Reviewerly/exHarmony huggingface dataset:
Beyond Triplet Loss: Meta Prototypical N-Tuple Loss for Person Re-identification

Person Re-identification (ReID) aims at matching a person of interest across images. In convolutional neural network (CNN) based approaches, loss design plays a vital role in pulling closer features of the same identity and pushing far apart features of different identities. In recent years, triplet loss achieves superior performance and is predominant in ReID. However, triplet loss considers only three instances of two classes in per-query optimization (with an anchor sample as query) and it is actually equivalent to a two-class classification. There is a lack of loss design which enables the joint optimization of multiple instances (of multiple classes) within per-query optimization for person ReID. In this paper, we introduce a multi-class classification loss, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> ., N-tuple loss, to jointly consider multiple ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> ) instances for per-query optimization. This in fact aligns better with the ReID test/inference process, which conducts the ranking/comparisons among multiple instances. Furthermore, for more efficient multi-class classification, we propose a new meta prototypical N-tuple loss. With the multi-class classification incorporated, our model achieves the state-of-the-art performance on the benchmark person ReID datasets
Retrofitting Typestates into Rust

As software permeates our lives, bugs become increasingly expensive; the best way to reduce their cost is to reduce the number of bugs. Of course, this is easier said than done and, at best, we can go after their root causes to mitigate them. One of such causes is state, whether it is the state of a light bulb (i.e. on/off), or the state of a complex protocol, reasoning about state is a complex process which developers are required to do with subpar tools.
A Discrete Differential Evolution Algorithm for Extractive Text Summarization

With the exponential growth of textual data on the web, automatic text summarization has become a key task in many natural language processing applications. Many works have been proposed to solve it, but the problem is still far from being totally solved. In this work, we propose a new extractive text summarizer that uses a discrete differential evolution algorithm. It looks for the best set of sentences to be put in the summary, in addition to the best order to be respected. To evaluate the performances of our approach, we used as benchmarks the DUC2001 and DUC2002 collections and opted for the ROUGE evaluation toolkit. The proposed approach is compared against eleven state-of-the-art methods. The obtained results show that our approach is very competitive. It outperforms many state-of-the-art methods; it ranks first out of 12 methods.
Bayesian Atlas Building with Hierarchical Priors for Subject-Specific Regularization

This paper presents a novel hierarchical Bayesian model for unbiased atlas building with subject-specific regularizations of image registration. We develop an atlas construction process that automatically selects parameters to control the smoothness of diffeomorphic transformation according to individual image data. To achieve this, we introduce a hierarchical prior distribution on regularization parameters that allows multiple penalties on images with various degrees of geometric transformations. We then treat the regularization parameters as latent variables and integrate them out from the model by using the Monte Carlo Expectation Maximization (MCEM) algorithm. Another advantage of our algorithm is that it eliminates the need for manual parameter tuning, which can be tedious and infeasible. We demonstrate the effectiveness of our model on 3D brain MR images. Experimental results show that our model provides a sharper atlas compared to the current atlas building algorithms with single-penalty regularizations. Our code is publicly available at https://github.com/jw4hv/HierarchicalBayesianAtlasBuild.
Vision Object-Oriented Augmented Sampling-Based Autonomous Navigation for Micro Aerial Vehicles

Autonomous navigation of micro aerial vehicles in unknown environments not only requires exploring their time-varying surroundings, but also ensuring the complete safety of flights at all times. The current research addresses estimation of the potential exploration value neglect of safety issues, especially in situations with a cluttered environment and no prior knowledge. To address this issue, we propose a vision object-oriented autonomous navigation method for environment exploration, which develops a B-spline function-based local trajectory re-planning algorithm by extracting spatial-structure information and selecting temporary target points. The proposed method is evaluated in a variety of cluttered environments, such as forests, building areas, and mines. The experimental results show that the proposed autonomous navigation system can effectively complete the global trajectory, during which an appropriate safe distance could always be maintained from multiple obstacles in the environment.
Guidelines for Promoting Software Product Line Experiments

