Reviewerly/exHarmony huggingface dataset:
Efficient Diversity-Driven Ensemble for Deep Neural Networks

The ensemble of deep neural networks has been shown, both theoretically and empirically, to improve generalization accuracy on the unseen test set. However, the high training cost hinders its efficiency since we need a sufficient number of base models and each one in the ensemble has to be separately trained. Lots of methods are proposed to tackle this problem, and most of them are based on the feature that a pre-trained network can transfer its knowledge to the next base model and then accelerate the training process. However, these methods suffer a severe problem that all of them transfer knowledge without selection and thus lead to low diversity. As the effect of ensemble learning is more pronounced if ensemble members are accurate and diverse, we propose a method named Efficient Diversity-Driven Ensemble (EDDE) to address both the diversity and the efficiency of an ensemble. To accelerate the training process, we propose a novel knowledge transfer method which can selectively transfer the previous generic knowledge. To enhance diversity, we first propose a new diversity measure, then use it to define a diversity-driven loss function for optimization. At last, we adopt a Boosting-based framework to combine the above operations, such a method can also further improve diversity. We evaluate EDDE on Computer Vision (CV) and Natural Language Processing (NLP) tasks. Compared with other well-known ensemble methods, EDDE can get highest ensemble accuracy with the lowest training cost, which means it is efficient in the ensemble of neural networks.
Efficient Cost-Reduced With High-Quality Image of Imperceptible Steganography Using Modulo and Magic Cube

Information hiding techniques are used to hide confidential data in digital carriers. It is difficult for malicious users to detect the existence of confidential data within the digital carriers. Audios, images, or videos can call be digital carriers. Information hiding includes steganography and watermarking. Watermarking is used for copyright protection. Steganography is used to protect confidential data transmission from malicious attackers. Imperceptibility and capacity are always the major concerns in steganography. In this paper, we propose an efficient steganography method for images using modulus operation and magic cube. The partial secret message bits are converted into decimals and then mapped to a predefined cube. The coordinates of the mapped position were embedded into the cover image using modulus operation. We obtain the stego-image quality better than 42 dB, while the payload is 3 bpp. The experimental results demonstrated that the proposed method has a higher capacity than previous works. Meanwhile, our method also maintain high imperceptibility. Furthermore, modulus operation can reduce the computational overhead of the proposed method.
Towards the Quantified Product-Product Lifecycle Support by Multi-Aspect Ontologies

Many physical products have been equipped with sensors, control units and actuators that allow monitoring and control via Internet. In this context, the paper defines “quantified products” (QP) exploiting the possibility of collecting and analyzing data during operation, not only on the level of a single product instance but for a complete fleet, which allows for new data services and service ecosystems. However, switching to production of QP is accompanied by substantial changes in the companies as different kinds of products and their lifecycles emerge and have to be coordinated. The paper structures the field by showing the different lifecycles for products, data services and ecosystems. Essential for QP is data integration, which is addressed in the paper using a multi-aspect ontology.
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists

Warning: This paper contains examples of language that some people may find offensive.Natural Language Processing (NLP) models risk overfitting to specific terms in the training data, thereby reducing their performance, fairness, and generalizability.E.g., neural hate speech detection models are strongly influenced by identity terms like gay, or women, resulting in false positives, severe unintended bias, and lower performance.Most mitigation techniques use lists of identity terms or samples from the target domain during training.However, this approach requires a-priori knowledge and introduces further bias if important terms are neglected.Instead, we propose a knowledge-free Entropy-based Attention Regularization (EAR) to discourage overfitting to training-specific terms.An additional objective function penalizes tokens with low selfattention entropy.We fine-tune BERT via EAR: the resulting model matches or exceeds stateof-the-art performance for hate speech classification and bias metrics on three benchmark corpora in English and Italian.EAR also reveals overfitting terms, i.e., terms most likely to induce bias, to help identify their effect on the model, task, and predictions.
Mukayese: Turkish NLP Strikes Back

Having sufficient resources for language X lifts it from the under-resourced languages class, but not necessarily from the under-researched class.In this paper, we address the problem of the absence of organized benchmarks in the Turkish language.We demonstrate that languages such as Turkish are left behind the stateof-the-art in NLP applications.As a solution, we present MUKAYESE, a set of NLP benchmarks for the Turkish language that contains several NLP tasks.We work on one or more datasets for each benchmark and present two or more baselines.Moreover, we present four new benchmarking datasets in Turkish for language modeling, sentence segmentation, and spell checking.
Redistributing Low-Frequency Words: Making the Most of Monolingual Data in Non-Autoregressive Translation

