Reviewerly/exHarmony huggingface dataset:
FunEasyLearn: An App for Learning Pronunciation?

The importance of both pronunciation learning/teaching and mobile-assisted language learning has been intensively pointed out in the language learning research. This study brings these two lines of interest together, by aiming at identifying the possibilities of using the FunEasyLearn language learning mobile app to promote the pronunciation learning by beginners of European Portuguese in an autonomous way or as a complement to language classes. After explaining the app’s selection and reviewing some important ideas for pronunciation training, the FunEasyLearn app is described and evaluated according to the Framework for Language Learning App Evaluation by Rosell-Aguilar, and its potential for pronunciation learning is analyzed. The results show that this app: features many strengths, especially in terms of user experience and technology; has several shortcomings in terms of language learning and pedagogy; and presents various properties which make it greatly helpful to support pronunciation learning in the relevant context (e.g., audio recordings, phonetic transcriptions, an embedded system of Automatic Speech Recognition). Some proposals are made to overcome the identified challenges and to turn it even more useful for pronunciation learning.
Formal Verification of a JavaCard Virtual Machine with Frama-C

Formal verification of real-life industrial software remains a challenging task. It provides strong guarantees of correctness, which are particularly important for security-critical products, such as smart cards. Security of a smart card strongly relies on the requirement that the underlying JavaCard virtual machine ensures necessary isolation properties. This case study paper presents a recent formal verification of a JavaCard Virtual Machine implementation performed by Thales using the Frama-C verification toolset. This is the first verification project for such a large-scale industrial smart card product where deductive verification is applied on the real-life C code. The target properties include common security properties such as integrity and confidentiality. The implementation contains over 7,000 lines of C code. After a formal specification in the ACSL specification language, over 52,000 verification conditions were generated and successfully proved. We present several issues identified during the project, illustrate them by representative examples and present solutions we used to solve them. Finally, we describe proof results, some lessons learned and desired tool improvements.
Robust supervised discrete hashing

In this paper, we proposed a more robust supervised hashing framework based on the Cauchy loss function and Supervised Discrete Hashing (SDH) called Robust Supervised Discrete Hashing (RSDH), which can learn a robust subspace consisted of binary codes. The Cauchy loss is used to measure the error between the label matrix and the product of the decomposed matrices. RSDH can not only reduce the outliers and noise of the hashing codes, but also achieve the more satisfactory retrieval effect. Image retrieval experiments demonstrate that RSDH performs better than the other hashing methods.
Research on Image Denoising and Super-Resolution Reconstruction Technology of Multiscale-Fusion Images

Image denoising and image super-resolution reconstruction are two important techniques for image processing. Deep learning is used to solve the problem of image denoising and super-resolution reconstruction in recent years, and it usually has better results than traditional methods. However, image denoising and super-resolution reconstruction are studied separately by state-of-the-art work. To optimally improve the image resolution, it is necessary to investigate how to integrate these two techniques. In this paper, based on Generative Adversarial Network (GAN), we propose a novel image denoising and super-resolution reconstruction method, i.e., multiscale-fusion GAN (MFGAN), to restore the images interfered by noises. Our contributions reflect in the following three aspects: (1) the combination of image denoising and image super-resolution reconstruction simplifies the process of upsampling and downsampling images during the model learning, avoiding repeated input and output images operations, and improves the efficiency of image processing. (2) Motivated by the Inception structure and introducing a multiscale-fusion strategy, our method is capable of using the multiple convolution kernels with different sizes to expand the receptive field in parallel. (3) The ablation experiments verify the effectiveness of each employed loss measurement in our devised loss function. And our experimental studies demonstrate that the proposed model can effectively expand the receptive field and thus reconstruct images with high resolution and accuracy and that the proposed MFGAN method performs better than a few state-of-the-art methods.
Learning Human-Object Interaction via Interactive Semantic Reasoning

Human-Object Interaction (HOI) detection devotes to learn how humans interact with surrounding objects via inferring triplets of 〈 human, verb, object 〉 . Recent HOI detection methods infer HOIs by directly extracting appearance features and spatial configuration from related visual targets of human and object, but neglect powerful interactive semantic reasoning between these targets. Meanwhile, existing spatial encodings of visual targets have been simply concatenated to appearance features, which is unable to dynamically promote the visual feature learning. To solve these problems, we first present a novel semantic-based Interactive Reasoning Block, in which interactive semantics implied among visual targets are efficiently exploited. Beyond inferring HOIs using discrete instance features, we then design a HOI Inferring Structure to parse pairwise interactive semantics among visual targets in scene-wide level and instance-wide level. Furthermore, we propose a Spatial Guidance Model based on the location of human body-parts and object, which serves as a geometric guidance to dynamically enhance the visual feature learning. Based on the above modules, we construct a framework named Interactive-Net for HOI detection, which is fully differentiable and end-to-end trainable. Extensive experiments show that our proposed framework outperforms existing HOI detection methods on both V-COCO and HICO-DET benchmarks and improves the baseline about 5.9% and 17.7% relatively, validating its efficacy in detecting HOIs.
Self-improving classification performance through GAN distillation

