Reviewerly/exHarmony huggingface dataset:
Adaptive pooling-based convolution factorization for deploying CNNs on energy-constrained IoT edge devices

Convolutional neural networks (CNNs) involve a tremendous amount of multiply-and-accumulate (MAC) computations, leading to energy issues when deployed on energy-constrained IoT edge devices for computer vision purposes. However, a great number of these computations are ineffectual since they lead to meaningless zero activations, which could be avoided if predicted appropriately. This has been the focus of many studies, whereas it has only been done through zero-activation simplifications. This paper goes beyond those simplifications through a convolution factorization approach to convert the MAC-intensive convolution operations into less complex pooling operations. The main characteristic of the proposed idea is that, given a target accuracy, the applicability and type of factorization are adaptively decided for each individual activation. This is achieved by providing a lightweight multi-class CNN-based predictor, namely RedZAP. The selected types of pooling operations for our convolution factorizations are supported by existing CNN architectures. The experimental results show that, given a top-1 accuracy loss constraint of 1%, the proposed approach saves another 8% MAC operations in comparison to the existing zero-prediction approaches in the literature.
Image semantic learning method based on social heterogeneous graph networks1

Image semantic learning techniques are crucial for image understanding and classification. In social networks, image data is widely disseminated thanks to convenient acquisition and intuitive expression characteristics. However, due to the freedom of users to publish information, the image has apparent context dependence and semantic fuzziness, which brings difficulties to image representation learning. Fortunately, social attributes such as hashtags carry rich semantic relations, which can be conducive to understanding the meaning of images. Therefore, this paper proposes a new method named Social Heterogeneous Graph Networks (SHGN) for image semantic learning in social networks. First, a heterogeneous graph is built to expand image semantic relations by social attributes. Then the consistent semantic space is reconstructed through cross-media feature alignment. Finally, an image semantic extended learning network is designed to capture and integrate the social semantics and visual feature, which obtains a rich semantic representation of images from a social context. The experiments demonstrate that SHGN can achieve efficient image representation, and favorably against many baseline algorithms.
Virtual Restoration of Ancient Wooden Ships Through Non-rigid 3D Shape Assembly with Ruled-Surface FFD

Abstract In recent years, 3D data has been widely used in archaeology and in the field of conservation and restoration of cultural properties. Virtual restoration, which reconstructs the original state in virtual space, is one of the promising applications utilizing 3D scanning data. Though many studies of virtual restoration have been conducted, it is still challenging to restore the cultural properties that consist of multiple deformable components because it is not feasible to identify the original shape uniquely. As a solution to such a problem, we proposed a non-rigid 3D shape assembly method for virtually restoring wooden ships that are composed of multiple timbers. The deformed timber can be well represented by ruled surface. We proposed a free-form deformation method with a ruled surface and an assembly method to align the deformable components mutually. The method employs a bottom-up approach that does not require reference data for target objects. The proposed framework narrows down the searching space for optimization using the physical constraints of wooden materials, and it can also obtain optimal solutions. We also showed an experimental result, where we virtually restored King Khufu’s first solar boat. The boat was originally constructed by assembling several timbers. The boat was reconstructed as a real object and is currently exhibited at a museum. However, unfortunately, the entire shape of the boat is slightly distorted. We applied the proposed method using archaeological knowledge and then showed the virtual restoration results using the acquired 3D data of the boat’s components.
Deep Learning-Based Segmentation of Cellular Membranes in Colorectal Immunohistochemical Images

The segmentation of cellular membranes is essential for getting crucial information in diagnosing several cancers, including lung, breast, colon, gastric cancer, etc. Manual segmentation of cellular membranes is a tedious, time-consuming routine and prone to error and inter-observer variation. So, it is one of the challenges that pathologists face in immunohistochemical (IHC) tissue images. Although automated segmentation of cellular membranes has recently gained considerable attention in digital pathology applications, little research is based on machine learning approaches. Therefore, this study proposes a deep framework for semantic segmenting cellular membranes using an end-to-end trainable Convolutional Neural Network (CNN) based on encoder and decoder architecture with Atreus Spatial Pyramid Pooling (ASPP). The backbone of the encoder depends on the residual architecture. The performance of the proposed framework was evalu ated and compared to other benchmark methods. As a result, we show that the proposed framework exhibits significant potential for cellular membranes segmentation in IHC images.
TPC, Where Art Thou?