Knowledge distillation (KD) is the preliminary step for training non-autoregressive translation (NAT) models, which eases the training of NAT models at the cost of losing important information for translating low-frequency words.In this work, we provide an appealing alternative for NAT -monolingual KD, which trains NAT student on external monolingual data with AT teacher trained on the original bilingual data.Monolingual KD is able to transfer both the knowledge of the original bilingual data (implicitly encoded in the trained AT teacher model) and that of the new monolingual data to the NAT student model.Extensive experiments on eight WMT benchmarks over two advanced NAT models show that monolingual KD consistently outperforms the standard KD by improving lowfrequency word translation, without introducing any computational cost.Monolingual KD enjoys desirable expandability, which can be further enhanced (when given more computational budget) by combining with the standard KD, a reverse monolingual KD, or enlarging the scale of monolingual data.Extensive analyses demonstrate that these techniques can be used together profitably to further recall the useful information lost in the standard KD.Encouragingly, combining with standard KD, our approach achieves 30.4 and 34.1 BLEU points on the WMT14 English-German and German-English datasets, respectively.Our code and trained models are freely available at https://github.com/alphadl/RLFW-NAT.mono.
Benchmarking Post-Hoc Interpretability Approaches for Transformer-based Misogyny Detection

Warning: This paper contains examples of language that some people may find offensive.Transformer-based Natural Language Processing models have become the standard for hate speech detection.However, the unconscious use of these techniques for such a critical task comes with negative consequences.Various works have demonstrated that hate speech classifiers are biased.These findings have prompted efforts to explain classifiers, mainly using attribution methods.In this paper, we provide the first benchmark study of interpretability approaches for hate speech detection.We cover four post-hoc token attribution approaches to explain the predictions of Transformer-based misogyny classifiers in English and Italian.Further, we compare generated attributions to attention analysis.We find that only two algorithms provide faithful explanations aligned with human expectations.Gradient-based methods and attention, however, show inconsistent outputs, making their value for explanations questionable for hate speech detection tasks.
Universal phase-shifting algorithm (UPSA) for nonuniform phase-step demodulation and null-testing criterion for phase accuracy gauging

We propose a non-iterative, universal phase-shifting algorithm (UPSA) for phase-demodulation regardless if the phase steps are linear, nonuniform, or even unknown. Nonlinear phase-step algorithms are important because those steps are not “floating-point” linear due to thermal drift, mechanical instabilities, or aging, even for commercial laser interferometers. Additionally, we propose a null-testing criterion to gauge the accuracy of the demodulated phase. Usually, novel PSAs are tested using simulations where we can compute the difference between the demodulated phase and the ideal one. This seems a vicious trap: usually, experimental phase gauging accuracy is unreachable having no prior access to the measuring phase. Here we offer a way to escape from this vicious circle by proposing a null-test that proves that the phase from experimental fringes is error-free. This null-test can be used for testing the phase quality of any PSA. In brief, we are proposing two interferometry tasks: 1) the UPSA for phase demodulation, and 2) a null-testing criterion which verify that the estimated phase is error-free.
Schools’ digital renewal: Steps to the “ideal school”

Schools’ digital renewal is seen as a schools’ changes process (learning goals, educational environment, teaching/learning process, school management) have started several decades ago and continues today in the growing digital environment. Schools are moving along the path of the digital renewal at different paces and are located at different stages of the process. The digital renewal stages (computerization, early and late informatization, digital transformation) are considered as groups of the developmental states where each specific school is located in the digital renewal process. At each of these stages, school communities have a specific vision of the possible/desired future, which becomes a base for the creation and implementation of the development programs. It is recommended to take this fact into account in the development of recommendations for the creation of an “ideal school”, and to prepare several such descriptions that correspond to the peculiarities of the development of the school at the appropriate stage.
Are You Really Muted?: A Privacy Analysis of Mute Buttons in Video Conferencing Apps

Video conferencing apps (VCAs) make it possible for previously private spaces — bedrooms, living rooms, and kitchens — into semi-public extensions of the office. For the most part, users have accepted these apps in their personal space without much thought about the permission models that govern the use of their private data during meetings. While access to a device’s video camera is carefully controlled, little has been done to ensure the same level of privacy for accessing the microphone. In this work, we ask the question: what happens to the microphone data when a user clicks the mute button in a VCA? We first conduct a user study to analyze users’ understanding of the permission model of the mute button. Then, using runtime binary analysis tools, we trace raw audio flow in many popular VCAs as it traverses the app from the audio driver to the network. We find fragmented policies for dealing with microphone data among VCAs — some continuously monitor the microphone input during mute, and others do so periodically. One app transmits statistics of the audio to its telemetry servers while the app is muted. Using network traffic that we intercept en route to the telemetry server, we implement a proof-of-concept background activity classifier and demonstrate the feasibility of inferring the ongoing background activity during a meeting — cooking, cleaning, typing, etc. We achieved 81.9% macro accuracy on identifying six common background activities using intercepted outgoing telemetry packets when a user is muted.
Number of necessary training examples for Neural Networks with different number of trainable parameters

