Reviewerly/exHarmony huggingface dataset:
The Distribution Properties of Consecutive Quadratic Residue Sequences

We consider any prime number <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> </math> . Let <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> <mo>,</mo> <mi>s</mi> </math> be two positive integers. We are interested in the arithmetic progressions (sequences) with the common difference <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>s</mi> </math> and length <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>k</mi> </math> , where the sequence entries are from the set of quadratic residue modulo <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>p</mi> </math> or the set of quadratic nonresidue modulo <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>p</mi> </math> . The numbers of such sequences are denoted as <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <mi>k</mi> <mo>,</mo> <mi>s</mi> </mrow> </mfenced> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <msubsup> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> </mrow> <mrow> <mo>′</mo> </mrow> </msubsup> <mfenced open="(" close=")" separators="|"> <mrow> <mi>k</mi> <mo>,</mo> <mi>s</mi> </mrow> </mfenced> </math> , respectively. In this paper, we apply analytic number theory methods, in particular, properties of Legendre’s symbol modulo <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mi>p</mi> </math> and character sums, to study the numbers <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> <mfenced open="(" close=")" separators="|"> <mrow> <mi>k</mi> <mo>,</mo> <mi>s</mi> </mrow> </mfenced> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M11"> <msubsup> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> </mrow> <mrow> <mo>′</mo> </mrow> </msubsup> <mfenced open="(" close=")" separators="|"> <mrow> <mi>k</mi> <mo>,</mo> <mi>s</mi> </mrow> </mfenced> </math> . Exact formulas are given for certain values of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M12"> <mi>k</mi> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M13"> <mi>s</mi> </math> under some restrictions. In addition, estimation formulas in other cases are given.
FineIBT: Fine-grain Control-flow Enforcement with Indirect Branch Tracking

We present the design, implementation, and evaluation of FineIBT: a CFI enforcement mechanism that improves the precision of hardware-assisted CFI solutions, like Intel IBT, by instrumenting program code to reduce the valid/allowed targets of indirect forward-edge transfers. We study the design of FineIBT on the x86-64 architecture, and implement and evaluate it on Linux and the LLVM toolchain. We designed FineIBT’s instrumentation to be compact, incurring low runtime and memory overheads, and generic, so as to support different CFI policies. Our prototype implementation incurs negligible runtime slowdowns (≈ 0%–1.94% in SPEC CPU2017 and ≈ 0%–1.92% in real-world applications) outperforming Clang-CFI. Lastly, we investigate the effectiveness/security and compatibility of FineIBT using the ConFIRM CFI benchmarking suite, demonstrating that our instrumentation provides complete coverage in the presence of modern software features, while supporting a wide range of CFI policies with the same, predictable performance.
MIFP: Selective Fat-Pointer Bounds Compression for Accurate Bounds Checking

Bounds compression for fat pointers can reduce the memory and performance overhead of maintaining pointer bounds and is necessary for efficient hardware implementation. However, compression can introduce inaccuracy to the bounds, making certain out-of-bounds accesses undetectable. Although the security threat can be mitigated by padding the objects, no known mitigations can detect these out-of-bounds accesses deterministically.
Raft: Hardware-assisted Dynamic Information Flow Tracking for Runtime Protection on RISC-V

Dynamic Information Flow Tracking (DIFT) is a fundamental computer security technique that tracks the data flow of interest at runtime, overcoming the limitations of discovering data dependencies statically at compilation time. However, software-based DIFT tools often suffer from unbearably high runtime overhead due to dynamic binary instrumentation or virtual machine, limiting the usefulness of DIFT. Even though hardware-assisted DIFT frameworks cut down the performance overhead effectively, it is still unacceptable for applications under rigorous time constraints.
WAVELET ESTIMATION OF A FUNCTION BELONGING TO LIPSCHITZ CLASS BY FIRST KIND CHEBYSHEV WAVELET METHOD

In this paper, the function of Lipschitz class and Chebyshev Wavelet method are studied. Four new wavelet estimations of a function f belonging to Lipschitz class by Chebyshev Wavelet method are estimated. The calculated estimators are best possible in Wavelet Analysis.
Quantum Complementarity Approach to Device-Independent Security

