Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Knowledge Gap: How Security Awareness Influences the Adoption of Industrial IoT

—The Internet-of-Things is no longer confined to end-users and private homes. Industrial IoT (IIoT) is supposed to improve industrial processes and make them more efficient. However, IIoT technologies may also pose (significant) security threats. Therefore, it is important to understand the balance between security awareness and willingness to adopt IIoT among manufacturing companies. In this paper, we explore companies’ willingness to adopt IIoT, their willingness to participate in trainings on IIoT, and contrast this to their current security awareness. We investigate classes of companies through latent class analysis based on a survey of over 130 industrial firms. We collected this sample from the Netherlands, as earlier research demonstrated that the Netherlands are generally comparable with other western countries in terms of technology adoption, while focusing on a single country reduces other potential noise effects. We find that the class of companies most susceptible and willingness to participate in educational awareness programs is comprised of companies with a high intention to adopt IIoT technologies, but with lowest awareness of their security threats, that is, companies that may be impacted the most by insecure IIoT devices. In contrast, the companies of the other class are highly aware of risks associated with IIoT, but also averse to adopting IIoT for their production processes. Furthermore, we find that smaller companies are more likely to be risk-aware and IIoT averse, while larger companies embrace IIoT while being risk unaware. The classes that we identified are robust to company age, market segment, current information and communication technology usage, and degree of production focus. Our findings highlight the need for policy makers to target their security awareness programs on adopting IIoT technologies to “smarten up” industrial processes to specific company classes, which increases the educational efforts’ efficacies. Otherwise, an apparent information imbalance skews the economic incentive model behind IIoT adoption, potentially leading to a future of dramatic IIoT security incidents.
Problematic Energy Differences between Cumulenes and Poly-ynes: Does This Point to a Systematic Improvement of Density Functional Theory?

Relative energetics of the C3H4 (allene (1) and propyne (2)), C5H4 (penta-1,2,3,4-tetraene (3) and penta-1,3-diyne (4)), and C7H4 (hepta-1,2,3,4,5,6-hexaene (5) and hepta-1,3,5-triyne (6)) cumulene and poly-yne structures were systematically examined using DFT (B3LYP, BLYP, and BP86), MP2, and CCSD(T) theories. The isomer energy separations, ΔE, were studied with three schemes: standard optimizations and energy determinations (CCSD(T)/cc-pVXZ//MP2/cc-pVTZ), heats of formation predictions via isodesmic and homodesmotic reactions (CCSD(T)/cc-pVTZ//MP2/cc-pVTZ), and BHLYP functional analysis fitted to experimental data. For the C3H4 isomers, ΔE was determined to be −1.4 and −1.4 kcal/mol for the first two schemes, with the BHLYP functional analysis being fit to the experimental ΔE of −1.4 ± 0.5 kcal/mol. The three schemes yield ΔE values of −8.8, −10.1, and −11.7 kcal/mol, respectively, for the C5H4 isomers 3 and 4. The C7H4 isomers 5 and 6 are separated by −14.3, −16.3, and −19.7 kcal/mol when the three sc.
Correcting Momentum with Second-order Information

We develop a new algorithm for non-convex stochastic optimization that ﬁnds an (cid:15) -critical point in the optimal O ( (cid:15) − 3 ) stochastic gradient and hessian-vector product computations. Our algorithm uses Hessian-vector products to “correct” a bias term in the momentum of SGD with momentum. This leads to better gradient estimates in a manner analogous to variance reduction methods. In contrast to prior work, we do not require excessively large batch sizes (or indeed any restrictions at all on the batch size), and both our algorithm and its analysis are much simpler. We validate our results on a variety of large-scale deep learning benchmarks and architectures, where we see improvements over SGD and Adam.
OFDM with Index Modulation in Orbital Angular Momentum Multiplexed Free Space Optical Links

