Reviewerly/exHarmony huggingface dataset:
Tacker: Tensor-CUDA Core Kernel Fusion for Improving the GPU Utilization while Ensuring QoS

The proliferation of machine learning applications has promoted both CUDA Cores and Tensor Cores&#x2019; integration to meet their acceleration demands. While studies have shown that co-locating multiple tasks on the same GPU can effectively improve system throughput and resource utilization, existing schemes focus on scheduling the resources of traditional CUDA Cores and thus lack the ability to exploit the parallelism between Tensor Cores and CUDA Cores.In this paper, we propose Tacker, a static kernel fusion and scheduling approach to improve GPU utilization of both types of cores while ensuring the QoS (Quality-of-Service) of co-located tasks. Tacker consists of a Tensor-CUDA Core kernel fuser, a duration predictor for fused kernels, and a runtime QoS-aware kernel manager. The kernel fuser enables the flexible fusion of kernels that use Tensor Cores and CUDA Cores, respectively. The duration predictor precisely predicts the duration of the fused kernels. Finally, the kernel manager invokes the fused kernel or the original kernel based on the QoS headroom of latency-critical tasks to improve the system throughput. Our experimental results show that Tacker improves the throughput of best-effort applications compared with state-of-the-art solutions by 18.6% on average, while ensuring the QoS of latency-critical tasks.
RID—Roof Information Dataset for Computer Vision-Based Photovoltaic Potential Assessment

Computer vision has great potential to accelerate the global scale of photovoltaic potential analysis by extracting detailed roof information from high-resolution aerial images, but the lack of existing deep learning datasets is a major barrier. Therefore, we present the Roof Information Dataset for semantic segmentation of roof segments and roof superstructures. We assessed the label quality of initial roof superstructure annotations by conducting an annotation experiment and identified annotator agreements of 0.15–0.70 mean intersection over union, depending on the class. We discuss associated the implications on the training and evaluation of two convolutional neural networks and found that the quality of the prediction behaved similarly to the annotator agreement for most classes. The class photovoltaic module was predicted to be best with a class-specific mean intersection over union of 0.69. By providing the datasets in initial and reviewed versions, we promote a data-centric approach for the semantic segmentation of roof information. Finally, we conducted a photovoltaic potential analysis case study and demonstrated the high impact of roof superstructures as well as the viability of the computer vision approach to increase accuracy. While this paper’s primary use case was roof information extraction for photovoltaic potential analysis, its implications can be transferred to other computer vision applications in remote sensing and beyond.
Opportunistic Routing in Quantum Networks

Unlike classical routing algorithms, quantum routing algorithms make use of entangled states—a type of resources that have a limited lifetime and need to be regenerated after consumption. In a nutshell, quantum routing algorithms have to use these resources efficiently, while optimizing some objectives such as the total waiting time. Current routing algorithms tend to keep a routing request waiting until all of the resources on its path are available. In this paper, we introduce a new way of managing entanglement resources in an opportunistic fashion: a request can move forward along its path as soon as possible (even if some resources on its path are not ready). We show that this opportunistic approach is fundamentally better than conventional approaches. In particular, our results indicate that this new approach achieves a 30-50% improvement in the average total waiting time and average link waiting time compared with several state-of-the-art routing algorithms. As a by-product of this work, we develop a new simulator for quantum routing, which can be used to evaluate various design choices under different scenarios.
Secure image block compressive sensing using complex Hadamard measurement matrix and bit‐level XOR

This paper proposes a novel image compression-encryption scheme based on compressive sensing and bit-level XOR. In the proposed scheme, the encrypted sequences are generated from a 4-D hyper-chaotic Lorenz system and a Logistic system. First, an original image is sampled by a secure block parallel compressive sensing (PCS) scheme. In the PCS phase, a key-controlled discrete cosine transform sparse basis matrix and a key-controlled complex Hadamard measurement matrix are employed. Next, the real part and imaginary part of the resulting complex-valued data are quantized and transformed into bit streams, respectively. After that, the two generated bit streams are combined into one bit stream. Then, the resulting bit stream is further encrypted by a bit-level XOR operation, where the encrypted key is generated from a chaotic system. The experiment results show the effectiveness and reliability of the proposed joint compression-encryption scheme. The proposed scheme can not only enhance the security of the compressed image but also improve the reconstructed quality of the compressed image.
Using WhatsApp to Enhance International Distance Education at the University of South Africa

