clouds since the public clouds available, can't surpass their expectations at affordable costs. With private cloud computing, users do not need to be in a physical location when accessing personified cloud resources, thus; leading to cost reduction, improved user experience (ux), staff effectiveness, and file sharing with organizational collaborations from remote sites. Despite lots of benefits offered, there have been constant challenges associated with cloud computing from Data loss & breaches, service vulnerabilities, insufficient due diligence, identity, access and credential management, poor footprint tracking for threats & malicious insider attacks etc. which threatens network security. This proposed application is set out to provide immediate solution to help fight against cloud insecurity, particularly in private cloud domains through restriction of access to cloud resources, giving access to only trusted members to prove their identities through Footprint Notifications, Logging Encryption and activities of the authenticated users, as it is essential to monitor information flow on the cloud for information assessment purposes. The implementation of a secured Private-Cloud Computing System is the central focus of this project; the application is designed by using advanced features of HTML, CSS, JAVASCRIPT, & JQUERY for the frontend interface while PHP & MYSQL were used to design the backend of the application and its log encryption.
Benchmarking Machine-Learning-Based Object Detection on a UAV and Mobile Platform

Object detection systems mounted on Unmanned Aerial Vehicles (UAVs) have gained momentum in recent years in light of the widespread use cases enabled by such systems in public safety and other areas. Machine learning has emerged as an enabler for improving the performance of object detection. However, there is little existing work that has studied the performance of the machine learning approach, which is computationally resource demanding, in a portable mobile platform for UAV based object detection in user mobility scenarios. This paper evaluates an integrated real-world testbed for this scenario, by employing commercial-off-the-shelf devices including a UAV system and a machine-learning-enabled mobile platform. It presents benchmarking results about the performance of popular machine learning and computer vision frameworks such as TensorFlow and OpenCV and the associated algorithms such as YOLO, embedded in a smartphone execution environment of limited resources. The results highlight opportunities and provide insights into technical gaps to be filled to realize real-time machine-learning-based object detection on a mobile platform with constrained resources.
Towards quantum internet and non-local communication in position-based qubits

Non-local communication among position based qubits is described for the system of the quantum electromagnetic resonator entangled to two semiconductor electrostatic qubits via interaction between matter and radiation by Jaynes-Cummings tight-binding Hamiltonian. Principle of quantum communication between position dependent qubits is explained with usage of simplistic model. All stages of derivation are presented. The case of two semiconductor position-dependent qubits interacting with quantum electromagnetic cavity is discussed and general form of tight-binding Hamiltonian is derived with renormalized tight-binding coefficients. The obtained results bring foundation for the construction of quantum networks and prospect of quantum internet. The presented work brings the perspective of creation of quantum communication networks between electrostatic position based qubits that are implementable in semiconductor single electron devices and in particular in current CMOS technologies. The case of two capacitively interacting qubits biased to quantum electromagnetic cavity is also described.
Sharing big data using blockchain technologies in local governments: Some technical, organizational and policy considerations

Governments have vast data resources related to a wide-variety of policies and programs. Integrating and sharing data across agencies and departments can add value to these data resources and bring about significant changes in public services as well as better government decisions. However, in addition to the lack of standards and an adequate information architecture, the main obstacles to a centralized government data-sharing strategy are security and privacy concerns. Blockchain - a decentralized peer-to-peer distributed ledger technology - provides a new way to develop sharing mechanisms. In addition, blockchain-based systems are difficult to tamper with and are highly traceable. Based on the current problems of a big data center in the city of Ningbo, China, this paper identifies limitations of this approach and explores the potential of some data sharing mechanism based on blockchain technology. Our analysis describes some potential advantages and the feasibility of using distributed data sharing and automated management mechanisms based on blockchain smart contracts. We also explore implementation challenges and provide some practical recommendations.
TaaS

We propose a mechanism for an m-party dishonest majority Multi-Party Computation (MPC) protocol to obtain the required pre-processing data (called Beaver Triples), from a subset of a set of cloud service providers; providing a form of TaaS (Triples-as-a-Service). The service providers used by the MPC computing parties can be selected dynamically at the point of the MPC computation being run, and the interaction between the MPC parties and the TaaS parties is via a single round of communication, logged on a public ledger. The TaaS is itself instantiated as an MPC protocol which produces the triples for a different access structure. Thus our protocol also acts as a translation mechanism between the secret sharing used by one MPC protocol and the other.
Verifiable Computation using Smart Contracts

