Reviewerly/exHarmony huggingface dataset:
Synthetical reasoning for hybrid tabular-textual question answering over multitasking model

Hybrid tabular-textual question answering (QA) is a crucial task in natural language processing that involves reasoning and locating answers from various information sources, primarily through numerical reasoning and span extraction. Cur-rent techniques in numerical reasoning often rely on autoregressive models to decode program sequences. However, these methods suffer from exposure bias and error propagation, which can significantly decrease the accuracy of program generation as the decoding process unfolds. To address these challenges, this paper proposes a novel multitasking hybrid tabular-textual question answering (MHTTQA) framework. Instead of generating operators and operands step by step, this framework can independently generate entire program tuples in parallel. This innovative approach solves the problem of error propagation and greatly improves the speed of program generation. The effectiveness of the method is demonstrated through experiments using the ConvFinQA and MultiHiertt datasets. Our proposed model outperforms the strong FinQANet baselines by 7% and 7.2% Exe/Prog Acc and the MT2Net baselines by 20.9% and 9.4% EM/F1. In addition, the program generation rate of our method far exceeds that of the baseline method. Additionally, our non-autoregressive program generation method exhibits greater resilience to an increasing number of numerical reasoning steps, further highlighting the advantages of our proposed framework in the field of hybrid tabular-textual QA.
Towards Anonymizing Intermodal Mobility Data for Smart Cities

As cities seek to optimize their resources for a sustainable and livable future, the concept of intermodal mobility has become increasingly important. However, the collection and analysis of intermodal mobility data is complicated by the need for robust anonymization methods, as privacy and security concerns remain paramount. Existing anonymization methods are either mode-specific or so complicated that they deter potential stakeholders. In this paper, we describe a variety of real mobility data sources for our upcoming field study. With that data, we plan to provide insights into infrastructure utilization and transitions between modes of transport. We further identified several anonymization techniques for mobility data to ensure privacy and acceptance among the citizens. To find suitable techniques for intermodal mobility data, we provide insights from our previous experience on anonymization and discuss the practicability of the identified techniques. Our paper highlights the need for explainable anonymization methods tailored to inter-modal mobility data that address privacy and security concerns and pave the way for more accessible privacy-compliant solutions.
Unforgeability in Stochastic Gradient Descent

Stochastic Gradient Descent (SGD) is a popular training algorithm, a cornerstone of modern machine learning systems. Several security applications benefit from determining if SGD executions are forgeable, i.e., whether the model parameters seen at a given step are obtainable by more than one distinct set of data samples. In this paper, we present the first attempt at proving impossibility of such forgery. We furnish a set of conditions, which are efficiently checkable on concrete checkpoints seen during training runs, under which checkpoints are provably unforgeable at that step. Our experiments show that the conditions are somewhat mild and hence always satisfied at checkpoints sampled in our experiments. Our results sharply contrast prior findings at a high level: We show that checkpoints we find to be provably unforgeable have been deemed to be forgeable using the same methodology and experimental setup suggested in prior work. This discrepancy arises because of unspecified subtleties in definitions. We experimentally confirm that the distinction matters, i.e., small errors amplify during training to produce significantly observable difference in final models trained. We hope our results serve as a cautionary note on the role of algebraic precision in forgery definitions and related security arguments.
Secure cloud storage for IoT based distributed healthcare environment using blockchain orchestrated and deep learning model

Internet of Things (IoT), a distributed healthcare system has integrated different medical resources with sensors and actuators. In this research paper proposes a secure healthcare monitoring system for IoT based distributed healthcare systems in the cloud using blockchain and deep learning (DL) mechanisms. The proposed system involved three phases: secure data transmission, data storage, and disease classification system. Initially, the patients are authenticated via blockchain mechanism and their data is encrypted via Effective Key-based Rivest Shamir Adelman (EKRSA), in which the keys are generated using Circle chaotic map and Linear inertia weight-based Honey Badger Optimization (CLHBO) algorithm. Next, in the data storage phase, these encrypted IoT data are securely stored in the cloud using blockchain technology in a distributed manner. Finally, in the disease classification, the data are gathered from the publicly available dataset, and these collected datasets are preprocessed to handle missing values and data normalization. After that, the proposed system applies a radial basis kernel-based linear discriminant analysis (RBKLDA) model to reduce the dimensionality of the dataset. At last, the disease classification is done by optimal parameter-centered bidirectional long short-term memory (OPCBLSTM). The proposed EKRSA system archives maximum throughput of 99.05% and reliability of 99.66, which is superior to the existing approaches. The OPCBLSTM is investigated for its disease classification process, the proposed one achieves 99.64% accuracy with less processing time of 6 ms, which is superior to the existing classifiers. The experimental analysis proves that the system attained better security and classification metrics results than the existing methods.
Cybercrime Bitcoin Revenue Estimations: Quantifying the Impact of Methodology and Coverage

