Reviewerly/exHarmony huggingface dataset:
FPPNet: Fast Privacy-Preserving Neural Network via Three-Party Arithmetic Secret Sharing

Jointing multi-source data for model training can improve the accuracy of neural network. To solve the raising privacy concerns caused by data sharing, data are generally encrypted and outsourced to a group of cloud servers for computing and processing. In this client-cloud architecture, we propose FPPNet, a fast and privacy-preserving neural network for secure inference on sensitive data. FPPNet is deployed in three cloud servers, who collaboratively execute privacy computing via three-party arithmetic secret sharing. We develop the secure conversion method between additive shares and multiplicative shares, and propose three secure protocols to calculate non-linear functions, such as comparison, exponent and division that are superior to prior three-party works. Some secure modules for running convolutional, ReLU, max-pooling and Sigmoid layers are designed to implement FPPNet. We theoretically analyze the security and complexity of the proposed protocols. With MNIST dataset and two types of neural networks, experimental results validate that our FPPNet is faster than the related works, and the accuracy is the same as that of plaintext neural network.
An Overview of Multimodal Fusion Learning

With the rapid development of modern science and technology, information sources have become more widely available and in more diverse forms, resulting in widespread interest in multimodal learning. With the various types of information captured by humans in understanding the world and perceiving objects, a single modality cannot provide all of the information about a specific object or phenomenon. Multimodal fusion learning opens up new avenues for tasks in deep learning, making them more scientific and human in their approach to solving many real-world problems. An important challenge confronting multimodal learning today is how to efficiently facilitate the fusion of multimodal features while retaining the integrity of modal information to reduce information loss. This paper summarizes the definition and development process of multimodality, analyzes and discusses briefly the main approaches to multimodal fusion, common models, and current specific applications, and finally discusses future development trends and research directions in the context of existing technologies.
PF-Net: Personalized Filter for Speaker Recognition from Raw Waveform

Speaker recognition using i-vector has been replaced by speaker recognition using deep learning. Speaker recognition based on Convolutional Neural Networks (CNNs) has been widely used in recent years, which learn low-level speech representations from raw waveforms. On this basis, a CNN architecture called SincNet proposes a kind of unique convolutional layer, which has achieved band-pass filters. Compared with standard CNNs, SincNet learns the low and high cut-off frequencies of each filter. This paper proposes an improved CNNs architecture called PF-Net, which encourages the first convolutional layer to implement more personalized filters than SincNet. PF-Net parameterizes the frequency domain shape and can realize band-pass filters by learning some deformation points in frequency domain. Compared with standard CNN, PF-Net can learn the characteristics of each filter. Compared with SincNet, PF-Net can learn more characteristic parameters, instead of only low and high cut-off frequencies. This provides a personalized filter bank for different tasks. As a result, our experiments show that the PF-Net converges faster than standard CNN and performs better than SincNet. Our code is available at github.com/TAN-OpenLab/PF-NET.
Proof of Storage with Corruption Identification and Recovery for Dynamic Group Users

With the widespread deployment of cloud storage, protecting the security and privacy of outsourced data becomes increasingly important. In the last decade, provable data possession/retrievability schemes with different flavors, such as public variability, efficient update, user revocation, protecting privacy, have been designed to protect the rights and interests of cloud users. Although extensive research have been conducted in protecting the security and privacy of group cloud users, there is still a lack of a way to efficiently identify the destroyed data and actualize cloud disaster recovery. In response to these problems, a publicly verifiable proof of storage scheme is proposed for group cloud users in this paper. We provide the construction details, where invalid files can be efficiently identificated and even recovered after being detected by a trusted third-party auditor. Further analysis indicate that the designed scheme is secure for dynamic group user data sharing.
The Optimization of Higher Vocational Accounting Curriculum System Under the Background of Intelligentization: A Web Crawler Based Method

Based on the OBE education concept, this paper adopts Python web crawler technology to conduct big data collection and analysis of accounting job recruitment information, sort out market demand for accounting talents’ knowledge, ability, and quality, and pays attention to the knowledge structure, post ability and professional quality that students should have in the real work situation, makes clear accounting talent cultivation objectives in the era of intelligence, reverse design and reconstruct the higher vocational accounting curriculum system, which is conducive to enhance the accounting graduates’ job adaptability and market competitiveness, and ultimately deepen the reform of talent cultivation mode, effectively improve the actual effect of talent cultivation.
A Novel Cross-Resolution Image Alignment for Multi-camera System

