Reviewerly/exHarmony huggingface dataset:
Indoor Human Fall Detection Algorithm Based on Wireless Sensing

As the main health threat to the elderly living alone and performing indoor activities, falls have attracted great attention from institutions and society.Currently, fall detection systems are mainly based on wear sensors, environmental sensors, and computer vision, which need to be worn or require complex equipment construction.However, they have limitations and will interfere with the daily life of the elderly.On the basis of the indoor propagation theory of wireless signals, this paper proposes a conceptual verification module using Wi-Fi signals to identify human fall behavior.The module can detect falls without invading privacy and affecting human comfort and has the advantages of noninvasive, robustness, universality, and low price.The module combines digital signal processing technology and machine learning technology.This paper analyzes and processes the channel state information (CSI) data of wireless signals, and the local outlier factor algorithm is used to find the abnormal CSI sequence.The support vector machine and extreme gradient boosting algorithms are used for classification, recognition, and comparative research.Experimental results show that the average accuracy of fall detection based on wireless sensing is more than 90%.This work has important social significance in ensuring the safety of the elderly.
Network Attached FPGAs in the Open Cloud Testbed (OCT)

The Open Cloud Testbed (OCT) provides nodes with Field Programmable Gate Arrays (FPGAs) that are under the complete control of the user and are directly attached to a network switch via two 100Gbps connections. We provide TCP and UDP stacks on the FPGAs. In addition, users have the ability to experiment with their own protocol. We present several experiments which make use of this capability including TCP throughput measurements, an encryption/decryption example, and machine learning inference split across two FPGAs where the images are input on one node and the labelled output available on a second node. The testbed is available for researchers to perform their own experiments, and includes a development platform that allows users to create FPGA applications. Network measurement results show we achieve close to peak bandwidth by tuning appropriate parameters.
Learning Based and Physical-layer Assisted Secure Computation Offloading in Vehicular Spectrum Sharing Networks

Massive computing tasks have been generated with the widespread applications of big data analysis in vehicular edge computing (VEC) networks. However, the offloading process of the VEC networks suffers a threat of information leakage. The physical layer security (PLS) technology is an effective security solution to protect confidential information. Furthermore, the contradiction between massive data transmission and limited communication resources promotes an urgent need for a proper scheme to improve resource utilization. In this paper, we design a joint secure offloading and resource allocation (SoRA) scheme based on PLS technology and spectrum sharing architecture. We aim at minimizing the system processing delay of all vehicular users (VUs) while ensuring the security of information, by jointly optimizing the spectrum access, transmit power and computing resource allocation. Then we adopt a multi-agent deep reinforcement learning algorithm to solve the optimization problem. With proper training, we demonstrate that the VU agents can successfully cooperate to improve the system processing delay and ensure the security of the offloading process.
Unsupervised Bias Evaluation of DNNs in non-IID Federated Learning Through Latent micro-Manifolds

Lately, Federated Learning (FL) is rapidly evolving in the field of health and quality of life systems. Its ability to train Machine Learning (ML) and Deep Learning (DL) models for the wide variety of Computer Vision (CV) fields that utilize them, one of them being the medical field, while at the same time fortifying the privacy of the sensitive information that describes them, makes the FL technology a necessary tool in modern health and medical CV systems. One of its disadvantages, though, has proven to be the quality of models used for the decentralized learning process and the ability to understand them. Low quality, unethical or biased models used for FL training, usually due to Non-IID (Independent and Identically Distributed) data, can have catastrophic consequences, especially in critical infrastructure in the medical field. In this paper, we tackle the problem of unfairness of DL models in the FL environment by leveraging the ability of latent mapping and representation learning in decision and augmentative DL models while striving to visualize their knowledge distribution. In particular, micro-Manifolds produced from the discovered latent deformations in a DL model are analyzed and, through a proposed quantization pipeline, the fairness of that model is measured in a summary of quantitative metrics. This methodology follows a fully unsupervised model and data agnostic manner to perform ethical evaluation, while it is documented with both medical and widely used benchmark data and DL architectures.
Adaptive Encrypted Traffic Characterization via Deep Representation Learning

