Reviewerly/exHarmony huggingface dataset:
Continuous test suite failure prediction

Continuous integration advocates to run the test suite of a project frequently, e.g., for every code change committed to a shared repository. This process imposes a high computational cost and sometimes also a high human cost, e.g., when developers must wait for the test suite to pass before a change appears in the main branch of the shared repository. However, only 4% of all test suite invocations turn a previously passing test suite into a failing test suite. The question arises whether running the test suite for each code change is really necessary. This paper presents continuous test suite failure prediction, which reduces the cost of continuous integration by predicting whether a particular code change should trigger the test suite at all. The core of the approach is a machine learning model based on features of the code change, the test suite, and the development history. We also present a theoretical cost model that describes when continuous test suite failure prediction is worthwhile. Evaluating the idea with 15k test suite runs from 242 open-source projects shows that the approach is effective at predicting whether running the test suite is likely to reveal a test failure. Moreover, we find that our approach improves the AUC over baselines that use features proposed for just-in-time defect prediction and test case failure prediction by 13.9% and 2.9%, respectively. Overall, continuous test suite failure prediction can significantly reduce the cost of continuous integration.
Multi-Label Sentiment Analysis on 100 Languages With Dynamic Weighting for Label Imbalance

We investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics, and social sciences. In particular, we introduce a sentiment analysis framework in multi-label setting as it obeys Plutchik's wheel of emotions. We introduce a novel dynamic weighting method that balances the contribution from each class during training, unlike previous static weighting methods that assign non-changing weights based on their class frequency. Moreover, we adapt the focal loss that favors harder instances from single-label object recognition literature to our multi-label setting. Furthermore, we derive a method to choose optimal class-specific thresholds that maximize the macro-f1 score in linear time complexity. Through an extensive set of experiments, we show that our method obtains the state-of-the-art performance in seven of nine metrics in three different languages using a single model compared with the common baselines and the best performing methods in the SemEval competition. We publicly share our code for our model, which can perform sentiment analysis in 100 languages, to facilitate further research.
Information-Theoretic Abstractions for Planning in Agents With Computational Constraints

In this paper, we develop a framework for path-planning on abstractions that are not provided to the agent a priori but instead emerge as a function of the available computational resources. We show how a path-planning problem in an environment can be systematically approximated by solving a sequence of easier to solve problems on abstractions of the original space. The properties of the problem are analyzed, and a number of theoretical results are presented and discussed. A numerical example is presented to show the utility of the approach and to corroborate the theoretical findings. We conclude by providing a discussion detailing the connections of the proposed approach to anytime algorithms and bounded rationality.
Einstein–Podolsky–Rosen paradox with position–momentum entangled macroscopic twin beams

Spatial entanglement is at the heart of quantum enhanced imaging applications and high-dimensional quantum information protocols. In particular, for imaging and sensing applications, quantum states with a macroscopic number of photons are needed to provide a real advantage over the classical state-of-the-art. We demonstrate the Einstein-Podolsky-Rosen (EPR) paradox in its original position and momentum form with bright twin beams of light by showing the presence of EPR spatial (position-momentum) entanglement. An electron-multiplying charge-coupled-device camera is used to record images of the bright twin beams in the near and far field regimes to achieve an apparent violation of the uncertainty principle by more than an order of magnitude. We further show that the presence of quantum correlations in the spatial and temporal degrees of freedom leads to spatial squeezing between the spatial fluctuations of the bright twin beams in both the near and far fields. This provides another verification of the spatial entanglement and points to the presence of hyperentanglement in the bright twin beams.
A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets

The need to measure bias encoded in tabular data that are used to solve pattern recognition problems is widely recognized by academia, legislators and enterprises alike. In previous work, we proposed a bias quantification measure, called fuzzy-rough uncertainty, which relies on the fuzzy-rough set theory. The intuition dictates that protected features should not change the fuzzy-rough boundary regions of a decision class significantly. The extent to which this happens is a proxy for bias expressed as uncertainty in a decision-making context. Our measure's main advantage is that it does not depend on any machine learning prediction model but a distance function. In this paper, we extend our study by exploring the existence of bias encoded implicitly in non-protected features as defined by the correlation between protected and unprotected attributes. This analysis leads to four scenarios that domain experts should evaluate before deciding how to tackle bias. In addition, we conduct a sensitivity analysis to determine the fuzzy operators and distance function that best capture change in the boundary regions.
Superposition with Lambdas

