Reviewerly/exHarmony huggingface dataset:
Adaptive Temporal Grouping for Black-box Adversarial Attacks on Videos

Deep-learning based video models, which have remarkable performance on action recognition tasks, are recently proved to be vulnerable to adversarial samples, even those generated in the black-box setting. However, these black-box attack methods are insufficient to attack videos models in real-world applications due to the requirement of lots of queries. To this end, we propose to boost the efficiency of black-box attacks on video recognition models. Although videos carry rich temporal information, they include redundant spatial information from adjacent frames. This motivates us to introduce the adaptive temporal grouping (ATG) method, which groups video frames by the similarity of their features extracted from the ImageNet-pretrained image model. By selecting one key-frame from each group, ATG helps any black-box attack methods to optimize the adversarial perturbations over key-frames instead of all frames, where the estimated gradient of key-frame is shared with other frames in each group. To balance the efficiency and precision of estimated gradients, ATG adaptively adjusts the group number by the magnitude of the current perturbation and the current query number. Through extensive experiments on the HMDB-51 dataset and the UCF-101 dataset, we demonstrate that ATG can significantly reduce the number of queries by more than 10% for the targeted attack.
Analysis of Different SaaS Architectures from a Trust Service Provider Perspective

The topic of cloud computing is becoming of extreme interest for trust service providers willing to move their services from a desktop-oriented approach into a more interconnected, mobile world. In our paper, we investigate and present from this perspective, several architectures that are currently available on the commercial market. The analysis covers some of the well-known commercial cloud platforms available: Amazon Web Services, Google Cloud Platform, IBM Cloud, Microsoft Azure, and Oracle Cloud. We have analyzed and identified several advantages associated with the use of these commercially available platforms with the objective of pondering the potential use of the Software-as-a-Service (SaaS) approach within an innovative Digital Enterprise making use of the traditional services of a trust service provider (including here digital signatures, digital notary, time stamping, etc.) The analysis of the above-mentioned commercial platforms is not only addressing their own intrinsic SaaS architectures but also the derived or personalized architectures that are provided for their tenants and clients.
THE USE Of BLENDED LEARNING MODEL TO ENHANCE STUDENT'S LEARNING INTEREST IN ENGLISH

The goal of this study is to ascertain whether employing a blended learning paradigm has increased students' interest in learning. In this study, a descriptive method is combined with a qualitative approach. The population of this study consisted of 36 students in class II-A of the STKIP Yasika Mathematics Education Study Program, which used purposive sampling as its sample technique. Data reduction, data presentation, and conclusion drawing or verification are used in the analysis of data gathering procedures, which include surveys, interviews, and observations. The results showed that, on average, 86 percent of students in class II-A of the Mathematics Education Study Program in STKIP Yasika exhibited a greater interest in studying English when blended learning is used. These results include the following indicators: 1) feelings of enjoyment with the very strong category (89%); 2) attention with the very strong category (83%); 3) very high interest in learning (91%); and 3) actively involved with the very strong category (81%)
Gradual Soundness: Lessons from Static Python

Context: Gradually-typed languages allow typed and untyped code to interoperate, but typically come with significant drawbacks. In some languages, the types are unreliable; in others, communication across type boundaries can be extremely expensive; and still others allow only limited forms of interoperability. The research community is actively seeking a sound, fast, and expressive approach to gradual typing. Inquiry: This paper describes Static Python, a language developed by engineers at Instagram that has proven itself sound, fast, and reasonably expressive in production. Static Python's approach to gradual types is essentially a programmer-tunable combination of the concrete and transient approaches from the literature. Concrete types provide full soundness and low performance overhead, but impose nonlocal constraints. Transient types are sound in a shallow sense and easier to use; they help to bridge the gap between untyped code and typed concrete code. Approach: We evaluate the language in its current state and develop a model that captures the essence of its approach to gradual types. We draw upon personal communication, bug reports, and the Static Python regression test suite to develop this model. Knowledge: Our main finding is that the gradual soundness that arises from a mix of concrete and transient types is an effective way to lower the maintenance cost of the concrete approach. We also find that method-based JIT technology can eliminate the costs of the transient approach. On a more technical level, this paper describes two contributions: a model of Static Python and a performance evaluation of Static Python. The process of formalization found several errors in the implementation, including fatal errors. Grounding: Our model of Static Python is implemented in PLT Redex and tested using property-based soundness tests and 265 tests from the Static Python regression suite. This paper includes a small core of the model to convey the main ideas of the Static Python approach and its soundness. Our performance claims are based on production experience in the Instagram web server. Migrations to Static Python in the server have caused a 3.7\% increase in requests handled per second at maximum CPU load. Importance: Static Python is the first sound gradual language whose piece-meal application to a realistic codebase has consistently improved performance. Other language designers may wish to replicate its approach, especially those who currently maintain unsound gradual languages and are seeking a path to soundness.
Reflectance Transformation Imaging for the quantitative characterization of experimental fracture surfaces of bonded assemblies

