Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Not Give a Dog Bread Every Time He Wags His Tail: Stealing Passwords through Content Queries (CONQUER) Attacks

—Android accessibility service was designed to assist individuals with disabilities in using Android devices. However, it has been exploited by attackers to steal user passwords due to design shortcomings. Google has implemented various countermeasures to make it difficult for these types of attacks to be successful on modern Android devices. In this paper, we present a new type of side channel attack called content queries (C ONQUER ) that can bypass these defenses. We discovered that Android does not prevent the content of passwords from being queried by the accessibility service, allowing malware with this service enabled to enumerate the combinations of content to brute force the password. While this attack seems simple to execute, there are several challenges that must be addressed in order to successfully launch it against real-world apps. These include the use of lazy query to differentiate targeted password strings, active query to determine the right timing for the attack, and timing-and state-based side channels to infer case-sensitive passwords. Our evaluation results demonstrate that the C ONQUER attack is effective at stealing passwords, with an average one-time success rate of 64.91%. This attack also poses a threat to all Android versions from 4.1 to 12, and can be used against tens of thousands of apps. In addition, we analyzed the root cause of the C ONQUER attack and discussed several countermeasures to mitigate the potential security risks it poses.
Adaptive self-learning controllers with disturbance compensation for automatic track guidance of industrial trucks

ABSTRACT This paper presents an extended control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a simplified linear model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step using a more complex nonlinear model, representing the real industrial truck. In this way, the controller is adapted to the actual truck variant and the corresponding parameter values. By using the nonlinear model, it can be ensured that the forklift's dynamic is approximated within the entire operating range, even at high steering angles. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significantly improves the control quality. In order to demonstrate this, the new approach is compared to an RL control concept, which is not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach.
Bayesian model for sustainable, resilient, and cost-effective concrete mixtures

ABSTRACT This work develops a Bayesian model that can be used to proportion sustainable, resilient, and cost-effective concrete mixtures based on stakeholder preferences. Traditionally, the decision-making process for proportioning concrete mixtures is centered around meeting target specifications, such as slump and compressive strength, and attributes such as sustainability and durability are given minimal importance. However, in the global push towards green infrastructure, sustainability plays a major role and must be considered to the same degree as engineering attributes. The model developed herein accounts for embodied carbon footprint, cost, and time to corrosion initiation, apart from the target specifications commonly used to proportion concrete mixtures. The developed model is demonstrated through two case studies: one targeting the average industrial embodied carbon footprint for a concrete mixture and the other targeting a reduction of 40% from the industry’s carbon footprint average. This model can also be used to perform data visualization and sensitivity analyses.
Comparing outcomes of ultra-low-cost hearing aids to programmable, refurbished hearing aids for adults with high frequency hearing loss in Malawi: a feasibility study

Introduction Access to ear and hearing health services are limited or non-existent in low-income countries, with less than 10% of the global production of hearing aids distributed to this population. The aim of this feasibility study was to compare the outcomes of an ultra-low-cost hearing aid (LoCHAid) to programmable, refurbished hearing aids for adults with high-frequency hearing loss, in Blantyre, Malawi. Methods Sixteen adults with high frequency hearing loss, and no prior experience of hearing aids, took part in this study, nine were fitted with the LoCHAid and seven were fitted with refurbished, programmable hearing aids, for a one-month trial. Five standardized hearing qualities questionnaires were used to compare outcomes pre and post device fitting and between devices. Questionnaire scales were analysed using general linear models and inductive thematic analysis was used to evaluate qualitative data. Results Overall, there was no significant difference found between LoCHAid and refurbished hearing aids, and the two device types each showed a similar degree of improvement after fitting. Qualitative data analysis identified two key themes: Sound Quality and User experience. Conclusion The results from this feasibility study are encouraging, but a comprehensive, larger clinical study is needed to draw firm conclusions about the LoCHAid's performance. This study has identified key improvement indicators required to enhance sound quality and user experience of the LoCHAid.
Prediction of MCI-to-AD progression with atrophy-weighted standard uptake value ratios of 18F-Florbetapir PET