Abstract We designed a superposition calculus for a clausal fragment of extensional polymorphic higher-order logic that includes anonymous functions but excludes Booleans. The inference rules work on $$\beta \eta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>β</mml:mi> <mml:mi>η</mml:mi> </mml:mrow> </mml:math> -equivalence classes of $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> -terms and rely on higher-order unification to achieve refutational completeness. We implemented the calculus in the Zipperposition prover and evaluated it on TPTP and Isabelle benchmarks. The results suggest that superposition is a suitable basis for higher-order reasoning.
Sampling, rates, and reaction currents through reverse stochastic quantization on quantum computers

The quest for improved sampling methods to solve statistical mechanics problems of physical and chemical interest has proceeded with renewed efforts since the invention of the Metropolis algorithm, in 1953. In particular, the understanding of thermally activated rare-event processes between long-lived metastable states, such as protein folding, is still elusive. In this case, one needs both the finite-temperature canonical distribution function and the reaction current between the reactant and product states to completely characterize the dynamic. Here we show how to tackle this problem using a quantum computer. We use the connection between a classical stochastic dynamics and the Schr\"odinger equation, also known as stochastic quantization, to variationally prepare quantum states, allowing us to unbiasedly sample from a Boltzmann distribution. Similarly, reaction rate constants can be computed as ground-state energies of suitably transformed operators, following the supersymmetric extension of the formalism. Finally, we propose a hybrid quantum-classical sampling scheme to escape local minima and explore the configuration space in both real-space and spin Hamiltonians. We indicate how to realize the quantum algorithms constructively, without assuming the existence of oracles or quantum walk operators. The quantum advantage concerning the above applications is discussed.
Collective multipartite Einstein-Podolsky-Rosen steering via cascaded four-wave mixing of rubidium atoms

Collective multipartite Einstein-Podolsky-Rosen (EPR) steering is a type of quantum correlation shared among $n$ parties, where the EPR paradox of one party can be realized only by performing local measurements on all the remaining $n\ensuremath{-}1$ parties. Here, we propose an efficient method to produce collective multipartite EPR steering via symmetrically and asymmetrically cascading parametric amplification processes, i.e., four-wave mixing (FWM) of rubidium atoms. The simplified collective-steering criterion is introduced using the Coffman-Kundu-Wootters monogamy relation. Moreover, by actively adjusting the parametric gains, the collective EPR steerability is optimized in our schemes. We find that the scale of collective steering can be extended by cascading more FWMs; in particular, introducing optical loss is useful for generating collective steering with more parties only in the asymmetry structure. Our results pave the way for the construction of quantum networks and provide a promising candidate for one-sided device-independent quantum cryptography among multiple users.
Multiplexed Photon Number Measurement

By using qubit frequencies to encode information, an experiment demonstrates an approach to quantum measurement that replaces sequential observations with simultaneous ones, greatly improving measurement time.
Double sparse quantum state preparation

Initializing classical data in a quantum device is an essential step in many quantum algorithms. As a consequence of measurement and noisy operations, some algorithms need to reinitialize the prepared state several times during its execution. In this work, we propose a quantum state preparation algorithm called CVO-QRAM with computational cost O(kM), where M is the number of nonzero probability amplitudes and $k$ is the maximum number of bits with value 1 in the patterns to be stored. The proposed algorithm can be an alternative to create sparse states in future NISQ devices.
TelecomNet: Tag-Based Weakly-Supervised Modally Cooperative Hashing Network for Image Retrieval

We are concerned with using user-tagged images to learn proper hashing functions for image retrieval. The benefits are two-fold: (1) we could obtain abundant training data for deep hashing models; (2) tagging data possesses richer semantic information which could help better characterize similarity relationships between images. However, tagging data suffers from noises, vagueness and incompleteness. Different from previous unsupervised or supervised hashing learning, we propose a novel weakly-supervised deep hashing framework which consists of two stages: weakly-supervised pre-training and supervised fine-tuning. The second stage is as usual. In the first stage, we propose two formulations Tag-basEd weakLy-supErvised Modally COoperative hashing Network (TelecomNet) and Generalized TelecomNet (GTelecomNet). Rather than performing supervision on tags, TelecomNet first learns an observed semantic embedding vector for each image from attached tags and then uses it to guide hashing learning. GTelecomNet introduces a novel semantic network to exploit more precise semantic information. By carefully designing the optimization problem, they can well leverage tagging information and image content for hashing learning. The framework is general and does not depend on specific deep hashing methods. Empirical results on real world datasets show that they significantly increase the performance of state-of-the-art deep hashing methods.
Behavioral responses to a cyber attack in a hospital environment

