Reviewerly/exHarmony huggingface dataset:
An Improved Water Strider Algorithm for Optimal Design of Skeletal Structures

Water Strider Algorithm (WSA) is a new metaheuristic method that is inspired by the life cycle of water striders. This study attempts to enhance the performance of the WSA in order to improve solution accuracy, reliability, and convergence speed. The new method, called improved water strider algorithm (IWSA), is tested in benchmark mathematical functions and some structural optimization problems. In the proposed algorithm, the standard WSA is augmented by utilizing an opposition-based learning method for the initial population as well as a mutation technique borrowed from the genetic algorithm. By employing Generalized Space Transformation Search (GSTS) as an opposition-based learning method, more promising regions of the search space are explored; therefore, the precision of the results is enhanced. By adding a mutation to the WSA, the method is helped to escape from local optimums which is essential for engineering design problems as well as complex mathematical optimization problems. First, the viability of IWSA is demonstrated by optimizing benchmark mathematical functions, and then it is applied to three skeletal structures to investigate its efficiency in structural design problems. IWSA is compared to the standard WSA and some other state-of-the-art metaheuristic algorithms. The results show the competence and robustness of the IWSA as an optimization algorithm in mathematical functions as well as in the field of structural optimization.
STAFF COMPETENCE AND TRAINING FOR DIGITAL INDUSTRY

Abstract The development of a digital economy based on effective work with big data, intel-ligent digital platforms, artificial intelligence, robotics technology and 3D printing requires the formation and improvement the level of employees’ competence as a set of new high-quality competencies. In recent years, the term “digital competence” has become a key one in discus-sions about what skills and knowledge are needed in a modern knowledge society. The term “Digital competency” has various interpretations in academic literature, in the practice of teaching, training and certification. In this article, we analyze the concept of “digital compe-tence” and related terms as well as possible ways of its formation. Moreover, we consider an example of the “digital competence” formation that has developed at the SPbPU Advanced Manufacturing Technologies Center of NTI. It is important to note that we distinguish the con-cepts of “digital competence” and “digital literacy”. These concepts are not identical, but “digi-tal competency” concept is considered as a key. In this article, we prove that the most suitable approach to training staff for the digital economy is formation of fundamental knowledge as well as developing applied skills. Due to digital platforms, the applied skills realizes funda-mental knowledge through solving practical problems and challenges in the global high-tech market of products and services.
Privacy-Preserving News Recommendation Model Learning

News recommendation aims to display news articles to users based on their personal interest. Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors. In this paper, we propose a privacy-preserving method for news recommendation model training based on federated learning, where the user behavior data is locally stored on user devices. Our method can leverage the useful information in the behaviors of massive number users to train accurate news recommendation models and meanwhile remove the need of centralized storage of them. More specifically, on each user device we keep a local copy of the news recommendation model, and compute gradients of the local model based on the user behaviors in this device. The local gradients from a group of randomly selected users are uploaded to server, which are further aggregated to update the global model in the server. Since the model gradients may contain some implicit private information, we apply local differential privacy (LDP) to them before uploading for better privacy protection. The updated global model is then distributed to each user device for local model update. We repeat this process for multiple rounds. Extensive experiments on a real-world dataset show the effectiveness of our method in news recommendation model training with privacy protection.
To Get Lost is to Learn the Way: Automatically Collecting Multi-step Social Engineering Attacks on the Web

By exploiting people's psychological vulnerabilities, modern web-based social engineering (SE) attacks manipulate victims to download malware and expose personal information. To effectively lure users, some SE attacks constitute a sequence of web pages starting from a landing page and require browser interactions at each web page, which we call multi-step SE attacks. Also, different browser interactions executed on a web page often branch to multiple sequences to redirect users to different SE attacks. Although common systems analyze only landing pages or conduct browser interactions limited to a specific attack, little effort has been made to follow such sequences of web pages to collect multi-step SE attacks.
Towards Topic-Guided Conversational Recommender System

Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. To develop an effective CRS, the support of high-quality datasets is essential. Existing CRS datasets mainly focus on immediate requests from users, while lack proactive guidance to the recommendation scenario. In this paper, we contribute a new CRS dataset named \textbf{TG-ReDial} (\textbf{Re}commendation through \textbf{T}opic-\textbf{G}uided \textbf{Dial}og). Our dataset has two major features. First, it incorporates topic threads to enforce natural semantic transitions towards the recommendation scenario. Second, it is created in a semi-automatic way, hence human annotation is more reasonable and controllable. Based on TG-ReDial, we present the task of topic-guided conversational recommendation, and propose an effective approach to this task. Extensive experiments have demonstrated the effectiveness of our approach on three sub-tasks, namely topic prediction, item recommendation and response generation. TG-ReDial is available at https://github.com/RUCAIBox/TG-ReDial.
Cascading Failure Pattern Identification in Power Systems Based on Sequential Pattern Mining

