Reviewerly/exHarmony huggingface dataset:
Semantic Roles or Syntactic Functions: The Effects of Annotation Scheme on the Results of Dependency Measures

Abstract The annotation scheme of dependency treebanks might have an impact on the results of linguistic analysis, thus leading to different interpretations of linguistic phenomena. This study compares the results of two widely used dependency measures, i.e., dependency direction and dependency distance, based on 18 parallel Universal Dependencies (UD) annotated treebanks and 18 corresponding Surface‐Syntactic Universal Dependencies (SUD) annotated treebanks. The results show that (1) Based on the semantic UD and syntactic SUD, dependency relations between function words and content words share the opposite dependency directions but similar dependency distances; (2) Annotation scheme has a significant impact on dependency direction, though the effect size is small. We find that the proportions of head‐final dependencies based on the syntactic SUD can better group language families than those based on semantic UD; (3) Annotation scheme also affects dependency distance significantly, though its effect size is small. Mean dependency distances (MDDs) based on UD are always higher than those based on SUD. However, the MDDs based on both annotation schemes are within a certain threshold, which shows that the linguistic universal of dependency distance minimization is independent of annotation schemes.
Broadcast Authenticated Encryption with Keyword Search

The emergence of public-key encryption with keyword search (PEKS) has provided an elegant approach to enable keyword search over encrypted content. Due to its high computational complexity proportional to the number of intended receivers, the trivial way of deploying PEKS for data sharing with multiple receivers is impractical, which motivates the development of a new PEKS framework for broadcast mode. However, existing works suffer from either the vulnerability to keyword guessing attacks (KGA) or high computation and communication complexity. In this work, a new primitive for keyword search in broadcast mode, named broadcast authenticated encryption with keyword search (BAEKS), is introduced, in which the sender not only encrypts the keyword but also authenticates it, eliminating the threats of KGA. Moreover, on top of keyword privacy, we formalize the notion of user anonymity (or key privacy) for BAEKS, which echoes the notion of key privacy for public-key encryption introduced by Bellare et al. (ASIACRYPT’01). We present a practical BAEKS construction that achieves all the desirable features, including keyword privacy of both searchable ciphertext and trapdoor, KGA-resistance, receiver anonymity of both searchable ciphertext and trapdoor, and universal keyword set scalability. Moreover, the trapdoor of our scheme achieves constant computation and communication cost, making it more suitable for broadcast mode where trapdoors are generated by multiple receivers in the search operations. The security of our scheme is proved under the standard DBDH assumption.
Artificial Intelligence (AI) in Renewable Energy Systems: A Condensed Review of its Applications and Techniques

This paper’s main objective is to examine the state of the art of artificial intelligence (AI) techniques and tools in power management, maintenance, and control of renewable energy systems (RES) and specifically to the solar power systems. The findings would allow researchers to innovate the current state of technologies and possibly use the standard and successful techniques in building AI-powered renewable energy systems, specifically for solar energy. Various peer-reviewed journal articles were examined to determine the condition and advancement of the AI techniques in the field of RES, specifically in solar power systems. Different theoretical and experimental AI techniques often used and reliable techniques determined were the Artificial Neural Network (ANN), Backpropagation Neural Network (BPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Genetic Algorithm (GA). These techniques are widely used in different types of solar predictions based on the findings of this review. However, ANN stood out as the best of these techniques. ANN’s specific advantages over its competition include short computing time, higher accuracy, and generalization capabilities over other modeling techniques. This would translate to cost efficiency over other modeling techniques.
The Hybrid Structure of a Self-Dual Built-In Control Circuit for Combinational Devices with Pre-Compression of Signals and Checking of Calculations by Two Diagnostic Parameters

The authors propose to use the concurrent checking approach for two diagnostic parameters with preliminary compression of signals from the diagnosed objects outputs when synthesizing self-checking digital devices. A hybrid structure of a self-dual built-in control circuit is presented. The advantages and disadvantages of the proposed structure are described. The main advantage of the proposed solution is the ability to reduce the structural redundancy of the self-checking device by using a compression circuit with a high detecting ability of the check circuit. As the main disadvantage, we emphasize the possibility of error concealment at the outputs of the signal compression elements. The paper discusses ways to eliminate the noted disadvantage in the form of circuitry methods. Experiments carried out with the MCNC Benchmarks system of combinational benchmarks have shown the high efficiency of the proposed approach for organizing built-in control circuits. We describe the features of the implementation of the proposed hybrid structure in the Logisim simulation environment, allowing other researchers to repeat the experiments and develop the results obtained.
Image classification with multi-view multi-instance metric learning

