Reviewerly/exHarmony huggingface dataset:
Parameters of Squares of Primitive Narrow-Sense BCH Codes and Their Complements

Studying the Schur square of a linear code is an important research topic in coding theory. Schur squares have important applications in cryptography and private information retrieval schemes, notably in secure multiparty computing or designing bilinear multiplication algorithms in finite extensions of finite fields through the notion of supercodes. Thanks to their exciting applications in cryptography, squares and powers of several linear codes have been investigated. In this paper, we will focus on the Schur square of a relevant well-known subclass of cyclic codes, Bose-Chaudhuri-Hocquenghem codes (BCH codes), which have wide applications in communication and storage systems and benefit from explicit defining sets that include consecutive integers, which gives the advantage of analyzing the parameters of BCH codes and their complements. Our main objective is to investigate the parameters of the squares of primitive narrow-sense BCH codes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal C(\delta)$ </tex-math></inline-formula> and their complements <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal C(\delta)^{c}$ </tex-math></inline-formula> . We will present two sufficient and necessary conditions to guarantee that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal C^{2}(\delta) \ne \Bbb F_{q}^{n}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal C^{2}(\delta)^{c} \ne \Bbb F_{q}^{n}$ </tex-math></inline-formula> by giving restrictions on designed distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \le \delta \le n$ </tex-math></inline-formula> . Based on these two characterizations, the dimensions and minimum distances of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal C^{2}(\delta)$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal C^{2}(\delta)^{c}$ </tex-math></inline-formula> are investigated in some cases. The dimensions of these squares are determined explicitly, and lower bounds on the minimum distance are given.
Authenticable Data Analytics Over Encrypted Data in the Cloud

Statistical analytics on encrypted data requires a fully-homomorphic encryption (FHE) scheme. However, heavy computation overheads make FHE impractical. In this paper we propose a novel approach to achieve privacy-preserving statistical analysis on an encrypted database. The main idea of this work is to construct a privacy-preserving calculator to calculate attributes’ count values for later statistical analysis. To authenticate these encrypted count values, we adopt an authenticable additive homomorphic encryption scheme to construct the calculator. We formalize the notion of an authenticable privacy-preserving calculator that has properties of broadcasting and additive homomorphism. Further, we propose a cryptosystem based on binary vectors to achieve complex logic expressions for statistical analysis on encrypted data. With the aid of the proposed cryptographic calculator, we design several protocols for statistical analysis including conjunctive, disjunctive and complex logic expressions to achieve more complicated statistical functionalities. Experimental results show that the proposed scheme is feasible and practical.
MLPKV: A Local Differential Multi-Layer Private Key-Value Data Collection Scheme for Edge Computing Environments

The existing solutions related to local differential privacy (LDP) in multi-layer networks for edge computing scenarios present several limitations in both key-value data heavy hitter identification and related frequency and mean estimation tasks. First, existing LDP approaches cannot effectively use edge nodes to improve their utility/performance. Secondly, there are many network transmission tasks in edge computing, which have relatively high requirements for communication and storage costs. Furthermore, the traditional privacy budget allocation cannot attain the best utilization. To solve the above problems, we propose MLPKV, a local differential multi-layer private key-value data collection scheme for edge computing, structured into three phases: dimensional reduction, padding-length estimation, and estimation. An improved EC-OLH algorithm is used to offload the computing efforts related to aggregation and estimation to edge nodes for achieving greater efficiency. In the dimensional reduction phase, a candidate set is generated to prune the domain of original data, which improves the estimation. In addition, our method groups users for completing the tasks in each phase to avoid additional errors caused by dividing the privacy budget, and proposes a new user division with an optimal grouping ratio. Finally, the proposed method was implemented in a proof-of-concept prototype system. We compare MLPKV with baseline methods such as PrivKV and PCKV. Experimental results on both synthetic and real-world datasets show that our method achieves better utility for heavy hitter identification, frequency, and mean estimations than other state-of-the-art mechanisms. For small data sets, our approach also provides high-accuracy estimation with a low privacy budget.
Information Leakage Attacks Exploiting Cache Replacement in Commercial Processors