In complicated computer vision tasks, the multi-camera system is more effective than a single camera owing to the image fusion of multiple cameras, in which the image alignment is the essential first step. Especially, the cross-resolution image alignment caused by focal length difference has been extensively studied. A usual solution is using pyramid based local feature matching to create the mapping between input images with high and low-resolution. However, this kind of algorithm has high time and space complexity and is not applicable to front-end equipment such as the multi-camera system. Therefore, this paper proposes a fast and novel cross-resolution image alignment method based on the approximate focal length difference, including the coarse feature matching in low-resolution and the matching model estimation in high-resolution. At last, two types of test experiments are carried out using the industrial camera and SLR camera respectively. And the experimental results show that the proposed method performs well for cross-resolution image alignment, which can be widely used for multi-camera system.
A genetic algorithm coupled with tree-based pruning for mining closed association rules

Due to the voluminous amount of itemsets that are generated, the association rules extracted from these itemsets contain redundancy, and designing an effective approach to address this issue is of paramount importance. Although multiple algorithms were proposed in recent years for mining closed association rules most of them underperform in terms of run time or memory. Another issue that remains challenging is the nature of the dataset. While some of the existing algorithms perform well on dense datasets others perform well on sparse datasets. This paper aims to handle these drawbacks by using a genetic algorithm for mining closed association rules. Recent studies have shown that genetic algorithms perform better than conventional algorithms due to their bitwise operations of crossover and mutation. Bitwise operations are predominantly faster than conventional approaches and bits consume lesser memory thereby improving the overall performance of the algorithm. To address the redundancy in the mined association rules a tree-based pruning algorithm has been designed here. This works on the principle of minimal antecedent and maximal consequent. Experiments have shown that the proposed approach works well on both dense and sparse datasets while surpassing existing techniques with regard to run time and memory.
A novel framework for extracting moment-based fingerprint features in specific emitter identification

Abstract Extensive experiments illustrate that moments and their derivations can act as effective fingerprint features for specific emitter identification. Nevertheless, the lack of mechanistic explanation restricts the moment-based fingerprint features to a trial-based and data-driven technique. To make up for theoretical weakness and enhance generalization ability, we analytically investigate how intentional modulation and unintentional modulation affect moments. A framework for extracting moment-based fingerprint features is proposed through fine-segmenting slices. Fingerprint features are extracted, followed by segmenting signals into a combination of sinewaves and calculating their moments. The proposed framework shows advantages in mechanism interpretability and generalizing ability. Simulations and experiments verified the correctness and effectiveness of the proposed framework.
Data-driven Digital Lighting Design for Residential Indoor Spaces

Conventionally, interior lighting design is technically complex yet challenging and requires professional knowledge and aesthetic disciplines of designers. This article presents a new digital lighting design framework for virtual interior scenes, which allows novice users to automatically obtain lighting layouts and interior rendering images with visually pleasing lighting effects. The proposed framework utilizes neural networks to retrieve and learn underlying design guidelines and the principles beneath the existing lighting designs, e.g., a newly constructed dataset of 6k 3D interior scenes from professional designers with dense annotations of lights. With a 3D furniture-populated indoor scene as the input, the framework takes two stages to perform lighting design: (1) lights are iteratively placed in the room; (2) the colors and intensities of the lights are optimized by an adversarial scheme, resulting in lighting designs with aesthetic lighting effects. Quantitative and qualitative experiments show that the proposed framework effectively learns the guidelines and principles and generates lighting designs that are preferred over the rule-based baseline and comparable to those of professional human designers.
MultiSense: Cross-labelling and Learning Human Activities Using Multimodal Sensing Data

To tap into the gold mine of data generated by Internet of Things (IoT) devices with unprecedented volume and value, there is an urgent need to efficiently and accurately label raw sensor data. To this end, we explore and leverage the hidden connections among the multimodal data collected by various sensing devices and propose to let different modal data complement and learn from each other. But it is challenging to align and fuse multimodal data without knowing their perception (and thus the correct labels). In this work, we propose MultiSense , a paradigm for automatically mining potential perception, cross-labelling each modal data, and then updating the learning models for recognizing human activity to achieve higher accuracy or even recognize new activities. We design innovative solutions for segmenting, aligning, and fusing multimodal data from different sensors, as well as model updating mechanism. We implement our framework and conduct comprehensive evaluations on a rich set of data. Our results demonstrate that MultiSense significantly improves the data usability and the power of the learning models. With nine diverse activities performed by users, our framework automatically labels multimodal sensing data generated by five different sensing mechanisms (video, smart watch, smartphone, audio, and wireless-channel) with an average accuracy 98.5%. Furthermore, it enables models of some modalities to learn unknown activities from other modalities and greatly improves the activity recognition ability.
School improvement and school efficiency: reflections and evidences in the liquid society ( <i>Mejora de la escuela y eficacia escolar: reflexiones y evidencias en la sociedad líquida</i> )

