Miracle-dz/UniFAR-FTUs huggingface dataset:
ComplAI: Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models

In this paper, we present ComplAI, a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model's behavior in drift scenarios, and to provide a single Trust Factor that evaluates different supervised ML models from an overall responsibility perspective. It helps users to (a) connect their models and enable explanations, (b) assess and visualize different aspects of the model and (c) compare different models from an overall perspective thereby facilitating actionable recourse for improvement of the models. ComplAI is model agnostic and works with different supervised machine learning scenarios and frameworks, seamlessly integrable with any ML life-cycle framework. Thus, this already deployed framework aims to unify critical aspects of Responsible AI systems for regulating the development process of such real systems. The theory version of the paper and a demo version is available in this link (use the Zip Password: Welcome2022! ). An detailed version of the paper is available in Arxiv.
Fluidity: Location-Awareness in Replicated State Machines

In planetary-scale replication systems, the overall response delay is greatly influenced by the geographical distances between client and server nodes. Current systems define the replica locations statically during startup time. However, the selected locations might be suboptimal for the clients, and the client request origin distribution may change over time, so a different replica placement may provide lower overall request latencies. In this work, we propose a location-aware replicated state machine that is able to adapt the geographic location of its replicas dynamically during runtime to locations geographically closer to client request origins. Our prototype is able to observe emerging optimization potentials and to reduce the overall request latency for the majority of clients by adapting its replica locations to the time-dependent optimum placement during real-world use case evaluations, whereby the absolute performance gain is dependent on the respective usage scenario.
MCRepair: Multi-Chunk Program Repair via Patch Optimization with Buggy Block

Automated program repair (APR) is a technology that identifies and repairs bugs automatically. However, repairing multi-chunk bugs remains a long-standing and challenging problem because an APR technique must consider dependencies and then reduce the large patch space. In addition, little is known about how to combine individual candidate patches even though multi-chunk bugs require combinations. Therefore, we propose a novel APR technique called multi-code repair (MCRepair), which applies a buggy block, patch optimization, and CodeBERT to target multi-chunk bugs. A buggy block is a novel method that binds buggy chunks into a multi-buggy chunk and preprocesses the chunk with its buggy contexts for patch space reduction and dependency problems. Patch optimization is a novel strategy that effectively combines the generated candidate patches with patch space reduction. In addition, CodeBERT, a BERT for source code datasets, is fine-tuned to address the lack of datasets and out-of-vocabulary problems. We conducted several experiments to evaluate our approach on six project modules of Defects4J. In the experiments using Defects4J, MCRepair repaired 66 bugs, including 22 multi-chunk bugs. Moreover, it fixed 19 unique bugs, including nine multi-chunk bugs, and improved 45--266% performance than the baselines.
Realism versus Performance for Adversarial Examples Against DL-based NIDS

The application of deep learning-based (DL) network intrusion detection systems (NIDS) enables effective automated detection of cyberattacks. Such models can extract valuable features from high-dimensional and heterogeneous network traffic with minimal feature engineering and provide high accuracy detection rates. However, it has been shown that DL can be vulnerable to adversarial examples (AEs), which mislead classification decisions at inference time, and several works have shown that AEs are indeed a threat against DL-based NIDS. In this work, we argue that these threats are not necessarily realistic. Indeed, some general techniques used to generate AE manipulate features in a way that would be inconsistent with actual network traffic. In this paper, we first implement the main AE attacks selected from the literature (FGSM, BIM, PGD, NewtonFool, CW, DeepFool, EN, Boundary, HSJ, ZOO) for two different datasets (WSN-DS and BoT-IoT) and we compare their relative performance. We then analyze the perturbation generated by these attacks and use the metrics to establish a notion of "attack unrealism". We conclude that, for these datasets, some of these attacks are performant but not realistic.
Show, Prefer and Tell: Incorporating User Preferences into Image Captioning

Current image captioning models produce fluent captions, but they rely on a one-size-fits-all approach that does not take into account the preferences of individual end-users. We present a method to generate descriptions with an adjustable amount of content that can be set at inference-time, thus providing a step toward a more user-centered approach to image captioning. *
Dynamic Controllability of Parameterized CSTNUs

