Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward a Realistic Benchmark for Out-of-Distribution Detection

Deep neural networks are increasingly used in a wide range of technologies and services, but remain highly susceptible to out-of-distribution (OOD) samples, that is, drawn from a different distribution than the original training set. A common approach to address this issue is to endow deep neural networks with the ability to detect OOD samples. Several benchmarks have been proposed to design and validate OOD detection techniques. However, many of them are based on farOOD samples drawn from very different distributions, and thus lack the complexity needed to capture the nuances of real-world scenarios. In this work, we introduce a comprehensive benchmark for OOD detection, based on ImageNet and Places365, that assigns individual classes as in-distribution or out-of-distribution depending on the semantic similarity with the training set. Several techniques can be used to determine which classes should be considered in-distribution, yielding benchmarks with varying properties. Experimental results on different OOD detection techniques show how their measured efficacy depends on the selected benchmark and how confidence-based techniques may outperform classifier-based ones on near-OOD samples.
Broadband optoelectronic properties of p-CuCrO2/n-Si Heterojunction prepared by Pulsed Laser Deposition

A broadband spectral response from ultraviolet to infrared photon energies is reported in p-CuCrO2/n-Si heterojunction prepared through Pulse Laser Deposition (PLD) technique. The device is fabricated at high oxygen pressure (7 ×10-1 mbar) by employing a Q-switched Nd: YAG Laser using p-type polycrystalline metastable 2H-Copper Chromium Oxide (CuCrO2) pellet and monocrystalline n-type Silicon wafer. X-ray diffraction analysis confirms the hexagonal phase of CuCrO2 on Si substrate. The low value of crystalline strain (0.14%) suggests the relaxed and stable formation of CuCrO2 layer. Atomic Force Microscopy data shows a uniform and crack-free nature of the heterojunction. The optical band gap of CuCrO2 layer is found to be 3.23eV via UV spectroscopy. The I-V characteristics of heterojunction under various spectral photon energies are performed and analyzed. Optoelectronic parameters such as photocurrent, responsivity, detectivity, and Noise Equivalent Power are examined for advanced optoelectronic applications including broadband photodetector.
How Android Apps Break the Data Minimization Principle: An Empirical Study

The Data Minimization Principle is crucial for protecting individual privacy. However, existing Android runtime permissions do not guarantee this principle. Moreover, the lack of an automatic enforcement mechanism leads to uncertainty as to whether apps strictly comply with this principle. To bridge this gap, we conduct the first systematic empirical study on violations of the Data Minimization Principle and design a new enforcement tool called GUIMind to detect them. GUIMind first utilizes a reinforcement learning model to explore app activities and monitor access to sensitive APIs that require sensitive permissions, and then it leverages an existing tool to detect such violations. We evaluate the performance of GUIMind using 120 real-world Android apps. The results indicate that GUIMind can achieve a detection accuracy of 96.1%, effectively accelerating the empirical study. Our empirical research is mainly focused on the prevalence of violations, the responses of administrators to violations, and the potential factors and characteristics that lead to violations, such as typical violations, app categories, and personal data types. Our study reveals that 83.5% of apps contain at least one privacy violation, with health apps being the most severe. In addition, telephony information is the most commonly leaked personal data type, accounting for 71.1%. Finally, we randomly selected 60 non-compliant apps for reporting to the administrator, whose responses confirm the effectiveness of our approach.
Sparse Recovery for ISAR Imaging via Nuclear Norm Minimization

Missing entries of the backscattered data matrix deteriorate the quality of the resulting ISAR images. In this paper a data recovery method based on nuclear norm minimization (NNM) to recover the missing samples is proposed. Real and imaginary parts of the data are completed separately and a higher quality ISAR image is obtained by the 2D Fourier trans-form of the recovered matrix. The method has been compared to inexact augmented Lagrangian multipliers (IALM) and 2D smoothed LO (2D-SLO) methods both visually and quantitatively under three missing scenarios for two different missing ratios. The performance improvement is handled for both quantitative metrics such as root mean square error (RMSE) and correlation. For proposed method, RMSE scores difference can be higher than % 50 for the some extreme cases, whereas improvements in the correlation scores generally vary between %5-%10 when compared to the other methods.
The Study of Complex Manipulation via Kinematic Hand Synergies: The Effects of Data Pre-Processing

