Reviewerly/exHarmony huggingface dataset:
Regulatory Compliance Modelling Using Risk Management Techniques

We describe a novel approach to regulatory compliance decision support that leverages an asset-based cyber security risk management approach following ISO 27005, and illustrate this with an example based on the General Data Protection Regulation (GDPR). Previous work in regulatory compliance modelling and decision support utilises semantic vocabularies and reasoning techniques, and our approach has the primary benefit that regulatory compliance is integrated with other domains of risk management, so that insight from domains such as cyber security combined with regulatory compliance for privacy can be gained based on a single model of the user's sociotechnical infrastructure. The main contributions of this paper are: to show how regulatory requirements may be modelled as “compliance threats” in an asset-based risk management framework; to illustrate mapping from the GDPR's legal text to domain assets, processes and relationships, compliance threats and control strategies to mitigate the threats; to show how the threats are triggered via recognition patterns based on asset configurations; to illustrate how the different types of regulatory requirement, e.g. obligations, prohibitions and derogating conditions, are represented in such a scheme; and finally to describe how to model causal dependencies between choices made to address a compliance threat and downstream additional compliance requirements.
Fast and Accurate Camera Scene Detection on Smartphones

AI-powered automatic camera scene detection mode is nowadays available in nearly any modern smartphone, though the problem of accurate scene prediction has not yet been addressed by the research community. This paper for the first time carefully defines this problem and proposes a novel Camera Scene Detection Dataset (CamSDD) containing more than 11K manually crawled images belonging to 30 different scene categories. We propose an efficient and NPU-friendly CNN model for this task that demonstrates a top-3 accuracy of 99.5% on this dataset and achieves more than 200 FPS on the recent mobile SoCs. An additional in-the-wild evaluation of the obtained solution is performed to analyze its performance and limitation in the real-world scenarios. The dataset and pre-trained models used in this paper are available on the project website.
Improving Barnes-Hut t-SNE Algorithm in Modern GPU Architectures with Random Forest KNN and Simulated Wide-Warp

The t-Distributed Stochastic Neighbor Embedding (t-SNE) is a widely used technique for dimensionality reduction but is limited by its scalability when applied to large datasets. Recently, BH-tSNE was proposed; this is a successful approximation that transforms a step of the original algorithm into an N-Body simulation problem that can be solved by a modified Barnes-Hut algorithm. However, this improvement still has limitations to process large data volumes (millions of records). Late studies, such as t-SNE-CUDA, have used GPUs to implement highly parallel BH-tSNE. In this research we have developed a new GPU BH-tSNE implementation that produces the embedding of multidimensional data points into three-dimensional space. We examine scalability issues in two of the most expensive steps of GPU BH-tSNE by using efficient memory access strategies , recent acceleration techniques , and a new approach to compute the KNN graph structure used in BH-tSNE with GPU. Our design allows up to 460% faster execution when compared to the t-SNE-CUDA implementation. Although our SIMD acceleration techniques were used in a modern GPU setup, we have also verified a potential for applications in the context of multi-core processors.
Graph compression based on transitivity for neighborhood query

In recent years, many graph compression methods have been introduced. One successful category of them is based on local decompression designed to answer neighborhood queries. These techniques mainly rely on local similarities of vertices. Besides, their performance is usually a function of graph sparsity. The proposed approach, in this paper, is a lossy compression technique used to answer neighborhood queries with a more general precondition, called transitivity. The output of this method is a sparse graph optimized to keep original adjacent vertices, in at most 2-distance from each other and vice versa. In other words, by traversing a compressed graph by depth of 2, from any desired vertex, its original adjacency list is reconstructed, with an acceptable error. This paper models an optimization problem to solve the inverse problem of finding the best compressed graph in order to minimize the reconstruction error. Then, this NP problem is approximated by a heuristic with a low degree polynomial time-complexity near to the complexity of the forward problem. The results of applying the proposed method on toy and real datasets are compared with the state of the art that improves compression ratio and performance with an acceptable query response time.
GDPR-Compliant Use of Blockchain for Secure Usage Logs

