Reviewerly/exHarmony huggingface dataset:
Real-time anomaly data detection method based on mixed kernel function PSO-SVR

To avoid the influence of abnormal data of micro-plant factory sensors on the control system, an abnormal data detection method based on mixed kernel function particle swarm optimization (PSO)-support vector regression (SVR) is proposed. First, the mixed kernel function constructed by the polynomial kernel and radial basis kernel is used as the kernel function of SVR, and PSO is introduced to optimize the hyperparameters of the SVR model and establish a prediction model. Then, the model completes a step-by-step prediction based on the data in the sliding window and calculates confidence intervals. Finally, the data are judged to be abnormal based on whether the measured values exceed the confidence interval, and the abnormal values are replaced with the predicted values. The results show that the mean absolute percentage error of the model is 0.0063, the accuracy is 99.63%, the prediction accuracy and detection accuracy are better than the comparison model, and the abnormal data in the sensor data flow can be effectively detected and processed.
Extensible Metatheory Mechanization via Family Polymorphism

With the growing practice of mechanizing language metatheories, it has become ever more pressing that interactive theorem provers make it easy to write reusable, extensible code and proofs. This paper presents a novel language design geared towards extensible metatheory mechanization in a proof assistant. The new design achieves reuse and extensibility via a form of family polymorphism, an object-oriented idea, that allows code and proofs to be polymorphic to their enclosing families. Our development addresses technical challenges that arise from the underlying language of a proof assistant being simultaneously functional, dependently typed, a logic, and an interactive tool. Our results include (1) a prototypical implementation of the language design as a Coq plugin, (2) a dependent type theory capturing the essence of the language mechanism and its consistency and canonicity results, and (3) case studies showing how the new expressiveness naturally addresses real programming challenges in metatheory mechanization.
Application of the ADDIE Model in the Teaching Practice of Basketball Skills

Abstract This paper establishes a mathematical model of basketball sports based on an ADDIE model. This paper takes basketball players’ pitching as the research object. Then use the ADDIE model method to analyze the best angle of the goal to improve the accuracy of basketball throwing. Firstly, this paper establishes the dynamic model of basketball movement and then shows the equation of basketball movement trajectory. Then this paper uses the ADDIE model to optimize and solve the best angle of basketball. At the same time, this paper proposes a motion control of basketball rotation motion using adaptive trajectory correction technology. Finally, this paper analyzes the optimal angle of a basketball's rotation projection. The numerical simulation proves that it is feasible to use the ADDIE model to solve the optimal angle of the basketball's rotating body. The method proposed in this paper achieves good results in angle correction and trajectory tracking. This method has a good application prospect in establishing the rotational projection motion model of sports tourism basketball.
Defunctionalization with Dependent Types

The defunctionalization translation that eliminates higher-order functions from programs forms a key part of many compilers. However, defunctionalization for dependently-typed languages has not been formally studied. We present the first formally-specified defunctionalization translation for a dependently-typed language and establish key metatheoretical properties such as soundness and type preservation. The translation is suitable for incorporation into type-preserving compilers for dependently-typed languages
Obstacle detection in snow covered terrain

This work describes an automatic detection method of obstacles covered by snow. The method is based on the detection of statistical anomalies relative to an estimated background image which contains no obstacles. The sensitivity of the detection can be adjusted by a specified probability of false alarms, and the obstacle detection confidence is characterized by a probability of detection. Statistical properties of the background image are estimated from the given image without additional information on the background. The visible height of obstacles above the snow is related to the actual height of the obstacles above the ground, so that an operator can estimate the actual height of the snow covered obstacle. The developed method requires no training, is self-calibrating to the cluttered images, operates with a single given image, and aligns with a detection quantification adopted in the receiver operating characteristic framework.
CommCSL: Proving Information Flow Security for Concurrent Programs using Abstract Commutativity

Information flow security ensures that the secret data manipulated by a program does not influence its observable output. Proving information flow security is especially challenging for concurrent programs, where operations on secret data may influence the execution time of a thread and, thereby, the interleaving between different threads. Such internal timing channels may affect the observable outcome of a program even if an attacker does not observe execution times. Existing verification techniques for information flow security in concurrent programs attempt to prove that secret data does not influence the relative timing of threads. However, these techniques are often restrictive (for instance because they disallow branching on secret data) and make strong assumptions about the execution platform (ignoring caching, processor instructions with data-dependent runtime, and other common features that affect execution time). In this paper, we present a novel verification technique for secure information flow in concurrent programs that lifts these restrictions and does not make any assumptions about timing behavior. The key idea is to prove that all mutating operations performed on shared data commute, such that different thread interleavings do not influence its final value. Crucially, commutativity is required only for an abstraction of the shared data that contains the information that will be leaked to a public output. Abstract commutativity is satisfied by many more operations than standard commutativity, which makes our technique widely applicable. We formalize our technique in CommCSL, a relational concurrent separation logic with support for commutativity-based reasoning, and prove its soundness in Isabelle/HOL. We implemented CommCSL in HyperViper, an automated verifier based on the Viper verification infrastructure, and demonstrate its ability to verify challenging examples.
Gear Profile Measurement Based on Thin Line Structured Light