Open distance e-learning (ODeL) is an essential form of e-learning for students in South Africa. It allows expanded access to higher education for students from rural or socioeconomically disadvantaged backgrounds. One of the key features of ODeL is that students can complete coursework modules virtually, without the need to step onto a physical university campus. Students from rural areas are able to participate in higher education, something they otherwise would not have had access to without ODeL. Information and Communication Technologies (ICTs), including messaging apps like WhatsApp, provide students with informal and formal learning opportunities, increase access to academic and community resources, and mitigate some Internet connectivity issues. Students are able to use WhatsApp to build connections with students and share class materials, without access to an Internet-connected computer. Using ICTs to enhance access to higher education is one way to bridge the wide socioeconomic gap in South Africa. This paper argues for additional non-Western research on socio-technical marginalization and access to ICTs for students enrolled at higher education institutions in South Africa.
Multi-Signal Detection Framework: A Deep Learning Based Carrier Frequency and Bandwidth Estimation

Multi-signal detection is of great significance in civil and military fields, such as cognitive radio (CR), spectrum monitoring, and signal reconnaissance, which refers to jointly detecting the presence of multiple signals in the observed frequency band, as well as estimating their carrier frequencies and bandwidths. In this work, a deep learning-based framework named SigdetNet is proposed, which takes the power spectrum as the network's input to localize the spectral locations of the signals. In the proposed framework, Welch's periodogram is applied to reduce the variance in the power spectral density (PSD), followed by logarithmic transformation for signal enhancement. In particular, an encoder-decoder network with the embedding pyramid pooling module is constructed, aiming to extract multi-scale features relevant to signal detection. The influence of the frequency resolution, network architecture, and loss function on the detection performance is investigated. Extensive simulations are carried out to demonstrate that the proposed multi-signal detection method can achieve better performance than the other benchmark schemes.
A novel privacy-preserving multi-level aggregate signcryption and query scheme for Smart Grid via mobile fog computing

With the vigorous development of edge computing, edge devices are more and more powerful and widely deployed. Mobile fog computing-based Smart Grid emerges as the times require and attracts much attention, by combining Smart Grid and mobile fog computing. Nonetheless, user privacy is also facing new challenges, which are caused by mobility. Particularly the key management problem when multiple users apply for key frequently. For the purpose of solving this problem, this paper proposes a novel privacy-preserving multi-level aggregate signcryption and query scheme for Smart Grid with mobile fog computing and quantum key pool. Firstly, we develop a multi-level and dynamic privacy-preserving aggregate signcryption method without trusted authority, including space-based data aggregation and time-based data aggregation. To enhance the security of key management, we introduce quantum key pool to fast distribute the keys. Secondly, we implement a data query and share method based on proxy re-encryption. It not only supports users to query historical data and bills, but also realizes secure data sharing with other third parties. Ultimately, we design formal security analysis and digital simulation experiments. The analysis results show that the proposed scheme can achieve the characteristics of secure, high efficiency, lightweight and robustness. In the background of the increasingly wide application of quantum technology, we hope this work is a trigger for other scholars and institutions in the hybrid application of quantum and classical technology.
Gradient eigendecomposition invariance biogeography-based optimization for mobile robot path planning

The path planning for mobile robots has attracted extensive attention, and evolutionary algorithms have been applied to this problem increasingly. In this paper, we propose a novel gradient eigendecomposition invariance biogeography-based optimization (GEI-BBO) for mobile robot path planning, which has the merits of high rotation invariance and excellent search performance. In GEI-BBO, we design an eigendecomposition mechanism for migration operation, which can reduce the dependency of biogeography-based optimization (BBO) on the coordinate system, improve the rotation invariance and share the information between eigensolutions more effectively. Meanwhile, to find the local optimal solution better, gradient descent is added, and the system search strategy can reduce the occurrence of local trapping phenomenon. In addition, combining the GEI-BBO with cubic spline interpolation will solve the problem of mobile robot path planning through a defined coding method and fitness function. A series of experiments are implemented on benchmark functions, whose results indicated that the optimization performance of GEI-BBO is superior to other algorithms. And the successful application of GEI-BBO for path planning in different environments confirms its effectiveness and practicability.
AirBC: A Lightweight Reputation-based Blockchain Scheme for Resource-constrained UANET

UAV Ad-hoc Network (UANET) has been widely used in many fields. However, the collaborative communication and data sharing among multiple UAVs in UANET are often attacked and threatened, due to the limited software and hardware capability of UAVs and the openness of wireless network environment. In this work, we introduce blockchain technology into UANET to enhance its security. Instead of directly adopting traditional blockchain which require huge storage, computation and communication resources, we propose a lightweight reputation-based blockchain scheme for resource-constrained UANET, named AirBC. Firstly, we present a lightweight storage strategy by elimination and compression to reduce storage overhead for UAV nodes. Secondly, we propose an improved reputation-enhanced Practical Byzantine Fault Tolerance (PBFT) consensus, as well as a reputation evaluation scheme based on reliable recording of UAV behaviors. In our scheme, UAVs with high reputation are selected into a miner committee to perform the consensus, thus improving efficiency. Meanwhile, the committee is updated at regular intervals to ensure scalability of UANET. Thirdly, we adopt a weighted proposal voting scheme to enhance the ability of group decision-making for UANET. Finally, to evaluate our approach, simulations are conducted and their results demonstrate that AirBC can reduce 63% storage overhead and 69% consensus latency on average for different scale UANET.
Exploring Unfairness on Proof of Authority