Multiple works have leveraged the public Bitcoin ledger to estimate the revenue cybercriminals obtain from their victims. Estimations focusing on the same target often do not agree, due to the use of different methodologies, seed addresses, and time periods. These factors make it challenging to understand the impact of their methodological differences. Furthermore, they underestimate the revenue due to the (lack of) coverage on the target's payment addresses, but how large this impact remains unknown.
Research and Application of Edge Computing and Deep Learning in a Recommender System

Recommendation systems play a pivotal role in improving product competitiveness. Traditional recommendation models predominantly use centralized feature processing to operate, leading to issues such as excessive resource consumption and low real-time recommendation concurrency. This paper introduces a recommendation model founded on deep learning, incorporating edge computing and knowledge distillation to address these challenges. Recognizing the intricate relationship between the accuracy of deep learning algorithms and their complexity, our model employs knowledge distillation to compress deep learning. Teacher–student models were initially chosen and constructed in the cloud, focusing on developing structurally complex teacher models that incorporate passenger and production characteristics. The knowledge acquired from these models was then transferred to a student model, characterized by weaker learning capabilities and a simpler structure, facilitating the compression and acceleration of an intelligent ranking model. Following this, the student model underwent segmentation, and certain computational tasks were shifted to end devices, aligning with edge computing principles. This collaborative approach between the cloud and end devices enabled the realization of an intelligent ranking for product listings. Finally, a random selection of the passengers’ travel records from the last five years was taken to test the accuracy and performance of the proposed model, as well as to validate the intelligent ranking of the remaining tickets. The results indicate that, on the one hand, an intelligent recommendation system based on knowledge distillation and edge computing successfully achieved the concurrency and timeliness of the existing remaining ticket queries. Simultaneously, it guaranteed a certain level of accuracy, and reduced computing resource and traffic load on the cloud, showcasing its potential applicability in highly concurrent recommendation service scenarios.
Poster: Detecting Adversarial Examples Hidden under Watermark Perturbation via Usable Information Theory

Image watermark is a technique widely used for copyright protection. Recent studies show that the image watermark can be added to the clear image as a kind of noise to realize fooling deep learning models. However, previous adversarial example (AE) detection schemes tend to be ineffective since the watermark logo differs from typical noise perturbations. In this poster, we propose Themis, a novel AE detection method against watermark perturbation. Different from prior methods, Themis neither modifies the protected classifier nor requires knowledge of the process for generating AEs. Specifically, Themis leverages usable information theory to calculate the pointwise score, thereby discovering those instances that may be watermark AEs. The empirical evaluations involving 5 different logo watermark perturbations demonstrate the proposed scheme can efficiently detect AEs, and significantly (over 15% accuracy) outperforms five state-of-the-art (SOTA) detection methods. The visualization results display our detection metric is more distinguishable between AEs and non-AEs. Meanwhile, Themis realizes a larger Area Under Curve (AUC) in a threshold-resilient manner, while only introducing ∼0.04s overhead.
Read Between the Lines: Detecting Tracking JavaScript with Bytecode Classification

Browsers and extensions that aim to block online ads and tracking scripts predominantly rely on rules from filter lists for determining which resource requests must be blocked. These filter lists are often manually curated by a community of online users. However, due to the arms race between blockers and ad-supported websites, these rules must continuously get updated so as to adapt to novel bypassing techniques and modified requests, thus rendering the detection and rule-generation process cumbersome and reactive (which can result in major delays between propagation and detection). In this paper, we address the detection problem by proposing an automated pipeline that detects tracking and advertisement JavaScript resources with high accuracy, designed to incur minimal false positives and overhead. Our method models script detection as a text classification problem, where JavaScript resources are documents containing bytecode sequences. Since bytecode is directly obtained from the JavaScript interpreter, our technique is resilient against commonly used bypassing methods, such as URL randomization or code obfuscation. We experiment with both deep learning and traditional ML-based approaches for bytecode classification and show that our approach identifies ad/tracking scripts with 97.08% accuracy, significantly outperforming cutting-edge systems in terms of both precision and the level of required features. Our experimental analysis further highlights our system's capabilities, by demonstrating how it can augment filter lists by uncovering ad/tracking scripts that are currently unknown, as well as proactively detecting scripts that have been erroneously added by list curators.
Analyzing the Real-World Security of the Algorand Blockchain

