Reviewerly/exHarmony huggingface dataset:
Estimating the structural diversity introduced by decision forest algorithms : A probabilistic approach

Structurally diverse decision trees are important for knowledge discovery and classification/prediction accuracy. Over the years, researchers have devoted much effort to the development of algorithms to increase diversity among the trees within an ensemble. While Kappa is commonly used to measure diversity among the decision trees, it does not measure the ability of the tree building algorithms to introduce diversity. Further, Kappa does not consider the structural diversity amongst the trees. Instead, Kappa measures the diversity of the predictions made from the trees produced, and are dependent on the datasets used. This paper presents a novel data-independent metric, called R index, for measuring the diversity that can be introduced by a decision forest algorithm without building the entire decision forest. The proposed measure is applied to five well-known algorithms that involve bagging and random subspacing. An efficient practical approach for calculating the R index empirically - R finder - is also proposed, and is implemented. Both R finder and Kappa were applied to thirty-two publicly available benchmark datasets under various algorithms to estimate the resulting diversity. The results indicate a generally strong negative correlation between R finder and Kappa, implying that R finder is effective at estimating the diversity of trees without the added computational costs associated with calculating Kappa.
Temporal ECDSA: A timestamp and signature mask enabled ECDSA algorithm for IoT client node authentication

Internet of Things (IoT) networks are vulnerable to various security threats, especially in client authentication. The current authentication methods require significant resources and are vulnerable to specific attacks. The Non-Interactive Zero Knowledge Proof (NIZKP) concept suits IoT device authentication. Elliptic Curve Digital Signature (ECDSA) is a popular algorithm for IoT device authentication based on elliptic curve cryptography (ECC) and NIZKP. However, an intelligent attacker who captures the ECDSA signatures generated by the same random number can recover the private key. This paper proposes a timestamp based approach to prevent signature reuse and fake signature generation in the ECDSA algorithm. The signature proof is generated differently at each occurrence to prevent the generation of valid signatures from leaked private keys. The proposed approach eliminates the expensive modular inversion operations in signature generation and verification phases, enhancing execution efficiency. The analysis results indicate that the proposed Temporal ECDSA algorithm effectively prevents most attacks on client authentication in IoT networks. Various metrics, including computational complexity and security measures, were used to evaluate the effectiveness of the proposed approach, demonstrating that it outperforms most ECDSA variants.
The Impact of VR-Based Learning on Student Engagement and Learning Outcomes in Higher Education

This research investigates virtual reality (VR) in higher education, covering types of VR, implementation in academic settings, effects on student engagement and learning outcomes, and exploring potential benefits for enhancing learning and developing graduate capabilities. A comprehensive review covered VR types like simulations, virtual labs, 360-degree experiences, and virtual classrooms, enabling experiential learning. The implementation process involves goal setting, content creation, hardware/software selection, infrastructure setup, curriculum integration, assessment, and evolution. Effective VR content and prepared instructors positively impact engagement, influencing comprehension, retention, and academic performance. AR/VR tech refines cognitive skills, practical knowledge, and personalised learning, fostering teamwork and critical thinking for graduates' career readiness.
Classification of direct optical signal inputs by Ag<sub>2</sub>S island network reservoir

Abstract We have reported that a physical reservoir with a silver sulfide island network can classify simple patterns of an irradiated light without converting it to a voltage signal input. In this study, we conducted experiments to verify whether the detection of dynamical change in an irradiating light, e.g., moving in a reservoir layer, can be available. We also investigated the possibility that the reservoir could detect a position of light exposure, in addition to the dependence on the wavelength and the exposure time. The technique was applied to a task of whether character-shaped light patterns could be recognized even if the irradiated position was changed.
Improvements to adversarial training for text classification tasks

Although deep learning models show powerful performance, they are still easily deceived by adversarial samples. Some methods for generating adversarial samples have the drawback of high time loss, which is problematic for adversarial training, and the existing adversarial training methods are difficult to adapt to the dynamic nature of the model, so it is still challenging to study an efficient adversarial training method. In this paper, we propose an adversarial training method, the core of which is the improved adversarial sample generation method AGFAT for adversarial training and the improved dynamic adversarial training method AGFAT-DAT. AGFAT uses a word frequency-based approach to identify significant words, filter replacement candidates, and use an efficient semantic constraint module as a means to reduce the time of adversarial sample generation; AGFAT-DAT is a dynamic adversarial training approach that uses a cyclic attack on the model after adversarial training and generates adversarial samples for adversarial training again. It is demonstrated that the proposed method can significantly reduce the generation time of adversarial samples, and the adversarial-trained model can also effectively defend against other types of word-level adversarial attacks.
AVID: A speech database for machine learning studies on vocal intensity

