Reviewerly/exHarmony huggingface dataset:
Ontological Scene Graph Engineering and Reasoning Over YOLO Objects for Creating Panoramic VR Content

Detecting objects in videos and harnessing their relationships for scene understanding is a challenging task in the computer vision domain. This has been attempted using the Scene Graph Generation (SGG) task. Recent YOLO models could track objects and establish spatial relationships among the detected objects. However, these deep neural networks are not capable of explaining the structural relationships among the objects that impair practical applications. The adoption of visual transformers is also not prudent as it leads to an increase in the complexity of the overall model. In this paper, an ontology-based scene graph engineering and reasoning approach over the extracted objects is proposed as a solution to this problem. First, the ontological model takes the detected objects from YOLO and generates corresponding entities and relationships. Then, the Semantic Web Rule (SWRL) is written on top of this model to discover the image sequence. And it also offers a machine-interpretable explanation for this sequence when a continuous stream (audio-visual) is constructed. Finally, this audio-visual is coupled with spatial media metadata to make it 360-degree panoramic viewable Virtual Reality (VR) content. It is found that the ontological model is a more versatile solution than the overall deep neural models. Overall, this methodology is helpful in various real-world scenarios for better learning and understanding of natural environments. For example, the audio explanation gadget helps specially-abled people navigate through cluttered environments such as metro rail stations.
Privacy-Preserving Decentralised Functional Encryption for Inner Product

To support secure data mining and privacy-preserving computation, partial access and selective computation on encrypted data are desirable. Functional encryption (FE) is a new paradigm of public-key encryption and allows authorized users to compute specific functions on encrypted data without knowing the data. However, in some FE schemes, a trusted central authority (CA) is required to generate secret keys for users according to the description of functions. In this paper, to reduce trust on the CA and protect users' privacy, a privacy-preserving decentralised FE for inner product (PPDFEIP) scheme is proposed where multiple authorities co-exist and work independently without any interaction. Especially, to resist collusion attacks, all secret keys of the same user are tied to his/her global identifier (GID), but authorities cannot know any information of the GID even if they collaborate. We formalize the definition and security model of our PPFEIP scheme, and propose a concrete construction. Furthermore, the proposed scheme is implemented and evaluated. Finally, the security of our PPDFEIP scheme is reduced to well-known complexity assumptions. The novelty is to reduce trust on the CA, protect users' privacy and enable authorized users to compute inner product on encrypted data without compromising confidentiality.
An Introduction to Deep Learning

Machine learningMachine Learning (ML) algorithmsDeep Learning try to learn the mapping from an input to output from data rather than through explicit programming. ML uses algorithms that iteratively learn from data to describe data or predict outcomes, i.e., to produce precise models based on that data. As the training data becomes larger, it is then possible to produce more precise models.
Book Recommendation Using Double-Stack BERT: Utilizing BERT to Extract Sentence Relation Feature for a Content-Based Filtering System

Traditional entertainment such as reading is still heavily enjoyed and is even having an increase in interest. But there is a significant discrepancy in search keywords “book recommendation” and “similar books to”, where the latter is higher than the former. This research proposed a method that attempted to recommend books by their story progression that is called Double-Stack BERT which generates a document embedding from a book synopsis as a representation in the vector space. This document embedding will then be measured by its similarity against each other using cosine similarity to retrieve a similar book recommendation. Double-Stack BERT builds the document embeddings by extracting and adding its sentence relation feature by treating each sentence in a document as a token. This addition proved to increase the performance of document embedding when tested on datasets with similar characteristics.
A Novel Pixel Value Predictor Using Long Short Term Memory (LSTM) Network

Pixel value prediction refers to predicting a pixel value using its neighboring pixel values. It is an important part of several image processing tasks, such as image compression and reversible data hiding. Prediction error is defined as the difference between the original pixel value and the corresponding predicted pixel value. Low prediction error helps to attain good performances in both image compression and reversible data hiding. This has motivated researchers to explore various techniques for pixel value prediction. Most of these techniques map the neighborhood to the predicted value using a mathematical relationship. This kind of rigid approach does not fit to the varied pixel neighborhood in images. On the contrary, a neural network can be trained to predict the pixel value instead of using a conventional mathematical approach for this mapping. In this paper, pixel value prediction is conceptualized as predicting the next value in a spatial sequence of pixels in a particular direction. Therefore, a novel pixel value predictor is proposed using a long short term memory (LSTM) network. Two different LSTM networks are trained separately for horizontal and vertical directions. Finally, the proposed approach leverages the predictors for both directions. For each pixel, it considers the direction with lesser variation in the input pixel sequence. Experimental results indicate that the proposed LSTM network based predictor performs better than the comparing state-of-the-art methods.
Vitiating Factors