Outsourcing computation has been widely used to allow weak clients to access computational resources of a cloud. A natural security requirement for the client is to be able to efficiently verify the received computation result. An attractive approach to verifying a general computation is to send the computation to multiple clouds, and use carefully designed protocols to compare the results and achieve verifiability. This however requires a Trusted Third Party (TTP) to manage the interactions of the client and the clouds. Our goal is to employ a smart contract to act as the TTP. This also relieves the client from directly interacting with the clouds, and engaging in possibly a complex stateful protocol. We focus on a verifiable computation protocol of Canetti, Riva and Rothbulm (CRR) with provable security against a malicious cloud, and show that direct employment of the protocol with a smart contract will result in an attack that will undermine the security of the system. We describe and analyze the attack, and extend CRR protocol to protect against this attack, resulting in a secure verifiable computation system using smart contracts. We also give the pseudocode of a smart contract and the required functions that can be used to implement the protocol, written in the Solidity language, and explain its working.
TUDocChain-Securing Academic Certificate Digitally on Blockchain

In the digital technology, Digital Documents become a part of organization, institution and educational field whether public or private. Digital Documents not only authorize the transition of information but also maintain the data in digital form. Academic Certificates approve the procurement of learning outcome. Certificates become necessary for people’s professional careers. It is essential to save these certificates in long-term available and tamper proof ledgers. A Blockchain stores transaction in a confirmable and persistent way, therefore it is appropriate to save certificate or learning certification. Blockchain divulge fraud of certificates and it substructure learning records. This is accomplished by keeping digital crypto-hashes of learning certificates and controlling authorization integrity through the development of smart contract on Blockchain. The TUDocChain is the platform that entitle authorize the academic certificates on public ledger in a reliable and sustainable format. In this paper, we present the Blockchain for education platform as a practical solution for issuing, validating and sharing of certificate.
Adversarial WiFi Sensing for Privacy Preservation of Human Behaviors

Recent research on WiFi sensing focuses on the identification of a wide range of human behaviors with high recognition accuracy. However, these well-studied recognition techniques can cause privacy concerns due to the ubiquity of WiFi signal and comprehensive behavior information embedded therein. In this letter, we take the first attempt to develop an adversarial deep network architecture for human behavior preservation. Our goal is to make desirable private behaviors of a human being not recognizable by general classifiers, while the recognition of other ones remaining unaffected. To achieve this, we propose a novel loss function, using which our network is capable of intentionally modifying CSI data extracted from received WiFi signal, constraining the new classification results to match the adversarial requirements. Experimental results demonstrate that, with the proposed adversarial scheme, the recognition rate of the human behaviors needed to be protected can be significantly decreased, while still maintaining the accuracy of other ones desired to be identified. Our source codes are available at https://github.com/siwangzhou/WiFi-ADG.
MinMaxScaler Binary PSO for Feature Selection

Particle Swarm Optimization (PSO) is a widely accepted optimization method which has shown merits in many fields. Particle Swarm Optimization has been modified several times to get the desired result in different problems. This paper presents a survey of developments and modifications of PSO, and from the survey, it is found that the major drawback of PSO is the trapping in local minima, due to which premature convergence takes place. Its performance is poor in high-dimensional data and in complex problems. That’s why PSO for feature selection problem has been surveyed further and a new MinMaxScaler Binary PSO is proposed for feature selection.
Visual Question Answering From Remote Sensing Images

Remote sensing images carry wide amounts of information beyond land cover or land use. Images contain visual and structural information that can be queried to obtain high level information about specific image content or relational dependencies between the objects sensed. This paper explores the possibility to use questions formulated in natural language as a generic and accessible way to extract this type of information from remote sensing images, i.e. visual question answering. We introduce an automatic way to create a dataset using OpenStreetMap <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> data and present some preliminary results. Our proposed approach is based on deep learning, and is trained using our new dataset.
Leveraging Ratings and Reviews with Gating Mechanism for Recommendation

