Reviewerly/exHarmony huggingface dataset:
Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images

Computational image analysis is one means for evaluating digitized histopathology specimens that can increase the reproducibility and reliability with which cancer diagnoses are rendered while simultaneously providing insight as to the underlying mechanisms of disease onset and progression. A major challenge that is confronted when analyzing samples that have been prepared at disparate laboratories and institutions is that the algorithms used to assess the digitized specimens often exhibit heterogeneous staining characteristics because of slight differences in incubation times and the protocols used to prepare the samples. Unfortunately, such variations can render a prediction model learned from one batch of specimens ineffective for characterizing an ensemble originating from another site. In this work, we propose to adopt unsupervised domain adaptation to effectively transfer the discriminative knowledge obtained from any given source domain to the target domain without requiring any additional labeling or annotation of images at the target site. In this paper, our team investigates the use of two approaches for performing the adaptation: (1) color normalization and (2) adversarial training. The adversarial training strategy is implemented through the use of convolutional neural networks to find an invariant feature space and Siamese architecture within the target domain to add a regularization that is appropriate for the entire set of whole-slide images. The adversarial adaptation results in significant classification improvement compared with the baseline models under a wide range of experimental settings.
Scale Driven Convolutional Neural Network Model for People Counting and Localization in Crowd Scenes

Counting and localization of people in videos consisting of low density to high density crowds encounter many key challenges including complex backgrounds, scale variations, nonuniform distributions, and occlusions. For this purpose, we propose a scale driven convolutional neural network (SD-CNN) model, which is based on the assumption that heads are the dominant and visible features regardless of the density of crowds. To deal with the problem of different scales of heads in different regions of the videos, we annotate a set of heads in random locations of the videos to develop a scale map representing the mapping of head sizes. We then extract scale aware proposals based on the scale map which are fed to the SD-CNN model acting as a head detector. Our model provides a response matrix rendering accurate head positions via nonmaximal suppression. For experimental evaluations, we consider three standard datasets presenting low density to high density crowd scenes. Our proposed SD-CNN model outperforms the state-of-the-art methods in terms of both frame-level and pixel-level analyses.
Extracting Resource Needs and Availabilities From Microblogs for Aiding Post-Disaster Relief Operations

Microblogging sites like Twitter are the important sources of real-time information during disaster/emergency events. During such events, the critical situational information posted is immersed in a lot of conversational content; hence, reliable methodologies are needed for extracting the meaningful information. In this paper, we focus on a particular application that is critical for efficient management of post-disaster relief operations - identifying tweets that inform about resource needs and resource availabilities. Two broad types of methodologies can be practically applied to identify such tweets during an ongoing disaster event: 1) supervised classification approaches, where the classifier models are trained on microblogs posted during prior events and applied on those posted during the ongoing event and 2) unsupervised pattern matching and information retrieval approaches that can be directly applied on the microblogs posted during the ongoing event. In this paper, we experiment with several supervised and unsupervised approaches to address the problem, including several neural network-based classification and retrieval models. We also propose two novel neural retrieval models (unsupervised) for the said application, which effectively combine word-level embeddings and character-level embeddings. We conduct experiments on tweets posted during two disaster events and observe that the two approaches perform well in different scenarios. Specifically, if good quality training data are available from prior events, then classification approaches perform better; however, if such training data are not available, then unsupervised retrieval methods outperform supervised classification approaches.
Mobile phones, gender, and female empowerment in sub-Saharan Africa: studies with African youth

ABSTRACTData from qualitative and survey research with young people in 24 locations (urban and rural) across Ghana, Malawi, and South Africa expose the complex interplay between phone ownership and usage, female empowerment, and chronic poverty in Africa. We consider gendered patterns of phone ownership and use before examining practices of use in educational settings, in business and in romantic and sexual relationships. While some reshaping of everyday routines is evident, in the specific context of female empowerment we find little support within our sites for the concept of the mobile phone as an instrument of positive transformative change. The phone's application in romantic and sexual relationships demonstrates particularly strongly the way phones are complicit in constraining women's empowerment and points to potential wider repercussions, including for educational and entrepreneurship trajectories. Women's agency is still mired within wider structures of patriarchy and chronic poverty: existing inequalities are being re-inscribed and reinforced.
Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking

Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches now use sequence models to solve this problem but their training can be affected by biases that decrease their efficiency. In this paper, we introduce a new training procedure that confronts the algorithm to its own mistakes while explicitly attempting to minimize the number of switches, which results in better training. We propose an iterative scheme of building a rich training set and using it to learn a scoring function that is an explicit proxy for the target tracking metric. Whether using only simple geometric features or more sophisticated ones that also take appearance into account, our approach outperforms the state-of-the-art on several MOT benchmarks.
Hierarchy Strengthened Grey Wolf Optimizer for Numerical Optimization and Feature Selection

Grey wolf optimizer (GWO) is an efficient swarm intelligence algorithm for kinds of optimization problems. However, GWO tends to be trapped in local optimum when solving large-scale problems. Social hierarchy is one of the main characteristics of GWO which affect the searching efficiency. Thus, an improved algorithm called hierarchy strengthened GWO (HSGWO) is proposed in this paper. First, the pack of wolves is roughly divided into two categories: dominant wolves and omega wolves. Second, the enhanced elite learning strategy is performed for dominant wolves to prevent the misguidance of low-ranking wolves and improve the collective efficiency. Then, the hybrid GWO and differential evolution (DE) strategy is executed for omega wolves to avoid falling into local optimum. In addition, a new hybrid one-dimensional and total-dimensional selection strategy is designed for omega wolves to balance the exploration and the exploitation during optimization. Finally, a perturbed operator is used to maintain the diversity of the population and further improve the exploration. To make a complete evaluation, the proposed HSGWO is first compared with six representative GWO variants for 50-dimensional problems based on CEC2014 benchmarks. The scalability of HSGWO is further tested by comparing it with eight state-of-the-art non-GWO algorithms for large-scale optimization problems with 100 decision variables. In addition, feature selection problem is used for testing the effectiveness of HSGWO on real-world applications. The experimental results demonstrated that the proposed algorithm outperforms other algorithms in terms of solution quality and convergence rate in most of the experiments.
Sets2Sets

Given past sequential sets of elements, predicting the subsequent sets of elements is an important problem in different domains. With the past orders of customers given, predicting the items that are likely to be bought in their following orders can provide information about the future purchase intentions. With the past clinical records of patients at each visit to the hospitals given, predicting the future clinical records in the subsequent visits can provide information about the future disease progression. These useful information can help to make better decisions in different domains. However, existing methods have not studied this problem well. In this paper, we formulate this problem as a sequential sets to sequential sets learning problem. We propose an end-to-end learning approach based on an encoder-decoder framework to solve the problem. In the encoder, our approach maps the set of elements at each past time step into a vector. In the decoder, our method decodes the set of elements at each subsequent time step from the vectors with a set-based attention mechanism. The repeated elements pattern is also considered in our method to further improve the performance. In addition, our objective function addresses the imbalance and correlation existing among the predicted elements. The experimental results on three real-world data sets showthat our method outperforms the best performance of the compared methods with respect to recall and person-wise hit ratio by 2.7-20.6% and 2.1-26.3%, respectively. Our analysis also shows that our decoder has good generalization to output sequential sets that are even longer than the output of training instances.
How enhancing gender inclusion affects inequality: Thresholds of complementary policies for sustainable development

Abstract This study investigates how enhancing gender inclusion affects inequality in 42 African countries for the period 2004–2014. The empirical evidence is based on the generalised method of moments. Three inequality indicators are used, namely, the Gini coefficient, Atkinson index, and Palma ratio. The two gender inclusion measurements used include female labour force participation and female employment. The following main findings are established. There are positive net effects on inequality from the enhancement of gender inclusion dynamics. An extended threshold analysis is used to assess critical masses at which further increasing gender inclusion enhances inequality. The established thresholds are as follows: (a) 55.555 “employment to population ratio, 15+, female (%)” for the nexus with the Gini coefficient. (b) 50 “labour force participation rate, female (% of female population ages 15+)” and between 50 and 55 “employment to population ratio, 15+, female (%)”, for the Atkinson index. (c) 61.87 “labour force participation rate, female (% of female population ages 15+)” for the Palma ratio. These established thresholds are worthwhile for sustainable development because, beyond the critical masses, policymakers should complement the gender inclusion policy with other measures designed to reduce income inequality. Some complementary measures that can be taken on board beyond the established thresholds could focus on enhancing, inter alia, information and communication technology, infrastructural development, financial inclusion, and inclusive education.
High-Capacity Convolutional Video Steganography with Temporal Residual Modeling

