Reviewerly/exHarmony huggingface dataset:
Authentication and Security Aspect of Information Privacy Using Anti-forensic Audio–Video Embedding Technique

Presently, fraud in online data transmission becomes a more critical and serious issue due to its security and authentication. This paper contributes and provides the correct path to these problems. Till date, many such aspects have been developed using the data embedding approach to embed the one-bit secret data into the video which produces less security and authentication. The implemented concept provides authentication and security aspects where more than three bits are embedded into video pixels using the forensic pixel adjustment mapping (FPAM) technique. The different types of image processing attacks are also tested which do not create any distortion. The extensive observed and verified software results of implemented approach have been compared to the state-of-the-art video data embedding methods. It is observed that the implemented forensic video data embedding technique is better as compared to any existing approaches.
Microblog Retrieval Based on Concept-Enhanced Pre-Training Model

Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have mostly been applied to conventional ad-hoc retrieval tasks over web pages and newswire articles. This article proposes a concept-enhanced pre-training model for microblog retrieval task, leveraging Semantic Matching Model (SMM) objective and Concept Correlation Model (CCM) objective. The proposed model is a novel neural ranking model specifically designed for ranking short-text microblog, which could merge the advantage of pre-training methodology for generating valid contextualized embedding with the superiority of the prior lexical knowledge (e.g., concept knowledge) for understanding short-text language semantic. We conduct experiments on widely used real-world datasets, and the experimental results demonstrate the efficiency of the proposed model, even compared with latest state-of-the-art neural-based models and pre-training based models.
Intrinsic stress determination based on the combination of photoelasticity and digital gradient sensing

A compact stress measurement system for transparent objects was proposed based on the combination of photoelascity and DGS methods. The interferometry fringe and speckle image of the deformed specimen were captured from perpendicular and slant directions, respectively. The fundamentals of the stress calculation and imaging model of oblique camera viewing were analyzed mathematically. After phase-shifting computation, image correlation, and geometric transformation, full-field distributions of the major and minor intrinsic principal stresses were obtained simultaneously. A validation experiment was conducted on a circular disk under diametral compression by the proposed method. Comparison between the experimental resulsts and theoretical data calculated according to the theory of elasticity verified the feasibility and accuracy. Moreover, the combined method was also successfully applied for the intrinsic principal stresses determination on transparent wrinkling thin film with defects subjected to shearing forces.
Enumeration of LCD and Self-dual Double Circulant Codes Over $${\boldsymbol{\mathbb {F}}}_q[v]/ &lt;\!\!v^2-1\!\!&gt;$$

Consider a finite field $$\mathbb {F}_q$$ having cardinality $$q=p^n$$ , for an odd prime p and a natural number n. In this chapter, we consider $$S=\mathbb {F}_q+v\mathbb {F}_q$$ , where $$v^2=1$$ and obtain conditions for double circulant (DC) codes over S to be LCD and self-dual. Using these conditions, we enumerate LCD and self-dual DC codes over S of length 2m. Further, we obtain some examples of DC codes by defining a Gray map $$\psi $$ from the ring $$S^m$$ to $$\mathbb {F}_q^{2m}$$ . Finally, some bounds are presented on the relative distances of the families of LCD and self-dual DC codes.
Do we behave differently on Twitter and Facebook: Multi-view social network user personality profiling for content recommendation

Human personality traits are key drivers behind our decision making, influencing our lives on a daily basis. Inference of personality traits, such as the Myers-Briggs personality type, as well as an understanding of dependencies between personality traits and user behavior on various social media platforms, is of crucial importance to modern research and industry applications such as recommender systems. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse; the impact of different social network data on profiling performance and of personality traits on applications such as recommender systems is yet to be evaluated. Furthermore, large-scale datasets are also lacking in the research community. To fill these gaps, in this work we develop a novel multi-view fusion framework PERS that infers Myers-Briggs personality type indicators. We evaluate the results not just across data modalities but also across different social networks, and also evaluate the impact of inferred personality traits on recommender systems. Our experimental results demonstrate that PERS is able to learn from multi-view data for personality profiling by efficiently leveraging highly varied data from diverse social multimedia sources. Furthermore, we demonstrate that inferred personality traits can be beneficial to other industry applications. Among other results, we show that people tend to reveal multiple facets of their personality in different social media avenues. We also release a social multimedia dataset in order to facilitate further research on this direction.
Multi-aspect detection and classification with multi-feed dynamic frame skipping in vehicle of internet things