Recommender system plays an important role to provide people with personalized information based on their history records. However, it is still a challenge to capture the preference of users accurately due to the sparsity of rating data and the heterogeneity of review data. In this paper, we propose a hybrid deep collaborative filtering model that jointly learns latent representations from ratings and reviews. Specifically, the model learns the rating feature and textual feature based on ratings and reviews simultaneously. Two embedding layers are employed to learn rating feature for users and items based on the user and item interactions, and two attention-based GRU networks learn context-aware representation from user and item reviews. Then a gating mechanism is used to leverage contributions from rating feature and textual feature. Experimental results on six real-world datasets demonstrate the superior performance of the proposed method over several state-of-the-art methods. Moreover, the keywords in reviews can be highlighted to interpret the predictions with the attention mechanism.
Gender-based speaker recognition from speech signals using GMM model

Speech is a convenient medium for communication among human beings. Speaker recognition is a process of automatically recognizing the speaker by processing the information included in the speech signal. In this paper, a new approach is proposed for speaker recognition through speech signal. Here, a two-level approach is proposed. In the first-level, the gender of the speaker is recognized, and in the second-level speaker is recognized based on recognized gender at first-level. After recognizing the gender of the speaker, search space is reduced to half for the second-level as speaker recognition system searches only in a set of speech signals belonging to identified gender. To identify gender, gender-specific features: Mel Frequency Cepstral Coefficients (MFCC) and pitch are used. Speaker is recognized by using speaker specific features: MFCC, Pitch and RASTA-PLP. Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) classifiers are used for identifying the gender and recognizing the speaker, respectively. Experiments are performed on speech signals of two databases: “IIT-Madras speech synthesis and recognition” (containing speech samples spoken by eight male and eight female speakers of eight different regions in English language) and “ELSDSR” (containing speech samples spoken by five male and five female in English language). Experimentally, it is observed that by using two-level approach, time taken for speaker recognition is reduced by 30–32% as compared to the approach when speaker is recognized without identifying the gender (single-level approach). The accuracy of speaker recognition in this proposed approach is also improved from 99.7% to 99.9% as compared to single-level approach. It is concluded through the experiments that speech signal of a minimum 1.12 duration (after neglecting silence parts) is sufficient for recognizing the speaker.
Arrow R-CNN for Flowchart Recognition

We propose Arrow R-CNN for recognizing the symbols and structure of offline handwritten flowcharts. Arrow R-CNN extends the Faster R-CNN object detection system with an arrow keypoint predictor. This keypoint predictor is used to reconstruct the flowchart structure. We propose a network architecture and data augmentation methods that allow us to train a very deep model on a small publicly available flowchart dataset. Evaluation results show that Arrow R-CNN outperforms existing offline systems by a wide margin. For comparison with existing online flowchart recognizers, we propose an extension for mapping strokes to recognized symbols. Results show that Arrow R-CNN also achieves state of the art in online recognition, even though it does not explicitly leverage stroke information. An ablation study reveals that data augmentation guided by domain knowledge is key to high accuracy on such a small dataset.
Trajectory Planning and the Target Search by the Mobile Robot in an Environment Using a Behavior-Based Neural Network Approach

SUMMARY Navigation and path analysis in a cluttered environment is a challenging task over the last few decades. In this paper, a behavior-based neural network (BNN) and reactive control architecture have been presented for navigation of the mobile robot. Two different reactive behaviors have been taken as inputs function. Obstacle position is the first reactive behavior given by u ( o ), whereas obstacle angle u ( n ) according to the target position is the second reactive behavior. The angular velocity and steering angle are the output of the controller. The backpropagation architecture reduces the errors of weight function and records the best weight data that match the BNN controller. Using the BNN algorithm, the robot reacts quickly as compared to other developed techniques. To validate the performance of the controller, simulation and experimental results have been compared in the common platforms. The deviation in results for both the scenarios is found to be within 10%. The results of the BNN algorithm have also been compared with other existing techniques. Effectiveness of the proposed technique is measured in terms of smoothness of the realistic path, collision point detection, path length, and performance time.
A knowledge-driven way to interpret the isometric log-ratio transformation and mixture distributions of geochemical data

