Reviewerly/exHarmony huggingface dataset:
Hybrid classical-quantum machine learning based on dissipative two-qubit channels

Abstract Although the environmental effects, i.e., dissipation and decoherence seem to be the strongest adversaries in the quantum information realm, here, we address how dissipation can be harnessed for quantum state preparation and universal quantum computation. In this line, we propose a realistic scheme for hybrid classical-quantum neural networks based on dissipative two-qubit channels. In particular, we design a variational quantum circuit consisting of a set of universal quantum gates. We encode classical information in the initial states of a two-qubit system interacting with a global environment. This composite system plays the role of a dissipative quantum channel (DQC). A pooling layer concatenates the output states of the DQCs resulting in the outcome of the circuit. Both the DCQs and the pooling layer provide superposition and entanglement which are the key ingredients of any universal quantum computation protocol. Finally, we investigate the capability and adaptability of this model by doing some machine learning tasks. It is reasonable to postulate that a quantum computer based on DQCs may outperform a classical computer because, in contrast to the latter, the former is capable of producing atypical patterns through non-classical phenomena.
Benchmarking the Role of Particle Statistics in Quantum Reservoir Computing

Abstract Quantum reservoir computing is a neuro‐inspired machine learning approach harnessing the rich dynamics of quantum systems to solve temporal tasks. It has gathered attention for its suitability for NISQ devices, for easy and fast trainability, and for potential quantum advantage. Although several types of systems have been proposed as quantum reservoirs, differences arising from particle statistics have not been established yet. In this work, the ability of bosons, fermions, and qubits are assessed and compared to store information from past inputs by measuring linear and nonlinear memory capacity. While, in general, the performance of the system improves with the Hilbert space size, it is shown that also the information spreading capability is a key factor. For the simplest reservoir Hamiltonian choice, and for each boson limited to at most one excitation, fermions provide the best reservoir due to their intrinsic nonlocal properties. On the other hand, a tailored input injection strategy allows the exploitation of the abundance of degrees of freedom of the Hilbert space for bosonic quantum reservoir computing and enhances the computational power compared to both qubits and fermions.
BipBip: A Low-Latency Tweakable Block Cipher with Small Dimensions

Recently, a memory safety concept called Cryptographic Capability Computing (C3) has been proposed. C3 is the first memory safety mechanism that works without requiring extra storage for metadata and hence, has the potential to significantly enhance the security of modern IT-systems at a rather low cost. To achieve this, C3 heavily relies on ultra-low-latency cryptographic primitives. However, the most crucial primitive required by C3 demands uncommon dimensions. To partially encrypt 64-bit pointers, a 24-bit tweakable block cipher with a 40-bit tweak is needed. The research on low-latency tweakable block ciphers with such small dimensions is not very mature. Therefore, designing such a cipher provides a great research challenge, which we take on with this paper. As a result, we present BipBip, a 24-bit tweakable block cipher with a 40-bit tweak that allows for ASIC implementations with a latency of 3 cycles at a 4.5 GHz clock frequency on a modern 10 nm CMOS technology.
A Runs Test-Based Kalman Filter With Both Adaptability and Robustness With Application to INS/GNSS Integration

Kalman filter (KF) is one of the most efficient solutions applied for the integration of inertial navigation system (INS) and global navigation satellite system (GNSS). However, for standard KF derived from the ideal system model assumption, various disturbance factors inevitably decrease its estimation accuracy in practical scenarios. Therefore, it is necessary to suppress the interference factors such as inaccurate noise covariance or irregular process uncertainty during filtering, thus improving the integrated system’s stability and accuracy. Nevertheless, existing solutions usually target specific individual issues and lack compatibility with nonstationary scenarios in that multiple uncertain factors coexist. In this article, an improved KF with a switching scheme based on runs hypothesis test is designed to enhance the algorithm’s performance in nonstationary environments, and a process uncertainty robust filter based on mixture correntropy is proposed to suppress the potential diverge risks. By integrating with the variational Bayesian approach into the switching scheme, the noise covariance adaptability of the proposed scheme is further improved, and an improved KF with both adaptivity and robustness is obtained. The simulation and field test results demonstrate that the proposed filter achieved the expected superior performance in challenging nonstationary conditions.
Hierarchical concept Bottleneck models for vision and their application to explainable fine classification and tracking