The unique properties of blockchain enable central requirements of distributed secure logging: Immutability, integrity, and availability. Especially when providing transparency about data usages, a blockchain-based secure log can be beneficial, as no trusted third party is required. Yet, with data governed by privacy legislation such as the GDPR or CCPA, the core advantage of immutability becomes a liability. After a rightful request, an individual's personal data need to be rectified or deleted, which is impossible in an immutable blockchain. To solve this issue, we exploit a legal property of pseudonymized data: They are only regarded personal data if they can be associated with an individual's identity. We make use of this fact by presenting P3, a pseudonym provisioning system for secure usage logs including a protocol for recording new usages. For each new block, a one-time transaction pseudonym is generated. The pseudonym generation algorithm guarantees unlinkability and enables proof of ownership. These properties enable GDPR-compliant use of blockchain, as data subjects can exercise their legal rights with regards to their personal data. The new-usage protocol ensures non-repudiation, and therefore accountability and liability. Most importantly, our approach does not require a trusted third party and is independent of the utilized blockchain software.
A Framework to Quantify Approximate Simulation on Graph Data

Simulation and its variants (e.g., bisimulation and degree-preserving simulation) are useful in a wide spectrum of applications. However, all simulation variants are coarse "yes-or-no" indicators that simply confirm or refute whether one node simulates another, which limits the scope and power of their utility. Therefore, it is meaningful to develop a fractional χ-simulation measure to quantify the degree to which one node simulates another by the simulation variant χ. To this end, we first present several properties necessary for a fractional χ-simulation measure. Then, we present FSim <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">χ</sub> , a general fractional χ-simulation computation framework that can be configured to quantify the extent of all χ-simulations. Comprehensive experiments and real-world case studies show the measure to be effective and the computation framework to be efficient.
Privacy and Utility-Assisted Data Protection Strategy for Secure Data Sharing and Retrieval in Cloud System

The outsourcing of Electronic Health Records (EHR) on cloud infrastructures has enabled medical data sharing among several healthcare applications. The blockchain offers security by authenticating users with encryption methods. The collaboration with the cloud provides better management but poses threats to the privacy of the patient. This paper devises a novel blockchain-assisted framework for effective data sharing and retrieval using cloud platforms. Here, the data protection model is devised in EHR application for secure transmission. The entities in the cloud platform include data user, data owner, smart agreement, transactional blockchain, and Inter-Planetary File System (IPFS). Here, the data owner includes a data protection model to secure EHR in which secured EHR is transferred to IPFS before sharing with the data user. The data protection is done by preserving data privacy using Tracy-Singh product and proposed Conditional Autoregressive Value at risk (CAViaR)-based Bird swarm algorithm (CAViaR-based BSA) combination of BSA and CAViaR for generating optimal privacy-preserving coefficients. The objective function is newly devised considering privacy and utility. The proposed CAViaR-based BSA outperformed other methods with minimal responsiveness of 251.339 s, maximal genuine user detection of 32.451%, maximal privacy of 96.5%, and minimal information loss of 3.5%.
GraphifyEvolution - A Modular Approach to Analysing Source Code Histories

The analysis of mobile applications has gained popularity in recent years. Multiple tools have been developed to find code smells in mobile applications. To analyse code evolution of mobile applications researchers have mostly written scripts to parse repository histories and apply existing code smell tools for each commit. Some specialised tools have been developed for analysing code smell histories but they only apply one specific method of finding code smells. We developed a modular and extendable tool called GraphifyEvolution that makes it possible to analyse code repositories by analysing changes in each commit and saving the application structure, including all changes, into a Neo4j graph database. We built the tool with Swift applications in mind, especially iOS applications and frameworks, but due to its modular nature it is possible to add support for other languages easily. Implementing analysers that allow for running external analysis tools for each commit and inserting the results into the application database are also possible. Combining information on the structure of applications and its evolution with results from external code analysis tools helps answer a multitude of research questions. Given its modular nature, we hope that our tool will be useful to researchers who wish to analyse source code histories. We have currently implemented support for the languages Swift, Java, C++ and for the external tools jscpd (code duplicate scanner) and insider (vulnerability scanner).
Enable Pipeline Processing of DNN Co-inference Tasks In the Mobile-Edge Cloud

