Reviewerly/exHarmony huggingface dataset:
A Multi-Scale Feature Attention Approach to Network Traffic Classification and Its Model Explanation

Network traffic classification, the task of associating network traffic with their generating application protocols or applications, is valuable for the control, allocation, and management of resources in today’s TCP/IP networks. In this paper, we propose Ulfar, a multi-scale feature attention approach to network traffic classification, which uses convolutional neural networks (CNN) as the building block of the deep packet analysis model. In Ulfar, we take only one packet per flow for network traffic classification. Ulfar is based on the key insight that format-related bytes appear at fixed offsets or in a specific pattern in the IP packet, and these format-related bytes are important for accurate network traffic classification. Our neural network model can automatically recover the format-related bytes by building high-level, multi-scale <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -gram features from raw byte sequences. In addition, at the representation learning side, we try to understand what patterns and signatures our neural network model learns from network traffic. We evaluate Ulfar using two publicly available datasets, and our experimental results show that Ulfar can conduct accurate network traffic classification. Also, we compare the results of Ulfar with four state-of-the-art approaches, and find that Ulfar has the ability to classify network traffic more accurately.
A systematic review examining the efficacy of commercially available foreign language learning mobile apps

A systematic review examining the efficacy of commercially available foreign language-learning apps (FLL) was completed. A database search of ERIC, PsychINFO, and LearnTechLib produced 1,786 journal articles. After applying specific inclusion and exclusion criteria based on Burston’s seminal study (2015) requiring a minimum number of 10 participants, quantitative learning outcome data and rigorous research design, eight studies remained. These studies were categorized in terms of the app studied, year of publication, language taught, age group of participants, setting, length of study, and device(s) used. Descriptive statistics demonstrate there is a dearth of studies examining app efficacy, that English was the most commonly taught language, and that vocabulary was the most commonly tested area. Although commercial apps were found to successfully support FLL, the included studies’ methods varied in ways that made direct comparison difficult.
Quantum Chain of Things (QCoT): A New Paradigm for Integrating Quantum Computing, Blockchain, and Internet of Things

Recently, Internet of Things (IoT) and Blockchain (BC) are coupled to present smart and secure applications. Quantum Computing (QC) is a new paradigm that imposes itself as a main technology in the near future, especially in the 6G era, due to its powerful computations. As a result, immigration of the IoT and BC to the QC has to be studied and prepared. This paper provides a brief review of relevant recent work to identify opportunities for optimal integration. Besides, a new paradigm called Quantum Chain of Things (QCoT) is proposed and its perspective architecture is presented and verified into two parts: (a) BC-IoT using Arduino, and (b) QC-IoT using IBM Qiskit simulator. Finally, presented knowledge such as current challenges, threads, and integration opportunities could help researchers to investigate new trends and future directions.
DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks

Quantum routing plays a key role in the development of the next-generation network system. In particular, an entangled routing path can be constructed with the help of quantum entanglement and swapping among particles (e.g., photons) associated with nodes in the network. From another side of computing, machine learning has achieved numerous breakthrough successes in various application domains, including networking. Despite its advantages and capabilities, machine learning is not as much utilized in quantum networking as in other areas. To bridge this gap, in this article, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a novel quantum routing model</i> for quantum networks that employs machine learning architectures to construct the routing path for the maximum number of demands (source–destination pairs) within a time window. Specifically, we present a deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA utilizes an empirically designed deep neural network that observes the current network states to accommodate the network’s demands, which are then connected by a qubit-preserved shortest path algorithm. The training process of DQRA is guided by a reward function that aims toward maximizing the number of accommodated requests in each routing window. Our experiment study shows that, on average, DQRA is able to maintain a rate of successfully routed requests at above 80% in a qubit-limited grid network and approximately 60% in extreme conditions, i.e., each node can be repeater exactly once in a window. Furthermore, we show that the model complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum networks.
Mobile Robot Navigation Functions Tuned by Sensor Readings in Partially Known Environments