Technical and organizational steps are necessary to mitigate cyber threats and reduce risks. Human behavior is the last line of defense for many hospitals and is considered as equally important as technical security. Medical staff must be properly trained to perform such procedures. This paper presents the first qualitative, interdisciplinary research on how members of an intermediate care unit react to a cyberattack against their patient monitoring equipment. We conducted a simulation in a hospital training environment with 20 intensive care nurses. By the end of the experiment, 12 of the 20 participants realized the monitors' incorrect behavior. We present a qualitative behavior analysis of high performing participants (HPP) and low performing participants (LPP). The HPP showed fewer signs of stress, were easier on their colleagues, and used analog systems more often than the LPP. With 40% of our participants not recognizing the attack, we see room for improvements through the use of proper tools and provision of adequate training to prepare staff for potential attacks in the future.
Hierarchy of Nonlinear Entanglement Dynamics for Continuous Variables

The entanglement produced by a bilinear Hamiltonian in continuous variables has been thoroughly studied and widely used. In contrast, the physics of entanglement resulting from nonlinear interaction described by partially degenerate high-order Hamiltonians remains unclear. Here, we derive a hierarchy of sufficient and necessary conditions for the positive-partial-transposition separability of bipartite nonlinear quantum states. The proposed criteria detect the nonpositive-partial-transposition inseparability of higher-order moments of states, which provides a systematic framework for the characterization of this kind of entanglement. Through numerical simulation of cubic and quartic Hamiltonians, we demonstrate the existence and competition of a hierarchy of entanglement witnesses, revealing the mechanism underlying such entanglement. Our results may provide a new direction in continuous variable quantum information processing.
Improving Visualization Interpretation Using Counterfactuals

Complex, high-dimensional data is used in a wide range of domains to explore problems and make decisions. Analysis of high-dimensional data, however, is vulnerable to the hidden influence of confounding variables, especially as users apply ad hoc filtering operations to visualize only specific subsets of an entire dataset. Thus, visual data-driven analysis can mislead users and encourage mistaken assumptions about causality or the strength of relationships between features. This work introduces a novel visual approach designed to reveal the presence of confounding variables via counterfactual possibilities during visual data analysis. It is implemented in CoFact, an interactive visualization prototype that determines and visualizes counterfactual subsets to better support user exploration of feature relationships. Using publicly available datasets, we conducted a controlled user study to demonstrate the effectiveness of our approach; the results indicate that users exposed to counterfactual visualizations formed more careful judgments about feature-to-outcome relationships.
Chaotic fitness-dependent optimizer for planning and engineering design

Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that mimics the reproduction behavior of the bee swarm in finding better hives. This algorithm is similar to Particle Swarm Optimization (PSO) but it works differently. The algorithm is very powerful and has better results compared to other common metaheuristic algorithms. This paper aims at improving the performance of FDO, thus, the chaotic theory is used inside FDO to propose Chaotic FDO (CFDO). Ten chaotic maps are used in the CFDO to consider which of them are performing well to avoid local optima and finding global optima. New technic is used to conduct population in specific limitation since FDO technic has a problem to amend population. The proposed CFDO is evaluated by using 10 benchmark functions from CEC2019. Finally, the results show that the ability of CFDO is improved. Singer map has a great impact on improving CFDO while the Tent map is the worst. Results show that CFDO is superior to GA, FDO, and CSO. Both CEC2013 and CEC2005 are used to evaluate CFDO. Finally, the proposed CFDO is applied to classical engineering problems, such as pressure vessel design and the result shows that CFDO can handle the problem better than WOA, GWO, FDO, and CGWO. Besides, CFDO is applied to solve the task assignment problem and then compared to the original FDO. The results prove that CFDO has better capability to solve the problem.
Semi-Autoregressive Image Captioning

