Reviewerly/exHarmony huggingface dataset:
Efficient Formal Verification of Galois-Field Arithmetic Circuits Using ZDD Representation of Boolean Polynomials

In this study, we present a new formal method for verifying the functionality of Galois-field (GF) arithmetic circuits.Assuming that the input-output relation (i.e., the specification of a GF arithmetic circuit) can be represented as polynomials over F2, the proposed method formally checks the equivalence between GF polynomials derived from a netlist and the specification.To efficiently verify the equivalence, we employ a zero-suppressed binary decision diagram (ZDD) to represent polynomials over F2.Even though polynomial reduction is the most time-consuming process of verification (i.e., equivalence checking), our new algorithm can efficiently reduce the GF polynomials in the form of a zero-suppressed binary decision diagram derived from the target netlist.The proposed algorithm derives the polynomials representing all intermediate nodes (i.e., the outputs of all gates) in the order from primary inputs to those primary outputs that are in accordance with the reverse topological traversal order.We demonstrated the efficiency and effectiveness of the proposed method via a set of experimental verifications.In particular, we confirmed that the proposed method can verify practical GF multipliers (including those used in standardized elliptic curve cryptography) approximately 30 times faster on average and at most 170 times faster than the best conventional method.
A Blockchain-based System for Secure Image Protection Using Zero-watermark

In the traditional image copyright protection system, watermarking technology is considered as an important technology to overcome the data protection problem and verify the data ownership relationship. However, the existing watermarking technology often needs a trusted third party to arbitrate in the implementation process, which may be difficult to find or costly. Meanwhile, the common image watermarking algorithm needs to operate the image data, which will inevitably lead to the image data loss. With the development of blockchain technology, the functions of de-trusted third parties and the fair and automatic processing characteristics of smart contracts attached to it have entered the public view. In this paper, we study the function of the zero-watermarking algorithm in image protection and its complete storage and authentication scheme, then propose a secure blockchain-based image copyright protection framework and build a system according to this framework. This framework combines blockchain and zero-watermark technology and uses interplanetary file system to solve the blockchain data expansion problem. Besides, the image owner can authenticate the image and realize the copyright traceability of the image. Furthermore, the keyword search function of the stored images in the system is realized based on a smart contract, which solves the problem of lack of trusted third parties. Experiment illustrates that the proposed scheme is feasible.
A Light in the Dark Web: Linking Dark Web Aliases to Real Internet Identities

Most users have several Internet names. On Face-book or LinkedIn, for example, people usually appear with the real one. On other standard websites, like forums, people often use aliases to protect their real identities with respect to the other users, with no real privacy against the web site and the authorities. Aliases in the Dark Web are different: users expect strong identity protection.In this paper, we show that using both aliases (aliases used in the standard Web) and Dark Web aliases can be dangerous per se. Indeed, we develop tools to link Dark Web to open aliases. For the first time, we perform a massive scale experiment on real scenarios. First between two Dark Web forums, then between the Dark Web forums and the standard forums. Due to a large number of possible pairs, we first reduce the search space cutting down the number of potential matches to a small set of candidates, and then on the selection of the correct alias among these candidates. We show that our methodology has excellent precision, from 87% to 94%, and recall around 80%.
Deciphering Undersegmented Ancient Scripts Using Phonetic Prior

Most undeciphered lost languages exhibit two characteristics that pose significant decipherment challenges: (1) the scripts are not fully segmented into words; (2) the closest known language is not determined. We propose a decipherment model that handles both of these challenges by building on rich linguistic constraints reflecting consistent patterns in historical sound change. We capture the natural phonological geometry by learning character embeddings based on the International Phonetic Alphabet (IPA). The resulting generative framework jointly models word segmentation and cognate alignment, informed by phonological constraints. We evaluate the model on both deciphered languages (Gothic, Ugaritic) and an undeciphered one (Iberian). The experiments show that incorporating phonetic geometry leads to clear and consistent gains. Additionally, we propose a measure for language closeness which correctly identifies related languages for Gothic and Ugaritic. For Iberian, the method does not show strong evidence supporting Basque as a related language, concurring with the favored position by the current scholarship. 1
Learning-based optical authentication in complex scattering media