Mobile robot navigation functions provide geometric path planning that can be used to control the physical mobile robot system. This letter extends the theory of mobile robot navigation functions to planar environments populated by unknown obstacles. The robot has knowledge of the environment outer boundary but must use an obstacle sensor with sector-shape footprint during navigation. The letter introduces on-line tuning of navigation functions based on sensor readings of obstacles and the robot state upon detection of new obstacles in the environment. The letter describes a feedback control law that changes its structure according to the current navigation function, with trajectory damping that ensures smooth monotonic approach to the target. The correctness of the navigation controller in terms of obstacle avoidance and arrival to the target is established. In addition to the analytic guarantees, simulation studies describe the robot system state parameters during navigation in an office floor environment populated by unknown obstacles.
Quantifying quantum-amplified metrology via Fisher information

We present a unified framework, based on quantum metrology concepts, for defining and quantifying deterministic noiseless quantum amplification of parameter-dependent processes, which plays an important role in increasing the precision of quantum sensing. Recent experiments [Burd et al., Science 364, 1163 (2019)] can be encompassed by this concept, which also leads to new suggestions of experimental work. The unified view presented here allows the identification of the basic steps for quantum amplification and of the measurements that lead to the best possible precision, beyond the quantum standard limit, in the estimation of parameters involved in the process. This is applied to the estimation of displacements of trapped ions and of the phase in SU(1,1) optical interferometers and atomic interferometry.Received 10 November 2021Accepted 21 January 2022DOI:https://doi.org/10.1103/PhysRevResearch.4.L012014Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum metrologyQuantum opticsQuantum sensingQuantum InformationAtomic, Molecular & Optical
Corruption, Chinese Investment, and Trade: Evidence from Africa

We investigate whether corruption in host countries drives the different routes of Chinese economic engagement with Africa. Using data from 49 African countries for 2000–2018, we find that corruption affects each route of China’s engagement with Africa differently. Corruption in Africa is significantly negatively associated with FDI from China, but significantly positive with both trade and construction. These relationships are moderated by the availability of natural resources but do not change after the implementation of the Xi Jinping anti-corruption campaign. By disaggregating China–Africa financial engagement into its different routes, we demonstrate that the relationship between corruption and China’s presence in Africa varies with the nature of the engagement. JEL Codes: F21, F23
RUFP: Reinitializing unimportant filters for soft pruning

Network pruning has become a popular method to reduce the storage and computational complexity of deep neural networks. In order to minimize the performance loss, soft pruning retains a large model capacity by setting unimportant weights to zero and allowing them to be updated. However, these weights are difficult to reactivate due to the small amplitude and frequent resets. In this paper, we propose a novel method, termed RUFP, to reinitialize unimportant filters according to the most important one, which not only gives these filters a chance to be reactivated, but also introduces more filter forms that may win the initialization lottery. By gradually increasing the reinitialization ratio and decreasing the reassigned values of factors in the batch normalization layer, soft pruning is achieved. Benefiting from the large model capacity and multiple reinitializations, the compressed model after fine-tuning achieves superior performance. Extensive experiments demonstrate the effectiveness of this method in improving the accuracy of the pruned model. The accuracy of ResNet-56 on CIFAR-10 is improved from 93.05% to 93.17% while reducing 57.7% calculations and 58.8% parameters. Compared with the traditional soft pruning method and other state-of-the-art methods, our RUFP obtains outstanding performance at various compression levels.
MOTS‐ACO: An improved ant colony optimiser for multi‐objective task scheduling optimisation problem in cloud data centres

Task scheduling in cloud data centres is an optimisation problem that aims to minimise power consumption and task makespan as well as ensures the quality of service delivered to cloud consumers. Although there are several existing task scheduling approaches, these methods mainly focus on optimising makespans of tasks while ignoring critical issues. This paper presents a comprehensive multi-objective task scheduling model based on an improved Ant Colony Optimisation (ACO) algorithm, referred to as MOTS-ACO. In order to promote the diversity of the Pareto set and accelerate the convergence speed, adaptive distribution probability is incorporated into the proposed algorithm, specifically in the process of updating the global rule. The performance of MOTS-ACO is compared with several existing multi-objectives task scheduling algorithms based on the makespan time, turnaround time, power efficiency and load balancing parameters. The results show the superiority of MOTS-ACO in terms of the makespan time, turnaround time, power efficiency and load balancing. Moreover, the proposed MOTS-ACO algorithm introduces more diversity in the search and accelerates the convergence speed towards the Pareto optimal solution.
Multi-task generative adversarial learning for nuclei segmentation with dual attention and recurrent convolution

