Reviewerly/exHarmony huggingface dataset:
Application of Dynamic Deformable Attention in Bird’s-Eye-View Detection

Recently, the performance improvement of BEV visual detection task has benefited from the extensive use of deformable attention. Deformable attention can easily transfer the features of the image space to the BEV space through the cross-attention mechanism. Compared with the global attention mechanism, when the feature resolution of the graph is larger, the computational consumption of deformable attention will be much smaller, so it can support larger Bird’s-Eye-View (BEV) feature resolution. However, there are also shortcomings such as a small receptive field and insufficient information exchange. We propose a deformable attention mechanism for dynamic reference points. This module is to accumulate the reference points of each cross-attention layer on the basis of the previous layer, thereby effectively expanding the perceptual field of BEV features for querying in the image space. Extensive experiments on the nuScenes benchmark demonstrate the effectiveness of our method.
Sliding Window Challenge Process for Congestion Detection

Many prominent smart contract applications such as payment channels, auctions, and voting systems often involve a mechanism in which some party must respond to a challenge or appeal some action within a fixed time limit. This pattern of challenge-response mechanisms poses great risks if, during periods of high transaction volume, the network becomes congested. In this case, fee market competition can prevent the inclusion of the response in blocks, causing great harm. As a result, responders are allowed long periods to submit their response and overpay in fees. To overcome these problems and improve challenge-response protocols, we suggest a secure mechanism that detects congestion in blocks and adjusts the deadline of the response accordingly. The responder is thus guaranteed a deadline extension should congestion arise. We lay theoretical foundations for congestion signals in blockchains and then proceed to analyze and discuss possible attacks on the mechanism and evaluate its robustness. Our results show that in Ethereum, using short response deadlines as low as 3 h, the protocol has >99% defense rate from attacks even by miners with up to $$33\%$$ of the computational power. Using shorter deadlines such as one hour is also possible with a similar defense rate for attackers with up to $$27\%$$ of the power.
Similarity Search with the Distance Density Model

The metric space model of similarity has become a standard formal paradigm of generic similarity search engine implementations. However, the constraints of identity and symmetry prevent from expressing the subjectivity and dependence on the context perceived by humans. In this paper, we study the suitability of the Distance density model of similarity for searching. First, we use the Local Outlier Factor (LOF) to estimate a data density in search collections and evaluate plenty of queries using the standard geometric model and its extension respecting the densities. We let 200 people assess the search effectiveness of the two alternatives using the web interface. Encouraged by the positive effects of the Distance density model, we propose an alternative way to estimate the data densities to avoid the quadratic LOF computation complexity with respect to the dataset size. The sketches with unbalanced bits are clarified to be in correlation with LOFs, which opens a possibility for an efficient implementation of large-scale similarity search systems based on the Distance density model.
ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation

Open compound domain adaptation (OCDA) considers the target domain as the compound of multiple unknown homogeneous subdomains. The goal of OCDA is to minimize the domain gap between the labeled source domain and the unlabeled compound target domain, which benefits the model generalization to the unseen domains. Current OCDA for semantic segmentation methods adopt manual domain separation and employ a single model to simultaneously adapt to all the target subdomains. However, adapting to a target subdomain might hinder the model from adapting to other dissimilar target subdomains, which leads to limited performance. In this work, we introduce a multi-teacher framework with bidirectional photometric mixing to separately adapt to every target subdomain. First, we present an automatic domain separation to find the optimal number of subdomains. On this basis, we propose a multi-teacher framework in which each teacher model uses bidirectional photometric mixing to adapt to one target subdomain. Furthermore, we conduct an adaptive distillation to learn a student model and apply consistency regularization to improve the student generalization. Experimental results on benchmark datasets show the efficacy of the proposed approach for both the compound domain and the open domains against existing state-of-the-art approaches.
Keeping Humans in the Loop: Teaching via Feedback in Continuous Action Space Environments

Interactive Reinforcement Learning (IntRL) allows human teachers to accelerate the learning process of Reinforcement Learning (RL) robots. However, IntRL has largely been limited to tasks with discrete-action spaces in which actions are relatively slow. This limits IntRL's application to more complicated and challenging robotic tasks, the very tasks that modern RL is particularly well-suited for. We seek to bridge this gap by presenting Continuous Action-space Interactive Reinforcement learning (CAIR): the first continuous action-space IntRL algorithm that is capable of using teacher feedback to out-perform state-of-the-art RL algorithms in those tasks. CAIR combines policies learned from the environment and the teacher into a single policy that proportionally weights the two policies based on their agreement. This allows a CAIR agent to learn a relatively stable policy despite potentially noisy or coarse teacher feedback. We validate our approach in two simulated robotics tasks with easy-to-design and - understand heuristic oracle teachers. Furthermore, we validate our approach in a human subjects study through Amazon Mechanical Turk and show CAIR out-performs the prior state-of-the-art in Interactive RL.
About cognitiveness of information retrieval