Discussions on the improvement and effectiveness of schools are high on the national and international education policy agendas. Despite the social and cultural complexity that frames educational processes, there is now sufficient research available on how to correctly orient theory and practice. The aim of the study is to reflect on the change and improvement of schools in the compulsory stage, based on the accumulated knowledge of the School Improvement and School Effectiveness Movements. The methodology used was that of documentary analysis of scientific production, based on the review, analysis and critical reflection of texts. The results show that in the current context of a liquid society, it is essential to have a management team that supports improvement processes, that the school as a whole is committed to renewal and, finally, that there is a participatory school culture. The conclusions reflect on and outline possible measures for improvement and innovation in schools, mainly related to the change of cultural imaginary.
Sustainable Artificial Intelligence: In Search of Technological Resilience

Great demands are placed on the role of digital technology and artificial intelligence for sustainable development. External shocks, like the current pandemic, as well as creeping degradation, like the effects of carbon economy on the climate, need convincing concepts to control lasting negative effects on society, environment, and economy. This paper is intended to contribute to the search for ways of enabling resilience through technology. High expectations are being put on data driven artificial intelligence in this respect. We consider that artificial intelligence tends to fall short of scientific rigor regarding cause-and-effect relations and discuss the inherent limitations of so-called formal systems that are at the bottom of artificial intelligence systems. We take into view what data analysis and reasoning can deliver regarding the discovery of empirical phenomena, arguing that targeted, reflective data reasoning can well help discover correlations worth further theoretical investigation. We suggest combining established methods of epistemic knowledge generation with data driven artificial intelligence, i.e. human intelligence with machine-based algorithmic intelligence, in support of advanced human-systems integration. For this concept of hybrid intelligence, we provide a procedural framework. This methodological approach gets exemplified by the description of recently published cases of a technical application resp. Scientific practice, illustrating the potential of hybrid intelligence for the scientific as well as technical solution of problems. Concluding remarks finally draw the line to future work on sustainable artificial intelligence as a pathway to resilience delivered by technological means.
TZEAMM: An Efficient and Secure Active Measurement Method Based on TrustZone

With the rapid development of computer and communication technology, embedded systems are widely used in smart devices. The increasing connectivity of these systems and the difficulties in providing comprehensive security have made such devices vulnerable to malicious attacks. Passive defense technologies and traffic-based intrusion detection technologies are not fully effective against such attacks. Trusted execution environment (TEE) technology can ensure system security against unknown attacks to some extent. Most researchers use TrustZone to implement TEE. However, the problem is that the API interface of the TEE module which provides the service is not secure. Therefore, to actively defend against attacks, we developed a trusted computing active measurement architecture based on TrustZone. To overcome the serious problem that modules in the trusted execution environment need to be passively invoked to provide services, we have proposed an active measurement closed-loop immune mechanism. To reduce the trusted computing base and reduce the performance overhead, we removed certain functional modules from the trusted execution environment. In addition, based on this architecture, we developed a trust chain and dynamic measurement method to ensure the security of the target applications. We changed the traditional attack response method, which requires the entire system to be restarted after an attack, by developing a fallback mechanism that is more suitable for the system. Finally, we verified the effectiveness of the architecture by developing an attack model. Performance testing and analysis showed that the architecture reduced the impact of the security mechanisms on the system. In the future, we will extend our research to more fine-grained measurements.
Mutual Authentication Protocol in a Distributed Heterogeneous Environment: A Blockchain-Based Approach

Most of the existing identity-based authentication solutions rely on a centralized key generation center, resulting in the potential risk of single point of failure. Furthermore, there is a lack of heterogeneous authentication schemes that can realize authentication between users in different cryptosystems. In this study, we first present an identity-based authentication protocol with distributed authorization centers based upon blockchain. Next, we extend our fundamental protocol to a heterogeneous environment by considering the mutual authentication between the Public Key Infrastructure (PKI)-based and the identity-based systems. Finally, the proposed work is validated through the comparisons with the benchmarks.
A Lattice-Based Multisignature Scheme for Blockchain-Enabled Systems

