Reviewerly/exHarmony huggingface dataset:
Investigating User Behavior in Legal Case Retrieval

Legal case retrieval is a specialized IR task aiming to retrieve supporting cases given a query case. While recent research efforts are committed to improving the automatic retrieval models' performances, little attention has been paid to the practical search interactions between users and systems in this task. Therefore, we focus on investigating user behavior in the scenario of legal case retrieval. Specifically, we conducted a laboratory user study that involved 45 participants majoring in law to collect users' rich interactions and relevance assessments. With the collected data, we first analyzed the characteristics of the search process in legal case retrieval practice. We observed significant differences between legal case retrieval and general web search in various search behavior. These differences highlight the necessity of in-depth investigating user behavior in legal case retrieval and re-thinking the application of related mechanisms developed based on the user models in Web search. Then we investigated factors that would influence search behavior from different perspectives, including task difficulty and domain expertise. Finally, we shed light on implicit feedback in legal case retrieval and designed a predictive model for relevance based on user behavior. Our work provides a better understanding of user interactions in the legal case retrieval process, which can benefit the design of the corresponding retrieval systems to support legal practitioners.
Hyperspectral-cube-based mobile face recognition: A comprehensive review

With the hyperspectral sensor technology evolving and becoming more cost-effective, hyperspectral imaging offers new opportunities for robust face recognition. Hyperspectral face cubes contain much more spectral information than face images from common RGB color cameras. Hyperspectral face recognition is robust to the impacts, such as illumination, pose, occlusion, and spoofing, which can heavily avoid the limitations of the visible-image-based face recognition. In this paper, we summarize the spectrum properties of hyperspectral face cubes and survey the hyperspectral face recognition methods in the literature. We categorize them into major groups for better understanding. We overview the existing hyperspectral face datasets, and establish our own dataset. We also discuss efficient neural networks used for mobile face recognition and conduct experiments on mobile hyperspectral face recognition. Results show that under harsh conditions like large illumination changing and pose variation, hyperspectral-cube-based methods have higher recognition accuracy than visible-image-based methods. Finally, we deliver insightful discussions and prospects for future works on mobile hyperspectral face recognition.
Maladaptive behaviour in response to email phishing threats: The roles of rewards and response costs

Email users are vulnerable to phishing threats and a greater understanding of how to protect them is needed. This research investigates how response costs and rewards influence users' protective and maladaptive security behaviours in the domain of phishing by testing a model that extends Protection Motivation Theory to more explicitly consider the role of maladaptive behaviour. The results show that rewards influence maladaptive behaviour rather than protective behaviour in response to email phishing threats, and that response costs influence both maladaptive and protective behaviours. That is, any perceived benefits from not performing protective behaviours against email phishing threats will result in an increase in the performance of maladaptive behaviours. Similarly, any increases in costs perceived to be incurred for performing protective behaviours against email phishing threats will result in a decrease in protective behaviour and an increase in maladaptive behaviour. These findings have both practical implications and implications for future research into protections against phishing threats.
Efficient and Robust Certification of Genuine Multipartite Entanglement in Noisy Quantum Error Correction Circuits

Ensuring the correct functioning of quantum error correction (QEC) circuits is crucial to achieve fault tolerance in realistic quantum processors subjected to noise. The first checkpoint for a fully operational QEC circuit is to create genuine multipartite entanglement across all subsystems of physical qubits. We introduce a conditional witnessing technique to certify genuine multipartite entanglement (GME) that is efficient in the number of subsystems and, importantly, robust against experimental noise and imperfections. Specifically, we prove that the detection of entanglement in a linear number of bipartitions by a number of measurements that also scales linearly, suffices to certify GME. Moreover, our method goes beyond the standard procedure of separating the state from the convex hull of biseparable states, yielding an improved finesse and robustness compared to previous techniques. We apply our method to the noisy readout of stabilizer operators of the distance-three topological color code and its flag-based fault-tolerant version. In particular, we subject the circuits to combinations of three types of noise, namely, uniform depolarizing noise, two-qubit gate depolarizing noise, and bit-flip measurement noise. We numerically compare our method with the standard, yet generally inefficient, fidelity test and to a pair of efficient witnesses, verifying the increased robustness of our method. Last but not least, we provide the full translation of our analysis to a trapped-ion native gate set that makes it suitable for experimental applications.
Lithological classification via an improved extreme gradient boosting: A demonstration of the Chang 4+5 member, Ordos Basin, Northern China