In recent years, optical encryption has become a promising method for securing information. However, optical cryptosystems have been demonstrated to be vulnerable to the attacking algorithms. Therefore, it is highly desirable that new optical cryptosystems can be continuously developed to achieve the higher security and withstand the attacks. In this paper, learning-based optical authentication in complex scattering media is proposed and experimentally verified. The ciphertext is generated by using an optical setup in complex scattering media, and a learning model is developed and trained to generate security keys. Moreover, other parameters, e.g., virtual phase-only masks, can be flexibly designed and used to further enlarge key space. The training pairs fed to the designed leaning model consist of specially processed images (i.e., acting as plaintexts) and speckle patterns (i.e., ciphertexts) recorded by CCD. The retrieved plaintexts obtained from the trained learning model cannot visually render recognizable information, and can be effectively authenticated by using nonlinear correlation algorithm. Optical experiments are conducted to verify effectiveness and applicability of the proposed learning-based optical authentication in complex scattering media to achieve the higher security and withstand the attacks. © Elsevier
Image Authentication Using QR Code Watermarking Approach Based on Image Segmentation

The rapid development of multimedia technology has brought huge challenges regarding the security of multimedia content. Digital watermarking is a widely-used form of protection, but it has proved hard to improve the robustness of digital watermarks and to find ways of preventing copy-based attacks. This paper proposes a new image authentication method that combines digital signatures and digital watermarking in the form of QR codes. Moving away from existing techniques, the proposed method uses image segmentation to exploit the error-correction capability of QR codes by converting the digital signature of each sub-block into a QR code before embedding it into the image. The digital signature is then saved as a watermark, which offsets the disadvantages of signature storage and is difficult to copy. Experiments show that the method is effective in terms of image noise and that the watermarks are robust and secure against copy-based attacks.
Online Partial Label Learning

A common assumption in online learning is that the data examples are precisely labeled. Unfortunately, it is intractable to obtain noise-free data in many real-world applications, and the datasets are usually adulterated with some irrelevant labels. To alleviate this problem, we propose a novel learning paradigm called Online Partial Label Learning (OPLL), where each data example is associated with multiple candidate labels. To learn from sequentially arrived data given partial knowledge of the correct answer, we propose three effective maximum margin-based algorithms. Theoretically, we derive the regret bounds for our proposed algorithms which guarantee their performance on unseen data. Extensive experiments on various synthetic UCI datasets and six real-world datasets validate the effectiveness of our proposed approaches.
Optimization by moving ridge functions: derivative-free optimization for computationally intensive functions

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Fiji-FIN: A Fault Injection Framework on Quantized Neural Network Inference Accelerator

In recent years, the big data booming has boosted the development of highly accurate prediction models driven from machine learning (ML) and deep learning (DL) algorithms. These models can be orchestrated on the customized hardware in the safety-critical missions to accelerate the inference process in ML/DL -powered IoT. However, the radiation-induced transient faults and black/white -box attacks can potentially impact the individual parameters in ML/DL models which may result in generating noisy data/labels or compromising the pre-trained model. In this paper, we propose Fiji-FIN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a suitable framework for evaluating the resiliency of IoT devices during the ML/DL model execution with respect to the major security challenges such as bit perturbation attacks and soft errors. Fiji-FIN is capable of injecting both single bit/event flip/upset and multi-bit flip/upset faults on the architectural ML/DL accelerator embedded in ML/DL -powered IoT. Fiji-FIN is significantly more accurate compared to the existing software-level fault injections paradigms on ML/DL -driven IoT devices.
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation

Video object segmentation plays a vital role to many robotic tasks, beyond the satisfied accuracy, quickly adapt to the new scenario with very limited annotations and conduct a quick inference are also important. In this paper, we are specifically concerned with the task of fast segmenting all pixels of a target object in all frames, given the annotation mask in the first frame. Even when such annotation is available, this remains a challenging problem because of the changing appearance and shape of the object over time. In this paper, we tackle this task by formulating it as a meta-learning problem, where the base learner grasping the semantic scene understanding for a general type of objects, and the meta learner quickly adapting the appearance of the target object with a few examples. Our proposed meta-learning method uses a closed form optimizer, the so-called "ridge regression", which has been shown to be conducive for fast and better training convergence. Moreover, we propose a mechanism, named "block splitting", to further speed up the training process as well as to reduce the number of learning parameters. In comparison with the state-of-the art methods, our proposed framework achieves significant boost up in processing speed, while having highly comparable performance compared to the best performing methods on the widely used datasets. Video demo can be found here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Subword Contextual Embeddings for Languages with Rich Morphology

