Reviewerly/exHarmony huggingface dataset:
Block Design-Based Key Agreement for Group Data Sharing in Cloud Computing

Data sharing in cloud computing enables multiple participants to freely share the group data, which improves the efficiency of work in cooperative environments and has widespread potential applications. However, how to ensure the security of data sharing within a group and how to efficiently share the outsourced data in a group manner are formidable challenges. Note that key agreement protocols have played a very important role in secure and efficient group data sharing in cloud computing. In this paper, by taking advantage of the symmetric balanced incomplete block design (SBIBD), we present a novel block design-based key agreement protocol that supports multiple participants, which can flexibly extend the number of participants in a cloud environment according to the structure of the block design. Based on the proposed group data sharing model, we present general formulas for generating the common conference key IC for multiple participants. Note that by benefiting from the (v, k + 1, 1)-block design, the computational complexity of the proposed protocol linearly increases with the number of participants and the communication complexity is greatly reduced. In addition, the fault tolerance property of our protocol enables the group data sharing in cloud computing to withstand different key attacks, which is similar to Yi's protocol.
Design of a Hybrid RF Fingerprint Extraction and Device Classification Scheme

Radio frequency (RF) fingerprint is the inherent hardware characteristics and has been employed to classify and identify wireless devices in many Internet of Things applications. This paper extracts novel RF fingerprint features, designs a hybrid and adaptive classification scheme adjusting to the environment conditions, and carries out extensive experiments to evaluate the performance. In particular, four modulation features, namely differential constellation trace figure, carrier frequency offset, modulation offset and I/Q offset extracted from constellation trace figure, are employed. The feature weights under different channel conditions are calculated at the training stage. These features are combined smartly with the weights selected according to the estimated signal to noise ratio at the classification stage. We construct a testbed using universal software radio peripheral platform as the receiver and 54 ZigBee nodes as the candidate devices to be classified, which are the most ZigBee devices ever tested. Extensive experiments are carried out to evaluate the classification performance under different channel conditions, namely line-of-sight (LOS) and nonline-of-sight scenarios. We then validate the robustness by carrying out the classification process 18 months after the training, which is the longest time gap. We also use a different receiver platform for classification for the first time. The classification error rate is as low as 0.048 in LOS scenario, and 0.1105 even when a different receiver is used for classification 18 months after the training. Our hybrid classification scheme has thus been demonstrated effective in classifying a large amount of ZigBee devices.
Ternary Adversarial Networks With Self-Supervision for Zero-Shot Cross-Modal Retrieval

Given a query instance from one modality (e.g., image), cross-modal retrieval aims to find semantically similar instances from another modality (e.g., text). To perform cross-modal retrieval, existing approaches typically learn a common semantic space from a labeled source set and directly produce common representations in the learned space for the instances in a target set. These methods commonly require that the instances of both two sets share the same classes. Intuitively, they may not generalize well on a more practical scenario of zero-shot cross-modal retrieval, that is, the instances of the target set contain unseen classes that have inconsistent semantics with the seen classes in the source set. Inspired by zero-shot learning, we propose a novel model called ternary adversarial networks with self-supervision (TANSS) in this paper, to overcome the limitation of the existing methods on this challenging task. Our TANSS approach consists of three paralleled subnetworks: 1) two semantic feature learning subnetworks that capture the intrinsic data structures of different modalities and preserve the modality relationships via semantic features in the common semantic space; 2) a self-supervised semantic subnetwork that leverages the word vectors of both seen and unseen labels as guidance to supervise the semantic feature learning and enhances the knowledge transfer to unseen labels; and 3) we also utilize the adversarial learning scheme in our TANSS to maximize the consistency and correlation of the semantic features between different modalities. The three subnetworks are integrated in our TANSS to formulate an end-to-end network architecture which enables efficient iterative parameter optimization. Comprehensive experiments on three cross-modal datasets show the effectiveness of our TANSS approach compared with the state-of-the-art methods for zero-shot cross-modal retrieval.
Analyzing the Structure of Attention in a Transformer Language Model

The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks.In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model.We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus.We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers.We also find that the deepest layers of the model capture the most distant relationships.Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads.
Diachronic Embedding for Temporal Knowledge Graph Completion

Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones–a problem known as KG completion. KG embedding approaches have proved effective for KG completion, however, they have been developed mostly for static KGs. Developing temporal KG embedding models is an increasingly important problem. In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time. This is in contrast to the existing temporal KG embedding approaches where only static entity features are provided. The proposed embedding function is model-agnostic and can be potentially combined with any static model. We prove that combining it with SimplE, a recent model for static KG embedding, results in a fully expressive model for temporal KG completion. Our experiments indicate the superiority of our proposal compared to existing baselines.
Multiple Kernel k-means with Incomplete Kernels