Caches have been used to construct various covert and side channels. Most existing cache channels exploit the timing difference between cache hits and cache misses. We highlight that cache misses in different states may have more significant time differences. This paper presents in detail how replacement latency differences can be used to construct timing-based channels (called WB channels) to leak information. Any modification to a cache line by a sender will set it to the dirty state, and the receiver can observe this through measuring the latency of replacing this cache set. This paper evaluates WB channels from implementation complexity, stability, scalability, bandwidth, and stealthiness. Experimental results show that WB channel can not only transmit information covertly with high bandwidth but also has the strong anti-interference ability. Moreover, many previous cache defense and detection mechanisms target attacks exploiting the timing difference between cache hits and cache misses. This paper discusses the effectiveness of the WB channels against some such strategies. Moreover, this paper shows how to use our WB channel to mount a side-channel attack against a real-world security-sensitive application, such as AES implemented in OpenSSL-1.0.1e.
A Mobile 3-D Object Recognition Processor With Deep-Learning-Based Monocular Depth Estimation

A 3-D object recognition system is a heavy task that consumes high sensor power and requires complex 3-D data processing. In this article, the proposed processor produces 3-D red, green, blue, and depth (RGB-D) data from an RGB image through a deep learning-based monocular depth estimation, and then its RGB-D data are sporadically calibrated with low-resolution depth data from a low-power depth sensor, lowering the sensor power by 27.3 times. Then, the proposed processor accelerates various convolution operations in the system by integrating the in-out skipping-based bit-slice-level computing processing elements and flexibly allocating workloads considering data properties. Moreover, the point feature (PF) aggregator is designed close to the global memory to support the PF reuse algorithm’s data aggregation. Additionally, the window-based search algorithm and its memory management are presented for efficient point processing in the point processing unit. Consequently, the 210-mW and 34-frames-per-second end-to-end 3-D object recognition processor is successfully demonstrated.
GALAMC: Guaranteed Authentication Level at Minimized Complexity Relying on Intelligent Collaboration

Conventional centralized authentication techniques based on both digital cryptography and physical-layer attributes are prone to single-point failure due to either compromised digital security keys or an abrupt change in the physical communication environment. Although these particular challenges could be mitigated by the joint use of decentralized authentication and physical-layer attributes, such schemes often exhibit unpredictable performance. Simultaneously, the necessary involvement of multiple parties and the imperfect observation of the physical communication environment can also significantly increase the latency and computational complexity. As a remedy, a decentralized authentication scheme is proposed in this paper to achieve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Guaranteed Authentication Level at Minimized Complexity</i> (GALAMC) based on the intelligent use of distributed collaboration and available distributive physical-layer attributes. Specifically, we aim for minimizing the complexity of the proposed collaborative authentication process by harnessing the minimum number of collaborative nodes and the selected authentication attributes at each node across the different environments while guaranteeing the required authentication level. The related physical-layer authentication scheme is implemented at each collaborative node where different physical-layer attributes can be selected based on their usefulness which is time-varying. The simulation results demonstrate that our scheme maintains the target level of authentication and it is more immune to sudden environmental changes than the conventional centralized physical-layer authentication scheme. It can also be observed that our proposed scheme can adaptively select the minimum number of collaborative nodes for adaptively minimizing the computational cost.
Cross-Domain Image-Object Retrieval Based on Weighted Optimal Transport

Given a 2D image query and a pool of 3D objects, the goal of image-object retrieval is to rank the 3D objects according to how well their content fits the query. Previous methods usually project 2D images and 3D objects into a joint embedding space and minimize the distance metric to complete the retrieval task. Since 2D images and 3D objects come from two different domains with large discrepancy, even when 3D objects and 2D images are mapped to a shared space, the gap in feature distribution remains significant, which always leads to domain misalignment. In this work, we propose a novel image-object retrieval method by leveraging optimal transport theory. Specifically, to tackle the dimensionality gap between 2D images and 3D objects, we first represent a 3D object via a sequence of its 2D projections. We then design a Cross-Domain View Attention module (CDVA) to automatically compute the optimal combination of 3D object projections given a 2D query image. Next, we exploit Weighted Optimal Transport (WOT)-based distance to depict the discrepancy between 2D images and 3D objects, and reduce the discrepancy to achieve instance-level alignment. Through this scheme, the transported 2D images and 3D objects with the same label are enforced to follow similar distributions. Finally, we design an explicit Category Centroid Alignment module (CCA) to achieve class-level alignment to improve the retrieval performance. Extensive experiments show that our method can achieve competitive performance on the MI3DOR and MI3DOR-2 benchmarks.
First-Break Picking Method Based on the Difference Between Multiwindow Energy Ratios