When extracting geo-information from geochemical data, it is essential to consider the compositional nature and geological signatures of the data. Isometric log-ratio transformation (ILR) produces an orthonormal basis of geochemical data and accounts for the compositional nature of the data. However, it still often remains difficult to interpret ILR-transformed variables because of the lack of a geological basis for a data-driven approach; therefore, it is necessary to find some geological knowledge-based criteria to help enable a more understandable interpretation following ILR transformation. Characterized by certain elements and elemental ratios, the chronological order of geological processes can be used to construct interpretable ILR transformation. This concept was applied to extract geo-information from stream sediment geochemical data in the Duolong mineral district, Tibet, China. Furthermore, the expectation-maximization (EM) algorithm modified by a minimum message length criterion (MML) was employed to investigate mixture distributions of the geochemical data. In the study area, mafic rocks with high concentrations of Cr and Ni were emplaced earlier than the porphyry Cu–Au deposits which have stable Cu/Au ratios. Based on these criteria and hierarchical cluster analysis, sequential binary partition was constructed among the Cu, Au, Cr, and Ni concentrations. The ILR-transformed variables follow either a bi-normal or tri-normal distribution, the subpopulations of which were interpreted as fingerprints inherited from mafic magmatic processes, Cu–Au hydrothermal systems, and later geological processes, respectively. The high-average subpopulation of ILR-transformed Cu corresponds to anomalies associated with the porphyry and epithermal Cu–Au systems, and two areas are predicted to have high Cu–Au mineralization potential. This study demonstrates that geological knowledge-driven ILR transformation is a promising way to efficiently extract geo-information from geochemical data.
Dempster-Shafer Theory for Fusing Face Morphing Detectors

Revealing that a human face on a biometric image is a mixture of two or more faces is of immense importance for document issuing authorities and document checking services. If not done, several persons can use the same photo-ID document for identity verification without being condemned. The development of automated face morphing detectors is currently in its early phase. The detectors reported so far are not mature for the market which is reflected in high error rates when tested with “unseen” data. Here, we demonstrate that fusion of several by far non-optimal detectors may lead to significant improvement of detection accuracy compared to that of individual detectors. Among the examined fusion approaches, Dempster's Rule of combination has the best accuracy allowing for coherent decision making even with contradicting decisions of individual detectors.
Deep Reinforcement Learning Based Personalized Health Recommendations

In this age of informatics, it has become paramount to provide personalized recommendations in order to mitigate the effects of information overload. This domain of biomedical and health care informatics is still untapped as far as personalized recommendations are concerned. Most of the existing recommender systems have, to some extent, not been able to address sparsity of data and non-linearity of user-item relationships among other issues. Deep reinforcement learning systems can revolutionize the recommendation architectures because of its ability to use non-linear transformations, representation learning, sequence modelling and flexibility for implementation of these architectures. In this paper, we present a deep reinforcement learning based approach for complete health care recommendations including medicines to take, doctors to consult, nutrition to acquire and activities to perform that consists of exercises and preferable sports. We try to exploit an “Actor-Critic” model for enhancing the ability of the model to continuously update information seeking strategies based on user’s real-time feedback. Health industry usually deals with long-term issues. Traditional recommender systems fail to consider the long-term effects, hence failing to capture dynamic sentiments of people. This approach treats the process of recommendation as a sequential decision process, which addresses the aforementioned issues. It is estimated that over 700 million people will possess wearable devices that will monitor every step they take. Data collected with these smart devices, combined with other sources like, Electronic Health Records, Nutrition Data and data collected from surveys can be processed using Big Data Analysis tools, and fed to recommendation systems to generate desirable recommendations. These data, after encoding (state) into appropriate format, will be fed to the Actor network, which will learn a policy for prioritizing a particular recommendation (action). The action, state pair is fed to the critic network, which generates a reward associated with the action, state pair. This reward is used to update the policy of the Actor network. The critic network learns using a pre-defined Expected Reward. Hence, we find that using tools for Big Data Analytics, and intelligent approaches like Deep Reinforcement Learning can significantly improve recommendation results for health care, aiding in creating seamlessly personalized systems.