Multisignature scheme enables a class of users to produce a signature interactively on a shared message, thus significantly reducing the signature size. This is especially important in blockchain-enabled systems where a consensus is required to complete a transaction. However, most existing approaches are constructed based upon traditional problems, for example, the integer factoring assumption and the discrete logarithm assumption, which in turn bear the risk of quantum computing attacks. Although lattice-based solutions are considered in some studies, the computational overhead due to high level of interaction becomes a major concern. In this paper, we present a new lattice-based multisignature scheme, which is constructed upon Bimodal Lattice Signature Scheme (BLISS). Next, we apply it to the blockchain-enabled systems through the demonstration of a cryptocurrency transaction. At last, we compare the proposed model with existing works on the performance. The result shows that our work can satisfy the security and efficiency concerns of the blockchain-enabled systems.
A Computing Efficient Algorithm Specialized for Pseudo-Constant Metrics

AIOps (Artificial Intelligence for IT Operations) improve operation and maintenance efficiency of complex IT systems by quickly locating the root causes of failures through machine learning methods. The key of AIOps is to quickly locate anomaly in huge amount of monitoring metric time series. In this paper, we show that there are a considerable number of Pseudo-Constant metrics in actual large systems, whose values vary little or occasionally change violently. Based on feature of this kind of metric, we propose a Binary Mixed Detection algorithm for Pseudo-Constant metric. We build a model to evaluate its detection time and also analyze its false negative and false positive probability. Finally, we show the performance of BMD on actual data set. The experimental results show that BMD can save 85% of the detection time.
Customer Experience and Delight in the Metaverse

Customer delight is defined as exceeding client expectations through an experience that is truly differentiated. Customer delight is not limited to delivering service quality, but it is also about how the service is delivered and customer experience of the service provided. Customer experience will traverse seamlessly across the virtual and physical worlds with the evolution of the metaverse resulting in ‘innovative disruption'. As the new playing field for corporates and engagement with consumers, it will transform customer experience. This will be augmented by turning historical notifications and transactions into real-time personalized communications. In this chapter the authors look at key pillars of how the metaverse will impact customer experience and delight. Through a literature survey, this chapter identifies the key pillars of the metaverse that will impact customer experience. The study then further drills into how customer experience is likely to be shaped in the metaverse for key sectors including retail, technology, and banking.
Semantical Analysis of the Logic of Bunched Implications

Abstract We give a novel approach to proving soundness and completeness for a logic (henceforth: the object-logic) that bypasses truth-in-a-model to work directly with validity. Instead of working with specific worlds in specific models, we reason with eigenworlds (i.e., generic representatives of worlds) in an arbitrary model. This reasoning is captured by a sequent calculus for a meta -logic (in this case, first-order classical logic) expressive enough to capture the semantics of the object-logic. Essentially, one has a calculus of validity for the object-logic. The method proceeds through the perspective of reductive logic (as opposed to the more traditional paradigm of deductive logic), using the space of reductions as a medium for showing the behavioural equivalence of reduction in the sequent calculus for the object-logic and in the validity calculus. Rather than study the technique in general, we illustrate it for the logic of Bunched Implications (BI), thus IPL and MILL (without negation) are also treated. Intuitively, BI is the free combination of intuitionistic propositional logic and multiplicative intuitionistic linear logic, which renders its meta-theory is quite complex. The literature on BI contains many similar, but ultimately different, algebraic structures and satisfaction relations that either capture only fragments of the logic (albeit large ones) or have complex clauses for certain connectives (e.g., Beth’s clause for disjunction instead of Kripke’s). It is this complexity that motivates us to use BI as a case-study for this approach to semantics.
Developing a Universal Identification Model: Integrating AI and IoT Tools with DNAFIDs to Recognize COVID-19 Patients

DNA fingerprinting identification systems based on artificial intelligence infuse almost every facet of life. Its impact on various aspects of human health can be seen today. It has also found its importance in the identification of global COVID-19 infections, whether suspected or proven, in patients. Because of the IoT and the application of sophisticated filtering techniques to big data, it’s conceivable. The technology of DNA profiling, which creates detailed profiles of individual characteristics, is a necessity. This can be incredibly helpful in the acquisition process in certain circumstances when paired with other data. Many candidates’ medical and physiological factors are included in the Human Clinical Profile (HCP), as are social profile-related services. The government spends significant tax dollars vetting the physical and medical characteristics of various candidates for clinical care and purposes related to public health response. We propose a technique that may help analyze the physical traits of candidates preemptively and conduct forensic investigation for human identification, which may help reduce the cost of check-ups and other medical processes. This technique combines DNA profiling with artificial intelligence tools to pre-screen candidates for COVID-19 patients who require physical and remote monitoring. Further experiments done in a targeted manner are justification for the hypothesis.