Controlling failure modes is an important issue for the development of bonded assemblies of multi-material structures in industry. The analysis of the failure modes of bonded assemblies, and in particular the evaluation of the adhesive/cohesive failure rate, constitute a significant issue. Generally, it is considered in industrial applications that obtaining a cohesive fracture makes it possible to generate better predictions regarding the rupture of the adhesive joints, even if recent work has shown that a good prediction of the mechanical behavior can also be obtained when adhesive or mixed failure modes occur. Currently, these evaluations are carried out by experts by means of a visual analysis, either directly on the fracture surfaces, or sometimes from high-resolution microscopy. The approach presented in this paper aims to set out a methodology based on the Reflectance Transformation Imaging technique for the objective characterization of experimental fracture surfaces of bonded assemblies. We show how this imaging technique can be used to quantify objective surface features, and how these local descriptors can be used to estimate and map out the failure mode on the fracture surfaces.
Murine Motion Behavior Recognition Based on DeepLabCut and Convolutional Long Short-Term Memory Network

Murine behavior recognition is widely used in biology, neuroscience, pharmacology, and other aspects of research, and provides a basis for judging the psychological and physiological state of mice. To solve the problem whereby traditional behavior recognition methods only model behavioral changes in mice over time or space, we propose a symmetrical algorithm that can capture spatiotemporal information based on behavioral changes. The algorithm first uses the improved DeepLabCut keypoint detection algorithm to locate the nose, left ear, right ear, and tail root of the mouse, and then uses the ConvLSTM network to extract spatiotemporal information from the keypoint feature map sequence to classify five behaviors of mice: walking straight, resting, grooming, standing upright, and turning. We developed a murine keypoint detection and behavior recognition dataset, and experiments showed that the method achieved a percentage of correct keypoints (PCK) of 87±1% at three scales and against four backgrounds, while the classification accuracy for the five kinds of behaviors reached 93±1%. The proposed method is thus accurate for keypoint detection and behavior recognition, and is a useful tool for murine motion behavior recognition.
Reflectance Transformation Imaging as a Tool for Computer-Aided Visual Inspection

This work investigates the use of Reflectance Transformation Imaging (RTI) rendering for visual inspection. This imaging technique is being used more and more often for the inspection of the visual quality of manufactured surfaces. It allows reconstructing a dynamic virtual rendering of a surface from the acquisition of a sequence of images where only the illumination direction varies. We investigate, through psychometric experimentation, the influence of different essential parameters in the RTI approach, including modeling methods, the number of lighting positions and the measurement scale. In addition, to include the dynamic aspect of perception mechanisms in the methodology, the psychometric experiments are based on a design of experiments approach and conducted on reconstructed visual rendering videos. The proposed methodology is applied to different industrial surfaces. The results show that the RTI approach can be a relevant tool for computer-aided visual inspection. The proposed methodology makes it possible to objectively quantify the influence of RTI acquisition and processing factors on the perception of visual properties, and the results obtained show that their impact in terms of visual perception can be significant.
Implementation of Information Technology into the Education of Music Teachers

The goal of the research is to study the process of introducing information technologies into the training of music teachers in Ukrainian pedagogical universities. To undertake the research, a study on the scientific literature on the professional training of music teachers in pedagogical colleges, as well as an analysis of the curricula of music pedagogy in general education schools and higher educational establishments. As a science, research on music education technologies focuses on phenomena related to music education technologies through the prism of a modern information society. There is a great interest in music technology creation in the context of constructivism. We propose to use constructivist methods as a philosophical basis for an online music education course for graduates. For Ukraine, it is necessary to study how students use this technology in preparation for a presentation in class, and the results were rated as moderately successful.
Hyper content e-module in information behavior course with the assistant of screencast

