Reviewerly/exHarmony huggingface dataset:
An Improved Clustering Algorithm Based on Density Peak and Nearest Neighbors

Aiming at the problems that the initial cluster centers are randomly selected and the number of clusters is manually determined in traditional clustering algorithm, which results in unstable clustering results, we propose an improved clustering algorithm based on density peak and nearest neighbors. Firstly, an improved density peak clustering method is proposed to optimize the cutoff distance and local density of data points. It avoids that random selection of initial cluster centers is easy to fall into the local optimal solution. Furthermore, a K-value selection method is presented to choose the optimal number of clusters, which is determined by the sum of the squared errors within the clusters. Finally, we employ the idea of the K-nearest neighbors to carry out the assignment for outliers. Experiments on the UCI real data sets indicate that our proposed algorithm can achieve better clustering results compared with several known algorithms.
The non-linear effects of fixed broadband on economic growth in Africa

Purpose The study assesses the non-linear nexus between fixed broadband and economic growth. The study focuses on data from 33 African countries for the period 2010 to 2020. Design/methodology/approach The empirical evidence is based on unit root tests, panel smooth transition regression and the generalized method of moments. Findings The following findings are established in this study. (1) The proportion of the population with access to electricity above and below which the relationship between fixed broadband and economic growth changes in sign is about 60%. (2) Below this threshold, each 1% increase in fixed broadband subscriptions induces a decline in economic growth of about 2.58%. Above the threshold, economic growth would increase by 2.43% when fixed broadband subscriptions increase by 1%. Sensitivity analyses and generalized method of moments (GMM) estimation show that these results are robust. Practical implications Due to the coronavirus disease (COVID-19) pandemic, which requires countries to take adequate measures to curb the spread of the pandemic, especially by means of virtual economic activities, any national policy aiming at improving the access of populations to high levels of fixed broadband services should be preceded by the implementation of an electrification program for at least 60% of the total population. Otherwise, providing a good quality internet connection for the benefit of the population would not produce the expected effects on economic growth and would, therefore, be counterproductive. Originality/value This study complements the extant literature by providing thresholds at which fixed broadband affects economic growth.
Fuzzy-Constrained Graph Pattern Matching in Medical Knowledge Graphs

Abstract The research on graph pattern matching (GPM) has attracted a lot of attention. However, most of the research has focused on complex networks, and there are few researches on GPM in the medical field. Hence, with GPM this paper is to make a breast cancer-oriented diagnosis before the surgery. Technically, this paper has firstly made a new definition of GPM, aiming to explore the GPM in the medical field, especially in Medical Knowledge Graphs (MKGs). Then, in the specific matching process, this paper introduces fuzzy calculation, and proposes a multi-threaded bidirectional routing exploration (M-TBRE) algorithm based on depth first search and a two-way routing matching algorithm based on multi-threading. In addition, fuzzy constraints are introduced in the M-TBRE algorithm, which leads to the Fuzzy-M-TBRE algorithm. The experimental results on the two datasets show that compared with existing algorithms, our proposed algorithm is more efficient and effective.
Retina

As network speeds have increased to over 100 Gbps, operators and researchers have lost the ability to easily ask complex questions of reassembled and parsed network traffic. In this paper, we introduce Retina, a software framework that lets users analyze over 100 Gbps of real-world traffic on a single server with no specialized hardware. Retina supports running arbitrary user-defined analysis functions on a wide variety of extensible data representations ranging from raw packets to parsed application-layer handshakes. We introduce a novel filtering mechanism and subscription interface to safely and efficiently process high-speed traffic. Under the hood, Retina implements an efficient data pipeline that strategically discards unneeded traffic and defers expensive processing operations to preserve computation for complex analyses. We present the framework architecture, evaluate its performance on production traffic, and explore several applications. Our experiments show that Retina is capable of running sophisticated analyses at over 100 Gbps on a single commodity server and can support 5--100× higher traffic rates than existing solutions, dramatically reducing the effort to complete investigations on real-world networks.
Dialing up the danger: Virtual reality for the simulation of risk