Deep Neural Network (DNN) based artificial intelligence help driving the great development of mobile Internet. However, the hardware of a mobile device may not be sufficiently to meet the computational requirements of a DNN inference task. Fortunately, computation offloading to the network edge can mitigate part of computation pressure for mobile devices. In this case, DNN computation in mobile devices can be accelerated by an edge-assistance collaborative inference scheme. Since co-inference tasks with multiple processing stages may continuously arrive at mobile devices, only considering one DNN-based task for acceleration is not practical. To solve the above challenge effectively, we formulate the problem of multiple co-inference tasks acceleration as a pipeline execution model. Based on the model, we design a fine-grained optimizer, which integrates model partition, model early-exit and intermediate data compression, to achieve tradeoff between accuracy and latency. Considering computational characteristics of a pipeline, the goal of the optimizer is designed to ensure the pipeline system's inference rate and single task execution performance. We implement the system prototype and do benchmark tests under a real-life testbed and the results prove the effectiveness of the optimizer.
Adaptive Strategy for Environment Exploration in Search and Rescue Missions by Autonomous Robot

In this research, a new adaptive strategy is proposed for the autonomous mobile robot, which explores the unknown search and rescue (SAR) environment. The robot, which implements the proposed strategy, operates on the frontier-based exploration approach and makes a decision of where to move next by applying a total of eight new strategies for candidate frontier assessment. The fuzzy logic controller is applied to determine the most suitable candidate frontier assessment strategy regarding the current robot state and the discovered environment information. The final decision of where to move next is made by the neutrosophic interval-valued multi-criteria decision-making method, namely WASPAS-IVNS, which enables the modelling of vagueness present in the initial sensor data. The proposed adaptive strategy is tested in the virtual Gazebo simulation. The obtained test results show the increased efficiency when comparing the proposed adaptive environment exploration strategy to the static environment exploration strategies and the standard greedy environment exploration approach.
Residential House Occupancy Detection: Trust-Based Scheme Using Economic and Privacy-Aware Sensors

Internet of Things (IoT) technologies (e.g., power-efficient occupancy-based energy management systems) are increasingly deployed in commercial buildings to reduce building energy consumption. However, the sensors involved in such systems are rarely adopted in residential houses due to their relatively high costs and users’ privacy concerns. Low-cost and nonintrusive IoT sensors have been proposed for residential houses for use with machine-learning algorithms. Furthermore, such sensors may be triggered very infrequently due to their nonintrusive nature, and it can take several days/weeks to collect sufficient training data. There is a research gap in accurately detecting occupancy information in residential houses with limited training data. This article proposes a trust-based occupancy detection scheme, which achieves high detection accuracy based on limited training data collected by nonintrusive, low-cost sensors. First, rather than directly taking raw sensor data as inputs, the semantic meanings (i.e., human activity sequences) are extracted from the data based on the order of triggered sensors. Second, the extracted human activity sequences are fed into the proposed trust-based sequence matching scheme for further occupancy detection. Comprehensive experimental results show that, when compared to existing occupancy detection algorithms, the proposed scheme can reliably achieve higher accuracy, especially when only limited training data is available.
Partitionable Asynchronous Cryptocurrency Blockchain

We consider operation of blockchain-based cryptocurrency in case of partitioning. We define the Partitionable Blockchain Consensus Problem. The problem may have an interesting solution if the partitions proceed independently by splitting accounts. We prove that this problem is not solvable in the asynchronous system. The peers in the two partitions may not agree on the last jointly mined block or, alternatively, on the starting point of independent concurrent computation. We introduce a family of detectors that enable a solution. We establish the relationship between detectors. We present the algorithm that solves the Partitionable Blockchain Consensus Problem using our detectors. We extend our solution to multiple splits, message loss and to partition merging. We simulate and evaluate the performance of detectors, discuss the implementation of the detectors and future work.
Integration of Dual Border Effects in Resource Estimation: A Cokriging Practice on a Copper Porphyry Deposit

Hierarchical or cascade resource estimation is a very common practice when building a geological block model in metalliferous deposits. One option for this is to model the geological domains by indicator kriging and then to estimate (by kriging) the grade of interest within the built geodomains. There are three problems regarding this. The first is that sometimes the molded geological domains are spotty and fragmented and, thus, far from the geological interpretation. The second is that the resulting estimated grades highly suffer from a smoothing effect. The third is related to the border effect of the continuous variable across the boundary of geological domains. The latter means that the final block model of the grade shows a very abrupt transition when crossing the border of two adjacent geological domains. This characteristic of the border effect may not be always true, and it is plausible that some of the variables show smooth or soft boundaries. The case is even more complicated when there is a mixture of hard and soft boundaries. A solution is provided in this paper to employ a cokriging paradigm for jointly modeling grade and geological domains. The results of modeling the copper in an Iranian copper porphyry deposit through the proposed approach illustrates that the method is not only capable of handling the mixture of hard and soft boundaries, but it also produces models that are less influenced by the smoothing effect. These results are compared to an independent kriging, where each variable is modeled separately, irrespective of the influence of geological domains.
Success Probability of Decoding Interleaved Alternant Codes

