Reviewerly/exHarmony huggingface dataset:
perf4sight: A toolflow to model CNN training performance on Edge GPUs

The increased memory and processing capabilities of today’s edge devices create opportunities for greater edge intelligence. In the domain of vision, the ability to adapt a Convolutional Neural Network’s (CNN) structure and parameters to the input data distribution leads to systems with lower memory footprint, latency and power consumption. However, due to the limited compute resources and memory budget on edge devices, it is necessary for the system to be able to predict the latency and memory footprint of the training process in order to identify favourable training configurations of the network topology and device combination for efficient network adaptation. This work proposes perf4sight, an automated methodology for developing accurate models that predict CNN training memory footprint and latency given a target device and network. This enables rapid identification of network topologies that can be retrained on the edge device with low resource consumption. With PyTorch as the framework and NVIDIA Jetson TX2 as the target device, the developed models predict training memory footprint and latency with 95% and 91% accuracy respectively for a wide range of networks, opening the path towards efficient network adaptation on edge GPUs.
On the algebra of data sketches

We consider the problem of designing a distributed data sketch for scenario in which data stream is observed by many independent network nodes. We require that a sketch apart from being computationally and memory efficient should also be mergeable in a way that mimics set theory operations on related data sets. For example, when monitoring network traffic, one may consider how many distinct packets passed through a given node (sum of sets for different time windows) or passed through two given nodes (intersection of sets from two locations) and what is their total size (intersection of weighted sets). In this paper we propose a sketch that allows to efficiently summarize sets constructed from a sequence of set theory operations. We also provide an analytical control over the trade-off between the accuracy and storage/computational requirements. In comparison to the previous works the proposed solution 1) allows the weights of elements, 2) allows performing set theory operations simultaneous on a large number of sketches, 3) does not require computationally expensive numerical calculations and guarantees low overheads.
Federated Learning for Non-IID Data: From Theory to Algorithm

Federated learning suffers from terrible generalization performance because the model fails to utilize global information over all clients when data is non-IID (not independently or identically distributed) partitioning. Meanwhile, the theoretical studies in this field are still insufficient. In this paper, we present an excess risk bound for federated learning on non-IID data, which measures the error between the model of federated learning and the optimal centralized model. Specifically, we present a novel error decomposition strategy, which decomposes the excess risk into three terms: agnostic error, federated error, and approximation error. By estimating the error terms, we find that Rademacher complexity and discrepancy distance are the keys to affecting the learning performance. Motivated by the theoretical findings, we propose FedAvgR to improve the performance via additional regularizers to lower the excess risk. Experimental results demonstrate the effectiveness of our algorithm and coincide with our theory.
Never ‘Drop the Ball’ in the Operating Room: An Efficient Hand-Based VR HMD Controller Interpolation Algorithm, for Collaborative, Networked Virtual Environments

In this work, we propose two algorithms that can be applied in the context of a networked virtual environment to efficiently handle the interpolation of displacement data for hand-based VR HMDs. Our algorithms, based on the use of dual-quaternions and multivectors respectively, impact the network consumption rate and are highly effective in scenarios involving multiple users. We illustrate convincing results in a modern game engine and a medical VR collaborative training scenario.
Implicit Weight Learning for Multi-View Clustering

Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering. In general, it is essential to measure the importance of each individual view, due to some noises, or inherent capacities in the description. Many previous works model the view importance as weight, which is simple but effective empirically. In this article, instead of following the traditional thoughts, we propose a new weight learning paradigm in the context of multi-view clustering in virtue of the idea of the reweighted approach, and we theoretically analyze its working mechanism. Meanwhile, as a carefully achieved example, all of the views are connected by exploring a unified Laplacian rank constrained graph, which will be a representative method to compare with other weight learning approaches in experiments. Furthermore, the proposed weight learning strategy is much suitable for multi-view data, and it can be naturally integrated with many existing clustering learners. According to the numerical experiments, the proposed implicit weight learning approach is proven effective and practical to use in multi-view clustering.
CVSS: Ubiquitous and Broken

The Common Vulnerability Scoring System is at the core of vulnerability management for systems of private corporations to highly classified government networks, allowing organizations to prioritize remediation in descending order of risk. With a lack of justification for its underlying formula, inconsistencies in its specification document, and no correlation to exploited vulnerabilities in the wild, it is unable to provide a meaningful metric for describing a vulnerability’s severity, let alone risk. As it stands, this standard compromises the security of America’s most sensitive information systems.
Application of Matrix Decomposition in Machine Learning

