Reviewerly/exHarmony huggingface dataset:
Proving Non-Termination by Acceleration Driven Clause Learning

We recently proposed Acceleration Driven Clause Learning (ADCL), a novel calculus to analyze satisfiability of Constrained Horn Clauses (CHCs). Here, we adapt ADCL to transition systems and introduce ADCL-NT, a variant for disproving termination. We implemented ADCL-NT in our tool LoAT and evaluate it against the state of the art.
SSE-Ship: A SAR Image Ship Detection Model with Expanded Detection Field of View and Enhanced Effective Feature Information

In this paper, we propose a SAR image ship detection model SSE-Ship that combines image context to extend the detection field of view domain and effectively enhance feature extraction information.This method aims to solve the problem of low detection rate in SAR images with ship combination and ship fusion scenes.Firstly, we propose STCSPB network to solve the problem of ship and non-ship object fusion by combining image contextual feature information to distinguish ship and non-ship objects.Secondly, we combine SE Attention to enhance the effective feature information and effectively improve the detection accuracy in combined ship driving scenes.Finally, we conducted extensive experiments on two standard base datasets, SAR-Ship and SSDD, to verify the effectiveness and stability of our proposed method.The experimental results show that the SSE-Ship model has P = 0.950, R = 0.946, mAP_0.5:0.95= 0.656 and FPS = 50 on the SAR-Ship dataset and mAP_0.5 = 0.964 and R = 0.940 on the SSDD dataset.
Scoreformer: Score Fusion-Based Transformers for Weakly-Supervised Violence Detection

Violence detection is an application of anomaly detection, which is used to detect violence content in video clips. Using multimodal as input can improve the performance of violence detection. However, the existing MML Transformers-based fusion methods do not take into account the differences between non-homologous modals. The fusion of non-homologous modals makes features become noise between each other. This paper proposes a score fusion-based transformer framework, named Scoreformer. First of all, the optical flow, RGB and audio features pass through the independent self-attention transformer blocks. Second, the optical flow and RGB features pass through the cross-modal transformer blocks, after that they are fused with the audio features through the score fusion block. This method avoids the noise interference caused by the direct fusion of audio features and visual features. Experiments on the XD-Violence dataset show that the proposed method achieves 84.54% of the AP value, which exceeds at least 2.85% compared with the most advanced method (e. g. MSL, CRFD).
Conflicts Between UX Designers, Front-End and Back-End Software Developers: Good or Bad for Productivity?

Modern software development processes side traditional development activities with user experience design, i.e. the process of designing software systems supporting and improving user interactions through the fulfilment of quality properties of the software systems such as usability, usefulness, and desirability. Commonly, developers and user experience designers have rather different backgrounds. Several researches have identified this difference as pivotal for the rising of miscommunication and conflicts, that may theoretically undermine productivity of the software development process. In this research, we investigated the kinds of conflict that are more likely to arise between developers and user experience designers. Besides, we analysed whether socio-cultural factors and geographic distribution of team members affect the rising of conflicts. Eventually, we related the presence of conflicts to the success of a software development project. We conducted this research as a questionnaire-based survey involving 56 professional software developers and user experience designers from various countries in Europe. The collected data showed that the most common type of conflict is task conflict. Age, gender and geographical distribution of team members do not affect the rising of conflicts. Conflicts are surprisingly perceived as beneficial for productivity in software development processes in several cases, although they led to the failure of projects in the 10.9% of the cases.
NISQ-Ready Community Detection Based on Separation-Node Identification

The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph&amp;#039;s adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which - upon its removal from the graph - yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ-ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.}
Extending the Frontier of ChatGPT: Code Generation and Debugging

Large-scale language models (LLMs) have emerged as a groundbreaking innovation in the realm of question-answering and conversational agents. These models, leveraging different deep learning architectures such as Transformers, are trained on vast corpora to predict sentences based on given queries. Among these LLMs, ChatGPT, developed by OpenAI, has ushered in a new era by utilizing artificial intelligence (AI) to tackle diverse problem domains, ranging from composing essays and biographies to solving intricate mathematical integrals. The versatile applications enabled by ChatGPT offer immense value to users. However, assessing the performance of ChatGPT's output poses a challenge, particularly in scenarios where queries lack clear objective criteria for correctness. For instance, evaluating the quality of generated essays becomes arduous and relies heavily on manual labor, in stark contrast to evaluating solutions to well-defined, closed-ended questions such as mathematical problems. This research paper delves into the efficacy of ChatGPT in solving programming problems, examining both the correctness and the efficiency of its solution in terms of time and memory complexity. The research reveals a commendable overall success rate of 71.875\%, denoting the proportion of problems for which ChatGPT was able to provide correct solutions that successfully satisfied all the test cases present in Leetcode. It exhibits strengths in structured problems and shows a linear correlation between its success rate and problem acceptance rates. However, it struggles to improve solutions based on feedback, pointing to potential shortcomings in debugging tasks. These findings provide a compact yet insightful glimpse into ChatGPT's capabilities and areas for improvement.
A new technique to incorporate multiple fermion flavors in tensor renormalization group method for lattice gauge theories