The Algorand consensus protocol is interesting both in theory and in practice. On the theoretical side, to achieve adaptive security, it introduces the novel idea of player replaceability, where each step of the protocol is executed by a different randomly selected committee whose members remain secret until they send their first and only message. The protocol provides consistency under arbitrary network conditions and liveness under intermittent network partitions. On the practical side, the protocol is used to secure the Algorand cryptocurrency, whose total value is approximately 850M at the time of writing.
Devil in Disguise: Breaching Graph Neural Networks Privacy through Infiltration

Graph neural networks (GNNs) have been developed to mine useful information from graph data of various applications, e.g., healthcare, fraud detection, and social recommendation. However, GNNs open up new attack surfaces for privacy attacks on graph data. In this paper, we propose Infiltrator, a privacy attack that is able to pry node-level private information based on black-box access to GNNs. Different from existing works that require prior information of the victim node, we explore the possibility of conducting the attack without any information of the victim node. Our idea is to infiltrate the graph with attacker-created nodes to befriend the victim node. More specifically, we design infiltration schemes that enable the adversary to infer the label, neighboring links, and sensitive attributes of a victim node. We evaluate Infiltrator with extensive experiments on three representative GNN models and six real-world datasets. The results demonstrate that Infiltrator can achieve an attack performance of more than 98% in all three attacks, outperforming baseline approaches. We further evaluate the defense resistance of Infiltrator against the graph homophily defender and the differentially private model.
Poster: Generic Multidimensional Linear Cryptanalysis of Feistel Ciphers

This poster presents new generic attacks on Feistel ciphers that incorporate the key addition at the input of the round function only. This feature leads to a specific vulnerability that can be exploited using multidimensional linear cryptanalysis. More specifically, our approach involves using key-independent linear trails so that the distribution of a combination of the plaintext and ciphertext can be computed, making it possible to use the likelihood-ratio test as a distinguisher. We provide theoretical estimates of the cost of our generic attacks, and verify these experimentally by applying the attacks to CAST-128 and LOKI91. The theoretical and experimental findings demonstrate that the proposed attacks lead to significant reductions in data or time complexity in several interesting cases.
Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems

The evolving landscape of data ecosystems (DEs) increasingly demands integrated and collaborative data-sharing mechanisms that simultaneously ensure data sovereignty. However, recently proposed federated platforms, e.g., Gaia-X, only offer a promising solution to share data among already trusted participants-they still lack features to establish and maintain trust. To address this issue, we propose transparency logs for data usage that retrospectively build trust among participants. Inspired by certificate transparency logs that successfully bridge trust gaps in PKIs, we equip data owners with credible evidence of data usage. We show that our transparency logs for data usage are well scalable to sizable DEs. Thus, they are a promising approach to bridge trust gaps in federated DEs with cryptographic guarantees, fostering more robust data sharing.
Universality class of machine learning for critical phenomena

Herein, percolation phase transitions on a two-dimensional lattice were studied using machine learning techniques. Results reveal that different phase transitions belonging to the same universality class can be identified using the same neural networks (NNs), whereas phase transitions of different universality classes require different NNs. Based on this finding, we proposed the universality class of machine learning for critical phenomena. Furthermore, we investigated and discussed the NNs of different universality classes. Our research contributes to machine learning by relating the NNs with the universality class.
Empowering Vision Transformer by Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation

U-Net, based on a deep convolutional network (CNN), has been clinically used to auto-segment normal organs, while still being limited to the planning target volume (PTV) segmentation. This work aims to address the problems in two aspects: 1) apply one of the newest network architectures such as vision transformers other than the CNN-based networks, and 2) find an appropriate combination of network hyper-parameters with reference to recently proposed nnU-Net ("no-new-Net"). VT U-Net was adopted for auto-segmenting the whole pelvis prostate PTV as it consisted of fully transformer architecture. The upgraded version (v.2) applied the nnU-Net-like hyper-parameter optimizations, which did not fully cover the transformer-oriented hyper-parameters. Thus, we tried to find a suitable combination of two key hyper-parameters (patch size and embedded dimension) for 140 CT scans throughout 4-fold cross validation. The VT U-Net v.2 with hyper-parameter tuning yielded the highest dice similarity coefficient (DSC) of 82.5 and the lowest 95% Haussdorff distance (HD95) of 3.5 on average among the seven recently proposed deep learning networks. Importantly, the nnU-Net with hyper-parameter optimization achieved competitive performance, although this was based on the convolution layers. The network hyper-parameter tuning was demonstrated to be necessary even for the newly developed architecture of vision transformers.
Poster: Efficient AES-GCM Decryption Under Homomorphic Encryption