Near ubiquitous encryption poses a challenge for security and quality of service (QoS) applications that rely on deep packet inspection (DPI) techniques for categorizing traffic types or threats. However, recent work has shown that machine learning (ML) on temporal flow statistics as well as convolutional neural network (CNN) methods applied to raw packets are able to classify network traffic even in the face of encryption. Unfortunately, many such methods often lack the ability to generalize to new categories, a critical requirement in our constantly evolving networks. Building on previous approaches we apply CNN models to the characterization task within a deep representation learning framework. The network acts as a feature extractor which is input to a lightweight support vector machine (SVM) classifier for the final output. By training the networks with an angular softmax loss in addition to the typical crossentropy loss, we can improve on the state of the art in terms of both classification accuracy and detection error. Furthermore we demonstrate that learned features provide the ability to label traffic categories not seen in neural network training.
PMAL: A Proxy Model Active Learning Approach for Vision Based Industrial Applications

Deep Learning models’ performance strongly correlate with availability of annotated data; however, massive data labeling is laborious, expensive, and error-prone when performed by human experts. Active Learning (AL) effectively handles this challenge by selecting the uncertain samples from unlabeled data collection, but the existing AL approaches involve repetitive human feedback for labeling uncertain samples, thus rendering these techniques infeasible to be deployed in industry related real-world applications. In the proposed Proxy Model based Active Learning technique (PMAL) , this issue is addressed by replacing human oracle with a deep learning model, where human expertise is reduced to label only two small subsets of data for training proxy model and initializing the AL loop. In the PMAL technique, firstly, proxy model is trained with a small subset of labeled data, which subsequently acts as an oracle for annotating uncertain samples. Secondly, active model's training, uncertain samples extraction via uncertainty sampling, and annotation through proxy model is carried out until predefined iterations to achieve higher accuracy and labeled data. Finally, the active model is evaluated using testing data to verify the effectiveness of our technique for practical applications. The correct annotations by the proxy model are ensured by employing the potentials of explainable artificial intelligence. Similarly, emerging vision transformer is used as an active model to achieve maximum accuracy. Experimental results reveal that the proposed method outperforms the state-of-the-art in terms of minimum labeled data usage and improves the accuracy with 2.2%, 2.6%, and 1.35% on Caltech-101, Caltech-256, and CIFAR-10 datasets, respectively. Since the proposed technique offers a highly reasonable solution to exploit huge multimedia data, it can be widely used in different evolutionary industrial domains.
Understanding general concepts of requirements engineering through design thinking: An experimental study with students

Abstract Teaching requirements engineering (RE) is one of the challenging parts of any software engineering (SE) education curriculum because it deals with understanding the problem from multiple perspectives and acquiring a holistic view of the system. Academicians have adopted several innovative approaches to teaching RE concepts to students, and design thinking (DT) is one of them. DT integrates the needs of people, technology, and requirements and can help learn and apply human‐centered techniques to solve problems. An experimental study is conducted with 315 undergraduate students to realize how they can learn the RE process effectively by combining DT methods with the RE concepts for an effective RE education. This study intends to identify how this integration help students in using multiple elicitation methods, better visualization and understanding of the system requirements, and achieving various teaching objectives. An experimental study was planned and executed with students by integrating DT methods for RE activities from the beginning of problem definition to the final solution. The evaluation was done by collecting students' responses to pre‐survey and post‐survey questions capturing the students' perspectives on the RE and DT techniques. The students' responses suggested that the DT drive RE activities to improve their learning, thinking abilities, engagement with the team, and teamwork. Additionally, the students' artifacts during the DT process have also been evaluated. The use of DT methods helped in understanding the elicitation process and, consequently, helped reach the solution.
A Novel High Capacity Reversible Data Hiding through Encryption Scheme by Permuting Encryption Key and Entropy Analysis

A Reversible data hiding through encryption (RDTE) scheme will consider an original image and a sequence of bits as the input and generate an encrypted image as the output. This encrypted image will be able to transmit through the network securely, and the authorized receiver can take out the hidden details along with the restoration of the actual image. This paper proposes a new RDTE scheme with a good rate of embedding without any issues during the restoration of the original image. We have used the well-known RC4 pseudo-random generator for the image encryption, and we performed data hiding during block-wise image encryption. In the proposed scheme, the original image is considered as non-overlapping blocks of size B × B pixels, and these blocks will be encrypted using a sequence of pseudo-random integers. During the RDTE process, all the possible unique permutations of the encryption key, K, will be generated, say (K <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> , K <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ,…, K <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</inf> ). Further, the sender will be capable of embedding one integer value from the set {0, 1,…, N} in a selected image block. A selected block will be encrypted using the pseudo-random sequence of integers using the key K <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</inf> to embed the integer Q in the selected block. The proposed scheme prefers to select keys with unique characters with sufficient length to ensure the maximum embedding capacity. The message extraction and image restoration are performed by analyzing the entropy measure from each block after attempting the decryption.
Active Fault Tolerant Control on Multi-rotor UAVs using ELM-based Neuro-Fuzzy System

