Reviewerly/exHarmony huggingface dataset:
CounTr: An End-to-End Transformer Approach for Crowd Counting and Density Estimation

Modeling context information is critical for crowd counting and desntiy estimation. Current prevailing fully-convolutional network (FCN) based crowd counting methods cannot effectively capture long-range dependencies with limited receptive fields. Although recent efforts on inserting dilated convolutions and attention modules have been taken to enlarge the receptive fields, the FCN architecture remains unchanged and retains the fundamental limitation on learning long-range relationships. To tackle the problem, we introduce CounTr, a novel end-to-end transformer approach for crowd counting and density estimation, which enables capture global context in every layer of the Transformer. To be specific, CounTr is composed of a powerful transformer-based hierarchical encoder-decoder architecture. The transformer-based encoder is directly applied to sequences of image patches and outputs multi-scale features. The proposed hierarchical self-attention decoder fuses the features from different layers and aggregates both local and global context features representations. Experimental results show that CounTr achieves state-of-the-art performance on both person and vehicle crowd counting datasets. Particularly, we achieve the first position (159.8 MAE) in the highly crowded UCF_CC_50 benchmark and achieve new SOTA performance (2.0 MAE) in the super large and diverse FDST open dataset. This demonstrates CounTr’s promising performance and practicality for real applications.
Enhancing Multisensory Experience and Brand Value: Key Determinants for Extended, Augmented, and Virtual Reality Marketing Applications

The rapid growth of extended reality (XR) technologies, such as augmented reality (AR), virtual reality (VR), and mixed reality (MR), provides avenues to turn market place into a highly innovative way of commerce. However, understanding customer’s demands, combined with scarce examples on successful marketing in virtual reality, opens a knowledge gap. To close this gap, the current chapter enrols auditing of literature from the perspective of consumer needs and market demands. Combining theoretical insights with different AR/VR technologies and their real market applications, we suggest a framework on how to augment experience, encompassing processing from attention to action. The conceptual framework could be implemented in development of appropriate high-tech AR/VR environments, providing appealing multisensory experience, and thus, enriching the brand portfolio with innovation going beyond traditional marketing practices.
Prospects for the Synthesis of the Language of Art, Science and Technology in Russian Science Art

The paper discusses the problems of integration of science and high-tech art in the interdisciplinary field of science art. It focuses on the changing position of this area of contemporary art in Russia. The authors analyze the innovative pathos of science art and the role of scientific and technical tools used in science art. The idea of science art is based on the development trends in science and art at the turn of the 19th and 20th centuries. Science art is considered to be a consequence rather than a harbinger of the synthesis of the language of art, science and technology, which became apparent in the last century. The growing interest in the phenomenon of science art is mainly due to the specifics of the art market and the demands of the state funds, scientific and educational organizations, as well as the administrative sector that manages them. The crisis of confidence in the new areas of contemporary art, science art especially, activates the search for support in science as one of the most authoritative and influential institutions of modern society. The authors come to the conclusion that science art can be considered to be one of the terms that is used in an effort to combine a number of already existing areas of contemporary art under the same scientific concept.
On the Measurement and Simulation of the BKZ Behavior for q-ary Lattices

The BKZ algorithm has been one of the most important tools for analyzing and assessing lattice-based cryptosystems. The second order statistical behavior of BKZ algorithm on random lattices has been well measured in the work of Yu and Ducas. Several simulators have been proposed to efficiently and accurately predict the profile shape for random lattices. But for the case q-ary lattices, the situation is much less understood. Recently, Albrecht and Li proposed a simulator that predicts the Z-shape of q-ary lattices a good accuracy. However, we find that this simulator still has its limitations when Z-shape disappears. Our experiments reveal more features for the shape profile of q-ary lattices. Based on some results on the distribution of the length of short vectors and reasonable heuristics, we propose a new simulator for random q-ary lattices with some extensions. Furthermore, the second order statistical behavior of BKZ algorithm on q-ary lattices is characterized, their similarities and differences compared to that for random lattices have been briefly analyzed.
Semi-automatic Pipeline for Large-Scale Dataset Annotation Task: A DMD Application

This paper concerns a methodology of a semi-automatic annotation strategy for the gaze estimation material of the Driver Monitoring Dataset (DMD). It consists of a pipeline of semi-automatic annotation that uses ideas from Active Learning to annotate data with an accuracy as high as possible using less human intervention. A dummy model (the initial model) that is improved by iterative training and other state-of-the-art (SoA) models are the actors of an automatic label assessment strategy that will annotate new material. The newly annotated data will be used as an iterative process to train the dummy model and repeat the loop. The results show a reduction of annotation work for the human by 60%, where the automatically annotated images have a reliability of 99%.
Cryptanalysis of Ciminion