The importance of experimentation for Software Engineering research has been notably established in the last years. The software engineering community has discussed how to proper report and evaluate experiments using different approaches, such as quality criteria, scales, and checklists. Nevertheless, there are no guidelines to support researchers and practitioners active in straightforward software engineering research areas, as in Software Product Lines (SPL), at conducting experiments. We hypothesize that experimentation guidelines may aid such a specific area by providing advice and actual excerpts reflecting good practices of SPL experimentation, thus experimentally evolving this area. Therefore, the goal of this paper is to provide guidelines for properly reporting and promoting SPL experiments. We defined such guidelines based on well-known software engineering experiment reports, quality evaluation checklists, and data extracted from 211 SPL experiments identified in a systematic mapping study. We evaluated the guidelines with a qualitative study with SPL and experimentation experts applying open and axial coding procedures. The evaluation enabled us to improve the guidelines. The resulting guidelines contain specific advice to researchers active in SPL and provide examples taken from published SPL experiments. The experts’ positive points indicate that the proposed guidelines can aid SPL researchers and practitioners. Sharing the resulting guidelines could support conducting SPL experiments and allow further area evolution based on prospective experiment replications and reproductions from well-designed and reported experiments.
Design, Analysis and Experiment of a Bridge-Type Piezoelectric Actuator for Infrared Image Stabilization

Piezoelectric actuators are widely used in the optical field due to their high precision, compact structure, flexible design, and fast response. This paper presents a novel piezoelectric actuator with a bridge-type mechanism, which can be used to stabilize the images of an infrared imaging system. The bridge amplification mechanism is used to amplify the actuation displacement, and its structural parameters are optimized by the response surface method. The control strategy of the image stabilization system is formulated, and the overall structure of the infrared image stabilization system is designed according to the principle of image stabilization and the control strategy. The prototype was fabricated and verified by a series of experiments. In the test, the laminated piezoelectric ceramics are used as the driving element, and its maximum output displacement was about 17 μm under a voltage of 100 V. Firstly, the performance of the piezoelectric amplification mechanism was tested, and the maximum displacement of the piezoelectric micro-motion mechanism was 115 μm. The displacement amplification ratio of the mechanism was 5.7. Then, the step distance and response time of the micro-displacement mechanism were measured by inputting the stepping signal. When the input voltage increased to 3 V, 5 V, and 7 V, the stepping displacements of the mechanism were 2.4 μm, 4.1 μm, and 5.8 μm. Finally, the image stabilization effect of the designed mechanism was verified by imaging timing control and feedback signal processing.
Network analysis on Bitcoin arbitrage opportunities

Many studies have shown that investment in Bitcoin could serve to diversify the stock market. The existing literature offers insight into how arbitrage trading may diminish the risks associated with cryptocurrencies. Using arbitrage data and a novel methodology, this paper uncovers pertinent insights for investors interested in hedging against losses. Evidence points to the dsx, kraken, and bitstamp exchanges as the most attractive for buying Bitcoin and the cexio, bitmarketlt, and coindeal exchanges as the most attractive for selling Bitcoin. We employ network analysis to explore the interrelationships in thirteen cryptocurrency exchanges, providing evidence that kraken and bitstamp are leaders in market-forming trends, while coindeal and dsx serve as intermediary exchanges. Overall, these findings show that investors can mitigate their trading risks by knowing precisely where to buy and sell Bitcoin and which exchanges offer arbitrage opportunities. Using these results, investors can develop long-term and algorithmic trading strategies.
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models

.We consider the problem of high-dimensional filtering of state-space models (SSMs) at discrete times. This problem is particularly challenging as analytical solutions are typically not available and many numerical approximation methods can have a cost that scales exponentially with the dimension of the hidden state. Inspired by lag-approximation methods for the smoothing problem [G. Kitagawa and S. Sato, Monte Carlo smoothing and self-organising state-space model, in Sequential Monte Carlo Methods in Practice, Springer, New York, 2001, pp. 178–195; J. Olsson et al., Bernoulli, 14 (2008), pp. 155–179], we introduce a lagged approximation of the smoothing distribution that is necessarily biased. For certain classes of SSMs, particularly those that forget the initial condition exponentially fast in time, the bias of our approximation is shown to be uniformly controlled in the dimension and exponentially small in time. We develop a sequential Monte Carlo (SMC) method to recursively estimate expectations with respect to our biased filtering distributions. Moreover, we prove for a class of SSMs that can contain dependencies amongst coordinates that as the dimension \(d\rightarrow \infty\) the cost to achieve a stable mean square error in estimation, for classes of expectations, is of \(\mathcal{O}(Nd^2)\) per unit time, where \(N\) is the number of simulated samples in the SMC algorithm. Our methodology is implemented on several challenging high-dimensional examples including the conservative shallow-water model.Keywordsfilteringsequential Monte Carlolag approximationshigh-dimensional particle filterMSC codes62M2060G3560J2060J1094A1293E11
dhCM: Dynamic and Hierarchical Event Categorization and Discovery for Social Media Stream

