Reviewerly/exHarmony huggingface dataset:
Fairness in Forecasting of Observations of Linear Dynamical Systems

In machine learning, training data often capture the behaviour of multiple subgroups of some underlying human population. This behaviour can often be modelled as observations of an unknown dynamical system with an unobserved state. When the training data for the subgroups are not controlled carefully, however, under-representation bias arises. To counter under-representation bias, we introduce two natural notions of fairness in time-series forecasting problems: subgroup fairness and instantaneous fairness. These notions extend predictive parity to the learning of dynamical systems. We also show globally convergent methods for the fairness-constrained learning problems using hierarchies of convexifications of non-commutative polynomial optimisation problems. We also show that by exploiting sparsity in the convexifications, we can reduce the run time of our methods considerably. Our empirical results on a biased data set motivated by insurance applications and the well-known COMPAS data set demonstrate the efficacy of our methods.
Quantum routing of information using chiral quantum walks

We address routing of classical and quantum information over quantum network and show how to exploit chirality (directionality) to achieve nearly optimal and robust transport. In particular, we prove how continuous-time chiral quantum walks over a minimal graph are able to model directional transfer of information over a network. At first, we show how classical information, encoded onto an excitation localized at one vertex of a simple graph, may be sent to any other chosen location with nearly unit fidelity by tuning a single phase. Then, we prove that high-fidelity transport is also possible for coherent superpositions of states, i.e., for routing of quantum information. Furthermore, we show that by tuning the phase parameter, one obtains universal quantum routing, i.e., independent on the input state. In our scheme, chirality is governed by a single phase, and the routing probability is robust against fluctuations of this parameter. Finally, we address characterization of quantum routers and show how to exploit the self-energies of the graph to achieve high precision in estimating the phase parameter.
Adversarial AI Testcases for Maritime Autonomous Systems

Contemporary maritime operations such as shipping are a vital component constituting global trade and defence. The evolution towards maritime autonomous systems, often providing significant benefits (e.g., cost, physical safety), requires the utilisation of artificial intelligence (AI) to automate the functions of a conventional crew. However, unsecured AI systems can be plagued with vulnerabilities naturally inherent within complex AI models. The adversarial AI threat, primarily only evaluated in a laboratory environment, increases the likelihood of strategic adversarial exploitation and attacks on mission-critical AI, including maritime autonomous systems. This work evaluates AI threats to maritime autonomous systems in situ. The results show that multiple attacks can be used against real-world maritime autonomous systems with a range of lethality. However, the effects of AI attacks vary in a dynamic and complex environment from that proposed in lower entropy laboratory environments. We propose a set of adversarial test examples and demonstrate their use, specifically in the marine environment. The results of this paper highlight security risks and deliver a set of principles to mitigate threats to AI, throughout the AI lifecycle, in an evolving threat landscape.
Dimensionality reduced training by pruning and freezing parts of a deep neural network: a survey

State-of-the-art deep learning models have a parameter count that reaches into the billions. Training, storing and transferring such models is energy and time consuming, thus costly. A big part of these costs is caused by training the network. Model compression lowers storage and transfer costs, and can further make training more efficient by decreasing the number of computations in the forward and/or backward pass. Thus, compressing networks also at training time while maintaining a high performance is an important research topic. This work is a survey on methods which reduce the number of trained weights in deep learning models throughout the training. Most of the introduced methods set network parameters to zero which is called pruning. The presented pruning approaches are categorized into pruning at initialization, lottery tickets and dynamic sparse training. Moreover, we discuss methods that freeze parts of a network at its random initialization. By freezing weights, the number of trainable parameters is shrunken which reduces gradient computations and the dimensionality of the model’s optimization space. In this survey we first propose dimensionality reduced training as an underlying mathematical model that covers pruning and freezing during training. Afterwards, we present and discuss different dimensionality reduced training methods—with a strong focus on unstructured pruning and freezing methods.
Hierarchical co-clustering with augmented matrices from external domains

Co-clustering simultaneously classifies row and column objects of data matrices and is considered to have better accuracy than conventional one-way clustering methods. In the era of big data, extracting classification knowledge about objects from several domains has become increasingly feasible. This study proposes a hierarchical co-clustering with augmented matrices (HICCAM), which co-clusters the row and column objects of a target matrix while utilizing the augmented data matrices of these target objects extracted from the external domains. The algorithm is designed to improve classification accuracy by transferring knowledge in augmented matrices and simultaneously improves cluster interpretability using hierarchical cluster structures. Experiments on document clustering confirmed that HICCAM achieved the highest accuracy among comparison methods with and without external knowledge. Its clusters exhibit hierarchical relationships according to their topics. In addition, we provide the experimental results with multiview synthetic datasets that demonstrate a clustering situation in which HICCAM can be effectively identified.
Impact of artificial intelligence text generators (AITGs) on libraries

