Reviewerly/exHarmony huggingface dataset:
A hierarchical approach based CBIR scheme using shape, texture, and color for accelerating retrieval process

In the traditional content-based image retrieval (CBIR) framework, images are retrieved based on the combined primitive features. However, such a kind of fusion is always not effective as one feature may overshadow other image feature. To overcome this issue, in this particular paper, we have suggested a hierarchical framework where features like color, texture, and shape are considered in a single hierarchy only. The main concern of the hierarchical system is the proper selection of the order of retrieving visual features. So, semantic-based image retrieval has been carried out. Here, at the first level, the salience map-based region of interest has been identified, and then edge histogram descriptor-based shape features are incorporated. In the second hierarchy, we have proposed a novel directional texture feature extraction based on the Tamura features’ directionality. Further, color is considered another primitive feature of an image, but human visual perception is not sensitive to each color. The image can be visualized by the salient colors, and in this work, we have developed a color image quantization-based approach. Now, to validate the system, extensive experimental results and its comparison with its contemporaries through Corel-1K, GHIM-10K, Olivia-2688, and Produce-1400 databases have been carried out.
On Quantum Optimal Transport

We analyze a quantum version of the Monge–Kantorovich optimal transport problem. The quantum transport cost related to a Hermitian cost matrix C is minimized over the set of all bipartite coupling states $$\rho ^{AB}$$ with fixed reduced density matrices $$\rho ^A$$ and $$\rho ^B$$ of size m and n. The minimum quantum optimal transport cost $$\textrm{T}^Q_{C}(\rho ^A,\rho ^B)$$ can be efficiently computed using semidefinite programming. In the case $$m=n$$ the cost $$\textrm{T}^Q_{C}$$ gives a semidistance if and only if C is positive semidefinite and vanishes exactly on the subspace of symmetric matrices. Furthermore, if C satisfies the above conditions, then $$\sqrt{\textrm{T}^Q_{C}}$$ induces a quantum analogue of the Wasserstein-2 distance. Taking the quantum cost matrix $$C^Q$$ to be the projector on the antisymmetric subspace, we provide a semi-analytic expression for $$\textrm{T}^Q_{C^Q}$$ for any pair of single-qubit states and show that its square root yields a transport distance on the Bloch ball. Numerical simulations suggest that this property holds also in higher dimensions. Assuming that the cost matrix suffers decoherence and that the density matrices become diagonal, we study the quantum-to-classical transition of the Monge–Kantorovich distance, propose a continuous family of interpolating distances, and demonstrate that the quantum transport is cheaper than the classical one. Furthermore, we introduce a related quantity—the SWAP-fidelity—and compare its properties with the standard Uhlmann–Jozsa fidelity. We also discuss the quantum optimal transport for general d-partite systems.
Line laser scanning microscopy based on the Scheimpflug principle for high-resolution topography restoration and quantitative measurement

A line laser scanning microscopy system with a larger depth of field based on the Scheimpflug principle is proposed for high-resolution surface topography restoration and quantitative measurement on miniature non-transparent samples. An imaging model based on the Scheimpflug principle is established, and a calibration method without system parameters is derived, which is further extended to a microscopic system. The measuring range of the system is 5mm×4mm×xmm, where x is the movement distance of the displacement stage. In the z-axis direction, the relative error of measurement is about 1% when z is of the millimeter level and less than 7% when z is of the micron level, and the spatial resolution is better than 3.8 µm. In the y-axis direction, the relative error of measurement is less than 5%. Finally, three-dimensional scanning of two samples with different surfaces is carried out to verify the feasibility of the system. The experimental results show that our system has the capability of high-resolution topography restoration and can be applied in industrial production scenarios such as automatic measurement and intelligent identification.
Towards Human-Centric Psychomotor Recommender Systems

Recommender Systems have been developed for years to guide the interaction of the users with systems in very diverse domains where information overload exists aimed to help humans in decision making. In order to better support the humans, the more the system knows about the user, the more useful recommendations the user can receive. In this sense, there is a need to explore which are the intrinsic human aspects that should be taken into account in each case when building the user models that provide the personalization. Moreover, there is a need to define and apply methodologies, guidelines and frameworks to develop this kind of systems in order to tackle the challenges of current artificial intelligence applications including issues such as ethics, transparency, explainability and sustainability. For our research, we have chosen the psychomotor domain. To provide some insights into this problem, in this paper we present the research directions we are exploring to apply a human-centric approach when developing the iBAID (intelligent Basket AID) psychomotor system, which aims to recommend the physical activities and movements to perform when training in basketball, either to improve the technique, to recover from an injury or even to keep active when getting older.
The Effect of Similarity Metric and Group Size on Outlier Selection &amp; Satisfaction in Group Recommender Systems