Pathological image is the gold standard for diagnosis and evaluation of cancer. Nuclei segmentation is the basis for quantitative analysis of the pathological image. Although the current deep learning-based nuclei segmentation methods generally perform better than the traditional ones, They are still plagued by over-segmentation and under-segmentation, especially when the nuclei are adherent and overlapping with each other. Therefore, how to effectively distinguish different nuclei has always been a challenging task. In this paper, we proposed a novel segmentation method for nuclei via integrating improved U-Net and generative adversarial learning. By introducing spatial and channel mapping table (SC-MT) attention mechanism, the issues about nuclei over-segmentation and under-segmentation have been alleviated and recurrent convolution units will contribute to the continuity of nuclei contours topology. Extensive experimental results on multiple nuclei segmentation datasets show that the proposed method can effectively distinguish the adherent and overlapping nuclei with robust performance. The code will be available at: https://github.com/antifen/Nuclei-Segmentation.
Data Collection Strategy Based on OSELM and Gray Wolf Optimization Algorithm for Wireless Sensor Networks

In order to effectively reduce the energy consumption, improve the efficiency of data collection in HWSNs, and prolong the lifetime of the overall network, the clustering method is one of the most effective methods in the data collection methods for HWSNs. The data collection strategy of HWSNs based on the clustering method mainly includes three stages: (1) selecting the appropriate cluster head, (2) forming between clusters, and (3) transferring data between clusters. Among them, the selection of the cluster heads in the first stage. The optimal number of cluster heads in the formation of clusters in the second stage is the core and key to the clustering data collection of HWSNs. In the stage of cluster head selection, a data collection strategy for HWSNs based on the clustering method is proposed. Sink establishes an extreme learning machine neural network model. The cluster member nodes select cluster heads based on the remaining energy of the sensor node, the number of the neighbor node, and the distance to the sink. The best cluster head node is selected through the adaptive learning of the online sequence extreme learning machine. Through comprehensive consideration of various factors to complete the clustering process, the gray wolf algorithm is used to optimize the number of clusters, balance the effect of clustering, and improve the efficiency of data collection while reducing energy consumption. An energy efficient and reliable clustering data collection strategy for HWSNs based on the online sequence extreme learning machine and the gray wolf optimization algorithm is proposed in this paper. The simulation results show that the proposed algorithm not only significantly improves the efficiency of the data collection and reduces energy consumption but also comprehensively improves the reliability of the network and prolongs the network's lifetime.
Spicing It Up: From Ubiquitous Devices to Tangible Things Through Provocation

In this pictorial, we conceptualize how modern designs take on the roles of either 'devices' or 'things' to shape everyday practices in fundamentally different ways. Drawing on Borgmann's device paradigm and provocative design, we visually recount how two designs, starting from the same brief using the same materials ultimately culminate in a 'device' and a 'thing'. As householders embedded the two designs in their sustainable practices, different design qualities emerged. We use these householders' experiences to discuss how 'spicing up' a design process with elements of provocation can help people engage more deeply with environmental challenges.
Tech companies and the public interest: the role of the state in governing social media platforms

In the early days of the internet, it was hoped that digital technology would bring about democracy and positive outcomes for society. Recently, the debate has shifted towards tech lash with many critics pointing towards technology companies undermining democracy, stability, and sustainability. As a result, a new consensus seems to be emerging among policymakers, companies, and civil societal actors that self-regulation has to move towards co-regulation. This Special Issue of Information, Communication and Society draws together cutting-edge contributions on three core themes in scholarly and policy discourse on platform regulation: First, the papers in this special issue enhance empirical understandings of the role of the state in governing social media platforms developing in the United States, Europe, and Sub-Saharan Africa. Second, they provide a holistic framework to understand policy problems that need to be addressed, which helps to develop and evaluate new policy initiatives. Finally, papers point towards three approaches in governing social media platforms and discuss the advantages and disadvantages of content moderation, process-based co-regulation, as well as competition regulation and alternative business models.
Cybercrime-as-a-Service Operations

