Reviewerly/exHarmony huggingface dataset:
ECOPY: Data-free model stealing for deep neural network

Deep neural networks are widely used increasingly in the real world, such as machine learning as a service (MLaas) which makes the owner of the model can deploy their fully trained model in the cloud for others to use. By paying remuneration and inputting the object into the model, customers can receive the predictions. Unfortunately, these network models are often threatened by model stealing attack. This attack uses the predictive information given by the original model to train a surrogate model with similar functions, so as to achieve the purpose of infringing the intellectual property of the model owner and the white-box adversarial sample attack against the clone model can transfer to the original model to a certain extent, which will result in more far-reaching damage and impact. But the model stealing process is often ineffective because the training data or related agent datasets of the original model cannot be obtained, for such data is often hidden by the model owner due to its privacy. On this basis, the model stealing attack without data has gradually entered people's view. The data generator will generate data to query the model to achieve the purpose of stealing. In this paper, a data-free model stealing attack frame based on gradient prediction and uniform sample generation has been proposed. Experiments show that this method can obtain a high imitation accuracy in a given query cost range. The performance (0.94x-0.99x) of the stand-in model is better than other MS attacks that rely on part of the original data set (such as JBDA, an attack that uses a part of the original data and other synthetic data generated from the original data by Jacobi data enhancement method to train the clone model, clone accuracy 0.13x-0.69x) or rely on the proxy data set (such as KnockoffNets which uses the surrogate data which has the homologous distribution with the original data to train the clone model, clone accuracy 0.52x-0.97x). At the same time, compared with other data-free MS attacks proposed recently (such as DFME, the SOTA method that uses the data randomly generated by generator model to train the clone model, clone accuracy 0.92x-0.99x), it also has the same or even higher performance. At the same time, the rate of success for adversarial examples attack through the surrogate model is also better than other data-free black-box attack.
Optimize value-flow analysis based static vulnerability detection by solver rating

Program static analysis is of great value of source code software vulnerability detection, but it is often limited by scalability bottlenecks. Constraint solvers are inefficient due to complex program dependencies on millions of lines of program source code. A single solver is difficult to get the balance between the accuracy and the time cost. This paper discusses the program dependence and constraint solving of static value-flow analysis, and specifically implements a solver rating system based on static taint analysis, which selects the most efficient solver for program dependence of critical path to reduce the false-positives and time cost of static vulnerability detection. Through testing for Juliet test sets and several real-world projects, we found that the overall performance of the system was better than other single SMT solvers or default scheduling strategies.
Multi-hop knowledge base question answering by case retrieval

Multi-hop Knowledge Base Question Answering (KBQA) aims to find the answer entities in a Knowledge Base (KB) that are multiple hops away from the topic entities mentioned in a question. The key challenge of multi-hop KBQA is the significant semantic gap between Natural Language (NL) and the relation paths in KB. Recently, the Case-Based Reasoning (CBR) approach has been successfully applied to KBQA, which first retrieves other similar questions from the training set and then referencing their solutions (e.g., reasoning path) to derive a solution for given question. However, existing CBR-based methods are hard to provide correct solutions while the gold reasoning paths are unavailable. Moreover, they are limited to the assumption that the reasoning path required to answer a question is repeats for similar questions, thus suffer from the generalization issue. To address above problems, we propose a novel framework, which consider both the semantic matching between NL question and KB paths, as well as the matching between KB paths of similar question and given question, to alleviate the inconsistency between NL and KB. Specifically, it uses above two matching mechanisms to generate more accurate solutions for training questions and then reuses it to rank the candidate reasoning paths of given question. To cope with the generalization issue mentioned above, our method adopts different ranking algorithm according to different NL similarity. Experiments over two benchmarks demonstrate the effectiveness of our approach, which achieves state of the art on MetaQA and new state of the art results on PathQuestion-Large.
Smooth Clip Advantage PPO in Reinforcement Learning

Abstract Deep reinforcement learning outperforms traditional methods in some domains. In this paper, we propose a novel reinforcement learning on policy (RL) algorithm, the Smoothing Clip Advantage Proximal Policy optimization Algorithm (SCAPPO), which extends the classical PPO algorithm where we exploit the smoothing properties of the sigmoid function to make full use of useful gradients. In addition, we provide more efficient gradients for policy networks effective gradients, aiming to solve the overfitting problem caused by the coupling of strategy and value functions. SCAPPO outperforms currently popular reinforcement learning algorithms in performance tasks in the Open AI Gym.
Ship type recognition method based on multi-view learning