A Conditional Simple Temporal Network with Uncertainty (CSTNU) models temporal constraint satisfaction problems in which the environment sets uncontrollable timepoints and conditions. The executor observes and reacts to such uncontrollable assignments as time advances with the CSTNU execution. However, there exist scenarios in which the occurrence of some future timepoints must be fixed as soon as the execution starts. We call these timepoints parameters. For a correct execution, parameters must assume values that guarantee the possibility of satisfying all temporal constraints, whatever the environment decides the execution time for uncontrollable timepoints and the truth value of conditions, i.e., dynamic controllability (DC). Here, we formalize the extension of the CSTNU with parameters. Furthermore, we define a set of rules to check the DC of such extended CSTNU. These rules additionally solve the problem inverse to checking DC: computing restrictions on parameter values that yield DC guarantees. The proposed rules can be composed into a sound and complete procedure.
NF-Log: Revisiting Log Writes in Relational Database for Efficient Persistent Memory Utilization

Non-volatile memory (NVM) is a promising storage technology that combines not only high performance and byte-addressability (like DRAM) but also durability (like SSD). However, as existing relational database management systems (RDBMS) are originally designed based on the assumption that all the data and log are stored on high latency block based devices, they are not able to take full advantage of this new technology yet. Consequently, write operations will under-utilize the device(NVM) and eventually downgrade the performance. In this work, we analyzed the redo-log mechanism in InnoDB and after discovering its severe impact on the overall performance, we decided to perform optimizations that will match with NVM features. We started by re-designing the log write path within InnoDB. After that, we worked on improving the log data access locality and finally, we optimized the write ahead mechanism. NF-log (Non Flushing Log) utilizes the NVM byte addressability and its persistence feature to reduce the write size by 30% and boost up the performance to up to 38% for sysbench write_intensive workloads and up to 16% for TPC-C.
Low-Power Footprint Inference with a Deep Neural Network offloaded to a Service Robot through Edge Computing

Recent advances in the field of Artificial Intelligence (AI) have enabled a vast variety of innovative digital services. Mobile smart devices usually resort to the cloud to run deep neural networks (DNN) due to insufficient computational power or severe power constraints that precludes the use of consumer-grade on-board processors and power-hungry Graphics Processing Units (GPU). However, the use of cloud computing in service robot deployments has shortcomings related with latency, privacy, security and reliability, which often makes it inconvenient or even impractical. A possible solution is the use of specialized edge computing devices with a trade-off between onboard robot computing resources and power footprint. This approach is exploited in this paper for a service robot programmed in ROS, equipped with a camera for image perception, a 2D LiDAR for autonomous navigation, and a system on module Nvidia Jetson AGX Xavier. The viability of running DNN aboard this robot to perform image classification with low-power footprint in a Covid-19 use case scenario is demonstrated.
Aberrant functional brain network organization is associated with relapse during 1-year follow-up in alcohol-dependent patients

Alcohol dependence (AD) is a debilitating disease associated with high relapse rates even after long periods of abstinence. Thus, elucidating neurobiological substrates of relapse risk is fundamental for the development of novel targeted interventions that could promote long-lasting abstinence. In the present study, we analyzed resting-state functional magnetic resonance imaging (rsfMRI) data from a sample of recently detoxified AD patients (n = 93) who were followed-up for 12 months after rsfMRI assessment. Specifically, we employed graph theoretic analyses to compare functional brain network topology and functional connectivity between future relapsers (REL, n = 59), future abstainers (ABS, n = 28) and age and gender matched controls (CON, n = 83). Our results suggest increased whole-brain network segregation, decreased global network integration and overall blunted connectivity strength in REL compared to CON. Conversely, we found evidence for a comparable network architecture in ABS relative to CON. At the nodal level, REL exhibited decreased integration and decoupling between multiple brain systems compared to CON, encompassing regions associated with higher-order executive functions, sensory and reward processing. Among AD patients, increased coupling between nodes implicated in reward valuation and salience attribution constitutes a particular risk factor for future relapse. Importantly, aberrant network organization in REL was consistently associated with shorter abstinence duration during follow-up, portending to a putative neural signature of relapse risk in AD. Future research should further evaluate the potential diagnostic value of the identified changes in network topology and functional connectivity for relapse prediction at the individual subject level.
In Search of Disentanglement in Tandem Mass Spectrometry Datasets

Generative modeling and representation learning of tandem mass spectrometry data aim to learn an interpretable and instrument-agnostic digital representation of metabolites directly from MS/MS spectra. Interpretable and instrument-agnostic digital representations would facilitate comparisons of MS/MS spectra between instrument vendors and enable better and more accurate queries of large MS/MS spectra databases for metabolite identification. In this study, we apply generative modeling and representation learning using variational autoencoders to understand the extent to which tandem mass spectra can be disentangled into its factors of generation (e.g., collision energy, ionization mode, instrument type, etc.) with minimal prior knowledge of the factors. We find that variational autoencoders can disentangle tandem mass spectra data with the proper choice of hyperparameters into meaningful latent representations aligned with known factors of variation. We develop a two-step approach to facilitate the selection of models that are disentangled which could be applied to other complex and high-dimensional data sets.
Mechanism of substrate binding and transport in BASS transporters