If a smart contract might be a legal agreement or might be relevant in the execution of a legal agreement, such a smart contract might be affected by a viating factor. Therefore, the situations that a party might be operating under the influence of a mistake, fraud, misrepresentation, duress, or undue influence should be explored. In order to do so, this chapter will give an outline of the rules on those aforementioned vitiating factors after which the impact of the technology on those rules will be considered. It will be shown that the technology might be leveraged by designers and operators of such platforms to prevent, to a certain extent, the occurrence of defects of consent. This can be done by ensuring, through smart and conscious use of the technology, a high degree of availability of information to the parties. It is argued that, if a vitiating factor occurs nonetheless and as a result of certain information being unavailable to one of the parties as a result of a strictly pseudonymous implementation of the technology, liability for the designer or operator might be the result. Moreover, in order to apply the current national systems on such vitiating factors the ‘location’ of a vitiating factor must be determined. A mistake might, for example, arise due to off-chain circumstances, but could also arise as a result of on-chain circumstances. Additionally, the impact of on-chain vitiating factors and off-chain vitiating factors is dependent on the scenario in which a smart contract exists. This chapter therefore expands on the four scenarios introduced in the previous chapter and outlines to which circumstances the applicable rules must be applied to.
Robust Proxy: Improving Adversarial Robustness by Robust Proxy Learning

Recently, it has been widely known that deep neural networks are highly vulnerable and easily broken by adversarial attacks. To mitigate the adversarial vulnerability, many defense algorithms have been proposed. Recently, to improve adversarial robustness, many works try to enhance feature representation by imposing more direct supervision on the discriminative feature. However, existing approaches lack an understanding of learning adversarially robust feature representation. In this paper, we propose a novel training framework called Robust Proxy Learning. In the proposed method, the model explicitly learns robust feature representations with robust proxies. To this end, firstly, we demonstrate that we can generate class-representative robust features by adding class-wise robust perturbations. Then, we use the class representative features as robust proxies. With the class-wise robust features, the model explicitly learns adversarially robust features through the proposed robust proxy learning framework. Through extensive experiments, we verify that we can manually generate robust features, and our proposed learning framework could increase the robustness of the DNNs.
ACB-Vote: Efficient, Flexible, and Privacy- Preserving Blockchain-Based Score Voting With Anonymously Convertible Ballots

Blockchain has emerged as a decentralized platform for e-voting. Among various blockchain-based voting systems, score voting provides flexible choices and better reflects public opinions. However, existing blockchain-based score voting systems suffer from heavy range proof overheads, and are much inefficient compared with other blockchain-based voting systems. Besides, voter anonymity in these systems is not rigorously addressed. In this paper, we propose an efficient, flexible and privacy-preserving score voting system, named ACB-Vote, from anonymously convertible ballots. ACB-Vote achieves voting anonymity with BBS+ signature and signature of knowledge. Driven by convertibly linkable signatures (CLS), ACB-Vote allows cast ballots to be converted, where the conversion mechanism prevents anonymous voters from multiple voting. Besides, the proposed system avoids heavy range proofs, enables batch ballot verification and facilitates flexible tallying methods. We formally define a security model for ACB-Vote and provide rigorous security proofs. Experiments show that the efficiency of ACB-Vote is competitive compared with the previous score voting systems and is affordable in blockchain environments.
A High-Coverage and Efficient Instruction-Level Testing Approach for x86 Processors

The processors have long been treated as trusted black boxes for running software. However, processors may have undocumented instructions and instruction flaws, which increase the attack surface of the computing system. Hardware-related attack surfaces can bypass malware detection tools, resulting in undefined system behavior, instability, and insecurity. Unfortunately, the existing testing methods for undocumented instructions and instruction flaws have issues of insufficient test coverage and low test efficiency. We proposed an approach Skipscan to address these issues, which tests both the legal instructions and the reserved instructions. For the first time, to improve the test coverage, Skipscan leverages an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimized combination algorithm</i> to generate instruction prefix combinations, which covers the entire types of legal prefix combinations. To improve the test efficiency, Skipscan skips a considerable number of redundant legal instructions by leveraging the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimal test set</i> of immediate and displacement operands. We evaluated Skipscan on eight x86 processors from Intel and AMD. The number of legal instructions and reserved instructions tested by Skipscan are 121.4 and 259.55 times that of Sandsifter on average, respectively. The test efficiency of Skipscan is on average 4 times that of Sandsifter. The ratio of legal instructions is reduced from 78.2% to 20.1% on average. Furthermore, we found more undocumented instructions on x86 processors and instruction flaws in x86 disassemblers.
A Proposal for Standardization of Internet of Musical Things (IoMusT) Environments

