Reviewerly/exHarmony huggingface dataset:
Heterogeneous Domain Adaptation With Structure and Classification Space Alignment

Domain adaptation (DA) aims at facilitating the target model training by leveraging knowledge from related but distribution-inconsistent source domain. Most of the previous DA works concentrate on homogeneous scenarios, where the source and target domains are assumed to share the same feature space. Nevertheless, frequently, in reality, the domains are not consistent in not only data distribution but also the representation space and feature dimensions. That is, these domains are heterogeneous. Although many works have attempted to handle such heterogeneous DA (HDA) by transforming HDA to homogeneous counterparts or performing DA jointly with domain transformation, nearly all of them just concentrate on the feature and distribution alignment across domains, neglecting the structure and classification space preservation for domains themselves. In this work, we propose a novel HDA model, namely, heterogeneous classification space alignment (HCSA), which leverages knowledge from both the source samples and model parameters to the target. In HCSA, structure preservation, distribution, and classification space alignment are implemented, jointly with feature representation by transferring both the source-domain representation and model knowledge. Moreover, we design an alternating algorithm to optimize the HCSA model with guaranteed convergence and complexity analysis. In addition, the HCSA model is further extended with deep network architecture. Finally, we experimentally evaluate the effectiveness of the proposed method by showing its superiority to the compared approaches.
Online handwritten Gurmukhi word recognition using fine-tuned Deep Convolutional Neural Network on offline features

The recognition of online handwriting is a vital application of pattern recognition, which involves the extraction of spatial and temporal information of handwritten patterns, and understanding the handwritten text while writing on the digital surface. Although, online handwriting recognition is a mature but exciting and fast developing field of pattern recognition, the same is not true for many of the Indic scripts. Gurmukhi is one of such popular scripts of India, and online handwriting recognition issues for larger units as words or sentences largely remained unexplored for this script till date. The existing study and first ever attempt for online handwritten Gurmukhi word recognition has relied upon the widely used hidden Markov model. This existing study evaluated against and performed very well in their chosen metrics. But, the available online handwritten Gurmukhi word recognition system could not obtain more than 90% recognition accuracy in data dependent environment too. The present study provided benchmark results for online handwritten Gurmukhi word recognition using deep learning architecture convolutional neural network, and obtained above 97% recognition accuracy in data dependent mode of handwriting. The previous Gurmukhi word recognition system followed the stroke based class labeling approach, whereas the present study has followed the word based class labeling approach. Present Online handwritten Gurmukhi word recognition results are quite satisfactory. Moreover, the proposed architecture can be used to improve the benchmark results of online handwriting recognition of several major Indian scripts. Experimental results demonstrated that the deep learning system achieved great results in Gurmukhi script and outperforms existing results in the literature.
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System

Text classification is usually studied by labeling natural language texts with relevant categories from a predefined set.In the real world, new classes might keep challenging the existing system with limited labeled data.The system should be intelligent enough to recognize upcoming new classes with a few examples.In this work, we define a new task in the NLP domain, incremental few-shot text classification, where the system incrementally handles multiple rounds of new classes.For each round, there is a batch of new classes with a few labeled examples per class.Two major challenges exist in this new task: (i) For the learning process, the system should incrementally learn new classes round by round without re-training on the examples of preceding classes; (ii) For the performance, the system should perform well on new classes without much loss on preceding classes.In addition to formulating the new task, we also release two benchmark datasets 1 in the incremental fewshot setting: intent classification and relation classification.Moreover, we propose two entailment approaches, ENTAILMENT and HY-BRID, which show promise for solving this novel problem.
Multi-Blockchain-Based IoT Data Processing Techniques to Ensure the Integrity of IoT Data in AIoT Edge Computing Environments

As the development of IoT technologies has progressed rapidly recently, most IoT data are focused on monitoring and control to process IoT data, but the cost of collecting and linking various IoT data increases, requiring the ability to proactively integrate and analyze collected IoT data so that cloud servers (data centers) can process smartly. In this paper, we propose a blockchain-based IoT big data integrity verification technique to ensure the safety of the Third Party Auditor (TPA), which has a role in auditing the integrity of AIoT data. The proposed technique aims to minimize IoT information loss by multiple blockchain groupings of information and signature keys from IoT devices. The proposed technique allows IoT information to be effectively guaranteed the integrity of AIoT data by linking hash values designated as arbitrary, constant-size blocks with previous blocks in hierarchical chains. The proposed technique performs synchronization using location information between the central server and IoT devices to manage the cost of the integrity of IoT information at low cost. In order to easily control a large number of locations of IoT devices, we perform cross-distributed and blockchain linkage processing under constant rules to improve the load and throughput generated by IoT devices.
Secure decentralized electronic health records sharing system based on blockchains