Multi-rotor UAVs have been used prevalently for a multitude of purposes and the reliability of these UAVs has to be ensured to enable safe operations, especially in urban airspace. Hence, an accurate and efficient fault tolerant control strategy is essential to actively detect hardware faults and perform controller reconfiguration. In this paper, an active fault tolerant control strategy is introduced, which uses Online Sequential Fuzzy Extreme Learning Machine (OS-Fuzzy-ELM) to diagnose actuator faults. A gain scheduled PID (GS-PID) controller then uses the fault information to reconfigure among pre-tuned PID controller gains in real-time. The extreme learning neuro-fuzzy based fault tolerant control algorithm has high generalization performance despite nonlinearities and uses low computational load, which makes it suitable for online application. Upon implementing the fault tolerant controller into the system model of a quadrotor UAV, testing is conducted for hovering and trajectory tracking flight scenarios. The results indicate that the fault tolerant controller is successful in quickly identifying faults, suppressing large deviations and subsequently perform quick recovery.
Quantum Data Networking for Distributed Quantum Computing: Opportunities and Challenges

Quantum Data Networking can significantly transform the landscape of quantum computing by enabling several small quantum computers (QCs) to form a distributed system to achieve the same computing power as a large quantum computer which is infeasible to build. However, this requires quantum state information, in the form of qubits, to be exchanged among multiple geographically distributed QCs, and there are many challenges associated with reliably transferring qubits from one QC to another efficiently. In this paper, we discuss various QDN design options, present main challenges and describe promising solutions to tackle the challenges.
WMDefense: Using Watermark to Defense Byzantine Attacks in Federated Learning

Federated learning enables data owners to train a global ML model without exchanging data. However, the unique pattern of training in federated learning can be exploited by malicious adversaries. These malicious adversaries degrade the accuracy of the federated training model by sending malicious inputs during the federated training process. Existing Byzantine robust federated learning algorithms remain vulnerable to customized local model poisoning attacks because they are not designed with a suitable malicious client detection mechanism. To defend against the latest Byzantine attacks, this work proposes an effective algorithm, WMDefense, which identifies malicious clients by embedding a watermark to the global model and tracking the degree of watermark recession after local model training. Our experiments apply WMDefense to two recent Byzantine attack algorithms and validate them using two publicly available datasets, showing that it can defend well against both attacks. Furthermore, we compare WMDefense with two current state-of-the-art Byzantine robustness federated learning algorithms and show our superior performance.
Data Centric DAO: When blockchain reigns over the Cloud

Nowadays, data has become more and more important. Most governments have passed laws to control how they are owned and used, such as the GDPR in Europe. However, any law is never perfect. We can see it when a company scattered around the world is hampered by the fact of not being able to exploit customer data outside the borders of a territory. Also, we can add that a data owner is not anymore once he sells them to a third party to be exploited. In this work, we propose a solution based on a permissioned blockchain, namely the Hyperledger Fabric, to allow any stakeholder with data to take part, with value providers, in creating Decentralized Autonomous organizations (DAOs). Once created, a DAO can attract other investors to expand it. The Hyperledger Fabric handles this whole process through channels. The infrastructure needed to run the business of the DAO is generated automatically through special transactions taking place at the blockchain level. In the opposite direction, the cloud infrastructure sends notifications to the Hyperledger for traceability and monitoring purposes. Finally, through simulations of wealth distribution models, we show that to keep control of a DAO based on the proposed architecture, the shares must be negotiated according to a Pareto law.
Sparse Communication for Federated Learning

Federated learning trains a model on a centralized server using datasets distributed over a massive amount of edge devices. Since federated learning does not send local data from edge devices to the server, it preserves data privacy. It transfers the local models from edge devices instead of the local data. However, communication costs are frequently a problem in federated learning. This paper proposes a novel method to reduce the required communication cost for federated learning by transferring only top updated parameters in neural network models. The proposed method allows adjusting the criteria of updated parameters to trade-off the reduction of communication costs and the loss of model accuracy. We evaluated the proposed method using diverse models and datasets and found that it can achieve comparable performance to transfer original models for federated learning. As a result, the proposed method has achieved a reduction of the required communication costs around 90% when compared to the conventional method for VGG16. Furthermore, we found out that the proposed method is able to reduce the communication cost of a large model more than of a small model due to the different threshold of updated parameters in each model architecture.
On Uploading Behavior and Optimizations of a Mobile Live Streaming Service