The Internet of Musical Things is an interdisciplinary area of knowledge that aims to improve the relationship between musicians, as well as between musicians and audience members, favoring concerts, studio productions, and music learning. Although emerging, this field already faces some challenges, such as lack of privacy and security, and mainly, lack of standardization and interoperability between its devices. Therefore, the present paper presents an environment design, called Sunflower, highlighting its architecture, protocols, and sound features that can contribute to solving the most recurrent problems in this area.
A Comprehensive Approach for Applying Threat Modeling to Internet of Things Systems

Internet of Things (IoT) technologies are being increasingly used by society. Industry, Agriculture, and Healthcare are examples of areas where IoT is intensively used. In this way, security became a primary concern for IoT systems since its lack can cause considerable damages from the disclosure of restricted information to the loss of lives. Threat Modeling is a well-known approach to mitigating security threats in information systems. However, to appropriately applying Threat Modeling to the IoT context is necessary to adjust the modeling to consider attacks that affect the security of those systems. This work presents a comprehensive approach for applying threat modeling to IoT systems. We performed survey-of-surveys on attacks and countermeasures for IoT and used this survey as input for traditional Threat Modeling techniques such as STRIDE and DREAD. We applied the adjusted approach to an open-source smart lock system finding some possible attacks. We present the process's steps to other practitioners performing threat modeling for IoT systems.
DiffNet Framework for On-Device Object Detection

In object detection, DNN models have hundreds of hidden layers and tens of millions of parameters to perform localization and classification tasks. Since DNN models require high computational capacity, object detection is limited in resource-constrained environments such as embedded boards and mobile devices. In this paper, we propose a DiffNet framework for on-device object detection. Existing DNN models contain deep convolutional layers to perform two tasks: We apply a divide and conquer approach to solve the complexity problem of object detection by dividing it into two simple problems: localization and classification. In other words, we train two models for object detection. The proposed backbone network is trained to regress the bounding box without class information. Therefore, it is about 80% lighter than the existing DNN model. In addition, when performing object detection in a real environment, the range of detectable objects is limited. We train a lightweight classifier for classifying objects that are highly related to the domain. The DiffNet framework uses the binding box detected by the differential backbone network as an input to the domain-specific classifier. To evaluate the detection accuracy of our framework, we conduct comparative experiments with existing lightweight models. In addition, we evaluate novelty to see whether the backbone can detect bounding boxes for novel classes that do not belong to the learning data.
ConShar: An Edge-based Context Sharing Model for the Internet of Things

Internet of Things (IoT) has become popular by its characteristics of embedded processing power and network capabilities into a wide range of everyday devices. It is well known that IoT devices are deployed with different characteristics for communication, data format, semantics. Context-awareness gives semantic meaning to IoT devices data by creating a high-level description of the entities situation, called context information. Unfortunately, many context-aware platforms only provide context information for itself and do not care in sharing it with whom may be interested. However, taking into account the heterogeneity of IoT environments, it is necessary to share the context information between different application domains to provide semantic interoperability. This paper presents ConShar, an edge-based context sharing model for IoT environments able to provide context interoperability. We also present related work in the area to clarify the novelty of the proposed architecture and a validation of ConShar's context Classification method.
Serverless Outlier Management for Environmental IoT Data - A Case Study of PuhatuMonitoring

PuhatuMonitoring (PM) is an IoT application for observing the water level changes in a wetland in the Puhatu Nature Protection Area (NPA), North West Estonia next to an open-pit oil-shale quarry. IoT devices are installed for monitoring the environment and the LORAWAN things network stack is used to collect raw sensor data into the cloud. A major challenge faced by geologists were outliers in the collected data, caused by natural and weather conditions or device/sensor failures. We propose a system for data collection, detection of outliers using unsupervised machine learning algorithms, tagging of outlier data, and storing the results. We utilize Serverless (FaaS) model to deploy individual data processing services as virtual functions, which can be executed in an event-driven manner on data streams. We designed two different serverless data pipeline (SDP) approaches: Apache NiFi and Message Queue (MQTT) based. We investigated the performance of the two proposed approaches and results showed that Apache NiFi-based SDP approach has a lower processing time and higher disk utilization. In contrast, the MQTT-based approach experienced higher processing time and lower disk and memory utilization. Our suitability analysis showed that Apache NiFi-based approach suits better for environment monitoring applications like PuhatuMonitoring.
SLA- Gα: The Next Generation of SLAs for IoT