Abstract Consumer demand for automobiles is changing because of the vehicle’s dependability and utility, and the superb design and high comfort make the vehicle a wealthy object class. The creation of object classes necessitates the creation of more sophisticated computer vision models. However, the critical issue is image quality, determined by lighting conditions, viewing angle, and physical vehicle construction. This work focuses on creating and implementing a deep learning-based traffic analysis system. Using a variety of video feeds and vehicle information, the developed model recognizes, categorizes, and counts vehicles in real-time traffic flow. The dynamic skipping method offered in the developed model speeds up the processing of a lengthy video stream while ensuring that the video picture is delivered accurately to the viewer. In real-time traffic, standard vehicle retrieval may assist in determining the make, model, and year of the vehicle. Previous MobileNet and VGG19 models achieved F-values of 0.81 and 0.91, respectively. However, the proposed solution raises MobileNet’s frame rate from 71.2 to 89.17 and VGG19’s frame rate from 48.2 to 59.14. The method may be applied to a wide range of applications that require a dedicated zone to monitor real-time data analysis and normal multimedia operations.
Inverse design of a vanadium dioxide based dynamic structural color via conditional generative adversarial networks

Dynamic structural color provides an additional time dimension in contrast to the static one, enriching the information load and functionalities. As a phase change material, vanadium dioxide offers great opportunities to implement dynamic structural color as its insulator-metal transition. Nevertheless, the multiple states also place a barrier to the efficient design of the structure configurations. This work firstly reports the dynamic structural color inverse design of asymmetric Fabry-Pérot cavity through a parameter-based conditional generative adversarial networks approach. The proposed structure attains a gamut as large as 117% of sRGB in the insulator state and can produce a 5% color coverage variation via the phase change of VO 2 layer. By using the trained conditional generative adversarial networks, the inverse design accuracy with the average color difference ΔE of 0.98 is achieved. A monochromatic pattern is designed by the trained networks to demonstrate different color dynamics of the various structures.
Equal in effectiveness but not yet perception: smartphones and laptops for completing brief academic tasks

Students frequently recruit digital devices to aid their academics. Past research has often focused on computers, with growing research on more mobile devices like tablets. Despite owning smartphones at high rates, little research has focused on college students’ use of smartphones for academics. In the present study, students were randomly assigned to use either their smartphone or laptop to complete six brief academic tasks. They then answered questions regarding perceptions of these devices for the research tasks as well as their more general experiences with, and perceptions of, such mobile devices in education. Results showed that smartphones and laptops were equally effective for completing the tasks, but student perceptions favored laptops over smartphones. Quantitative and qualitative data also suggested that, while students saw significant educational value in laptops, views on smartphones were mixed. That said, students also identified approaches where smartphones could be particularly useful for classroom activities. If implemented in ways that highlight their affordances, both laptops and smartphones can be effective educational tools allowing for diverse approaches in the classroom.
A lightweight deep neural network with higher accuracy

To improve accuracy of the MobileNet network, a new lightweight deep neural network is designed based on the MobileNetV2 network. Firstly, it modifies the network depth of MobileNetV2 to balance the image resolution, network width and depth to keep the gradient stable, which reduces the generation of gradient vanishing or gradient exploding. Secondly, it proposes an improved Bottleneck module by introducing channel attention mechanism. It assigns different weights for different channels according to the degree of relevance between the object features and channels. Therefore, the network can extract more effective features from a complex background. In the end, a new usage strategy of the improved Bottleneck is proposed. It uses the improved Bottleneck module in the second, fourth and fifth stages of MobileNetV2, and uses the original Bottleneck module in other states. Compared with MobileNetV2, MobileNetV3, ShuffleNetV2, GhostNet and HBONetmethods, the proposed method has the highest classification accuracy on the ImageNet-1K dataset, CIFAR-10 and CIFAR-100. Compared with YOLOV4-Lite methods based on these lightweight network networks, YOLOV4-Lite based on our proposed network also has the highest detection accuracy on the PASCAL VOC07+12 dataset.
Governmental Anti-Pandemic and Subsidy Strategies for Blockchain-Enabled Food Supply Chains in the Post-Pandemic Era