Proof of Authority (PoA) is a type of permissioned consensus algorithm with a fixed committee. PoA has been widely adopted by communities and industries due to its better performance and faster finality. In this paper, we explore the unfairness issue existing in the current PoA implementations. We have investigated 2,500+ in the wild projects and selected 10+ as our main focus (covering Ethereum, Binance smart chain, etc.). We have identified two types of order manipulation attacks to separately break the transaction-level (a.k.a. transaction ordering) and the block-level (sealer position ordering) fairness. Both of them merely rely on honest-but-profitable sealer assumption without modifying original settings. We launch these attacks on the forked branches under an isolated environment and carefully evaluate the attacking scope towards different implementations. To date (as of Nov 2021), the potentially affected PoA market cap can reach up to 681,087 million USD. Besides, we further dive into the source code of selected projects, and accordingly, propose our recommendation for the fix. To the best of knowledge, this work provides the first exploration of the unfairness issue in PoA algorithms.
Deep transfer learning-based gaze tracking for behavioral activity recognition

Computational Ethology studies focused on human beings is usually referred as Human Activity Recognition (HAR). Specifically, this paper belongs to a line of work on the identification of broad cognitive activities that users carry out with computers. The keystone of this kind of systems is the noninvasive detection of the subject's gaze fixations in selected display areas. Noninvasiveness is ensured by using the conventional laptop cameras without additional illumination or tracking devices. The gaze ethograms, composed as sequences of gaze fixations, are the basis to identify the user activities. To determine the gaze fixation display areas with the highest accuracy, this paper explores the use of a transfer learning approach applied to several well-known deep learning network (DLN) architectures whose input is the eye area extracted from the face image,and output is the identification of the gaze fixation area in the computer screen. Two different datasets are created and used in the validation experiments. We report encouraging results that may allow the general use of the system.
Stegozoa

Several totalitarian states around the world deploy sophisticated censorship apparatuses to prevent citizens from freely accessing the Internet. To counter these restrictions, some censorship-circumven-tion tools establish covert channels through the media streams of popular conferencing applications. A recent tool named Protozoa allows for establishing high-performing, peer-to-peer covert channels over WebRTC media streams. However, Protozoa is vulnerable to potential man-in-the-middle attacks. This may occur in cases where WebRTC applications rely on WebRTC gateways to mediate users' connections. In such cases, an adversary that controls the WebRTC gateway can inspect the content of the media streams and trivially detect the transmission of covert payload.
Research-Creations for Speculating About Digitized Automation: Bringing Creative Writing Prompts and Vital Materialism into the Sociology of Futures

In this article, we present ideas about developing innovative methods for the sociology of futures. Our approach brings together the literature on sociotechnical imaginaries and the sociology of futures with vital materialism theories and research-creation methods. We draw on our research-creation materials from a series of online workshops. The workshops involved the use of creative writing prompts with participants across a diverse range of age groups and locations. The article ends with some reflections on the implications of our approach for researching the futures of emerging digital technologies and the methodological and theoretical development of the sociology of futures.
Attempting cardiac arrest prediction using artificial intelligence on vital signs from Electronic Health Records

This work attempts to determine whether it is possible to predict the occurrence of in-hospital Cardiac Arrest (CA) using vital signs routinely recorded in a hospital's Electronic Health Record (EHR) system. The goal is to give caregivers advance notice of the possibility so they can intervene before its occurrence or prepare for immediate response in case it occurs. The work concentrates on three different aspects: which prediction model, which vital signs, and what time window produce the most accurate prediction. A number of experiments were conducted using six artificial intelligence algorithms and vital sign recordings ranging from 1 to 12 h. The work demonstrates that Random Forest produces the best prediction accuracy, reaching higher than 80%. Accuracy improves by more than 10% when prediction is done using vital sign data from the immediately preceding 60 min. Additionally, performance improves by only 3% when the observation range is increased from 1 h to 12 h.
Mitigating Frontrunning Attacks in Ethereum