Group recommender systems (GRS) are a specific case of recommender systems (RS), where recommendations are constructed to a group of users rather than an individual. GRS has diverse application areas including trip planning, recommending movies to watch together, or music in shared environments. However, due to the lack of large datasets with group decision-making feedback information, or even the group definitions, GRS approaches are often evaluated offline w.r.t. individual user feedback and artificially generated groups. These synthetic groups are usually constructed w.r.t. pre-defined group size and inter-user similarity metric. While numerous variants of synthetic group generation procedures were utilized so far, its impact on the evaluation results was not sufficiently discussed. In this paper, we address this research gap by investigating the impact of various synthetic group generation procedures, namely the usage of different user similarity metrics and the effect of group sizes. We consider them in the context of "outlier vs. majority" groups, where a group of similar users is extended with one or more diverse ones. Experimental results indicate a strong impact of the selected similarity metric on both the typical characteristics of selected outliers as well as the performance of individual GRS algorithms. Moreover, we show that certain algorithms better adapt to larger groups than others.
Supporting a Group Member to Make a Group Choice

We often make choices that involve a group of people, such as selecting a movie to watch with friends or choosing a travel destination to visit with the family. Sometimes, a single member of the group may be in charge of making the decision for the group, by playing the role of "organizer". Although some tools for supporting Group Decision-Making have been proposed, none of them have considered the case where a single group member is autonomously making such a decision, hence entering the preferences of the group members, interacting with the system, and finally selecting a proper recommendation. In this paper, we introduce MyFoodGRS, a web application for a single user to find a proper restaurant for their group, that supports the previously mentioned tasks. We introduce an interaction design to follow the Attribute and Socially-based group decision patterns, and we report the positive result of the conducted system usability evaluation.
μManycore: A Cloud-Native CPU for Tail at Scale

Microservices are emerging as a popular cloud-computing paradigm. Microservice environments execute typically-short service requests that interact with one another via remote procedure calls (often across machines), and are subject to stringent tail-latency constraints. In contrast, current processors are designed for traditional monolithic applications. They support global hardware cache coherence, provide large caches, incorporate microarchitecture for long-running, predictable applications (such as advanced prefetching), and are optimized to minimize average latency rather than tail latency.
NeuRex: A Case for Neural Rendering Acceleration

This paper presents NeuRex, an accelerator architecture that efficiently performs the modern neural rendering pipeline with an algorithmic enhancement and supporting hardware. NeuRex leverages the insights from an in-depth analysis of the state-of-the-art neural scene representation to make the multi-resolution hash encoding, which is the key operational primitive in modern neural renderings, more hardware-friendly and features a specialized hash encoding engine that enables us to effectively perform the primitive and the overall rendering pipeline. We implement and synthesize NeuRex using a commercial 28nm process technology and evaluate two versions of NeuRex (NeuRex-Edge, NeuRex-Server) on a range of scenes with different image resolutions for mobile and high-end computing platforms. Our evaluation shows that NeuRex achieves up to 9.88× and 3.11× speedups against the mobile and high-end consumer GPUs with a substantially small area overhead and lower energy consumption.
On the Amortized Communication Complexity of Byzantine Broadcast

Designing an efficient solution for Byzantine broadcast is an important problem for many distributed computing and cryptographic tasks. There have been many attempts to achieve sub-quadratic communication complexity in several directions, both in theory and practice, all with pros and cons. This paper initiates the study of another attempt: improving the amortized communication complexity of multi-shot Byzantine broadcast. Namely, we try to improve the average cost when we have sequential multiple broadcast instances. We present a protocol that achieves optimal amortized linear complexity under an honest majority. Our core technique is to efficiently form a network for disseminating the sender's message by keeping track of dishonest behaviors over multiple instances. We also generalize the technique for the dishonest majority to achieve amortized quadratic communication complexity.
A Neural Bag-of-Words Point Process Model for User Return Time Prediction in E-commerce