Aiming to explore whether governments should choose anti-pandemic or subsidy strategies in the post-pandemic era, this study constructed a three-level food supply chain that was composed of a leading third-party logistics provider, a supplier and a retailer, in which the third-party logistics provider used blockchain technology for food traceability to address consumer concerns about food safety. We then used game theory to analyze the pricing decisions, traceability levels, anti-pandemic effort levels and subsidy levels of the supply chain under different governmental anti-pandemic or subsidy strategies. Our results showed that in all scenarios, the higher the consumer preference for traceability information, the larger the traceability levels and anti-pandemic effort levels and the more favorable the outcome for all parties; thus, governments should improve consumer awareness of pandemic prevention. For the benefit of all parties, governments should adopt anti-pandemic and subsidy strategies simultaneously, even in the post-pandemic era. Interestingly, for the scenario in which governments could only adopt one strategy, when the cost coefficient of traceability was small, the governmental subsidies would actually lead to lower traceability levels of the 3PL. This study could provide decision-making references for governments during the post-pandemic era and a new possibility for blockchain application.
Analysis of Chinese Machine Translation Training Based on Deep Learning Technology

With the advent of the information age, people can establish good communication through Internet technology. Mechanical translation has become a key means to solve people's communication problems. However, there are still obstacles to communication between different languages. In order to solve this problem, this paper uses existing neural network technology to the English-Chinese bidirectional machine translation model in the field of marine science and technology. Based on deep learning technology, we collect Chinese and English abstracts and partial full texts of Chinese and English papers with marine science and technology as the key words and build a professional corpus in English and Chinese about marine science and technology. In the Chinese-English bidirectional translation model, the local weight sharing is introduced into the Chinese encoder and the English encoder, and the output of the Chinese encoder sublayer and the English encoder sublayer is fused as the output of the respective encoders, and the performance of the translation model is evaluated using the BLEU parameters. Through the training of the translation model, compared with the transformer model, the BLEU value of the model with local weight sharing and encoder sublayer fusion output is improved by 1.6 and 3.8 in the Chinese-English and English-Chinese translation directions, respectively. The PPL values in the Chinese-English and English-Chinese translation directions decreased by 18.72% and 14.62%, respectively. We demonstrate the effectiveness of the language translation model. Experiments show that the research on machine language adaptive technology based on deep learning can more smoothly realize the two-way translation of literature in the field of marine science and technology. Compared with traditional mechanical translation, this paper proposes a translation model based on the deep neural algorithm, which improves the effect of model training by constructing a Chinese-English corpus with the theme of marine science and technology.
In-situ noncontact measurement system for nozzle throat deformation in high-temperature gas heating via laser speckle digital image correlation with wavelet smoothing of displacement field

The shape of nozzle throat in an engine could greatly change the amount of thrust or heat transfer performance. To in-situ evaluate nozzle throat deformation in high-temperature gas heating tests, a non-contact full-field deformation measurement system via laser speckle Digital Image Correlation with wavelet smoothing of displacement field is proposed. For high-temperature thermal radiation suppression, high-power laser speckle pattern is used for digital image correlation calculation and multi-step filters with polarizers are used. A displacement field wavelet smoothing algorithm is proposed to mitigate imaging distortion caused by airflow disturbance. Gas heating experiments on a nozzle sample made of carbon fiber high silica phenolic resin composites were carried out. The full-field displacement measurement results show that compared with the commonly used air blade method, the proposed method can provide smoother displacement field for deformation evaluation, the displacement measurement difference of the proposed method and a dial indicator was within 0.01 mm.
Responsible research and innovation in practice: Driving both the ‘How’ and the ‘What’ to research

There have been ongoing discussions in research communities, including the field of trustworthy autonomous systems (TAS), on how researchers may meaningfully engage with responsible research and innovation (RRI). By critically reflecting on the RRI aspects of an ongoing research project focusing on the efficient capture of richer quantitative human response data (e.g., from consumer surveys), this paper offers a case study on how research development can be ethically driven. The role of RRI in the project is unpicked against the broader considerations of its possible interactions with researchers in a typology we developed: as a research safeguard, research subject, and research driver. Going beyond the more common practice of using RRI simply to safeguard how research should be conducted, it is demonstrated that it can also serve as a positive driving force to explore what should be researched. Experiences and challenges are elaborated within the main stages of research development, potentially applicable to a wider range of future projects in the field.
A Privacy-Assured Data Lifecycle for Epidemic-Handling Systems

Although the use of personal data from different contexts is essential to curbing the spread of COVID-19 in epidemic-handling systems (EHSs), it increases the chances of privacy breaches and personal data misuse. This article analyses the data lifecycle and proposes various technical requirements for privacy preservation in EHSs.
Can You Trust Zero Trust?