With the rising popularity of Ethereum, there is also an uptick in the number of smart contract based decentralized applications (DApps). Consequently, Ethereum transaction volume is growing steadily over the last few years, but so are the various types of attacks on it. In Ethereum vulnerable smart contracts are always taken advantage of by adversaries. One of the primary ways of exploiting Ethereum with malicious intent is through frontrunning attacks that take advantage of the waiting time of transactions in the pending pool by adjusting the gas price. Attackers willing to execute such attacks constantly monitor the pending transaction pool and try to frontrun transactions. Mitigating such attacks is a critical step for ensuring secure DApp operations in Ethereum. In this paper, we propose a model-based attack detection and prevention scheme. We extract specific features for each transaction and transform each transaction into a feature vector which is then analyzed by a machine learning model to detect if it is a frontrunning attack transaction or not in real time. Extensive experiments on a large dataset of transactions establish the effectiveness of our approach.
Digital image self-recovery using CNN networks

This paper proposes a novel CNN-based image tampering detection and self-recovery scheme, this method focus on protecting digital images from forensic manipulation. For this purpose, the watermark algorithm should have two main features first, detecting the tampered zone of the received image by using an authentication watermark; second, recovering the tampered zone by utilizing an image digest watermark. For tamper detection, we propose the CNN-based authentication watermark generation. The image digest is generated using a DCT transform and a jpeg quantization table. "An End-to-End Compression Framework Based on CNN" is used to compress the image digest and improve the quality of recovering the tampered area. We code the compressed image digest by Reed-Solomon error correction code to protect it from a high tampering ratio. Finally, we use Arnold transform for embedding the authentication watermark and image digest watermark in the host image. This embedding method improves security issues. To show the proposed scheme's performance, conduct many experiments. The precision and recall score are calculated to evaluate tampering detection, while for evaluation of image self-recovery, calculate the PSNR and SSIM values of the recovered image. The comparisons with the state-of-the-art methods show that the proposed scheme is superior in imperceptibility, security, and recovery capability.
Understanding Disparate Effects of Membership Inference Attacks and their Countermeasures

Machine learning algorithms, when applied to sensitive data, can pose severe threats to privacy. A growing body of prior work has demonstrated that membership inference attack (MIA) can disclose whether specific private data samples are present in the training data to an attacker. However, most existing studies on MIA focus on aggregated privacy leakage for an entire population, while leaving privacy leakage across different demographic subgroups (e.g., females and males) in the population largely unexplored. This raises two important issues: (1) privacy unfairness (i.e., if some subgroups are more vulnerable to MIAs than the others); and (2) defense unfairness (i.e., if the defense mechanisms provide more protection to some particular subgroups than the others).
Cluster center consistency guided sampling learning for multiple kernel clustering

• This work proposes a novel dynamic anchor sampling scheme. • This work proposes a new multiple kernel clustering method . • Our method gives better results than the state-of-the-art competitors. Multiple kernel clustering (MKC) is popular in processing non-linear data over the past years. The main challenge is that the kernel matrix with the size n × n leads to high memory and computational consumption, where n denotes the number of samples. Although the widely used static anchor sampling can mitigate such a challenging considerably, how to dynamically select anchor points with a learning manner from kernel matrix is a difficult problem. To address the issue, this paper proposes a novel method dubbed as cluster center consistency guided sampling learning (3CSL) for multiple kernel clustering (3CSL-MKC). Specifically, by taking the cluster center consistency between the original partial kernel data and anchor points into consideration, 3CSL-MKC learns the shared anchor sampling matrix gradually. With the help of high-quality anchors, the essential clustering information of each kernel partition can be transformed largely into a concentrated low-dimensional representation matrix. Meanwhile, based on dictionary learning, 3CSL-MKC fuses these candidate representation matrixes to produce the resulting consensus representation, such that the clustering assignments can be directly obtained relying on simple k -means. A large number of experiments are conducted on different multiple kernel datasets to verify the effectiveness and efficiency of the proposed method.
Discovering Engagement Personas in a Digital Diabetes Prevention Program

Digital health technologies are shaping the future of preventive health care. We present a quantitative approach for discovering and characterizing engagement personas: longitudinal engagement patterns in a fully digital diabetes prevention program. We used a two-step approach to discovering engagement personas among n = 1613 users: (1) A univariate clustering method using two unsupervised k-means clustering algorithms on app- and program-feature use separately and (2) A bivariate clustering method that involved comparing cluster labels for each member across app- and program-feature univariate clusters. The univariate analyses revealed five app-feature clusters and four program-feature clusters. The bivariate analysis revealed five unique combinations of these clusters, called engagement personas, which represented 76% of users. These engagement personas differed in both member demographics and weight loss. Exploring engagement personas is beneficial to inform strategies for personalizing the program experience and optimizing engagement in a variety of digital health interventions.