A brief specification of concept of “cognitive information search” is provided, which is considered as the construction of a “path of cognition” – an interactive iterative and significantly dependent on the previous result formation of a target fact on a chaotic set of found facts. The result of such a search will be (1) the selection of fragments of documents that meet the real information need (and not copies of documents that meet the expressed need, as in traditional documentary retrieval system), and (2) an interactively generated semantic graph - a conceptual image of solving the user's problem. The assessment of the “cognitiveness” of interactive semantic information search based on an ontographic representation is carried out by comparing the “information” properties of objects and processes characteristic of information search and cognition. The model of the cognition process is presented in the form of interacting functional blocks of processing and storage of information structures.
Verifiable DOPE from Somewhat Homomorphic Encryption, and the Extension to DOT

Distributed oblivious polynomial evaluation (DOPE) is a special case of two-party computation where a sender party holds a polynomial f(x) of degree t and a receiver party has an input $$x_2$$ . They communicate with a set of distributed cloud servers to implement a secure computation such that the receiver party obtains $$f(x_2)$$ , while the privacy of their inputs is preserved. We present a verifiable and private DOPE protocol using additive homomorphic encryption in the presence of k distributed servers where k does not depend on the degree t. The sender is involved in the offline phase which can be implemented at any time well in advance of the actual online computation phase. Our protocol holds the unconditional security against a malicious sender in the offline phase and a static active adversary corrupting a coalition of at most $$k-1$$ dishonest servers in the online computation phase with negligible probability of error. In addition, it preserves strong privacy conditions for a DOPE system. The communication complexity is determined by the term kt which improves the DOPE approaches of [18] and [5]. Also, the proposed protocol can be extended to a protocol of secure $$\left( {\begin{array}{c}1\\ 2\end{array}}\right) $$ distributed oblivious transfer with the linear communication complexity O(k) where the same setting of security is achieved.
Generated Tests in the Context of Maintenance Tasks: A Series of Empirical Studies

Maintenance tasks are often time-consuming and hard to manage. To cope with that, developers often use failing test cases to guide their maintenance efforts. Therefore, working with good test cases is essential to the success of maintenance. Automatically generated tests can save time and lead to higher code coverage. However, they often include test smells and are hard to read. Moreover, it is not clear whether generated tests can be effective when guiding maintenance tasks, nor if developers fully accept them. This paper presents a series of empirical studies that evaluates whether automatically generated tests can support developers when maintaining code. First, we ran an empirical study with 20 real developers to compare how they perform maintenance tasks with automatically generated (Evosuite or Randoop) and manually-written tests. Then, we applied a survey with 82 developers that assessed developers’ perceptions of using refactored Randoop tests. Finally, a third empirical study with 24 developers focused on evaluating the impact of refactored Randoop tests on the performance of maintenance. Our results and statistical analyses show that automatically generated tests can be a great help for identifying faults during maintenance. Developers were more accurate at identifying maintenance tasks when using Evosuite tests and equally effective to create bug fixes (manually-written, Evosuite, and Randoop). Developers preferred refactored Randoop tests to the original ones. However, the refactorings applied did not improve their performance on fault detection. On the other hand, developers were more effective to fix the faults using refactored Randoop tests.
Book Recommender System Using Singular Value Decomposition Combined with Slope One Algorithm

As information spreads on the internet, users need more personalized information. A recommender system can be applied to achieve this. Singular Value Decomposition (SVD) is a method that can be applied to a recommender system to find hidden features in the relationship matrix between items and between users. However, the recommender system faces several problems, one of which is data sparsity. The SVD algorithm itself considers an empty rating value as 0. In solving this problem, the SVD algorithm can be combined with rating predictions using the Slope One algorithm. The Slope One algorithm is applied to complete the empty rating data so that the model can be trained with more complete data. So, the Slope-SVD method can improve the accuracy of the recommender system. This study aims to apply the Slope-SVD algorithm to a book recommender system using a rating dataset from the Goodreads web. The performance of the system was tested with the Mean Square Error (MAE). The test results from this study indicate that combining the Slope One algorithm with SVD gives better results than each Slope One or SVD alone.
A Practice-Based Approach to Developing First-Year Higher Education Students' Digital Literacy

