Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-layer Path Selection in Multi-path Transport Protocol for Mobile Devices

MPTCP is a new transport protocol that enables mobile devices to use multiple physical paths simultaneously through several network interfaces, such as WiFi and Cellular. However, wireless path capacities change frequently in the mobile environments, causing challenges for path selection. For example, WiFi associated paths often become poor as devices walk away, since WiFi has intermittent connectivity caused by the short signal coverage and stochastic interference. MPTCP's native decision based on hysteretic TCP-layer estimation will miss the real switching point of wireless quality, which may cumulate packets on the broken path and causes serious packets reinjection. Through analyzing a unique dataset in the wild, we quantitatively study the impact of MAC-layer factors on the aggregated performance of MPTCP. We then propose a decision tree approach for cross-layer path selection that decides which path to carry the incoming packets dynamically according to the prior learned schemes. A prototype of the path selection system named SmartPS, which proactively probes the wireless environments, is realized and deployed in Linux and Android. Evaluation results demonstrate that our SmartPS can efficiently utilize the faster path, with goodput improvements of up to 29%.
Smartphone Security Behavioral Scale: A New Psychometric Measurement for Smartphone Security

Despite widespread use of smartphones, there is no measurement standard targeted at smartphone security behaviors. In this paper we translate a well-known cybersecurity behavioral scale into the smartphone domain and show that we can improve on this translation by following an established psychometrics approach surveying 1011 participants. We design a new 14-item Smartphone Security Behavioral Scale (SSBS) exhibiting high reliability and good fit to a two-component behavioural model based on technical versus social protection strategies. We then demonstrate how SSBS can be applied to measure the influence of mental health issues on smartphone security behavior intentions. We found significant correlations that predict SSBS profiles from three types of MHIs. Conversely, we are able to predict presence of MHIs using SSBS profiles.We obtain prediction AUCs of 72.1% for Internet addiction,75.8% for depression and 66.2% for insomnia.
Light-induced topological magnons in two-dimensional van der Waals magnets

Driving a two-dimensional Mott insulator with circularly polarized light breaks time-reversal and inversion symmetry, which induces an optically-tunable synthetic scalar spin chirality interaction in the effective low-energy spin Hamiltonian. Here, we show that this mechanism can stabilize topological magnon excitations in honeycomb ferromagnets such as CrI$_3$ and in optical lattices. We find that the irradiated quantum magnet is described by a Haldane model for magnons that hosts topologically-protected edge modes. We study the evolution of the magnon spectrum in the Floquet regime and via time propagation of the magnon Hamiltonian for a slowly varying pulse envelope. Compared to similar but conceptually distinct driving schemes based on the Aharanov-Casher effect, the dimensionless light-matter coupling parameter $\lambda = eEa/\hbar\omega$ at fixed electric field strength is enhanced by a factor $\sim 10^5$. This increase of the coupling parameter allows to induce a topological gap of the order of $\Delta \approx 2$ meV with realistic laser pulses, bringing an experimental realization of light-induced topological magnon edge states within reach.
Improving Event Detection using Contextual Word and Sentence Embeddings

The task of Event Detection (ED) is a subfield of Information Extraction (IE) that consists in recognizing event mentions in natural language texts. Several applications can take advantage of an ED system, including alert systems, text summarization, question-answering systems, and any system that needs to extract structured information about events from unstructured texts. ED is a complex task, which is hampered by two main challenges: the lack of a dataset large enough to train and test the developed models and the variety of event type definitions that exist in the literature. These problems make generalization hard to achieve, resulting in poor adaptation to different domains and targets. The main contribution of this paper is the design, implementation and evaluation of a recurrent neural network model for ED that combines several features. In particular, the paper makes the following contributions: (1) it uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for the ED task; (2) the proposed model has the ability to use its first layer to learn good feature representations; (3) a new public dataset with a general definition of event; (4) an extensive empirical evaluation that includes (i) the exploration of different architectures and hyperparameters, (ii) an ablation test to study the impact of each attribute, and (iii) a comparison with a replication of a state-of-the-art model. The results offer several insights into the importance of contextual embeddings and indicate that the proposed approach is effective in the ED task, outperforming the baseline models.
Efficient computation and analysis of distributional Shapley values