There is an increasing expectation that SLAs become fully automated for IoT environments. This automation may involve monitoring of adherence to an SLA, discretionary encryption of data, enforcement of SLAs and compliance with legal requirements such as the General Data Protection Regulation (GDPR) or comparable regulations. Furthermore, such SLAs need to be customised to a specific IoT vertical, IoT use case or IoT scenario. However, designing such customised IoT scenario-specific SLAs on the fly is very challenging, nuanced, complex and requires domain-specific knowledge. To address such challenges, we propose a platform agnostic, trustworthy, reliable, secure and GDPR compliant end-to-end automated on the fly SLA generation platform. The architecture follows the GDPR Privacy by Design approach, and we demonstrate how this framework can be used for processing of data irrespective from where the data generated, and the SLA attached to it.
Decentralising Access Control for IoT Environment

IoT has a profound impact on businesses and individuals with growing adoption. Security and scalability are key subjects for scaling the adoption. Cyber attacks increase each year, and the emerging technologies' addition such as Machine Learning introduce vulnerabilities with additional complexity. Access control can mitigate security threats with proper rights management. XACML is an appropriate way to enforce complex policies in heterogeneous environments like IoT due to its flexibility. Furthermore, the blockchain's advantages like data immutability and availability can aid in building a trustworthy access control system for IoT. Blockchain can support a de-centralised architecture for policy evaluation and avoid single points of failure for the policy evaluation resulting in enhanced security of the IoT network. Smart contracts accommodate the access control policies' evaluation for delivering a decentralised and tamper-proof system with consistent outcomes. This paper proposes a decentralised access control approach following the XACML standard and enabling the access control decision evaluation using smart contracts. The implementation's impact on a complex real-world environment is described. The reference implementation is extensible to a great degree as it has flexibility in including services on top of the blockchain, such as an audit mechanism on the access decisions.
Data Augmentation for Edge-AI on-chip Learning

This study applied data augmentation to improve the inference accuracy of edge-artificial intelligence on-chip learning, which uses the fine-tuning technique with limited knowledge and without a cloud server. Subsequently, keyword spotting was adopted as an example of the edge-AI application to evaluate inference accuracy. Investigations revealed that all four data augmentation types contributed to inference accuracy improvements, boosting data augmentation by 5.7 times rather than the one-shot boost without data augmentation recorded previously.
Agri-food Traceability Using Blockchain Technology to Ensure Value Chain Management and Fair Pricing in Bangladesh

The agri-food supply chain plays the most vital role in the socioeconomic sphere to the producer and consumer's avail. In the traditional agri-food supply chain in Bangladesh, the farmers need to hinge on the intermediaries for selling their products as there is a limitation of communicative medium between producers and consumers. Also, the fair pricing of agri-food products and transparency are significant issues in the traditional supply chain system. In this paper, we propose a conceptual blockchain-based agri-food supply chain model for Bangladesh where the farmers can communicate with consumers and sell products directly without any inter-agency. This model helps to incorporate transparency in the agri-food supply chain and fair pricing of the agri-food products between producers and consumers.
Fast, Robust &amp; Light Machine Learning for Signal Classification in Next Generation Mobile Networks

Next generation mobile networks bring unprecedented opportunities coupled with unique challenges thanks to the integration of multiple families of devices. Fast and robust signal classification and modulation identification become critical to meet the sustained demand on capacity. This paper presents a comparative study of data-centric and conventional approaches to signal identification at different noise levels on a real-world application. We demonstrate that a standard lightweight classifier can detect multiple modulation schemes with and without data compression and outperforms current state-of-the-art by as much as 6% on average across 15 different noise levels. More importantly, the detection speed is improved by at least 50-fold without a significant loss in accuracy when using feature compression.
Things Data Interoperability: Linking Annotated Entities in oneM2M

The semantic graphs have been subject to promising research for representing and enhancing Internet of Things data, in which RDF triples and LPG have been widely considered each with its own features and usages. Different Internet of Things standards have supported semantic graphs in their specifications, however large amount of Internet of Things data is utilized and exchanged in their conventional formats, which needs to be annotated in order to utilize the full potential of semantic graphs in such environments. This paper proposes a mapping protocol for interpreting and translating the non-semantic oneM2M resources to NGSI-LD format with an emphasis on creating relationships among the annotated NGSI-LD Entities. As proof of concept, an annotation example has been illustrated based on the public parking use case scenario.