The availability of a large dataset can be a key factor in achieving good generalization capabilities when training deep learning models. Unfortunately, dataset collection is an expensive and time-consuming task, especially in specific application domains (e.g., medicine). In this paper, we present an approach for overcoming dataset size limitations by combining a classifier with a generative adversarial network (GAN) trained to synthesize "hard" samples through a triplet loss, to encourage the model to learn class features which may be under-represented or ambiguous in a small dataset. We evaluate the proposed approach on subsets of CIFAR-10 in order to simulate a low data availability, and compare the results achieved by our method with those obtained when training in a standard supervised setting over the same reduced set of data. Performance analysis shows a significant improvement in accuracy when training the model on GAN-generated hard samples: our GAN distillation approach improves accuracy in the reduced dataset scenario by about 5 percent points, compared to standard supervised training. Ablation studies and feature visualization confirm that our generative approach is able to consistently produce synthetic images that allow the model to improve its performance even with low data availability.
Efficient probability-oriented feature matching using wide field-of-view imaging

Feature matching is a key technique for a wide variety of computer vision and image processing applications such as visual localization. It permits finding correspondences of significant points within the environment that eventually determine the localization of a mobile agent. In this context, this work evaluates an Adaptive Probability-Oriented Feature Matching (APOFM) method that dynamically models the visual knowledge of the environment in terms of the probability of existence of features. Several improvements are proposed to achieve a more robust matching in a visual odometry framework: a study on the classification of the matching candidates, enhanced by a nearest neighbour search policy; a dynamic weighted matching that exploits the probability of feature existence in order to tune the matching thresholds; and an automatic false positive detector. Additionally, a comparison of performance is carried out, considering a publicly available dataset composed of two kinds of wide field-of-view images: catadioptric and fisheye. Overall, the results validate the appropriateness of these contributions, which outperform other well-recognized implementations within this framework, such as the standard visual odometry, a visual odometry method based on RANSAC, as well as the basic APOFM. The analysis shows that fisheye images provide more visual information of the scene, with more feature candidates. Contrarily, omnidirectional images produce fewer feature candidates, but with higher ratios of feature acceptance. Finally, it is concluded that improved precision is obtained when the location problem is solved by this method.
Robust Ensembling Network for Unsupervised Domain Adaptation

Recently, in order to address the unsupervised domain adaptation (UDA) problem, extensive studies have been proposed to achieve transferrable models. Among them, the most prevalent method is adversarial domain adaptation, which can shorten the distance between the source domain and the target domain. Although adversarial learning is very effective, it still leads to the instability of the network and the drawbacks of confusing category information. In this paper, we propose a Robust Ensembling Network (REN) for UDA, which applies a robust time ensembling teacher network to learn global information for domain transfer. Specifically, REN mainly includes a teacher network and a student network, which performs standard domain adaptation training and updates weights of the teacher network. In addition, we also propose a dual-network conditional adversarial loss to improve the ability of the discriminator. Finally, for the purpose of improving the basic ability of the student network, we utilize the consistency constraint to balance the error between the student network and the teacher network. Extensive experimental results on several UDA datasets have demonstrated the effectiveness of our model by comparing with other state-of-the-art UDA algorithms.
Information Chatbot for College Management System Using Multinomial Naive Bayes

A Chatbot is software that makes an interaction between user and machine where the automatic responses will be generated. Chatbot can make the user to think that he/she is chatting with another human by using these bots. The usage of a smart phone with many applications increased. The motive of the chatbot is to generate a quick response within less time to the user&#x2019;s query and reduce the workload of the management system. Every year, there is a breakthrough in the field of artificial intelligence. Bots are now utilized for communication to provide required or predetermined acknowledgment. The Chatbot should be forthright and communicative. They assist us by providing distraction, saving time, and reducing stress. These are designed to simulate the behavior of a human to engage with the real-time conversations. A remarkable customer experience can be built by using chatbots. These can be utilized by a large number of people at the same time. Our concept of considering chatbot can help students in pandemic and socially distant situations. The proposed system is developed by using Multinomial Na&#x00EF;ve Bayes algorithm and will be able to act as user side and generate responses to the user queries accurately.
Spectral Clustering Effect in Software Development Effort Estimation

Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can increase the estimation accuracy. The research methods employ symmetrical data processing and experimentation. Four experimental models based on function point analysis, stepwise regression, spectral clustering, and categorical variables have been conducted. The results indicate that the most advantageous variant is a combination of stepwise regression and spectral clustering. The proposed method provides the most accurate estimates compared to the baseline method and other tested variants.
Compiler-Assisted Hardening of Embedded Software Against Interrupt Latency Side-Channel Attacks

Recent controlled-channel attacks exploit timing differences in the rudimentary fetch-decode-execute logic of processors. These new attacks also pose a threat to software on embedded systems. Even when Trusted Execution Environments (TEEs) are used, interrupt latency attacks allow untrusted code to extract application secrets from a vulnerable enclave by scheduling interruption of the enclave. Constant-time programming is effective against these attacks but, as we explain in this paper, can come with some disadvantages regarding performance. To deal with this new threat, we propose a novel algorithm that hardens programs during compilation by aligning the execution time of corresponding instructions in secret-dependent branches. Our results show that, on a class of embedded systems with deterministic execution times, this approach eliminates interrupt latency side-channel leaks and mitigates limitations of constant-time programming. We have implemented our approach in the LLVM compiler infrastructure for the San-cus TEE, which extends the openMSP430 microcontroller, and we discuss applicability to other architectures. We make our implementation and benchmarks available for further research.
Muzzle the Shuttle: Efficient Compilation for Multi-Trap Trapped-Ion Quantum Computers

Trapped-ion systems can have a limited number of ions (qubits) in a single trap. Increasing the qubit count to run meaningful quantum algorithms would require multiple traps where ions need to shuttle between traps to communicate. The existing compiler has several limitations which result in a high number of shuttle operations and degraded fidelity. In this paper, we target this gap and propose compiler optimizations to reduce the number of shuttles. Our technique achieves a maximum reduction of $51.17\%$ in shuttles (average $\approx 33\%$) tested over $125$ circuits. Furthermore, the improved compilation enhances the program fidelity up to $22.68$X with a modest increase in the compilation time.
Analyzing the Relationship between Software Metrics and Bad Smells Using Critical Metric Value (CMV)

User requirements for a software system frequently evolve with time, and developers sometimes make incorrect implementation choices while meeting such requirements. These choices tend to introduce system flaws, i.e., bad smells in the program's source code. Bad smells do not disturb the normal functioning of a system, but they might worsen the software quality by increasing its complexity. Software metrics play a significant role in the analysis of object-oriented properties of a system. Also, change in software metrics’ values are often used to predict bad smells in a code. In the current study, an investigation has been conducted on four open-source projects. The concept of Critical Metric Value (CMV) has been introduced in the current study, and its impact on the occurrence of five selected bad smells has been examined to establish a relationship between software metrics and bad smells. CMV is that value of any software metric that is considered an outlier when compared to the rest of the metric values. The selected bad smells have been categorized as Null, Only, and Multiple based on the presence of CMV in a class. This study shows that 37.3% of the total classes have been affected by bad smells. Long statement bad smell pre-dominantly affects the selected systems as it is present in 25.74% classes. The experiment's findings show that 82.5% of the total bad smells occur in a class that consists of at least one CMV. The current study helps to establish a relationship between the bad smells and critical software metrics.
Meta-learning for compressed language model: A multiple choice question answering study

Model compression is a promising approach for reducing the model size of pretrained-language-models (PLMs) on low resource edge devices and applications. Unfortunately, the compression process always accompanies a cost of performance degradation, especially for the low resource downstream tasks, i.e., multiple-choice question answering. To address the degradation issue of model compression on PLMs, we proposed an end-to-end reptile (ETER) meta-learning approach to improving the performance of PLMs on the low resource multiple-choice question answering task. Specifically, our ETER improves the traditional two-stage meta-learning to an end-to-end manner, integrating the target finetuning stage into the meta training stage. To strengthen the generic meta-learning, ETER employs two-level meta-task construction from instance-level and domain-level to enrich its task generalization. What is more, ETER optimizes meta-learning by parameter constraints to reduce its parameter learning space. Experiments demonstrate that ETER significantly improved the performance of compressed PLMs and achieved large superiority over the baselines on different datasets.
CALText: Contextual Attention Localization for Offline Handwritten Text