Purpose The purpose of studying the impact of artificial intelligence text generators (AITGs) on libraries is to examine the effect of AITGs on the library landscape, including the services offered, the resources provided and the roles of library staff. Design/methodology/approach The current study examined how AITGs impact libraries. The researcher was able to comprehend the problem by critically analyzing and reviewing the pertinent published works, such as books, journals and articles. Findings This study concludes AITGs can assist libraries in streamlining operations, enhancing services and making collections more accessible. It is vital to highlight that AITGs are not intended to dissuade its users from visiting physical libraries or to replace them with virtual ones. Instead, they are a tool that can improve and supplement the services and resources provided by virtual libraries. Originality/value The study’s observations add to the corpus of information on AITGs in libraries and help users comprehend their technological foundations. Further empirical research is recommended on the effects of AITGs and their impact on libraries.
Theoretical Models Explaining the Level of Digital Competence in Students

In the new global scene, digital skills are a key skill for students to seize new learning opportunities, train to meet the demands of the labor market, and compete in the global market, while also communicating effectively in their everyday and academic lives. This article presents research aimed at relating the impact of personal variables on the digital competence of technical problem solving in Spanish students from 12 to 14 years old. A quantitative methodology with a cross-sectional design was employed. A sample of 772 students from 18 Spanish educational institutions was used. For data collection, an assessment test was designed (ECODIES®) based on a validated indicator model to evaluate learners’ digital competence (INCODIES®), taking as a model the European framework for the development of digital competence. Mediation models were used and theoretical reference models were created. The results allowed us to verify the influence of personal, technology use, and attitudinal variables in the improvement of digital skill in technical problem solving. The findings lead to the conclusion that gender, acquisition of digital devices, and regular use do not determine a better level of competence.
A Blockchain-Enabled Serverless Approach for IoT Healthcare Applications

This chapter will examine the interaction between serverless computing and blockchain technology for IoT healthcare applications as a novel approach to solving unreliable function problems and resource allocation on serverless computing providers. The existence and execution of functions with malicious code and the lack of confidence and trust in the validity and safety of executive functions can always create concerns for customers during the process of using these functions. In particular, the importance of ensuring the safety and privacy of customers increases when these functions are used in sensitive areas such as health. On the other hand, because there are limited resources available in the Internet of Things devices, serverless service providers must employ a strategy that can provide a basis for addressing requests in the best possible manner by implementing a proper scheduling process. Especially when it comes to health-related programs, this is of utmost importance. We consider the potential and ability of continuous interaction between the two technologies of serverless computing and Hyper Ledger Fabric’s private blockchain as a suitable and efficient solution to respond to the needs raised. By enabling the interaction between these two technologies, it is essential to note that this new approach can be suitable for use in the healthcare industry since it can provide the context in which necessary requests can be responded to by performing the appropriate scheduling process. Furthermore, serverless service providers should provide functions that can ensure customers that the service provider will approve the functions they use and that there will be no unreliable origins and functions that use malicious code. Consequently, considering the concerns in sensitive areas such as health, it can be said that this approach tries to respond to these concerns by presenting a novel solution. The proposed approach tries to respond to the sent requests by considering the privacy of its users, along with the appropriate and timely allocation of resources, while providing reliable and trustworthy conditions for customers. It is clear that this provides the basis for good utilization of the resources, which can ultimately increase the satisfaction of the users and the quality of service they receive.
Neutrosophic Theory Framework for Building Mathematics Teachers Capacity in Assessment of High School Students in the United Arab Emirates

Better classroom evaluation may have positive effects on students' learning, according to research and practice from the past ten years. United Arab Emirates (UAE) values the assessment of procedures used in teaching, as an integral part in the evaluation of their effectiveness. Through evaluation, the results realized, help in measuring the effectiveness of curricula and methods used in teaching. This, therefore, affects stakeholders in education, the teachers, and most importantly, the students. This study aims at cross-examining students’ performance in mathematics, especially at the high school level, in the UAE. Also, this evaluation has a multi-criteria so the concept of multi-criteria decision-making is used in this paper. But this process has vague and uncertain information, so the neutrosophic theory is used to solve this problem. The neutrosophic sets integrated with the MCDM methodology. The neutrosophic AHP method is used to compute the weights of criteria and evaluate the classroom.
Adaptive Dynamic Path Planning Method for Autonomous Vehicle Under Various Road Friction and Speeds