In the 21st century, continuous innovation and adaptation in every component of learning is required in education, both in the competency component, material content, strategy and media, as well as its evaluation. This study emphasized the development of an electronic or digital module in the information behavior course, it is one of the courses regarding the characteristics of the student activity level that accommodates information retrieval and strives to use appropriate information. The e-module design is developed utilize the 4D model by define, design, develop and disseminate. The participants of this research involved media experts, material experts and students of the Library and Information Science study program at two universities, which are are Universitas Pendidikan Indonesia and Universitas Negeri Malang, there are 20 people in total who used e-modules in limited dissemination on information behavior course. This research revealed the electronic module in the course, the emphasis of the module content design which is enriched with digital-based information sources or in another word called as hyper content that becomes a differentiator in presenting the module content, coupled with the power of audio and visual media or called as screencast. Through this research, lecturers able to prepare independent learning materials by utilizing digital technology.
Comprehensive-perception dynamic reasoning for visual question answering

The goal of Visual Question Answering (VQA) is to answer questions based on an image. In the VQA task, reasoning plays an important role in dealing with relations because this task has a high requirement for modeling complex features. In most existing models, the features are only extracted and integrated between adjacent layers. This pattern arguably affects the integrity of information interaction during reasoning. In this paper, we propose a comprehensive-perception dynamic reasoning (CPDR) model to utilize the cross-layer object features for multi-step compound reasoning. It calculates the interactions among the object features from all previous layers and integrates these interactions to generate new object features, iteratively. Finally, the object features of all layers will be used for the final prediction. Empirical results show that our model achieves superior performance among VQA models which are not VLP-based, and incorporating the CPDR module into the VLP models brings considerable performance improvements.
Mitigating Reporting Bias in Semi-supervised Temporal Commonsense Inference with Probabilistic Soft Logic

Acquiring high-quality temporal common sense (TCS) knowledge from free-form text is a crucial but challenging problem for event-centric natural language understanding, due to the language reporting bias problem: people rarely report the commonly observed events but highlight the special cases. For example, one may rarely report "I get up from bed in 1 minute", but we can observe "It takes me an hour to get up from bed every morning'' in text. Models directly trained upon such corpus would capture distorted TCS knowledge, which could influence the model performance. Prior work addresses this issue mainly by exploiting the interactions among temporal dimensions (e.g., duration, temporal relation between events) in a multi-task view. However, this line of work suffers the limitation of implicit, inadequate and unexplainable interactions modeling. In this paper, we propose a novel neural-logic based Soft Logic Enhanced Event Temporal Reasoning (SLEER) model for acquiring unbiased TCS knowledge, in which the complementary relationship among dimensions are explicitly represented as logic rules and modeled by t-norm fuzzy logics. SLEER can utilize logic rules to regularize its inference process. Experimental results on four intrinsic evaluation datasets and two extrinsic datasets show the efficiency of our proposed method.
Is neural language acquisition similar to natural? A chronological probing study

The probing methodology allows one to obtain a partial representation of linguistic phenomena stored in the inner layers of the neural network, using external classifiers and statistical analysis. Pre-trained transformer-based language models are widely used both for natural language understanding (NLU) and natural language generation (NLG) tasks making them most commonly used for downstream applications. However, little analysis was carried out, whether the models were pre-trained enough or contained knowledge correlated with linguistic theory. We are presenting the chronological probing study of transformer English models such as MultiBERT and T5. We sequentially compare the information about the language learned by the models in the process of training on corpora. The results show that 1) linguistic information is acquired in the early stages of training 2) both language models demonstrate capabilities to capture various features from various levels of language, including morphology, syntax, and even discourse, while they also can inconsistently fail on tasks that are perceived as easy. We also introduce the open-source framework for chronological probing research, compatible with other transformer-based models. https://github.com/EkaterinaVoloshina/chronological_probing
NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency

We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data. In NaturalInversion, we propose: (1) a Feature Transfer Pyramid which uses enhanced image prior of the original data by combining the multi-scale feature maps extracted from the pre-trained classifier, (2) a one-to-one approach generative model where only one batch of images are synthesized by one generator to bring the non-linearity to optimization and to ease the overall optimizing process, (3) learnable Adaptive Channel Scaling parameters which are end-to-end trained to scale the output image channel to utilize the original image prior further. With our NaturalInversion, we synthesize images from classifiers trained on CIFAR-10/100 and show that our images are more consistent with original data distribution than prior works by visualization and additional analysis. Furthermore, our synthesized images outperform prior works on various applications such as knowledge distillation and pruning, demonstrating the effectiveness of our proposed method.
Asyncval