Cascading failure simulation data contain many fault chains (FCs), which can present cascading failure propagation paths. In cascading failure process, some component outages play dominating roles in propagation, indicating the common characteristics among different FCs. The commonness is embodied as combinations of components which are vulnerable to trip and cause serious blackout consequences. In addition, a component outage will increase the outage probability of relevant components and induce dependent outage in subsequent stage. Such relevance between two components can be called component outage causality. A combination of sequential component outages with outage causalities, which exists in different FCs and leads to system load loss, can be regarded as a cascading failure pattern (CFP). Statistical characteristics of FCs indicate that CFPs are variously distributed in different FCs, can present propagation paths and cause different impacts on system blackouts. This paper proposes a cascading failure pattern (CFP) identification method based on sequential pattern mining approach. The proposed method focuses on mining CFPs from massive FCs, quantifies the influence of CFP on system blackout and identifies the critical ones. The proposed method is verified with FCs data on IEEE 39-bus and 118-bus test systems.
Identifying Use Case Elements from Textual Specification: A Preliminary Study

Software requirements are described in some form of natural language (NL) text so that stakeholders with limited experience can also comprehend them easily. However, the NL text written document is inherently ambiguous, and this makes it hard to examine requirements manually to find inconsistencies, duplicates, and/or missing requirements. Use Case Analysis is a graphical depiction used to explain the interaction between the user and the system for the given user's task. Additionally, it denotes the extension/dependency of one use case to another to understand the system flow. It is often used to identify, clarify, and categorize system requirements. However, generating use cases from a textual written description of requirements is an arduous task involving a significant manual work, which can be automated using data-driven techniques. In this poster paper, we present an initial approach for the automated identification of use case names and actor names from the textual requirements specification using machine learning techniques.
PolymoRF

Today's wireless technologies are largely based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. To address this key issue, wireless receivers will need to (i) infer on-the-fly the physical-layer parameters currently used by transmitters; and if needed, (ii) change their hardware and software structures to demodulate the incoming waveform. In this paper, we introduce PolymoRF, a deep learning-based polymorphic receiver able to reconfigure itself in real time based on the inferred waveform parameters. Our key technical innovations are (i) a novel embedded deep learning architecture, called RFNet, which enables the solution of key waveform inference problems; (ii) a generalized hardware/software architecture that integrates RFNet with radio components and signal processing. We prototype PolymoRF on a custom software-defined radio platform, and show through extensive over-the-air experiments that PolymoRF achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.
General resource theory of quantum coherence in multipartite system

量子相干理论是一类重要的量子资源理论, 其自由操作是各种类型的非相干操作. 在单体相干资源理论中, 最大相干态是最重要的量子资源态, 它被定义为在非相干操作下可以转化为任何其他纯态的量子态. 但是, 这一情形在多体系统中发生了巨大改变: 不仅在有些相干度量下不存在唯一的最大相干态, 而且在有些非相干操作下几乎所有纯的相干多体态都不可比较(非相干操作下, 量子态之间的转换几乎不可能). 为了解决这一问题, 把非相干操作的定义扩展为一种不能产生相干的量子操作, 即研究一般化的相干资源理论. 具体地说, 基于量子资源是否来源于多体相干或者真的多体相干研究两类可能的量子资源理论框架, 并且指出在这些理论框架下存在合理的偏序关系(每个纯态都可在非相干操作下转换为相干度更弱的纯态). 另外, 还证明了真的多体相干资源理论下存在唯一的最大相干态.
Quantum circuit design methodology for multiple linear regression

Multiple linear regression assumes an imperative role in supervised machine learning. In 2009, Harrow et al. [Phys. Rev. Lett. 103, 150502 (2009)] showed that their HHL algorithm can be used to sample the solution of a linear system $\mathbf{Ax=b}$ exponentially faster than any existing classical algorithm, with some manageable caveats. The entire field of quantum machine learning gained considerable traction after the discovery of this celebrated algorithm. However, effective practical applications and experimental implementations of HHL are still sparse in the literature. Here, we demonstrate a potential practical utility of HHL, in the context of regression analysis, using the remarkable fact that there exists a natural reduction of any multiple linear regression problem to an equivalent linear systems problem. We put forward a $7$-qubit quantum circuit design, motivated from an earlier work by Cao et al. [Mol. Phys. 110, 1675 (2012)], to solve a $3$-variable regression problem, using only elementary quantum gates. We also implement the Group Leaders Optimization Algorithm (GLOA) [Mol. Phys. 109 (5), 761 (2011)] and elaborate on the advantages of using such stochastic algorithms in creating low-cost circuit approximations for the Hamiltonian simulation. We believe that this application of GLOA and similar stochastic algorithms in circuit approximation will boost time- and cost-efficient circuit designing for various quantum machine learning protocols. Further, we discuss our Qiskit simulation and explore certain generalizations to the circuit design.
Decision Tree Algorithm with Class Overlapping-Balancing Entropy for Class Imbalanced Problem