This paper contributes to the conceptualisation of digital literacy as a social practice. While previous studies have focused on student digital skills and digital practices in well-resourced environments, there is a research gap concerning digital literacy as a social practice in resource constrained environments, such as South African universities. A qualitative approach is used to explore the acquisition of discipline-specific digital literacy practices by a purposive sample of three first-year students from two extended degree programme courses. The findings reveal that the discipline-specific learning and assessment activities instantiated students' digital literacy practices in the technical, cognitive, and social-emotional dimensions. Furthermore, the participants acquired digital literacies in the technical and cognitive dimension. The research findings provide insight to how other higher education educators in developing contexts could integrate digital literacies into course curricula as a means of building students' capacity on discipline-specific digital literacies.
Dynamic Template Selection Through Change Detection for Adaptive Siamese Tracking

Deep Siamese trackers have recently gained much attention in recent years since they can track visual objects at high speed. Additionally, adaptive tracking methods, where target samples collected by the tracker are employed for online learning, have achieved state-of-the-art accuracy. However, single object tracking (SOT) remains a challenging task in real-world application due to changes and deformations in a target object's appearance. Learning on all the collected samples may lead to catastrophic forgetting, and thereby corrupt the tracking model. In this paper, SOT is formulated as an online incremental learning problem. A new method is proposed for dynamic sample selection and memory replay, preventing template corruption. In particular, we propose a change detection mechanism to detect gradual changes in object appearance, and select the corresponding samples for online adaption. In addition, an entropy-based sample selection strategy is introduced to maintain a diversified auxiliary buffer for memory replay. Our proposed method can be integrated into any object tracking algorithm that leverages online learning for model adaptation. Extensive experiments conducted on the OTB-100, LaSOT, UAV123, and TrackingNet datasets highlight the cost-effectiveness of our method, along with the contribution of its key components. Results indicate that integrating our proposed method into state-of-art adaptive Siamese trackers can increase the potential benefits of a template update strategy, and significantly improve performance. Code: https://github.com/madhukiranets/Adaptive-Siamese-Dimp
English Learning Motivation of College Students Based on probability Distribution

Abstract In order to systematically study the motivation tendency of college students in English learning, researchers in various countries have used a variety of test scales to conduct a questionnaire survey on college students on the basis of understanding the current situation of practical language knowledge learning. The final results show that motivation, as a major factor affecting successful language learning, can not only arouse the interest of college students in learning, but also improve their learning efficiency in practice and development.Therefore, after understanding the bucket sorting algorithm of probability distribution, this paper, according to the types and research status of English learning motivation, uses a questionnaire to select a college student to conduct a survey on English learning motivation, so as to provide effective basis for practical education management.
Understanding Spatio-Temporal Relations in Human-Object Interaction using Pyramid Graph Convolutional Network

Human activities recognition is an important task for an intelligent robot, especially in the field of human-robot collaboration, it requires not only the label of sub-activities but also the temporal structure of the activity. In order to automatically recognize both the label and the temporal structure in sequence of human-object interaction, we propose a novel Pyramid Graph Convolutional Network (PGCN), which employs a pyramidal encoder-decoder architecture consisting of an attention based graph convolution network and a temporal pyramid pooling module for downsampling and upsampling interaction sequence on the temporal axis, respectively. The system represents the 2D or 3D spatial relation of human and objects from the detection results in video data as a graph. To learn the human-object relations, a new attention graph convolutional network is trained to extract condensed information from the graph representation. To segment action into sub-actions, a novel temporal pyramid pooling module is proposed, which upsamples compressed features back to the original time scale and classifies actions per frame. We explore various attention layers, namely spatial attention, temporal attention and channel attention, and combine different upsampling decoders to test the performance on action recognition and segmentation. We evaluate our model on two challenging datasets in the field of human-object interaction recognition, i.e. Bimanual Actions and IKEA Assembly datasets. We demonstrate that our classifier significantly improves both framewise action recognition and segmentation, e.g., F1 micro and F1@50 scores on Bimanual Actions dataset are improved by 4.3% and 8.5% respectively.
Image Registration of Very-High-Resolution Satellite Images Using Deep Learning Model for Outlier Elimination

Very-high-resolution (VHR) satellite image contains reliable various information over large areas, so that, it has been used as key data in the field of remote sensing. Image registration must be conducted to effectively use the multitemporal VHR satellite images. Conjugate points (CPs) extracted from the same region between images are required to perform image registration. However, outliers included in the CPs cause distortion when they were used for the image registration. Here we propose a deep learning-based technique to effectively remove the outliers. A Siamese network was built as a purpose of an outlier removal, and the network was trained using data based on the patch pirs centered on each CP. Experimental results demonstrate that the proposed method can remove outliers more effectively than a random sample consensus (RANSAC) technique thus and achieves improved registration accuracy.
Digital Signature Scheme to Match Generalized Reed-Solomon Code over GF(q)

