Reviewerly/exHarmony huggingface dataset:
A Novel Siamese Network for Few/Zero-Shot Handwritten Character Recognition Tasks

Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks.It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks (CNNs) for learning a distance function that can map input data from the input space to the feature space.Instead of determining the class of each sample, the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not.The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly initialized weights and trained by binary cross-entropy loss.Building two CNNs from scratch is a trial and error and time-consuming phase.In addition, training with binary crossentropy loss sometimes leads to poor margins.In this paper, a novel Siamese network is proposed and applied to few/zero-shot Handwritten Character Recognition (HCR) tasks.The novelties of the proposed network are in.1) Utilizing transfer learning and using the pre-trained AlexNet as a feature extractor in the Siamese architecture.Fine-tuning a pre-trained network is typically faster and easier than building from scratch.2) Training the Siamese architecture with contrastive loss instead of the binary cross-entropy.Contrastive loss helps the network to learn a nonlinear mapping function that enables it to map the extracted features in the vector space with an optimal way.The proposed network is evaluated on the challenging Chars74K datasets by conducting two experiments.One is for testing the proposed network in few-shot learning while the other is for testing it in zero-shot learning.The recognition accuracy of the proposed network reaches to 85.6% and 82% in few-and zero-shot learning respectively.In addition, a comparison between the performance of the proposed Siamese network and the traditional Siamese CNNs is conducted.The comparison results show that the proposed network achieves higher recognition results in less time.The proposed network reduces the training time from days to hours in both experiments.
Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

Forecasting on success or failure of software has become an interesting and, in fact, an essential task in the software development industry.In order to explore the latest data on successes and failures, this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors, misinterpretation, and miscommunication of requirements and decision-making processes play their roles in software success forecasting.We discussed a potential relationship between forecasting of software success or failure and the development processes.We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study, and they were more likely to rate software success forecasting linking to the development processes.Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate.The results of this empirical study also validated that requirements' misinterpretation and miscommunication were the main causes behind software systems' failure.It has been seen that reliable, relevant, and trustworthy sources of information help in decision-making to predict software systems' success in the software industry.This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects.Future investigation can be performed to identify the other human factors that may impact software systems' success.
Selective Layer Tuning and Performance Study of Pre-Trained Models Using Genetic Algorithm

Utilizing pre-trained models involves fully or partially using pre-trained parameters as initialization. In general, configuring a pre-trained model demands practitioners’ knowledge about problems or an exhaustive trial–error experiment according to a given task. In this paper, we propose tuning trainable layers using a genetic algorithm on a pre-trained model that is fine-tuned on single-channel image datasets for a classification task. The single-channel dataset comprises images from grayscale and preprocessed audio signals transformed into a log-Mel spectrogram. Four deep-learning models used in the experimental evaluation employed the pre-trained model with the ImageNet dataset. The proposed genetic algorithm was applied to find the highest fitness for every generation to determine the selective layer tuning of the pre-trained models. Compared to the conventional fine-tuning method and random layer search, our proposed selective layer search with a genetic algorithm achieves higher accuracy, on average, by 9.7% and 1.88% (MNIST-Fashion), 1.31% and 1.14% (UrbanSound8k), and 2.2% and 0.29% (HospitalAlarmSound), respectively. In addition, our searching method can naturally be applied to various datasets of the same task without prior knowledge about the dataset of interest.
Faster Deep Inertial Pose Estimation with Six Inertial Sensors

We propose a novel pose estimation method that can predict the full-body pose from six inertial sensors worn by the user. This method solves problems encountered in vision, such as occlusion or expensive deployment. We address several complex challenges. First, we use the SRU network structure instead of the bidirectional RNN structure used in previous work to reduce the computational effort of the model without losing its accuracy. Second, our model does not require joint position supervision to achieve the best results of the previous work. Finally, since sensor data tend to be noisy, we use SmoothLoss to reduce the impact of inertial sensors on pose estimation. The faster deep inertial poser model proposed in this paper can perform online inference at 90 FPS on the CPU. We reduce the impact of each error by more than 10% and increased the inference speed by 250% compared to the previous state of the art.
Plastic Waste Management through the Development of a Low Cost and Light Weight Deep Learning Based Reverse Vending Machine

