Reviewerly/exHarmony huggingface dataset:
Automatic Controllable Product Copywriting for E-Commerce

Automatic product description generation for e-commerce has witnessed significant advancement in the past decade. Product copy- writing aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. As the services provided by e-commerce platforms become diverse, it is necessary to adapt the patterns of automatically-generated descriptions dynamically. In this paper, we report our experience in deploying an E-commerce Prefix-based Controllable Copywriting Generation (EPCCG) system into the JD.com e-commerce product recommendation platform. The development of the system contains two main components: 1) copywriting aspect extraction; 2) weakly supervised aspect labelling; 3) text generation with a prefix-based language model; and 4) copywriting quality control. We conduct experiments to validate the effectiveness of the proposed EPCCG. In addition, we introduce the deployed architecture which cooperates the EPCCG into the real-time JD.com e-commerce recommendation platform and the significant payoff since deployment. The codes for implementation are provided at https://github.com/xguo7/Automatic-Controllable-Product-Copywriting-for-E-Commerce.git.
Adaptive Correlation Integration for Deep Image Clustering

Deep clustering methods often estimate data correlations to guide unsupervised learning. However, since different kinds of correlations capture different data characteristics, a strong data correlation may not be appropriate for every case. In this paper, we propose a novel Teacher-Student framework to adaptively estimate correlation and interactively learn a deep clustering model for various data distributions. Specifically, a teacher module mines and integrates various kinds of correlations from different perspectives. To adapt to various cases, we propose a novel Ad aptive I ntegration Gate (ADI-Gate) to selectively and dynamically integrate different data correlations in a teaching-feedback manner. Furthermore, a student module performs unsupervised clustering inference with the estimated correlation and provides a preference for teacher module. We also design a Pairwise-Weighted loss (PW-loss) to enhance high-confident correlation guidance of data pairs during the learning process of student module. In image clustering experiments on four public datasets, our model achieves consistent improvements over state-of-the-art models.
Optimizing Two-Way Partial AUC With an End-to-End Framework

The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive Rates (FPRs). Based on the knowledge that a skillful classifier should simultaneously embrace a high TPR and a low FPR, we turn to study a more general variant called Two-way Partial AUC (TPAUC), where only the region with <inline-formula><tex-math notation="LaTeX">$\mathsf {TPR} \geq \alpha, \mathsf {FPR} \leq \beta$</tex-math></inline-formula> is included in the area. Moreover, a recent work shows that the TPAUC is essentially inconsistent with the existing Partial AUC metrics where only the FPR range is restricted, opening a new problem to seek solutions to leverage high TPAUC. Motivated by this, we present the first trial in this article to optimize this new metric. The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss. To address this issue, we propose a generic framework to construct surrogate optimization problems, which supports efficient end-to-end training with deep learning. Moreover, our theoretical analyses show that: 1) the objective function of the surrogate problems will achieve an upper bound of the original problem under mild conditions, and 2) optimizing the surrogate problems leads to good generalization performance in terms of TPAUC with a high probability. Finally, empirical studies over several benchmark datasets speak to the efficacy of our framework.
Construction and Optimization of TRNG Based Substitution Boxes for Block Encryption Algorithms

Internet of Things is an ecosystem of interconnected devices that are accessible through the internet. The recent research focuses on adding more smartness and intelligence to these edge devices. This makes them susceptible to various kinds of security threats. These edge devices rely on cryptographic techniques to encrypt the pre-processed data collected from the sensors deployed in the field. In this regard, block cipher has been one of the most reliable options through which data security is accomplished. The strength of block encryption algorithms against different attacks is dependent on its nonlinear primitive which is called Substitution Boxes. For the design of S-boxes mainly algebraic and chaos-based techniques are used but researchers also found various weaknesses in these techniques. On the other side, literature endorse the true random numbers for information security due to the reason that, true random numbers are purely non-deterministic. In this paper firstly a natural dynamical phenomenon is utilized for the generation of true random numbers based S-boxes. Secondly, a systematic literature review was conducted to know which metaheuristic optimization technique is highly adopted in the current decade for the optimization of S-boxes. Based on the outcome of Systematic Literature Review (SLR), genetic algorithm is chosen for the optimization of s-boxes. The results of our method validate that the proposed dynamic S-boxes are effective for the block ciphers. Moreover, our results showed that the proposed substitution boxes achieve better cryptographic strength as compared with state-of-the-art techniques.
Instance Shadow Detection with A Single-Stage Detector