Vocal intensity, which is quantified typically with the sound pressure level (SPL), is a key feature of speech. To measure SPL from speech recordings, a standard calibration tone (with a reference SPL of 94 dB or 114 dB) needs to be recorded together with speech. However, most of the popular databases that are used in areas such as speech and speaker recognition have been recorded without calibration information by expressing speech on arbitrary amplitude scales. Therefore, information about vocal intensity of the recorded speech, including SPL, is lost. In the current study, we introduce a new open and calibrated speech/electroglottography (EGG) database named Aalto Vocal Intensity Database (AVID). AVID includes speech and EGG produced by 50 speakers (25 males, 25 females) who varied their vocal intensity in four categories (soft, normal, loud and very loud). Recordings were conducted using a constant mouth-to-microphone distance and by recording a calibration tone. The speech data was labelled sentence-wise using a total of 19 labels that support the utilisation of the data in machine learing (ML) -based studies of vocal intensity based on supervised learning. In order to demonstrate how the AVID data can be used to study vocal intensity, we investigated one multi-class classification task (classification of speech into soft, normal, loud and very loud intensity classes) and one regression task (prediction of SPL of speech). In both tasks, we deliberately warped the level of the input speech by normalising the signal to have its maximum amplitude equal to 1.0, that is, we simulated a scenario that is prevalent in current speech databases. The results show that using the spectrogram feature with the support vector machine classifier gave an accuracy of 82% in the multi-class classification of the vocal intensity category. In the prediction of SPL, using the spectrogram feature with the support vector regressor gave an mean absolute error of about 2 dB and a coefficient of determination of 92%. We welcome researchers interested in classification and regression problems to utilise AVID in the study of vocal intensity, and we hope that the current results could serve as baselines for future ML studies on the topic.
Single image super-resolution based on Bendlets analysis and structural dictionary learning

High-resolution biological tissue slice images provide opportunities for more precise observation and analysis of histopathological features. These images typically consist of enclosed contours, and Bendlets transform is an effective means to approximate such structures. In this study, we leverage Bendlet transform to incorporate multi-level image data, thereby establishing a multi-scale pyramid feature set. This approach aids in achieving a sparser representation of image textures and structures. We apply Bendlets transform to extract multi-frequency wavelet subband features and formulate structured dictionaries for both high and low resolutions based on these features. After creating these dictionaries, we compute sparse representation coefficients using the low-resolution dictionary and subsequently integrate them with the high-resolution dictionary to generate high-resolution subbands. Ultimately, through the process of inverse wavelet transformation, we completed the reconstruction of high-resolution images. This method not only significantly enhances the restoration of image details and clarity but also effectively preserves the overall structural characteristics of the original images.
Color crosstalk compensation method for color phase-shifting fringe projection profilometry based on phase correction matrix

Color phase-shifting fringe projection profilometry is one of the single-shot three-dimensional shape measurement techniques. The color crosstalk of the projector-camera system yields undesired phase errors when using phase-shifting method. In this paper, a color crosstalk compensation method based on phase correction matrix is proposed. In this method, the phase correction matrix is established to compensate the deviations between the actual phase-shift values in the acquired fringes and the standard ones in the ideal fringes. Only two fringe patterns are utilized to obtain the phase correction matrix. The quadratic equations for calculating the actual phase-shift values of the fringes in the three color channels are derived. The actual phase-shift values and the corresponding standard ones are employed to form the equilibrium equations for computing the phase correction coefficients in the matrix. Experimental results demonstrate the feasibility of the proposed method and it can effectively reduce the induced overall phase error caused by the color crosstalk.
RANGO: A Novel Deep Learning Approach to Detect Drones Disguising from Video Surveillance Systems

Video surveillance systems provide means to detect the presence of potentially malicious drones in the surroundings of critical infrastructures. In particular, these systems collect images and feed them to a deep-learning classifier able to detect the presence of a drone in the input image. However, current classifiers are not efficient in identifying drones that disguise themselves with the image background, e.g., hiding in front of a tree. Furthermore, video-based detection systems heavily rely on the image’s brightness, where darkness imposes significant challenges in detecting drones. Both these phenomena increase the possibilities for attackers to get close to critical infrastructures without being spotted and hence be able to gather sensitive information or cause physical damages, possibly leading to safety threats. In this article, we propose RANGO, a drone detection arithmetic able to detect drones in challenging images where the target is difficult to distinguish from the background. RANGO is based on a deep learning architecture that exploits a Preconditioning Operation (PREP) that highlights the target by the difference between the target gradient and the background gradient. The idea is to highlight features that will be useful for classification. After PREP, RANGO uses multiple convolution kernels to make the final decision on the presence of the drone. We test RANGO on a drone image dataset composed of multiple already-existing datasets to which we add samples of birds and planes. We then compare RANGO with multiple currently existing approaches to show its superiority. When tested on images with disguising drones, RANGO attains an increase of 6.6% mean Average Precision (mAP) compared to YOLOv5 solution. When tested on the conventional dataset, RANGO improves the mAP by approximately 2.2%, thus confirming its effectiveness also in the general scenario.
Multimodal consistency-specificity fusion based on information bottleneck for sentiment analysis