Communication using orbital angular momentum (OAM) modes has recently received a considerable interest in free space optical (FSO) communications. Propagating OAM modes through free space may be subject to atmospheric turbulence (AT) distortions that cause signal attenuation and crosstalk which degrades the system capacity and increases the error probability. In this paper, we propose to enhance the OAM FSO communications in terms of bit error rate and spectral efficiency, for different levels of AT regimes. The performance gain is achieved by introducing orthogonal frequency division multiplexing (OFDM) with index modulation technique to the OAM FSO system.
Implicit Integration of Superpixel Segmentation into Fully Convolutional Networks

Superpixels are a useful representation to reduce the complexity of image data. However, to combine superpixels with convolutional neural networks (CNNs) in an end-to-end fashion, one requires extra models to generate superpixels and special operations such as graph convolution. In this paper, we propose a way to implicitly integrate a superpixel scheme into CNNs, which makes it easy to use superpixels with CNNs in an end-to-end fashion. Our proposed method hierarchically groups pixels at downsampling layers and generates superpixels. Our method can be plugged into many existing architectures without a change in their feed-forward path because our method does not use superpixels in the feed-forward path but use them to recover the lost resolution instead of bilinear upsampling. As a result, our method preserves detailed information such as object boundaries in the form of superpixels even when the model contains downsampling layers. We evaluate our method on several tasks such as semantic segmentation, superpixel segmentation, and monocular depth estimation, and confirm that it speeds up modern architectures and/or improves their prediction accuracy in these tasks.
Compositional Explanations for Image Classifiers

Existing algorithms for explaining the output of image classiﬁers perform poorly on inputs where the object of interest is partially occluded. We present a novel, black-box algorithm for computing explanations that uses a principled approach based on causal theory. We implement the method in the tool CET (Compositional Explanation Tool). Owing to the compositionality in its algorithm, CET computes explanations that are much more accurate than those generated by the existing explanation tools on images with occlusions and delivers a level of performance comparable to the state of the art when explaining images without occlusions.
Lord of the Ring(s): Side Channel Attacks on the CPU On-Chip Ring Interconnect Are Practical

We introduce the first microarchitectural side channel attacks that leverage contention on the CPU ring interconnect. There are two challenges that make it uniquely difficult to exploit this channel. First, little is known about the ring interconnect's functioning and architecture. Second, information that can be learned by an attacker through ring contention is noisy by nature and has coarse spatial granularity. To address the first challenge, we perform a thorough reverse engineering of the sophisticated protocols that handle communication on the ring interconnect. With this knowledge, we build a cross-core covert channel over the ring interconnect with a capacity of over 4 Mbps from a single thread, the largest to date for a cross-core channel not relying on shared memory. To address the second challenge, we leverage the fine-grained temporal patterns of ring contention to infer a victim program's secrets. We demonstrate our attack by extracting key bits from vulnerable EdDSA and RSA implementations, as well as inferring the precise timing of keystrokes typed by a victim user.
Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach

Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for datasets highly contaminated with outliers is, however, still challenging due to the underlying computational complexity. Recent literature has focused on learning-based algorithms. However, most approaches are supervised (which require a large amount of labelled training data). In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting. Unlike other methods, our work is agnostic to the underlying input features, and can be easily generalized to a wide variety of LP-type problems with quasi-convex residuals. We empirically show that our method out-performs existing unsupervised learning approaches, and achieves competitive results compared to traditional methods on several important computer vision problems1.
Formation of 2s-State Hydrogen Atom in Proton-Lithium Inelastic Scattering

Elaborate coupled static formalism is employed for treatment of proton-lithium collisions at wide range of incident energies between 10 and 1000 Kev. Coupled static and frozen core approximations are employed for calculating partial and total cross sections. Only elastic and formation of excited hydrogen, H(2s), channels are considered. Total cross sections are calculated using seven partial waves Green’s function expansion technique of total angular momentum . Proposed iterative approach allows for reliable representation of the core potentials using elaborate variational calculation of target orbitals. Polarization potential of lithium atom is taken into consideration in calculating corresponding total cross sections. Quite interesting reliable results were obtained in comparison with other theoretical approaches.
A Hybrid CNN-BiLSTM Voice Activity Detector