Accurate prediction of MCI-to-AD progression is an important yet challenging task. We introduce a new quantitative parameter: the atrophy-weighted standard uptake value ratio (awSUVR), defined as the PET SUVR divided by the hippocampal volume measured with MR, and evaluate whether it may provide better prediction of the MCI-to-AD progression. Materials and Methods: We used ADNI data to evaluate the prediction performances of the awSUVR against SUVR. 571, 363 and 252 18-F-Florbetaipir scans were selected based on criteria of conversion at the third, fifth and seventh year after the PET scans, respectively. Corresponding MR scans were segmented with Freesurfer and applied on PET for SUVR and awSUVR computation. We also searched for the optimal combination of target and reference regions. In addition to evaluating the overall prediction performances, we also evaluated the prediction for APOE4 carriers and non-carriers. For the scans with false predictions, we used 18-F-Flortaucipir scans to investigate the potential source of error. Results: awSUVR provides more accurate prediction than the SUVR in all three progression criteria. The 5-year prediction accuracy/sensitivity/specificity is 90/81/93% for awSUVR and 86/81/88% for SUV. awSUVR also yields good 3- and 7-year prediction accuracy/sensitivity/specificity of 91/57/96 and 92/89/93, respectively. APOE4 carriers generally are slightly more difficult to predict for the progression. False negative prediction is found to either due to a near-cutoff mis-classification or potentially non-AD dementia pathology. False positive prediction is mainly due to the slightly delayed progression than the expected progression time. Conclusion: We demonstrated with ADNI data that 18-F-Florbetapir SUVR weighted with hippocampus volume may provide good prediction power with over 90% accuracy in MCI-to-AD progression.
Adsorption of Therapeutic and Recreational Drugs During Prolonged Storage of Plasma Samples in Gel Separator Tubes

Abstract Hospital samples collected in gel separator tubes are often submitted to forensic toxicology laboratories for analysis in impaired driving and death investigations. Drug adsorption to the gel separator material may lead to underestimation of the drug concentration present at the time of sample collection, potentially affecting the interpretation of analytical results. Using liquid chromatography--tandem mass spectrometry (LC--MS-MS), decreases in plasma concentration of 53 drugs and metabolites relevant to forensic toxicology casework were investigated in samples stored in BD Vacutainer® PSTTM tubes for up to 3 months. After storage for only 1 day, approximately 50% of the drugs and metabolites had significantly lower concentrations in plasma separation tubes (PSTs) compared to non-gel tubes (up to 27% lower). After storage for 3 months, approximately 75% of the drugs and metabolites had significantly lower concentrations in PSTs compared to non-gel tubes (up to 69% lower). Fentanyl, carfentanil, ketamine, diphenhydramine and several antidepressants were among the drugs most susceptible to adsorption. Central nervous system stimulants (e.g., methamphetamine and amphetamine) as well as naturally-occurring and semi-synthetic opioids (e.g., morphine, hydromorphone and oxycodone) were among the drugs least susceptible to adsorption and displayed only minimal relative decreases in concentration (if any) over the 3-month sample storage period. The potential for decreases in drug concentration due to adsorption of drugs to the gel material should be considered for toxicological interpretation based on the analysis of a sample collected in a gel separator tube.
The oxygen dissociation curve of blood in COVID-19–An update

An impressive effect of the infection with SARS-Co-19 is the impairment of oxygen uptake due to lung injury. The reduced oxygen diffusion may potentially be counteracted by an increase in oxygen affinity of hemoglobin. However, hypoxia and anemia associated with COVID-19 usually decrease oxygen affinity due to a rise in [2,3-bisphosphoglycerate]. As such, COVID-19 related changes in the oxygen dissociation curve may be critical for oxygen uptake and supply, but are hard to predict. A Pubmed search lists 14 publications on oxygen affinity in COVID-19. While some investigations show no changes, three large studies found an increased affinity that was related to a good prognosis. Exact causes remain unknown. The cause of the associated anemia in COVID-19 is under discussion. Erythrocytes with structural alterations of membrane and cytoskeleton have been observed, and virus binding to Band 3 and also to ACE2 receptors in erythroblasts has been proposed. COVID-19 presentation is moderate in many subjects suffering from sickle cell disease. A possible explanation is that COVID-19 counteracts the unfavorable large right shift of the oxygen dissociation curve in these patients. Under discussion for therapy are mainly affinity-increasing drugs.
Histological diagnosis of unprocessed breast core-needle biopsy via stimulated Raman scattering microscopy and multi-instance learning