Distributional data Shapley value (DShapley) has been recently proposed as a principled framework to quantify the contribution of individual datum in machine learning. DShapley develops the foundational game theory concept of Shapley values into a statistical framework and can be applied to identify data points that are useful (or harmful) to a learning algorithm. Estimating DShapley is computationally expensive, however, and this can be a major challenge to using it in practice. Moreover, there has been little mathematical analyses of how this value depends on data characteristics. In this paper, we derive the first analytic expressions for DShapley for the canonical problems of linear regression and non-parametric density estimation. These analytic forms provide new algorithms to compute DShapley that are several orders of magnitude faster than previous state-of-the-art. Furthermore, our formulas are directly interpretable and provide quantitative insights into how the value varies for different types of data. We demonstrate the efficacy of our DShapley approach on multiple real and synthetic datasets.
Diagnostic Uncertainty Calibration: Towards Reliable Machine Predictions in Medical Domain

Label disagreement between human experts is a common issue in the medical domain and poses unique challenges in the evaluation and learning of classification models. In this work, we extend metrics for probability prediction, including calibration, i.e., the reliability of predictive probability, to adapt to such a situation. We further formalize the metrics for higher-order statistics, including inter-rater disagreement, in a unified way, which enables us to assess the quality of distributional uncertainty. In addition, we propose a novel post-hoc calibration method that equips trained neural networks with calibrated distributions over class probability estimates. With a large-scale medical imaging application, we show that our approach significantly improves the quality of uncertainty estimates in multiple metrics.
Evaluation of proteases produced by Erwinia chrysanthemi for the deproteinization of crustacean waste in a chitin production process.

This study evaluated the potential use of proteases produced by Erwinia chrysanthemi for the extraction of pure chitin from crustacean wastes using standard methods. The crude bacterial protease had activity of 22.4 U/ml. Protein removal by the commercial protease were similar in both the mineralized and demineralized wastes and amounted to about 75 and 80%, respectively, in 16 h. A similar trend was observed with higher protein removal efficiency (95%) for the crude protease from both the mineralized and demineralized wastes. Also, treatment of the powder with 5% NaOH resulted in the removal of protein in the demineralized and mineralized wastes decreased by about 96 and 87.6%, respectively, in same period from an initial concentration of 0.3 mg/ml. It was concluded that proteases produced by E. chrysanthemi could be used to transform waste from sea-food processing industries into products of commercial value. Key words: Erwinia chrysanthemi, extra cellular, proteases, chitin, value-added products.
Confidence-Aware Learning for Deep Neural Networks

Despite the power of deep neural networks for a wide range of tasks, an overconfident prediction issue has limited their practical use in many safety-critical applications. Many recent works have been proposed to mitigate this issue, but most of them require either additional computational costs in training and/or inference phases or customized architectures to output confidence estimates separately. In this paper, we propose a method of training deep neural networks with a novel loss function, named Correctness Ranking Loss, which regularizes class probabilities explicitly to be better confidence estimates in terms of ordinal ranking according to confidence. The proposed method is easy to implement and can be applied to the existing architectures without any modification. Also, it has almost the same computational costs for training as conventional deep classifiers and outputs reliable predictions by a single inference. Extensive experimental results on classification benchmark datasets indicate that the proposed method helps networks to produce well-ranked confidence estimates. We also demonstrate that it is effective for the tasks closely related to confidence estimation, out-of-distribution detection and active learning.
A New Species of Callulina (Anura: Microhylidae) from the West Usambara Mountains, Tanzania

Abstract The description of the species Callulina kreffti was based on specimens collected in the East Usambara Mountains of Tanzania. Successive collecting has shown this species to be widely distributed through the Eastern Arc Mountains. Advertisement calls from populations in the type locality of Callulina kreffti were compared with calls from populations in the West Usambara Mountains. Analysis of the calls suggested that these two populations of Callulina represent two separate taxa. Subsequent morphological and molecular investigations indicated that these two populations are distinct. Herein, we describe a new Callulina species on the basis of call, morphology and molecular sequences.
Identification and Remediation of Self-Admitted Technical Debt in Issue Trackers

Technical debt refers to taking shortcuts to achieve short-term goals, which might negatively influence software maintenance in the long-term. There is increasing attention on technical debt that is admitted by developers in source code comments (termed as self-admitted technical debt or SATD). But SATD in issue trackers is relatively unexplored. We performed a case study, where we manually examined 500 issues from two open source projects (i.e. Hadoop and Camel), which contained 152 SATD items. We found that: 1) eight types of technical debt are identified in issues, namely architecture, build, code, defect, design, documentation, requirement, and test debt; 2) developers identify technical debt in issues in three different points in time, and a small part is identified by its creators; 3) the majority of technical debt is paid off, 4) mostly by those who identified it or created it; 5) the median time and average time to repay technical debt are 25.0 and 872.3 hours respectively.
Scattering invariance for arbitrary polarizations protected by joint spatial-duality symmetries