Multiple kernel clustering (MKC) algorithms optimally combine a group of pre-specified base kernel matrices to improve clustering performance. However, existing MKC algorithms cannot efficiently address the situation where some rows and columns of base kernel matrices are absent. This paper proposes two simple yet effective algorithms to address this issue. Different from existing approaches where incomplete kernel matrices are first imputed and a standard MKC algorithm is applied to the imputed kernel matrices, our first algorithm integrates imputation and clustering into a unified learning procedure. Specifically, we perform multiple kernel clustering directly with the presence of incomplete kernel matrices, which are treated as auxiliary variables to be jointly optimized. Our algorithm does not require that there be at least one complete base kernel matrix over all the samples. Also, it adaptively imputes incomplete kernel matrices and combines them to best serve clustering. Moreover, we further improve this algorithm by encouraging these incomplete kernel matrices to mutually complete each other. The three-step iterative algorithm is designed to solve the resultant optimization problems. After that, we theoretically study the generalization bound of the proposed algorithms. Extensive experiments are conducted on 13 benchmark data sets to compare the proposed algorithms with existing imputation-based methods. Our algorithms consistently achieve superior performance and the improvement becomes more significant with increasing missing ratio, verifying the effectiveness and advantages of the proposed joint imputation and clustering.
NIC: Detecting Adversarial Samples with Neural Network Invariant Checking

Deep Neural Networks (DNN) are vulnerable to adversarial samples that are generated by perturbing correctly classified inputs to cause DNN models to misbehave (e.g., misclassification).This can potentially lead to disastrous consequences especially in security-sensitive applications.Existing defense and detection techniques work well for specific attacks under various assumptions (e.g., the set of possible attacks are known beforehand).However, they are not sufficiently general to protect against a broader range of attacks.In this paper, we analyze the internals of DNN models under various attacks and identify two common exploitation channels: the provenance channel and the activation value distribution channel.We then propose a novel technique to extract DNN invariants and use them to perform runtime adversarial sample detection.Our experimental results of 11 different kinds of attacks on popular datasets including ImageNet and 13 models show that our technique can effectively detect all these attacks (over 90% accuracy) with limited false positives.We also compare it with three state-of-theart techniques including the Local Intrinsic Dimensionality (LID) based method, denoiser based methods (i.e., MagNet and HGD), and the prediction inconsistency based approach (i.e., feature squeezing).Our experiments show promising results.
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations

Messages in human conversations inherently convey emotions.The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks.However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions.In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism.Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance.In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score.
Forward-Chaining Partial-Order Planning

Over the last few years there has been a revival of interest in the idea of least-commitment planning with a number of researchers returning to the partial-order planning approaches of UCPOP and VHPOP. In this paper we explore the potential of a forward-chaining state-based search strategy to support partial-order planning in the solution of temporal-numeric problems. Our planner, POPF, is built on the foundations of grounded forward search, in combination with linear programming to handle continuous linear numeric change. To achieve a partial ordering we delay commitment to ordering decisions, timestamps and the values of numeric parameters, managing sets of constraints as actions are started and ended. In the context of a partially ordered collection of actions, constructing the linear program is complicated and we propose an efficient method for achieving this. Our late-commitment approach achieves flexibility, while benefiting from the informative search control of forward planning, and allows temporal and metric decisions to be made - as is most efficient - by the LP solver rather than by the discrete reasoning of the planner. We compare POPF with the approach of constructing a sequenced plan and then lifting a partial order from it, showing that our approach can offer improvements in terms of makespan, and time to find a solution, in several benchmark domains.
APPA: An anonymous and privacy preserving data aggregation scheme for fog-enhanced IoT

Fog computing is a modern computing platform that connects the cloud with the edge smart devices located at the edge of the network. The fog computing platform has several characteristics desirable for Internet of Things (IoT) systems, such as the efficient data access, low latency, and location awareness. Data aggregation is a common operation in IoT systems. However, for data aggregation applications in the fog-enhanced IoT environment, how to efficiently preserve the privacy of sensitive data is a major concern. To address this challenge, we propose APPA: a device-oriented Anonymous Privacy-Preserving scheme with Authentication for data aggregation applications in fog-enhanced IoT systems, which also supports multi-authority to manage smart devices and fog nodes locally. In APPA scheme, the anonymity and authenticity of the device is guaranteed with pseudonym and pseudonym certificate, which can be updated autonomously. Taking the advantage of a local certification authority, the pseudonym management can be shifted to specialized fogs at the network edge, which provide real-time service for device registration and update. The data privacy can be ensured during data aggregation by using the Paillier algorithm. Detailed security analysis is conducted to show that our scheme can achieve security and privacy-preservation properties in the fog-enhanced IoT systems. Additionally, we compare our scheme with existing schemes to demonstrate the effectiveness and efficiency of our proposed scheme in terms of low computational complexity and communication overhead.
Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning

Counterfactuals about what could have happened are increasingly used in an array of Artificial Intelligence (AI) applications, and especially in explainable AI (XAI). Counterfactuals can aid the provision of interpretable models to make the decisions of inscrutable systems intelligible to developers and users. However, not all counterfactuals are equally helpful in assisting human comprehension. Discoveries about the nature of the counterfactuals that humans create are a helpful guide to maximize the effectiveness of counterfactual use in AI.
Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images