Computation delegation to untrusted third-party while maintaining data confidentiality is possible with homomorphic encryption (HE). However, in many cases, the data was encrypted using another cryptographic scheme such as AES-GCM. Hybrid encryption (a.k.a Transciphering) is a technique that allows moving between cryptosystems, which currently has two main drawbacks: 1) lack of standardization or bad performance of symmetric decryption under FHE; 2) lack of input data integrity.
Objective Video Quality Assessment and Ground Truth Coordinates for Automatic License Plate Recognition

In the realm of modern video processing systems, traditional metrics such as the Peak Signal-to-Noise Ratio and Structural Similarity are often insufficient for evaluating videos intended for recognition tasks, like object or license plate recognition. Recognizing the need for specialized assessment in this domain, this study introduces a novel approach tailored to Automatic License Plate Recognition (ALPR). We developed a robust evaluation framework using a dataset with ground truth coordinates for ALPR. This dataset includes video frames captured under various conditions, including occlusions, to facilitate comprehensive model training, testing, and validation. Our methodology simulates quality degradation using a digital camera image acquisition model, representing how luminous flux is transformed into digital images. The model’s performance was evaluated using Video Quality Indicators within an OpenALPR library context. Our findings show that the model achieves a high F-measure score of 0.777, reflecting its effectiveness in assessing video quality for recognition tasks. The proposed model presents a promising avenue for accurate video quality assessment in ALPR tasks, outperforming traditional metrics in typical recognition application scenarios. This underscores the potential of the methodology for broader adoption in video quality analysis for recognition purposes.
Breaking Boundaries Between Linguistics and Artificial Intelligence

There is a wide connection between linguistics and artificial intelligence (AI), including the multimodal language matching. Multi-modal robots possess the capability to process various sensory modalities, including vision, auditory, language, and touch, offering extensive prospects for applications across various domains. Despite significant advancements in perception and interaction, the task of visual-language matching remains a challenging one for multi-modal robots. Existing methods often struggle to achieve accurate matching when dealing with complex multi-modal data, leading to potential misinterpretation or incomplete understanding of information. Additionally, the heterogeneity among different sensory modalities adds complexity to the matching process. To address these challenges, we propose an approach called vision-language matching with semantically aligned embeddings (VLMS), aimed at improving the visual-language matching performance of multi-modal robots.
Federated learning‐based privacy‐preserving electricity load forecasting scheme in edge computing scenario

Summary In the era of big data, massive amounts of data hold great value. However, much data exists as isolated islands, and the maximum value of the data cannot be fully utilized. Federated learning allows each client to train local data and then share the training model parameters securely, which can address the isolated data island problem and exploit data value while ensuring data privacy and security. Accordingly, in order to securely complete the electric power load forecasting using existing data, this paper constructs a federated learning‐based privacy‐preserving scheme to support electricity load forecasting in edge computing scenarios. To address the problems of the data‐isolated islands and data privacy in electric power systems, this paper proposes a decentralized distributed solution based on the federated learning technique. Our scheme achieves electricity load forecasting for power systems through the federated learning‐based framework and uses edge computing architecture to improve real‐time data capability and reduce network latency. For the hierarchical scheduling structure in power systems, we divide the system into a cloud‐side‐device three‐layer architecture, which achieves structural coordination and balance, and each layer collects information according to the scheduling control tasks, promoting scheduling effectiveness. Finally, different privacy protection methods are used on the cloud‐edge and edge‐device sides to significantly enhance data security. Moreover, We have conducted extensive experimental simulations for our proposed scheme. The experimental results show that the relative error of electricity load forecasting is around 1.580%. Meanwhile, our scheme achieves high accuracy and low memory usage. The security analysis proves the feasibility and security of our scheme.
Language Outlay for the Sokoto Cement Production System Using Deterministic Finite Automata Scheme

This paper constructs the compact, detailed and extended models, and focuses on the algebraic theoretic strings and language of each transition of the finite automata scheme. It was discovered that from the initial stage to the final stage of cement production processes, each transition or production process can have a particular language. In addition, a language scheme is developed for each of the sub-states (sub-model) that leads to a theoretic study of language scheme and semantics of the model. It can be deduced that when represented as binary codes, the established schemes in the sub-states can be studied as a Boolean algebraic scheme.