Interleaved Reed–Solomon codes admit efficient decoding algorithms which correct burst errors far beyond half the minimum distance in the random errors regime, e.g., by computing a common solution to the Key Equation for each Reed–Solomon code, as described by Schmidt et al. If this decoder does not succeed, it may either fail to return a codeword or miscorrect to an incorrect codeword, and good upper bounds on the fraction of error matrices for which these events occur are known. The decoding algorithm immediately applies to interleaved alternant codes as well, i.e., the subfield subcodes of interleaved Reed–Solomon codes, but the fraction of decodable error matrices differs, since the error is now restricted to a subfield. In this paper, we present new general lower and upper bounds on the fraction of decodable error matrices by Schmidt et al.’s decoding algorithm, thereby making it the only decoding algorithm for interleaved alternant codes for which such bounds are known.
An Ensemble Learning-Based No Reference Qoe Model For User Generated Contents

The explosive growth of user-generated content (UGC) videos demands new Quality of Experience model for this new type of content. In this paper, we propose a novel No Reference QoE model for UGC videos. The proposed model takes into account not only bitstream-level and packet-level information, but also encoding parameters. Features are extracted from input videos at frame level, then combined by a pooling layer. In addition, ensemble learning is utilized to improve overall prediction performance. Experimental results show that our model can predict MOS values of UGC videos with very high accuracy. Also, the proposed model is simple, and so can be used for real-time QoE monitoring.
Preferences for Teaching Materials: A Survey on a Multimodal World

In an age of mass teaching, student-teacher contact time is not always enough to ensure an effective teaching-learning process. For the assimilation of concepts it is necessary that, outside the classroom, without the presence and support of the teacher, the student uses materials that allow to remember and eventually complement the learning during the class and simultaneously facilitate and guide, leading to the achievement of the defined objectives. As technology evolves, study materials and support platforms are increasingly diverse and it is important for the teacher to know which means students use and their motivations. A survey was conducted with a group of students from an undergraduate degree in biomedical engineering from a Portuguese higher education institution in order to understand their study habits and the materials they use when out of the classroom. From the analysis of the answers it was possible to infer that the students show preference for studying in a family environment, alone and using as materials the class notes provided by the teachers. Despite the growing use of videos and other multimodal media, the written, personalized notes and orientations set by the teacher are still the most preferred.
Efficient Cryptographic protocol for Sorting with Data-oblivious

Sorting is a primitive operation in data processing. Multiparty sorting includes a set of players P and a list L. Each element a <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ε L is shared among the players P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</sub> ε P (sharing is done over a prime field F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ). The players collaboratively execute the sorting without computing the values of the elements. The existing multiparty sorting protocols are based on compare, swap and data-oblivious with more time complexity. The existing compare protocol use either the bit-decomposition or bit-sharing and complexity of these protocols depends on size of prime. Since these protocols provide the privacy to individual elements, they need large computation. Here, we design a multiparty sorting protocol with privacy and data-obliviousness based on multiparty compare protocol. This proposed compare protocol does not use bit-decomposition or bit-sharing and complexity of this protocol depends on number of players. The proposed multiparty sorting protocol incurs constant number of multiparty message exchanges that depends only on the number of elements in the list. Ours multiparty sorting protocol sorting the elements with less time complexity.
Deep Learning: Systems and Responsibility

Deep learning enables numerous applications across diverse areas. Data systems researchers are also increasingly experimenting with deep learning to enhance data systems performance. We present a tutorial on deep learning, highlighting the data systems nature of neural networks as well as research opportunities for advancements through data management techniques. We focus on three critical aspects: (1) classic design tradeoffs in neural networks which we can enrich through a systems and data management perspective, e.g., thinking critically about storage, data movement, and computation; (2) classic design problems in data systems which we can reconsider with neural networks as a viable design option, e.g., to replace or help system components that make complex decisions such as database optimizers; and (3) essential considerations for responsible application of neural networks in critical human-facing problems in society and how these also link to data management and performance considerations. While these are seemingly a diverse set of rich topics, they are strongly interconnected through data management, and their combination offers rich opportunities for future research.