There is a growing interest the use of virtual reality (VR) to simulate unsafe spaces, scenarios, and behaviours. Environments that might be difficult, costly, dangerous, or ethically contentious to achieve in real life can be created in virtual environments designed to give participants a convincing experience of “being there.” There is little consensus in the academic community about the impact of simulating risky content in virtual reality, and a scarcity of evidence to support the various hypotheses which range from VR being a safe place to rehearse challenging scenarios to calls for such content creation to be halted for fear of irreversible harm to users. Perspectives split along disciplinary lines, with competing ideas emerging from cultural studies and games studies, from psychology and neuroscience, and with industry reports championing the efficacy of these tools for information retention, time efficiency and cost, with little equivalence in information available regarding impact on the wellbeing of participants. In this study we use thematic analysis and close reading language analysis to investigate the way in which participants in a VR training scenario respond to, encode and relay their own experiences. We find that participants overall demonstrate high levels of “perceptual proximity” to the experience, recounting it as something that happened to them directly and personally. We discuss the impact of particular affordances of VR, as well as a participant’s prior experience on the impact of high-stress simulations. Finally, we consider the ethical mandate for training providers to mitigate the risk of traumatizing or re-traumatizing participants when creating high-risk virtual scenarios.
Electromagnetic Side-Channel Attack Resilience against PRESENT Lightweight Block Cipher

Lightweight cryptography is a novel diversion from conventional cryptography that targets internet-of-things (IoT) platform due to resource constraints. In comparison, it offers smaller cryptographic primitives such as shorter key sizes, block sizes and lesser energy drainage. The main focus can be seen in algorithm developments in this emerging subject. Thus, verification is carried out based upon theoretical (mathematical) proofs mostly. Among the few available side-channel analysis studies found in literature, the highest percentage is taken by power attacks. PRESENT is a promising lightweight block cipher to be included in IoT devices in the near future. Thus, the emphasis of this paper is on lightweight cryptology, and our investigation shows unavailability of a correlation electromagnetic analysis (CEMA) of it. Hence, in an effort to fill in this research gap, we opted to investigate the capabilities of CEMA against the PRESENT algorithm. This work aims to determine the probability of secret key leakage with a minimum number of electromagnetic (EM) waveforms possible. The process initially started from a simple EM analysis (SEMA) and gradually enhanced up to a CEMA. This paper presents our methodology in attack modelling, current results that indicate a probability of leaking seven bytes of the key and upcoming plans for optimisation. In addition, introductions to lightweight cryptanalysis and theories of EMA are also included.
SVD-Embedded Deep Autoencoder for MIMO Communications

Using a deep autoencoder (DAE) for end-to-end communication in multiple-input multiple-output (MIMO) systems is a novel concept with significant potential. DAE-aided MIMO has been shown to outperform singular-value decomposition (SVD)-based precoded MIMO in terms of bit error rate (BER). This paper proposes embedding left- and right-singular vectors of the channel matrix into DAE encoder and decoder to further improve the performance of the MIMO DAE. SVD-embedded DAE largely outperforms theoretic linear precoding in terms of BER. This is remarkable since it demonstrates that DAEs have significant potential to exceed the limits of current system design by treating the communication system as a single, end-to-end optimization block. Based on the simulation results, at SNR=10dB, the proposed SVD-embedded design can achieve a BER of about 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> and reduce the BER at least 10 times compared with existing DAE without SVD, and up to 18 times compared with theoretical linear precoding. We attribute this to the fact that the proposed DAE can match the input and output as an adaptive modulation structure with finite alphabet input. We also observe that adding residual connections to the DAE further improves the performance.
Small-Sample Interval Prediction Model based on Transfer Learning for Solar Power Prediction

This paper proposes a small-sample interval prediction (SIP) model based on Transfer Learning (TL) method for solar power prediction under the condition of limited amount of training data. Extreme Learning Machine (ELM) is employed as base predictor to construct interval boundaries and Direct Quantile Regression (DQR) method is utilized for training. Based on TL approach, TrAdaBoost for interval prediction method is proposed to enhance the prediction performance in the scenario of limited data. Comprehensive experiments based on realistic solar power data are conducted to confirm the superiority of the proposed model.
TopEx: topic exploration of COVID-19 corpora - Results from the BioCreative VII Challenge Track 4

TopEx is a natural language processing application developed to facilitate the exploration of topics and key words in a set of texts through a user interface that requires no programming or natural language processing knowledge, thus enhancing the ability of nontechnical researchers to explore and analyze textual data. The underlying algorithm groups semantically similar sentences together followed by a topic analysis on each group to identify the key topics discussed in a collection of texts. Implementation is achieved via a Python library back end and a web application front end built with React and D3.js for visualizations. TopEx has been successfully used to identify themes, topics and key words in a variety of corpora, including Coronavirus disease 2019 (COVID-19) discharge summaries and tweets. Feedback from the BioCreative VII Challenge Track 4 concludes that TopEx is a useful tool for text exploration for a variety of users and tasks.http://topex.cctr.vcu.edu.
Birthday-Bound Slide Attacks on TinyJAMBU’s Keyed-Permutations for All Key Sizes