This paper presents a new hybrid architecture for voice activity detection (VAD) incorporating both convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) layers trained in an end-to-end manner. In addition, we focus specifically on optimising the computational efficiency of our architecture in order to deliver robust performance in difficult in-the-wild noise conditions in a severely under-resourced setting. Nested k-fold cross-validation was used to explore the hyperparameter space, and the trade-off between optimal parameters and model size is discussed. The performance effect of a BiLSTM layer compared to a unidirectional LSTM layer was also considered. We compare our systems with three established baselines on the AVA-Speech dataset. We find that significantly smaller models with near optimal parameters perform on par with larger models trained with optimal parameters. BiLSTM layers were shown to improve accuracy over unidirectional layers by ≈2% absolute on average. With an area under the curve (AUC) of 0.951, our system outperforms all baselines, including a much larger ResNet system, particularly in difficult noise conditions.
Cosmological Constraints from Galaxy Cluster Sparsity, Cluster Gas Mass Fraction, and Baryon Acoustic Oscillation Data

In recent years, the availability of large, complete cluster samples has enabled numerous cosmological parameter inference analyses using cluster number counts. These have provided constraints on the cosmic matter density Ω m and the amplitude of matter density fluctuations σ 8 alternative to that obtained from other standard probes. However, systematics uncertainties, such as the mass calibration bias and selection effects, may still significantly affect these data analyses. Hence, it is timely to explore other proxies of galaxy cluster cosmology that can provide cosmological constraints complementary to those obtained from cluster number counts. Here we use measurements of the cluster sparsity from weak-lensing mass estimates of the LC2-single and HSC-XXL cluster catalogs to infer constraints on a flat ΛCDM model. The cluster sparsity has the advantage of being insensitive to selection and mass calibration bias. On the other hand, it primarily constrains a degenerate combination of Ω m and σ 8 (along approximately constant curves of and, to a lesser extent, the reduced Hubble parameter h. Hence, in order to break the internal parameter degeneracies, we perform a combined likelihood analysis of the cluster sparsity estimates with cluster gas mass fraction measurements and BAO data. We find marginal constraints that are competitive with those from other standard cosmic probes: Ω m = 0.316 ± 0.013, σ 8 = 0.757 ± 0.067 (corresponding to S 8 = 0.776 ± 0.064), and h = 0.696 ± 0.017 at 1σ. Moreover, assuming a conservative Gaussian prior on the mass bias of gas mass fraction data, we find a lower limit on the gas depletion factor Y b,500c ≳ 0.89.
Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments

For robots navigating using only a camera, illumination changes in indoor environments can cause re-localization failures during autonomous navigation. In this paper, we present a multi-session visual SLAM approach to create a map made of multiple variations of the same locations in different illumination conditions. The multi-session map can then be used at any hour of the day for improved re-localization capability. The approach presented is independent of the visual features used, and this is demonstrated by comparing re-localization performance between multi-session maps created using the RTAB-Map library with SURF, SIFT, BRIEF, BRISK, KAZE, DAISY, and SuperPoint visual features. The approach is tested on six mapping and six localization sessions recorded at 30 min intervals during sunset using a Google Tango phone in a real apartment.
Moving from Cross-Project Defect Prediction to Heterogeneous Defect Prediction: A Partial Replication Study