The management of the abundant amount of used plastic bottle waste is a major concern nowadays, because it is a major contributor to landfills and overburdens waste processing facilities. Once disposed of, plastic can take centuries to break down, hence, recycling not only manages the waste efficiently, but it reduces the environmental impact and creates economic opportunities, as well. An incentive-based Reverse Vending machine (RVM) is an effective way to involve the general public in the management of plastic waste. The existing solutions are either very expensive, from a computation and cost perspective, or they lack the robustness and durability necessary for deployment. In this research, we have developed an incentive-based low-cost RVM for the collection, identification, classification, and sorting of used plastic bottles with the addition of a reward-based user application. The developed RVM includes a low-cost computing device running a lightweight image processing algorithm, sensors, camera, and a self-designed mechanical arm. To support the low computing device in the RVM, a lightweight MobileNet model has been trained using transfer learning. A dataset of 10,983 pet bottle images was collected using a camera installed inside the machine for the classification model. The results of the study show that MobileNet achieved 99.2% testing accuracy, which is better than the existing bottle-classification approaches. Moreover, the weight of the developed model was only 12 Mb, which is fourteen and six times less than inceptionV3 and Res-Net (Residual Neural Networks), respectively. Furthermore, the developed RVM costs a fraction of the price, compared to the existing solutions. Two versions of the machine have been deployed at a University for more than 6 months, collecting over 650 kg of plastic waste.
Deep Learning Analysis of English Education Blended Teaching in Virtual Reality Environment

The rapid development of computer software and hardware, network technology, and various Internet platforms has brought mankind into a new era. In recent years, “virtual reality” can be regarded as a huge hot spot, whether in the field of industry, education, or research. At present, although the heat has subsided a little, the technical teams involved in various fields are also working collectively to continuously innovate. Based on the mixed teaching mode of English education, this article conducts in-depth research on deep learning and virtual reality technology, integrates deep learning and virtual reality learning environment, and builds a learning model of English education learning environment based on virtual reality. The teaching design of the course aims to fully combine the main content of deep learning with the virtual reality environment. Through experimental research, it is explored whether the learning environment based on virtual reality can promote deep learning. The relevant data of the experimental class and the control class are collected through questionnaires, starting from the four dimensions of motivation dimension, investment dimension, strategy dimension, and result dimension, and conduct a comparative analysis, and use the auxiliary interview method to understand the experience of students and teachers on virtual reality equipment and put forward relevant suggestions.
Effectiveness of Artificial Intelligence (AI) in Improving Pupils’ Deep Learning in Primary School Mathematics Teaching in Fujian Province

Several primary school students in Fujian Province have perceived studying mathematics as challenging. To deal with this issue, computer technology advancements, specifically artificial intelligence (AI), present an opportunity to evaluate individual students' learning challenges and give individualized support to optimize their success in mathematics classes. It is also possible to use virtual reality (VR) to assist learners in acquiring complex mathematical and logical ideas and to lessen learners' mistakes. As a result, researchers, particularly beginners, are missing out on a complete perspective of the study of AI in teaching mathematics. That is why we are exploring the role of AI in math education by developing a "fuzzy-based tweakable convolution neural network with a long short-term memory (FT-CNN-LSTM-AM)" method. For this investigation, the students' datasets are taken and educated by mathematical teaching via the application of AI. The proposed method is utilized to predict the students' performance in mathematical education. A grey wolf optimizer is employed to boost the effectiveness of the proposed method. Furthermore, the performance of the proposed method is analyzed and compared with existing approaches to gain the highest reliability.
A Methodology for Training Homomorphic Encryption Friendly Neural Networks

Privacy-preserving deep neural network (DNN) inference is a necessity in different regulated industries such as healthcare, finance and retail. Recently, homomorphic encryption (HE) has been used as a method to enable analytics while addressing privacy concerns. HE enables secure predictions over encrypted data. However, there are several challenges related to the use of HE, including DNN size limitations and the lack of support for some operation types. Most notably, the commonly used ReLU activation is not supported under some HE schemes. We propose a structured methodology to replace ReLU with a quadratic polynomial activation. To address the accuracy degradation issue, we use a pre-trained model that trains another HE-friendly model, using techniques such as trainable activation functions and knowledge distillation. We demonstrate our methodology on the AlexNet architecture, using the chest X-Ray and CT datasets for COVID-19 detection. Experiments using our approach reduced the gap between the F1 score and accuracy of the models trained with ReLU and the HE-friendly model to within a mere 0.32-5.3 percent degradation. We also demonstrate our methodology using the SqueezeNet architecture, for which we observed 7 percent accuracy and F1 improvements over training similar networks with other HE-friendly training methods.
A New Key Recovery Side-Channel Attack on HQC with Chosen Ciphertext