The study of kinematic hand synergies through matrix decomposition techniques, such as singular value decomposition, supports the theory that humans might control a subspace of predefined motions during manipulation tasks. These subspaces are often referred to as synergies. However, different data pre-processing methods lead to quantitatively different conclusions about these synergies. In this work, we shed light on the role of data pre-processing on the study of hand synergies by analyzing both numerical simulation and real kinematic data from a complex manipulation task, i.e., piano playing. The results obtained suggest that centering the data, by removing the mean, appears to be the most appropriate preprocessing technique for studying kinematic hand synergies.
Stratigraphy and tectonic implications of Upper Cretaceous rocks in the Powder River basin, northeastern Wyoming and southeastern Montana

Tl Introduction Tl Stratigraphy T10 Rocks of Cenomanian and early Turonian age T14 Mowry Shale T14 Belle Fourche Shale and Formation T17 Belle Fourche Member of the Frontier Formation T26 Greenhorn Formation T29 Regional hiatus for late Cenomanian and early Turonian time T30 Rocks of middle Turonian age T30 Pool Creek Member of the Carlile Shale and strata in the Carlile Member of the Cody Shale T30 Emigrant Gap Member of the Frontier Formation T31 Regional hiatus for middle and late Turonian time T32 Rocks of late Turonian age T32 Turner Sandy Member of the Carlile Shale and strata in the Carlile Member of the Cody Shale T32 Wall Creek Member of the Frontier Formation T34 Rocks of late Turonian and Coniacian age T36 Sage Breaks Member of the Carlile Shale and strata in the Carlile Member of the Cody Shale T36 Sage Breaks Member of the Cody Shale T37 Regional hiatus mainly for Coniacian time T37 Rocks of late Coniacian, Santonian, and early Campanian age T38 Niobrara Formation T38 Niobrara Member of the Cody Shale T41 Gammon Member of the Pierre Shale and Gammon Member of the Cody Shale T41 Lower part of the Steele Member and the enclosed Shannon Sandstone and Sussex Sandstone Beds of the Cody Shale T44 Regional hiatus for early Campanian time T47 Rocks of early to late Campanian age T47 Sharon Springs Member of the Pierre Shale T47 Mitten Member of the Pierre Shale and Claggett Member of the Cody Shale T48 Upper part of the Steele Member of the Cody Shale T48 Red Bird Silty Member of the Pierre Shale and Parkman Sandstone Member of the Judith River Formation T52 Parkman Sandstone Member of the Mesaverde Formation T55 Lower unnamed shale member of the Pierre Shale and laterally equivalent beds in the Judith River Formation and Bearpaw Shale T57 Unnamed marine shale member of the Mesaverde Formation T63 Regional hiatus for latest Campanian time T64 Rocks of latest Campanian and Maastrichtian age T64 Kara Bentonitic Member and upper unnamed shale member of the Pierre Shale and an upper part of the Bearpaw Shale T64 Teapot Sandstone Member of the Mesaverde Formation T65 Lewis Shale T66
Fortifying IoT Devices: AI-Driven Intrusion Detection via Memory-Encoded Audio Signals