Ciminion is a symmetric cryptographic algorithm proposed by Dobraunig et al. in EUROCRYPT 2021, which is based on Toffoli-Gates over $$\mathbb {F}_{2^n}$$ or $$\mathbb {F}_p$$ . This cipher is a multiparty computation (MPC), fully-homomorphic encryption (FHE) and zero-knowledge (ZK) friendly symmetric-key primitive due to its low multiplicative complexity. There is currently no published third-party cryptanalysis of this algorithm. In this paper, we give the first analysis on Ciminion based on higher order differential cryptanalysis and integral cryptanalysis. We consider the three sets of instances, i.e., “standard” set, “conservative” set and the instances used in MPC application, and construct the corresponding reduced-round distinguishers over $$\mathbb {F}_{2^n}$$ and $$\mathbb {F}_p$$ , respectively. On the other hand, we observe a linear relation between the input and output of the round function and conclude a new set of weak random numbers based on this observation. For an aggressive evolution of Ciminion called Aiminion, we recover the subkeys under these weak random numbers. Although we cannot recover the master key, the information disclosure of the subkeys also poses certain potential threats to the cryptographic algorithm. Our results can provide guidance for designers to choose round random numbers.
Clustering Effect of Iterative Differential and Linear Trails

Differential and linear cryptanalysis are two of the most important kinds of cryptanalysis for symmetric-key primitives. In this paper, we propose a graph-based method of evaluating the clustering effect of iterative differential and linear trails. We also exploit the iterative trails to find exploitable difference and linear propagations. We apply our method to four lightweight SPN primitives including PRESENT, GIFT-64, RECTANGLE and KNOT-256. For KNOT-256, we improve the best difference and linear propagations by 5 and 9 rounds respectively. For RECTANGLE, we improve the best 14-round linear propagation. Our other results are consistent with the best known results. We illustrate the dominance of iterative trails by showing the proportion of trails that are incorporated in our method in a difference or linear propagation. Additionally, for the primary version of KNOT, we find difference and linear propagations leading to different differential and linear attacks. We stress here that our results do not threaten the security of KNOT.
Practical Multi-party Private Set Intersection Cardinality and Intersection-Sum Under Arbitrary Collusion

Private set intersection cardinality (PSI-CA) and private intersection-sum with cardinality (PSI-CA-sum) are two primitives that enable data owners to learn the intersection cardinality of their data set, with the difference that PSI-CA-sum additionally outputs the sum of the associated integer values of all the data that belongs to the intersection (i.e., intersection-sum). In this paper, we investigate the practical constructions of these two primitives, focusing on the multi-party setting. To our knowledge, all existing multi-party PSI-CA (MPSI-CA) protocols are either impractical or vulnerable to arbitrary collusion (i.e., the adversary can corrupt any proper subset of all parties), and as for multi-party PSI-CA-sum (MPSI-CA-sum), there is even no formalization for this notion at present, not to mention secure constructions for it. So in this paper, we first propose the first MPSI-CA protocol that achieves simultaneous practicality and security against arbitrary collusion (in the semi-honest adversary model). We also conduct implementation to verify its practicality (while the previous results under arbitrary collusion only present theoretical analysis of performance, lacking real implementation). Numeric results show that it only takes 12.805 s to finish the online computation by shifting expensive operations to an offline phase, even in the dishonest majority setting with 15 parties each holding $$2^{16}$$ data. Among all parties, the cost of clients is especially lower compared to that of the known results, which is only 0.3 s in finishing their tasks. Second, we formalize the notion of MPSI-CA-sum and give the first realization which admits simultaneous practicality and security against arbitrary collusion as well. The computational complexity of it is roughly double that of our MPSI-CA protocol. Besides the main results, we introduce the notions and provide efficient constructions of two new building blocks: multi-party secret-shared shuffle and oblivious zero-sum check, which may be of independent interest.
Rank Metric Codes and Their Galois Duality

In this paper, we describe the Galois dual of rank metric codes in the ambient space FQn×m and FQmn, where Q=qe. We obtain connections between the duality of rank metric codes with respect to distinct Galois inner products. Furthermore, for 0 ≤ s < e, we introduce the concept of qsm-dual bases of FQm over FQ and obtain some conditions about the existence of qsm-self-dual basis.
Tweakable $$\mathcal {S}_{\text{ leeve }}$$: A Novel $$\mathcal {S}_{\text{ leeve }}$$ Construction Based on Tweakable Hash Functions