Software defect prediction heavily relies on the metrics collected from software projects. Earlier studies often used machine learning techniques to build, validate, and improve bug prediction models using either a set of metrics collected within a project or across different projects. However, techniques applied and conclusions derived by those models are restricted by how identical those metrics are. Knowledge coming from those models will not be extensible to a target project if no sufficient overlapping metrics have been collected in the source projects. To explore the feasibility of transferring knowledge across projects without common labeled metrics, we systematically integrated Heterogeneous Defect Prediction (HDP) by replicating and validating the obtained results. Our main goal is to extend prior research and explore the feasibility of HDP and finally to compare its performance with that of its predecessor, Cross-Project Defect Prediction. We construct an HDP model on different publicly available datasets. Moreover, we propose a new ensemble voting approach in the HDP context to utilize the predictive power of multiple available datasets. The result of our experiment is comparable to that of the original study. However, we also explored the feasibility of HDP in real cases. Our results shed light on the infeasibility of many cases for the HDP algorithm due to its sensitivity to the parameter selection. In general, our analysis gives a deep insight into why and how to perform transfer learning from one domain to another, and in particular, provides a set of guidelines to help researchers and practitioners to disseminate knowledge to the defect prediction domain.
External gas accretion provides a fresh gas supply to the active S0 galaxy NGC 5077

In early-type galaxies, externally accreted gas is thought to be the main source of gas replenishment at late times. We use MUSE integral field spectroscopy data to study the active S0 galaxy NGC 5077, which is known to have disturbed dynamics that are indicative of a past external interaction. We confirm the presence of a stellar kinematically distinct core with a diameter of 2.8 kpc that is counter-rotating with respect to the main stellar body of the galaxy. We find that the counter-rotating core consists of an old stellar population that is not significantly different from the rest of the galaxy. The ionised gas is strongly warped and extends out to 6.5 kpc in the polar direction and in a filamentary structure. The gas dynamics is complex, with significant changes in the position angle as a function of radius. The ionised gas line ratios are consistent with LINER excitation by the active galactic nucleus, both in the nucleus and at kiloparsec scales. We discover a nuclear outflow with projected velocity V ∼ 400 km s−1, consistent with a hollow outflow cone intersecting the plane of the sky. The properties of the misaligned gas match predictions from numerical simulations of misaligned gas infall after a gas-rich merger. The warp and change in the gas orientation as a function of radius are consistent with gas relaxation due to stellar torques; these are stronger at small radii where the gas aligns faster than in the outer regions, driving gas to the nucleus. The stellar and gas dynamics indicate that NGC 5077 has had at least two external interactions, one that resulted in the formation of the counter-rotating core, followed by the second, late-time external gas accretion. NGC 5077 illustrates the importance of external interactions in the replenishment of the galaxy gas reservoir and the nuclear gas content available for black hole fuelling.
Energy-Efficient Task Offloading and Resource Allocation for Multiple Access Mobile Edge Computing

In this paper, the problem of joint radio and computation resource management over multi-channel is investigated for multi-user partial offloading mobile edge computing (MEC) system. The target is to minimize the weighted sum of energy consumption by jointly optimizing transmission time, local and edge computation capacity allocation, bandwidth allocation and data partition. An optimization problem is formulated, which is nonconvex and can not be solved directly. Then, we transform the origin optimization problem into an equivalent convex optimization problem. For general case of multi-user multi-channel, we decouple the convex optimization problem into subproblems and an optimal resource management strategy is obtained by adopting block coordinate descent (BCD) method. To gain further insight, we investigate the optimal resource management strategy for two special cases. First, consider the case of multi-user shares single channel. Since the single-channel optimization problem is reduced from the multi-channel optimization problem, the solution approach of general case can be applied to this case, and the solving algorithm for this case has low computation complexity, which is a combination of analytical and bisection-search methods. Then, for the case of single-user occupies all channel, the optimization problem is simplified and an optimal solving algorithm with closed-form solutions is proposed.
Study of the Lipophilicity and Retention Behavior of Some s-Triazine Derivatives on Aminoplast and Cellulose