Hamming Quasi-Cyclic (HQC) is a code-based candidate of NIST post-quantum standardization procedure. The decoding steps of code-based cryptosystems are known to be vulnerable to side-channel attacks and HQC is no exception to this rule. In this paper, we present a new key recovery side-channel attack on HQC with chosen ciphertext. Our attack takes advantage of the reuse of a static secret key on a micro-controller with a physical access. The goal is to retrieve the static secret key by targeting the Reed-Muller decoding step of the decapsulation and more precisely the Hadamard transform. This function is known for its diffusion property, a property that we exploit through side-channel analysis. The side-channel information is used to build an Oracle that distinguishes between several decoding patterns of the Reed-Muller codes. We show how to query the Oracle such that the responses give a full information about the static secret key. Experiments show that less than 20.000 electromagnetic attack traces are sufficient to retrieve the whole static secret key used for the decapsulation. Finally, we present a masking-based countermeasure to thwart our attack.
Forward-Secure Revocable Secret Handshakes from Lattices

Secret handshake ( $$\textsf{SH}$$ ), as a fundamental privacy-preserving primitive, allows members from the same organization to anonymously authenticate each other. Since its proposal by Balfanz et al., numerous constructions have been proposed, among which only the ones separately designed by Zhang et al. over coding and An et al. over lattice are secure against quantum attacks. However, none of known schemes consider the issue of key exposure, which is a common threat to cryptosystem implementations. To guarantee users’ privacy against the key exposure attack, forward-secure mechanism is believed to be a promising countermeasure, where secret keys are periodically evolved in such a one-way manner that, past transactions of users are protected even if a break-in happens. In this work we formalize the model of forward-secure secret handshake and present the first lattice-based instantiation, where ABB $$\textsf{HIBE}$$ is applied to handle key evolution process through regarding time periods as hierarchies. In particular, dynamic revocability is captured by upgrading the static verifier-local revocation techniques into updatable ones. To achieve anonymous handshake with ease, we present a generic way of transforming zero-knowledge argument systems termed as Fiat-Shamir with abort, into mutual authentication protocols. Our scheme is proved secure under the Short Integer Solution ( $$\textsf{SIS}$$ ) and Learning With Errors ( $$\textsf{LWE}$$ ) assumptions in the random oracle model.
Frequency estimation under non-Markovian spatially correlated quantum noise

We study the estimation precision attainable by entanglement-enhanced Ramsey interferometry in the presence of spatiotemporally correlated non-classical noise. Our analysis relies on an exact expression of the reduced density matrix of the qubit probes under general zero-mean Gaussian stationary dephasing, which is established through cumulant-expansion techniques and may be of independent interest in the context of non-Markovian open dynamics. By continuing and expanding our previous work [Beaudoin et al., Phys. Rev. A 98, 020102(R) (2018)], we analyze the effects of a non-collective coupling regime between the qubit probes and their environment, focusing on two limiting scenarios where the couplings may take only two or a continuum of possible values. In the paradigmatic case of spin-boson dephasing noise from a thermal environment, we find that it is possible to suppress, on average, the effect of spatial correlations by randomizing the location of the probes, as long as enough configurations are sampled where noise correlations are negative. As a result, superclassical precision scaling is asymptotically restored for initial entangled states, including experimentally accessible one-axis spin-squeezed states.
Strategic Rituals of Loyalty: When Israeli Journalists Face Digital Hate

This article examines how and why Israeli journalists use their military service as a shield in response to online violence and digital hate. This practice, termed here the military-as-alibi strategy, is highly consequential. First, it excludes Israeli citizens who are exempt from military service (mostly Palestinian citizens of Israel and ultra-orthodox Jews). Second, it affirms the presumption that “good journalists” are not to be measured by their reporting, but rather by external loyalty tests that allegedly demonstrate their commitment to the national cause. Drawing on analyses of interviews with 20 Israeli journalists, media coverage and social media content, this article frames the military-as-alibi strategy within the local context of a militarised society, but also as part of journalists’ global struggle to win the hearts of their audiences in challenging times. Building on Tuchman (1972), the article labels journalists’ references to their military backgrounds as a strategic ritual of loyalty. The article proposes an alternative strategy to counter anti-press attacks: if journalism is indeed a public good (Pickard 2019), then “good journalism” should be considered “good citizenship”. This approach could free journalists from surrendering to nationalist loyalty tests, and lay better foundations for journalists–audiences relationships in the future.
Variational Entanglement-Assisted Quantum Process Tomography with Arbitrary Ancillary Qubits