First-break picking is an important step during processing of both passive and active seismic data. Many automated algorithms have been developed to detect first-break points in large volumes of seismic data. However, it remains difficult to determine precise first-break points in seismograms with low signal-to-noise ratios. Therefore, we present a new approach based on the differences between multi-window energy ratios (DERs) that minimizes the effects of noise. First, the DER is defined and a thresholding method detecting first-break points using the DERs is proposed. Thresholding can be varied depending on the DER parameters, which ensures reliable results even if the parameters change. We use two types of seismic data to establish and verify the DER method: big data derived via global earthquake monitoring (STanford EArthquake Dataset) and ocean bottom cable (OBC) data acquired offshore of Pohang, Republic of Korea. We investigated the effects of parameter changes on the DER picking results. Good picking performance was verified under low signal-to-noise conditions and compared to conventional first-break picking methods. The DER accuracy was higher than that of conventional methods and outliers were rare.
Certificateless Searchable Encryption With Trapdoor Unlinkability for Industrial Internet of Things

Industrial Internet of Things (IIoT) is a promising application of Internet of Things (IoT) in the industrial sector, which greatly improves the manufacturing efficiency and enables the intelligent production. Nowadays, cloud computing has been introduced to handle the data management problem in IIoT. Nevertheless, how to retrieve ciphertext data remains a challenging problem. Recently, certificateless searchable encryption (CLSE) was proposed to tackle with this problem. CLSE allows the receiver to search the ciphertext data directly, meanwhile it protects the data privacy. However, existing CLSE schemes do not achieve the trapdoor unlinkability since deterministic algorithms are used to generate the trapdoors. Additionally, they cannot support multiple keywords search in one-time test operation. To address these issues, we propose a certificateless searchable encryption scheme with trapdoor unlinkability (CLSETU) for IIoT. We then demonstrate the semantic security of our scheme against insider keyword guessing attacks (KGA). Finally, the performance evaluation shows its feasibility in the deployment of IIoT.
A Palletizing System for Microchannel Heat Exchangers Based on 3D Visual Guidance

Aiming at the problems of slow manual palletizing, high risk and easy damage to products after the assembly of microchannel heat exchanger, a palletizing system based on 3D visual guidance is presented in this paper.Firstly, an automatic calibration method for RGB-D camera extrinsic parameters was adopted: used random sample consensus algorithm RANSAC for plane fitting, and the pallet was idealized as a plane.The camera extrinsic parameters were calculated according to the rotation relationship between the plane normal vector and the camera coordinate system.When it was performing detection, converted depth data into point cloud data and preprocessed, including coordinate transformation, downsampling, etc.After that, according to the characteristics of stacking, a point cloud segmentation algorithm based on depth and the number of points was used, which could not only segment the pallet and sponge strips, but also effectively detect obstacles.For different parts segmented, the pallet was positioned by fitting the minimum bounding rectangle, and the stacking depths were obtained by calculating the centroid of the sponge strips.Finally, a series of constraints were used to determine whether the unloading conditions were met.The experimental results show that the automatic calibration is effective, the time consuming is about 0.43s, and the depth range of the pallet is 11mm after calibration.The algorithm can accurately identify various situations above the stacking and the time consuming is about 1.63s.
Spatial Transformer K-Means

The K-means algorithm is one of the most employed centroid-based clustering algorithms. Unfortunately, it often requires intricate data embeddings for good performance, which comes at the cost of reduced theoretical guarantees and loss of interpretability. Instead, we propose to use the intrinsic data space and augment K-means with a similarity measure invariant to non-rigid transformations. This enables (i) the reduction of intrinsic nuisances associated with the data, making the clustering task simpler and improving performance, leading to state-of-theart results, (ii) clustering in the input space of the data, providing a fully interpretable clustering algorithm, and (iii) the benefit of convergence guarantees.
Grading of Prostate Whole-slide Images Using Weak Self-supervised Learning

Prostate cancer is the second most common cancer among men worldwide. For prognosis and treatment, pathologists assign International Society of Urological Pathology (ISUP) grades to Whole Slide Imaging (WSI) of sampled prostate tissues to express the severity of the cancer. The manual approach suffers from human error and person-to-person variations. Existing approaches that use machine learning for automatic grading of WSI images includes using expensive datasets in which different regions of the slide are annotated by pathologists to show different levels of cancer. However, most of the existing realworld datasets contain weak labels; i.e. each slide is labeled with just one grade number. In this work, we present a self-supervised learning method to automatically grade prostate slides using datasets of weakly labeled slides. Our algorithm achieves a 5-way classification accuracy of 54.1% which which surpasses the performance of 41.5% using state of the art multiple-instance labeling methods.
Unsupervised Cross‐Media Hashing Learning via Knowledge Graph