Blockchain technology has a great potential for improving efficiency, security and privacy of Electronic Health Records (EHR) sharing systems. However, existing solutions relying on a centralized database are susceptible to traditional security problems such as Denial of Service (DoS) attacks and a single point of failure similar to traditional database systems. In addition, past solutions exposed users to privacy linking attacks and did not tackle performance and scalability challenges. In this paper, we propose a permissioned Blockchain based healthcare data sharing system that integrates Blockchain technology, decentralized file system and threshold signature to address the aforementioned problems. The proposed system is based on Istanbul Byzantine Fault Tolerant (IBFT) consensus algorithm and Interplanetary File System (IPFS). We implemented the proposed system on an enterprise Ethereum Blockchain known as Hyperledger Besu. We evaluated and compared the performance of the proposed system based on various performance metrics such as transaction latency, throughput and failure rate. Experiments were conducted on a variable network size and number of transactions. The experimental results indicate that the proposed system performs better than existing Blockchain based systems. Moreover, the decentralized file system provides better security than existing traditional centralized database systems while providing the same level of performance.
Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF)

Heavy metals in road dust pose a significant threat to human health. This study investigated the concentrations, patterns, and sources of eight hazardous heavy metals (Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg) in the street dust of Zhengzhou city of PR China. Fifty-eight samples of road dust were analyzed based on three methods of risk assessment, i.e., Geo-Accumulation Index (Igeo), Potential Ecological Risk Assessment (RI), and Nemerow Synthetic Pollution Index (PIN). The results exhibited higher concentrations of Hg and Cd 14 and 7 times higher than their background values, respectively. Igeo showed the risks of contamination in a range of unpolluted (Cr, Ni) to strongly polluted (Hg and Cd) categories. RI came up with the contamination ranges from low (Cr, Ni, Cu, Zn, As, and Pb) to extreme (Cd and Hg) risk of contamination. The risk of contamination based on PIN was from safe (Cu, As, and Pb) to seriously high (Cd and Hg). The results yielded by PIN indicated the extreme risk of Cd and Hg in the city. Positive Matrix Factorization was used to identify the sources of contamination. Factor 1 (vehicular exhaust), Factor 2 (coal combustion), Factor 3 (metal industry), and Factor 4 (anthropogenic activities), respectively, contributed 14.63%, 35.34%, 36.14%, and 13.87% of total heavy metal pollution. Metal’s presence in the dust is a direct health risk for humans and warrants immediate and effective pollution control and prevention measures in the city.
MedShard: Electronic Health Record Sharing Using Blockchain Sharding

Electronic health records (EHRs) are important assets of the healthcare system and should be shared among medical practitioners to improve the accuracy and efficiency of diagnosis. Blockchain technology has been investigated and adopted in healthcare as a solution for EHR sharing while preserving privacy and security. Blockchain can revolutionize the healthcare system by providing a decentralized, distributed, immutable, and secure architecture. However, scalability has always been a bottleneck in blockchain networks due to the consensus mechanism and ledger replication to all network participants. Sharding helps address this issue by artificially partitioning the network into small groups termed shards and processing transactions parallelly while running consensus within each shard with a subset of blockchain nodes. Although this technique helps resolve issues related to scalability, cross-shard communication overhead can degrade network performance. This study proposes a transaction-based sharding technique wherein shards are formed on the basis of a patient’s previously visited health entities. Simulation results show that the proposed technique outperforms standard-based healthcare blockchain techniques in terms of the number of appointments processed, consensus latency, and throughput. The proposed technique eliminates cross-shard communication by forming complete shards based on “the need to participate” nodes per patient.
Knowing More About Questions Can Help: Improving Calibration in Question Answering

We study calibration in question answering, estimating whether model correctly predicts answer for each question.Unlike prior work which mainly rely on the model's confidence score, our calibrator incorporates information about the input example (e.g., question and the evidence context).Together with data augmentation via back translation, our simple approach achieves 5-10% gains in calibration accuracy on reading comprehension benchmarks.Furthermore, we present the first calibration study in the open retrieval setting, comparing the calibration accuracy of retrieval-based span prediction models and answer generation models.Here again, our approach shows consistent gains over calibrators relying on the model confidence.Our simple and efficient calibrator can be easily adapted to many tasks and model architectures, showing robust gains in all settings. 1
NFTs and copyright: challenges and opportunities