We propose a new technique to incorporate multiple fermion flavors in the tensor renormalization group method for lattice gauge theories, where fermions are treated by the Grassmann tensor network formalism. The basic idea is to separate the site tensor into multiple layers associated with each flavor and to introduce the gauge field in each layer as replicas, which are all identified later. This formulation, after introducing an appropriate compression scheme in the network, enables us to reduce the size of the initial tensor with high efficiency compared with a naive implementation. The usefulness of this formulation is demonstrated by investigating the chiral phase transition and the Silver Blaze phenomenon in 2D Abelian gauge theories with $N_{\rm f}$ flavors of Wilson fermions up to $N_{\rm f}=4$.
Proper Scoring Loss Functions Are Simple and Effective for Uncertainty Quantification of White Matter Hyperintensities

Uncertainty quantification is an important tool for improving the trustworthiness and clinical usefulness of medical imaging segmentation models, and many techniques exist for quantifying segmentation uncertainty. However, popular segmentation losses such as Dice loss lead to poorly calibrated models and silent failure in uncertainty maps. We compare common proper scoring rule based losses, which encourage well-calibrated models, to Dice loss and calibrated Dice loss variants, for white matter hyperintensity (WMH) segmentation in FLAIR and T1w MRI. We show that scoring rules yield strong performance (e.g., Spherical-TopK: Dice of 0.763, vs 0.717 for Dice loss) and low WMH instance detection failure rate in axial slices (Logarithmic yields 11% missing instances in the uncertainty map vs 28% for Dice). Furthermore, proper scoring rule methods do not exhibit the performance degradation in calibration error and WMH burden prediction of Dice loss in low WMH burden patients. Finally, we show temperature scaling is insufficient to overcome the drawbacks of Dice loss.
Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models

Large language models (LLMs) exhibit positional bias in how they use context, which especially complicates listwise ranking. To address this, we propose permutation self-consistency, a form of self-consistency over ranking list outputs of black-box LLMs. Our key idea is to marginalize out different list orders in the prompt to produce an order-independent ranking with less positional bias. First, given some input prompt, we repeatedly shuffle the list in the prompt and pass it through the LLM while holding the instructions the same. Next, we aggregate the resulting sample of rankings by computing the central ranking closest in distance to all of them, marginalizing out prompt order biases in the process. Theoretically, we prove the robustness of our method, showing convergence to the true ranking in the presence of random perturbations. Empirically, on five list-ranking datasets in sorting and passage reranking, our approach improves scores from conventional inference by up to 7-18% for GPT-3.5 and 8-16% for LLaMA v2 (70B), surpassing the previous state of the art in passage reranking. Our code is at https://github.com/castorini/perm-sc.
Enhanced Techniques to Secure Medical Image and Data Transit

This paper displays the work related to the secured medical picture transmission in light of watermarking and encryption. Client particular watermark is installed into the LSB of unique picture. Implanting watermark in LSB does not influence the nature of picture. This watermarked picture is then encoded by utilizing a pixel repositioning calculation. Every pixel is repositioned in light of the rest of after division by number 10. This leftover portion lattice goes about as encryption key and is required at the season of unscrambling as well. Comprehensive analyses are completed on proposed approach. The outcomes demonstrate that the watermark inserted is intangible and can be effectively separated at the recipient. Likewise, the encoded picture has no visual importance with the first picture and histogram of scrambled picture is changed. Encoded picture can be decoded with no loss of data from the picture. From this decoded picture watermark can be removed which endures no misfortune in the watermark. PSNR esteems for an arrangement of medicinal pictures are fulfilling.
VQNet: Library for a Quantum-Classical Hybrid Neural Network