Matrix decomposition is a mathematical method widely adopted in computer science for its reliability and outstanding performance. It is often chosen for use in the computer vision field, recommender system, image compression, and denoising. This paper introduces the classic matrix decomposition algorithms, including eigenvalue decomposition, UV decomposition, singular value decomposition (SVD), and their application in computer science. Additionally, the paper discusses the flaws of the adoption of singular value decomposition in image compression and denoising, a common problem in matrix decomposition‐‐‐sparse matrix. Finally, this paper introduces the PCANet, the combination of matrix decomposition and neural network, which inspires improving the interpretability of the deep learning model.
Evaluating the effectiveness of takeoff assignment strategies under irregular configurations

The use of UAVs has been growing steadily over the last years. Now that even the industry is adopting them for a wide range of activities, it can be said with certainty that UAVs will become an important asset for many enterprises. We foresee that, due to affordable prices, applications with groups of UAVs, also called swarms, will become mainstream. Swarms of UAVs can perform tasks faster and/or with more redundancy, and other tasks are only possible by collaborative work of UAVs. However, there are still many challenges to be solved before swarms of UAVs can be used safely. One of the challenges is the takeoff; i.e., takeoff should be safe (no collisions) and fast at the same time. An important part of the takeoff is the assignment task; i.e., determining which UAV goes where. In this work we will compare the effectiveness of three assignment algorithms, in terms of total distance travelled, number of flight paths crossing, and calculation time. We specially focus on irregular patterns. Our results show that the Kuhn-Munkres Algorithm (KMA) is preferable in almost all cases. It ensures that the total distance travelled by all UAVs is minimal, and most importantly it reduces the number of flight paths crossing each other (i.e. potential collisions). This is a very important metric because it allows for fast (semi) simultaneous takeoff procedures, which are not possible if the chances of collision are high.
In-Browser Attendance System using Face Recognition and Serverless Edge Computing

Because of Covid-19, schools, colleges, and institutions have moved to online learning. The education system has encountered and continues to encounter various challenges in this online format in managing the attendance of the students. The teacher used to call out the students' roll numbers or names when they were in the physical education mode. Nowadays as the world is developing towards a digital era, numerous techniques of collecting attendance such as attendance via biometric technologies like eye recognition, face scanning, voice recognition, fingerprint analysis have earned a lot of fame. Face recognition is the most efficient of these approaches as the face can be captured using a camera and compared using a trained model, but the others are more complex to implement at the user end, and some even need hardware. A lot of research work has been already done related to face recognition using models such as YOLO, MTCNN, FaceNet, HOG, LBPH, C2D-CNN. Models are usually loaded in the backend which causes latency issues and makes the system inefficient to use. Our proposed system aims to perform face recognition within the browser itself with the help of serverless edge computing. For the students, a simple web portal is developed, from which they can navigate to our plugin extension, where the model will capture attendance and dynamically update it in a Google Sheet. Face detection was done with Tiny Face Detector, while face recognition was done with Face Recognition Net. A few more models operate in conjunction with these two, recognizing the student from his or her livestream, checking the student's authenticity using logged in credentials, and updating the attendance in real-time across the browser.
A novel decentralized cross‐domain identity authentication protocol based on blockchain

Abstract In the context of cloud computing, the interaction between clients in different application domains becomes more frequent, which makes cross‐domain identity authentication safely and efficiently become an important research topic. Public key infrastructure (PKI) is a technology to solve cross‐domain authentication. However, there are problems such as difficulty in mutual trust between multiple certificate authority nodes (CA), failure of single point, and low efficiency in the traditional PKI method. Blockchain is a promising technology for decentralized trust management by providing consistent data storage, which gives impetus to the further development of cross‐domain identity authentication. Thus, this article apply blockchain to cross‐domain identity authentication. To solve the defects of the traditional PKI method, the design requirements are analyzed firstly, based on the analysis result, we proposed a double‐layer cross‐domain identity authentication model by constructing a consortium blockchain which is comprised of authentication server nodes (AS) and some internal blockchain, the model can highly improve the scalability of the PKI system without changing the internal architecture. Then a novel authentication protocol was put forward. The protocol can improve the efficiency of online cross‐domain identity authentication transactions by verifying the hash instead of the signature of their certificate. By putting the generation process of the blockchain certificate and the storage process of its hash in the registration operation and reducing the authentication process for AS and CA, the efficiency is further improved. Finally, the protocol was evaluated by security and performance analysis. The results display our protocol can guarantee security and has an excellent performance in cross‐domain identity authentication transactions.
Cformer