Core-needle biopsy (CNB) plays a vital role in the initial diagnosis of breast cancer. However, the complex tissue processing and global shortage of pathologists have hindered traditional histopathology from timely diagnosis on fresh biopsies. In this work, we developed a full digital platform by integrating label-free stimulated Raman scattering (SRS) microscopy with weakly-supervised learning for rapid and automated cancer diagnosis on un-labelled breast CNB. Methods: We first compared the results of SRS imaging with standard hematoxylin and eosin (H&E) staining on adjacent frozen tissue sections. Then fresh unprocessed biopsy tissues were imaged by SRS to reveal diagnostic histoarchitectures. Next, weakly-supervised learning, i.e., the multi-instance learning (MIL) model was conducted to evaluate the ability to differentiate between benign and malignant cases, and compared with the performance of supervised learning model. Finally, gradient-weighted class activation mapping (Grad-CAM) and semantic segmentation were performed to spatially resolve benign/malignant areas with high efficiency. Results: We verified the ability of SRS in revealing essential histological hallmarks of breast cancer in both thin frozen sections and fresh unprocessed biopsy, generating histoarchitectures well correlated with H&E staining. Moreover, we demonstrated that weakly-supervised MIL model could achieve superior classification performance to supervised learnings, reaching diagnostic accuracy of 95% on 61 biopsy specimens. Furthermore, Grad-CAM allowed the trained MIL model to visualize the histological heterogeneity within the CNB. Conclusion: Our results indicate that MIL-assisted SRS microscopy provides rapid and accurate diagnosis on histologically heterogeneous breast CNB, and could potentially help the subsequent management of patients.
TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions

Recently, more and more people study online for the convenience of access to massive learning materials (e.g. test questions/notes), thus accurately understanding learning materials became a crucial issue, which is essential for many educational applications. Previous studies focus on using language models to represent the question data. However, test questions (TQ) are usually heterogeneous and multi-modal, e.g., some of them may only contain text, while others half contain images with information beyond their literal description. In this context, both supervised and unsupervised methods are difficult to learn a fused representation of questions. Meanwhile, this problem cannot be solved by conventional methods such as image caption, as the images may contain information complementary rather than duplicate to the text. In this paper, we first improve previous text-only representation with a two-stage unsupervised instance level contrastive based pre-training method (MCL: Mixture Unsupervised Contrastive Learning). Then, TQ-Net was proposed to fuse the content of images to the representation of heterogeneous data. Finally, supervised contrastive learning was conducted on relevance prediction-related downstream tasks, which helped the model to learn the representation of questions effectively. We conducted extensive experiments on question-based tasks on large-scale, real-world datasets, which demonstrated the effectiveness of TQ-Net and improve the precision of downstream applications (e.g. similar questions +2.02% and knowledge point prediction +7.20%). Our code will be available, and we will open-source a subset of our data to promote the development of relative studies.
Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision

Many engineering organizations are reimplementing and extending deep neural networks from the research community. We describe this process as deep learning model reengineering. Deep learning model reengineering - reusing, reproducing, adapting, and enhancing state-of-the-art deep learning approaches - is challenging for reasons including under-documented reference models, changing requirements, and the cost of implementation and testing. In addition, individual engineers may lack expertise in software engineering, yet teams must apply knowledge of software engineering and deep learning to succeed. Prior work has examined on DL systems from a"product"view, examining defects from projects regardless of the engineers' purpose. Our study is focused on reengineering activities from a"process"view, and focuses on engineers specifically engaged in the reengineering process. Our goal is to understand the characteristics and challenges of deep learning model reengineering. We conducted a case study of this phenomenon, focusing on the context of computer vision. Our results draw from two data sources: defects reported in open-source reeengineering projects, and interviews conducted with open-source project contributors and the leaders of a reengineering team. Our results describe how deep learning-based computer vision techniques are reengineered, analyze the distribution of defects in this process, and discuss challenges and practices. Integrating our quantitative and qualitative data, we proposed a novel reengineering workflow. Our findings inform several future directions, including: measuring additional unknown aspects of model reengineering; standardizing engineering practices to facilitate reengineering; and developing tools to support model reengineering and model reuse.
Fast and accurate AMS-02 antiproton likelihoods for global dark matter fits

The antiproton flux measurements from AMS-02 offer valuable information about the nature of dark matter, but their interpretation is complicated by large uncertainties in the modeling of cosmic ray propagation. In this work we present a novel framework to efficiently marginalise over propagation uncertainties in order to obtain robust AMS-02 likelihoods for arbitrary dark matter models. The three central ingredients of this framework are: the neural emulator , which provides highly flexible predictions of the antiproton flux; the likelihood calculator , which performs the marginalisation, taking into account the effects of solar modulation and correlations in AMS-02 data; and the global fitting framework , which allows for the combination of the resulting likelihood with a wide range of dark matter observables. We illustrate our approach by providing updated constraints on the annihilation cross section of WIMP dark matter into bottom quarks and by performing a state-of-the-art global fit of the scalar singlet dark matter model, including also recent results from direct detection and the LHC.
Water dissociation on pristine low-index TiO2 surfaces