Deep learning is a modern approach to realize artificial intelligence. Many frameworks exist to implement the machine learning task; however, performance is limited by computing resources. Using a quantum computer to accelerate training is a promising approach. The variational quantum circuit (VQC) has gained a great deal of attention because it can be run on near-term quantum computers. In this paper, we establish a new framework that merges traditional machine learning tasks with the VQC. Users can implement a trainable quantum operation into a neural network. This framework enables the training of a quantum-classical hybrid task and may lead to a new area of quantum machine learning.
Secured Decentralized Archiving Healthcare Data Using Blockchain with IOT

Blockchain technology has unprotected its immense adaptability in hot off the press years as a departure from the norm of super convenience store sectors. In which sought ways in incorporating its abilities directed toward their operations. While so far most of the focus has been on the financial service industry, several projects in other service-related areas such as healthcare start showing innumerable change. Numerous starting points for Blockchain technology in the healthcare industry are the focus of this paper.
Modeling and Analysis of Tagging Networks in Stack Exchange Communities

Large Question-and-Answer (Q&A) platforms support diverse knowledge curation on the Web. While researchers have studied user behavior on the platforms in a variety of contexts, there is relatively little insight into important by-products of user behavior that also encode knowledge. Here, we analyze and model the macroscopic structure of tags applied by users to annotate and catalog questions, using a collection of 168 Stack Exchange websites. We find striking similarity in tagging structure across these Stack Exchange communities, even though each community evolves independently (albeit under similar guidelines). Using our empirical findings, we develop a simple generative model that creates random bipartite graphs of tags and questions. Our model accounts for the tag frequency distribution but does not explicitly account for co-tagging correlations. Even under these constraints, we demonstrate empirically and theoretically that our model can reproduce a number of statistical properties of the co-tagging graph that links tags appearing in the same post.
Robust Outdoor Vehicle Visual Tracking Based on k-Sparse Stacked Denoising Auto-Encoder

Robust visual tracking for outdoor vehicle is still a challenging problem due to large object appearance variations caused by illumination variation, occlusion, and fast motion. In this chapter, k-sparse constraint is added to the encoder part of stacked auto-encoder network to learn more invariant feature of object appearance, and a stacked k-sparse-auto-encoder–based robust outdoor vehicle tracking method under particle filter inference is further proposed to solve the problem of appearance variance during the tracking. Firstly, a stacked denoising auto-encoder is pre-trained to learn the generic feature representation. Then, a k-sparse constraint is added to the stacked denoising auto-encoder, and the encoder of k-sparse stacked denoising auto-encoder is connected with a classification layer to construct a classification neural network. Finally, confidence of each particle is computed by the classification neural network and is used for online tracking under particle filter framework. Comprehensive tracking experiments are conducted on a challenging single-object tracking benchmark. Experimental results show that our tracker outperforms most state-of-the-art trackers.
A tensorized logic programming language for large-scale data

We introduce a new logic programming language T-PRISM based on tensor embeddings. Our embedding scheme is a modification of the distribution semantics in PRISM, one of the state-of-the-art probabilistic logic programming languages, by replacing distribution functions with multidimensional arrays, i.e., tensors. T-PRISM consists of two parts: logic programming part and numerical computation part. The former provides flexible and interpretable modeling at the level of first order logic, and the latter part provides scalable computation utilizing parallelization and hardware acceleration with GPUs. Combing these two parts provides a remarkably wide range of high-level declarative modeling from symbolic reasoning to deep learning. To embody this programming language, we also introduce a new semantics, termed tensorized semantics, which combines the traditional least model semantics in logic programming with the embeddings of tensors. In T-PRISM, we first derive a set of equations related to tensors from a given program using logical inference, i.e., Prolog execution in a symbolic space and then solve the derived equations in a continuous space by TensorFlow. Using our preliminary implementation of T-PRISM, we have successfully dealt with a wide range of modeling. We have succeeded in dealing with real large-scale data in the declarative modeling. This paper presents a DistMult model for knowledge graphs using the FB15k and WN18 datasets.
Outis: Crypto-Assisted Differential Privacy on Untrusted Servers