Path planning is a crucial technology for autonomous vehicle (AV). However, it is difficult to adapt to dynamic driving environment, and AV may lose lateral dynamic stability due to high speed and various friction. This paper presents an adaptive dynamic path planning method (ADPPM) for AV to address the challenges. The ADPPM is comprised of three components: 1) A dynamic state-fused steering decision method based on a hierarchical fuzzy inference system is designed to calculate the steering position of AV in each iteration step, and the method fuses the multi-state of the dynamic environment; 2) dynamic path optimization method is designed to reduce the mean curvature of the path based on the particle swarm optimization method, which improves the lateral dynamics stability of AV; 3) adaptive speed inference method is proposed to provide desired steering speed for AV according to various road friction and reduce AV’s steering burden. The ADPPM provides path planning in the dynamic environment, and it also improves the stability of AV under various road friction and speeds. Finally, the proposed method is verified by CarSim.
Dynamic Message Propagation Network for RGB-D and Video Salient Object Detection

Exploiting long-range semantic contexts and geometric information is crucial to infer salient objects from RGB and depth features. However, existing methods mainly focus on excavating local features within fixed regions by continuously feeding forward networks. In this article, we introduce Dynamic Message Propagation (DMP) to dynamically learn context information within more flexible regions. We integrate DMP into a Siamese-based network to process the RGB image and depth map separately and design a multi-level feature fusion module to explore cross-level information between refined RGB and depth features. Extensive experiments show clear improvements of our method over 17 methods on six benchmark datasets for RGB-D salient object detection (SOD). Additionally, our method outperforms its competitors for the video SOD task. Code is available at https://github.com/chenbaian-cs/DMPNet .
<scp>CodeEditor</scp> : Learning to Edit Source Code with Pre-trained Models

Developers often perform repetitive code editing activities (up to 70%) for various reasons (e.g., code refactoring) during software development. Many deep learning (DL) models have been proposed to automate code editing by learning from the code editing history. Among DL-based models, pre-trained code editing models have achieved the state-of-the-art (SOTA) results. Pre-trained models are first pre-trained with pre-training tasks and fine-tuned with the code editing task. Existing pre-training tasks mainly are code infilling tasks (e.g., masked language modeling), which are derived from the natural language processing field and are not designed for automatic code editing. In this article, we propose a novel pre-training task specialized in code editing and present an effective pre-trained code editing model named CodeEditor . Compared to previous code infilling tasks, our pre-training task further improves the performance and generalization ability of code editing models. Specifically, we collect lots of real-world code snippets as the ground truth and use a powerful generator to rewrite them into mutated versions. Then, we pre-train our CodeEditor to edit mutated versions into the corresponding ground truth, to learn edit patterns. We conduct experiments on four code editing datasets and evaluate the pre-trained CodeEditor in three settings (i.e., fine-tuning, few-shot, and zero-shot). (1) In the fine-tuning setting, we train the pre-trained CodeEditor with four datasets and evaluate it on the test data. CodeEditor outperforms the SOTA baselines by 15%, 25.5%, 9.4%, and 26.6% on four datasets. (2) In the few-shot setting, we train the pre-trained CodeEditor with limited data and evaluate it on the test data. CodeEditor substantially performs better than all baselines, even outperforming baselines that are fine-tuned with all data. (3) In the zero-shot setting, we evaluate the pre-trained CodeEditor on the test data without training. CodeEditor correctly edits 1,113 programs, while the SOTA baselines cannot work. The results show that the superiority of our pre-training task and the pre-trained CodeEditor is more effective in automatic code editing.
A Solomonic Solution to Blockchain Front-Running

Blockchain front-running involves multiple agents, other than the legitimate agent, claiming a payment from performing a contract. It arises because of the public nature of blockchain transactions and potential network congestion. This paper notes that disputes over payments are similar to classic ownership disputes (such as King Solomon's dilemma). We propose a simultaneous report mechanism that eliminates blockchain front-running. In each case, the mechanism relies on threats to remove ownership from all claimants and preferences from the legitimate claimant over allocations to other agents.
Abnormal Activity Detection Techniques in Intelligent Video Surveillance: A Survey