Steganography represents the art of unobtrusively concealing a secret message within some cover data. The key scope of this work is about high-capacity visual steganography techniques that hide a full-sized color video within another. We empirically validate that high-capacity image steganography model doesn't naturally extend to the video case for it completely ignores the temporal redundancy within consecutive video frames. Our work proposes a novel solution to this problem(i.e., hiding a video into another video). The technical contributions are two-fold: first, motivated by the fact that the residual between two consecutive frames is highly-sparse, we propose to explicitly consider inter-frame residuals. Specifically, our model contains two branches, one of which is specially designed for hiding inter-frame residual into a cover video frame and the other hides the original secret frame. And then two decoders are devised, revealing residual or frame respectively. Secondly, we develop the model based on deep convolutional neural networks, which is the first of its kind in the literature of video steganography. In experiments, comprehensive evaluations are conducted to compare our model with classic steganography methods and pure high-capacity image steganography models. All results strongly suggest that the proposed model enjoys advantages over previous methods. We also carefully investigate our model's security to steganalyzer and the robustness to video compression.
Multiple Robustness Enhancements for Image Adaptive Steganography in Lossy Channels

Considering that traditional image steganography technologies suffer from the potential risk of failure under lossy channels, an enhanced adaptive steganography with multiple robustness against image processing attacks is proposed, while maintaining good detection resistance. First, a robust domain constructing method is proposed utilizing robust element extraction and optimal element modification, which can be applied to both spatial and JPEG images. Then, a robust steganography is proposed based on “Robust Domain Constructing + RS-STC Codes,” combined with cover selection, robust cover extraction, message coding, and embedding with minimized costs. In addition, to provide a theoretical basis for message extraction integrity, the fault tolerance of the proposed algorithm is deduced using error model based on burst errors and decoding damage. Finally, on the basis of parameter discussion about robust domain construction, performance experiments are conducted, and the recommended coding parameters are given for lossy channels with different attacks using the analytic results for fault tolerance. A series of experimental results demonstrate that the proposed algorithm can extract embedded messages with significantly higher accuracy after different attacks, such as compression, noising, scaling and other attacks, compared with the state-of-the-art adaptive steganography, and robust watermarking algorithms, while maintaining good detection resistant performance.
Central Bank Digital Currency: Financial System Implications and Control

IT progress and its application to the financial industry have inspired central banks and academics to reflect about the merits of central bank digital currencies (CBDC) accessible to the broad public. This paper first briefly recalls the advantages that have been associated with CBDC and reviews some relevant background from the history of the issuance of different forms of central bank money. It then discusses two key arguments against CBDC, namely (i) risk of structural disintermediation of banks and centralization of the credit allocation process within the central bank and (ii) risk of facilitation systemic runs on banks in crisis situations. The paper proposes as solution a two-tier remuneration of CBDC, as a tested and simple tool to control the quantity of CBDC both in normal and crisis times. It is, however, also acknowledged that controlling the quantity of CBDC is not necessarily sufficient to control its impact on the financial system. Finally, the paper compares the financial account implications of CBDC with the one of crypto assets, stable coins, and narrow bank digital money, noting the similarity and differences in terms of implications on the financial system. It is concluded that well-controlled CBDC seems feasible, without this implying that CBDC would not catalyze change in the financial system.
When Code Completion Fails: A Case Study on Real-World Completions

Code completion is commonly used by software developers and is integrated into all major IDE's. Good completion tools can not only save time and effort but may also help avoid incorrect API usage. Many proposed completion tools have shown promising results on synthetic benchmarks, but these benchmarks make no claims about the realism of the completions they test. This lack of grounding in real-world data could hinder our scientific understanding of developer needs and of the efficacy of completion models. This paper presents a case study on 15,000 code completions that were applied by 66 real developers, which we study and contrast with artificial completions to inform future research and tools in this area. We find that synthetic benchmarks misrepresent many aspects of real-world completions; tested completion tools were far less accurate on real-world data. Worse, on the few completions that consumed most of the developers' time, prediction accuracy was less than 20% -- an effect that is invisible in synthetic benchmarks. Our findings have ramifications for future benchmarks, tool design and real-world efficacy: Benchmarks must account for completions that developers use most, such as intra-project APIs; models should be designed to be amenable to intra-project data; and real-world developer trials are essential to quantifying performance on the least predictable completions, which are both most time-consuming and far more typical than artificial data suggests. We publicly release our preprint [https://doi.org/10.5281/zenodo.2565673] and replication data and materials [https://doi.org/10.5281/zenodo.2562249].
Novel stochastic methods to predict short-term solar radiation and photovoltaic power