Image classification is a critical and meaningful task in image retrieval, recognition and object detection. In this paper, three-side efforts are taken to accomplish this task. First, visual features with multi-instance representation are extracted to characterize the image due to the merits of bag-of-words representations. And a new distance function is designed for bags, which measures the relationship between bags more precisely. Second, the idea of multi-view learning is implemented since multiple views encourage the classifier to be more consistent and accurate. Last but not least, the metric learning technique is explored by optimizing the joint conditional probability to pursue view-dependent metrics and the importance weights of the newly-designed distance in multi-view scenario. Therefore, we propose a multi-view multi-instance metric learning method named MVMIML for image classification, which integrates the advantages of the multi-view multi-instance representation and metric learning into a unified framework. To solve MVMIML, we adopt the alternate iteration optimization algorithm and analyze the corresponding computational complexity. Numerical experiments verify the advantages of the new distance function and the effectiveness of MVMIML.
Learning Multiple Intent Representations for Search Queries

Representation learning has always played an important role in information retrieval (IR) systems. Most retrieval models, including recent neural approaches, use representations to calculate similarities between queries and documents to find relevant information from a corpus. Recent models use large-scale pre-trained language models for query representation. The typical use of these models, however, has a major limitation in that they generate only a single representation for a query, which may have multiple intents or facets. The focus of this paper is to address this limitation by considering neural models that support multiple intent representations for each query. Specifically, we propose the NMIR (Neural Multiple Intent Representations) model that can generate semantically different query intents and their appropriate representations. We evaluate our model on query facet generation using a large-scale dataset of real user queries sampled from the Bing search logs. We also provide an extrinsic evaluation of the proposed model using a clarifying question selection task. The results show that NMIR significantly outperforms competitive baselines.
Gas consumption analysis of Ethereum blockchain transactions

Abstract In Ethereum blockchain, whenever a transaction of smart contract is executed, transaction fee is charged in terms of Ethers. To calculate the transaction fee, a computational unit, gas is introduced in smart contracts. Gas consumption is calculated against the smart contract source code execution. The transaction initiator sets the gas price against per unit of gas and the total gas limit. If the gas limit is sufficient, the transaction will be mined otherwise it will be reverted. Smart contracts of Ethereum can be written in any high‐level language such as Solidity, Vyper, Python, Java and so forth, but Solidity is massively used for smart contracts creation. In this article, we have examined the 5000 transactions of Solidity based smart contracts from Etherscan and performed statistical analysis on opcodes and source code parameters used in these transactions to identify gas costly patterns. Our statistical results (correlation and regression) analyze the relationship of Solidity parameters and opcodes with the gas consumption. Factors causing an increase or decrease in the gas consumption of smart contracts are highlighted in this article. The regression analysis showed that 87.8% of the variability in the response variable (gas consumption) is due to the parameters used in this analysis. Our results will help the smart contract developers to write the gas optimized smart contracts. The results can be beneficial for end users as they will have to pay gas price for less number of gas units.
HybridDeepRx: Deep Learning Receiver for High-EVM Signals

In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural network receiver is devised, containing layers in both time- and frequency domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Extensive set of numerical results is provided, in the context of 5G NR uplink incorporating also measured terminal power amplifier characteristics. The obtained results show that the proposed receiver system is able to clearly outperform classical linear receivers as well as existing ML receiver approaches, especially when the EVM is high in comparison with modulation order. The proposed ML receiver can thus facilitate pushing the terminal power amplifier (PA) systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage.
Flexibly Focusing on Supporting Facts, Using Bridge Links, and Jointly Training Specialized Modules for Multi-Hop Question Answering

With the help of the detailed annotated question answering dataset HotpotQA, recent question answering models are trained to justify their predicted answers with supporting facts from context documents. Some related works train the same model to find supporting facts and answers jointly without having specialized models for each task. The others train separate models for each task, but do not use supporting facts effectively to find the answer; they either use only the predicted sentences and ignore the remaining context, or do not use them at all. Furthermore, while complex graph-based models consider the bridge/connection between documents in the multi-hop setting, simple BERT-based models usually drop it. We propose FlexibleFocusedReader (FFReader), a model that 1) Flexibly focuses on predicted supporting facts (SFs) without ignoring the important remaining context, 2) Focuses on the bridge between documents, despite not using graph architectures, and 3) Jointly learns predicting SFs and answering with two specialized models. Our model achieves consistent improvement over the baseline. In particular, we find that flexibly focusing on SFs is important, rather than ignoring remaining context or not using SFs at all for finding the answer. We also find that tagging the entity that links the documents at hand is very beneficial. Finally, we show that joint training is crucial for FFReader.
State-Aware Meta-Evaluation of Evaluation Metrics in Interactive Information Retrieval