Performance has and still is perceived by customers as one of the main differentiators of computer systems. Hence, having access to widely accepted, objective and verifiable performance data is paramount for customers in making informed buying decisions. The last ten years have seen the rise of many new DBMSs. With them came a flurry of benchmark definitions. Even if technically sound, these benchmarks alone cannot achieve the goal of delivering widely accepted, objective and verifiable performance data. In addition they need to be agreed upon by a large body of vendors, and they need an independent oversight authority that assures correctness of their results. There are two major standard organizations that work on achieving these goals: Standard Performance Evaluation Corporation (SPEC) and Transaction Processing Performance Council (TPC). This article centers around the TPC, its history, challenges and steps it is taking to stay relevant in the years ahead.
Adaptive Color Quantization Method with Multi-level Thresholding

Abstract In this study, a novel color quantization approach which automatically estimates the number of colors by multi-level thresholding based on the histogram is proposed. The method consists of three stages. First, red–green–blue is clustered by threshold values. Thus, the pixels are positioned in a cluster or sub-prism. Second, the color palette is produced by determining the centroids of the clusters. Finally, the pixels are reassigned to clusters based on their distance from each centroid. The average of the pixels included in each cluster also represents the color of that cluster. While conventional methods are user-dependent, the proposed algorithm automatically generates the number of colors by considering the pixels assigned to the clusters. Additionally, the multi-level thresholding approach is also a solution to the initialization problem, which is another important issue for quantization. Consequently, the experimental results of the method tested with various images show better performance than many frequently used quantization techniques.
Mediating Role of Crisis Management between Knowledge Management and Employee Commitment

The purpose of the study is to examine the mediating role of crisis management between knowledge management and employee commitment. Committed employees are needed to keep the workforce motivated and knowledge is required to keep them committed, however, crisis issues develop loyalty among the employees, and this can only be achieved if the employees effectively manage knowledge. For the purpose of achieving the objectives of the study, primary study has been conducted. A sample of 384 employees was used to run structural equation modeling. The findings revealed that knowledge management has a significant impact over employee commitment, likewise, crisis management significantly mediates the positive relationship between knowledge management and employee commitment. The study used the underpinning support of knowledge-based view, and it is an attempt to enrich the theory by adding crisis management in the theory as a mediator.
A Data Enhancement Method toward Real-World Single Image Super-Resolution

Abstract Introducing convolutional neural networks (CNNs) into single image super-resolution (SISR) has achieved remarkable performance in experimental scenarios over the past decades. However, maintaining constant mapping complexity, most existing CNN-based SISR approaches make it challenging to reconstruct high-quality images in real scenarios because of considerable variation between authentic degradation and simulated degradation. In this paper, to improve SISR models’ capabilities in the real world, we propose a data enhancement method, consisting of frequency domain data augmentation (FDA) and dynamical frequency loss (DFL). By introducing high-resolution images’ frequency information, the FDA enhances the quality and quantity of data, regularizing the model to learn “how” to appropriately apply super-resolution on different frequency components of a given image. In addition, to further strengthen the regularization effect, we design DFL to dynamically assign the loss trade-off hyperparameters of distinct frequency components, guiding the model to allocate computational overhead reasonably. Considering quantitative analysis and visual quality in combination, extensive experiments prove that the proposed enhancement method significantly improves SR models’ capabilities in real scenarios without adding any computational cost to the evaluation procedure.
Research on the Grouping Method of Side-Channel Leakage Detection

Power analysis attack is a method to obtain the key of the cryptographic chip by analyzing the correlation between power consumption information leaked by the cryptographic chip during the computing process and the key. The efficiency of power analysis attack poses a serious threat to the software and hardware implementation of cryptographic algorithms. In order to detect whether a cryptographic chip has information leakage, it is necessary to assess it by using detection techniques. The t-test is a hypothesis test used in the field of statistics to test whether there is a significant difference in the means of two normally distributed populations with unknown variance, and is also a useful tool in side-channel information leakage assessment. In this paper, two grouping methods are proposed based on the characteristics of the AES algorithm to investigate the construction of two overall groups before the implementation of the Welch’s t-test. Experimental verification of the DPA contest V4 dataset shows that both grouping methods were effective in detecting a large number of leakage points on power traces, but the grouping method by AES first round S-box output Hamming weight has a higher proportion of both the number of leakage points and the high t-statistic distribution than the method of grouping by bit value.
Message Recovery Attack of Kyber Based on Information Leakage in Decoding Operation

In this work, we propose practical side-channel attacks for message recovery in post-quantum key encapsulation mechanisms (KEM). As a target scheme, Kyber is a standardized algorithm in the ongoing NIST standardization process. Notably, this work is the first one that implements message recovery by exploiting the information leaked on computational operations during Kyber decoding. The main findings include 1. analyzing computational operations during decoding by power consumption information to effectively recover message; 2. recovering message by analyzing the time differences existing in decoding single bits; 3. by way of simple power analysis, using incremental storage leakage to recover the message.
PREFHE, PREFHE-AES and PREFHE-SGX: Secure Multiparty Computation Protocols from Fully Homomorphic Encryption and Proxy ReEncryption with AES and Intel SGX