Acquiring reliable lithology information is a critical step for geological analysis since many basic jobs in early exploration have to be completed under the application of lithological materials. Lithology prediction then is always regarded as a research hotspot in geosciences. XGBoost is proved to be more powerful on pattern recognition than classic models, as it takes advantages of gradient boosting, classification tree, regularization, and other advanced machine learning techniques, thus being more potential to provide an ideal solution for lithology prediction. Nonetheless, this model is difficult to obtain optimal results due to the employment of many hyper-parameters, and will be low-efficient when dealing with many variables. Therefore, two computing techniques, continuous restricted Boltzmann machine (CRBM) and particle swarm optimization (PSO), are introduced to improve prediction performance of XGBoost. CRBM can extract fewer while more significant features from original data, and PSO will automatically optimize hyper-parameters during training process. Data used for validation is derived from tight sandstone reservoirs of member of Chang 4 + 5, western Jiyuan Oilfield, Ordos Basin, Northern China. Three experiments are designed to verify prediction capability of the proposed model. In order to highlight validation effect, two classic predictors named support vector machine (SVM) and gradient boosting decision tree (GBDT) are applied to create a contrast. The total prediction accuracy and the respective accuracy of each lithology produced by CRBM-PSO-XGBoost are all the highest in three experiments, well demonstrating the proposed model is effective to predict the lithology of tight sandstone reservoirs and has better robustness.
Learning With Noisy Labels via Self-Reweighting From Class Centroids

Although deep neural networks have been proved effective in many applications, they are data hungry, and training deep models often requires laboriously labeled data. However, when labeled data contain erroneous labels, they often lead to model performance degradation. A common solution is to assign each sample with a dynamic weight during optimization, and the weight is adjusted in accordance with the loss. However, those weights are usually unreliable since they are measured by the losses of corrupted labels. Thus, this scheme might impede the discriminative ability of neural networks trained on noisy data. To address this issue, we propose a novel reweighting method, dubbed self-reweighting from class centroids (SRCC), by assigning sample weights based on the similarities between the samples and our online learned class centroids. Since we exploit statistical class centers in the image feature space to reweight data samples in learning, our method is robust to noise caused by corrupted labels. In addition, even after reweighting the noisy data, the decision boundaries might still suffer distortions. Thus, we leverage mixed inputs that are generated by linearly interpolating two random images and their labels to further regularize the boundaries. We employ the learned class centroids to evaluate the confidence of our generated mixed data via measuring feature similarities. During the network optimization, the class centroids are updated as more discriminative feature representations of original images are learned. In doing so, SRCC will generate more robust weighting coefficients for noisy and mixed data and facilitates our feature representation learning in return. Extensive experiments on both the synthetic and real image recognition tasks demonstrate that our method SRCC outperforms the state of the art on learning with noisy data.
Increasing Throughput and Reducing Storage Bloating Problem Using IPFS and Dual-Blockchain Method

Blockchain is a revolutionary technology that has been underlying behind many cryptocurrencies for many years. Due to the scalability issue, this decentralized system lags behind the centralized currency systems, and cannot be adopted by other platforms though it has a lot of unique features. This paper is motivated due to the lack of enough scalability on the existing blockchain technologies. A theoretical method is proposed in this paper to increase throughput and reduce storage dependencies. A distributed storage system IPFS is used to bypass the storing liabilities and to increase throughput. The dual-blockchain method serves the core features of the blockchain by adding the references of the main block into the ledger in place of the original block. The analysis shows that our proposed method can achieve up to 25.8 times greater throughput and almost 1685 times lesser ledger size compared to Bitcoin Core.
ThamizhiMorph: A morphological parser for the Tamil language

Abstract This paper presents an open source and extendable Morphological Analyser cum Generator (MAG) for Tamil named Thamizhi Morph. Tamil is a low-resource language in terms of NLP processing tools and applications. In addition, most of the available tools are neither open nor extendable. A morphological analyser is a key resource for the storage and retrieval of morphophonological and morphosyntactic information, especially for morphologically rich languages, and is also useful for developing applications within Machine Translation. This paper describes how Thamizhi Morph is designed using a Finite-State Transducer (FST) and implemented using Foma. We discuss our design decisions based on the peculiarities of Tamil and its nominal and verbal paradigms. We specify a high-level meta-language to efficiently characterise the language’s inflectional morphology. We evaluate Thamizhi Morph using text from a Tamil textbook and the Tamil Universal Dependency treebank version 2.5. The evaluation and error analysis attest a very high performance level, with the identified errors being mostly due to out-of-vocabulary items, which are easily fixable. In order to foster further development, we have made our scripts, the FST models, lexicons, Meta-Morphological rules, lists of generated verbs and nouns, and test data sets freely available for others to use and extend upon.
Towards an understanding of benefits and challenges in the use of design thinking in requirements engineering