Morphological information is important for many sequence labeling tasks in Natural Language Processing (NLP). Yet, existing approaches rely heavily on manual annotations or external software to capture this information. In this study, we propose using subword contextual embeddings for languages with rich morphology. Evaluated on Dependency Parsing (DEP) and Named Entity Recognition (NER) tasks, which are shown to benefit highly from morphological information, subword contextual embeddings consistently outperformed other approaches on all languages tested (Hungarian, Finnish, Czech and Turkish). Our proposed method enables achieving state-of-the-art results with little annotation requirements compared to the previous work. Besides, the novel network architecture we propose, coupled with a Bayesian hyperparameter optimization suite, achieved state-of-the-art results for both tasks for the Turkish language. Finally, we experimented with different multi-task learning architectures to analyze the effect of jointly learning the two tasks.
Folded Integer Multiplication for FPGAs

Encryption - especially the key exchange algorithms such as RSA - is an increasing use-model for FPGAs, driven by the adoption of the FPGA as a SmartNIC in the datacenter. While bulk encryption such as AES maps well to generic FPGA features, the very large multipliers required for RSA are a much more difficult problem. Although FPGAs contain thousands of small integer multipliers in DSP Blocks, aggregating them into very large multipliers is very challenging because of the large amount of soft logic required - especially in the form of long adders, and the high embedded multiplier count. In this paper, we describe a large multiplier architecture that operates in a multi-cycle format and which has a linear area/throughput ratio. We show results for a 2048-bit multiplier that has a latency of 118 cycles, inputs data every 9th cycle and closes timing at 377MHz in an Intel Arria 10 FPGA, and over 400MHz in a Stratix 10. The proposed multiplier uses 1/9 of the DSP resources typically used in a 2048-bit Karatsuba implementation, showing a perfectly linear throughput to DSP-count ratio. Our proposed solution outperforms recently reported results, in either arithmetic complexity - by making use of the Karatsuba techniques, or in scheduling efficiency - embedded DSP resources are fully utilized.
Approximate String Matching with SIMD

Abstract We consider the $k$ mismatches version of approximate string matching for a single pattern and multiple patterns. For these problems, we present new algorithms utilizing the single instruction multiple data (SIMD) instruction set extensions for patterns of up to 32 characters. We apply SIMD computation in three ways: in counting of mismatches, in comparison of substrings and in calculation of fingerprints. We show the competitiveness of the new algorithms by practical experiments.
WRGPruner: A new model pruning solution for tiny salient object detection

The model pruning is one of the predominant model compression tasks to decrease the demands in computing power and memory footprint. However, most existing pruning methods have overly broad application areas, which defects in a sub-optimal solution specifically to solve certain specified difficult problems in the tasks of salient object detection. In this paper, we propose a novel solution, dubbed as WRGPruner, based on the concept of salient energy level (SEL) for tiny salient object detection. The concept of SEL defines the level of assessing the distinguishing ability of parameters in the trained model between background and salient objects. To exploit the SEL, the WRGPruner is proposed, which considers three factors for model compression including the weight in the filter, the mathematical rank of the feature map matrix, and the gradient in the backward propagation. We mathematically prove the effectiveness of the WRGPruner for tiny salient objects. Besides, a tiny salient object dataset (TSOD) is constructed for evaluation. Extensive experiments show that WRGPruner reduces 60% of parameters with slight enhancement in terms of six accuracy metrics for VGG16 on TSOD. This demonstrates that the SEL is suitable for measure parameters and the effectiveness of WRGPruner.
The E-darning Sampler

Extending the life of our clothes is the most effective intervention of all current sustainable textile practices. Taking this into consideration, this paper explores how we can upcycle and repair our clothes with e-textiles, and how to share these techniques with other makers and crafters. To do so the author interviewed 4 visible mending educators about their teaching practices and personal experiences with mending. These interviews were then used to inform the design of the E-Darning Sampler for the TEI 2021 swatchbook, which includes examples of different e-textile darning patterns and functionalities made with darning looms. This paper contributes insights on how to design educational samples for encouraging sustainable making practices.
An Efficient Blockchain-based Cross-domain Authentication and Secure Certificate Revocation Scheme

The existing cross-domain authentication mechanisms are established based on well-known Public Key Infrastructure (PKI) systems, where digital certificates issued by Certificate Authority(CA) serve to authenticate the identity of entities. The complex authentication path, accompanied by multiple signatures and verifications, causes low authentication efficiency. In addition, resulting from an “update gap” between Certificate Revocation List(CRL) and Online Certificate Status Protocol(OCSP) mechanism, the revoked certificates will be exposed to DDoS attacks. For addressing aforementioned two challenges, an efficient blockchain-based authentication and secure certificate revocation scheme is proposed. Based on the non-tamperability and traceability of the blockchain, the signature module of X.509 digital certificate is replaced by the certificate hash value which serves as the trust certificate between domains. Furthermore, a consensus algorithm is adopted for random number hash broadcasting to improve efficiency. Theoretical analysis shows the proposed scheme has the security characteristics of non-repudiation, anonymity and anti DDoS attack. The experimental results demonstrate the proposed scheme has the advantage over existing cross-domain authentication and certificate revocation scheme at communication and computing cost and security respectively.
Fides: Managing Data on Untrusted Infrastructure