We build our secure multiparty computation (MPC) protocols on top of the fully homomorphic encryption (FHE) scheme, BFVrns, and augment it with Proxy Re-Encryption (PRE). We offer three distinct secure MPC protocols that make use of the Advanced Encryption Standard (AES) and Intel Software Guardian Extension (SGX). The PREFHE protocol is based on FHE and PRE that offers a reasonable computational time of milliseconds or seconds, depending on the function computed jointly on the parties’ encrypted data. It offers 4 rounds and a communication cost that only depends on the parties’ ciphertext size. PREFHE-AES employs AES-128 encryption, which reduces the cost of communication to bits rather than kilobytes or megabytes while maintaining the same number of rounds as PREFHE. PREFHE-SGX is another novel approach that reduces the number of rounds from 4 to 3 by utilizing only one untrusted server. Additionally, it delivers a reasonable level of performance that is applicable to real-world use cases. We pioneer the use of SGX and FHE in secure MPC protocols, resulting in reduced number of rounds. In the protocols, after parties send their encrypted data to the server, they are not required to be online that improves practicality in the protocols. Additionally, the parties are not required to collaborate on any computations during the encryption and decryption phases that makes our protocols more efficient than other proposed protocols.
Granting Access Privileges Using OpenID Connect in Permissioned Distributed Ledgers

Permissioned distributed ledger technology (DLT), in which only authenticated entities participate, assumes trust among the participants and implicit consent for data manipulation. In light of international regulations such as the GDPR, it is necessary to clarify the access privileges of user data, even for systems that assume the trust of the participants. In this paper, we propose an access privilege granting method for service providers that need to access user data in permissioned DLT systems. The proposed method separates the access privilege for user data in the distributed ledger from the execution privilege for smart contracts. By requesting a user to grant the access privilege, the participants can manipulate user data using smart contracts. The access privilege is represented by a token issued by OpenID Connect (OIDC). Smart contracts can directly verify the token without the participant’s interference. In this way, all the participants in the DLT network can reach a consensus that data manipulation is based on the user’s consent. We implemented the prototype system with Keycloak, an OIDC-compliant identity provider, and Hyperledger Fabric, a permissioned DLT, and then evaluated its performance. Finally, the overhead of access control is 0.21%, from which we conclude that the load on the system is very small.
Replicated Additive Secret Sharing with the Optimized Number of Shares

Replicated additive secret sharing (RSS) schemes introduce the threshold for additive secret sharing, and are known for computational efficiency and flexibility. While the traditional RSS schemes usually have a huge storage overhead with each server holding multiple shares, recent variations have tried reducing storage overhead but at the expense of computational performance. In this work, we focus on optimizing the number of shares to reduce storage overhead without introducing excessive computational cost. First, we construct a 2-of-n RSS (i.e., any two among n servers could reconstruct the secret value), which generates secret shares incrementally so that the storage increases almost linearly with n, and achieves the optimal number of shares as we proved. Then, we extend 2-of-n RSS to a general t-of-n RSS. Moreover, the incrementally-generate mechanism makes our scheme support a server to join dynamically that refrain existing shares from being modified. Our empirical study across 60 servers supports that our scheme largely reduces the storage overhead while obtaining an efficient runtime. Storage efficiency shows an improvement of up to two orders of magnitude and online runtime is within microsecond scale in our experimental settings.
Generic 2-Party PFE with Constant Rounds and Linear Active Security, and Efficient Instantiation

The paper considers generic construction of 2-party private function evaluation (PFE) in the malicious adversary model. There is hitherto only one concrete design of actively secure 2-party PFE protocol (Liu et al. at PKC 2022, and LWY hereafter) with constant rounds and linear complexity. One interesting feature of LWY is its function reusability (i.e., the same function is involved in multiple executions of LWY) which makes its execution more efficiently from the second execution. Nevertheless, in its first execution (in particular for those settings where only one invocation of the function is required), LWY is quite involved and too inefficient to be of practical use. For these settings (of non-reusable private functions), we initiate a generic construction of 2-party PFE protocol with constant rounds and linear complexity in the malicious adversary model based on Yao’s garbled circuit and singly homomorphic encryption. When instantiated with ElGamal encryption and Groth secret shuffle (J. Cryptology 2010), the generic construction effectuates a novel concrete design of 2-party PFE, which has better performance and reduces 51.2% communication bits and 52.4% computation costs, compared to LWY (in its first execution) at the same security level. It even outperforms several 2-party PFE protocols (Katz and Malka at AISACRYPT 2011, and Mohassel and Sadeghian at EUROCRYPT 2013) that are secure in the semi-honest adversary model from the communication perspective. The proposed PFE and LWY thus make optimal solutions available for non-reusable and reusable private functions, respectively.
A Random Reversible Watermarking Scheme for Relational Data