In this work, the network complexity should be reduced with a concomitant reduction in the number of necessary training examples. The focus thus was on the dependence of proper evaluation metrics on the number of adjustable parameters of the considered deep neural network. The used data set encompassed Hematoxylin and Eosin (H&E) colored cell images provided by various clinics. We used a deep convolutional neural network to get the relation between a model's complexity, its concomitant set of parameters, and the size of the training sample necessary to achieve a certain classification accuracy. The complexity of the deep neural networks was reduced by pruning a certain amount of filters in the network. As expected, the unpruned neural network showed best performance. The network with the highest number of trainable parameter achieved, within the estimated standard error of the optimized cross-entropy loss, best results up to 30% pruning. Strongly pruned networks are highly viable and the classification accuracy declines quickly with decreasing number of training patterns. However, up to a pruning ratio of 40%, we found a comparable performance of pruned and unpruned deep convolutional neural networks (DCNN) and densely connected convolutional networks (DCCN).
Multi-feature sparse similar representation for person identification

Person identification with a single feature (e.g., face recognition, speaker verification, person re-identification, etc.) has been studied extensively for many years, while few works focus on multi-feature person identification. Though promising performance has been achieved by only using the information of facial images, voice, or pedestrian appearance, it is still challenging to recognize a person with only a single feature in some situations (e.g., a person at a distance or occluded by other objects, and a partial person out of view). In this paper, we present a multi-feature sparse similar representation (MFSSR) method to effectively fuse face features, body features, and global image features for the task of person identification. In MFSSR, we designed a reconstructed deep spatial feature for representing the appearance of human body by using the spatial correlation coding of partial deep spatial features. Then we presented a multi-feature sparse similar representation model for jointly using different features, e.g., face, body, and the global image. Besides, considering that the coding coefficients associated with good samples but not outliers should be more similar among different features, we jointly represent different features by imposing a weighted ℓ1-norm distance regularization, instead of the conventional ℓ2-norm regularization, on the coefficients. Experimental results on several multi-feature person identification databases have clearly shown the superior performance of the proposed model.
Joint hyperbolic and Euclidean geometry contrastive graph neural networks

Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in a wide variety of analytical tasks. Current GNN approaches focus on learning representations in a Euclidean space, which are effective in capturing non-tree-like structural relations, but they fail to model complex relations in many real-world graphs, such as tree-like hierarchical graph structure. This paper instead proposes to learn representations in both Euclidean and hyperbolic spaces to model these two types of graph geometries. To this end, we introduce a novel approach – Joint hyperbolic and Euclidean geometry contrastive graph neural networks (JointGMC). JointGMC is enforced to learn multiple layer-wise optimal combinations of Euclidean and hyperbolic geometries to effectively encode diverse complex graph structures. Further, the performance of most GNNs relies heavily on the availability of large-scale manually labeled data. To mitigate this issue, JointGMC exploits proximity-based self-supervised information in different geometric spaces (i.e., Euclidean, hyperbolic, and Euclidean-hyperbolic interaction spaces) to regularize the (semi-) supervised graph learning. Extensive experimental results on eight real-world graph datasets show that JointGMC outperforms eight state-of-the-art GNN models in diverse graph mining tasks, including node classification, link prediction, and node clustering tasks, demonstrating JointGMC’s superior graph representation ability. Code is available at https://github.com/chachatang/jointgmc.
Vehicloak: A Blockchain-Enabled Privacy-Preserving Payment Scheme for Location-Based Vehicular Services

The Internet of Vehicles (IoV) technology enables vehicles to communicate with each other, with pedestrians and with roadside infrastructures, to realize more efficient, safer and more environmentally friendly transportation. IoV also promises rich location-based services for vehicles, such as parking and toll highway. However, preserving privacy for location-based service payments emerges as a critical and challenging problem in IoV. Existing schemes rely on centralized banks for payment processing, resulting in location privacy leakage to centralized entities. In this paper, we propose a decentralized privacy-preserving payment scheme named Vehicloak for IoV based on the blockchain technology. The biggest challenge is to provide location privacy for vehicles while guaranteeing correct service payments using the transparent blockchain. To tackle this challenge, we introduce a new cryptographic technique called zk-GSigproof that integrates zero-knowledge proof with group signature. Vehicloak implements this technique in a smart contract to process payment, which verifies zero-knowledge proof and group signature without leaking location information. It is not limited to IoV and can be applied in many payment scenarios. To evaluate the performance of our scheme, we implement Vehicloak on a private blockchain of 100 nodes on Aliyun, and conduct a test with up to 4,000 transactions. The experimental results prove the feasibility of Vehicloak.
Tri-staged feature selection in multi-class heterogeneous datasets using memetic algorithm and cuckoo search optimization