This chapter explores the cybercrime-as-a-service operations that have changed the cybercrime marketplace from a direct sales model to a managed service model. As cybercrime evolved, so did the motivation and skill of the hackers. What began as a highly skilled activity undertaken by individuals driven by curiosity and research grew to a horde of lightly trained yet motivated young people looking for notoriety and/or a quick profit as tools became easier to use and more readily available. As the ability to profit from cybercrime grew exponentially, hackers began to sell their services, and eventually it was more profitable and less risky to sell a packaged cybercrime as a service than commit the crime. The cybercrime-as-a-service operations now involve many types of cybercrime including botnets, distributed denial of service attacks (DDoS), credit card fraud, malware, spam, and phishing attacks. The services are sold through hacker forums, direct web sales, and on the dark web using cryptocurrency. The world’s law enforcement agencies have recognized the threat of cybercrime-as-a-service operations, and there have been recent high-profile arrests of the operators and takedowns of the cybercrime-as-a-service operations.
Building Semantic Based Recommender System Using Knowledge Graph Embedding

Recommendation systems are information filtering mechanisms used in E-commerce, media and entertainment industry. It essentially facilitate the customers for a better user experience by processing the content user-specific. This is known as personalization. However, though leveraged by machine learning algorithms existing recommendation systems, still suffers from the problem of cold-start and sparcity. These problems could be resolved by using knowledge graphs since it gives a semantic explanation of recommendations. Also, graph learning method overcomes the problems of manual feature extraction and is effective for feature learning in predicting tasks. In this research, we develop a semantic based recommender through link prediction in a knowledge graph. We apply graph embedding techniques for extracting the semantics of explicable recommendations. The proposed method is validated by building a knowledge graph using the MovieLens dataset. We observed that factorization based scoring functions such as HolE and DistMult provides better semantic recommendations.
Cross-Lingual Word Embeddings

The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese
An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors

Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we investigated the possibility to extract these statistics from acceleration sensor data generated by a previously developed sensor garment. We used deep learning-based models to recognize five football-related activities (jogging, sprinting, passing, shooting and jumping) in an accurate, robust, and fast manner. A combination of convolutional (CNN) layers followed by recurrent (bidirectional) LSTM layers achieved up to 98.3% of accuracy. Our results showed that deep learning models performed better in evaluation time and prediction accuracy than traditional machine learning algorithms. In addition to an increase in accuracy, the proposed deep learning architecture showed to be 2.7 to 3.4 times faster in evaluation time than traditional machine learning methods. This demonstrated that deep learning models are accurate as well as time-efficient and are thus highly suitable for cost-effective, fast, and accurate human activity recognition tasks.
CAE: Contextual auto-encoder for multivariate time-series anomaly detection in air transportation

The Automatic Dependent Surveillance-Broadcast protocol is one of the latest compulsory advances in air surveillance. While it supports the tracking of the ever-growing number of aircraft in the air, it also introduces cybersecurity issues that must be mitigated e.g., false data injection attacks where an attacker emits fake surveillance information. The recent data sources and tools available to obtain flight tracking records allow the researchers to create datasets and develop Machine Learning models capable of detecting such anomalies in En-Route trajectories. In this context, we propose a novel multivariate anomaly detection model called Contextual Auto-Encoder (CAE). It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase (e.g. climbing, cruising or descending) during its training. To illustrate the CAE’s efficiency, an evaluation dataset was created using real-life anomalies as well as realistically crafted trajectory modifications, with which the CAE as well as three anomaly detection models from the literature were evaluated. Results show that the CAE achieves better results in both accuracy and speed of detection. The dataset, the models implementations and the evaluation results are available in an online repository, thereby enabling replicability and facilitating future experiments.
Enhancing robustness of noisy qutrit teleportation with Markovian memory

Abstract Quantum teleportation is the fundamental communication unit in quantum communication. Here, a three-level system is selected for storing and transmitting quantum information, due to its unique advantages, such as lower cost than a higher-level system and higher capacity and security than a two-level system. It is known that the key procedure for perfect teleportation is the distribution of entanglement through quantum channel. However, amounts of noise existing in the quantum channel may interfere the entangled state, causing the degradation of quantum entanglement. In the physical implementations of quantum communication schemes, noise acting on the carriers of successive transmissions often exhibits some correlations, which is the so called quantum memory channel. In this paper, a memory channel model during the entanglement distribution phase is constructed and the uniform expression of the evolution of a two-qutrit entangled state under different kinds of correlated noise is derived. Finally, Pauli noise and amplitude damping noise as the typical noise source are considered to analyze the influence of memory effects of noise on qutrit teleportation. It is expected to show that three-level teleportation under these two types of channels can generally enhance the robustness to noise if the Markovian correlations of quantum channel are taken into consideration.