Complementarity is an essential feature of quantum mechanics. The preparation of an eigenstate of one observable implies complete randomness in its complementary observable. In quantum cryptography, complementarity allows us to formulate security analyses in terms of phase-error correction. However, the concept becomes much subtler in the device-independent regime that offers security without device characterization. Security proofs of device-independent quantum cryptography tasks are often complex and quite different from those of their more standard device-dependent cousins. The existing proofs pose huge challenges to experiments, among which large data-size requirement is a crux. Here, we show the complementarity security origin of the device-independent tasks. By linking complementarity with quantum nonlocality, we recast the device-independent scheme into a quantum error correction protocol. Going beyond the identical-and-independent-distribution case, we consider the most general attack. We generalize the sample entropy in classical Shannon theory for the finite-size analysis. Our method exhibits good finite-size performance and brings the device-independent scheme to a more practical regime. Applying it to the data in a recent ion-trap-based device-independent quantum key distribution experiment, one could reduce the requirement on data size to less than a third. Furthermore, the operational meaning of complementarity naturally extends device-independent scenarios to advantage key distillation, easing experiments by tolerating higher loss and lower transmittance.
UAV Real-Time Path Planning Based on Heuristic Angle Search Strategy in an Unknown Environment

For the problem of poor safety and real-time tracking of the paths planned by UAVs with unknown map information, an improved JPS algorithm based on a heuristic angle search strategy is proposed on the basis of the traditional JPS algorithm. This algorithm improves the jump point screening rules and introduces the visual camera's field of view angle as a heuristic factor to obtain obstacle information ahead of time, improving the real-time safety of path planning. The path is also smoothed by using a B-spline curve. Finally, the simulation and comparison experiments of the A* algorithm, the JPS algorithm, the hybrid A* algorithm, and this algorithm are conducted in a gazebo environment. The experimental results show that the path planned by this algorithm can effectively reduce the threat caused by unknown obstacles, and real flight experiments are being conducted on a UAV based on the ROS operating system to prove the feasibility and safety of this algorithm.
The Application of AI Speech Evaluation Technology in College English Teaching under the Smart Classroom Teaching Mode

Spoken English proficiency is an important manifestation of college students' comprehensive English ability, and it is also an inevitable requirement for college students when adapting to the current globalization. However, due to the the particularity of spoken English and the lack of teaching efficiency, it increases the difficulty for students to improve their spoken English proficiency, and also makes it a difficult point in language teaching for teachers. Aiming at these problems, a college English smart classroom teaching model is constructed based on the iFlytek FiF Spoken English Training System. This system employs AI speech evaluation technology, which relying on Deep Neural Network Model. A teaching quasi-experiment have been carried out for one semester among undergraduates. The research findings have shown that the application of AI Speech Evaluation Technology in college English teaching can improve students' participation in learning and teaching efficiency dramatically.
Research Data Without Borders: How the Sections of the NFDI Work on Cross-Cutting Topics Across Disciplines and Consortia

In this panel, the sections of the NFDI initiative will report on their organizational structures and their collaboration with the NFDI consortia to design cross-disciplinary services for a German national research data infrastructure. The panelists will share early experiences, insights and best practices from their interdisciplinary work. They will also highlight how their work links to European activities in the EOSC and other international initiatives.
MINE - A Text Analysis Service for Digital Humanities Scientists

This paper introduces a newly developed service called MINE. The service assists Digital Humanities scientists in solving two problems: data acquisition and text analysis on a large scale. MINE consists of two main components: a search engine and a workspace. The search engine does not only provide a way to search across various data sources, but also facilitates access to difficult-to-access sources. Meanwhile, the workspace allows users to analyze data using provided models or writing their own analysis workflows. Thanks to this, a new approach to text analysis in Digital Humanities can be taken, which enables large-scale analyses.
A Path Planing Algorithm for Mobile Robot Based on Particle Swarm

Aiming at known global static environment, this paper conducts research on path planning strategy for mobile robot from the aspect of single robot. One stepwise path planning method is proposed as follows. Model of robot operation space is established through link graph at first, then the shortest path of link graph is obtained by graph theory algorithm. Particle Swarm Optimization algorithm is employed to optimize the obtained path so as to achieve global optimal path at last. Simulation results show that the proposed method can accomplish the planning of robot moving path from the from the start state to target state at short length without any collision. The proposed method is simple and feasible, able to satisfy high real-time requirement of mobile robot navigation, which provides a satisfactory solution for robot path planning.
Factory Automatic Meter Reading Robot