• Proposes Tri-Staged Feature Selection (TFS) for multi-class heterogeneous datasets. • Initial features are selected using Kruskal Wallis Test. • Refinement of obtained features using Memetic Algorithm with local beam search. • Final feature set refinement using Cuckoo search algorithm for better classification. • Experiments conducted on 12 real datasets for validation of proposed method. Classification algorithms and their preprocessing operations usually performs on feature selection on homogeneous or heterogeneous attributes, binary or multi-class labels separately. Only very few methods attempt to perform feature selection on datasets with heterogeneous multi-class attributes. In order to bridge this gap with better classification performance, the paper proposes a Tri-staged Feature Selection (TFS) methodology which performs (i) Feature selection using Kruskal Wallis test (ii) Refinement of feature selection using a new Memetic Algorithm with local beam search and genetic algorithm operations and (iii) Further refinement of feature selection using Cuckoo Search algorithm. Proper tradeoff between both exploration and exploitation is maintained in the proposed method. The experimental results on 12 datasets show that the proposed method is better than that of state-of-the-art methods used for feature selection in terms of multi-class accuracy, hamming loss, ranking loss, normalized coverage and convergence rate for multi-class heterogeneous datasets.
EDALoCo: Enhancing the accessibility of blockchains through a low-code approach to the development of event-driven applications for smart contract management

Blockchain is a cutting-edge technology based on a distributed, secure and immutable ledger that facilitates the registration of transactions and the traceability of tangible and intangible assets without requiring central governance. The agreements between the nodes participating in a blockchain network are defined through smart contracts. However, the compilation, deployment, interaction and monitoring of these smart contracts is a barrier compromising the accessibility of blockchains by non-expert developers. To address this challenge, in this paper, we propose a low-code approach, called EDALoCo, that facilitates the development of event-driven applications for smart contract management. These applications make blockchain more accessible for software developers who are non-experts in this technology as these can be modeled through graphical flows, which specify the communications between data producers, data processors and data consumers. Specifically, we have enhanced the open-source Node-RED low-code platform with blockchain technology, giving support for the creation of user-friendly and lightweight event-driven applications that can compile and deploy smart contracts in a particular blockchain. Additionally, this platform extension allows users to interact with and monitor the smart contracts already deployed in a blockchain network, hiding the implementation details from non-experts in blockchain. This approach was successfully applied to a case study of COVID-19 vaccines to monitor and obtain the temperatures to which these vaccines are continuously exposed, to process them and then to store them in a blockchain network with the aim of making them immutable and traceable to any user. As a conclusion, our approach enables the integration of blockchain with the low-code paradigm, simplifying the development of lightweight event-driven applications for smart contract management. The approach comprises a novel open-source solution that makes data security, immutability and traceability more accessible to software developers who are non-blockchain experts.
Taming model uncertainty in self-adaptive systems using bayesian model averaging

Research on uncertainty quantification and mitigation of software-intensive systems and (self-)adaptive systems, is increasingly gaining momentum, especially with the availability of statistical inference techniques (such as Bayesian reasoning) that make it possible to mitigate uncertain (quality) attributes of the system under scrutiny often encoded in the system model in terms of model parameters. However, to the best of our knowledge, the uncertainty about the choice of a specific system model did not receive the deserved attention.
Mental Imagery, Product Involvement and Presence at Virtual Reality Supermarket

Virtual Reality (VR) is a technology that creates fully immersive and virtual experiences and have been used to create virtual supermarkets and explore consumer behaviour on such alternate reality. The current study uses a VR scenario to analyse the effects of mental imagery, product involvement and presence on customer’s emotions and on the customers purchase intention, during a virtual shopping experience. The proposed model was tested with 108 participants in the laundry detergent category on a VR scenario. Findings reveal that on a virtual setting, mental imagery has the most significant impact on emotions, whereas presence has a strong influence on the purchase decision. Yet product involvement has any effect only on emotions. Moreover, the obsession for laundry products highly decreased the effect of product involvement on purchase intention.