The attention paid to blockchain technology has always been shadowed by the ever-popular Bitcoin. Bitcoin is not the only life-altering outcome of the blockchain revolution. Each day we come across another useful aspect of blockchain technology: first, it was smart contracts, then initial coin offerings, then security token offerings and, since the beginning of March this year, there has been a lot of hype around non-fungible tokens (NFTs). While different digital art and collectibles are tokenized and sold for mind-boggling amounts, there are some legal questions, including regarding copyright law, that need to be tackled. This article will develop as follows: In Part II, we first describe the main characteristics of blockchain technology, and its important features that impact NFTs. Then, we consider where NFTs should be classified within the different groups of tokens. We define NFTs and consider why they are so popular nowadays and how they interact with...
Asymmetric image encryption scheme based on the Quantum logistic map and cyclic modulo diffusion

<abstract> In this study, a novel asymmetric image encryption scheme based on the Rivest-Shamir-Adleman (RSA) algorithm and Arnold transformation is proposed. First, the asymmetric public key RSA algorithm is used to generate the initial values for a quantum logistic map. Second, the parameters of the Arnold map are calculated. Then, Arnold scrambling operation is performed on the plain image to achieve the rough hiding of image information. Third, each row and each column of the image are taken as different units respectively and then exclusive-OR (XOR) diffusion is applied. Finally, the generated keystream is used to perform an end-to-start cyclic modulo diffusion operation for all rows and columns to produce the final cipher image. In addition, the keystream is related to the plain image, which can enhance the ability to resist chosen plaintext attack and known plaintext attack. The test results also show that the proposed encryption algorithm has strong plain sensitivity and key sensitivity. </abstract>
DNNV: A Framework for Deep Neural Network Verification

Abstract Despite the large number of sophisticated deep neural network (DNN) verification algorithms, DNN verifier developers, users, and researchers still face several challenges. First, verifier developers must contend with the rapidly changing DNN field to support new DNN operations and property types. Second, verifier users have the burden of selecting a verifier input format to specify their problem. Due to the many input formats, this decision can greatly restrict the verifiers that a user may run. Finally, researchers face difficulties in re-using benchmarks to evaluate and compare verifiers, due to the large number of input formats required to run different verifiers. Existing benchmarks are rarely in formats supported by verifiers other than the one for which the benchmark was introduced. In this work we present DNNV, a framework for reducing the burden on DNN verifier researchers, developers, and users. DNNV standardizes input and output formats, includes a simple yet expressive DSL for specifying DNN properties, and provides powerful simplification and reduction operations to facilitate the application, development, and comparison of DNN verifiers. We show how DNNV increases the support of verifiers for existing benchmarks from 30% to 74%.
Self-Supervised Adversarial Distribution Regularization for Medication Recommendation

Medication recommendation is a significant healthcare application due to its promise in effectively prescribing medications. Avoiding fatal side effects related to Drug-Drug Interaction (DDI) is among the critical challenges. Most existing methods try to mitigate the problem by providing models with extra DDI knowledge, making models complicated. While treating all patients with different DDI properties as a single cohort would put forward strict requirements on models' generalization performance. In pursuit of a valuable model for a safe recommendation, we propose the Self-Supervised Adversarial Regularization Model for Medication Recommendation (SARMR). SARMR obtains the target distribution associated with safe medication combinations from raw patient records for adversarial regularization. In this way, the model can shape distributions of patient representations to achieve DDI reduction. To obtain accurate self-supervision information, SARMR models interactions between physicians and patients by building a key-value memory neural network and carrying out multi-hop reading to obtain contextual information for patient representations. SARMR outperforms all baseline methods in the experiment on a real-world clinical dataset. This model can achieve DDI reduction when considering the different number of DDI types, which demonstrates the robustness of adversarial regularization for safe medication recommendation.
Positive-Unlabeled Learning from Imbalanced Data

