Reviewerly/exHarmony huggingface dataset:
The Risk and Return of Cryptocurrency Carry Trade

This paper provides a comprehensive analysis of the risk and return dynamics associated with cryptocurrency carry trade. The cross-sectional carry trade strategy using cryptocurrencies yields an annualized return of 49.3% with a Sharpe ratio of 0.81. Our investigation shows that the cryptocurrency returns cannot be explained by prevailing crytocurrency factors, including market, size, momentum, volatility, downside-risk, and platform collapse risks. Moreover, our analysis fails to identify any substantial connection between these returns and geopolitical risks or fiat-currency carry trade. Our findings suggest that a significant portion of cryptocurrency carry trade returns can be regarded as a premium for equity market volatility risk. Our research highlights the inter-asset class linkages between equity risk factors and cryptocurrency returns.
Generative AI and Copyright: Exception, Compensation or Both?

The emergence of generative artificial intelligence (“AI”) has increased existing concerns and raised new issues both for AI developers and right holders. These include possible copyright infringement when copyright subject matter is used in AI training (including generative AI) modules, absence of viable licensing models, and a lack of transparency around the use of works in AI training. This article proposes a four-step approach that might help mitigate these challenges by taking into account the interests of both AI developers and Australian right holders. It suggests differentiating between the so-called “non-creative AI” and “creative AI” modules. While the former “non-creative AI” modules could be subjected to a new copyright exception, the use of works to train “creative AI” modules should require compensation that could be best collected via a statutory licensing scheme. In addition, the article calls for transparency around the use of works in AI training, which is indispensable for the efficient exercise and enforcement of rights.
OPENAi, Money, Cryptocurrencies and the Crisis of Technology

Computers have changed how people work and how they communicate in recent years. They have also produced an ideology of the nature of computer language and its supremacy in thought and action. A recent conflict with a company that promoted the idea of establishing means of control of Artificial Intelligence and whose corporate structure was specially designed to prevent external influences has spotlighted the problems with ideas of computer science, finance and belief systems.
Federated learning-outcome prediction with multi-layer privacy protection

Learning-outcome prediction (LOP) is a long-standing and critical problem in educational routes. Many studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection issue. To this end, this study proposes a distributed grade prediction model, dubbed FecMap, by exploiting the federated learning (FL) framework that preserves the private data of local clients and communicates with others through a global generalized model. FecMap considers local subspace learning (LSL), which explicitly learns the local features against the global features, and multi-layer privacy protection (MPP), which hierarchically protects the private features, including model-shareable features and not-allowably shared features, to achieve client-specific classifiers of high performance on LOP per institution. FecMap is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part, a local part, and a classification head in clients and averaging the global parts from clients on the server. To evaluate the FecMap model, we collected three higher-educational datasets of student academic records from engineering majors. Experiment results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP, compared with the state-of-the-art models. This study makes a fresh attempt at the use of federated learning in the learning-analytical task, potentially paving the way to facilitating personalized education with privacy protection.
Electronic libraries as the key structural component of e-librarianship

The author examines e-libraries as a subdivision of the new discipline of e-librarianship. The e-libraries as a particular subject of the library information environment emerged in 1990s, and in the mid-decade, the Russian National Public Library for Science and Technology (RNPLS&amp;T) introduced its first e-collections. In 2020–2022, within the framework of the new research theme “Electronic Librarianship”, the RNPLS&amp;T was developing the subjects of e-librarianship entity and definition, as well as of the terminological system. Today, the RNPLS&amp;T’s research team has been preparing the multi-authored monograph “Electronic Librarianship”. The author discusses definitions for e-libraries offered by several scientists, with the preference given to that introduced in the textbook “Electronic libraries. Information and communication environment” [in Russian]. The author characterizes the major e-libraries, both national and global. Further intensive development and improvement of e-libraries is beyond doubt. The author suggests that the National Electronic Library (NEL) and Open Information Archives are to play the leading role in this process. The e-library is one of the subjects of studies in the area of e-librarianship. The generic affinity of the concepts of “library” and “e-library” is emphasized, which makes the e-librarianship the subdiscipline of general librarianship. Meanwhile, the e-librarianship covers a particular wide substantive area. The e-librarianship structure has to become the focus of further studies. The terms “e-library” and “digital library” are specified. The key trends of developing e-libraries as a component of library systems and libraries in the digital environment are defined, e. g. consolidation of providers of e-resource management systems, introduction of open source projects and information systems to support science, etc. Today, almost every university (high school), research institute, large or even medium-size library get their e-libraries, which stipulates and actualizes the development of new discipline of e-librarianship by RNPLS&amp;T’s team. The modern libraries’ integration into the general information space is the key to further success. The article is prepared under the Government Order No. 075-01235-23-01 of August 9, 2023, theme No. 1021062311368-2-5.8.3 “Development pf electronic librarianship as a scientific and academic discipline in the circumstances of transforming library collections, reference, bibliographic and document services in the digital environment (FNEG-2022-0004)”
Research Progress of Location Privacy Protection Technology in the Internet Era