We study the security of the underlying keyed-permutations of NIST LWC finalist TinyJAMBU. Our main findings are key-recovery attacks whose data and time complexities are close to the birthday bound $$2^{64}$$ . The attack idea works for all versions of TinyJAMBU permutations having different key sizes, irrespective of the number of rounds repeated in the permutations. Most notably, the attack complexity is only marginally increased even when the key size becomes larger. Concretely, for TinyJAMBU permutations of key sizes 128, 192, and 256 bits, the data/time complexities of our key-recovery attacks are about $$2^{65}$$ , $$2^{66}$$ , and $$2^{69.5}$$ , respectively. Our attacks are on the underlying permutations and not on the TinyJAMBU AEAD scheme; the TinyJAMBU mode of operation limits the applicability of our attacks. However, our results imply that TinyJAMBU’s underlying keyed-permutations cannot be expected to provide the same security levels as robust block ciphers of the corresponding block and key sizes. Furthermore, the provable security of TinyJAMBU AEAD scheme should be carefully revisited, where the underlying permutations have been assumed to be almost ideal.
KDPM: Kernel Data Protection Mechanism Using a Memory Protection Key

The kernel data of an operating system kernel can be modified through memory corruption by exploiting kernel vulnerabilities. Memory corruption allows privilege escalation and defeats security mechanisms. The kernel control flow integrity verifies and guarantees the order of invoking kernel codes. The kernel address space layout randomization randomizes the virtual address layout of the kernel code and data. The additional kernel observer focuses on the unintended privilege modifications to restore the original privileges. However, these existing security mechanisms do not prevent writing to the kernel data. Therefore, kernel data can be overwritten by exploiting kernel vulnerabilities. Additionally, privilege escalation and the defeat of security mechanisms are possible. We propose a kernel data protection mechanism (KDPM), which is a novel security design that restricts the writing of specific kernel data. This mechanism protects privileged information and the security mechanism to overcome the limitations of existing approaches. The KDPM adopts a memory protection key (MPK) to control the write restriction of kernel data. The KDPM with the MPK ensures that the writing of privileged information for user processes is dynamically restricted during the invocation of specific system calls. To prevent the security mechanisms from being defeated, the KDPM dynamically restricts the writing of kernel data related to the mandatory access control during the execution of specific kernel codes. Further, the KDPM is implemented on the latest Linux with an MPK emulator. We also evaluated the possibility of preventing the writing of privileged information. The KDPM showed an acceptable performance cost, measured by the overhead, which was from 2.96% to 9.01% of system call invocations, whereas the performance load on the MPK operations was 22.1 ns to 1347.9 ns.
Anomaly Diagnosis in Cyber-Physical Systems

Cyber-Physical Systems (CPS) constitute the operational basis for a number of critical national infrastructure (CNI) sectors including but not limited to manufacturing, smart electrical grids and water utilities, where programmable networked systems enable physical processes. Programmable Logic Controllers (PLCs) play a vital role in this by controlling CPS processes and consequently have become a primary target for cyber attacks that aim to disrupt CPS. By contrast with conventional networked setups, the operational and safety-critical importance of PLCs introduce challenges for CNI operators on empirically determining if an incident is a cyber-attack or a system fault as both occurrences can display similar outputs on the physical process. Moreover, existing anomaly detection techniques explicit to PLCs primarily give indication of an incident rather than attempting to categorise what the incident is. In this paper, we introduce a novel PLC anomaly diagnosis framework defined by a two-stage identification and classification approach based on novelty detection. Through the use of PLC run-time and network communication data generated by physical processes on a representational CPS testbed, we achieve an average of 99.35% on anomaly profiling accuracy and highlight the distinctions between system faults and cyber-attacks. In general, we demonstrate a practical approach that can be adopted by next generation CPS cyber defence tools.
Secure and Flexible Data Sharing for Distributed Storage with Efficient Key Management

In this paper, we propose a Secure and Flexible Data Sharing (SFDS) scheme for distributed storage, where data owners can outsource their data to a distributed storage network and share the data with authorized users. To preserve confidentiality, all data are encrypted by data owners’ secret keys before being outsourced, and fine-grained access policies are enforced on the encrypted data (ciphertexts) to achieve flexible data sharing. Furthermore, based on the ciphertext puncturable encryption and the hierarchical identity-based encryption, we design an efficient key and ciphertext update mechanism, which enables data owners to update their secret keys and the corresponding ciphertexts periodically to deal with side-channel attacks and system vulnerabilities. Update tokens are constructed to directly derive new keys and ciphertexts. Through detailed security analysis, it is demonstrated that SFDS can achieve all three essential security properties, i.e., forward security, post-compromise security, and collusion attack resistance.
A Topological Dummy-based Location Privacy Protection Mechanism for the Internet of Drones