We reveal how to exploit joint spatial-electromagnetic duality symmetries to obtain invariant scattering properties (including extinction, scattering, absorption) of self-dual scattering systems for incident waves of arbitrary polarizations. The electromagnetic duality ensures the helicity preservation along all scattering directions, and thus intrinsically eliminates the interferences between the two scattering channels originating from the circularly polarized components of incident waves. This absence of interference directly secures invariant scattering properties for all polarizations located on the same latitude circle of the Poincare sphere, which are characterized by polarization ellipses of the same eccentricity and handedness. Further incorporations of mirror and/or inversion symmetries would lead to such invariance throughout the whole Poincare sphere, guaranteeing invariant scattering properties for all polarizations. Simultaneous exploitations of composite symmetries of different natures render an extra dimension of freedom for scattering manipulations, offering new insights for both fundamental explorations and optical device engineering related to symmetry dictated light-matter interactions.
On Actual Preparation of Dicke State on a Quantum Computer

The exact number of CNOT and single qubit gates needed to implement a Quantum Algorithm in a given architecture is one of the central problems of Quantum Computation. In this work we study the importance of concise realizations of Partially defined Unitary Transformations for better circuit construction using the case study of Dicke State Preparation. The Dicke States $(\left|D^n_k \right>)$ are an important class of entangled states with uses in many branches of Quantum Information. In this regard we provide the most efficient Deterministic Dicke State Preparation Circuit in terms of CNOT and single qubit gate counts in comparison to existing literature. We further observe that our improvements also reduce architectural constraints of the circuits. We implement the circuit for preparing $\left| D^4_2 \right>$ on the "ibmqx2" machine of the IBM QX service and observe that the error induced due to noise in the system is lesser in comparison to the existing circuit descriptions. We conclude by describing the CNOT map of the generic $\left| D^n_k \right>$ preparation circuit and analyze different ways of distributing the CNOT gates in the circuit and its affect on the induced error.
Joint Beam Training and Data Transmission Design for Covert Millimeter-Wave Communication

Covert communication prevents legitimate transmission from being detected by a warden while maintaining certain covert rate at the intended user. Prior works have considered the design of covert communication over conventional low-frequency bands, but few works so far have explored the higher-frequency millimeter-wave (mmWave) spectrum. The directional nature of mmWave communication makes it attractive for covert transmission. However, how to establish such directional link in a covert manner in the first place remains as a significant challenge. In this paper, we consider a covert mmWave communication system, where legitimate parties Alice and Bob adopt beam training approach for directional link establishment. Accounting for the training overhead, we develop a new design framework that jointly optimizes beam training duration, training power and data transmission power to maximize the effective throughput of Alice-Bob link while ensuring the covertness constraint at warden Willie is met. We further propose a dual-decomposition successive convex approximation algorithm to solve the problem efficiently. Numerical studies demonstrate interesting tradeoff among the key design parameters considered and also the necessity of joint design of beam training and data transmission for covert mmWave communication.
Extraction of baryonia from the lightest antiprotonic atoms

Anti-protonic hydrogen and helium atoms are analyzed. Level shifts and width are expressed in terms of $\bar{p}$-nucleon sub-threshold scattering lengths and volumes. Experimental data are compared to results obtained from the 2009 version of the Paris $N\bar{N}$ interaction potential. Comparison with 1999 version is also made. Effects of $N\bar{N}$ quasi-bound states are discussed. Atomic 2P hyperfine structure is calculated for antiprotonic deuterium and the significance of new measurements is indicated.
Developing a predictive signature for two trial endpoints using the cross-validated risk scores method

Summary The existing cross-validated risk scores (CVRS) design has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using high-dimensional data (such as genetic data). The design is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure. In some settings, it is desirable to consider the tradeoff between two outcomes, such as efficacy and toxicity, or cost and effectiveness. With this motivation, we extend the CVRS design (CVRS2) to consider two outcomes. The design employs bivariate risk scores that are divided into clusters. We assess the properties of the CVRS2 using simulated data and illustrate its application on a randomized psychiatry trial. We show that CVRS2 is able to reliably identify the sensitive group (the group for which the new treatment provides benefit on both outcomes) in the simulated data. We apply the CVRS2 design to a psychology clinical trial that had offender status and substance use status as two outcomes and collected a large number of baseline covariates. The CVRS2 design yields a significant treatment effect for both outcomes, while the CVRS approach identified a significant effect for the offender status only after prefiltering the covariates.
JAMPI: efficient matrix multiplication in Spark using Barrier Execution Mode