The chromatographic behavior of four groups of s-triazine derivatives (15 compounds) has been studied on aminoplast and cellulose thin layers with two mobile phases, methanol-water-acetonitrile, 30 + 20 + X, where X = 1–10 (v/v), and methanol–dilute acetic acid, 30 + 20 (v/v). The mechanism of retention was investigated by changing of volume fraction of acetonitrile in the first mobile phase. Reversed-phase chromatography occurs on both supports. Retention constants, RM° were determined by extrapolation and good correlation was obtained between retention constants, RM° and log P. These retention constants can be used as the measure of the lipophilicity of compounds. The effect of mobile phase pH on the chromatographic retention of s-triazine derivatives was examined and approximate protonation constants, pKa, were determined on the basis of the dependence of retention on pH. These constants correlated well with Hammett substituent constants σ.
Planck Cold Clumps in the λ Orionis Complex. III. A Chemical Probe of Stellar Feedback on Cores in the λ Orionis Cloud

Massive stars have a strong impact on their local environments. However, how stellar feedback regulates star formation is still under debate. In this context, we studied the chemical properties of 80 dense cores in the Orion molecular cloud complex composed of the Orion A (39 cores), B (26 cores), and λ Orionis (15 cores) clouds using multiple molecular line data taken with the Korean Very Long Baseline Interferometry Network 21 m telescopes. The λ Orionis cloud has an H ii bubble surrounding the O-type star λ Ori, and hence it is exposed to the ultraviolet (UV) radiation field of the massive star. The abundances of C2H and HCN, which are sensitive to UV radiation, appear to be higher in the cores in the λ Orionis cloud than in those in the Orion A and B clouds, while the HDCO to H2CO abundance ratios show the opposite trend, indicating warmer conditions in the λ Orionis cloud. The detection rates of dense gas tracers such as the N2H+, HCO+, and H13CO+ lines are also lower in the λ Orionis cloud. These chemical properties imply that the cores in the λ Orionis cloud are heated by UV photons from λ Ori. Furthermore, the cores in the λ Orionis cloud do not show any statistically significant excess in the infall signature of HCO+ (1–0), unlike those in the Orion A and B clouds. Our results support the idea that feedback from massive stars impacts star formation in a negative way by heating and evaporating dense materials, as in the λ Orionis cloud.
Phase separation of active Brownian particles in two dimensions: anything for a quiet life

Active Brownian particles display self-propelled movement, which can be modelled as arising from a one-body force. Although their interparticle interactions are purely repulsive, for strong self-propulsion, the swimmers phase separate into dilute and dense phases. We describe in detail a recent theory for such motility induced phase-separation. Starting from the continuity equation and the force density balance, the description is based on four superadiabatic contributions to the internal force density. Here the superadiabatic forces are due to the flow in the system and they act on top of the adiabatic forces that arise from the equilibrium free energy. Phase coexistence is described by bulk state functions and agrees quantitatively with Brownian dynamics simulation results from the literature. We describe in detail all analytical steps to fully resolve the spatial and orientational dependence of the one-body density and current. The decomposition into angular Fourier series leads to coupling of total density, polarisation and all higher modes. We describe the power functional approach, including the kinematic dependence of the superadiabatic force fields and the quiet life effect that pushes particles from fast to slow regions and hence induces the phase separation.
Generating Images with Sparse Representations

The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models. Previous approaches such as VQ-VAE use deep autoencoders to obtain compact representations, which are more practical as inputs for likelihood-based models. We present an alternative approach, inspired by common image compression methods like JPEG, and convert images to quantized discrete cosine transform (DCT) blocks, which are represented sparsely as a sequence of DCT channel, spatial location, and DCT coefficient triples. We propose a Transformer-based autoregressive architecture, which is trained to sequentially predict the conditional distribution of the next element in such sequences, and which scales effectively to high resolution images. On a range of image datasets, we demonstrate that our approach can generate high quality, diverse images, with sample metric scores competitive with state of the art methods. We additionally show that simple modifications to our method yield effective image colorization and super-resolution models.