Reviewerly/exHarmony huggingface dataset:
Understanding the Achieved Rate Multiplication Effect in FlowQueue-based AQM Bottleneck

The progressive adoption of Active Queue Management (AQM) and the popularity of Dynamic Adaptive Streaming over HTTP (DASH)-based streaming services have motivated the development of adaptive chunklets. Chunklets significantly improves the Quality of Experience (QoE) of video streaming applications in the presence of cross-traffic, which is known as the Achieved Rate (AR) multiplication effect. However, the detailed behaviour of chunklets and their implications on queuing dynamics have not been well-explored. In this paper, we instrumented a fine-grained, packet-driven FreeBSD queue measurement kernel module, and present an experimentally validated system model to help us better understand the AR multiplication effect in FlowQueue-based AQM bottlenecks. We experimentally demonstrated the accuracy of our system model across a wide range of network settings, along with providing fine-grained insights into queuing behaviours, including the detection of hash collisions. Our approach can provide network operators and researchers with greater network visibility where AQM schemes are deployed.
Quantum Broadcast Channels with Cooperating Decoders: An Information-Theoretic Perspective on Quantum Repeaters

Communication over a quantum broadcast channel with cooperation between the receivers is considered. The first form of cooperation addressed is classical conferencing. Another cooperation setting involves quantum conferencing, where Receiver 1 can teleport a quantum state to Receiver 2. The conferencing setting is intimately related to quantum repeaters, as the sender, Receiver 1, and Receiver 2 can be viewed as the transmitter, the repeater, and the destination receiver, respectively. We develop lower and upper bounds on the capacity region in each setting. At last, we show that as opposed to the MAC with entangled encoders, entanglement between decoders does not increase the classical communication rates for the broadcast dual.
A Certificate-Based Provable Data Possession Scheme in the Standard Model

Certificate-based cryptosystem can not only resolve the private key escrow problem inherent in the identity-based cryptosystem but also reduce the cost of public key certificate management in the public key infrastructure-(PKI-) based cryptosystem. Provable data possession (PDP) can ensure the integrity of users’ data stored in the cloud at a very high probability. By combining these two concepts, we propose a certificate-based PDP scheme. We prove that our scheme is secure in the standard model assuming that the Squ-CDH problem is hard. Based on the index logic table, our scheme can be extended to support dynamic operations easily. Efficiency analysis shows that our scheme has high efficiency.
Can Text Summarization Enhance the Headline Stance Detection Task? Benefits and Drawbacks

This paper presents an exploratory study that analyzes the benefits and drawbacks of summarization techniques for the headline stance detection. Different types of summarization approaches are tested, as well as two stance detection methods (machine learning vs deep learning) on two state-of-the-art datasets (Emergent and FNC–1). Journalists' headlines sourced from the Emergent dataset have demonstrated with very competitive results that they can be considered a summary of the news article. Based on this finding, this work evaluates the effectiveness of using summaries as a substitute for the full body text to determine the stance of a headline. As for automatic summarization methods, although there is still some room for improvement, several of the techniques analyzed show greater results compared to using the full body text—Lead Summarizer and PLM Summarizer are among the best-performing ones. In particular, PLM summarizer, especially when five sentences are selected as the summary length and deep learning is used, obtains the highest results compared to the other automatic summarization methods analyzed.
Distributed Quantum Faithful Simulation and Function Computation Using Algebraic Structured Measurements

We consider the task of faithfully simulating a distributed quantum measurement and function computation, and demonstrate a new achievable rate-region. For this, we develop the technique of randomly generating algebraic structured POVMs. To overcome the challenges caused by algebraic construction, we develop (i) a Pruning Trace inequality which is a tighter version of the known operator Markov inequality and (ii) a covering lemma which does not require the operator Chernoff inequality and hence applicable to pairwise-independent codewords. We demonstrate rate gains for this problem over traditional coding schemes and provide a multi-party distributed faithful simulation and function computation protocol.
Children’s Perceptions of Sponsorship Disclosures in Online Influencer Videos

As sponsoring in online influencer videos is gaining popularity, advertising- and media regulators are tightening the guidelines for commercial content in YouTube videos (e.g., the European Union's Audiovisual Media Services Directive [AVMSD], and the United States' Federal Trade Commission). An important part of these stricter guidelines is the obligatory inclusion of disclosures, in order to increase the transparency and fairness of sponsored online influencer videos (European Commission, 2018; Einstein, 2015; Federal Trade Commission 2010). In these videos, persuasive messages are embedded into the entertaining content of influencers which makes it hard for audiences to recognize its persuasive nature. Disclosures can help audiences to better recognize the commercial nature of sponsored online influencer videos and potentially facilitate more critical processing of these videos.
Stereo digital image correlation with improved depth of field using tilt-shift photography