Monitoring and predicting user engagement is essential to gauge the overall health of an E-commerce platform. A healthy active user base indicates that the platform is able to retain users and is performing well on the user satisfaction metric. To measure long-term user satisfaction, predicting the return rate of a user is essential. The frequent return of the user indicates that they are overall satisfied with the platform. To this end, we consider the problem of predicting users’ return time on the platform given their historical interactions.
Chaos in a two dimensional fractional discrete Hopfield neural network and its control

A novel two-dimensional fractional discrete Hopfield neural network is presented in this study, which is based on discrete fractional calculus. This network incorporates both constant and variable orders, and its behavior is examined using phase plots, time evolution, bifurcation, Lyapunov exponents, and complexity analysis. Compared to integer and constant fractional orders, the numerical simulations demonstrate that the proposed variable-order fractional HNN exhibits more complex characteristics, and by selecting different fractional variable orders, novel attractors with chaotic behavior can be obtained. Additionally, a control scheme is proposed to stabilize the suggested neural network by utilizing the stability theorem for fractional discrete time systems. This control scheme is applied to both states in the study.
MetaNMP: Leveraging Cartesian-Like Product to Accelerate HGNNs with Near-Memory Processing

Heterogeneous graph neural networks (HGNNs) based on metapath exhibit powerful capturing of rich structural and semantic information in the heterogeneous graph. HGNNs are highly memory-bound and thus can be accelerated by near-memory processing. However, they also suffer from significant memory footprint (due to storing metapath instances as intermediate data) and severe redundant computation (when vertex features are aggregated among metapath instances). To address these issues, this paper proposes MetaNMP, the first DIMM-based near-memory processing HGNNs accelerator with reduced memory footprint and high performance. Specifically, we first propose a cartesian-like product paradigm to generate all metapath instances on the fly for heterogeneous graphs. In this way, metapath instances no longer need to be stored as intermediate data, avoiding significant memory consumption. We then design a data flow for aggregating vertex features on metapath instances, which aggregates vertex features along the direction of the metapath instances dispersed from the starting vertex to exploit shareable aggregation computations, eliminating most of the redundant computations. Finally, we integrate specialized hardware units in DIMM to accelerate HGNNs with near-memory processing, and introduce a broadcast mechanism for edge data and vertex features to mitigate the inter-DIMM communication. Our evaluation shows that MetaNMP achieves the memory space reduction of 51.9% on average and the performance improvement by 415.18× compared to NVIDIA Tesla V100 GPU.
Synchronized Multi-list User Interfaces for Fashion Catalogs

Several online catalogs use carousels to present thematic lists of products, based on different optimization criteria. While this makes it possible to search for items according to diverse relevance perspectives, it hardly supports an integrated evaluation, which is key to critical consuming behavior. To address this issue, we propose a synchronized multi-list model that (i) enriches item presentation by visualizing its evaluation and (ii) enables the user to simultaneously center the carousels of the multi-list on the item in her/his focus of attention, showing its ranking in each list. This type of visualization is aimed at enhancing the transparency of results by enabling the user to simultaneously compare products across all the evaluation criteria applied within the multi-list.
A Usability Evaluation of a Software Framework for Designing Persuasive Games.

With the rise of persuasive game design for health, there is a need for easy, quick, and effective ways of developing and testing out persuasive games across multiple domains. This paper presents the design and usability evaluation of P-Gamer – a software framework for developing persuasive games and evaluating the effectiveness of various strategies. In line with the user-centred design approach, we designed a prototype of the system and conducted a usability evaluation with six persuasive system designers to (a) understand how usable the P-Gamer platform is, (b) understand the ease of use for each of the six major features in the platform, and (c) identify and correct the design issues existing in the platform. Our results showed that the overall system was perceived to be useable. The system had an overall system usability score of 87.92, which is within the excellent score range in the SUS scale. Our results also showed that five of the six major sections of the platform were significantly easy to use. We reflect on the results and also discuss the design issues and insights into addressing them.
Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models

The rapid growth of the volume and variety of online media content has made it increasingly challenging for users to discover fresh content that meets their particular needs and tastes. Recommender Systems are digital tools that support users in navigating the plethora of available items. While these systems may offer several benefits, they may also create or reinforce certain undesired effects, including Popularity Bias, i.e., a short list of popular items becoming more popular while a long list of unpopular ones becoming even more unpopular.
Efficient Evaluation of Concrete Fracture Surface Roughness Using Fringe Projection Technology