In the era of the Internet, location information has become an indispensable part of people's daily lives. However, when users enjoy the enormous convenience brought by LBS, they must also face many privacy and security issues brought by location based services. In order to ensure the quality of service while maintaining user privacy, the researchers collated some major advances in the current location privacy protection technology, and described in detail the syntactic privacy model, including K - anonymity, L diversity and δ-existence. Secondly, some main technologies used in the literature to protect the privacy of location services were pointed out, including location obfuscation technology and differential privacy technology. Finally, prospects for future technological development trends and future research work were presented.
Research on Sensitive Frame Recognition Algorithm for Home Monitoring Video Based on Mixture of Gaussians Background Modeling

Surveillance videos have become ubiquitous in our daily lives, and to mitigate the risk of information leakage during the upload process to the cloud, privacy protection is of utmost importance. This paper proposes an application scenario based on home surveillance videos, mainly using foreground-background separation technology to protect the privacy area in surveillance videos. In home surveillance videos, privacy information mainly refers to people in motion. The paper improves the traditional mixture of Gaussian model by replacing Gaussian distribution with t-distribution for background modeling and identifies moving targets in the video through foreground-background separation detection. Morphological operations are applied to the results of target detection to eliminate noise and ensure the accuracy of subsequent threshold judgment. Then, by setting appropriate thresholds, video frames with dense human activity are detected and considered as sensitive information frames. Finally, the sensitive information frames are extracted from the video stream and encrypted to protect the privacy information. Through simulation experiments, the improved mixture of Gaussian background modeling algorithm can effectively separate foreground and background in surveillance videos, improve the accuracy of moving target detection, and accurately identify sensitive information frames. By adjusting the size of the threshold, the number of sensitive information frames that need to be encrypted can be flexibly controlled, which enhances the encryption level of the video, effectively protects users' privacy information, and reduces the risk of information leakage.
Unsupervised Image Hashing Using a Deep Convolutional Encoder-Decoder Model for Fast Image Retrieval

Image hashing methods transform high-dimensional image features into low-dimensional binary codes while preserving semantic similarity. Among image hashing techniques, supervised image hashing approaches outperform unsupervised and semisupervised methods. However, labelling image data requires extra time and expert effort. In this study, we proposed a deep learning-based unsupervised image hashing method for unlabeled image data. The proposed hashing method is built in an end-to-end fashion. It consists of an encoder-decoder model. As a novel idea, we used a supervised pre-trained network as an encoder model, which provides fast convergence in the training phase and efficient image features. Hash codes are extracted by optimizing those intermediate features. Experiments performed on two benchmark image datasets demonstrate the competitive results compared to unsupervised image hashing methods.
Consumer behavior in the metaverse

Abstract The rising interest in the marketing potential of the metaverse and its influence on consumer behavior is undeniable. Experts in the field have identified gaps in understanding consumer behavior in the metaverse and have highlighted the need of further research in the domain. This study addresses this void by focusing on Gen Z and their interactions within the metaverse. Gathering qualitative data through semi‐structured interviews with 63 participants, the study employs a deductive thematic analysis, using the Engel‐Kollat‐Blackwell (EKB) model as its theoretical framework. The findings align with the stages of the EKB model– awareness, information search, engagement, and post‐engagement evaluation. We find that despite facing initial challenges, participants display eagerness to access the metaverse and actively seek engagement opportunities, even encouraging others to join. This research provides empirical insights into challenges and opportunities that exist for consumers and brands within the metaverse. Further, it also enhances our understanding of immersive time in the metaverse and elucidates consumer engagement with digital technologies. Finally, the study also offers practical implications for managers, tech developers, and policymakers looking to enhance consumer interest and involvement in the metaverse.
A Voice-Based Personal Assistant for Mental Health in Kreol Morisien