Quantum process tomography is a pivotal technique in fully characterizing quantum dynamics. However, exponential scaling of the Hilbert space with the increasing system size extremely restrains its experimental implementations. Here, we put forward a more efficient, flexible, and error-mitigated method: variational entanglement-assisted quantum process tomography with arbitrary ancillary qubits. Numerically, we simulate up to eight-qubit quantum processes and show that this tomography with m ancillary qubits (0≤m≤n) alleviates the exponential costs on state preparation (from 4^{n} to 2^{n-m}), measurement settings (at least a 1 order of magnitude reduction), and data postprocessing (efficient and robust parameter optimization). Experimentally, we first demonstrate our method on a silicon photonic chip by rebuilding randomly generated one-qubit and two-qubit unitary quantum processes. Further using the error mitigation method, two-qubit quantum processes can be rebuilt with average gate fidelity enhanced from 92.38% to 95.56%. Our Letter provides an efficient and practical approach to process tomography on the noisy quantum computing platforms.
Specificity ratings for Italian data

Abstraction enables us to categorize experience, learn new information, and form judgments. Language arguably plays a crucial role in abstraction, providing us with words that vary in specificity (e.g., highly generic: tool vs. highly specific: muffler). Yet, human-generated ratings of word specificity are virtually absent. We hereby present a dataset of specificity ratings collected from Italian native speakers on a set of around 1K Italian words, using the Best-Worst Scaling method. Through a series of correlation studies, we show that human-generated specificity ratings have low correlation coefficients with specificity metrics extracted automatically from WordNet, suggesting that WordNet does not reflect the hierarchical relations of category inclusion present in the speakers' minds. Moreover, our ratings show low correlations with concreteness ratings, suggesting that the variables Specificity and Concreteness capture two separate aspects involved in abstraction and that specificity may need to be controlled for when investigating conceptual concreteness. Finally, through a series of regression studies we show that specificity explains a unique amount of variance in decision latencies (lexical decision task), suggesting that this variable has theoretical value. The results are discussed in relation to the concept and investigation of abstraction.
Football Event Classification Using Convolutional Autoencoder and Multilayer Extreme Learning Machine

Event detection and classification in sports are particularly important in the field of sports data analytics. In this era, various AI methods are utilized to detect events in a football (soccer) game. The use of advanced computational techniques in this area can help to achieve higher accuracy in detecting events. This letter proposes a novel architecture to classify events in a football game. The dataset, which is used, consists of numerous images pertaining to different events in a regular football match. From this dataset, the objective is to detect specific and semantically meaningful events, such as pass, dribble, or shoot. The proposed model consists of a convolutional autoencoder (AE) coupled with a multilayer extreme learning machine to classify the given images into defined events. The convolutional AE module is placed before the extreme learning machine (ELM) in the model pipeline as it helps with data compression and dimensionality reduction so as to ensure fast computation. Thereby, a multilayer extreme learning machine achieves better performance compared with the other existing approaches.
Incorporation of Human Knowledge Into Data Embeddings to Improve Pattern Significance and Interpretability

Embedding is a common technique for analyzing multi-dimensional data. However, the embedding projection cannot always form significant and interpretable visual structures that foreshadow underlying data patterns. We propose an approach that incorporates human knowledge into data embeddings to improve pattern significance and interpretability. The core idea is (1) externalizing tacit human knowledge as explicit sample labels and (2) adding a classification loss in the embedding network to encode samples' classes. The approach pulls samples of the same class with similar data features closer in the projection, leading to more compact (significant) and class-consistent (interpretable) visual structures. We give an embedding network with a customized classification loss to implement the idea and integrate the network into a visualization system to form a workflow that supports flexible class creation and pattern exploration. Patterns found on open datasets in case studies, subjects' performance in a user study, and quantitative experiment results illustrate the general usability and effectiveness of the approach.