In the era of Big Data, relational data is at risk of piracy and misuse when distributed, shared and used. The use of digital watermarking technology is a reliable way to protect the copyright of relational data. In order to protect the copyright of relational data and recover the original data, many reversible watermarking schemes have been proposed in recent years. But most of them cannot extract the watermark information completely under severe attacks. To address this problem, a random reversible watermarking scheme is proposed. Watermark embedding algorithm, watermark integrity checking algorithm, watermark detection algorithm and data recovery algorithm are designed. The watermark capacity is increased by embedding multiple watermarks in selected tuples, and the randomness of the watermark information distribution is increased by embedding unequal proportions of watermarks in different tuples. In extracting the watermark, the attacked bits are discarded to improve the accuracy of watermark detection. In addition, only a partition with complete watermark information is selected for watermark extraction. This not only improves the speed of watermark extraction, but also avoids the risk of key leakage from other partitions. The experimental results show that the complete watermark information can be extracted even when more than 90% of the tuples are under attack.
Bootstrapping Trust in Community Repository Projects

Community repositories such as PyPI and NPM are immensely popular and collectively serve more than a billion packages per day. However, existing software certification mechanisms such as code signing, which seeks to provide to end users authenticity and integrity for a piece of software, are not suitable for community repositories and are not used in this context. This is very concerning, given the recent increase in the frequency and variety of attacks against community repositories. In this work, we propose a different approach for certifying the validity of software projects hosted on community repositories. We design and implement a Software Certification Service (SCS) that receives certification requests from a project owner for a specific project and then issues a project certificate once the project owner successfully completes a protocol for proving ownership of the project. The proposed certification protocol is inspired from the highly-successful ACME protocol used by Let’s Encrypt and can be fully automated on the SCS side. It is, however, fundamentally different in its attack mitigation capabilities and in how ownership is proven. It is also compatible with existing community repositories such as PyPI, RubyGems, or NPM, without requiring changes to these repositories. To support this claim, we instantiate the proposed certification service with several practical deployments.
Another Lattice Attack Against ECDSA with the wNAF to Recover More Bits per Signature

In the resource-constrained environment such as the Internet of Things, the windowed Non-Adjacent-Form (wNAF) representation is usually used to improve the calculation speed of the scalar multiplication of ECDSA. This paper presents a practical cache side channel attack on ECDSA implementations which use wNAF representation. Compared with existing works, our method exploits more information from the cache side channels, which is then efficiently used to construct lattice attacks in the ECDSA private key recovery. First, we additionally monitor the invert function which is related to the sign of the wNAF digits, and obtain a Double-Add-Invert chain through the Flush+Flush cache side channel. Then, we develop effective methods extracting 154.2 bits information of the ephemeral key per signature for 256-bit ECDSA from this chain, much more than the best known result which extracts 105.8 bits per signature. Finally, to efficiently use the extracted information, we convert the problem of recovering the private key to the Hidden Number Problem (HNP) and the Extended Hidden Number Problem (EHNP) respectively, which are solved by lattice reduction algorithms. We applied the attack on ECDSA with the secp256k1 curve in OpenSSL 1.1.0h. The experimental results show that only 3 signatures are enough to recover the private key. To the best of our knowledge, this work exploits the signs of the wNAF representation, along with the Double-Add chain against ECDSA, to recover the private key with the least number of signatures.
Towards High Transferability on Neural Network for Black-Box Adversarial Attacks

Adversarial examples are one of the biggest potential risks faced by the modern neural networks, threatening the application with high sensitiveness. To improve the efficiency of black-box attacks, and eventually achieve the purpose of reducing the query number by a large margin when keeping a high attack success rate, we propose a NES-based gradient estimation method, which greatly reduces the queries via a heuristic way. We also use ADAM-based perturbation update rules to improve the strength of iterative attacks. Besides, to make the whole method more flexible, meta learning is introduced to generate gradients on multiple substitute models and train an initial meta model with stronger generalization ability for online attacks. Experiments on MNIST and CIFAR10 show that META-NES-ADAM attack greatly reduces query number while sacrificing a little attack success rate when attacking black-box models.