In interactive IR (IIR), users often seek to achieve different goals (e.g. exploring a new topic, finding a specific known item) at different search iterations and thus may evaluate system performances differently. Without state-aware approach, it would be extremely difficult to simulate and achieve real-time adaptive search evaluation and recommendation. To address this gap, our work identifies users' task states from interactive search sessions and meta-evaluates a series of online and offline evaluation metrics under varying states based on a user study dataset consisting of 1548 unique query segments from 450 search sessions. Our results indicate that: 1) users' individual task states can be identified and predicted from search behaviors and implicit feedback; 2) the effectiveness of mainstream evaluation measures (measured based upon their respective correlations with user satisfaction) vary significantly across task states. This study demonstrates the implicit heterogeneity in user-oriented IR evaluation and connects studies on complex search tasks with evaluation techniques. It also informs future research on the design of state-specific, adaptive user models and evaluation metrics.
PoSAT: Proof-of-Work Availability and Unpredictability, Without the Work

An important feature of Proof-of-Work (PoW) blockchains is full dynamic availability, allowing miners to go online and offline while requiring only 50% of the online miners to be honest. Existing Proof-of-stake (PoS), Proof-of-Space and related protocols are able to achieve this property only partially, either requiring the additional assumption that adversary nodes are online from the beginning and no new adversary nodes come online afterwards, or use additional trust assumptions for newly joining nodes. We propose a new PoS protocol PoSAT which can provably achieve dynamic availability fully without any additional assumptions. The protocol is based on the longest chain and uses a Verifiable Delay Function for the block proposal lottery to provide an arrow of time. The security analysis of the protocol draws on the recently proposed technique of Nakamoto blocks as well as the theory of branching random walks. An additional feature of PoSAT is the complete unpredictability of who will get to propose a block next, even by the winner itself. This unpredictability is at the same level of PoW protocols, and is stronger than that of existing PoS protocols using Verifiable Random Functions.
OneEdge

Resource management for geo-distributed infrastructures is challenging due to the scarcity and non-uniformity of edge resources, as well as the high client mobility and workload surges inherent to situation awareness applications. Due to their centralized nature, state-of-the-art schedulers that work well in datacenters lack the performance and feature requirements of such applications. We present OneEdge, a hybrid control plane that enables autonomous decision-making at edge sites for localized, rapid single-site application deployment. Edge sites handle mobility, churn, and load spikes, by cooperating with a centralized controller that allows coordinated multi-site scheduling and dynamic reconfiguration.
Privacy-Preserving Batch-based Task Assignment in Spatial Crowdsourcing with Untrusted Server

In this paper, we study the privacy-preserving task assignment problem in spatial crowdsourcing, where the locations of both workers and tasks, prior to their release to the server, are perturbed with Geo-Indistinguishability (a differential privacy notion for location-based systems). Different from the previously studied online setting, where each task is assigned immediately upon arrival, we target the batch-based setting, where the server maximizes the number of successfully assigned tasks after a batch of tasks arrive. To achieve this goal, we propose the k-Switch solution, which first divides the workers into small groups based on the perturbed distance between workers/tasks, and then utilizes Homomorphic Encryption (HE) based secure computation to enhance the task assignment. Furthermore, we expedite HE-based computation by limiting the size of the small groups under k. Extensive experiments demonstrate that, in terms of the number of successfully assigned tasks, the k-Switch solution improves batch-based baselines by 5.9X and the existing online solution by 1.74X, with no privacy leak.
Structural Deep Incomplete Multi-view Clustering Network

In recent years, incomplete multi-view clustering has drawn increasing attention due to the existence of large amounts of unlabeled incomplete data whose views are not fully observed in the practical applications. Although many traditional methods have been extended to address the incomplete learning problem, most of them exploit the shallow models and ignore the geometric structure. To address these issues, we proposed a structural deep incomplete multi-view clustering network. Specifically, the proposed method can simultaneously explore the high-level features and high-order geometric structure information of data with several view-specific graph convolutional encoder networks and can directly obtain the optimal clustering indicator matrix in one stage. Experimental results on several datasets with the comparison of state-of-the-art methods validate the superiority of the proposed method.
Progressive development of spin wave chaos in active-ring oscillators