Current state-of-the-art approaches for image captioning typically adopt an autoregressive manner, i.e., generating descriptions word by word, which suffers from slow decoding issue and becomes a bottleneck in real-time applications. Non-autoregressive image captioning with continuous iterative refinement, which eliminates the sequential dependence in a sentence generation, can achieve comparable performance to the autoregressive counterparts with a considerable acceleration. Nevertheless, based on a well-designed experiment, we empirically proved that iteration times can be effectively reduced when providing sufficient prior knowledge for the language decoder. Towards that end, we propose a novel two-stage framework, referred to as Semi-Autoregressive Image Captioning (SAIC), to make a better trade-off between performance and speed. The proposed SAIC model maintains autoregressive property in global but relieves it in local. Specifically, SAIC model first jumpily generates an intermittent sequence in an autoregressive manner, that is, it predicts the first word in every word group in order. Then, with the help of the partially deterministic prior information and image features, SAIC model non-autoregressively fills all the skipped words with one iteration. Experimental results on the MS COCO benchmark demonstrate that our SAIC model outperforms the preceding non-autoregressive image captioning models while obtaining a competitive inference speedup.
Analysis of Video Game Players’ Emotions and Team Performance: An Esports Tournament Case Study

Video gaming and eSports is a quickly developing industry already involving billions of players worldwide. Gaming and eSports tournaments require strong mental abilities to avoid severe stress and other negative consequences upon completing the game. In this article, we report on the impact of emotions on a team performance. For this reason, we collect audio recordings and game logs from the players in real conditions at an eSports tournament. This data is further used in trained machine learning models for analysis of players’ emotional conditions from the voice during the game. We considered recognition of several types of emotions as well as the background sounds. To do this, we trained 92.7% accuracy classifier of six most common classes of emotions and sounds in eSports audio and applied it to eSports data. As a result, we demonstrate that there is an opportunity to measure the eSports team’s performance from the players’ emotional conditions obtained from the voice communication. We found that there is a strong correlation among the performance of the team, communication between the players, and emotional sentiment of communication. The teams achieve much better results when they had much more internal conversations during the game.
Temporally informed random forests for suicide risk prediction

Abstract Objective Suicide is one of the leading causes of death worldwide, yet clinicians find it difficult to reliably identify individuals at high risk for suicide. Algorithmic approaches for suicide risk detection have been developed in recent years, mostly based on data from electronic health records (EHRs). Significant room for improvement remains in the way these models take advantage of temporal information to improve predictions. Materials and Methods We propose a temporally enhanced variant of the random forest (RF) model—Omni-Temporal Balanced Random Forests (OT-BRFs)—that incorporates temporal information in every tree within the forest. We develop and validate this model using longitudinal EHRs and clinician notes from the Mass General Brigham Health System recorded between 1998 and 2018, and compare its performance to a baseline Naive Bayes Classifier and 2 standard versions of balanced RFs. Results Temporal variables were found to be associated with suicide risk: Elevated suicide risk was observed in individuals with a higher total number of visits as well as those with a low rate of visits over time, while lower suicide risk was observed in individuals with a longer period of EHR coverage. RF models were more accurate than Naive Bayesian classifiers at predicting suicide risk in advance (area under the receiver operating curve = 0.824 vs. 0.754, respectively). The proposed OT-BRF model performed best among all RF approaches, yielding a sensitivity of 0.339 at 95% specificity, compared to 0.290 and 0.286 for the other 2 RF models. Temporal variables were assigned high importance by the models that incorporated them. Discussion We demonstrate that temporal variables have an important role to play in suicide risk detection and that requiring their inclusion in all RF trees leads to increased predictive performance. Integrating temporal information into risk prediction models helps the models interpret patient data in temporal context, improving predictive performance.
The Nomadic Office: A Location Independent Workspace Through Mixed Reality

The worldwide spread of the coronavirus forcefully affected our lives, the economy, and social culture. Unforeseen, a significant number of workforces are requested or required to work from home, impacting creativity, work performance, and social interaction. Video conferencing tools are consequently substituting in-person meetings; new workplaces are arranged in domestic environments, causing a shift in how employees work and interact with their environment. At the same time, recent developments in mixed reality (MR) enable us to synthesize virtual office environments and experience them on the go. This article consolidates how the latest research in MR supports the transition of desktop computing into the virtual realm, enriching traditional office environments with virtual elements. We conceptualize augmented domestic workspaces and truly nomadic offices that overcome the physical constraints and unfavorable effects of continuous telecommuting. To promote future research, we highlight open research questions and outline a nomadic MR office of the future.