IoT devices have become an increasingly accessible target for evasive attacks, such as botnets, due to insecure network services, deprecated software components, unencrypted data communication, and other vulnerabilities. To address these security concerns, our work makes several significant contributions toward curating datasets and designing and developing a robust and effective Host-Based Intrusion Detection algorithm (HIDS) for IoT devices. The proposed algorithm leverages memory-based fingerprints to train a convolutional neural network (CNN) model. Our approach is based on the premise that despite the heterogeneity of IoT devices, the functionality of each IoT device is often unique and remains relatively constant throughout its lifespan. Thus, to develop an effective IDS algorithm based on anomaly detection, we encode the dynamic IoT device memory into sound wave signals, extract discriminable features such as MFCCs and Chroma features, and pass these deterministic fingerprints as feature vectors to a device-specific CNN model. When trained using three different testbed datasets, our model achieved 100% accuracy for known and anomaly memory instances. Additionally, the evaluation of our features and feature engineering process showed that our model, which is trained with the features from memory-encoded signals, is more reliable and robust than those algorithms that leverage raw memory bytes as feature vectors.
cegar-pt: A Tool for Abstraction by Program Transformation

Abstraction is an important approach for proving the correctness of computer programs. There are many implementations of this approach available, but unfortunately, the various implementations are difficult to reuse and combine, and the successful techniques have to be re-implemented in new tools again and again. We address this problem by contributing the tool cegar-pt, which views abstraction as program transformation and integrates different verification components off-the-shelf. The idea is to use existing components without having to change their implementation, while still adjusting the precision of the abstraction using the successful CEGAR approach. The approach of cegar-pt is largely general: It only restricts the abstraction to transform, given a precision that defines the level of abstraction, one program into another program. The abstraction by program transformation can over-approximate the data flow (e.g., havoc some variables, use more abstract types) or the control flow (e.g., loop abstraction, slicing). To illustrate our tool, we provide a demonstration video, accessible at https://youtu.be/ASZ6hoq8asE.
The Incidence, Common Presentation, Management and Outcome of Children with Bronchiolitis Admitted in Dubai Hospital Between 2016 ‐

In a retrospective study done over a period of 1 year in Dubai Hospital, the course of children admitted with Bronchiolit-is under 2 years of age was studied (2016 - 2017). A total of 119 children were included in the study. Their common presenting signs and symptoms such as cough, respiratory distress, fever and appetite, along with their management with either bronchodilator alone or a combination of: bronchodilators with hypertonic saline antibiotics, steroids was analyzed. Their length of stay, organism isolated either RSV or others, age & season at admission along with their gender and nationality was also recorded. Using this data, it was evaluated what was the overall number of admissions of children with bronchiolitis that year, most common presenting features and most common organism isolated along with seasonal variation & most commonly used medications for management. Further analysis was done on various treatments used in association with signs and symptoms, different seasons and with length of hospitalization. It also looked into age & gender association with presenting signs/symptoms, length of hospital stay & treatment. Our results showed that incidence of bronchiolitis over 1 year, amongst children under 2 years of age was as high as 91%. In concordance with other studies, (13) majority of children presented at nearly 4 – 5 months of age & the most common presenting feature was cough with respiratory distress & fever. RSV was the most common causative organism and more prevalent in winter months. Predominant treatment plan used was a combination of bronchodilators with either hypertonic saline and/or antibiotics, with an average length of hospitalization of 2 days. There was no statistically significant difference in age of admission or length of hospitalization in boys or girls. Older children tended to have a shorter hospital stay and there was a statistically significant correlation between use of bronchodilators with or without other medications to length of hospitalization.
INSPIIRE – A Modular and Passive Exoskeleton to Investigate Human Walking and Balance

Powered exoskeletons for SCI patients are mainly limited by their inability to balance dynamically during walking. To investigate and understand the control strategies of human bipedal locomotion, we developed INSPIIRE, a passive exoskeleton. This device constrains the movements of able-bodied subjects to only hip and knee flexions and extensions, similar to most current active exoskeletons. In this paper, we detail the modular design and the mechanical implementation of the device. In preliminary experiments, we tested whether humans are able to handle dynamic walking without crutches, despite the limitation of lateral foot placement and locked ankles. Five healthy subjects showed the ability to stand and ambulate at an average speed of 1 m/s after 5 minutes of self-paced training. We found that while the hip abduction/adduction is constrained, the foot placement was made possible thanks to the pelvis yaw and residual flexibility of the exoskeleton segments in the lateral plan. This result points out that INSPIIRE is a reliable instrument to learn sagitally-constrained human locomotion, and the potential of investigating more dynamic walking, which is shown as possible in this implementation, even if only flexion/extension of the hip and knee are allowed.
Bioengineered gas vesicle nanostructure increases the ultrasound backscattered signal amplitude in presence of tumoral enzyme Cathepsin B