This Letter reports the first experimental demonstration of progressive chaos development through four-wave mixing of spin wave amplified harmonics in magnonic active-ring oscillators. We find that the route to chaos represents a sequence of quasiperiodic regimes of oscillations and ultimately yields formation of a 3D torus in the phase space. The transition from the quasiperiodic oscillations to chaos proceeds through progressive spectrum stochastization during which the amplified harmonics gradually disappear.
Communication Over Quantum Channels With Parameter Estimation

Communication over a random-parameter quantum channel when the decoder is required to reconstruct the parameter sequence is considered. We study scenarios that include either strictly-causal, causal, or non-causal channel side information (CSI) available at the encoder, and also when CSI is not available. This model can be viewed as a form of quantum metrology, and as the quantum counterpart of the classical rate-and-state channel with state estimation at the decoder. Regularized formulas for the capacity-distortion regions are derived. In the special case of measurement channels, single-letter characterizations are derived for the strictly-causal and causal settings. Furthermore, in the more general case of entanglement-breaking channels, a single-letter characterization is derived when CSI is not available. As a consequence, we obtain regularized formulas for the capacity of random-parameter quantum channels with CSI, generalizing previous results by Boche <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2016, on classical-quantum channels. Bosonic dirty paper coding is introduced as a consequence, where we demonstrate that the optimal coefficient is not necessarily that of minimum mean-square error estimation as in the classical setting.
Application of Extreme Learning Machine in the Survival Analysis of Chronic Heart Failure Patients With High Percentage of Censored Survival Time

Objective: To explore the application of the Cox model based on extreme learning machine in the survival analysis of patients with chronic heart failure. Methods: The medical records of 5,279 inpatients diagnosed with chronic heart failure in two grade 3 and first-class hospitals in Taiyuan from 2014 to 2019 were collected; with death as the outcome and after the feature selection, the Lasso Cox, random survival forest (RSF), and the Cox model based on extreme learning machine (ELM Cox) were constructed for survival analysis and prediction; the prediction performance of the three models was explored based on simulated data with three censoring ratios of 25, 50, and 75%. Results: Simulation results showed that the prediction performance of the three models decreased with increasing censoring proportion, and the ELM Cox model performed best overall; the ELM Cox model constructed with 21 highly influential survival predictors screened from actual chronic heart failure data showed the best performance with C-index and Integrated Brier Score (IBS) of 0.775(0.755, 0.802) and 0.166(0.150, 0.182), respectively. Conclusion: The ELM Cox model showed good discrimination performance in the survival analysis of patients with chronic heart failure; it performs consistently for data with a high proportion of censored survival time; therefore, the model could help physicians identify patients at high risk of poor prognosis and target therapeutic measures to patients as early as possible.
Algebraic Attacks on Round-Reduced Keccak

Since Keccak was selected as the SHA-3 standard, both its hash mode and keyed mode have attracted lots of third-party cryptanalysis. Especially in recent years, there is progress in analyzing the collision resistance and preimage resistance of round-reduced Keccak. However, for the preimage attacks on round-reduced Keccak-384/512, we found that the linear relations leaked by the hash value are not well exploited when utilizing the current linear structures. To make full use of the \(320+64\times 2=448\) and 320 linear relations leaked by the hash value of Keccak-512 and Keccak-384, respectively, we propose a dedicated algebraic attack by expressing the output as a quadratic boolean equation system in terms of the input. Such a quadratic boolean equation system can be efficiently solved with linearization techniques. Consequently, we successfully improved the preimage attacks on 2/3/4 rounds of Keccak-384 and 2/3 rounds of Keccak-512.
SlowFast Rolling-Unrolling LSTMs for Action Anticipation in Egocentric Videos

Action anticipation in egocentric videos is a difficult task due to the inherently multi-modal nature of human actions. Additionally, some actions happen faster or slower than others depending on the actor or surrounding context which could vary each time and lead to different predictions. Based on this idea, we build upon RULSTM architecture, which is specifically designed for anticipating human actions, and propose a novel attention-based technique to evaluate, simultaneously, slow and fast features extracted from three different modalities, namely RGB, optical flow and extracted objects. Two branches process information at different time scales, i.e., frame-rates, and several fusion schemes are considered to improve prediction accuracy. We perform extensive experiments on EpicKitchens55 and EGTEA Gaze+ datasets, and demonstrate that our technique systematically improves the results of RULSTM architecture for Top-5 accuracy metric at different anticipation times.