Differential privacy (DP) has steadily become the de-facto standard for achieving privacy in data analysis, which is typically implemented either in the or model. The local model has been more popular for commercial deployments as it does not require a trusted data collector. This increased privacy, however, comes at a cost of utility and algorithmic expressibility as compared to the central model. In this work, we propose, Crypt$\epsilon$, a system and programming framework that (1) achieves the accuracy guarantees and algorithmic expressibility of the central model (2) without any trusted data collector like in the local model. Crypt$\epsilon$ achieves the best of both worlds by employing two non-colluding untrusted servers that run DP programs on encrypted data from the data owners. Although straightforward implementations of DP programs using secure computation tools can achieve the above goal theoretically, in practice they are beset with many challenges such as poor performance and tricky security proofs. To this end, Crypt$\epsilon$ allows data analysts to author logical DP programs that are automatically translated to secure protocols that work on encrypted data. These protocols ensure that the untrusted servers learn nothing more than the noisy outputs, thereby guaranteeing DP (for computationally bounded adversaries) for all Crypt$\epsilon$ programs. Crypt$\epsilon$ supports a rich class of DP programs that can be expressed via a small set of transformation and measurement operators followed by arbitrary post-processing. Further, we propose performance optimizations leveraging the fact that the output is noisy. We demonstrate Crypt$\epsilon$'s feasibility for practical DP analysis with extensive empirical evaluations on real datasets.
Solution attractor of local search in travelling salesman problem (part 2): computational study

This paper is the second part of our study. In the first part, we introduced the concept of solution attractor of local search system for the travelling salesman problem (TSP), described a procedure for constructing the solution attractor, and presented an attractor-based search system to solve the dynamic multi-objective TSP. In this paper, we report the results of our recent empirical study on some important properties of the solution attractor of local search system for the TSP. These properties include the nature of convergence of local search trajectories, the size of the constructed solution attractor, the relationship between the size of the problem and the size of the constructed solution attractor, the best tour in the solution attractor, and computational complexity in the attractor-based search system.
Position Control of Servo Drive System Based on Interval Type-2 Fuzzy Logic Algorithm

This paper is extension of major research of application of fuzzy logic for position control of precision electromechanical system specifically pointing and tracking system of telescope. Proposed interval type-2 fuzzy logic algorithm is evolution type-1 algorithm for improving the efficiency of a drive system due to the fact that in type-2 fuzzy systems membership functions are tridimensional and include in their entire period footprint of uncertainty. To prove the effectiveness of the presented method of fuzzy type-2 logic we compared it with the traditional PI controller. The obtained results show good steady-state and dynamic performance and workability of the presented algorithm.
A Probabilistic Two-Pile Game

We consider a game with two piles, in which two players take turn to add $a$ or $b$ chips ($a$, $b$ are not necessarily positive) randomly and independently to their respective piles. The player who collects $n$ chips first wins the game. We derive general formulas for $p_n$, the probability of the second player winning the game by collecting $n$ chips first and show the calculation for the cases $\{a,b\}$ = $\{-1,1\}$ and $\{-1,2\}$. The latter case was asked by Wong and Xu \cite{WX}. At the end, we derive the general formula for $p_{n_1,n_2}$, the probability of the second player winning the game by collecting $n_2$ chips before the first player collects $n_1$ chips.
High precision Taylor Expansion and Piecewise Calculation Square Root algorithm of microcomputer protection

Microcomputer Protection System usually needs to carry on Square Root Operations, such as Newton iterative algorithm, traditional Taylor Expansion algorithm, etc. However, there is not enough high precision to calculate for traditional Square Root Operations. Address to this issue, Taylor Expansion and Piecewise Calculation Square Root algorithm is purposed. In this algorithm, square root algorithm of Taylor Expansion and piecewise calculation are used, and then by successive approximation method the error is reduced. At last, by piecewise calculation the radicand is lessening. Simulation shows that, compared error between uniform segment method and non-uniform segment method, Taylor Expansion and Piecewise Calculation Square Root algorithm has the characters such as high precision and low computation. The precision of this algorithm is high enough to the Microcomputer Protection System, in which there is not more than the error 1.5 × 10-8.
Balanced Risk Assessment System for the Elderly Based on Factor Analysis and EWM

This paper discusses how to evaluate the balance ability of the elderly and puts forward specific suggestions to prevent falls. Through literature review, we have established the first level of indicators including age, posture, walking posture, medical history [1][2]. Then different diseases and different characteristics of the body during walking were taken as secondary indicators. Factor analysis and stepwise regression were used to screen index and extract information. Finally, entropy weight method is used to quantify the index. It was found that the three secondary indicators of walking posture in the system of men and women all had an impact of 1/3. In men, lead leg and disease have basically the same effect on the elderly's falls, while in women, age and disease have basically the same effect.