We propose a semi-supervised learning method called Cformer for automatic clustering of text documents in cases where clusters are described by a small number of labeled examples, while the majority of training examples are unlabeled. We motivate this setting with an application in contextual programmatic advertising, a type of content placement on news pages that does not exploit personal information about visitors but relies on the availability of a high-quality clustering computed on the basis of a small number of labeled samples.
Design and ARM-Based Implementation of Bitstream-Oriented Chaotic Encryption Scheme for H.264/AVC Video

In actual application scenarios of the real-time video confidential communication, encrypted videos must meet three performance indicators: security, real-time, and format compatibility. To satisfy these requirements, an improved bitstream-oriented encryption (BOE) method based chaotic encryption for H.264/AVC video is proposed. Meanwhile, an ARM-embedded remote real-time video confidential communication system is built for experimental verification in this paper. Firstly, a 4-D self-synchronous chaotic stream cipher algorithm with cosine anti-controllers (4-D SCSCA-CAC) is designed to enhance the security. The algorithm solves the security loopholes of existing self-synchronous chaotic stream cipher algorithms applied to the actual video confidential communication, which can effectively resist the combinational effect of the chosen-ciphertext attack and the divide-and-conquer attack. Secondly, syntax elements of the H.264 bitstream are analyzed in real-time. Motion vector difference (MVD) coefficients and direct-current (DC) components in Residual syntax element are extracted through the Exponential-Golomb decoding operation and entropy decoding operation based on the context-based adaptive variable length coding (CAVLC) mode, respectively. Thirdly, the DC components and MVD coefficients are encrypted by the 4-D SCSCA-CAC, and the encrypted syntax elements are re-encoded to replace the syntax elements of the original H.264 bitstream, keeping the format compatibility. Besides, hardware codecs and multi-core multi-threading technology are employed to improve the real-time performance of the hardware system. Finally, experimental results show that the proposed scheme, with the advantage of high efficiency and flexibility, can fulfill the requirement of security, real-time, and format compatibility simultaneously.
ICARUS - Incremental Design and Verification of Software Updates in Safety-Critical Product Lines

The lifecycles of software updates for Cyber Physical Systems are significantly decreasing. Especially for safety-critical functions, these must be carefully tested for compatibility to target configurations. In order to formalize the requirements of the system and to validate software changes in a modular way, contract-based design can be used for formal verification. A contract is defined as a pair of an assumption describing the required conditions for the working environment of a component, and a guarantee, which specifies its expected behavior including timing properties and value ranges of interfaces. In this work, we present a concept for efficient verification of a software update in a contract-based development environment with consideration of several system variants. The concept is based on an incremental refinement verification methodology which uses deltas, i.e. differences between variants, to automatically propagate changes and retest only the incrementally relevant contracts. By applying the methodology in a case study for a network representing a variable Adaptive Cruise Control system, we could demonstrate its applicability and its advantages in reducing the total verification effort for product line evolution.
Does Self-attribution Impact on Investor Perception About Cryptocurrency Market Efficiency? The Mediating Role of Overconfidence

The paper aims to explore the self-attribution bias impact on investor perception about cryptocurrency market efficiency with mediating role overconfidence in the relation of self-attribution with perceived market efficiency. We opted to collect data via a convenience sampling technique by using electronic questionnaires and limiting our study to European cryptocurrency investors. Structural Equation Modelling outcomes indicate no significant impact of self-attribution bias on perceived market efficiency. Whereas it was found that overconfidence bias has a significant effect on perceived efficiency. Further, the decomposition analysis indicated the presence of the mediating effect of overconfidence in the relationship of self-attribution with perceived market efficiency.
Unsupervised Domain Adaptation for Static Malware Detection based on Gradient Boosting Trees