The recent advancement of drone technologies and communication protocols allows us to envision a robust and dynamic mobile vehicular network paradigm called the Internet of Drones (IoD). In this environment, drones will perform several location-based services (LBSs) for users, awakening the interest of malicious entities whose intention is to hamper the service. Hence, drones need high protection regarding their localization in LBSs. However, there is a lack of Location Privacy Protection Mechanisms (LPPMs) for an IoD scenario. Dummy-based LPPMs provide proper location privacy in traditional mobile networks, mainly for sparse configurations. The design of this mechanism for IoD can overcome this deficiency. This study proposes a novel dummy-based LPPM for the IoD, called TDG, that focuses on the IoD topology characteristics regardless of near drones being the first approach presented in this context. Through extensive experiments, we show that TDG can provide proper location privacy for drones in sparse configurations, reducing the use of the wireless communication channel. TDG can protect the real drone’s trajectory up to more than 90% of the time, leaking less than 25% of the drone’s real coordinates.
Resource Allocation for Time-triggered Federated Learning over Wireless Networks

The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalization of classic synchronous and asynchronous FL. Taking the resource-constrained and unreliable nature of wireless networks into account, we jointly consider the user selection and bandwidth optimization problem to minimize the FL training loss. The optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed greedy search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead.
Fast Parameter Synchronization for Distributed Learning with Selective Multicast

Recent advances in distributed machine learning show theoretically and empirically that, for many models, provided workers would participate in the synchronizations eventually, i) the training still converges, even if only p workers take part in each round of synchronization, and ii) a larger p generally leads to a faster rate of convergence. These findings shed light on eliminating the bottleneck effects of parameter synchronization in large-scale data-parallel distributed training, having motivated several optimization designs.In this paper, we focus on optimizing the parameter synchronization for peer-to-peer distributed learning, in which workers generally broadcast or multicast their updated parameters to others for synchronization, and propose SELMCAST, an expressive and Pareto-optimal multicast receiver selection algorithm, to achieve the goal. Compared with the state-of-the-art design that randomly selects exactly p receivers for each worker’s multicast in a bandwidth-agnostic way, SELMCAST chooses receivers based on the global view of their available bandwidth and loads, yielding two advantages. Firstly, it could optimize the bottleneck sending rate, thus cutting down the time cost of parameter synchronization. Secondly, when more than p receivers are with sufficient bandwidth, they would be selected as many as possible, bringing benefits to the convergence of training. Extensive evaluations show that SELMCAST is efficient and always achieves near-optimal performance.
Satellite Assisted QKD Key Encapsulation

The ever-increasing quantum computational power may pose a serious threat to classical Public Key Exchange (PKE) algorithms in the next few years. The need for quantum resilient key exchange schemes is becoming more topical as quantum computers become more powerful. Quantum Key Distribution (QKD) promises unconditionally secure key distribution between two remote parties. The distilled keys of a QKD link can be used by classical symmetric algorithms such as the Advanced Encryption Standard (AES). Unfortunately, QKD has come across some major limitations in distance reach and Secure Key Rates (SKR)s that make the idea of a global quantum secured network debatable. In this work, we propose a satellite assisted Key Encapsulation Mechanism (KEM) that takes advantage of the high security of a quantum AES-256-bit key encrypted link, to deliver secret keys through classical communication layer in high rates, using QKD only for link establishment. In this way, the keys produced by the QKD do not directly encrypt the data traffic, allowing therefore longer lifespans. This scheme would be able to support up to thousands terminal nodes with a total average SKR as low as 4.23 bps.
Policy-based Fully Spiking Reservoir Computing for Multi-Agent Distributed Dynamic Spectrum Access

In the midst of the machine learning revolution, there is hope to thrive the ever-growing demand for limited spectrum resources imposed by the growth of wireless devices with a paradigm shift to more intelligent ways to manage and share the radio spectrum. This requirement mandates very energy-efficient solutions that can tackle the rapid changes of the wireless environment. This work considers spiking neural networks, which have been shown to drastically reduce the energy consumption compared to conventional neural networks in a reinforcement learning setup designed for the dynamic spectrum sharing scenario. Moreover, the temporal aspect of the problem and the necessity of sample efficiency motivates incorporating liquid state machines into this design. However, the agents’ state- and time-variant inputs impose a burden of a posteriori hyperparameter optimization for liquid state machines, rendering the deployment of reliable models whose reservoirs operate in favorable regimes very challenging in such a setting. Therefore, we employ a homeostatic learning rule for adaptively tuning small-world reservoir connections to maintain near-chaotic behavior during operation. Simulation results prove the performance of the introduced solution compared with several existing techniques.