Developing and sustaining a “zero-trust architecture” is essentially impossible today. The concept is currently a moving target, and its meaning is in the eye of the beholder. One thing we know for certain is that it’s a misnomer.
Enhancing pretrained language models with structured commonsense knowledge for textual inference

Transformer-based pretrained language models have shown promising performances in various textual inference tasks. However, additional relational knowledge between the semantic units within the input text may also play a critical role in the inference process. To equip the pretrained language models with the relational knowledge, previous methods employ a retrieval-based strategy, which obtain the relational features from prebuilt knowledge bases by a lookup operation. However, the inherent sparsity of part of the knowledge bases would prevent the direct retrieval of the relational features. To address this issue, we propose a MIX-strategy based Structural commonsense integration framework (Mix-Sci). In addition to the traditional retrieval strategy which only adapts to the knowledge bases with high coverage, Mix-Sci introduces an additional generative strategy to incorporate the sparse knowledge bases with the pretrained language models. In specific, in the training process, Mix-Sci learns to generate the structural information of the knowledge base, including the embedding of nodes and the connection relationship between the nodes. So that in the test process, the structural information can be generated to enhance the inference process. Experimental results on two textual inference tasks: machine reading comprehension and event prediction show that Mix-Sci can effectively utilize both the dense and the sparse knowledge bases, to consistently improve the performance of pretrained language models on textual inference tasks.
VLSI architecture design and implementation of 5/3 and 9/7 lifting Discrete Wavelet Transform

Discrete Wavelet Transform (DWT) is considered among the few computationally expensive block of multimedia compression standards. In this work, we proposed a reduced hardware complexity VLSI architectures of 5/3 and 9/7 lifting bi-orthogonal DWT for multimedia applications. The architecture uses a combination of Distribute Arithmetic (DA) and Canonical Signed Digit (CSD) based implementation to reduce the hardware complexity. The resulting lifting based architecture discretely finds optimized number of sum of products to give minimum realization. The architecture is implemented on Field Programmable Gate Array (FPGA) with results compared with known classical and other optimized DWT architectures.
Automated Testing with Machine Learning Frameworks: A Critical Analysis

As software systems are becoming more and more complex and standard testing practices are exhausting, we need smart solutions to reduce the time, efforts and resources spent on software testing. The aim of this paper was to critically analyze machine learning (ML) frameworks related to software automation. We measured the performance of testing tools on the basis of the manual labor (effort) required, in addition to the test performance, accuracy or error rate, scope, time required and prerequisite knowledge requirements. These factors play a vital role to ensure ML frameworks with automation software can produce great results and hence improve software quality.
Pixels: An Efficient Column Store for Cloud Data Lakes

To benefit from the cloud's higher elasticity and price-efficiency, most modern data-lake engines support S3-like cloud object storage (COS) services as their optional or preferred underlying storage. Meanwhile, the widespread column stores, such as Parquet, are applied in these data lakes to improve analytical performance. However, as these column stores were designed for on-premise HDFS, they often suffer from the high latency of COS and deliver sub-optimal query performance. We observe that by optimizing the storage layout and data access pattern, we can effectively hide and mitigate the high latency. In this paper, we present Pixels, a column store optimized for the cloud that solves the problem by (1) the workload-driven storage layout optimization within and across the row group boundaries; (2) the I/O scheduling concerning the optimized storage layout and the performance characteristics of COS. They collectively improve the analytical performance in a transparent way that does not affect data ingestion and query execution in data lakes. Evaluations show that Pixels outperforms the state-of-the-art column store on COS by more than one order of magnitude on real-world workload and by 1.93x on TPC-H. Moreover, the performance of Pixels is also portable to HDFS.
Field-aware Variational Autoencoders for Billion-scale User Representation Learning

User representation learning plays an essential role in Internet applications, such as recommender systems. Though developing a universal embedding for users is demanding, only few previous works are conducted in an unsupervised learning manner. The unsupervised method is however important as most of the user data is collected without specific labels. In this paper, we harness the unsupervised advantages of Variational Autoencoders (VAEs), to learn user representation from large-scale, high-dimensional, and multi-field data. We extend the traditional VAE by developing Field-aware VAE (FVAE) to model each feature field with an independent multinomial distribution. To reduce the complexity in training, we employ dynamic hash tables, a batched softmax function, and a feature sampling strategy to improve the efficiency of our method. We conduct experiments on multiple datasets, showing that the proposed FVAE significantly outperforms baselines on several tasks of data reconstruction and tag prediction. Moreover, we deploy the proposed method in real-world applications and conduct online A/B tests in a look-alike system. Results demonstrate that our method can effectively improve the quality of recommendation. To the best of our knowledge, it is the first time that the VAE-based user representation learning model is applied to real-world recommender systems.