Sentiment analysis, a subtask of affective computing, endows machines with the ability to sense and comprehend emotions. Recently, research attention has shifted from traditional isolated modality to ubiquitous multi-modalities, requiring to model the complex relationships between modalities and extract the task-relevant information. However, current methods only focus on modality consistency neglecting the complementary relationship and specificity information within each modality. Furthermore, most studies regard multimodal fusion as a mere process of information integration without considering the control of information flow. We propose a variational model that explicitly decomposes unimodal representations into two types, capturing consistency and specificity information, respectively. Following the information bottleneck principle, the implementation optimizes the fusion representation by maximizing its mutual information with consistency and specificity representations while minimizing mutual information with raw inputs. With information integrity constraints and task label supervision, the fusion representation preserves task-relevant information and discards irrelevant noise. Finally, quantitative and qualitative experiments on two benchmark datasets show that our method achieves competitive performance compared to recent baselines, for multimodal sentiment analysis.
DRC: Chromatic aberration intensity priors for underwater image enhancement

Underwater imaging technology is a crucial tool for monitoring marine flora and fauna. However, selective light absorption and scattering properties of water make underwater imagery frequently appear blurred and exhibit color biases, hindering the extraction of vital aquacultural insights. To address this challenge, we propose a method, namely DRC, which is a holistic approach to enhancing underwater image clarity and color fidelity. This method comprises three integral components: D-procedure, R-procedure, and C-procedure. The D-procedure intricately accounts for the trichromatic underwater attenuation dynamics, formulating a chromatic aberration intensity prior. This prior counters the disparities in degradation levels seen in conventional single-channel prior depth estimations, achieving a dynamic depth representation approaching binocular image precision. The R-procedure, utilizing an adaptive dark-pixel prior, pinpoints corresponding points across varied depth zones to counteract backscattering, thereby mitigating the image's hazy appearance. The C-procedure bolsters image luminance and color fidelity through opponent channel rectification and amalgamates pronounced image contrasts and intricate details via Gaussian pyramid fusion. The method was tested on several publicly available datasets and compared with nine popular underwater image enhancement techniques. Both subjective and objective assessments underscore the superiority of our DRC method over existing underwater image enhancement techniques.
Adaptive knowledge distillation and integration for weakly supervised referring expression comprehension

Weakly supervised referring expression comprehension (REC) aims to ground target objects in images according to given referring expressions, while the mappings between image regions and referring expressions are unavailable during the model training phase. Existing models typically reconstruct the multimodal relationships to ground targets by utilizing off-the-shelf information, and ignore to further exploit helpful knowledge to enhance the model performance. To address this issue, we propose an adaptive knowledge distillation architecture to enrich the predominant pattern of weakly supervised REC and transfer the target-aware and interaction-aware knowledge from a pre-trained teacher grounder to enhance the grounding performance of the student model. Specifically, in order to encourage the teacher to impart more reliable knowledge, we present a Knowledge Confidence-Based Adaptive Temperature (KCAT) learning approach to learn optimal temperatures to transfer the target-aware and interaction-aware knowledge with higher prediction confidence. Moreover, to urge the student to absorb more helpful knowledge, we introduce a Student Competency-Based Adaptive Weight (SCAW) learning strategy to dynamically integrate the distilled target-aware and interaction-aware knowledge to enhance the student's grounding certainty. We conduct extensive experiments on three benchmark datasets, RefCOCO, RefCOCO+, and RefCOCOg, to validate the proposed approach. Experimental results demonstrate that our approach achieves superior performance over state-of-the-art methods with the aid of adaptive knowledge distillation and integration. The code and trained models are available at: https://github.com/dami23/WREC_AdaptiveKD.
MFQS: Multichannel Floating-Point Quantum Representation of Digital Signals