Currently, CCTV (Closed Circuit Television) cameras are used for surveillance by alerting the security officer if any malfunction or abnormal activity happens. Abnormal activities may be theft, violence, or explosion. CCTV cameras are used in public places like city streets, parks, communities, and neighborhoods to help detect crime and enhance public safety. Manual surveillance for this is tedious and time-consuming. Detecting abnormal crowd behavior in real-time is an exciting research area. Presently, most researchers are interested in developing Dynamic abnormal detection mechanisms to ensure security. However, this is challenging due to climate change, human movement, occlusions, and low video quality. Due to the high dimensionality of video data, Space and time complexity are also increased. This paper explains the various methods of abnormal activity detection under deep learning and the handcrafted approach.
Zero-Knowledge Arguments for Lattice-Based Accumulators: Logarithmic-Size Ring Signatures and Group Signatures Without Trapdoors

Abstract An accumulator is a function that hashes a set of inputs into a short, constant-size string while preserving the ability to efficiently prove the inclusion of a specific input element in the hashed set. It has proved useful in the design of numerous privacy-enhancing protocols, in order to handle revocation or simply prove set membership. In the lattice setting, currently known instantiations of the primitive are based on Merkle trees, which do not interact well with zero-knowledge proofs. In order to efficiently prove the membership of some element in a zero-knowledge manner, the prover has to demonstrate knowledge of a hash chain without revealing it, which is not known to be efficiently possible under well-studied hardness assumptions. In this paper, we provide an efficient method of proving such statements using involved extensions of Stern’s protocol. Under the Small Integer Solution assumption, we provide zero-knowledge arguments showing possession of a hash chain. As an application, we describe new lattice-based group and ring signatures in the random oracle model. In particular, we obtain: (i) the first lattice-based ring signatures with logarithmic size in the cardinality of the ring and (ii) the first lattice-based group signature that does not require any GPV trapdoor and thus allows for a much more efficient choice of parameters.
Offering Two-way Privacy for Evolved Purchase Inquiries

Dynamic and flexible business relationships are expected to become more important in the future to accommodate specialized change requests or small-batch production. Today, buyers and sellers must disclose sensitive information on products upfront before the actual manufacturing. However, without a trust relation, this situation is precarious for the involved companies as they fear for their competitiveness. Related work overlooks this issue so far: existing approaches protect the information of a single party only, hindering dynamic and on-demand business relationships. To account for the corresponding research gap of inadequately privacy-protected information and to deal with companies without an established trust relation, we pursue the direction of innovative privacy-preserving purchase inquiries that seamlessly integrate into today’s established supplier management and procurement processes. Utilizing well-established building blocks from private computing, such as private set intersection and homomorphic encryption, we propose two designs with slightly different privacy and performance implications to securely realize purchase inquiries over the Internet. In particular, we allow buyers to consider more potential sellers without sharing sensitive information and relieve sellers of the burden of repeatedly preparing elaborate yet discarded offers. We demonstrate our approaches’ scalability using two real-world use cases from the domain of production technology. Overall, we present deployable designs that offer two-way privacy for purchase inquiries and, in turn, fill a gap that currently hinders establishing dynamic and flexible business relationships. In the future, we expect significantly increasing research activity in this overlooked area to address the needs of an evolving production landscape.
The COVID-19 pandemic, economic policy uncertainty and the hedging role of cryptocurrencies: a global perspective

Purpose The COVID-19 pandemic has led to global economic policy uncertainty, which has increased the need to investigate ways to mitigate the uncertainty. This study aims to examine the potential of cryptocurrencies as a hedge and safe haven avenue against economic policy uncertainty. Design/methodology/approach This study investigates the behavior of the five leading cryptocurrencies in relation to country-level and group-level economic policy uncertainty indices, as measured by the text-based method developed by Baker et al . ( The Quarterly Journal of Economics , 2016, 131, 1593–1636). The research covers a broad range of emerging and developed economies from July 2013 to September 2020. The study employs the approach of Narayan et al . ( Economic Modelling , 2016, 53, 388–397) to examine the hedging and safe-haven properties of cryptocurrencies. Findings This study finds that the top cryptocurrencies play a hedging role against economic policy uncertainty, with some exceptions. Additionally, there is evidence to support the idea that cryptocurrencies can serve as a safe haven during the COVID-19 pandemic. As a result, investors may benefit from using cryptocurrencies as a risk-management avenue during times of uncertainty. Originality/value This research contributes to the existing literature by testing the cryptocurrencies' hedging and safe haven properties in a new way, by analyzing their lead and lag behaviors using a recent and innovative approach. Additionally, it examines a wide range of emerging and advanced markets, providing insight into the potential of using cryptocurrencies as a risk mitigation avenue.