Improving the explainability of Computer Vision models based on Deep Learning has recently become a compelling problem, ensuring reliable predictions to the end-user and enabling more fine-grained classifications. Recently, Concept Bottleneck models have been proposed for images classification, partitioning the problem in two stages and thereby defining a hierarchy of concepts. So far, however, little work has been done to investigate the applicability of this approach to other datasets with higher intra-class variability and ambiguity, and to discuss its flexibility to tasks different from whole-images classification. In this work we develop and discuss a Concept Bottleneck model for images segmentation, objects fine classification and tracking, and compare it to more classical methods based on Mask R-CNN and images similarity algorithms. All our models are trained and tested on a dataset comprised of pictures of fridges filled with various objects, however the method can be applied to any fine classification task. The proposed model makes full use of the hierarchy in concepts, exploiting the relationships between different categories at the same hierarchical level and relying on a novel method for handling multi-labels classifications. We show that the performance on fine classification is on par with a regular Mask R-CNN, but with a significant increase in explainability and in handling classes confusion. New explainable metrics are proposed to quantitatively evaluate the increase in explainability. We also demonstrate the effectiveness of the derived Concept Bottleneck features on related tasks, i.e., the tracking of objects between consecutive pictures in a sequence. The code is released as open source and available at https://opensource.silicon-austria.com/pittinof/hierarchical-concept-bottleneck.
Surround-View Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-Insensitive Multi-Task Framework

Surround-view fisheye perception under valet parking scenes is fundamental and crucial in autonomous driving. Environmental conditions in parking lots perform differently from the common public datasets, such as imperfect light and opacity, which substantially impacts on perception performance. Most existing networks based on public datasets may generalize suboptimal results on these valet parking scenes, also affected by the fisheye distortion. In this article, we introduce a new large-scale fisheye dataset called Fisheye Parking Dataset(FPD) to promote the research in dealing with diverse real-world surround-view parking cases. Notably, our compiled FPD exhibits excellent characteristics for different surround-view perception tasks. In addition, we also propose our real-time distortion-insensitive multi-task framework Fisheye Perception Network (FPNet), which improves the surround-view fisheye BEV perception by enhancing the fisheye distortion operation and multi-task lightweight designs. Extensive experiments validate the effectiveness of our approach and the dataset's exceptional generalizability.
Evidence of personality traits on phishing attack menace among selected university undergraduates in Nigerian

Access ease, mobility, portability, and improved speed have continued to ease the adoption of computing devices; while, consequently proliferating phishing attacks. These, in turn, have created mixed feelings in increased adoption and nosedived users’ trust level of devices. The study recruited 480-students, who were exposed to socially-engineered attack directives. Attacks were designed to retrieve personal data and entice participants to access compromised links. We sought to determine the risks of cybercrimes among the undergraduates in selected Nigerian universities, observe students’ responses and explore their attitudes before/after each attack. Participants were primed to remain vigilant to all forms of scams as we sought to investigate attacks’ influence on gender, students’ status, and age to perceived safety on susceptibility to phishing. Results show that contrary to public beliefs, age, status, and gender were not among the factors associated with scam susceptibility and vulnerability rates of the participants. However, the study reports decreased user trust levels in the adoption of these new, mobile computing devices.
Diversity-Based Evolutionary Population Dynamics: A New Operator for Grey Wolf Optimizer

Evolutionary Population Dynamics (EPD) refers to eliminating poor individuals in nature, which is the opposite of survival of the fittest. Although this method can improve the median of the whole population of the meta-heuristic algorithms, it suffers from poor exploration capability to handle high-dimensional problems. This paper proposes a novel EPD operator to improve the search process. In other words, as the primary EPD mainly improves the fitness of the worst individuals in the population, and hence we name it the Fitness-Based EPD (FB-EPD), our proposed EPD mainly improves the diversity of the best individuals, and hence we name it the Diversity-Based EPD (DB-EPD). The proposed method is applied to the Grey Wolf Optimizer (GWO) and named DB-GWO-EPD. In this algorithm, the three most diversified individuals are first identified at each iteration, and then half of the best-fitted individuals are forced to be eliminated and repositioned around these diversified agents with equal probability. This process can free the merged best individuals located in a closed populated region and transfer them to the diversified and, thus, less-densely populated regions in the search space. This approach is frequently employed to make the search agents explore the whole search space. The proposed DB-GWO-EPD is tested on 13 high-dimensional and shifted classical benchmark functions as well as 29 test problems included in the CEC2017 test suite, and four constrained engineering problems. The results obtained by the proposal upon implemented on the classical test problems are compared to GWO, FB-GWO-EPD, and four other popular and newly proposed optimization algorithms, including Aquila Optimizer (AO), Flow Direction Algorithm (FDA), Arithmetic Optimization Algorithm (AOA), and Gradient-based Optimizer (GBO). The experiments demonstrate the significant superiority of the proposed algorithm when applied to a majority of the test functions, recommending the application of the proposed EPD operator to any other meta-heuristic whenever decided to ameliorate their performance.
Cloud cover bias correction in numerical weather models for solar energy monitoring and forecasting systems with kernel ridge regression