Mobile Live Streaming (MLS) services are now one of the most popular types of mobile apps. They involve a (often amateur) user broadcasting content to a potentially large online audience via unreliable networks (e.g., LTE). Although prior work has focused on viewer-side behavior, it is equally important to study and improve broadcaster operations. Using detailed logs obtained from a major MLS provider, we first conduct an in-depth measurement study of uploading behavior. Our key findings include large wasteful uploads, strong viewing locality, and traffic dominance of loyal viewers. Specifically, 33.3% of uploads go unwatched, and the viewership of broadcasters tends to be localized to a small set of broadcaster-specific network regions. Inspired by our findings, we propose two system innovations to streamline MLS systems: adaptive uploading and edge server pre-fetching. These optimizations leverage machine learning for reduced waste and improved QoE. Trace-driven experiments show that the adaptive uploading reduces the resources wastage by 63%, and the pre-fetching boosts the startup by 29.5%.
Using UML to Describe the Development of Software Products Using an Object Approach

One of the most popular languages of graphic modeling is Unified Modeling Language (UML). This abstract presents an overview of the language UML. A wide range of diagrams were considered, together with a brief description of their application, the main advantages and disadvantages of the UML language, its object-oriented approach. Strengths (Strengths), Weaknesses (Weaknesses), Capabilities (Opportunities) and Threats (Threats) have been formulated for SWOT analysis. The SWOT-matrix is based on them. A detailed structure of diagram types was presented, dividing the above-mentioned diagrams into structural and behavioral diagrams. Among all the applications of the UML language in more detail considered the possibilities of its use in software engineering, and specifically - in software development. According to the study, it was found that the UML diagram language is one of the best tools.
When Deep Learning Meets Steganography: Protecting Inference Privacy in the Dark

While cloud-based deep learning benefits for high-accuracy inference, it leads to potential privacy risks when exposing sensitive data to untrusted servers. In this paper, we work on exploring the feasibility of steganography in preserving inference privacy. Specifically, we devise GHOST and GHOST+, two private inference solutions employing steganography to make sensitive images invisible in the inference phase. Motivated by the fact that deep neural networks (DNNs) are inherently vulnerable to adversarial attacks, our main idea is turning this vulnerability into the weapon for data privacy, enabling the DNN to misclassify a stego image into the class of the sensitive image hidden in it. The main difference is that GHOST retrains the DNN into a poisoned network to learn the hidden features of sensitive images, but GHOST+ leverages a generative adversarial network (GAN) to produce adversarial perturbations without altering the DNN. For enhanced privacy and a better computation-communication trade-off, both solutions adopt the edge-cloud collaborative framework. Compared with the previous solutions, this is the first work that successfully integrates steganography and the nature of DNNs to achieve private inference while ensuring high accuracy. Extensive experiments validate that steganography has excellent ability in accuracy-aware privacy protection of deep learning.
PhoneyTalker: An Out-of-the-Box Toolkit for Adversarial Example Attack on Speaker Recognition

Voice has become a fundamental method for human-computer interactions and person identification these days. Benefit from the rapid development of deep learning, speaker recognition exploiting voice biometrics has achieved great success in various applications. However, the shadow of adversarial example attacks on deep neural network-based speaker recognition recently raised extensive public concerns and enormous research interests. Although existing studies propose to generate adversarial examples by iterative optimization to deceive speaker recognition, these methods require multiple iterations to construct specific perturbations for a single voice, which is input-specific, time-consuming, and non-transferable, hindering the deployment and application for non-professional adversaries. In this paper, we propose PhoneyTalker, an out-of-the-box toolkit for any adversary to generate universal and transferable adversarial examples with low complexity, releasing the requirement for professional background and specialized equipment. PhoneyTalker decomposes an arbitrary voice into phone combinations and generates phone-level perturbations using a generative model, which are reusable for voices from different persons with various texts. Experiments on mainstream speaker recognition systems with large-scale corpus show that PhoneyTalker outperforms state-of-the-art methods with overall attack success rates of 99.9% and 84.0% under white-box and black-box settings respectively.