Agile approaches arose as a way of addressing some of the main challenges in software development. Some of the challenges are changing requirements, lack of understanding about the system's scope, and out-of-sync between code, requirements, and documentation. Software companies' professionals have adopted Design Thinking (DT) to support software development to understand better what customers want, fostering the creation of features and products. Due to the importance of requirement engineering to software development success, this study aimed to characterize what DT benefits and challenges were perceived in requirements engineering by the Brazilian software development community. The current study used mixed methods combining two qualitative methods, a focus group to collect and understand the professionals' DT usage opinions and a survey to confirm the professionals' challenges and benefits. This study identified that DT has helped the professionals improve the requirements gathering and specification, reflecting on better users' real needs understanding and building fittable solutions to support them. The results serve to understand better DT potential, perceived by software industry professionals, to anticipate and support these issues with other professionals.
Wavelength variable generation and detection of photon pairs in visible and mid-infrared regions via spontaneous parametric downconversion

We report an experimental demonstration of wavelength variable generation and detection of photon pairs in the visible and mid-infrared (MIR) regions over a wide spectral range of 2–5 µm via spontaneous parametric downconversion. Despite the recent increase in interest in such a photon-pair source, there have been few detailed evaluations of emitted photons generated via the downconversion process in the low gain regime, due to the lack of suitable single-photon detectors. By changing the angle of the nonlinear crystal, we continuously tune the phase-matching condition and generate photon pairs as signal photons in the wavelength range of 600–965 nm and idler photons in the wavelength range of 1186–4694 nm. We evaluate the generated photon pairs using single and coincidence counts by superconducting nanowire single-photon detectors up to a wavelength of 2 µm and detect the intensity using an InSb detector with a lock-in detection system up to 5 µm. From this analysis, a pair generation rate of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>5</mml:mn> </mml:msup> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">s</mml:mi> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−<!-- − --></mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> per mW of pump power is experimentally obtained for this wavelength range. This work provides a basis for the realization of applications such as heralded MIR single-photon sources, infrared imaging, and infrared spectroscopy based on quantum technologies in the MIR region.
Zilch: A Framework for Deploying Transparent Zero-Knowledge Proofs

As cloud computing becomes more popular, research has focused on usable solutions to the problem of verifiable computation (VC), where a computationally weak device (Verifier) outsources a program execution to a powerful server (Prover) and receives guarantees that the execution was performed faithfully. A Prover can further demonstrate knowledge of a secret input that causes the Verifier’s program to satisfy certain assertions, without ever revealing which input was used. State-of-the-art <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Zero-Knowledge</i> Proofs of Knowledge (ZKPK) methods encode a computation using arithmetic circuits and preserve the privacy of Prover’s inputs while attesting the integrity of program execution. Nevertheless, developing, debugging, and optimizing programs as circuits remains a daunting task, as most users are unfamiliar with this programming paradigm. In this work, we present Zilch, a framework that accelerates and simplifies the deployment of VC and ZKPK for any application <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transparently</i> , i.e., without the need of trusted setup. Zilch uses traditional instruction sequences rather than static arithmetic circuits that would need to be regenerated for each different computation. Towards that end, we have implemented Z MIPS: a MIPS-like processor model that allows verifying each instruction independently and compose a proof for the execution of the target application. To foster usability, Zilch incorporates a novel cross-compiler from an object-oriented Java-like language tailored to ZKPK and optimized our Z MIPS model, as well as a powerful API that enables integration of ZKPK within existing C/C++ programs. In our experiments, we demonstrate the flexibility of Zilch using two real-life applications, and evaluate Prover and Verifier performance on a variety of benchmarks.
CWOA: Hybrid Approach for Task Scheduling in Cloud Environment