Recently, Chaum et al. (ACNS’21) introduced $$\mathcal {S}_{\text{ leeve }}$$ , which describes an extra security layer for signature schemes, i.e., ECDSA. This distinctive feature is a new key generation mechanism, allowing users to generate a “back up key” securely nested inside the secret key of a signature scheme. Using this novel construction, the “back up key”, which is secret, can be used to generate a “proof of ownership”, i.e., only the rightful owner of this secret key can generate such a proof. This design offers a quantum secure fallback, i.e., a brand new quantum resistant signature, ready to be used, nested in the ECDSA secret key. In this work, we rely on the original $$\mathcal {S}_{\text{ leeve }}$$ definition to generalize the construction to a modular design based on Tweakable Hash Functions, thus yielding a cleaner design of the primitive. Furthermore, we provide a thorough security analysis taking into account the security of the ECDSA signature scheme, which is lacking in the original work. Finally, we provide an analysis based on formal methods using Verifpal assuring the security guarantees our construction provides.
Market Equilibria and Risk Diversification in Blockchain Mining Economies

The success of blockchain-based applications, most notably cryptocurrencies, has brought the allocation of mining resources at the epicenter of academic and entrepreneurial attention. Critical for the stability of these markets is the question of how miners should adjust their allocations over time in response to changes in their environment and in other miners’ strategies. In this paper, we present a proportional response (PR) protocol that makes these adjustments for any risk profile of a miner. The protocol has low informational requirements and is particularly suitable for such distributed settings. When the environment is static, we formally show that the PR protocol attains stability by converging to the market equilibrium. For dynamic environments, we carry out an empirical study with actual data from four popular cryptocurrencies. We find that running the PR protocol with higher risk diversification is beneficial both to the market by curbing volatile re-allocations (and, thus, increasing market stability), and to individual miners by improving their profits after accounting for factor mobility (switching) costs.
Knowledge Representation for Hierarchical and Interconnected Business Contexts

Although business context has been introduced as an important concept for message-standards usage and maintenance, its usability depends on the technique used to represent contextual knowledge. This paper investigates a logic-based business-context-modeling technique, which is an alternative technique that can overcome some of the issues identified and discussed in this paper. For other issues, we propose future research directions.
Introduction to Everyday Virtual and Augmented Reality

Due to emerging consumer hardware solutions, virtual and augmented reality technologies are gaining increasing relevance in everyday contexts, such as living rooms or office spaces. This raises various challenges such as getting immersed in small and cluttered spaces, integrating immersive tools into existing processes and workflows, as well as the involvement of highly heterogeneous user groups in VR and AR applications. The current chapter aims to introduce and characterise this emerging research field by identifying various challenges in terms of the development and investigation of everyday VR and AR systems. Therefore, we give an overview of everyday VR and AR, discuss challenges for the field that we deem central to the continued adoption and integration of VR and AR into the wider public, as well as provide an overview of current everyday VR and AR in various application contexts and discuss some things from a users’ perspective. We then review works from previous WEVR workshops, which were established as a platform for the exchange of everyday VR and AR research, to face the main challenges and provide possible solutions. Finally, we discuss the WEVR impact and point out future research avenues.
PRIORITY: An Intelligent Problem Indicator Repository

We report the design, development and deployment of PRIORITY, an intelligent portal aimed at reducing the workload of instructors, tutors and teaching assistants in large programming courses of creating lab, assignment and exam problems every week. PRIORITY offers a scalable, user friendly and indexed repository of problems that can be queried to retrieve problems related to a particular programming concept, say for loops. PRIORITY accomplishes this by casting problem retrieval as a multi-label learning problem and using solving it using novel feature selection and AI-techniques. We also report the results of an A/B test and user survey, both conducted while PRIORITY was being used to offer a CS1 course taught at IIT Kanpur with over 500 students. PRIORITY has been in deployment at IIT Kanpur for almost 2 years now and our experience thus far suggests that it not only presents a valuable tool for course administrators, but also opens up several intriguing problems at the intersection of programming instruction, pedagogy, machine learning, semi-supervised learning and information retrieval. Code for PRIORITY is available at https://github.com/purushottamkar/priority/
Computational Analysis of Quantitative Characteristics of Some Residual Properties of Solvable Baumslag–Solitar Groups

Let Gk be defined as Gk = $$\left\langle {a,b;{{a}^{{ - 1}}}ba = {{b}^{k}}} \right\rangle $$ , where k ≠ 0. It is known that if p is a prime number then Gk is residually a finite p-group iff p|k – 1. It is also known that if p and q are primes not dividing k – 1, p < q and π = {p, q} then Gk is residually a finite π-group iff (k, q) = 1, p|q – 1 and the order of k in the multiplicative group of the field ℤq is a p-number. This paper examines the question of the number of two-element sets of prime numbers that satisfy the conditions of the last criterion. More precisely, let fk(x) be the number of sets {p, q} such that p < q, p $$\nmid $$ k – 1, q $$\nmid $$ k – 1, (k, q) = 1, p|q – 1, the order of k modulo q is a p-number, and p and q are chosen among the first x primes. We state that, if 2 ≤ |k| ≤ 10 000 and 1 ≤ x ≤ 50 000, then for almost all considered k the function fk(x) can be approximated quite accurately by the function αkx0.85, where the coefficient αk is different for each k and {αk|2 ≤ |k| ≤ 10 000} ⊆ (0.28; 0.31]. In addition, the dependence of the value fk(50 000) on k is investigated and an effective algorithm for checking a two-element set of prime numbers for compliance with the conditions of the last criterion is proposed. The results may have applications in the theory of computational complexity and algebraic cryptography.
Augmenting Feature Representation with Gradient Penalty for Robust Text Categorization

