Reviewerly/exHarmony huggingface dataset:
Problems of Developing User Identification Systems by Keyboard Handwriting

This article is devoted to the analysis of problems in the development of user recognition systems by keyboard handwriting. The works of famous scientists in this field are analyzed. Based on the results of the analysis of the works, the problems of developing systems for identifying and authenticating users by keyboard handwriting were identified. It is shown that the use of standard built-in events in modern high-level object-oriented programming languages, pressing and releasing keys, leads to errors in measuring the time of bigrams, where the results shifted to values of 200, 240, 280 and 320 ms. In addition, studies were conducted when working on various keyboards, the results were different related to design features and ergonomics. On the example of three different keyboards, the time distributions of the user's bigrams were obtained. For accuracy, an approximation based on a mixture of the distribution of Gaussian components was used. This paper proposes methods for solving these problems in order to increase the reliability of authentication.
Attribute-Based Approaches for Secure Data Sharing in Industrial Contexts

The sharing of data is becoming increasingly important for the process and manufacturing industries that are using data-driven models and advanced analysis to assess production performance and make predictions, e.g., on wear and tear. In such environments, access to data needs to be accurately controlled to prevent leakage to unauthorized users while providing easy to manage policies. Data should further be shared with users outside trusted domains using encryption. Finally, means for revoking access to data are needed. This paper provides a survey on attribute-based approaches for access control to data, focusing on policy management and enforcement. We aim to identify key properties provided by attribute-based access control (ABAC) and attribute-based encryption (ABE) that can be combined and used to meet the abovementioned needs. We describe such possible combinations in the context of a proposed architecture for secure data sharing. The paper concludes by identifying knowledge gaps to provide direction to future research on attribute-based approaches for secure data sharing in industrial contexts.
Joint Gradient Sparsification and Device Scheduling for Federated Learning

Federated learning is an attractive distributed learning framework where edge server coordinates edge devices to collaboratively train an artificial intelligence (AI) model while training data stays in edge devices. However, implementing federated learning in an energy-efficient way still faces some problems in practice: i) uploading high-dimensional parameters of edge devices is energy-costly and causes communication congestion; ii) The heterogeneity of edge devices, i.e., non-i.i.d. data, different software/hardware configurations, and time-varying channel conditions, affects training accuracy and convergence rate of federated learning. To address these issues, in this paper, we aim at minimizing energy use of the heterogeneous edge devices while maintaining the learning performance. Note that the effects of sparsification and the number of participating devices are highly coupled, and they have opposite effects on both learning performance and energy consumption. Therefore, the gradient sparsification and device scheduling should be jointly optimized for federated learning. We consider the case where a fraction of heterogeneous devices are scheduled to upload their sparsified local gradients for global aggregation at the server. First, a tradeoff between the sparsification and the number of participating devices is revealed through the convergence rate analysis. Then, a joint optimization problem is formulated to minimize energy consumption under the convergence guarantee. An efficient algorithm is proposed to find the globally optimal solution of the considered non-convex problem. Simulations verify the efficiency of the proposed scheme.
Universal BlackMarks: Key-Image-Free Blackbox Multi-Bit Watermarking of Deep Neural Networks

Existing methods for Deep Neural Networks (DNN) watermarking either require accessing the internal parameters of the DNN models (white-box watermarking), or rely on backdooring to enforce a desired behavior of the model when the DNN is fed with a specific set of key input images (black-box watermarking). In this letter, we propose a black-box multi-bit DNN watermarking algorithm, suitable for multiclass classification networks, whereby the presence of the watermark can be retrieved from the output of the network in correspondence to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">any</i> input. To read the watermark, we first apply a power function to the softmax output of the DNN model to map it from an impulse-like to a smooth distibution. Then, we extract the watermark bits by projecting the output of the DNN onto a pseudorandom key vector. Watermark embedding is achieved by adding a proper regularizer term to the training loss. The effectiveness of the proposed method is demonstrated by applying it to various network architectures working on different datasets. The experimental results demonstrate the possibility to embed a robust watermark into the output of the host DNN with a negligible impact on the accuracy of the original task.
An Instability-Resilient Renewable Energy Allocation System for a Cloud Datacenter