The Bile Acid Sodium Symporter (BASS) family transports a wide array of molecules across membranes, including bile acids in humans, and small metabolites in plants. These transporters, many of which are sodium-coupled, have been shown to use an elevator mechanism of transport, but exactly how substrate binding is coupled to sodium ion binding and transport is not clear. Here we solve the crystal structure at 2.3 Å of a transporter from Neisseria Meningitidis (ASBTNM) in complex with pantoate, a potential substrate of ASBTNM. The BASS family is characterised by two helices that cross-over in the centre of the protein in an arrangement that is intricately held together by two sodium ions. We observe that the pantoate binds, specifically, between the N-termini of two of the opposing helices in this cross-over region. During molecular dynamics simulations the pantoate remains in this position when sodium ions are present but is more mobile in their absence. Comparison of structures in the presence and absence of pantoate demonstrates that pantoate elicits a conformational change in one of the cross-over helices. This modifies the interface between the two domains that move relative to one another to elicit the elevator mechanism. These results have implications, not only for ASBTNM but for the BASS family as a whole and indeed other transporters that work through the elevator mechanism.
End-User Programming for Smart Educational Devices: Exploring the Role of Linguistic Aspects, Mental Models, and Reasoning Strategies in Trigger-Action Rules Composition and Debugging

Enabling individuals to personalize and control the functionality of their smart devices is essential, particularly in educational environments where customized curricula are necessary for each student. To achieve this goal, End-User Programming through trigger-action rules seems to be a promising approach to empower teachers to reach this goal. However, to handle complex scenarios, End-User Programming systems must support naive users in adopting effective reasoning strategies and mental models. My Ph.D. research intends to explore specific linguistic aspects that can guide teachers in creating and debugging trigger-action rules for programming their smart educational devices, supporting them to assume effective mental models and reasoning strategies.
Body-centric and shadow-based interaction for large wall displays

We are entering an era in human-computer interaction where new display form factors, including large displays, promise to efficiently support an entire class of tasks that were not properly supported by traditional desktop computing interfaces. We develop a “body-centric” model of interaction appropriate for use with very large wall displays. We draw on knowledge of how the brain perceives and operates in the physical world, including the concepts of proprioception, interaction spaces, and social conventions, to drive the development of novel interaction techniques. The techniques we develop include an approach for embodying the user as a virtual shadow on the display, which is motivated by physical shadows and their affordances. Other techniques include methods for selecting and manipulating virtual tools, data, and numerical values by enlisting different parts of the user’s body, methods for easing multi-user collaboration by exploiting social norms, and methods for mid-air text input. We then present a body-centric architecture for supporting the implementation of interaction techniques such as the ones we designed. The architecture maintains a computational geometric model of the entire scene, including users, displays, and any relevant physical objects, which a developer can query in order to develop novel interaction techniques or applications. Finally, we investigate aspects of low-level human performance relevant to a body-centric model. We conclude that traditional models of performance, particularly Fitts’ law, are inadequate when applied to physical pointing on large displays where controldisplay gain can vary widely, and we show that an approach due to Welford is more suitable. Our investigations provide a foundation for a comprehensive body-centric model of interaction with large wall displays that will enable a number of future research directions.
Who is publishing in ecology and evolution? the underrepresentation of women and the Global South

Introduction: Most global biodiversity is in developing economies. Decades of capacity building should have built sufficient in-country capacity to develop biodiversity baselines; yet has effort provided the expertise to build these baselines? Methods: Grants and access to research opportunities are often linked to success in publishing, with the H-index providing the main metric of academic success. Recent compilations of “Top Researchers in Ecology and Evolution” included 5,419 researchers, but where these researchers are and how representative they are has not been well studied. We explored the global distribution of “Top Researchers in Ecology and Evolution” and explored the representation of Women, non-Caucasians, and non-Caucasian women, as well as the representation of “local” top researchers in different regions. Results: Over half Top Researchers in Ecology and Evolution are from just three countries (United States, United Kingdom, and Australia), and 83% come from 12 higher-income countries. Even in lower-income economies the majority of the few “high impact” researchers are originally from higher-income economies. Only China had a high proportion of their high-impact non-Caucasian researchers, with the majority of researchers coming from that region. Women were also underrepresented across the globe, only three countries had more than 20% of top-performing ecologists being female. Discussion: Ultimately, despite decades of capacity building, we are still failing to build in-country capacity for research or to provide sufficient support for female ecologists to publish and lead the field. Here we discuss why these issues persist, and how we might improve representation and access to opportunity and support for all groups, and provide the analysis needed to provide solutions to global challenges in biodiversity conservation, which require diverse representation to develop effective, and nuanced solutions.
Governor vessel moxibustion for cancer-related fatigue in colorectal patients: a randomized trial