The new barrier mode in Apache Spark allows for embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage does not depend on any other tasks in the same stage, and hence it can be scheduled independently. However, several algorithms require more sophisticated inter-task communications, similar to the MPI paradigm. By combining distributed message passing (using asynchronous network IO), OpenJDK’s new auto-vectorization and Spark’s barrier execution mode, we can add non-map/reduce-based algorithms, such as Cannon’s distributed matrix multiplication to Spark. We document an efficient distributed matrix multiplication using Cannon’s algorithm, which significantly improves on the performance of the existing MLlib implementation. Used within a barrier task, the algorithm described herein results in an up to 24% performance increase on a 10,000 × 10,000 square matrix with a significantly lower memory footprint. Applications of efficient matrix multiplication include, among others, accelerating the training and implementation of deep convolutional neural network-based workloads, and thus such efficient algorithms can play a ground-breaking role in the faster and more efficient execution of even the most complicated machine learning tasks.
LOL: Lidar-only Odometry and Localization in 3D point cloud maps*

In this paper we deal with the problem of odometry and localization for Lidar-equipped vehicles driving in urban environments, where a premade target map exists to localize against. In our problem formulation, to correct the accumulated drift of the Lidar-only odometry we apply a place recognition method to detect geometrically similar locations between the online 3D point cloud and the a priori offline map. In the proposed system, we integrate a state-of-the-art Lidaronly odometry algorithm with a recently proposed 3D point segment matching method by complementing their advantages. Also, we propose additional enhancements in order to reduce the number of false matches between the online point cloud and the target map, and to refine the position estimation error whenever a good match is detected. We demonstrate the utility of the proposed LOL system on several Kitti datasets of different lengths and environments, where the relocalization accuracy and the precision of the vehicle’s trajectory were significantly improved in every case, while still being able to maintain real-time performance.
Genetic characterization of sunflower breeding resources from Argentina: assessing diversity in key open-pollinated and composite populations

Open-pollinated (OPs) and composite populations (CPs) represent a valuable resource for sunflower breeding programmes. However, little is known about the levels and distribution of genetic variation within each of these populations. In this study, quantitative and qualitative traits along with molecular markers were used to evaluate 14 populations from the Instituto Nacional de Tecnología Agropecuaria (INTA) sunflower germplasm collection. These populations were chosen to represent historically important accessions that still play a central role within the INTA sunflower breeding programme due to their extensive variation in diverse agronomically important traits. Nine quantitative and eight qualitative agro-morphological descriptors were recorded and compared with those of a larger set of accessions representative of the phenotypic diversity of the sunflower collection. Molecular characterization was conducted on a total of 311 individuals using 16 microsatellite markers. Overall, the average gene diversity was 0.56 and the average number of alleles per locus was 6.25. No statistically significant differences in genetic diversity were detected between the OPs and CPs. Global estimates of FST revealed very high levels of differentiation among accessions (FST= 0.413, P< 0.05). Population structure analyses were consistent with the observed levels of differentiation and identified two major groups. The results of this work show that high global diversity is preserved within the accessions analysed here. Additionally, this study provides a set of reliable and discriminant markers for the cost-effective molecular characterization of sunflower accessions, along with the guidelines for the delineation of sampling strategies for OPs and CPs, thus aiding the efficient management and exploitation of sunflower germplasm collections.
Users' Concern for Privacy in Context-Aware Reasoning Systems

Context-aware reasoning systems allow drawing sophisticated inferences about users' behaviour and physiological condition, by aggregating data from seemingly unrelated sources. We conducted a general population online survey to evaluate users' concern about the privacy of data gathered by these systems. We found that people are more concerned about third parties accessing data gathered by environmental sensors as compared to physiological sensors. Participants also indicated greater concern about unfamiliar third parties (e.g., private companies) as opposed to familiar third parties (e.g., relatives). We further found that these concerns are predicted and (to a lesser degree) causally affected by people's beliefs about how much can be inferred from these types of data, as well as by their background in computer science.
New Non-parametric Tests for Multivariate Paired Data.

Paired data are common in many fields, such as medical diagnosis and longitudinal data analysis, where measurements are taken for the same set of objects under different conditions. In many of these studies, the number of measurements could be large. Existing methods either impose strong assumptions or have power decrease fast as the number of measurements increases. In this work, we propose new non-parametric tests for paired data. These tests exhibit substantial power improvements over existing methods under moderate- to high- dimensional data. We also derived asymptotic distributions of the new tests and the approximate $p$-values based on them are reasonably accurate under finite samples through simulation studies, making the new tests easy-off-the-shelf tools for real applications. The proposed tests are illustrated through the analysis of a real data set on the Alzheimer's disease research.