The evaluation of concrete surface roughness is crucial in the field of civil engineering. The purpose of this study is to propose a no-contact and efficient method for the measurement of the roughness of concrete fracture surfaces based on fringe-projection technology. A simple phase-correction method using one additional strip image is presented for the phase unwrapping to improve the measurement efficiency and accuracy. The experimental results indicate that the measuring error for plane height is less than 0.1mm, and the relative accuracy for measuring a cylindrical object is about 0.1%, meeting the requirements for concrete fracture-surface measurement. On this basis, three-dimensional reconstructions were carried out on various concrete fracture surfaces to evaluate the roughness. The results reveal that the surface roughness (R) and fractal dimension (D) decrease as the concrete strength increases or the water-to-cement ratio decreases, consistent with previous studies. In addition, compared with the surface roughness, the fractal dimension is more sensitive to the change in concrete surface shape. The proposed method is effective for detecting concrete fracture-surface features.
Gold metallogeny in Iran; implications for gold exploration and conceptual modelling

Significant progress in the classification, definition and understanding of the main Au deposit types could significantly aid improvements in Au exploration. Because of the wide occurrence of Au in the central part of the Tethyan Eurasian Metallogenic Belt, Iranian structures composed of more than 17 zones (arcs and blocks) are considered as having one of the largest Au reserves in the Middle East. Without attempts at understanding the tectono-magmatic evolution of Iran and the geodynamic settings of Au deposition, the establishment of a reliable predictive exploration model for Au-type deposits in Iran and other parts of the world will be unsuccessful. By considering a total of 33 Au deposits and prospects in Iran, a mineralization sequence is revealed from orogenic and volcanogenic massive sulfide (VMS), orogenic, Carlin-type, epithermal/porphyry Cu–Au/skarn, epithermal/iron oxide–copper–gold (IOCG). The trend of deposition gradually changes in the SW–NE axis to intrusion-related, epithermal and porphyry Cu–Au deposits at the Urumieh–Dokhtar Magmatic Arc (UDMA) and post-arc magmatism. VMS deposits occur adjacent to the NE Zagros Fold and Thrust Belt, at the Sanandaj–Sirjan Zone. The Zagros Orogeny and associated post-collisional magmatism at UDMA host many porphyry, epithermal and intrusion-related Au deposits, with a major magmatism peak in the Miocene. This work reveals that orogenic and Carlin-type Au mineralization are linked genetically. After each associated subduction for Palaeotethys (286–215 Ma) and Neotethys (210–68 Ma) in Iran, VMS and orogenic Au-deposits are formed in the border of the subduction (±obduction) zone. The porphyry, intrusion-related, epithermal and IOCG mineralization are emplaced in appropriate formations and structures during collision and post-collision processes.
Library and information education in the context of legislative and social transformations

The strategic legal documents of the federal level that define the key requirements for the professional activities of library and information specialists are elaborated. The analysis of the external and internal environment of libraries and educational organizations was conducted and the trends affecting their development were formulated: digitalization, the growth of open data, the strengthening of the evaluation and analytical functions of library specialists, the anonymization of users. Directions of the development of higher LIS education, considering current social and professional needs, are proposed: differentiation of specialized competencies, formation of a mixed educational environment, conjugation of curricula, practice needs, forward-looking forms of activity, regulatory requirements and expectations of applicants, conducting fundamental and applied research, ensuring the effectiveness of intra-industry interaction.
Evaluating the Performance of PRISMA Shortwave Infrared Imaging Sensor for Mapping Hydrothermally Altered and Weathered Minerals Using the Machine Learning Paradigm

Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework.
Saturation-Induced Phase Error Compensation Method Using Complementary Phase

Intensity saturation can induce phase error and, thus, measurement error in fringe projection profilometry. To reduce saturation-induced phase errors, a compensation method is developed. The mathematical model of saturation-induced phase errors is analyzed for N-step phase-shifting profilometry, and the phase error is approximately N-folder of the frequency of the projected fringe. Additional N-step phase-shifting fringe patterns with initial phase-shift π/N are projected for generating a complementary phase map. The final phase map is obtained by averaging the original phase map extracted from the original fringe patterns and the complementary phase map, and then the phase error can be canceled out. Both simulations and experiments demonstrated that the proposed method can substantially reduce the saturation-induced phase error and realize accurate measurements for a highly dynamic range of scenarios.