The online event discovery in social media based documents is useful, such as for disaster recognition and intervention. However, the diverse events incrementally identified from social media streams remain accumulated, ad hoc, and unstructured. They cannot assist users in digesting the tremendous amount of information and finding their interested events. Further, most of the existing work is challenged by jointly identifying incremental events and dynamically organizing them in an adaptive hierarchy. To address these problems, this article proposes d ynamic and h ierarchical C ategorization M odeling (dhCM) for social media stream. Instead of manually dividing the timeframe, a multimodal event miner exploits a density estimation technique to continuously capture the temporal influence between documents and incrementally identify online events in textual, temporal, and spatial spaces. At the same time, an adaptive categorization hierarchy is formed to automatically organize the documents into proper categories at multiple levels of granularities. In a nonparametric manner, dhCM accommodates the increasing complexity of data streams with automatically growing the categorization hierarchy over adaptive growth. A sequential Monte Carlo algorithm is used for the online inference of the dhCM parameters. Extensive experiments show that dhCM outperforms the state-of-the-art models in terms of term coherence, category abstraction and specialization, hierarchical affinity, and event categorization and discovery accuracy.
Materialities of care for older people: caring together/apart in the political economy of caring apparatus

By applying a posthuman perspective to the analysis of care for older people (COP), we analyse the agential cuts (together/apart) enacted by humans (mainly caregivers and older people) and more-than-humans (a camera intra-acting with other objects) whose agential entanglement configures and reconfigures the political economy of the caring apparatus. Our study identifies 'targeting', 'monitoring', and 'aligning' as interrelated caring practices, thus contributing to advance a posthuman understanding of welfare technology, and advancing a critical use of the possibilities enacted by technologies.
An Empirical Analysis of Practitioners' Perspectives on Security Tool Integration into DevOps

Background: Security tools play a vital role in enabling developers to build secure software. However, it can be quite challenging to introduce and fully leverage security tools without affecting the speed or frequency of deployments in the DevOps paradigm. Aims: We aim to empirically investigate the key challenges practitioners face when integrating security tools into a DevOps workflow in order to provide recommendations for overcoming the challenges. Method: We conducted a study involving 31 systematically selected webinars on integrating security tools in DevOps. We used a qualitative data analysis method, i.e., thematic analysis, to identify the challenges and emerging solutions related to integrating security tools in rapid deployment environments. Results: We find that whilst traditional security tools are unable to cater for the needs of DevOps, the industry is moving towards new generations of security tools that have started focusing on the needs of DevOps. We have developed a DevOps workflow that integrates security tools and a set of guidelines by synthesizing practitioners' recommendations in the analyzed webinars. Conclusion: Whilst the latest security tools are addressing some of the requirements of DevOps, there are many tool-related drawbacks yet to be adequately addressed.
Applying and Comparing Policy Gradient Methods to Multi-echelon Supply Chains with Uncertain Demands and Lead Times

In the present work, we have applied and compared Deep Reinforcement Learning techniques to solve a problem usually addressed with Operations Research tools. State-of-the-art Policy Gradient methods are used in a production planning and product distribution problem, considering a four-echelon supply chain with uncertain lead times, to minimize total operating costs while meeting uncertain seasonal demands. Two-phases experiments are conducted regarding eight different scenarios. Firstly, A2C, DDPG, PPO, SAC, and TD3 are used considering fixed training budgets. In the second phase, the best two algorithms from the first phase are tuned considering different stopping criteria. Results show that PPO and SAC perform better for the problem addressed. They achieve comparable learning behavior, final performance, and sample complexity, while PPO is faster in terms of wall-clock time.
Towards a Knowledge Centric Semantic Approach for Text Summarization

Text Summarization is one of the important process for extracting important data from a text document. In the proposed method, the useful text or data collected is obtained as abridged form of the document and it is provided to the user as summary. In this current world where there is almost limitless information online, we must understand what data and its context is relevant to our objective for a certain task. As the enormous information is getting collected on the internet, it is a tedious and a challenging task and to go through the accessible information on the Web. This paper proposes a knowledge centric approach for text summarization. The dataset used for training this system is the DUC 2007 which contains manually created summaries, automatic baseline summaries and additional supporting data with results, documents etc. which is combined with TF-IDF algorithm. A domain-based ontology is created in addition Cross Entropy and Normalized Point Wise Mutual Information and ANOVA Normalized Point Wise Mutual Information is calculated based on which the sentences are grouped and eliminated. The proposed approach is superior in terms of performance and recorded F-Measure and False Negative Rate is 88.20% and 0.14 respectively
Fuzzy entity alignment via knowledge embedding with awareness of uncertainty measure