Stereo digital image correlation (DIC) is a non-contacting technique to obtain full-field displacement and strain fields from the surface of a deforming object by comparing images recorded before and after deformation. With stereo measurement techniques, out-of-plane measurements are often limited by the depth of field achieved in an image. Tilt-shift photography uses the Scheimpflug principle to improve depth of field by rotating the image plane with respect to the camera sensor. This is implemented using a tilt-shift lens mount, which imposes an angle of tilt between the lens and the camera sensor. In this stereo-DIC study, images were taken using a range of stereo camera angles with either the lens tilted or not tilted to impose the Scheimpflug effect. A series of rigid body displacements were then measured for each combination of stereo and tilt angles. An evaluation of the means and standard deviations of the measurements shows that Scheimpflug adjustment offers minimal improvement when measuring rigid body displacements, but significant improvement at reducing artificial strains.
Feature Selection and Software Defect Prediction by Different Ensemble Classifiers

Software defect prediction can improve its quality and is actively studied during the last decade. This paper focuses on the improvement of software defect prediction accuracy by proper feature selection techniques and using ensemble classifier. The software code metrics were used to predict the defective modules. JM1 public NASA dataset from PROMISE Software Engineering Repository was used in this study. Boruta, ACE, regsubsets and simple correlation are used for feature selection. The results of selection are formed based on hard voting of all features selectors. A new stacking classifier for software defects prediction is presented in this paper. The stacking classifier for defects prediction algorithm is based on combination of 5 weak classifiers. Random forest algorithm is used to combine the predictions. The obtained prediction accuracy was up to 96.26%.
Fast keyword search over encrypted data with short ciphertext in clouds

Nowadays, it is convenient for people to store their data on clouds. To protect the privacy, people tend to encrypt their data before uploading them to clouds. Due to the widespread use of cloud services, public key searchable encryption is necessary for users to search the encrypted files efficiently and correctly. However, the existing public key searchable encryption schemes supporting monotonic queries suffer from either infeasibility in keyword testing or inefficiency such as heavy computing cost of testing, large size of ciphertext or trapdoor, and so on. In this work, we first propose a novel and efficient anonymous key-policy attribute-based encryption (KP-ABE). Then by applying Shen et al.’s generic construction to the proposed anonymous KP-ABE, we obtain an efficient and expressive public key searchable encryption, which to the best of our knowledge achieves the best performance in testing among the existing such schemes. Only 2 pairings are needed in testing. By applying our searchable encryption, one is able to expressively and efficiently search their encrypted data on clouds, without leaking the keyword information. Besides, we also implement our scheme and others with Python for comparing the performance. From the implementation results, our scheme owns the best performance on testing, and the size of ciphertexts and trapdoors are smaller than most of the existing schemes.
Faster Public-Key Compression of SIDH With Less Memory

In recent years, the isogeny-based protocol, namely supersingular isogeny Diffie-Hellman (SIDH) has become highly attractive for its small public key size. In addition, one can utilize several techniques to further compress the public key. However, compared to other post-quantum protocols, the computational cost of SIDH is relatively high, and so is that of its public-key compression. On the other hand, the storage for pairing computation and discrete logarithms to speed up the current implementation of the key compression is somewhat large. In this paper, we mainly improve the performance of public-key compression of SIDH, especially the efficiency and the storage of pairing computation involved. Our experimental results show that the memory requirement for pairing computation is reduced by a factor of about 1.5. Meanwhile, the instantiation of public-key compression of SIDH is <inline-formula><tex-math notation="LaTeX">$6.95\%--10.44\%$</tex-math></inline-formula> faster than the current state-of-the-art. Although SIKE is broken now, the techniques in this paper may benefit other isogeny-based cryptosystems which are still secure.
Modulation Recognition Algorithm Based on ResNet50 Multi-feature Fusion