Positive-unlabeled (PU) learning deals with the binary classification problem when only positive (P) and unlabeled (U) data are available, without negative (N) data. Existing PU methods perform well on the balanced dataset. However, in real applications such as financial fraud detection or medical diagnosis, data are always imbalanced. It remains unclear whether existing PU methods can perform well on imbalanced data. In this paper, we explore this problem and propose a general learning objective for PU learning targeting specially at imbalanced data. By this general learning objective, state-of-the-art PU methods based on optimizing a consistent risk can be adapted to conquer the imbalance. We theoretically show that in expectation, optimizing our learning objective is equivalent to learning a classifier on the oversampled balanced data with both P and N data available, and further provide an estimation error bound. Finally, experimental results validate the effectiveness of our proposal compared to state-of-the-art PU methods.
Toward a Design of Blood Donation Management by Blockchain Technologies

Nowadays, due to rapid changes in the population structure, which results in lower fertility rates and rapid aging, the demand for blood supply is increasing a lot. In most countries, blood quality and origin are managed by blood management information systems, such as national institutions. However, the existing system lacks limitations in that it lacked detailed blood information, which causes difficulties in managing blood quality, supply, and demand. Therefore, to solve this problem, this paper proposes an innovative system that supports managing the blood information, providing more detailed blood information such as blood consumption and disposal. A blockchain is currently a standard tool implemented in research, especially in supply chain management such as the medical field or financial system. Notably, private blockchain techniques with limited participants are relatively fast and reliable, making them suitable for B2B (Business-to-Business) transactions. Therefore, the proposed system is based on the architecture of Hyperledger Fabric. Moreover, the recorded information in blockchain technology will support dealing with the supply and demand of blood in the national institutions so that it is easier for national institutions to solve emergencies.
Blockchain-Based Wireless Sensor Networks for Malicious Node Detection: A Survey

Wireless Sensor Networks (WSNs) are broadly applied for various applications in tracking and surveillance due to their ease of use and other distinctive characteristics compelled by real-time cooperation among the sensor nodes. In WSNs, security is becoming a critical issue, as the techniques for malicious node detection adopt a one-time, centralized decision-making approach. With this paradigm, errors are difficult to avoid, and reproducibility and traceability are challenging. Hence, malicious node discovery technologies in conventional WSNs cannot assure traceability and fairness of the detection method. Herein, this paper discusses an in-depth survey of a blockchain-based approach for malicious node detection, an exhaustive examination of the integration of blockchain techniques with WSNs (BWSN), and insights into this novel concept. This survey discusses the architecture, sector-wise applications, and uses of BWSN. Moreover, this survey describes malicious node detection based on BWSN in two parts: 1) the BWSN architecture for detecting the malicious nodes and 2) the smart contract aspects in malicious node detection. Next, this survey explains the contributions of blockchain for WSN data management, which involves online information aggregation and may include auditing, event logs, and storage for information analysis and offline query processing. This survey first presents the conventional WSN solutions then the blockchain-based WSN solutions for data management. Additionally, this survey discusses the contributions of blockchain for WSN security management. It first examines the centralized WSN models for security problems, followed by a discussion of the blockchain-based WSN solutions for security management, such as offering access control, preserving information integrity, guaranteeing privacy, and ensuring WSNs’ node longevity.
Bestie: Very Practical Searchable Encryption with Forward and Backward Security

Dynamic searchable symmetric-key encryption (DSSE) is a promising crypto-tool that enables secure keyword searching over dynamically added or deleted ciphertexts. Currently, many works on DSSE devote their efforts to obtaining forward and backward security and practical performance. However, it is still challenging to design a single DSSE scheme that simultaneously achieves this security, high performance, and real deletion. Note that real deletion is a critical feature to guarantee the right of the user to be forgotten stipulated by GDPR. Due to this fact, we propose a new forward-and-backward secure DSSE scheme named Bestie. To achieve high search performance, Bestie takes the traditional hash and pseudorandom functions and symmetric-key encryption as building blocks and supports parallel keyword search. Bestie also achieves non-interactive real deletion for avoiding the client to do a clean-up process. This feature not only guarantees the above GDPR rule but also makes Bestie more suitable for managing large-scale data. Bestie also saves the client’s computation and communication costs. Finally, we experimentally compare Bestie with five previous well-known works and show that Bestie is much better in most respects. For example, Bestie requires approximately 3.66 microseconds to find a matching ciphertext. In contrast, Bestie has search performance at least 2 times faster than both \(\texttt {Mitra}^*\) (CCS’18) and \(\texttt {Diana}_{del}\) (CCS’17), 1,032\(\times \) faster than Fides (CCS’17), and 38,332\(\times \) faster than Janus++ (CCS’18), respectively. Compared with Mitra (CCS’18), Bestie saves at least 80% client time cost during a search.