Prediction of Total Cloud Cover (TCDC) from numerical weather simulation models, such as Global Forecast System (GFS), can aid renewable energy engineers in monitoring and forecasting solar photovoltaic power generation. A major challenge is the systematic bias in TCDC simulations induced by the errors in the numerical model parameterization stages. Correction of GFS-derived cloud forecasts at multiple time steps can improve energy forecasts in electricity grids to bring better grid stability or certainty in the supply of solar energy. We propose a new kernel ridge regression (KRR) model to reduce bias in TCDC simulations for medium-term prediction at the inter-daily, e.g., 2–8 day-ahead predicted TCDC values. The proposed KRR model is evaluated against multivariate recursive nesting bias correction (MRNBC), a conventional approach and eight machine learning (ML) methods. In terms of the mean absolute error (MAE), the proposed KRR model outperforms MRNBC and ML models at 2–8 day ahead forecasts, with MAE ≈ 20–27%. A notable reduction in the simulated cloud cover mean bias error of 20–50% is achieved against the MRNBC and reference accuracy values generated using proxy-observed and non-corrected GFS-predicted TCDC in the model's testing phase. The study ascertains that the proposed KRR model can be explored further to operationalize its capabilities, reduce uncertainties in weather simulation models, and its possible consideration for practical use in improving solar monitoring and forecasting systems that utilize cloud cover simulations from numerical weather predictions.
Learning the User’s Deeper Preferences for Multi-modal Recommendation Systems

Recommendation system plays an important role in the rapid development of micro-video sharing platform. Micro-video has rich modal features, such as visual, audio, and text. It is of great significance to carry out personalized recommendation by integrating multi-modal features. However, most of the current multi-modal recommendation systems can only enrich the feature representation on the item side, while it leads to poor learning of user preferences. To solve this problem, we propose a novel module named Learning the User’s Deeper Preferences (LUDP) , which constructs the item-item modal similarity graph and user preference graph in each modality to explore the learning of item and user representation. Specifically, we construct item-item similar modalities graph using multi-modal features, the item ID embedding is propagated and aggregated on the graph to learn the latent structural information of items; The user preference graph is constructed through the historical interaction between the user and item, on which the multi-modal features are aggregated as the user’s preference for the modal. Finally, combining the two parts as auxiliary information enhances the user and item representation learned from the collaborative signals to learn deeper user preferences. Through a large number of experiments on two public datasets (TikTok, Movielens), our model is proved to be superior to the most advanced multi-modal recommendation methods.
GSNNet: Group semantic-guided neighbor interaction network for co-salient object detection

The critical challenge for co-salient object detection (CoSOD) is to extract common saliency information from a group of relevant images. Most of the existing CoSOD methods do not fully explore the semantic commonality of co-salient objects, which can be strong guidance for collaborative feature learning, and do not take full advantage of rich hierarchical features of different layers, resulting in inferior performance. To this end, we propose a Group Semantic-guided Neighbor interaction network (GSNNet) for co-salient object detection. Specifically, the proposed network contains the group semantic module (GSM), neighbor interaction module (NIM), and feature enhancement module (FEM). The network first learns semantic consensus from a group of relevant images by the GSM, which uses the reverse guidance strategy and the group-wise combination strategy to distill the group semantic cues from the forward and complementary features. With the powerful guidance of group semantic, the NIM is employed to conduct the neighbor feature interaction of adjacent layers to excavate the contextual information and enhance feature representation. Then the FEM is adopted to refine the critical cues by the attention mechanism, which enhances the compactness of feature representation. The proposed GSNNet is evaluated on three challenging CoSOD benchmark datasets using four widely-used metrics, which demonstrates that our proposed method is superior to the other twelve cutting-edge methods for co-salient object detection.
Quantum-inspired permanent identities

The permanent is pivotal to both complexity theory and combinatorics. In quantum computing, the permanent appears in the expression of output amplitudes of linear optical computations, such as in the Boson Sampling model. Taking advantage of this connection, we give quantum-inspired proofs of many existing as well as new remarkable permanent identities. Most notably, we give a quantum-inspired proof of the MacMahon master theorem as well as proofs for new generalizations of this theorem. Previous proofs of this theorem used completely different ideas. Beyond their purely combinatorial applications, our results demonstrate the classical hardness of exact and approximate sampling of linear optical quantum computations with input cat states.
Research Trends in E-Government Interoperability: Mapping Themes and Concepts Based on The Scopus Database