Recognition of Arabic-like scripts such as Persian and Urdu is more challenging than Latin-based scripts. This is due to the presence of a two-dimensional structure, context-dependent character shapes, spaces and overlaps, and placement of diacritics. We present an attention based encoder-decoder model that learns to read handwritten text in context. A novel localization penalty is introduced to encourage the model to attend only one location at a time when recognizing the next character. In addition, we comprehensively refine the only complete and publicly available handwritten Urdu dataset in terms of ground-truth annotations. We evaluate the model on both Urdu and Arabic datasets. For Urdu, contextual attention localization achieves $$82.06\%$$ character recognition rate and $$51.97\%$$ word recognition rate which represent more than $$2\times $$ improvement over existing bi-directional LSTM models. For Arabic, the model outperforms multi-directional LSTM models with $$77.47\%$$ character recognition rate and $$37.66\%$$ word recognition rate without performing any slant or skew correction. Code and pre-trained models for this work are available at https://github.com/nazar-khan/CALText .
Generating Cartoon Images from Face Photos with Cycle-Consistent Adversarial Networks

The generative adversarial network (GAN) is first proposed in 2014, and this kind of network model is machine learning systems that can learn to measure a given distribution of data, one of the most important applications is style transfer.Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image.CYCLE-GAN is a classic GAN model, which has a wide range of scenarios in style transfer.Considering its unsupervised learning characteristics, the mapping is easy to be learned between an input image and an output image.However, it is difficult for CYCLE-GAN to converge and generate high-quality images.In order to solve this problem, spectral normalization is introduced into each convolutional kernel of the discriminator.Every convolutional kernel reaches Lipschitz stability constraint with adding spectral normalization and the value of the convolutional kernel is limited to [0, 1], which promotes the training process of the proposed model.Besides, we use pretrained model (VGG16) to control the loss of image content in the position of l1 regularization.To avoid overfitting, l1 regularization term and l2 regularization term are both used in the object loss function.In terms of Frechet Inception Distance (FID) score evaluation, our proposed model achieves outstanding performance and preserves more discriminative features.Experimental results show that the proposed model converges faster and achieves better FID scores than the state of the art.
A Survey of Spam Bots Detection in Online Social Networks

Online Social networks (OSN) have become an integral part of people's lives. People from all over the world interact instantly between each other by sharing pictures and content. They can also express their opinion about politics, sport, and be part of influencing users in OSN. So, with the large growth of the number of users of OSN, it has become a target for the vicious people that post spam contents and messages. The malicious social bots (MSB) are one of the biggest threats that menace the social networks security and several studies have been conducted to detect them. In this work we focus on spam bots and reviewed all the existing bot detection techniques based on different features extracted from users' profiles and interactions. The paper analyzed and compared the proposed techniques between 2014 and 2021 to get the most relevant features that improve the spam bot detection and the most efficient Machine learning ML and Deep learning DL techniques from OSN. An investigation on existing datasets is proposed, some limitations of the studied approaches are outlined and future directions for social bot techniques detection improvement are proposed.
Resource Allocation in Cloud Using Enhanced Max-Min Algorithm

Cloud computing technology is getting more advanced these days. Cloud computing is a pay-as-you-go service since it allows for variable resource allocation for services. Task scheduling and resource allocation are becoming major requirements for efficient and effective load balancing among cloud resources. For scheduling resources in cloud environment, different tasks need to be executed simultaneously by the available resources in order to minimize the completion time. To achieve this, we proposed a new technique called Enhanced Max-Min Algorithm (EMM). This algorithm takes less time to complete all the tasks with minimum completion time. In the proposed algorithm, small size tasks are concurrently executed and resource allocation is done accordingly. When compared to Min-Min algorithm the Enhanced Max-Min algorithm provides high resource utilization and reduces the makespan.
Design and Implementation of Data Sharing Traceability System Based on Blockchain Smart Contract

“Traceability” is an important method often used in modern supply management. The traceability system is a system based on the Internet of Things technology. In this process, users will share resources through cloud service providers, so how to ensure data security is also one of the issues we consider. Blockchain technology is an emerging technology in the field of information technology. Its decentralized nature, distributed storage, and difficult data modification provide us with fair exchange and sharing of data. Feasible solutions: in this article, we have studied the key issues of fair exchange and safe sharing of data based on blockchain and designed a multigroup data sharing scheme based on alliance chain. To solve the various existing traceability systems problem, this paper designs a new traceability system based on blockchain technology and implements a system prototype to verify the feasibility of the system.