Abstract A cloud computing system typically comprises of a huge number of interconnected servers that are organized in a datacentre. Such servers dynamically cater to the on-demand requests put forward by the clients seeking solutions to their applications through an interface. The scheduling activity concerned with scientific applications is designated under the NP hard problem category since they make use of heterogeneous resources of dynamic capabilities. Recently cloud computing researchers had developed numerous meta-heuristic approaches for providing solutions to the challenges arising in the task scheduling activities. Scheduling of tasks poses a major concern in cloud computing environment. This decreases the efficiency of the system considerably, if not handled properly. Hence, an improvised task scheduling algorithm that enhances the performance of the cloud is needed. There are two factors that affect the cloud environment: service quality and energy usage. To increase the performance in above suggested factors (memory, makespan and energy efficiency), an efficient hybridized algorithm, obtained by integrating the Cuckoo Search Algorithm (CSA) and Whale Optimization Algorithm (WOA), called the CWOA had been proposed in this work. The performance of our proposed CWOA algorithm had been compared with Ant Colony Optimization, CSA and WOA and it was found to produce an improvement of 5.62%, 4.36% and 2.27% with respect to makespan, 16.36%, 19.19% and 13.13% with respect to memory utilization and 19.08%, 19.34% and 16.75% with respect to energy consumption parameters, respectively. Comprehensive results have been tabulated in the result section of this article.
T-VLAD: Temporal vector of locally aggregated descriptor for multiview human action recognition

Robust view-invariant human action recognition (HAR) requires effective representation of its temporal structure in multi-view videos. This study explores a view-invariant action representation based on convolutional features. Action representation over long video segments is computationally expensive, whereas features in short video segments limit the temporal coverage locally. Previous methods are based on complex multi-stream deep convolutional feature maps extracted over short segments. To cope with this issue, a novel framework is proposed based on a temporal vector of locally aggregated descriptors (T-VLAD). T-VLAD encodes long term temporal structure of the video employing single stream convolutional features over short segments. A standard VLAD vector size is a multiple of its feature codebook size (256 is normally recommended). VLAD is modified to incorporate time-order information of segments, where the T-VLAD vector size is a multiple of its smaller time-order codebook size. Previous methods have not been extensively validated for view-variation. Results are validated in a challenging setup, where one view is used for testing and the remaining views are used for training. State-of-the-art results have been obtained on three multi-view datasets with fixed cameras, IXMAS, MuHAVi and MCAD. Also, the proposed encoding approach T-VLAD works equally well on a dynamic background dataset, UCF101.
Analysis of lifting scheme based Double Density Dual-Tree Complex Wavelet Transform for de-noising medical images

Medical images play a vital role in diagnosis of various diseases. This has paved a path to the extensive use of CT, mammogram, MRI and ultrasound images in the recent days which has caused a rising concern about the radiation dosage that is involved in medical screening process. Owing to this concern low dose screening is widely being performed and has resulted in the introduction of noise, artifacts thus producing low image quality which can adversely affect the judgment of the radiologists. This in turn has led to the demand of enhanced image de-noising techniques. This work is an approach to remove multiple types of noises from low dose medical images using lifting based Double Density Dual-Tree Complex Wavelet Transform (DDDTCWT) and a modified Bernoulli based thresholding technique enhanced by fuzzy optimization technique. The parameters observed from the simulation results of the proposed method were compared with the existing de-noising techniques and results of the proposed method have shown significant improvement over the conventional techniques. The proposed work not only efficiently de-noises the image but also enhances its visual appearance. The Lifting scheme used provides augmented memory for decomposition, thus speeding up the entire de-noising process.
Hand Gesture Recognition using Flex Sensor and Machine Learning Algorithms

In this paper, we have proposed a mechanism of hand gesture recognition using flex sensors and Arduino UNO. The data acquired from the sensors corresponding to different hand gestures are analyzed with the help of different traditional machine learning algorithms. We also proposed an adversarial learning approach that performs better classification in comparison with these traditional learning models. The results of the proposed approach records a commendable accuracy of 88.32% with a precision of 81.77%, a recall of 84.37% and F1-score of 82.78%. The proposed model yields better results in terms of all these four performance metrics.
The More the Merrier: Reducing the Cost of Large Scale MPC

Secure multi-party computation (MPC) allows multiple parties to perform secure joint computations on their private inputs. Today, applications for MPC are growing with thousands of parties wishing to build federated machine learning models or trusted setups for blockchains. To address such scenarios we propose a suite of novel MPC protocols that maximize throughput when run with large numbers of parties. In particular, our protocols have both communication and computation complexity that decrease with the number of parties. Our protocols buildon prior protocolsbased on packed secret-sharing, introducing new techniques to build more efficient computation for general circuits. Specifically, we introduce a new approach for handling linear attacks that arise in protocols using packed secret-sharing and we propose a method for unpacking shared multiplication triples without increasing the asymptotic costs. Compared with prior work, we avoid the $$\log |C|$$ overhead required when generically compiling circuits of size |C| for use in a SIMD computation, and we improve over folklore "committee-based" solutions by a factor of O(s), the statistical security parameter. In practice, our protocol is up to 10X faster than any known construction, under a reasonable set of parameters.