Abstract In order to achieve the high precision 3D measurement of gear tooth profile, a telecentric optical measurement system with large FOV (field of view), WD (working distance) and measuring range was proposed, the system was applied to the gear tooth profile measurement experiment and obtained a good effect. Firstly, the composition and the principle of the system were introduced. The lights of projection and imaging with an angle of 45° from the normal line of the measured surface can reduce the requirement of DOF (depth of field) of the imaging objective lens. Secondly, in combine with the application background of small modulus gear tooth profile measurement, the magnification, NA (numerical aperture) and other optical parameters were calculated, the optical system was constructed and the system performance parameters are listed. Then, the calibration model of the system was demonstrated and the calibrations of depth pixel resolution and horizontal pixel resolution were completed through experiments. Finally, an involute artifact with a modulus of 2.9992mm and a total tooth profile deviation of 1.1μm was measured. The results show that the deviation of the total tooth profile measured is about 2μm, which is consistent with the calibration curve of the involute artifact.
Ancilla-Assisted Generation of Photons from Vacuum via Time-Modulation of Extracavity Qubit

We propose a scheme for generation of photons from vacuum due to time-modulation of a quantum system coupled indirectly to the cavity field through some ancilla quantum subsystem. We consider the simplest case when the modulation is applied to an artificial 2-level atom (we call t-qubit), while the ancilla is a stationary qubit coupled via the dipole interaction both to the cavity and t-qubit. We find that tripartite entangled states with a small number of photons can be generated from the system ground state under resonant modulations, even when the t-qubit is far detuned from both the ancilla and the cavity, provided its bare and modulation frequencies are properly adjusted as function of other system parameters. We attest our approximate analytic results by numeric simulations and show that photon generation from vacuum persists in the presence of common dissipation mechanisms.
Non-local affinity adaptive acceleration propagation network for generating dense depth maps from LiDAR

Depth completion aims to generate dense depth maps from the sparse depth images generated by LiDAR. In this paper, we propose a non-local affinity adaptive accelerated (NL-3A) propagation network for depth completion to solve the mixing depth problem of different objects on the depth boundary. In the network, we design the NL-3A prediction layer to predict the initial dense depth maps and their reliability, non-local neighbors and affinities of each pixel, and learnable normalization factors. Compared with the traditional fixed-neighbor affinity refinement scheme, the non-local neighbors predicted by the network can overcome the propagation error problem of mixed depth objects. Subsequently, we combine the learnable normalized propagation of non-local neighbor affinity with pixel depth reliability in the NL-3A propagation layer, so that it can adaptively adjust the propagation weight of each neighbor during the propagation process, which enhances the robustness of the network. Finally, we design an accelerated propagation model. This model enables parallel propagation of all neighbor affinities and improves the efficiency of refining dense depth maps. Experiments on KITTI depth completion and NYU Depth V2 datasets show that our network is superior to most algorithms in terms of accuracy and efficiency of depth completion. In particular, we predict and reconstruct more smoothly and consistently at the pixel edges of different objects.
Deep Learning Model CNN With LSTM For Speaker Recognition

Speech recognition is one of the most important research fields nowadays because of its necessity in our daily lives and to raise the fields of security to the highest level, It's a task of speech processing, and our main scope in this paper is on speaker verification, which is to identify persons from their voices where the process depends on digitizing the sound waves into a form that allows the system to deal with it.The verification process is based on the characteristics of the speaker's voice (voice biometrics) and sends it to a further process to extract the features of that voice using the feature extraction method and using AI techniques to perform the task of identification.MFCC is used for the task of features extraction and obtains the spectrogram of a given voice signal where it represents a bank of information about the voice and sends it to the CNN model for further processing for training the model on that signal to verify if the voice belongs to a user in the system or it's a new enrollment.
Evaluating deep generative models on cognitive tasks: a case study