Renewable energy supply is a promising solution for datacenters' increasing electricity monetary cost, energy consumption and harmful gas emissions. However, due to the instability of renewable energy, insufficient renewable energy supply may lead to the use of stored energy or brown energy. To handle this problem, in this paper, we propose an instability-resilient renewable energy allocation system. We define a job's service-level-objective (SLO) as the successful running probability by only using supplied renewable energy. The system allocates jobs with the same SLO level to the same physical machine (PM) group, and powers each PM group with renewable energy generators that have probability no less than its SLO to produce the amount no less than its energy demand. We use a deep learning technique to predict the probability of producing the amount no less than each value of each renewable energy source, and predict the energy demands of each PM area. We formulate an optimization problem to match renewable energy resources with different instabilities to different PM groups for supply, and use reinforcement learning method and linear programming method to solve it. We further propose an energy-driven computing resource assignment method, which adjusts the amount of computing resource of each job based on job deadline and failure probability in each PM group, and a failure prediction based energy saving method. Real trace driven experiments show that our methods achieve much lower SLO violations, total energy monetary cost and total carbon emission compared to other methods and the effectiveness of individual methods.
Factors affecting forensic electric network frequency matching – A comprehensive study

The power system frequency fluctuations could be captured by digital recordings and extracted to compare with a reference database for forensic timestamp verification. It is known as the Electric Network Frequency (ENF) criterion, enabled by the properties of random fluctuations and intra-grid consistency. In essence, this is a task of matching a short random sequence within a long reference, whose accuracy is mainly concerned with whether this match could be uniquely correct. In this paper, we comprehensively analyze the factors affecting the reliability of ENF matching, including the length of test recording, length of reference, temporal resolution, and Signal-to-Noise Ratio (SNR). For synthetic analysis, we incorporate the first-order AutoRegressive (AR) ENF model and propose an efficient Time-Frequency Domain noisy ENF synthesis method. Then, the reliability analysis schemes for both synthetic and real-world data are respectively proposed. Through a comprehensive study, we quantitatively reveal that while the SNR is an important external factor to determine whether timestamp verification is viable, the length of test recording is the most important inherent factor, followed by the length of reference. However, the temporal resolution has little impact on performance. Finally, a practical workflow of the ENF-based audio timestamp verification system is proposed, incorporating the discovered results.
Security and Privacy Research Artifacts: Are We Making Progress?

According to the Annual Computer Security Applications Conference (ACSAC):1
Katie Moussouris: Vulnerability Disclosure and Security Workforce Development

Bob Blakley: Welcome everyone to episode four of the IEEE Over the Rainbow podcast. I’m Bob Blakley, and I’m here with my cohost Lorrie Cranor. Our guest today is Katie Moussouris. Katie is the CEO of Luta Security, a company that helps organizations design and operate bug bounty and vulnerability reporting programs. Katie has a background in molecular biology and worked on the Human Genome Project at MIT. While she was at MIT, she became a system administrator, which led her in time to a career in information security. She was a penetration tester for @Stake; after Symantec acquired @Stake, she established the Symantec Vulnerability Research Program. From Symantec, she moved to Microsoft, where she created Microsoft’s Vulnerability Research Program in 2008; in 2014, Katie moved to HackerOne, where she served as chief policy officer. In 2016, she left HackerOne to found Luta Security. Katie has done both academic and policy work in vulnerability research and disclosure; she was a driving force in ensuring that the 2013 revision of the Wassenaar Arrangement would exempt software tools used for defense from export controls, and she served as editor of both major ISO/IEC vulnerability standards (ISO/IEC 29147 and ISO/IEC 30111).
HADES: Hash-Based Audio Copy Detection System for Copyright Protection in Decentralized Music Sharing

Preventive measures to stop copyright infringement are yet to be implemented on current decentralized music-sharing platforms. There is no mechanism to reject modified audio before they go online, so some decentralized music platforms become places full of pirated audio files. To address this problem, a perceptual hash-based audio detection method for copyright protection in decentralized music sharing was proposed. Chromaprint, an open-source audio fingerprint program, generates a hash value to detect copyright infringement. To assess the perceptual hash technique’s robustness, Chromaprint generates a hash value from an audio file that can be modified with several signal processing attacks. The results of the detection system show that Chromaprint is very effective at spotting copyright infringement, with an average match rate of 92.64%. Deployed using a public Ethereum blockchain test network, the execution time from hashing to uploading to the IPFS distributed storage is only 616.3 ms.
Nothing Is Good Enough: Fast and Cheap Are Undervalued as Influencers of Security Tool Adoption