In order to solve the problem of low accuracy of modulation pattern recognition under the condition of low signal-to-noise ratio in traditional modulation pattern recognition methods, this paper proposes a modulation recognition algorithm based on ResNet50 multi-feature fusion. This method uses the ResNet50 network that introduces an attention mechanism, and on this basis, through the extraction of three data characteristics of IQ signal, signal spectrogram and constellation diagram, and the multi-feature fusion of the three characteristic data as the input of the network. By comparing the improved ResNet5 and ResNet50, VGG-19, VGG-16 under the conditions of -10dB, -5dB, 0dB, 5dB, and 10dB in different signal-to-noise ratios. The experimental result shows that the improved method can effectively identify 7 types of digital signal modulation modes, namely 2FSK, 4FSK, BPSK, QPSK, 8PSK, 16QAM, and 64QAM under the condition of low signal-to-noise ratio. Especially when the signal-to-noise ratio is -5dB, the average recognition rate of the ReSNet50 network based on multi-feature fusion can reach 93.41%.
HMSG: Heterogeneous graph neural network based on Metapath SubGraph learning

Heterogeneous graph neural network (HGNN) models, capable of learning low-dimensional dense vectors from heterogeneous graphs for downstream graph-mining tasks, have attracted increasing attention in recent years. For these models, metapath-based methods have been widely adopted. However, most existing metapath-based HGNN models either discard intermediate nodes within a metapath, resulting in information loss, or indiscriminately aggregate information along a metapath containing different types of nodes, resulting in unavoidable learning bias. To overcome these limitations, a new HGNN model named HMSG, is proposed in this paper to comprehensively capture structural, semantic and attribute information from both homogeneous and heterogeneous neighbors more purposefully. To achieve this, a type-specific linear transformation is first applied to transfer the node attributes to different types of nodes with the same latent factor space. In the new model, the heterogeneous graph is decomposed into multiple metapath-based homogeneous and heterogeneous subgraphs where each subgraph associates specific semantic and structural information; this is different from existing models, which mainly rely on symmetric metapaths. Subsequently, tailored attention-based message aggregation methods are independently applied to each subgraph such that information learning can be more targeted. Finally, information from different subgraphs is fused through graph-level attention to obtain a complete representation. The learned representations are evaluated by several graph-mining tasks. Results indicate that the HMSG attains the best performance in all evaluation metrics than state-of-the-art baselines. Further ablation experiments demonstrate the effectiveness of the modules designed for the HMSG.
Interactive Cardiovascular Surgical Planning via Augmented Reality

The current practice for planning complex cardiovascular surgeries includes printing and cutting physical heart models. Unfortunately, such cuts are permanent, thus it is not possible to interactively experiment with different cuts, slowing the planning process. In collaboration with Children’s Healthcare of Atlanta Heart Center (CHOA), we are exploring new ways to improve cardiovascular, or heart, surgical planning through augmented reality (AR). We are developing CardiacAR , an iOS AR application that enables interactive surgical planning on mobile devices. CardiacAR offers powerful and flexible tools critical for surgical planning, such as omni-directional slicing of patients’ 3D heart models and virtual annotation to assist planning. We believe the ubiquity of iOS devices will help broaden access to the CardiacAR technology and streamline its deployment.
A Refutation of Finite-State Language Models through Zipf’s Law for Factual Knowledge

We present a hypothetical argument against finite-state processes in statistical language modeling that is based on semantics rather than syntax. In this theoretical model, we suppose that the semantic properties of texts in a natural language could be approximately captured by a recently introduced concept of a perigraphic process. Perigraphic processes are a class of stochastic processes that satisfy a Zipf-law accumulation of a subset of factual knowledge, which is time-independent, compressed, and effectively inferrable from the process. We show that the classes of finite-state processes and of perigraphic processes are disjoint, and we present a new simple example of perigraphic processes over a finite alphabet called Oracle processes. The disjointness result makes use of the Hilberg condition, i.e., the almost sure power-law growth of algorithmic mutual information. Using a strongly consistent estimator of the number of hidden states, we show that finite-state processes do not satisfy the Hilberg condition whereas Oracle processes satisfy the Hilberg condition via the data-processing inequality. We discuss the relevance of these mathematical results for theoretical and computational linguistics.
Smart Contract: A Literature-Based Analysis and Development of a Taxonomy Framework

So far, the term ‘smart contract’ is mainly reflected in research addressing technology, IT applications or peculiar businesses like stock exchange or bitcoin. However, a smart contract is an issue of purchasing and supply management (PSM) due to its potential to connect suppliers with customers through state-of-the-art digital means. Thus, we identify the need to define the concept of smart contracts within the PSM domain. Therefore, the main research aim is to clarify the construct of a smart contract. The core finding is that a deconstruction of existing definitions reveals peculiarities in the perspectives and domains but gives insufficient validity to PSM analysis. Thus, the result of this research is a definition proposal for smart contract. The implications of this analysis could pave the way for a wider discussion of how smart contracts affect PSM outside the typical blockchain and bitcoin arenas.
Universal Quantitative Steganalysis Using Deep Residual Networks