Abstract We present a detailed case study evaluating selective cognitive abilities (decision making and spatial reasoning) of two recently released generative transformer models, ChatGPT and DALL-E 2. Input prompts were constructed following neutral a priori guidelines, rather than adversarial intent. Post hoc qualitative analysis of the outputs shows that DALL-E 2 is able to generate at least one correct image for each spatial reasoning prompt, but most images generated are incorrect, even though the model seems to have a clear understanding of the objects mentioned in the prompt. Similarly, in evaluating ChatGPT on the rationality axioms developed under the classical Von Neumann-Morgenstern utility theorem, we find that, although it demonstrates some level of rational decision-making, many of its decisions violate at least one of the axioms even under reasonable constructions of preferences, bets, and decision-making prompts. ChatGPT’s outputs on such problems generally tended to be unpredictable: even as it made irrational decisions (or employed an incorrect reasoning process) for some simpler decision-making problems, it was able to draw correct conclusions for more complex bet structures. We briefly comment on the nuances and challenges involved in scaling up such a ‘cognitive’ evaluation or conducting it with a closed set of answer keys (‘ground truth’), given that these models are inherently generative and open-ended in responding to prompts.
Malicious source code detection using a translation model

Modern software development often relies on open-source code sharing. Open-source code reuse, however, allows hackers to access wide developer communities, thereby potentially affecting many products. An increasing number of such "supply chain attacks" have occurred in recent years, taking advantage of open-source software development practices. Here, we introduce the Malicious Source code Detection using a Translation model (MSDT) algorithm. MSDT is a novel deep-learning-based analysis method that detects real-world code injections into source code packages. We have tested MSDT by embedding examples from a dataset of over 600,000 different functions and then applying a clustering algorithm to the resulting embedding vectors to identify malicious functions by detecting outliers. We evaluated MSDT's performance with extensive experiments and demonstrated that MSDT could detect malicious code injections with precision@k values of up to 0.909.
Speed-up and suppression effect of side chain in quantum searching on a graph

Searching efficiency, which biologists and roboticists are ever concerned about, has become important in physics and information science nowadays. In classical probability-based searching problems, as stigmergy, the increase in graph complexity will decrease the searching efficiency. Here we study the searching efficiency based on the first-passage probability and find a counterintuitive phenomenon in quantum walk on a graph with floating vertices. Connecting one vertex in the floating layer to that in the base layer will speed up the searching rapidity, but such a speed-up effect will be suppressed at once if one more vertex is connected to the already connected vertex in the floating layer. This is a counterintuitive phenomenon in comparison to its classical counterpart, where additional vertices at the side chain will retard the searching rapidity. We also propose an ancillary model that bridges the measurement of the probability and the first-passage probability, which is expected to provide new ideas for quantum simulation by means of qubit chips.
An Evaluation Method of Equipment Development Priority Combining Subjective and Objective

Abstract This article proposes a Subjective And Objective Evaluation (SAOE) method for equipment development priorities. By establishing the evaluation index system of equipment development priority evaluation, using fuzzy analytic hierarchy process to determine the subjective weights of each indicator in the indicator system, and then use the deviation maximization method to determine the objective weight of each indicator in the indicator system. Finally, based on least square method and relative entropy principle, combining subjective and objective weights to obtain the combined weight value of indicators, to obtain the equipment development priority. This method uses SAOE method to evaluate the priority of equipment development, which improves the scientific and reliability of the evaluation results, and is more practical, and provides a reliable basis for equipment development planning.
Future trends in IT project management – large organizations perspective

Purpose of the study: This article aims to identify and better understand the current trends in IT project management methodologies, focusing on the perspective of large organizations.&#x0D; Methodology: Systematic literature review has been conducted in SCOPUS and Web of Science databases to understand the current state of knowledge. Also as an additional verification of the results, a narrative search was done later in the SCOPUS database to confirm the growing interest in particular project management methodologies over time.&#x0D; Main Findings: The review has shown that the top two trends in project management methodologies are hybrid and agile approaches. The other two that consistently popped up and were also confirmed by follow-up narrative search, were AI-driven and sustainable project management.&#x0D; Applications of the study: Results of this review are useful for both researchers and practitioners, pointing out areas for future research and development, as well as showing what are the best approaches to project management.&#x0D; Novelty/Originality of the study: This systematic literature review has shown the latest state of trends related to research done recently, with explaining of how to use new trends to fight the nowadays challenges related to AI, sustainability, and performance of teams in large organizations, from a project management perspective.
The number of rational points on a class of hypersurfaces in quadratic extensions of finite fields