This paper formulates a new problem, instance shadow detection, which aims to detect shadow instance and the associated object instance that cast each shadow in the input image. To approach this task, we first compile a new dataset with the masks for shadow instances, object instances, and shadow-object associations. We then design an evaluation metric for quantitative evaluation of the performance of instance shadow detection. Further, we design a single-stage detector to perform instance shadow detection in an end-to-end manner, where the bidirectional relation learning module and the deformable maskIoU head are proposed in the detector to directly learn the relation between shadow instances and object instances and to improve the accuracy of the predicted masks. Finally, we quantitatively and qualitatively evaluate our method on the benchmark dataset of instance shadow detection and show the applicability of our method on light direction estimation and photo editing.
The Effect of Geometry Teaching Designed with the Developed Mobile Application on the Academic Achievement of the Students

In order to ensure active participation of students in the lessons during distance education period, different learning tools were sought. For this purpose, within framework of the ADDIE design model, it was desired to develop a mobile application called GeoHepta to be used in learning the 7th grade mathematics course Geometry and Measurement learning field subjects. The study was carried out in the distance education process with 7th grade students in the 2020-2021 academic year. The research was carried out with a quasi-experimental design. In the study, "Geometry and Measurement Learning Field Achievement Test" was used. As a result of the research, a significant difference was reached between the post-test scores of the students in the experimental group, in which the mobile application-supported teaching was implemented, and the control group students, in which the textbook-based teaching was implemented. While there was a significant difference between the pre-test post-test scores of the experimental group students, there was no significant difference between the pre-test post-test scores of the control group students.
Communication by Means of Thermal Noise: Toward Networks With Extremely Low Power Consumption

In this paper, the paradigm of thermal noise communication (TherCom) is put forward for future wired/wireless networks with extremely low power consumption.Taking backscatter communication (BackCom) and reconfigurable intelligent surface (RIS)-based radio frequency chain-free transmitters one step further, a thermal noise-driven transmitter might enable zero-signal-power transmission by simply indexing resistors or other noise sources according to information bits.This preliminary paper aims to shed light on the theoretical foundations, transceiver designs, and error performance derivations as well as optimizations of two emerging TherCom solutions: Kirchhofflaw-Johnson-noise (KLJN) secure bit exchange and wireless thermal noise modulation (TherMod) schemes.Our theoretical and computer simulation findings reveal that noise variance detection, supported by sample variance estimation with carefully optimized decision thresholds, is a reliable way of extracting the embedded information from noise modulated signals, even with limited number of noise samples.
Deterministic Gaussian conversion protocols for non-Gaussian single-mode resources

In the context of quantum technologies over continuous variables, Gaussian states and operations are typically regarded as freely available, as they are relatively easily accessible experimentally. In contrast, the generation of non-Gaussian states, as well as the implementation of non-Gaussian operations, pose significant challenges. This divide has motivated the introduction of resource theories of non-Gaussianity. As for any resource theory, it is of practical relevance to identify free conversion protocols between resources, namely Gaussian conversion protocols between non-Gaussian states. Via systematic numerical investigations, we address the approximate conversion between experimentally relevant single-mode non-Gaussian states via arbitrary deterministic one-to-one mode Gaussian maps. First, we show that cat and binomial states are approximately equivalent for finite energy, while this equivalence was previously known only in the infinite-energy limit. Then we consider the generation of cat states from photon-added and photon-subtracted squeezed states, improving over known schemes by introducing additional squeezing operations. The numerical tools that we develop also allow to devise conversions of trisqueezed into cubic-phase states beyond previously reported performances. Finally, we identify various other conversions which instead are not viable.
Convergence analysis of asynchronous stochastic recursive gradient algorithms

Asynchronous stochastic algorithms with variance reduction techniques have been empirically shown to be useful for many large-scale machine learning problems. By making a parallel optimization algorithm asynchronous, one can reduce the synchronization cost and improve the practical efficiency. Recently, the stochastic recursive gradient algorithm has shown superior theoretical performance; however, it is not scalable enough in the current big data era. To make it more practical, we propose a class of asynchronous stochastic recursive gradient methods and analyze them in the shared memory model. The analysis results show that our asynchronous algorithms can linearly converge to the solution in the strongly convex case and complete the iteration faster. In addition, we analyze the “price of asynchrony” and give the sufficient conditions required for linear speedup. To the best of our knowledge, our speedup conditions match the optimal bounds of asynchronous stochastic algorithms known thus far. Finally, we demonstrate our theoretical analyses from a practical implementation on a multicore machine.
Techniques of medical image encryption taxonomy