Solar forecasting has evolved towards becoming a key component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents two novel stochastic forecasting models for solar PV by utilizing historical measurement data to outline a short-term high-resolution probabilistic behavior of solar. First, an uncertain basis functions method is used to forecast both solar radiation and PV power. Three possible distributions are considered for the uncertain basis functions - Gaussian, Laplace, and Uniform distributions. Second, stochastic state-space models are applied to characterize the behaviors of solar radiation and PV power output. A filter-based expectation-maximization and Kalman filtering mechanism is employed to recursively estimate the system parameters and state variables. This enables the system to accurately forecast small as well as large fluctuations of the solar signals. The introduced forecasting models are suitable for real-time tertiary dispatch controllers and optimal power controllers. The PV forecasting models are tested using solar radiation and PV power measurement data collected from a 13.5 kW PV panel installed on the rooftop of our laboratory. The results are compared with standard time series forecasting mechanisms and show a substantial improvement in the forecasting accuracy of the total energy produced.
Squeezed Light Induced Symmetry Breaking Superradiant Phase Transition

We theoretically investigate the quantum phase transition in the collective systems of qubits in a high quality cavity, where the cavity field is squeezed via the optical parametric amplification process. We show that the squeezed light induced symmetry breaking can result in quantum phase transition without the ultrastrong coupling requirement. Using the standard mean field theory, we derive the condition of the quantum phase transition. Surprisingly, we show that there exists a tricritical point where the first- and second-order phase transitions meet. With specific atom-cavity coupling strengths, both the first- and second-order phase transition can be controlled by the nonlinear gain coefficient, which is sensitive to the pump field. These features also lead to an optical switching from the normal phase to the superradiant phase by just increasing the pump field intensity. The signature of these phase transitions can be observed by detecting the phase space Wigner function distribution with different profiles controlled by the squeezed light intensity. Such superradiant phase transition can be implemented in various quantum systems, including atoms, quantum dots, and ions in optical cavities as well as the circuit quantum electrodynamics system.
An efficient attribute-based online/offline searchable encryption and its application in cloud-based reliable smart grid

Recently the cloud computing popularity is becoming high, and most of the organizations turned to outsource their sensitive data and maintenance to the cloud server. However, outsourcing sensitive data to the cloud raises both data security and confidentiality question. Consequently, encryption techniques play an important role in securing data within the cloud. Although outsourcing data in an encrypted form protect the data, searching and data retrieving encrypted data is a challenge. Therefore, to facilitate the burden of working on encrypted data, this paper introduces an attribute-based online/offline searchable encryption scheme with the following contributions: First, encryption and trapdoor algorithms separated into two phases. Second, both the message encryption and attribute control policy are performed at offline phase. Third, we prove that the proposed scheme is secure against both chosen plaintext and chosen keyword attacks. Finally, we explain the applicability of the proposed scheme in cloud-based smart grid. Cloud computing, due to its nature, needs adaptive scheme to tackle some of its challenges.
Optimized Ensemble Extreme Learning Machine for Classification of Electrical Insulators Conditions

The classification of distinct problems of insulators in the distribution networks is a task that requires operator's experience. The applications of techniques to automate the inspection of electrical systems with the objective of detecting faults in insulators have shown to be reasonable alternatives to improve reliability in power grid. In this paper, based on the development of an experimental setup, signals are acquired considering three distinct faults in insulators. In this case, 13.8 kV (rms) is applied in drilled, contaminated, and good insulators considering an ultrasound detector connected to a computer. In the sequence, a multiclass classification method is proposed considering the ensemble of classifiers. The method considers the association of five distinct techniques, Bottom-Up segmentation, wavelet energy coefficient, principal component analysis, and particle swarm optimization associated with ensemble extreme learning machine (EN-ELM). Named optimized ensemble extreme learning machine, the present approach outperforms the original EN-ELM method. Finally, results show significant increase in robustness and faster training procedure when compared to classical approaches.