Aiming at the problem of low reliability of ship model recognition from remote sensing images, we propose a ship model recognition method based on multi-view learning. Firstly, the multi-view feature data is constructed by different feature operators. Then the single feature view data is used to train SVM classifier respectively while the multi-view data is fused and used to train classifier by CPM-Nets. Finally, we fuse the results of single feature view classifier and multi-feature view classifier by classifier aggregation to enhance the accuracy rate. Experiments show that the proposed method can improve the accuracy of ship type recognition.
Roof Construction and Structural Transformation on Batak Toba Traditional House on Samosir Island

Abstract The Batak Toba Traditional House, one of the main attractions of Samosir Island, a volcanic island in the center of Lake Toba, is an example of a well-preserved cultural heritage. However, the Batak Toba Traditional House has undergone a transformation in recent decades, influenced by technological advances, material availability, and modernization. The authors aim to discover the roof transformation of structure and construction of Batak Toba Traditional Houses in Samosir Island’s four villages, namely Huta Siallagan, Lumban Sigiro, Lumban Parmonangan, and Sosor Batu. This study employs a descriptive qualitative approach, where the authors will produce descriptive information about the problems studied in written and oral form. The authors will observe and analyze the transformation of roof construction and structural components on Batak Toba Traditional Houses on Samosir Island. The result of this study is that there are slight transformations in the roof structure and construction system on Batak Toba Traditional House on Samosir Island. First, there is a transformation in the roof materials. Second, a transformation in the structural design. Third, the construction phases of the traditional houses. Last, a transformation in the roof structural joint systems.
Research on Chinese word segmentation model based on RoBERTa and conditional radom field

This paper proposes a Chinese word segmentation model based on RoBERTa and Conditional Random Field. The Chinese word segmentation model based on RoBERTa and CRF deals with Chinese word segmentation in the way of sequence labeling. The training of the model takes the character sequence of the sentence as the input, which is converted into number according to vocabulary and sent into RoBERTa layer. The output results of RoBERTa layer were converted into the probability distribution of word position label by linear transformation and softmax activation function. The predicted word position label sequence was obtained by Viterbi decoding operation. After experiments, the accuracy rate, precision rate, recall rate and f1 scores of RoBERTa + CRF model on SIGHAN's PKU and MSR corpora are 96.33%, 95.75%, 95.00% and 95.37% respectively (pick the highest score), which has preliminary application value and certain room for improvement.
Bad video classification based on deep learning

In modern society, violence and pornography often occur in KTV entertainment venues and on campus. It is of great significance to protect students' physical and mental health and maintain the order of KTV entertainment venues if violent and pornographic clips can be instantly identified from surveillance videos. However, pornographic video identification networks in general are different from violent video identification networks. Moreover, the traditional video classification network is not suitable for the classification of bad videos. Therefore, we propose a new deep learning network architecture for bad video classification. The network consists of a primary branch that uses optical flow information and a secondary branch that uses keyframe information. Experimental results show that this network structure is more accurate than the existing related video classification networks.
Visible Joint Classification and Temporal Segment Matching based 3D Pose Refinement for Volleyball Receive Analysis

Abstract Receive action pose analysis is very meaningful in volleyball games for training and strategy. Because receive action is lack high-quality labeled data sets and with problems like occlusion, body overlap, and abnormal pose. Conventional work fails to obtain high accurate pose results. This paper proposes visible joint refinement and receive action template matching for volleyball receive action analysis. Firstly, the visible joint is using pixel color feature and the potential constraint features between space and joints to classify the visible joint and refine the error visible joint. Secondly, the template is to realize the pose segment refinement at 3D level by matching. It is based on a multi-view system for a real volleyball competition scene. The dataset video is from the Game of 2014 Japan Inter High School of Men Volleyball. The experiment result achieves 95.33 %, 96.92 %, and 98.43 % success rate at the 30 mm, 50 mm, and 70 mm error ranges.
Improving Gaze Tracking in Large Screens With Symmetric Gaze Angle Amplification and Optimization Technique

Many gaze tracking applications focus on use in personal devices such as mobile phones and PCs. However, gaze tracking in large screens poses challenges because with an increase in screen size, gaze tracking in the edge region decreases owing to the restricted range of human eye movement. In addition, as large screens are often exposed to the public, anyone can use the gaze tracking module. This makes it difficult to apply personalized calibration as in personal devices. To acquire accurate gaze in the edge region, we propose a novel approach—symmetric angle amplifying function—for the gaze angle, which amplifies angles when a user is looking at the edge area of the large screen. Our function is designed particularly for the case where the screen is divided into grid-shaped regions. Furthermore, for the better user experience, we optimize neural networks using the network-optimization framework and also propose a center gravity function that pulls gaze coordinates presented on the screen to the predefined center of the region to compensate for the person-wise difference in movement of the human eyes. Experimental results revealed the superiority of the proposed methods over the baseline and different types of fitting functions. The gaze tracking module serves as a part of an aggregated system and is implemented for use in autonomous vehicles.
Manta Ray Foraging Optimization Algorithm: Modifications and Applications