Although digitization has become the main trend of instrument industry development, the finger-type instrument is widely used for its simple structure, high reliability, and high accuracy. At present, the majority of finger-type meter reading methods are still stuck in the traditional manual meter reading and manual meter reading can not guarantee real-time performance, and manual meter reading is more expensive in high temperature, high pressure, and high radiation areas. Jetson Xavier NX is proposed to be used as the master machine to design an automatic meter reading robot to meet the needs of automatic meter reading in machine vision. Finally, the automatic meter reading robot designed in this paper can run smoothly in the room de-artificially, automatically read the pointer meter value, and reduce labor costs effectively.
End of Term Web Archive Dataset: Longitudinal Web Archive of .GOV and .MIL Domains

The End of Term (EOT) Web Archive Dataset presents a longitudinal dataset of the US federal web which includes publicly available .gov and .mil domains, created during the 2008, 2012, 2016, and 2020 presidential elections in the United States. Based on the End of Term Web Archive, this dataset presents 461TB of WARC data and accompanying derivative files in WAT, WET, and CDX format. A metadata sidecar file is also provided that contains content-based characterizations, including languages, character sets, format identifiers, and mime types. In addition to these derivative formats, CDX indexes in the ZipNum and Parquet formats that provide additional functionality to the dataset are included. The EOT dataset is freely available on the Amazon S3 platform as part of the Amazon Open Data Program.
PADME-SoSci: A Platform for Analytics and Distributed Machine Learning for the Social Sciences

Data privacy and ownership are significant in social data science, raising legal and ethical concerns. Sharing and analyzing data is difficult when different parties own different parts of it. An approach to this challenge is to apply de-identification or anonymization techniques to the data before collecting it for analysis. However, this can reduce data utility and increase the risk of re-identification. To address these limitations, we present PADME-SoSci, a distributed analytics tool that federates model implementation and training. PADME-SoSci uses a federated approach where the model is implemented and deployed by all parties and visits each data location incrementally for training. This enables the analysis of data across locations while still allowing the model to be trained as if all data were in a single location. Training the model on data in its original location preserves data ownership. Furthermore, the results are not provided until the analysis is completed on all data locations to ensure privacy and avoid bias in the results.
Efficient Ultrafine Typing of Named Entities

Ultrafine named entity typing (UFET) refers to the assignment of predefined labels to entity mentions in a given context. In contrast to traditional named entity typing, the number of potential labels is in the thousands and one mention can have more than one assigned type. Previous approaches either depend on large training datasets, or require inefficient encoding of all input-type combinations. Therefore, there is a need for investigating the efficiency during training and prediction of entity typing models in the ultrafine-grained setting, considering its distinctively bigger search space, compared to the coarse- and fine-grained tasks. To efficiently solve UFET, we propose Decent, a lightweight model that encodes, using a pretrained language model, the input sentences separately from the type labels. Additionally, we make use of negative oversampling to speed up the training while improving the generalization of unseen types. Using an openly available UFET dataset, we evaluated the classification and runtime performance of Decent and observed that training and prediction runtime is orders of magnitude faster than the current state-of-the-art approaches, while maintaining a competitive classification performance.
Path Planning Based on Improved RRT Algorithm

The traditional path-exploring Random Trees (RRT) algorithm has the defects of low search efficiency and high path curvature. Based on the basic RRT algorithm, target bias strategy and pruning optimization algorithm are adopted to reduce unnecessary blank search and improve the computational speed. By introducing stretch function, the generated path is searched forward along the end node, and the two non-adj acent nodes with no obstacles in the middle are merged to reduce redundant paths. The cubic B-spline function is used to make the path curve smoother. In the matlab simulation environment simulation. Simulation results show that compared with the traditional RRT algorithm, the improved path planning algorithm greatly reduces the length of path planning and the path search time, and verifies the effectiveness of the path planning algorithm.
Construction Methods, Devices, Computer Equipment and Storage Media of Temporary Entity Model

The construction method, device, computer equipment and storage media of temporary entity model, obtain the list of temporary entity, and create the standardized database data of each type of temporary entity in the list of temporary entity through the building model construction program; Based on format conversion plug-in, format conversion of the file data of the standardized improvised family library is carried out and then imported to the game development engine; Based on the import results, a background model family library is constructed in the game development engine with the usage information, usage material and specification data of the temporary entity; According to the click instruction, the selected ad-hoc entity model in the background model family library is displayed through the game development engine, and the modeling error correction of the selected ad-hoc entity model is carried out. Therefore, it is convenient to simplify the operation of user construction scheme, facilitate the preparation of temporary construction scheme, and meet the calculation demand of temporary construction.
Task-Dependent Visual Behavior in Immersive Environments: A Comparative Study of Free Exploration, Memory and Visual Search