With the rapid growth of multimedia data, cross-media hashing has become an important technology for fast cross-media retrieval. Because the manual annotations are difficult to obtain in real-world application, unsupervised cross-media hashing is studied to address the hashing learning without manual annotations. Existing unsupervised cross-media hashing methods generally focus on calculating the similarities through the features of multimedia data, while the learned hashing code cannot reflect the semantic relationship among the multimedia data, which hinders the accuracy in the cross-media retrieval. When humans try to understand multimedia data, the knowledge of concept relations in our brain plays an important role in obtaining high-level semantic. Inspired by this, we propose a knowledge guided unsupervised cross-media hashing (KGUCH) approach, which applies the knowledge graph to construct high-level semantic correlations for unsupervised cross-media hash learning. Our contributions in this paper can be summarized as follows: 1) The knowledge graph is introduced as auxiliary knowledge to construct the semantic graph for the concepts in each image and text instance, which can bridge the multimedia data with high-level semantic correlations to improve the accuracy of learned hash codes for cross-media retrieval. 2) The proposed KGUCH approach constructs correlation of the multimedia data from both the semantic and the feature aspects, which can exploit complementary information to promote the unsupervised cross-media hash learning. The experiments are conducted on three widely-used datasets, which verify the effectiveness of our proposed KGUCH approach.
Think Talk Write: An Effort to Improve Students' Information Literacy

This study aims to examine the effect of the Think Talk Write learning model as an effort to improve students' information literacy in the study of the concept of the immune system. This type of research includes a action research approach with a quasi-experimental method (using pre-test and post-test). The research subject was a population of 52 students in class IX science. The research data was collected based on the results of the pre-test and post-test that had been carried out on grade IX students. Based on the results of this study, it was found that the class that applied the think talk write learning model got a higher average score (4.07) than the class that applied the conventional learning model (4.05). Thus, it is concluded that learning that applies the think talk write model can improve information literacy in students.
Magnetic survey in the process of large-scale construction sites

Archaeogeophysical prospection is necessary when planning large constructions. Motorized magnetometry systems offer an effective method to identify archaeology prior the construction. A sufficiently large area must be surveyed to estimate the archaeological potential correctly.
Assisted Process Knowledge Graph Building Using Pre-trained Language Models

The automated construction of knowledge graphs from procedural documents is a challenging research area. Here, the lack of annotated data, as well as raw text repositories describing real-world procedural documents, make it extremely difficult to adopt deep learning approaches. Pre-trained language models have shown promising results concerning the knowledge extraction tasks from the models themselves. Although several works explored this strategy to build knowledge graph, the viability of knowledge base construction by using prompt-based learning strategy from such language models has not yet been investigated deeply. In this work, we present a prompt-based in-context learning strategy to extract, from natural language process descriptions, conceptual information that can be converted into their equivalent knowledge graphs. Such a strategy is performed in a multi-turn dialog fashion. We validate the accuracy of the proposed approach from both quantitative and qualitative perspectives. The results highlight the feasibility of the proposed approach within low-resource scenarios.
Adventures with Datalog: Walking the Thin Line Between Theory and Practice

This keynote paper features a concise introduction to Datalog, which is followed by an overview of some theoretical results about the complexity and expressive power of a number of Datalog variants. This will be interleaved with a tale of four Datalog-related companies co-founded by the author: DLVSystem, Lixto, Wrapidity, and DeepReason.ai.
Neural Networks Reduction via Lumping

The increasing size of recently proposed Neural Networks makes it hard to implement them on embedded devices, where memory, battery and computational power are a non-trivial bottleneck. For this reason during the last years network compression literature has been thriving and a large number of solutions has been published to reduce both the number of operations and the parameters involved with the models. Unfortunately, most of these reducing techniques are actually heuristic methods and usually require at least one re-training step to recover the accuracy. The need of procedures for model reduction is well-known also in the fields of Verification and Performances Evaluation, where large efforts have been devoted to the definition of quotients that preserve the observable underlying behaviour. In this paper we try to bridge the gap between the most popular and very effective network reduction strategies and formal notions, such as lumpability, introduced for verification and evaluation of Markov Chains. Elaborating on lumpability we propose a pruning approach that reduces the number of neurons in a network without using any data or fine-tuning, while completely preserving the exact behaviour. Relaxing the constraints on the exact definition of the quotienting method we can give a formal explanation of some of the most common reduction techniques.