A novel multichannel floating-point quantum representation of digital signals (or simply MFQS) is proposed in this paper. The MFQS model uses three entangled qubit sequences where real amplitude values, channel, and position can be stored. The amplitude is represented by a (p + q)-bit floating-point number based on the Institute of Electrical and Electronics Engineers (IEEE-754) standard, including one sign qubit, p qubits exponent, and q − 1 qubits mantissa. We present two MFQS models including 1D signals (MFQS1 model) and 2D signals (MFQS2 model), and give the model preparation procedure and the corresponding complexity analysis. In addition, the MFQS model provides some operations, including signal merging, addition, reversal, and channel merging, swap, and reversal. Then, we design the quantum circuits that manipulate these basic operations, which provide the necessary building blocks for future applications of quantum signal processing. Compared to available quantum signal representations, the proposed MFQS provides highly accurate predictions in many real-world scenarios.
Sampling Cardinality-Based Feature Models

The goal of sample-based testing of variant-rich software systems is to reduce usually very large configuration spaces to significantly smaller, yet still representative subsets of configurations to be tested for quality assurance. Recent sampling techniques and tools are restricted to finite-dimensional, Boolean configuration spaces specified by a feature model. However, in many modern application domains like cloud computing and cyber-physical systems, customers not only decide about the presence or absence of features in a configuration but also about the multiplicity (number of instances) of configurable resources. Cardinality-based feature models extend Boolean feature models by cardinality annotations and respective constraints to enable multiple, and even potentially a-priori unbounded, copies of features and their respective sub-trees. The resulting infinite and inherently non-convex configuration spaces are no longer tractable by established sampling criteria and corresponding sampling algorithms for Boolean feature models like pairwise feature interaction coverage. In this paper, we first revisit the subtleties of the configuration semantics of cardinality-based feature models. We propose novel sampling criteria explicitly taking multiplicity of feature selections into account. Finally, we present evaluation results gained from applying our tool implementation to a collection of example models, showing applicability of the proposed approach.
From Decision Models To User-Guiding Configurators Using SMT

As decision models for the correct derivation of customizable products grow ever more complex, user-facing configurators are developed to manage this complexity by emulating the models as closely as possible. By design, decision models closely resemble the structures of configurators. Therefore, a classical configurator that implements a decision model often copies the model's (linear) ordering of decisions. To enable users to jump into arbitrary points of a decision model, non-linear analysis is necessary. We present a novel way of automatically encoding a complex decision model into Satisfiability Modulo Theories (SMT) formulas, which are then solved by an SMT solver. Users interact with a configurator front-end, which internally calls an incremental SMT solver that returns open choices. In this paper, we present the architecture of our framework, introduce a general direct SMT encoding of decision models, and illustrate with a small case study the potential of our approach for product configuration and reconfiguration. Our approach successfully converts a legacy ordered decision model from an industrial application into a fully interactive configurator.
UnWise: High T-Wise Coverage from Uniform Sampling

Configuration spaces of industrial product lines are typically too large to be tested exhaustively. Therefore, testing in practice is often carried out on samples, sets of configurations which satisfy the requirements of the testing scenario. For t-wise sampling, the objective is to cover all t-wise interactions between configurable options with as few configurations as possible. However, a trade-off needs to be made between t, sampling time, sample size, and achieved coverage. In addition, it is infeasible for larger systems to even compute the set of all 2-wise interactions in practicable time. In this work, we reevaluate the performance of uniform samplers in terms of 2-wise coverage and come to a more positive result than previous research. We also present completion and reduction algorithms that greatly improve said performance. As a baseline for comparison, we additionally evaluate the two state-of-the-art dedicated t-wise samplers Baital and YASA. In doing so, we are the first to evaluate and compare these samplers on a large set of industrial feature models.
Partial Proofs to Optimize Deductive Verification of Feature-Oriented Software Product Lines

Software product lines (SPLs) are a technique to efficiently develop families of software products. Code is implemented in functional features which are composed to individual software variants. SPLs are oftentimes used in safety-critical systems, which is why functional correctness is more important than ever. As an advanced approach, deductive verification offers the possibility to verify the behaviour of software against a formal specification. When deductive verification is applied for SPLs, it meets the challenges of an SPLs variability. Since most verification approaches do not scale for variant-rich product lines, we take up existing approaches of reuse of proof parts to develop our concept of partial proofs. We split proofs into a feature-specific and a product-specific part. The feature-specific part is only proven once for all products enabling advanced proof reuse. We implement our concept of partial proofs in the tool VarCorC and evaluate it on three case studies. We found that both the number of proof steps and the verification time can be reduced by using partial proofs. Further, we determine a trend of increasing improvements of verification costs for large-scale SPLs.