Quantitative steganalysis helps to predict length of payload message in stego images. Payload estimation is vital tool for forensic analysts. With recent development in deep learning, convolutional neural networks are extensively used for image steganalysis. CNN helps to easily capture statistical characteristics of images and learn features automatically. But as depth of convolutional neural network increases, accuracy suffers. Deep residual network makes use of residual blocks to address this problem. Residual network (ResNet) contains hundreds or even thousands of layers to learn demographic features of object implicitly. We propose blind payload length estimation using ResNet-50, deep residual network. We have used pre-trained model ResNet-50 for estimating payload length. Experiments are performed in Keras Python library on gray scale images from standard BOSSBase dataset. Mean absolute error has been used as performance metric. Use of residual network, ResNet-50, for universal payload estimation demonstrates good result both in spatial and in JPEG domain.
The use of satellite in 5G networks: the technological step forward and perspectives due to the VIBeS project

5G system is a really disruptive new concept of telecommunication network because it will make use of all the up to date technologies both at physical and network management layer. It will represent a revolution especially for what concerns the latter aspects in particular introducing the concept of slicing. Thanks to their peculiar characteristics satellite systems can contribute to realize this revolution, offering performance in some very important scenario that will guarantee to satisfy 5G target requirements. The VIBeS project, funded by ESA, has reached very meaningful achievements in this line. The paper summarizes the main outcomes of the project and draws the significant perspectives that are now opened.
Building Reproducible Video Streaming Traffic Generators

Video streaming traffic dominates Internet traffic. However, there is a dearth of tools to generate such traffic on emulation-based testbeds. In this paper we present tools to create representative and reproducible video streaming traffic to evaluate the next generation of traffic classification, Quality of Service (QoS) algorithms and traffic engineering systems. We discuss 27 different combinations of streaming video traffic types in this preliminary work, and illustrate the diversity of network-level dynamics in these protocols.
Information collection system for fall detection of stroke patients under cascade algorithm in the context of multi‐modal information fusion and e‐health

Abstract To effectively detect stroke patients after falling under e‐health, the fall situation of patients is graded, and warnings are given to improve the survival probability of stroke patients after falling. First, the fall model of stroke patients is analysed. According to the model, multi‐modal information fusion fall detection technology is proposed, including data fusion algorithm and feature recognition technology. Also, various sensor data are adjusted. The fall detection cascade algorithm is proposed to classify data with different features in order, thereby completing target detection. Finally, combined with heart rate sensor, height sensor and microcontroller unit software and patients' e‐health information, the research and development of an information collection system for fall detection are realized. Six young volunteers are selected to test the system performance. The results show that for the six testers, the heart rate detected by the ordinary device and the device of this investigation is the same when it is resting in different states of resting, walking, as well as walking and falling. The heart rate difference between walking and falling detection is not large (within the allowable error range of the device). But the best detection effect is to measure after the patient falls, which not only reduces power consumption but also keeps the detection error to a minimum. The height sensor is in the static state, increased by 75 m in the vertical direction and decreased by 75 cm from the static position in the vertical direction. The height difference of the data information exported from these three cases has some errors compared with the actual 75 cm. The tester's three situations of resting, walking and falling, standing up after sitting still and falling are observed. The waveform when resting is stable, and the acceleration information also fluctuates significantly when walking and after standing up. The accuracy of the developed system is above 80%, which achieves the expected effect and assisted medical treatment of fall detection based on patients' e‐health information. The results show that the proposed information collection system for fall detection of stroke patients under cascade algorithm in the context of multi‐modal information fusion has good performance and high practical value.
Adaptive Online Sequential Extreme Learning Machine with Kernels for Online Ship Power Prediction

With the in-depth penetration of renewable energy in the shipboard power system, the uncertainty of its output power and the variability of sea conditions have brought severe challenges to the control of shipboard integrated power system. In order to provide additional accurate signals to the power control system to eliminate the influence of uncertain factors, this study proposed an adaptive kernel based online sequential extreme learning machine to accurately predict shipboard electric power fluctuation online. Three adaptive factors are introduced, which control the kernel function scale adaptively to ensure the accuracy and speed of the algorithm. The electric power fluctuation data of real-ship under two different sea conditions are used to verify the effectiveness of the algorithm. The simulation results clearly demonstrate that in the case of ship power fluctuation prediction, the proposed method can not only meet the rapidity demand of real-time control system, but also provide accurate prediction results.