Gas vesicles (GVs), nanostructure gas-filled protein-shelled compartments, act as ultrasound contrast agents (UCAs). Recent works report bioengineered GvpC, one of the shell proteins, potentializes interactions with specific molecules in tumor microenvironment. An important target of investigation is Cathepsin B (CTSB), an enzyme produced by several tumors. CTSB acts in the tumoral microenvironment to increase tumor growth and invasion. This work objective relied on bioengineering the GVs to promote the GvpC-CTSB interaction with a subsequent increase of ultrasound (US) signal backscattered. GvpC was engineered to contain a known CTSB cleavage sequence re-added in striped GV (ΔGV). Western Blot (WB) test was used to compare the GVwt (wild type) and GV* (GV engendered) before and after 24 hours reaction with CTSB. US images for PBS (negative control), GVtw, ΔGV, GV* and CTSB+GV*, were obtained in 21MHz (5% total power) and regions-of-interest (ROIs) over the final B-mode image were determined. The WB test for GVwt resulted in mean (±SD) GvpC concentrations of 25169 (±8574) and 32873 (±9113) and GV* was 32487 (±6867) and 12763 (±2846), at t0 and t24, respectively. The US image gray level intensities for the ROIs related to the samples of PBS, GVwt, ΔGV, GV* and CTSB+GV* were 55.8 (±17.6), 47.4 (±16.6), 46.1 (±22.8) and 150.2 (±24), respectively. These results reveal an increase in the US signal backscattered from GV* after CTSV reaction. This increase is likely related to a direct GvpC degradation by CTSB, without affecting the GV* structure. In conclusion, high frequency US B-mode images showed the GV* with the potential to act as an UCA sensitive to the CTSB presence in tumoral microenvironment.
Validation and Implementation of Key Technologies for the Application of 5G NR in LEO Satellite Communication

With the advancement of fifth-generation (5G) new radio (NR) technology, there has been a growing interest in the study of non-terrestrial networks (NTN), particularly in the context of satellite communication. The validation and implementation of low earth orbit (LEO) satellite communication within NTN has gained considerable attention. However, this field poses various challenges, including significant time delays and time-frequency synchronization issues due to Doppler shift. This paper focuses on addressing the time-frequency synchronization problem encountered in LEO satellite communication and mobile phone connectivity. It proposes two experimental approaches that utilize an ultra-wideband channel emulator. These approaches encompass a two-step random access (RA) with mapping relationship experiments involving RACH occasions (RO) and PUSCH occasions (PO), as well as dynamic scheduling implementation. The proposed schemes aim to facilitate initial access and dynamic scheduling for effective communication between satellites and mobile phones.
Production Model Based on Lean Tools to Increase the Overall Efficiency of the Equipment in the Sole Pressing Process of a Footwear Company

The present investigation was carried out in a rubber soles production company in the footwear industry in Peru, which belongs to the manufacturing sector. In this sector, several problems affecting the Overall Equipment Effectiveness (OEE) of small and medium-sized companies were identified, either due to a large amount of downtime, low availability and performance of equipment, or low quality, among others; this study has its main objective to improve OEE according to the global industry standard (85%). The proposed solution implemented a production model in which SMED, TPM, and Poka-Yoke methodologies were applied. With this, the simulation of the current and the improved situation was carried out with the Arena software. Additionally, it was demonstrated that the proposed model improved the OEE to 86.17% because availability, performance, and quality improved to 95.85%, 89.95%, and 99.94%, respectively. Finally, this research was done to contribute to the sector's growth and to be a reference for companies with similar processes that want to increase OEE.
Assessing Cheating in MOOCs