Code-based public key cryptography is one of the most widely studied cryptographic algorithms against quantum computing attacks. The main issue today is determining how to choose parameters that strike a balance between security and efficiency. The key reason is that public key size is far too large to be practical. This paper is to investigate and select generalized Reed-Solomon (GRS) codes over the q-ary Galois Field (GF(q)), and attempts to build a code-based classic public key cryptographic algorithm (CFS) signature scheme and investigates its feasibility and related performance optimization, providing a full security proof and analysis. Constructing a cryptographic algorithm based on GF(q) coding can effectively reduce the size of the public key size while maintaining security. While the GRS code is preferred over GF (q), it allows for more parameter selection flexibility. It has higher security and a smaller public key size than other code-based digital signature schemes. In the case of slightly improved security, the public key size is only 4.1% of the original CFS scheme.
Teacher Guidance in the Digitalization Era: Efforts to Improve Student Achievement by Strengthening Online Sourced Learning Materials

The article aimed to analyze the teacher guidance in the digitalization era: an effort to improve student achievement by strengthening online-sourced learning materials at Madrasah Aliyah Negeri (MAN) Insan Cendikia Pelaihari Banjarmasin. Through semi-structured interviews with several teacher councils, the researchers have received many vital inputs regarding the dedication of teachers in guiding students to learn applications in the digital era in the distance learning situation. The researchers used a phenomenological approach to understand how teachers monitor online learning activities in the current era of technology. The result showed that the teachers was able to get the students care and using learning resources in various forms of application. The dedication of the teachers and the students in Madrasah Aliyah teachers from Pelaihari Banjarmasin has become strengthening in online sourced learning materials. The stduents has changed to get learning achievement to be better result. Hopefully, these findings help developing educational studies in the current era of technology. Keywords: Online Learning Materials, Improve Students Achievement, Online Learning
Combining Internal and External Constraints for Unrolling Shutter in Videos

Videos obtained by rolling-shutter (RS) cameras result in spatially-distorted frames. These distortions become significant under fast camera/scene motions. Undoing effects of RS is sometimes addressed as a spatial problem, where objects need to be rectified/displaced in order to generate their correct global shutter (GS) frame. However, the cause of the RS effect is inherently temporal, not spatial. In this paper we propose a space-time solution to the RS problem. We observe that despite the severe differences between their xy frames, a RS video and its corresponding GS video tend to share the exact same xt slices – up to a known sub-frame temporal shift. Moreover, they share the same distribution of small 2D xt-patches, despite the strong temporal aliasing within each video. This allows to constrain the GS output video using video-specific constraints imposed by the RS input video. Our algorithm is composed of 3 main components: (i) Dense temporal upsampling between consecutive RS frames using an off-the-shelf method, (which was trained on regular video sequences), from which we extract GS "proposals". (ii) Learning to correctly merge an ensemble of such GS "proposals" using a dedicated MergeNet. (iii) A video-specific zero-shot optimization which imposes the similarity of xt-patches between the GS output video and the RS input video. Our method obtains state-of-the-art results on benchmark datasets, both numerically and visually, despite being trained on a small synthetic RS/GS dataset. Moreover, it generalizes well to new complex RS videos with motion types outside the distribution of the training set (e.g., complex non-rigid motions) – videos which competing methods trained on much more data cannot handle well. We attribute these generalization capabilities to the combination of external and internal constraints.
Differential Cryptanalysis in the Fixed-Key Model

A systematic approach to the fixed-key analysis of differential probabilities is proposed. It is based on the propagation of 'quasidifferential trails', which keep track of probabilistic linear relations on the values satisfying a differential characteristic in a theoretically sound way. It is shown that the fixed-key probability of a differential can be expressed as the sum of the correlations of its quasidifferential trails. The theoretical foundations of the method are based on an extension of the difference-distribution table, which we call the quasidifferential transition matrix. The role of these matrices is analogous to that of correlation matrices in linear cryptanalysis. This puts the theory of differential and linear cryptanalysis on an equal footing. The practical applicability of the proposed methodology is demonstrated by analyzing several differentials for RECTANGLE, KNOT, Speck and Simon. The analysis is automated and applicable to other SPN and ARX designs. Several attacks are shown to be invalid, most others turn out to work only for some keys but can be improved for weak-keys.