Voice-based smart personal assistants (VSPAs) are applications that recognize speech-based input and perform a task. In many domains, VSPA can play an important role as it mimics an interaction with another human. For low-resource languages, developing a VSPA can be challenging due to the lack of available audio datasets. In this work, a VSPA in Kreol Morisien (KM), the native language of Mauritius, is proposed to support users with mental health issues. Seven conversational flows were considered, and two speech recognition models were developed using CMUSphinx and DeepSpeech, respectively. A comparative user evaluation was conducted with 17 participants who were requested to speak 151 sentences of varying lengths in KM. It was observed that DeepSpeech was more accurate with a word error rate (WER) of 18% compared to CMUSphinx at 24%, that is, DeepSpeech fully recognized 76 sentences compared to CMUSphinx where only 57 sentences were fully recognized. However, DeepSpeech could not fully recognize any 7-word sentences, and thus, it was concluded that the contributions of DeepSpeech to automatic speech recognition in KM should be further explored. Nevertheless, this research is a stepping stone towards developing more VSPA to support various activities among the Mauritian population.
What is the Difference between Image Cognition in Humans and Computer Vision?

There exist some Computer Vision Models (CVMs) such as CNN, Vision Transformer (ViT), and CLIP, which were pre-trained on a huge amount of training data. The image cognition power of these CVMs is very high. In our environmental cognition research using photos, we manually measured the inter-photo visual similarity. Our previous study found that CVM-based photo similarity and visual similarity were quite similar, when compared by photo MDS. However, it was also suggested that the difference in image cognition between humans and CVM was related to representation of humans. We investigated here numerically in detail the difference between CVM-based photo similarity and visual similarity, using six types of photo sets. The influence of representation could be evaluated by cluster size on MDS. It was shown that representation influences the cognition of shrines and temples, foods, insects, buildings, greens, garden styles, perspective views, night views, the symbol tree, and so on.
SmartGate: Accelerate Cloud Gateway with SmartNIC

With the development of cloud computing, more and more enterprises and organizations are migrating their businesses to the cloud for resource sharing, cost reduction, and improved operational efficiency. Infrastructure as a Service (IaaS) is an important service model in cloud computing, providing users with virtualized computing, storage, and network resources. Cloud gateway, a key component of IaaS architecture, performs various functions such as traffic management, load balancing, and security protection. This paper presents SmartGate, a cloud gateway based on SmartNIC that leverages offloading capabilities to overcome the performance bottleneck of traditional software-based solutions. SmartGate enhances the network traffic processing model for SmartNIC and designs the lifecycle of offloading flow rules to improve the coordination efficiency between hardware and software. Our evaluation shows that SmartGate can reduces the latency by ∼76.1% and achieves a nearly 4x increase in energy efficiency.
Optimizing IT Human Capital: Evaluating and Crafting an Innovative Management System with COBIT 2019

Information Technology (IT) is vital for every part, even for organizational purposes. At the Faculty of Industrial Engineering at XYZ University, there are several stages that include the spread of unavoidable IT resources in several areas, such as the competence of adjustable IT resources, and the decrease in the value of IT resources. IT management efforts to reduce the obstacles that arise start with the knowledge of the ability to manage information technology, so that the administration and processes necessary to analyze research aim to provide recommendations related to IT resource management. refers to the COBIT 2019 framework that focuses on the APO07 Managed Human Resource Domain Process at the XYZ College of Engineering. The research process starts with the collection of data through document analysis and interviews, thus resulting in gap analysis by Cobit 2019. Recommendations will be given to improve the IT resources management process. In case, people, processes, and technologies based on gap analysis results, provide such as a variety of software or application choices, compile documentation, employment instructions and services, and solve IT problems. On the process aspect resulting draft employment contract. On the technological aspects of decommissioning recommendations related to the selection of tools.
Research on Multi-level Trust Mechanism of Internet of Things

With 5G, the Internet of Things and other technologies becoming popular, cloud edge end integration gradually replaced centralized cloud computing has become the current development trend. However, due to the complex network environment, the security of massive and diverse terminal devices has become a challenge, and the traditional iot trust mechanism has been unable to apply to the cloud-side architecture. As a decentralized distributed ledger technology, blockchain has the characteristics of data immutable, unfalsifiable, traceable and jointly maintained by many aspects, etc. Integrating blockchain into the cloud-side architecture can enable data interaction between multiple participants with mutual trust, which can ensure data integrity and security. This paper proposes to integrate blockchain into the cloud edge architecture of the Internet of Things to solve the trust problem, and the edge node and the cloud node jointly maintain the blockchain environment, and adopt the fragmentation idea to form a temporary dynamic "community" to improve the throughput of trust evidence of the blockchain and thus improve the overall security of the Internet of Things system. An improved algorithm for detecting abnormal trust information based on Apriori is designed and deployed on cloud nodes. Experiments show that the improved detection algorithm can improve the detection efficiency and reduce the calculation consumption.
Robust watermarking based on dual-random phase encryption against collusion attacks