&lt;abstract&gt;&lt;p&gt;Let $ q $ be an even prime power and let $ \mathbb{F}_{q} $ be the finite field of $ q $ elements. Let $ f $ be a nonzero polynomial over $ \mathbb{F}_{q^2} $ of the form $ f = a_{1}x_{1}^{m_{1}}+\dots+a_{s}x_{s}^{m_{s}}+y_{1}y_{2}+\dots+y_{n-1}y_{n}+y_{n-2t-1}^{2}+\dots +y_{n-3}^2+y_{n-1}^{2}$ $+b_{t}y_{n-2t}^{2}+ $ $ \dots +b_{1}y_{n-2}^{2}+b_{0}y_{n}^{2} $, where $ a_{i}, b_j\in \mathbb{F}_{q^{2}}^{*}, $ $ m_i\ne 1, $ $ (m_{i}, m_{k}) = 1, $ $ i\ne k, $ $ m_{i}|(q+1), $ $ m_{i}\in \mathbb{Z}^{+}, $ $ 2|n $, $ n &amp;gt; 2 $, $ 0\leq t\leq \frac{n}{2}-2 $, $ {\mathrm{Tr}}_{\mathbb{F}_{q^2}/\mathbb{F}_{2}}(b_{j}) = 1 $ for $ i, k = 1, \dots, s $ and $ j = 0, 1, \dots, t $. For each $ b \in \mathbb{F}_{q^2} $, let $ N_{q^2}(f = b) $ denote the number of $ \mathbb{F}_{q^2} $-rational points on the affine hypersurface $ f = b $. In this paper, we obtain the formula of $ N_{q^2}(f = b) $ by using the Jacobi sums, Gauss sums and the results of quadratic form in finite fields.&lt;/p&gt;&lt;/abstract&gt;
A Double-Teacher Model Capable of Exploiting Isomorphic and Heterogeneous Discrepancy Information for Medical Image Segmentation

Deep learning, with continuous development, has achieved relatively good results in the field of left atrial segmentation, and numerous semi-supervised methods in this field have been implemented based on consistency regularization to obtain high-performance 3D models by training. However, most semi-supervised methods focus on inter-model consistency and ignore inter-model discrepancy. Therefore, we designed an improved double-teacher framework with discrepancy information. Herein, one teacher learns 2D information, another learns both 2D and 3D information, and the two models jointly guide the student model for learning. Simultaneously, we extract the isomorphic/heterogeneous discrepancy information between the predictions of the student and teacher model to optimize the whole framework. Unlike other semi-supervised methods based on 3D models, ours only uses 3D information to assist 2D models, and does not have a fully 3D model, thus addressing the large memory consumption and limited training data of 3D models to some extent. Our approach shows excellent performance on the left atrium (LA) dataset, similar to that of the best performing 3D semi-supervised methods available, compared to existing techniques.
Factors determining research productivity in Ethiopian Universities: The case of Madda Walabu University

This study aimed to explore factors determining research production at Madda Walabu University. The study has employed a case study design to collect data through interviews, focus group discussions (FGDs), and document analysis. The study recruited six participants using purposive sampling techniques for interviews. Besides, seven discussants were selected using a purposeful sampling technique for FGD. The study revealed that the research practices at the three colleges were in their infancy stage. The primary factors that hindered research practice were lengthy research administrations, a lack of budget, a lack of facilities, and a lack of interest in conducting research. Hence, the study recommends that the university revisit research policies and provide authority and responsibility for schools to manage research in their respective schools. The study also recommended that the university should fulfill its facilities to achieve its vision.
Thai Word Segmentation with a Brain-Inspired Sparse Distributed Representations Learning Memory

Word segmentation is necessary for many natural language processing, especially Thai language, that is, unsegmented words. However, wrong segmentation causes terrible performance in the final result. In this study, we propose two new brain-inspired methods based on Hawkins' approach to address Thai word segmentation. Sparse Distributed Representations (SDRs) are used to model the neocortex structure of the brain to store and transfer information. The first proposed method, THDICTSDR, improves the dictionary-based approach by utilizing SDRs to learn the surrounding context and combine with n-gram to select the correct word. The second method uses SDRs instead of a dictionary and is called THSDR. The evaluation uses the BEST2010 and LST20 standard datasets for segmentation words by comparing them with the longest matching, newmm, and Deepcut, which is state-of-the-art in the deep learning approach. The result shows that the first method provides the accuracy, and performances are significantly better than other dictionary bases. The first new method can achieve F1-Score at 95.60%, comparable to the state-of-the-art and Deepcut F1-Score at 96.34%. However, it provides a better performance F1-Score at 96.78% in learning all vocabularies. In addition, it can achieve 99.48% F1-Score beyond Deepcut 97.65% in case of all sentences being learnt. The second method has fault tolerance to noise and provides overall result over deep learning in all cases.
An Image-Based Human-Robot Collision Avoidance Scheme: A Proof of Concept

OCCUPATIONAL APPLICATIONSIn modern industrial plants, collisions between humans and robots pose a significant risk to occupational safety. To address this concern, we sought to devise a reliable system for human-robot collision avoidance system employing computer vision. This system enables the proactive prevention of dangerous collisions between humans and robots. In contrast to previous approaches, we used a standard RGB camera, making implementation more convenient and cost-effective. Furthermore, the proposed method greatly extends the effective detection range compared to previous studies, thereby enhancing its utility for monitoring large-scale workplaces.