Medical images are one of the most significant and sensitive data types in computer systems. Sending medical images over the internet necessitates using a robust encryption scheme that is resistant to cryptographic attacks. Confidentiality is the most critical part of the three security objectives for information systems security, namely confidentiality, integrity, and availability. Confidentiality is the most critical aspect for the secure storage and transfer of medical images. In this study, we attempt to classify various encryption methods in order to assist researchers in selecting the optimal strategy for protecting sensitive patient information while transferring medical images without alteration and outline the measures that should be adopted to address challenges and concerns relevant to techniques of medical image encryption.
Post-OCR Document Correction with Large Ensembles of Character Sequence-to-Sequence Models

In this paper, we propose a novel method to extend sequence-to-sequence models to accurately process sequences much longer than the ones used during training while being sample- and resource-efficient, supported by thorough experimentation. To investigate the effectiveness of our method, we apply it to the task of correcting documents already processed with Optical Character Recognition (OCR) systems using sequence-to-sequence models based on characters. We test our method on nine languages of the ICDAR 2019 competition on post-OCR text correction and achieve a new state-of-the-art performance in five of them. The strategy with the best performance involves splitting the input document in character n-grams and combining their individual corrections into the final output using a voting scheme that is equivalent to an ensemble of a large number of sequence models. We further investigate how to weigh the contributions from each one of the members of this ensemble. Our code for post-OCR correction is shared at https://github.com/jarobyte91/post_ocr_correction.
Cross-Modal Federated Human Activity Recognition via Modality-Agnostic and Modality-Specific Representation Learning

In this paper, we propose a new task of cross-modal federated human activity recognition (CMF-HAR), which is conducive to promote the large-scale use of the HAR model on more local devices. To address the new task, we propose a feature-disentangled activity recognition network (FDARN), which has five important modules of altruistic encoder, egocentric encoder, shared activity classifier, private activity classifier and modality discriminator. The altruistic encoder aims to collaboratively embed local instances on different clients into a modality-agnostic feature subspace. The egocentric encoder aims to produce modality-specific features that cannot be shared across clients with different modalities. The modality discriminator is used to adversarially guide the parameter learning of the altruistic and egocentric encoders. Through decentralized optimization with a spherical modality discriminative loss, our model can not only generalize well across different clients by leveraging the modality-agnostic features but also capture the modality-specific discriminative characteristics of each client. Extensive experiment results on four datasets demonstrate the effectiveness of our method.
Optimal instance subset selection from big data using genetic algorithm and open source framework

Abstract Data is accumulating at an incredible rate, and the era of big data has arrived. Big data brings great challenges to traditional machine learning algorithms, it is difficult for learning tasks in big data scenario to be completed on stand-alone. Data reduction is an effective way to solve this problem. Data reduction includes attribute reduction and instance reduction. In this study, we focus on instance reduction also called instance selection, and view the instance selection as an optimal instance subset selection problem. Inspired by the ideas of cross validation and divide and conquer, we defined a novel criterion called combined information entropy with respect to a set of classifiers to measure the importance of an instance subset, the criterion uses multiple independent classifiers trained on different subsets to measure the optimality of an instance subset. Based on the criterion, we proposed an approach which uses genetic algorithm and open source framework to select optimal instance subset from big data. The proposed algorithm is implemented on two open source big data platforms Hadoop and Spark, the conducted experiments on four artificial data sets demonstrate the feasibility of the proposed algorithm and visualize the distribution of selected instances, and the conducted experiments on four real data sets compared with three closely related methods on test accuracy and compression ratio demonstrate the effectiveness of the proposed algorithm. Furthermore, the two implementations on Hadoop and Spark are also experimentally compared. The experimental results show that the proposed algorithm provides excellent performance and outperforms the three methods.
Can We use Authentic Learning to Educate Students about Secure Infrastructure as Code Development?

Despite yielding benefits for organizations, infrastructure as code (IaC) scripts are susceptible to security weaknesses, such as hard-coded passwords. Existence of such security weaknesses necessitate integration of education materials related to secure development of IaC scripts. In this preliminary work, we describe our experiences of how application of authentic learning helped students learn about secure development of IaC scripts. Our paper shows education materials based on authentic learning to help students learn about secure IaC development.
The dynamic relationship of cryptocurrencies with supply chain and logistics stocks – the impact of COVID-19