Digital watermarking was an important method for video copyright protection, but it may face inter-frame linear attack in two kinds of collusion scenarios. By analyze attack principle and the essence of the problem, an improved double random phase encryption technology based on the principle of optical holography was proposed as a method against inter-frame collusion attacks. The experimental results show that the improved method has stronger convergence and adjustment of inter-frame redundancy than the prototype. After applying noise interference and geometric attack, the average peak signal-to-noise ratio (PSNR) of the first and last frames of frame averaging and frame cutting are decreasing by 23.12%, and the average amplitude of PSNR between the extreme values of frame reconstruction and frame cutting is 2.88%, which is significantly better than the 42.20% of the comparison method. It shows that the method can ensure stronger robustness of the watermark when dealing with common noise interference and attacks. In summary, the proposed method satisfies the evaluation criteria and is superior to the comparison method, ensuring that the co-conspirator cannot use data redundancy to obtain collusion rules and implement inter-frame attacks, which has the effect of resistance.
Chaotic Digital Image Encryption Based on Fourier Transform

In order to improve the security of chaotic digital image, the chaotic digital image encryption method is designed by the fusion optimization of Fourier transform technology. The initial input chaotic digital image is segmented, and Fourier transform technology is used to realize chaotic scrambling of the digital image and replace the pixel value of the image. By determining the parameters such as watermark embedding position and embedding strength, the watermark is embedded in the chaotic digital image, and the encryption of the chaotic digital image is completed by combining the generation result of the image key. Compared with the traditional encryption methods, it is found that the peak signal-to-noise ratio of the encrypted image obtained by the optimized design method is increased by about 5.0, and the average correlation coefficient of adjacent pixels is reduced by about 0.15. At the same time, the anti-attack ability of the encrypted image is significantly improved.
Frequency-domain Generative Adversarial Image Super-Resolution Algorithm Based on Phase Attention Loss

Deep learning-based image super-resolution algorithms have been widely used, greatly improving the quality of super-resolution images. After the experiment, it is found that the phase information of the image contains more detailed components Compared with the amplitude spectrum, while existing algorithms do not notice this. In this paper, a phase-attention based frequency-domain generative adversarial network is proposed and used in an image super-resolution algorithm to further improve the quality of the generated image by enhancing the proportion of spectral phase information in the loss function. Specifically, firstly, the image is subjected to Fourier transform, and the real and imaginary parts of the half-sheet spectrum are simultaneously input into a dual-channel frequency-domain super-resolution network, and combined with the self-designed frequency-domain discriminator, a generative adversarial model in the frequency domain is formed, and the image is reconstructed by the inverse Fourier transform at the output. The experimental results show that our method achieves excellent super-resolution results, both in terms of subjective visualization and objective evaluation indicators.
Anomalous Traffic Detection in Campus Networks based on NetFlow and CNN

With the popularization and massive use of Internet, various types of attacks and abnormal traffic in campus networks are emerging. In this paper, based on NetFlow technology and convolutional neural network to analyze abnormal traffic in campus network, we add point group convolution and channel randomization on the basis of traditional neural network, and solve the problems of group convolution by shuffling different channels, which greatly reduces the amount of model operations and speeds up the speed of model classification. The improved neural network model achieves 93.8%. in accuracy, which greatly improves the accuracy of abnormal traffic classification. The method can realize the real-time collection of campus network traffic and realize the real-time monitoring of campus network condition through traffic analysis and comparison, which can strongly guarantee the security and stability of university network.
A Time-bound Data Access Control Scheme based on Attribute-based Encryption

Data protection in data sharing refers to the measures and practices implemented to ensure shared data's security, privacy, and integrity. It involves taking steps to protect data from unauthorized access and misuse. By utilizing encryption algorithms, users can securely store data using cloud servers while safeguarding it during the process of transmission. Ciphertext-policy attribute-based encryption (CP-ABE) is suitable for scenarios involving multi-party data sharing. We present an improved time-bound CP-ABE scheme access control for balancing extensive data caching overheads and implements it on the InterPlanetary File System (IPFS). The data owner employs our encryption to secure the shared data and subsequently stores data by IPFS, thereby maximizing the capacity of distributed storage of the scheme. Our method separates the time constraints from the attributes, allowing for the modification of the time-bound key without changing the user attribute key. It avoids the frequent encryption issues that arise due to changes in shared time while keeping other attributes unchanged.