Significant amounts of data are currently being stored and managed on third-party servers. It is impractical for many small scale enterprises to own their private datacenters, hence renting third-party servers is a viable solution for such businesses. But the increasing number of malicious attacks, both internal and external, as well as buggy software on third-party servers is causing clients to loose their trust in these external infrastructures. While small enterprises cannot avoid using external infrastructures, they need the right set of protocols to manage their data on untrusted infrastructures. In this paper, we propose TFCommit, a novel atomic commitment protocol that executes transactions on data stored across multiple untrusted servers. To our knowledge, TFCommit is the first atomic commitment protocol to execute transactions in an untrusted environment without using expensive Byzantine replication. Using TFCommit, we propose an auditable data management system, Fides, residing completely on untrustworthy infrastructure. As an auditable system, Fides guarantees the detection of potentially malicious failures occurring on untrusted servers using tamper-resistant logs with the support of cryptographic techniques. The experimental evaluation demonstrates the scalability of our approach and the relatively low overhead of executing transactions on untrusted infrastructure.
PapyRow: A Dataset of Row Images from Ancient Greek Papyri for Writers Identification

Papyrology is the discipline that studies texts written on ancient papyri. An important problem faced by papyrologists and, in general by paleographers, is to identify the writers, also known as scribes, who contributed to the drawing up of a manuscript. Traditionally, paleographers perform qualitative evaluations to distinguish the writers, and in recent years, these techniques have been combined with computer-based tools to automatically measure quantities such as height and width of letters, distances between characters, inclination angles, number and types of abbreviations, etc. Recently-emerged approaches in digital paleography combine powerful machine learning algorithms with high-quality digital images. Some of these approaches have been used for feature extraction, other to classify writers with machine learning algorithms or deep learning systems. However, traditional techniques require a preliminary feature engineering step that involves an expert in the field. For this reason, publishing a well-labeled dataset is always a challenge and a stimulus for the academic world as researchers can test their methods and then compare their results from the same starting point. In this paper, we propose a new dataset of handwriting on papyri for the task of writer identification. This dataset is derived directly from GRK-Papyri dataset and the samples are obtained with some enhancement image operation. This paper presents not only the details of the dataset but also the operation of resizing, rotation, background smoothing, and rows segmentation in order to overcome the difficulties posed by the image degradation of this dataset. It is prepared and made freely available for non-commercial research along with their confirmed ground-truth information related to the task of writer identification.
Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning

Detecting failure cases is an essential element for ensuring the safety self-driving system. Any fault in the system directly leads to an accident. In this paper, we analyze the failure of semantic segmentation, which is crucial for autonomous driving system, and detect the failure cases of the predicted segmentation map by predicting mean intersection of union (mIoU). Furthermore, we design a deep neural network for predicting mIoU of segmentation map without the ground truth and introduce a new loss function for training imbalance data. The proposed method not only predicts the mIoU, but also detects failure cases using the predicted mIoU value. The experimental results on Cityscapes data show our network gives prediction accuracy of 93.21% and failure detection accuracy of 84.8%. It also performs well on a challenging dataset generated from the vertical vehicle camera of the Hyundai Motor Group with 90.51% mIoU prediction accuracy and 83.33% failure detection accuracy.
A New Adaptive Bidirectional Region-of-Interest Detection Method for Intelligent Traffic Video Analysis

Real-time intelligent video-based traffic surveillance applications play an important role in intelligent transportation systems. To reduce false alarms as well as to increase computational efficiency, robust road segmentation for automated Region of Interest (RoI) detection becomes a popular focus in the research community. A novel Adaptive Bidirectional Detection (ABD) of region-of-interest method is presented in this paper to automatically segment the roads with bidirectional traffic flows into two regions of interest. Specifically, a foreground segmentation method is first applied along with the flood-fill algorithm to estimate the road regions. Then the Lucas-Kanade's optical flow algorithm is utilized to track and divide the estimated road into regions of interest in real-time. Experimental results using a dataset of real traffic videos illustrate the feasibility of the proposed method for automatically determining the RoIs in real-time.