The capabilities of deep models are constantly mined for extraction and representation of features among text classification tasks. However, these models are sensitive to changes in input data, resulting in poor robustness. Meanwhile, the model lacks information interaction and weak representation ability. In this work, for feature extraction, a joint model that consists of a convolutional neural network, a bidirectional gated recurrent unit, and an attention mechanism is proposed. This new model can improve versatility and fully discover category information in text. For feature representation, a projector under the supervised contrastive learning method is introduced. The method can improve the representation of an encoder and realize aggregation of the same category. Considering the robustness of the PCRA, the gradient penalty is added to a contrastive loss function. Experiments are performed on four datasets to assess the proposed model (PCRA and PCRA-GP) using an accuracy metric. The experimental results show that our model is suitable for variable-length and bilingual texts. Compared with the baseline model, it remains competitive, and it reaches SOTA on the 20 Newsgroups dataset. Moreover, the performance of the model is evaluated under different hyperparameters to clarify its working mechanism.
Feature Point Detection and Description Networks Based on Asymmetric Convolution and the Cross-ResolutionImage-Matching Method

Image matching can be transformed into the problem of feature point detection and matching of images. The current neural network methods have a weak detection effect on feature points and cannot extract enough sparse and uniform feature points. In order to improve the detection and description ability of feature points, this paper proposes a self-supervised feature point detection and description network based on asymmetric convolution: ACPoint. Specifically, first, feature point pseudolabels are learned from an unlabeled dataset, and pseudolabels are used for supervised learning; then, the learned model is used to update pseudolabels. Through multiple iterations of model training and label updating, high-quality labels and high-accuracy models are obtained adaptively. The asymmetric convolution feature point (ACPoint) network adopts an asymmetric convolution module to simultaneously train three convolution branches to learn more feature information, which uses two one-dimensional convolutions to enhance the backbone of square convolution from both horizontal and vertical directions and improve the representation of local features during inference. Based on the ACPoint network, a cross-resolution image-matching method is proposed. Experiments show that our proposed network model has higher localization accuracy and homography estimation ability on the HPatches dataset.
A Multimodal Network Security Framework for Healthcare Based on Deep Learning

As the network is closely related to people’s daily life, network security has become an important factor affecting the physical and mental health of human beings. Network flow classification is the foundation of network security. It is the basis for providing various network services such as network security maintenance, network monitoring, and network quality of service (QoS). Therefore, this field has always been a hot spot of academic and industrial research. Existing studies have shown that through appropriate data preprocessing techniques, machine learning methods can be used to classify network flows, most of which, however, are based on manually and expert-originated feature sets; it is a time-consuming and laborious work. Moreover, only features extracted by a single model can be used in classification tasks, which can easily make the model inefficient and prone to overfitting. In order to solve the abovementioned problems, this study proposes a multimodal automatic analysis framework based on spatial and sequential features. The framework is completely based on the deep learning method and realizes automatic extraction of two types of features, which is very suitable for processing large-flow information; this improves the efficiency of network flow classification. There are two types of frameworks based on pretraining and joint-training, respectively, with analyzing the advantages and disadvantages of them in practice. In terms of evaluation, compared with the previous methods, the experimental results show that the framework has good performance in both accuracy and stability.
Using GQIM Model and Gompertz Model to Measure, Analyze, and Optimize the Whole Process of Software Project

This paper proposes a novel concept of using defect management technology to measure, predict, and optimize the management of the entire software project cycle. It first utilizes the software defect measurement GQIM model and prediction Gompertz model to track and quantify the defects of the entire project cycle. Then, the specific optimization strategy is proposed to manage the core processes of the project management, including project scope management, project time management, project quality management, project human resource management, project communication management, and project risk management. This study solves the quantification problems that are common in the current software project management process, such as complex testing procedures, high defect repair costs, and high communication costs. Furthermore, it contributes to promote the overall level of project management in the software industry. Taking the SDLM’s electronic banking software project management as an example, the various management quality indicators before and after the optimization of the project management process strongly indicate that using software defect management technology to optimize the software project management process is feasible and effective.
Interview with Spain's National Cryptologic Centre (CCN)

The interview with Spain's National Cryptologic Centre was conducted by Rubn Arcos through email on 24 October 2022