Objective This study aimed to investigate the efficacy and mechanism of action of Governor Vessel Moxibustion (GVM) in the treatment of Cancer-Related Fatigue (CRF) in patients who have completed treatment for colorectal cancer. Methods We randomly assigned 80 CRF patients in a 1:1 ratio to either the experimental group or the control group. During the three-week treatment period, both groups of patients received usual care for CRF provided by professional nurses. The experimental group received additional GVM treatment (three times a week, nine times total). The primary outcome was the mean change in total fatigue score from baseline to the end of treatment, assessed using the Chinese version of the Piper Fatigue Scale. Results At baseline, the total fatigue scores were 6.20 ± 0.12 in the experimental group and 6.16 ± 0.14 in the control group. At the end of treatment, the total fatigue scores decreased by 2.03 points (32.7% decrease from baseline) in the experimental group and by 0.99 points (15.6% decrease from baseline) in the control group. The absolute reduction in total fatigue scores in the experimental group was 1.04 points higher than in the control group (95% CI, 0.93 to 1.15; P<0.001), corresponding to a relative difference of 17.1% (95% CI, 15.2% to 18.9%; P<0.001). At the end of treatment, the experimental group showed greater reductions in interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) levels compared to the control group. No serious adverse events related to GVM treatment were observed. Conclusion GVM appears to be safe and effective for alleviating CRF in patients who have completed colorectal cancer treatment, which may be related to the modulation of IL-6 and TNF-α levels. Trial registration Chinese Clinical Trials Registry: ChiCTR2300069208.
Reservoir based spiking models for univariate Time Series Classification

A variety of advanced machine learning and deep learning algorithms achieve state-of-the-art performance on various temporal processing tasks. However, these methods are heavily energy inefficient—they run mainly on the power hungry CPUs and GPUs. Computing with Spiking Networks, on the other hand, has shown to be energy efficient on specialized neuromorphic hardware, e.g., Loihi, TrueNorth, SpiNNaker, etc. In this work, we present two architectures of spiking models, inspired from the theory of Reservoir Computing and Legendre Memory Units, for the Time Series Classification (TSC) task. Our first spiking architecture is closer to the general Reservoir Computing architecture and we successfully deploy it on Loihi; the second spiking architecture differs from the first by the inclusion of non-linearity in the readout layer. Our second model (trained with Surrogate Gradient Descent method) shows that non-linear decoding of the linearly extracted temporal features through spiking neurons not only achieves promising results, but also offers low computation-overhead by significantly reducing the number of neurons compared to the popular LSM based models—more than 40x reduction with respect to the recent spiking model we compare with. We experiment on five TSC datasets and achieve new SoTA spiking results (—as much as 28.607% accuracy improvement on one of the datasets), thereby showing the potential of our models to address the TSC tasks in a green energy-efficient manner. In addition, we also do energy profiling and comparison on Loihi and CPU to support our claims.
Psychometric properties of the Sport Courage Scale for Chinese athletes

Objective To revise Sport Courage Scale (SCS) suitable for Chinese athletes. Methods Six hundred and eighty three athletes were selected for verification factor analysis, correlation analysis, reliability analysis, and independent sample t-test using the method of random sampling of the entire group. Results Confirmatory factor analysis model showed that model 1 (25 items) failed to fit the data; while model 2 (20 items) was finally accepted with its five-factor model. The factor structure consists of 5 dimensions (χ2/df = 2.262;CFI = 0.969;TLI = 0.963; RMSEA = 0.043; SRMR = 0.044). Cronbach’s α of the final version of SCS was 0.845, and the corrected correlation coefficient between the items and the total score of the scale was between 0.352 and 0.788. Conclusion Revised SCS has good reliability and validity and can be used as a measurement tool for the sports courage of athletes in China.