Static malware detection is important for protection against malware by allowing for malicious files to be detected prior to execution. It is also especially suitable for machine learning-based approaches. Recently, gradient boosting decision trees (GBDT) models, e.g., LightGBM (a popular implementation of GBDT), have shown outstanding performance for malware detection. However, as malware programs are known to evolve rapidly, malware classification models trained on the (source) training data often fail to generalize to the target domain, i.e., the deployed environment. To handle the underlying data distribution drifts, unsupervised domain adaptation techniques have been proposed for machine learning models including deep learning models. However, unsupervised domain adaptation for GBDT has remained challenging. In this paper, we adapt the adversarial learning framework for unsupervised domain adaptation to enable GBDT learn domain-invariant features and alleviate performance degradation in the target domain. In addition, to fully exploit the unlabelled target data, we merge them into the training dataset after pseudo-labelling. We propose a new weighting scheme integrated into GBDT for sampling instances in each boosting round to reduce the negative impact of wrongly labelled target instances. Experiments on two large malware datasets demonstrate the superiority of our proposed method.
An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model

COVID-19, being the virus of fear and anxiety, is one of the most recent and emergent of various respiratory disorders. It is similar to the MERS-COV and SARS-COV, the viruses that affected a large population of different countries in the year 2012 and 2002, respectively. Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty. The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution, and Random Forest Model. The analysis upon dataset has been performed considering the time duration from January 1<sup>st</sup> 2020 to16<sup>th</sup> July 2021. The model has been developed to obtain the forecast values till September 2021. This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country. In India, the cases are rapidly increasing day-by-day since mid of Feb 2021. The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave. To empower the prediction for future validation, the proposed model works effectively.
An AUV Path Planner for Large-scale Search and Rescue based on A* Algorithm

Abstract For large-scale search and rescue (SAR) tasks that require complete coverage of the workspace, it is important to increase the efficiency and obtained sensor data quality. A novel path planner named SAR-A* to this problem is introduced, which takes into account the sensor performance and practical prior information. Firstly, the workspace is decomposed into plenty of hexagonal cells which are treated as waypoints for A* algorithm. Target present probability is then modeled to Gaussian distribution and the performance of the side-scan sonar (SSS) is evaluated. The proposed path planner is validated in a complex terrain scenario which proves that the SAR-A* path planner can increase confidence in locating the target quickly, and is suitable for the large-scale SAR.
Mobile Education Resource Sharing Method for Wireless Broadband Connection

The recall rate and precision rate of current mobile education resource sharing methods are low, and the resource sharing time is long, so a mobile education resource sharing method based on wireless broadband connection is proposed. Ant colony algorithm was used to optimize the focused crawler, the optimized crawler was used to capture the data of mobile education resources and extract the semantic features of the captured data, and the K-means clustering algorithm was used to cluster all the data. Based on the calculation results of network centrality and network density, a wireless broadband connection is used to schedule mobile education resources to complete the sharing of mobile education resources. The experimental results show that the proposed method’s recall rate and precision rate are above 95% and 96%, and the average resource sharing time is 0.63 s. The comprehensive performance of the proposed method is better.
Influential Factors In Adopting Blockchain Technology for eGovernment: A Systematic Review of Empirical Research

Over the last few years, the increasing interest in blockchain technology has caused many attempts to implement it in the public sector. Some countries have adopted blockchain in their government and realize the benefits of technology, and some others have not met yet even postpone the initiative of using blockchain. Several factors have influenced blockchain technology adoption for eGovernment in some countries. This review study aims to indicate the factors that influence blockchain adoption in the government sector by conducting a systematic literature review of empirical studies to gain a different insight and gain more evidence.
Performance Measurement of a Rule-Based Ontology Framework (ROF) for Auto-Generation of Requirements Specification

Documenting requirements specification requires a lot of effort from stakeholders and developers. Time and knowledge limitations are also obstacles in creating structured requirements document. Our previous works proposed a framework for automated generation of requirements specifications called Rule-Based Ontology Framework (ROF).The requirements documentation phase produces two outputs: process modeling according to the Business Process Model and Notation (BPMN) standard and Software Requirements Specification (SRS) documents following the ISO/IEC/IEEE 29148:2018 standard. In this paper, we do performance measurement of ROF in the IS project case study which includes validating ROF prototype by performing User Acceptance Test (UAT); measuring effectiveness by calculating notation error and requirements error; and measuring efficiency by calculating the time spent in producing documents. The efficiency and effectiveness of both are measured by comparing BPMN graph and SRS document generated by ROF with BPMN graph and SRS document that are created manually by the stakeholders.