Advances in information and communication technology, in the provision of public services, have affected all aspects of electronic governance. This widespread use has resulted in vast amounts of data, which governments and other entities can collect and analyse to measure, assess and improve service interoperability. Interoperability is the ability of software to collaborate. Currently, studies on the interoperability of e-government applications are the focus of research by previous researchers to develop information and communication technology to realize the smooth exchange of information in all elements of government. This study aims to determine the development of research on interoperability in e-government by conducting an in-depth analysis of research trends and dominant themes in e-government interoperability. This research is a systematic review using VosViewer and Nvivo Qualitative Software Analysis, and both are used to uncover trending issues and research themes on e-government interoperability. This research data was obtained through the Scopus database using a search strategy to find relevant documents. The results show that research trends related to e-government interoperability issues have declined recently. In addition, this study also presents the highest citations, funding sponsors, and research connectivity between countries. Furthermore, this study also produces six dominant themes related to e-government interoperability, namely Interoperability, E-government, Systems, Technology, Data, and Services. Some of the findings produced in this study are expected to provide a comprehensive picture of research trends in e-government interoperability for researchers in the future.
A netnography study examined consumer perception towards cryptocurrency investment during the COVID-19 pandemic

There is an increasing interest in cryptocurrencies, especially with the change brought to our lives by the COVID-19 pandemic regarding digital platforms. In this context, it is seen that many YouTube channels and Twitter accounts have digital interaction and produce current content about the circulation of digital currency that users have an intense interest in Turkey. Therefore, a three and half years (2019-2022) netnographic field study was conducted focusing on YouTube and Twitter platforms to evaluate interaction trends in the attitudes and behaviours of users about cryptocurrency investments in Turkey and compared to the pre-pandemic period. The study results show that, with the economic recession resulting from the pandemic, people and companies have an increasing interest in cryptocurrencies, especially in terms of saving and investment. This reveals that the increase in interest has transformed the pre-pandemic negative perception, and users have taken steps to interact and invest more.
A UTXO-based Sharding Method for Stablecoin

We propose a UTXO-based sharding method to achieve horizontal scalability in a token-based system, including stablecoin. The critical challenge of the current sharding method is that introducing extra cross-shard transactions significantly impacts transaction latency. Previous solutions use account-based sharding to improve transaction volume, increasing latency. We propose a new method that improves transaction throughput linearly while keeping latency low by reducing cross-shard transactions. We were able to verify our solution through experiments.
Character Recognition By Implementing FPGA-Based Artificial Neural Network

A non-linear applied math knowledge modelling tool, Artificial Neural Networks (ANN) are predominantly used to model complicated interactions between inputs and outputs or to look for patterns within the data. Using VHDL coding, we developed a generic hardware-based ANN. This classifier has been trained to recognize letters on a 4x4 binary grid that a user fills out using 16 toggle switches. An LCD shows the most likely classification that the ANN proposed. The ANN was taught to recognize 20 English character patterns and 9 Arabic character patterns on a 4x4 grid to showcase the viability of the FPGA execution of ANN. The Altera DE2 development and education board use the generated design file to flash the Altera Cyclone II FPGA. A training supervisor is also included in the design, which is responsible for training the ANN to recognize a set of 29 standard characters drawn from the English and Arabic alphabets. Positive patterns were found, with the ANN successfully recognizing all training characters. Finally, this study demonstrates the adaptability and boundless possibility of hardware-based implementation of ANN and the infinite possibility of prototyping and building recent systems with VHDL coding on an FPGA platform.
Emotion Quantification Using Variational Quantum State Fidelity Estimation

Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset.
Path Planning Research for Outdoor Mobile Robot

We present an improved A* algorithm based on road boundary constrainted for road scenes such as campuses, communities, and industrial parks. Firstly, the LIDAR scans the surrounding environment and saves it in the point cloud data, pre-processes the point cloud and acquires the road boundary, then translates the acquired road boundary to generate a minimal cost feasible domain, and finally adds the feasible domain to the A* algorithm for path planning. To verify the feasibility of the method, experiments were conducted in both simulated and real environments. The experimental results show that the method can improve the problem of the paths planned by the A* algorithm being too close to the road edges and reduce the influence of road boundaries when the mobile robot is moving outdoors.