Purpose The COVID-19 pandemic is known to have affected the logistics and supply chains; however, there is no adequate empirical evidence to prove in which way it has affected the relationship between the stocks related to this field with the corresponding cryptocurrencies. This paper aims to test the dynamic relationship of cryptocurrencies with supply chain and logistics stocks. Design/methodology/approach In this paper, the author tests the causal and long-run relationship between logistics and supply chain stocks with the corresponding cryptocurrencies related to these fields, or those that are known to exhibit characteristics that can be utilized by these fields, testing also whether the COVID-19 pandemic affected this relationship. To do so, the author performs the variable-lag causality to test the causal relationship, and examines if this relationship changed due to COVID-19. The author then implements the multifractal detrended cross-correlation analysis to investigate the characteristics of a possible long-run relationship, testing also whether they changed due to COVID-19. Findings The results indicate that there is a positive long-run relationship between each logistics and supply chain stocks and the corresponding cryptocurrencies, before and also during COVID-19, but during COVID-19 this relationship becomes weaker, in most cases. Moreover, before COVID-19, the majority of the cases indicate a causal direction from cryptocurrencies to the stocks, while during COVID-19, the causal relationships decrease in multitude, and most cases unveil a causal direction from the stocks to cryptocurrencies. Originality/value The causal pattern changed during COVID-19, and the long-run relationship became weaker, showing a change in the dynamics in the relationship between logistics and supply chain stocks with cryptocurrencies.
Twisted hybrid algorithms for combinatorial optimization

Abstract Proposed hybrid algorithms encode a combinatorial cost function into a problem Hamiltonian and optimize its energy by varying over a set of states with low circuit complexity. Classical processing is typically only used for the choice of variational parameters following gradient descent. As a consequence, these approaches are limited by the descriptive power of the associated states. We argue that for certain combinatorial optimization problems, such algorithms can be hybridized further, thus harnessing the power of efficient non-local classical processing. Specifically, we consider combining a quantum variational ansatz with a greedy classical post-processing procedure for the MaxCut-problem on three-regular graphs. We show that the average cut-size produced by this method can be quantified in terms of the energy of a modified problem Hamiltonian. This motivates the consideration of an improved algorithm which variationally optimizes the energy of the modified Hamiltonian. We call this a twisted hybrid algorithm since the additional classical processing step is combined with a different choice of variational parameters. We exemplify the viability of this method using the quantum approximate optimization algorithm (QAOA), giving analytic lower bounds on the expected approximation ratios achieved by twisted QAOA. We observe that for levels p = 1, …, 5, these lower bounds are comparable to the known lower bounds on QAOA at level p + 1 for high-girth graphs. This suggests that using twisted QAOA can reduce the circuit depth by 4 and the number of variational parameters by 2.
A computational model of Ostrom's Institutional Analysis and Development framework

The Institutional Analysis and Development (IAD) framework developed by Elinor Ostrom and colleagues provides great conceptual clarity on the immensely varied topic of social interactions. In this work, we propose a computational model to examine the impact that any of the variables outlined in the IAD framework has on the resulting social interactions. Of particular interest are the rules adopted by a community of agents, as they are the variables most susceptible to change in the short term. To provide systematic descriptions of social interactions, we define the Action Situation Language (ASL) and provide a game engine capable of automatically generating formal game-theoretical models out of ASL descriptions. Then, by incorporating any agent decision-making models, the connection from a rule configuration description to the outcomes encouraged by it is complete. Overall, our model enables any community of agents to perform what-if analysis, where they can foresee and examine the impact that a set of regulations will have on the social interaction they are engaging in. Hence, they can decide whether their implementation is desirable.
HistoML, a markup language for representation and exchange of histopathological features in pathology images

The study of histopathological phenotypes is vital for cancer research and medicine as it links molecular mechanisms to disease prognosis. It typically involves integration of heterogenous histopathological features in whole-slide images (WSI) to objectively characterize a histopathological phenotype. However, the large-scale implementation of phenotype characterization has been hindered by the fragmentation of histopathological features, resulting from the lack of a standardized format and a controlled vocabulary for structured and unambiguous representation of semantics in WSIs. To fill this gap, we propose the Histopathology Markup Language (HistoML), a representation language along with a controlled vocabulary (Histopathology Ontology) based on Semantic Web technologies. Multiscale features within a WSI, from single-cell features to mesoscopic features, could be represented using HistoML which is a crucial step towards the goal of making WSIs findable, accessible, interoperable and reusable (FAIR). We pilot HistoML in representing WSIs of kidney cancer as well as thyroid carcinoma and exemplify the uses of HistoML representations in semantic queries to demonstrate the potential of HistoML-powered applications for phenotype characterization.