Properties of practices in industrial use are rarely studied. Security workers satisfice, selecting a tool that’s “good enough,” and in practice, that turns out to be nothing at all. This article advocates valuing lightweight methods and presents studies and evaluation criteria for such tools.
Local Decoding in Distributed Compression

A recent result says that the lossless compression of a single source Xn is achievable with a strong locality property; any Xi can be decoded from a constant number of compressed bits, with a vanishing in n probability of error. By contrast, we show that for two separately encoded sources pXn, Y nq, lossless compression and strong locality is generally not possible. Specifically, we show that for the class of “confusable” sources, strong locality cannot be achieved whenever one of the sources is compressed below its entropy. Irrespective of n, there always exists at least one index i for which the probability of incorrectly decoding pXi, Yiq is lower bounded by 2’Opdq, where d denotes the worst-case (over indices) number of compressed bits accessed by the local decoder. Conversely, if the source is not confusable, strong locality is possible even if one of the sources is compressed below its entropy. Results extend to an arbitrary number of sources.
LFEformer: Local Feature Enhancement Using Sliding Window With Deformability for Automatic Speech Recognition

A module using sliding window with deformablity, abbreviated as SWD, has been proposed for local feature enhancement. In particular, the proposed SWD module adopts windows with variable size based on the depth of the embedded network layers. Moreover, the proposed SWD module is inserted into the Transformer network, referred as LFEformer, for automatic speech recognition. Such network is particularly good at capturing both local and global features, and this is beneficial for model improvement. It is worth mentioning that the local and global features are extracted by SWD module and the attention mechanism in Transformer network, respectively. The effectiveness of the LFEformer has been validated on three widely used datasets, which are Aishell-1, HKUST and WSJ ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dev93</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eval92</i> ). The experimental results demonstrate that 0.5% CER, 0.8% CER and 0.7%/0.3% WER improvement can be obtained in the correspondent datasets.
Central Cohesion Gradual Hashing for Remote Sensing Image Retrieval

With the recent development of remote sensing technology, large image repositories have been collected. In order to retrieve the desired images of massive remote sensing data sets effectively and efficiently, we propose a novel central cohesion gradual hashing (CCGH) mechanism for remote sensing image retrieval. First, we design a deep hashing model based on ResNet-18 which has a shallow architecture and extracts features of remote sensing imagery effectively and efficiently. Then, we propose a new training model by minimizing a central cohesion loss which guarantees that remote-sensing hash codes are as close to their hash code centers as possible. We also adopt a quantization loss which promotes that outputs are binary values. The combination of both loss functions produces highly discriminative hash codes. Finally, a gradual sign-like function is used to reduce quantization errors. By means of the aforementioned developments, our CCGH achieves state-of-the-art accuracy in the task of remote sensing image retrieval. Extensive experiments are conducted on two public remote sensing image data sets. The obtained results support the fact that our newly developed CCGH is competitive with other existing deep hashing methods.
Backward Support Computation Method for Positive and Negative Frequent Itemset Mining

Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today's big data environment.The conventional association rule mining focuses mainly on positive itemsets generated from frequently occurring itemsets (PFIS).However, there has been a significant study focused on infrequent itemsets with utilization of negative association rules to mine interesting frequent itemsets (NFIS) from transactions.In this work, we propose an efficient backward calculating negative frequent itemset algorithm namely EBC-NFIS for computing backward supports that can extract both positive and negative frequent itemsets synchronously from dataset.EBC-NFIS algorithm is based on popular e-NFIS algorithm that computes supports of negative itemsets from the supports of positive itemsets.The proposed algorithm makes use of previously computed supports from memory to minimize the computation time.In addition, association rules, i.e. positive and negative association rules (PNARs) are generated from discovered frequent itemsets using EBC-NFIS algorithm.The efficiency of the proposed algorithm is verified by several experiments and comparing results with e-NFIS algorithm.The experimental results confirm that the proposed algorithm successfully discovers NFIS and PNARs and runs significantly faster than conventional e-NFIS algorithm.
IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution

Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues.These challenges are increasing the interest in the quality of medical images.Recent research has proven that the rapid progress in convolutional neural networks (CNNs) has achieved superior performance in the area of medical image super-resolution.However, the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance (MR) images, adding extra noise in the models and more memory consumption.Furthermore, conventional deep CNN approaches used layers in series-wise connection to create the deeper mode, because this later end layer cannot receive complete information and work as a dead layer.In this paper, we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS.In our proposed approach, a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters.Furthermore, a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image.Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.
Federated Learning With Over-the-Air Aggregation Over Time-Varying Channels