Visual behavior depends on both bottom-up mechanisms, where gaze is driven by the visual conspicuity of the stimuli, and top-down mechanisms, guiding attention towards relevant areas based on the task or goal of the viewer. While this is well-known, visual attention models often focus on bottom-up mechanisms. Existing works have analyzed the effect of high-level cognitive tasks like memory or visual search on visual behavior; however, they have often done so with different stimuli, methodology, metrics and participants, which makes drawing conclusions and comparisons between tasks particularly difficult. In this work we present a systematic study of how different cognitive tasks affect visual behavior in a novel within-subjects design scheme. Participants performed free exploration, memory and visual search tasks in three different scenes while their eye and head movements were being recorded. We found significant, consistent differences between tasks in the distributions of fixations, saccades and head movements. Our findings can provide insights for practitioners and content creators designing task-oriented immersive applications.
Model Poisoning Attack In Federated Learning Via Adversarial Examples

The emergence of federated learning framework sets protects user privacy and has solved the Isolated Data Island problem. However, existing federation attack methods mostly use label flipping for data poisoning attacks, while federated backdoor attacks require patching the target samples. The attacker can also arrange other generative networks at local nodes but has limited data reconstruction capability. Thus, this paper proposes a new poisoning attack method named adversarial example poisoning attack (AEP A). The attacker uses the distributed global model to create adversarial examples and uses the original clean label for federation training and attacks against the specific target class in the dataset. In the evaluation process, we conducted extensive experiments on the CfF AR-l 0 dataset. We also explored the toxicity of the adversarial examples under different generation methods, compared our method with the label-flipping attack, and finally showed the effectiveness of AEPA.
Semantification of Space Data - A Feasibility Study

This paper presents a new approach to the semantic representation of NASA planetary mission data. The Open Archival Information Systems (OAIS) Information Model (IM) is used as a model to represent this data in a Knowledge Graph (KG). To prepare the data for this requirement, a machine learning approach is used to extract information (entities) and connect to Wikidata. A demo shows the advantages of using federated SPARQL queries over the created KG and Wikidata.
Collision Detection Algorithm Based on Rigid Body and Soft Body Mixed Hierarchical Bounding Box

A collision detection algorithm based on mixed-level bounding boxes is proposed. Firstly, a balanced bounding box tree is established for the detected object, and then the tree node level is divided by the split plane division method. According to the characteristics of different bounding boxes, the triangular surface of geometry is divided by combining two bounding boxes to reduce the number of intersection tests, which can quickly complete the intersection test of geometry. The experimental results show that compared with the classical algorithm, it has a certain time advantage and can be used in collision detection between rigid and soft objects.
The Optimization of path Planning for a Picking Robot Based on an Improved Ant Colony Algorithm

Regarding the issue that the convergence speed of traditional ant colony algorithms is often slow, the quality of the solution needs to be improved, and it is easy to fall into local optimal solutions. This article proposes an improved ant colony algorithm. Based on the traditional ant colony algorithm, combined with the suboptimal solution path obtained by optimizing the A* algorithm, introducing heuristic factors and pheromone volatilization factors and adjusting the deadlock update strategy to reduce the number of turns and avoid entering a deadlock state. Speed up the convergence speed of the algorithm and improve the quality of the solution. Experimental results show that compared with traditional ant colony algorithms, the improved ant colony algorithm in this paper can effectively reduce the number of turns while reducing the shortest path length and has strong stability.
A MCMC Algorithm for Improved Bayesian Network Structure Learning

The Bayesian network is a probabilistic graph model based on causal modeling and uncertainty reasoning. It gives the probability relationship between variables so that the posterior probability of the desired result can be calculated. In the fields of machine learning and artificial intelligence, it is commonly employed. The structural learning of Bayesian networks is the focus and complexity of study since the variables can build multiple network topologies and so form complex models. The application of an enhanced MCMC approach for network structure search is investigated in this research. A multi-step proposed distribution based on the Metropolis-Hastings method decreases computational cost, optimizes the solution process, and enhances the MCMC chain convergence speed. The approach is also used to solve a Bayesian network structure learning problem. The results of the experiments demonstrate.