The problem of handling a class imbalanced problem by modifying decision tree algorithm has received widespread attention in recent years.A new splitting measure, called class overlapping-balancing entropy (OBE), is introduced in this paper that essentially pay attentions to all classes equally.Each step, the proportion of each class is balanced via the assigned weighted values.They not only depend on equalizing each class, but they also take into account the overlapping region between classes.The proportion of weighted values corresponding to each class is used as the component of Shannon's entropy for splitting the current dataset.From the experimental results, OBE significantly outperforms the conventional splitting measures like Gini index, gain ratio and DCSM, which are used in the well-known decision tree algorithms.It also exhibits superior performance compared to AE and ME that are designed for handling the class imbalanced problem specifically.
Bottom-Up Foreground-Aware Feature Fusion for Person Search

The key to efficient person search is jointly localizing pedestrians and learning discriminative representation for person re-identification (re-ID). Some recently developed task-joint models are built with separate detection and re-ID branches on top of shared region feature extraction networks, where the large receptive field of neurons leads to background information redundancy for the following re-ID task. Our diagnostic analysis indicates the task-joint model suffers from considerable performance drop when the background is replaced or removed. In this work, we propose a subnet to fuse the bounding box features that pooled from multiple ConvNet stages in a bottom-up manner, termed bottom-up fusion (BUF) network. With a few parameters introduced, BUF leverages the multi-level features with different sizes of receptive fields to mitigate the background-bias problem. Moreover, the newly introduced segmentation head generates a foreground probability map as guidance for the network to focus on the foreground regions. The resulting foreground attention module (FAM) enhances the foreground features. Extensive experiments on PRW and CUHK-SYSU validate the effectiveness of the proposals. Our Bottom-Up Foreground-Aware Feature Fusion (BUFF) network achieves considerable gains over the state-of-the- arts on PRW and competitive performance on CUHK-SYSU.
FedAT: A Communication-Efficient Federated Learning Method with Asynchronous Tiers under Non-IID Data.

Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized. This form of collaborative learning exposes new tradeoffs among model convergence speed, model accuracy, balance across clients, and communication cost, with new challenges including: (1) straggler problem, where the clients lag due to data or (computing and network) resource heterogeneity, and (2) communication bottleneck, where a large number of clients communicate their local updates to a central server and bottleneck the server. Many existing FL methods focus on optimizing along only one dimension of the tradeoff space. Existing solutions use asynchronous model updating or tiering-based synchronous mechanisms to tackle the straggler problem. However, the asynchronous methods can easily create a network communication bottleneck, while tiering may introduce biases as tiering favors faster tiers with shorter response latencies. To address these issues, we present FedAT, a novel Federated learning method with Asynchronous Tiers under Non-i.i.d. data. FedAT synergistically combines synchronous intra-tier training and asynchronous cross-tier training. By bridging the synchronous and asynchronous training through tiering, FedAT minimizes the straggler effect with improved convergence speed and test accuracy. FedAT uses a straggler-aware, weighted aggregation heuristic to steer and balance the training for further accuracy improvement. FedAT compresses the uplink and downlink communications using an efficient, polyline-encoding-based compression algorithm, therefore minimizing the communication cost. Results show that FedAT improves the prediction performance by up to 21.09%, and reduces the communication cost by up to 8.5x, compared to state-of-the-art FL methods.
Optimal Subarchitecture Extraction For BERT

We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by applying recent breakthroughs in algorithms for neural architecture search. This optimal subset, which we refer to as "Bort", is demonstrably smaller, having an effective (that is, not counting the embedding layer) size of $5.5\%$ the original BERT-large architecture, and $16\%$ of the net size. Bort is also able to be pretrained in $288$ GPU hours, which is $1.2\%$ of the time required to pretrain the highest-performing BERT parametric architectural variant, RoBERTa-large (Liu et al., 2019), and about $33\%$ of that of the world-record, in GPU hours, required to train BERT-large on the same hardware. It is also $7.9$x faster on a CPU, as well as being better performing than other compressed variants of the architecture, and some of the non-compressed variants: it obtains performance improvements of between $0.3\%$ and $31\%$, absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks.
Brain Magnetic Resonance Image Tumor Detection and Segmentation Using Edgeless Active Contour