We study federated learning (FL) with over-the-air aggregation over time-varying wireless channels. Independent workers compute local gradients based on their local datasets and send them to a parameter server (PS) through a time-varying multipath fading multiple access channel via orthogonal frequency-division multiplexing (OFDM). We assume that the workers do not have channel state information, hence the PS employs multiple antennas to alleviate the fading effects. Wireless channel variations result in inter-carrier interference, which has a detrimental effect on the performance of OFDM systems, especially when the channel is rapidly varying. We examine the effects of the channel time variations on the convergence of the FL with over-the-air aggregation, and show that the resulting undesired interference terms have only limited destructive effects, which do not prevent the convergence of the learning algorithm. We also validate our results via extensive simulations, which corroborate the theoretical expectations.
Decision-making support system for diagnosis of oncopathologies by histological images

The aim of the study is to increase the functional efficiency of machine learning decision support system (DSS) for the diagnosis of oncopathology on the basis of tissue morphology. The method of hierarchical information-extreme machine learning of diagnostic DSS is offered. The method is developed within the framework of the functional approach to modeling of natural intelligence cognitive processes at formation and acceptance of classification decisions. This approach, in contrast to neuronal structures, allows diagnostic DSS to adapt to arbitrary conditions of histological imaging and flexibility in retraining the system by expanding the recognition classes alphabet that characterize different structures of tissue morphology. In addition, the decisive rules built within the geometric approach are practically invariant to the multidimensionality of the diagnostic features space. The developed method allows to create information, algorithmic, and software of the automated workplace of the histologist for diagnosing oncopathologies of different genesis. The machine learning method is implemented on the example of diagnosing breast cancer.
Real-Time Lightweight Bangla Sign Language Recognition Model Using Pre-trained MobileNetV2 and Conditional DCGAN

Sign language is one of the major communication mediums for deaf and dumb people. An interpreter can help disabled people in their daily life. Most of the works on Bangla sign language (BdSL) recognition are either on grayscale images or need heavy preprocessing. This paper proposes a lightweight, less preprocessing-based BdSL recognizing model with good accuracy. We used a dataset that has 38 classes. We used a pre-trained MobileNetV2 because of the small dataset, which is a lightweight model. To generalize the model, we used conditional deep convolutional generative adversarial network. We focused on creating a lightweight Bangla sign language recognizing model that smartphones can run and does not rely on preprocessing. We achieved 94.74% testing accuracy. Although our proposed model could not surpass the overall state-of-the-art accuracy, the model achieved better accuracy in classifying all individual signs, which was one of the major problems of many research works.
An Analysis of Bitcoin Dust Through Authenticated Queries

Dust refers to the amounts of cryptocurrency that are smaller than the fees required to spend them in a transaction. Due to its “economically irrational” nature, dust is often used to achieve some external side effect, rather than exchanging value. In this paper we study this phenomenon by conducting an analysis of dust creation and consumption in the Bitcoin blockchain. We do so by exploiting a new method that allows resource-constrained nodes to retrieve blockchain data by sending authenticated queries to possibly untrusted but more powerful nodes. We validate the method effectiveness experimentally and then analyze the collected data. Results show that a large amount of dust can be traced back to on-chain betting services.
Microscopy Cancer Cell Imaging in B-lineage Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is a type of white blood cancer, in which the B- and T- lymphocytes are affected. This cancer constitutes approximately 20% of the pediatric cancers. Morphologically, the normal progenitor cells and cancer blood cells when present at low numbers appear similar under the microscope to the naked eye, and the preliminary tests are based on the cancerous cell count. Moreover, the conventional tests are very expensive and are not available widely in pathology laboratories or hospitals, particularly in rural areas. Because microscopic examination is readily available and cost effective, conferring the ability of distinguishing cancer cells from normal cells to microscopic image processing evaluation will provide several benefits in terms of scaling and cost. The complete workflow of such a tool consists of the following steps: (1) capture of images and preparation of the dataset, (2) normalization of color stain to correct for abnormalities during the staining process, (3) segmentation of cells of interest, and (4) classification of cells as cancer cells or normal cells. In this chapter, all the four stages are discussed in detail.