Water absorption and dissociation processes on pristine low-index TiO$_2$ interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To understand these, we construct three sets of machine learning potentials that are simultaneously applicable to various TiO$_2$ surfaces, based on three density-functional-theory approximations. We compute the water dissociation free energies on seven pristine TiO$_2$ surfaces, and predict that anatase (100), anatase (110), rutile (001), and rutile (011) surfaces favor water dissociation, anatase (101) and rutile (100) surfaces have mostly molecular absorption, while the calculated dissociation fraction on rutile (110) surface may depend on the density functional assumed. Moreover, using an automated algorithm, we reveal that these surfaces follow different types of atomistic mechanisms for proton transfer and water dissociation: one-step, two-step, or both. Our finding thus demonstrates that the different pristine TiO$_2$ surfaces react with water in distinct ways, and cannot be represented using just the low-energy anatase (101) and rutile (110) surfaces.
FPT Constant-Approximations for Capacitated Clustering to Minimize the Sum of Cluster Radii

Clustering with capacity constraints is a fundamental problem that attracted significant attention throughout the years. In this paper, we give the first FPT constant-factor approximation algorithm for the problem of clustering points in a general metric into $k$ clusters to minimize the sum of cluster radii, subject to non-uniform hard capacity constraints. In particular, we give a $(15+\epsilon)$-approximation algorithm that runs in $2^{0(k^2\log k)}\cdot n^3$ time. When capacities are uniform, we obtain the following improved approximation bounds: A (4 + $\epsilon$)-approximation with running time $2^{O(k\log(k/\epsilon))}n^3$, which significantly improves over the FPT 28-approximation of Inamdar and Varadarajan [ESA 2020]; a (2 + $\epsilon$)-approximation with running time $2^{O(k/\epsilon^2 \cdot\log(k/\epsilon))}dn^3$ and a $(1+\epsilon)$-approximation with running time $2^{O(kd\log ((k/\epsilon)))}n^{3}$ in the Euclidean space; and a (1 + $\epsilon$)-approximation in the Euclidean space with running time $2^{O(k/\epsilon^2 \cdot\log(k/\epsilon))}dn^3$ if we are allowed to violate the capacities by (1 + $\epsilon$)-factor. We complement this result by showing that there is no (1 + $\epsilon$)-approximation algorithm running in time $f(k)\cdot n^{O(1)}$, if any capacity violation is not allowed.
GANN: Graph Alignment Neural Network for Semi-Supervised Learning

Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning. Most methods fail to exploit adequate graph information when labeled data is limited, leading to the problem of oversmoothing. To overcome this issue, we propose the Graph Alignment Neural Network (GANN), a simple and effective graph neural architecture. A unique learning algorithm with three alignment rules is proposed to thoroughly explore hidden information for insufficient labels. Firstly, to better investigate attribute specifics, we suggest the feature alignment rule to align the inner product of both the attribute and embedding matrices. Secondly, to properly utilize the higher-order neighbor information, we propose the cluster center alignment rule, which involves aligning the inner product of the cluster center matrix with the unit matrix. Finally, to get reliable prediction results with few labels, we establish the minimum entropy alignment rule by lining up the prediction probability matrix with its sharpened result. Extensive studies on graph benchmark datasets demonstrate that GANN can achieve considerable benefits in semi-supervised node classification and outperform state-of-the-art competitors.
Exploring ChatGPT's Ability to Rank Content: A Preliminary Study on Consistency with Human Preferences

As a natural language assistant, ChatGPT is capable of performing various tasks, including but not limited to article generation, code completion, and data analysis. Furthermore, ChatGPT has consistently demonstrated a remarkable level of accuracy and reliability in terms of content evaluation, exhibiting the capability of mimicking human preferences. To further explore ChatGPT's potential in this regard, a study is conducted to assess its ability to rank content. In order to do so, a test set consisting of prompts is created, covering a wide range of use cases, and five models are utilized to generate corresponding responses. ChatGPT is then instructed to rank the responses generated by these models. The results on the test set show that ChatGPT's ranking preferences are consistent with human to a certain extent. This preliminary experimental finding implies that ChatGPT's zero-shot ranking capability could be used to reduce annotation pressure in a number of ranking tasks.
Selective excitation and photoinduced bleaching of defects in InAlGaAs/GaAs high-power diode lasers

Mounting-induced defects in semiconductor quantum-well (QW) lasers are investigated by photocurrent spectroscopy. The defects are located in the laser waveguides and give rise to an absorption band below the QW band gap with a maximum absorption cross section of σ=2×10−15 cm2. We observe a nonlinear fully reversible photobleaching of the defects and a resulting increase of QW photocurrent upon continuous wave irradiation of the devices, demonstrating a direct interaction between quantum-confined carriers and a defect level.