Entity alignment refers to associate entities in different knowledge graphs if they are semantically identical. Embedding-based entity alignment approaches encode entities in a continuous embedding space where entities are aligned based on the similarity of learned embeddings. However, there exists ambiguity and uncertainty in entity alignment caused by single alignment metric. In this paper, a fuzzy entity alignment method FuzzyEA is proposed to model the uncertainty in alignment process based on intuitionistic fuzzy set (IFS). Iterative TransE model is designed to learn relational structure of knowledge graphs, where mutual selection and error correction mechanism is proposed to enhance the effect of iteration. The alignment results obtained by name/description embedding and structure embedding are fused based on Dempster's combination rule. Experiments on three benchmark datasets demonstrate that the proposed FuzzyEA consistently outperforms other entity alignment methods and contributes to promising improvement in alignment accuracy and discrimination ability.
Sparse representation of approximation to identity

The Dirac-[Formula: see text] distribution may be realized through sequences of convolutions, the latter being also regarded as approximation to the identity. This paper proposes the real form of pre-orthogonal adaptive Fourier decomposition (POAFD) method to realize fast approximation to the identity. It belongs to sparse representation of signals having potential applications in signal and image analysis.
The BABEL Generator and E-Rater: 21st Century Writing Constructs and Automated Essay Scoring (AES)

Author(s): Perelman, Les | Abstract: Automated essay scoring (AES) machines use numerical proxies to approximate writing constructs. The BABEL Generator was developed to demonstrate that students could insert appropriate proxies into any paper, no matter how incoherent the prose, and receive a high score from any one of several AES engines. Cahill, Chodorow, and Flor (2018), researchers at Educational Testing Service (ETS), reported on an Advisory for the e-rater AES machine that can identify and flag essays generated by the BABEL Generator. This effort, however, solves a problem that does not exist. Since the BABEL Generator was developed as a research tool, no student could use the BABEL Generator to create an essay in a testing situation. However, testing preparation companies are aware of e-rater's flaws and incorporate the strategies designed to help students exploit these flaws. This test prep does not necessarily make the students stronger writers just better test takers. The new version of e-rater still appears to reward lexically complex, but nonsensical essays demonstrating that current implementations of AES technology continue to be unsuitable for scoring summative, high stakes writing examinations. Keywords: Automated essay scoring (AES), BABEL generator, writing constructs, writing assessments, fairness
Data Processing on Distributed Systems Storage Challenges

Hadoop is an open-source software framework for storing data and launching applications on standard machine clusters. This solution provides massive storage space for all types of data, tremendous processing power, and the ability to support virtually any amount of work. Based on Java, this framework is part of the Apache project, sponsored by Apache Software Foundation [Hadoop official site. http://hadoop.apache.org/]. Thanks to the MapReduce framework, it can handle huge amounts of data. Rather than having to move the data to a network for processing, MapReduce allows you to directly move the processing software to the data. No one will be able to neglect the results realized by the Hadoop technique nor to question its power and its performance to manage important volumetrics of the data of any type: structured, semi-structured, or unstructured [https://www.lebigdata.fr/hadoop]. But it does not shine by its processing speed when datasets are smaller and even smaller. This problem, the “small files” problem has been well-defined by the Hadoop community, and also by the researchers, the majority of the proposed solutions and concepts only deal with the performance and the pressure exerted on the NameNode memory; some new approaches have been proposed as the casi-random, grouping of small heterogeneous files and in different formats, this solves the problem of memory because the number of metadata has been considerably reduced; the real challenge companies face is the performance of the Hadoop cluster when processing a very large number of small files.
Smart production: features of assessing the level of personnel digital readiness

The article considers contemporary digital transformation experiences. The analytical tools to support decision-making of its organizational and digital infrastructure development are offered here. In particular, directions for measuring the digital maturity of production in terms of institutional infrastructure are being explored. An evaluation methodology is proposed to identify the most important signs of the digital transformation of production, the degree of transformation of the production system, the identification of development the problem zones and the optimum trajectory of further development of production. The development of such a trajectory depends on the level of development of the Staff Digital Readiness Index (Smart Production). It includes such indicators as Digital Literacy Index (Initial Digitization), Digital Management Index (Digital Manageability), Digital Confidence Index (Digital Transformation). The authors see the possibility of optimizing the production system activity by simulating the possibility of transition from one state of the production system to another one, corresponding to the requirements of the digital maturity assessment. A Markov chain machine is used. The tools developed can provide a knowledge base for the analytical infrastructure of the production system. It focuses on the information infrastructure of smart manufacturing that allows to take advantage of intellectual technologies such as Big Data, machine learning, the industrial Internet of things.