The process of model checkpoint validation refers to the evaluation of the performance of a model checkpoint executed on a held-out portion of the training data while learning the hyperparameters of the model. This model checkpoint validation process is used to avoid over-fitting and determine when the model has converged so as to stop training. A simple and efficient strategy to validate deep learning checkpoints is the addition of validation loops to execute during training. However, the validation of dense retrievers (DR) checkpoints is not as trivial -- and the addition of validation loops is not efficient. This is because, in order to accurately evaluate the performance of a DR checkpoint, the whole document corpus needs to be encoded into vectors using the current checkpoint before any actual retrieval operation for checkpoint validation can be performed. This corpus encoding process can be very time-consuming if the document corpus contains millions of documents (e.g., 8.8M for MS MARCO v1 and 21M for Natural Questions). Thus, a naïve use of validation loops during training will significantly increase training time. To address this issue, we propose Asyncval: a Python-based toolkit for efficiently validating DR checkpoints during training. Instead of pausing the training loop for validating DR checkpoints, Asyncval decouples the validation loop from the training loop, uses another GPU to automatically validate new DR checkpoints and thus permits to perform validation asynchronously from training. Asyncval also implements a range of different corpus subset sampling strategies for validating DR checkpoints; these strategies allow to further speed up the validation process. We provide an investigation of these methods in terms of their impact on validation time and validation fidelity. Asyncval is made available as an open-source project at https://github.com/ielab/asyncval.
Supply Chain Finance in Blockchain-based Digital Asset Platform

Assets such as warehouse receipts and pre-sale certificates are important for enterprises,which can be used to pledge in supply chain finance. In China, traditional supply chain finance is done manually by the bank side, which inevitably encounters problems such as multiple pledges and unclear rights on assets. In this paper, we propose a blockchain-based digital asset platform (DAP), which not only embeds digital signatures and smart contracts to improve the reliability of supply chain finance between enterprises and banks but also the data stored on the DAP blockchain makes traceability possible. Besides, DAP is built on DNA-based Consortium blockchain and threshold ECDSA Algorithm, which is autonomous and controllable. Moreover, subsequent performance tests show that DAP has a high ability to process transactions and has a greater potential for application.
Implementation of CNN-MLP and CNN-LSTM for MitM Attack Detection System

Man in the Middle (MitM) is one of the attack techniques conducted for eavesdropping on data transitions or conversations between users in some systems secretly. It has a sizeable impact because it could make the attackers will do another attack, such as website or system deface or phishing. Deep Learning could be able to predict various data well. Hence, in this study, we would like to present the approach to detect MitM attacks and process its data, by implementing hybrid deep learning methods. We used 2 (two) combinations of the Deep Learning methods, which are CNN-MLP and CNN-LSTM. We also used various Feature Scaling methods before building the model and will determine the better hybrid deep learning methods for detecting MitM attack, as well as the feature selection methods that could generate the highest accuracy. Kitsune Network Attack Dataset (ARP MitM Ettercap) is the dataset used in this study. The results prove that CNN-MLP has better results than CNN-LSTM on average, which has the accuracy rate respectively at 99.74%, 99.67%, and 99.57%, and using Standard Scaler has the highest accuracy (99.74%) among other scenarios.&#x0D;
An Efficient Diffusion Approach for Chaos-Based Image Encryption and DNA Sequences

Experts and researchers in the field of information security have placed a high value on the security of image data in the last few years. They have presented several image encryption techniques that are more secure. To increase the security level of image encryption algorithms, this article offers an efficient diffusion approach for image encryption methods based on one-dimensional Logistic, three-dimensional Lorenz, DNA encoding and computing, and SHA-256. The encryption test demonstrates that the method has great security and reliability. This article, also, examines the security of encryption methods, such as secret key space analysis, key sensitivity test, histogram analysis, information entropy process, correlation examination, and differential attack. When the image encryption method described in this article is compared to several previous image encryption techniques, the encryption algorithm has higher information entropy and a lower correlation coefficient.