The research conducts a systematic literature review on assessment and cheating in MOOCs. We highlight emerging issues in MOOC pedagogy and the need for further evaluation research, regarding the empirically identified ways in which students engage in acts of dishonesty while taking MOOCs. Analyzing research between 2011 and 2022, we identified the geographical distribution of publications and their association with Sustainable Development Goals and Quacquarelli Symonds (QS) Classification. The most cited papers are categorized according to types of research. Finally, the research focuses on the works directly engaging in assessment, cheating, and ethics. The analysis reveals that depending on the subject of the paper, types of research might vary. We conclude by noting that while assessment and cheating are part of research on MOOC, the number of papers focused solely on those subjects is considerably smaller. Depending on the main focus, types of research vary, showing trends but also areas in which the discipline could further develop.
Cross-spectral matrix fitting for passive mapping of the ultrasonic cavitation based on Elastic-net regularization

Cavitation produced by high-intensity focused ultrasound needs to be monitored for specific applications such as sonoporation. Passive acoustic mapping using adaptive or non-adaptive beamforming methods has been proposed in the literature for this task, but lacks of spatial (primarily axial) resolution. This is mainly due to the absence of a time reference, a common specificity of passive imaging compared to standard pulse-echo ultrasound. In this paper, we propose an inverse problem-based method for passive cavitation imaging, aiming to increase the spatial resolution through sparsity constraints. Results on simulated data are presented and compared to state-of-the-art approaches, such as the frequential delay-and-sum and the frequential robust Capon beamformer.
LSFuseNet: Dual-Fusion of Landsat-8 and Sentinel-2 Multispectral Time Series for Permutation Invariant Applications

Satellite data provides valuable insights into environmental changes and natural resource management, such as monitoring deforestation, mapping land use changes, and identifying areas at risk of soil degradation. Landsat-8 and Sentine1-2 are the publicly available high spatial resolution satellites launched in recent years. But, both have a moderate temporal resolution which limits their use in the applications like precision agriculture, land cover mapping, disaster monitoring, etc. For such applications, daily or weekly monitoring is better suited. Fusing data from the two satellites can provide enhanced observations. Both Landsat-8 and Sentine1-2 satellites have the same geographic coordinate systems which makes them amiable for fusion. But, fusing data at the pixel level for these satellites is challenging as they visit the same location on different days. The proposed model `LSFuseNet’ effectively fuses data at the feature level. It is a dual-fusion model in which bi-directional cross-modal attention is used to identify and exchange the hotspot information in the two modalities. A feature alignment module learns the fine-grained features and mitigates the noise in the data. We have innovatively applied contrastive learning to improve the quality of the learned representations of the data from the two satellites. We evaluate our model for two applications - crop yield prediction and snow cover prediction. For crop yield prediction, we have taken two crops, viz. corn, and soybean, for approximately 500 counties in the US. For snow cover prediction, we considered approximately 1300 US counties. Our extensive experiments show that LSFuseNet outperforms competing models. Also, the benefit of fusing the data from two satellites over using the data from a single satellite is evident from the results of both applications. We have further modified the model to include meteorological and/or soil data (if applicable) to further enhance the performance of the model.
Individualized Training of Back Muscles Using Iterative Learning Control of a Compliant Balance Board

Here we present the GyroTrainer, a bespoke mechatronic balance board system designed to trigger activation of the back muscles while the user engages in a balance-challenging game. The GyroTrainer uses admittance control coupled with an iterative learning approach so as to tailor the admittance control parameters, i.e. difficulty level, according to the user's skill. Our experimental evaluation demonstrated that an individualized admittance control stiffness could be identified for each user, which corresponds with a desired level of difficulty and increased back muscle activity. A first game implementation demonstrates the feasibility of utilizing the GyroTrainer system and the individually identified admittance control stiffness for gamification of back muscle training.