Tumor detection and segmentation from a brain Magnetic Resonance Imaging (MRI) slice is a task of identifying a case of tumor, and masking out the tumor tissue region. It becomes a challenging task due to the complex distribution of the gray-scale intensities in the brain MRI. Additionally, skull stripping has been an essential pre-processing step, which removes the non-brain regions from the image. The existing skull stripping methods suffer from low accuracy and high complexity problems due to the variation in skull regions, depending upon the MRI slices obtained on different planes. These problems are solved in our work by selecting a region of interest using an Active Contour(AC), and using the symmetry characteristics of the human brain anatomy. The proposed method uses AC at multiple steps, and it is fully automatic in both tumor detection and segmentation. The algorithm is validated on a dataset of 3064 T1-weighted contrast-enhanced MRI images with three kinds of brain tumors. The method records detection efficiencies of 81%, 56% and 71% in meningioma, glioma and pituitary tumor classes respectively. Further, it exhibits a higher tumor segmentation performance as compared to a recent AC-based brain tumor segmentation method in each of the tumor classes separately.
Hybrid Unscented Kalman Filter with Rare features for Underwater Target tracking using Passive Sonar Measurements

A Novel estimator called as Hybrid Unscented Kalman Filter(HUKF) is developed in the paper to tackle the issue of passive target tracking in underwater scenarios using bearing-only measurements (captured by a towed array). As the name indicates the algorithm is a hybrid one obtained by combining three existing algorithms namely UKF, Integration technique and Pre-processing mechanism to yield much better performance than any of them individually. The sensor noise reduction in spatial measurements caused by integration technique together with noise reduction in time measurements caused by Pre-processing mechanism in successive time iterations cumulatively contribute to the improvement in performance. Moreover the montecarlo simulations in Matlab(R2009a) provide evidence that, HUKF also display attractive features like Optimal Performance(produce less estimation errors at less computational complexity), Efficient Tracking in critical conditions(long ranges/severe noise) and Non-Divergence even with nasty initialization. Lastly the algorithm can be helpful in promoting TA usage on regular basis despite of the associated maneuvering issues.
Recognition and Effective Handling of Negations in Enhancing the Accuracy of Urdu Sentiment Analyzer

Although work has been done in Urdu Sentiment Analysis by researchers but still there is a lot of room for improvement in the form of achieving higher accuracy. Therefore, in this research, the accuracy of Urdu Sentiment Analysis in multiple domains is enhanced by dealing negations using Lexicon-based approach, one of the broadly used approaches for performing Sentiment Analysis. Negations in Urdu Sentiment Analysis are particularly focused in this research because of their effective role in Sentiment Analysis. Both local and long distance negations are considered. For achieving this goal, a corpus with 6025 Urdu sentences, from 151 blogs that belong to 14 different genres is taken in which use of negations is carefully observed. Two major steps are taken in this regard. First, to deal with the morphological negations, this type of negations is included in the negative word file of the Urdu Sentiment Lexicon developed for performing Sentiment Analysis of Urdu blogs. Secondly, rule-based approach is used for handling the implicit and explicit negations. Rules are designed that can deal with both implicit and explicit negations effectively. Implementation of these rules increased the accuracy of Sentiment Analyzer from 73.88% to 78.32% with 0.745, 0.788 and 0.745 Precision, Recall and Fmeasure respectively, which is statistically significant improvement.
Dynamics and control of entangled electron-photon states in nanophotonic systems with time-variable parameters

We study the dynamics of strongly coupled nanophotonic systems with time-variable parameters. The approximate analytic solutions are obtained for a broad class of open quantum systems including a two-level fermion emitter strongly coupled to a multimode quantized electromagnetic field in a cavity with time-varying cavity resonances or the electron transition energy. The coupling of the fermion and photon subsystems to their dissipative reservoirs is included within the stochastic equation of evolution approach, which is equivalent to the Lindblad approximation in the master equation formalism. The analytic solutions for the quantum states and the observables are obtained under the approximation that the rate of parameter modulation and the amplitude of the frequency modulation are much smaller than the optical transition frequencies. At the same time, they can be arbitrary with respect to the generalized Rabi oscillations frequency which determines the coherent dynamics. Therefore, our analytic theory can be applied to an arbitrary modulation of the parameters, both slower and faster than the Rabi frequency, for complete control of the quantum state. In particular, we demonstrate protocols for switching on and off the entanglement between the fermionic and photonic degrees of freedom, swapping between the quantum states, and the decoupling of the fermionic qubit from the cavity field due to modulation-induced transparency.