This paper proposes a method for simultaneous segmentation of multi-source images, using the multivariate mixture model (MvMM) and maximum of log-likelihood (LL) framework. The segmentation is a procedure of texture classification, and the MvMM is used to model the joint intensity distribution of the images. Specifically, the method is applied to the myocardial segmentation combining the complementary texture information from multi-sequence (MS) cardiac magnetic resonance (CMR) images. Furthermore, there exist inter-image mis-registration and intra-image misalignment of slices in the MS CMR images. Hence, the MvMM is formulated with transformations, which are embedded into the LL framework and optimized simultaneously with the segmentation parameters. The proposed method is able to correct the inter- and intra-image misalignment by registering each slice of the MS CMR to a virtual common space, as well as to delineate the indistinguishable boundaries of myocardium consisting of pathologies. Results have shown statistically significant improvement in the segmentation performance of the proposed method with respect to the conventional approaches which can solely segment each image separately. The proposed method has also demonstrated better robustness in the incongruent data, where some images may not fully cover the region of interest and the full coverage can only be reconstructed combining the images from multiple sources.
Experimental Twin-Field Quantum Key Distribution through Sending or Not Sending

Channel loss seems to be the most severe limitation on the practical application of long distance quantum key distribution. The idea of twin-field quantum key distribution can improve the key rate from the linear scale of channel loss in the traditional decoy-state method to the square root scale of the channel transmittance. However, the technical demanding is rather tough because it requests single photon level interference of two remote independent lasers. Here, we adopt the technology developed in the frequency and time transfer to lock two independent lasers' wavelengths and utilize additional phase reference light to estimate and compensate the fiber fluctuation. Further with a single photon detector with high detection rate, we demonstrate twin field quantum key distribution through the sending-or-not-sending protocol with realistic phase drift over 300 km optical fiber spools. We calculate the secure key rates with finite size effect. The secure key rate at 300 km ($1.96\times10^{-6}$) is higher than that of the repeaterless secret key capacity ($8.64\times10^{-7}$).
An Energy-Efficient SDN Controller Architecture for IoT Networks With Blockchain-Based Security

Internet of Things (IoT) is a disruptive technology in many aspects of our society, ranging from communications to financial transactions to national security (e.g., Internet of Battlefield / Military Things), and so on. There are long-standing challenges in IoT, such as security, comparability, energy consumption, and heterogeneity of devices. Security and energy aspects play important roles in data transmission across IoT and edge networks, due to limited energy and computing (e.g., processing and storage) resources of networked devices. Whether malicious or accidental, interference with data in an IoT network potentially has real-world consequences. In this article, we explore the potential of integrating blockchain and software-defined networking (SDN) in mitigating some of the challenges. Specifically, we propose a secure and energy-efficient blockchain-enabled architecture of SDN controllers for IoT networks using a cluster structure with a new routing protocol. The architecture uses public and private blockchains for Peer to Peer (P2P) communication between IoT devices and SDN controllers, which eliminates Proof-of-Work (POW), as well as using an efficient authentication method with the distributed trust, making the blockchain suitable for resource-constrained IoT devices. The experimental results indicate that the routing protocol based on the cluster structure has higher throughput, lower delay, and lower energy consumption than EESCFD, SMSN, AODV, AOMDV, and DSDV routing protocols. In other words, our proposed architecture is demonstrated to outperform classic blockchain.
Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature extraction, and we empirically prove that the preprocessing of the two-stage sliding window will be useful for high-level feature extraction in the integrated model. After data preprocessing, spatial and temporal features are extracted in CNN and recurrent autoencoder for the classification in fully connected networks. Empiric results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection.
Combining Fact Extraction and Verification with Neural Semantic Matching Networks

The increasing concern with misinformation has stimulated research efforts on automatic fact checking. The recentlyreleased FEVER dataset introduced a benchmark factverification task in which a system is asked to verify a claim using evidential sentences from Wikipedia documents. In this paper, we present a connected system consisting of three homogeneous neural semantic matching models that conduct document retrieval, sentence selection, and claim verification jointly for fact extraction and verification. For evidence retrieval (document retrieval and sentence selection), unlike traditional vector space IR models in which queries and sources are matched in some pre-designed term vector space, we develop neural models to perform deep semantic matching from raw textual input, assuming no intermediate term representation and no access to structured external knowledge bases. We also show that Pageview frequency can also help improve the performance of evidence retrieval results, that later can be matched by using our neural semantic matching network. For claim verification, unlike previous approaches that simply feed upstream retrieved evidence and the claim to a natural language inference (NLI) model, we further enhance the NLI model by providing it with internal semantic relatedness scores (hence integrating it with the evidence retrieval modules) and ontological WordNet features. Experiments on the FEVER dataset indicate that (1) our neural semantic matching method outperforms popular TF-IDF and encoder models, by significant margins on all evidence retrieval metrics, (2) the additional relatedness score and WordNet features improve the NLI model via better semantic awareness, and (3) by formalizing all three subtasks as a similar semantic matching problem and improving on all three stages, the complete model is able to achieve the state-of-the-art results on the FEVER test set (two times greater than baseline results).1