The novel metaheuristic manta ray foraging optimization (MRFO) algorithm is based on the smart conduct of manta rays.The MRFO algorithm is a newly developed swarm-based metaheuristic approach that emulates the supportive conduct performed by manta rays in search of food.The MRFO algorithm efficiently resolves several optimization difficulties in various domains due to its ability to provide an equilibrium between global and local searches during the search procedure, resulting in nearly optimal results.Thus, researchers have developed several variants of MRFO since its introduction.This paper provides an in-depth examination of recent MRFO research.First, the paper introduces the natural inspiration context of MRFO and its conceptual optimization framework, and then MRFO modifications, hybridizations, and applications across different domains are discussed.Finally, a meta-analysis of the developments of the MRFO is presented along with the possible future research directions.This study can be useful for researchers and practitioners in optimization, engineering design, machine learning, scheduling, image processing, and other fields.
Bridging the Gap between Synthetic and Natural Questions via Sentence Decomposition for Semantic Parsing

Abstract Semantic parsing maps natural language questions into logical forms, which can be executed against a knowledge base for answers. In real-world applications, the performance of a parser is often limited by the lack of training data. To facilitate zero-shot learning, data synthesis has been widely studied to automatically generate paired questions and logical forms. However, data synthesis methods can hardly cover the diverse structures in natural languages, leading to a large gap in sentence structure between synthetic and natural questions. In this paper, we propose a decomposition-based method to unify the sentence structures of questions, which benefits the generalization to natural questions. Experiments demonstrate that our method significantly improves the semantic parser trained on synthetic data (+7.9% on KQA and +8.9% on ComplexWebQuestions in terms of exact match accuracy). Extensive analysis demonstrates that our method can better generalize to natural questions with novel text expressions compared with baselines. Besides semantic parsing, our idea potentially benefits other semantic understanding tasks by mitigating the distracting structure features. To illustrate this, we extend our method to the task of sentence embedding learning, and observe substantial improvements on sentence retrieval (+13.1% for Hit@1).
Impact of Synchronizing Visual Cues With Switch of Foot Contact State on the Presence of Virtual Flight While Seated

Providing extraordinary experiences is an important aspect of virtual reality (VR). Among these unusual experiences, flying in the sky has been investigated in various studies that propose methods for providing this experience. The presentation of a floating sensation is essential for improving the virtual flight experience; however, conventional systems that present floating sensations tend to be massive. To simplify the systems presenting floating sensations, we focused on the changes in the tactile ground of the sole during takeoff and landing. This study aims to clarify how synchronization of sole contact switching and visual stimuli affects the sensation of floating in a virtual flight experience. We created an experimental device that can switch between grounded and ungrounded soles in accordance with takeoff and landing in the VR image while the user is seated. The experimental results show that synchronizing the grounding/ungrounding of the sole in a virtual environment improves the sensations of presence, floating, moving, as well as enjoyment of the flight experience. Additionally, objective evaluations using physiological data, specifically electrodermal activity (EDA), suggested that switching the physical ground contact of the sole according to takeoff and landing significantly increases the excitement level during the flight experience. These results indicate the importance of tactile ground-state manipulation synchronized with visual stimuli in the construction of a high sense of presence in a virtual flight system.
Economic appraisal standards for planning earthquake early warning infrastructure acquisition

Abstract Earthquake early warning systems can reduce potential economic losses caused by earthquakes. However, they are expensive to implement and maintain, as examples from around the word show. These examples also show the challenges in accurately estimating their costs during the acquisition phase and the consequences of poor cost estimating practices. Indeed, current approaches to early cost estimates lack consistency and a well thought out structure as there are currently no established standards to support cost planning for EEW systems. This paper argues that standards for estimating capital and maintenance costs are important during the infrastructure acquisition phase to ensure the financial viability and effective cost management of earthquake early warning systems. By comparing cost estimates of selected existing EEW systems against generic economic appraisal standards and using cost data from a research project which developed and tested a new EEW system, this paper provides insights into the factors that need to be considered when estimating the capital and maintenance costs of new EEW systems. As such, it provides the foundation for the development of economic appraisal standards for EEW systems. Standards for estimating costs could help decision makers develop well thought through estimates for capital and maintenance costs for major investments such as earthquake early warning systems and, assist subsequent cost controlling activities throughout the project life cycle.
Research on Asynchronous Information Fusion Technology of the Distributed Active/Passive Imaging Detection System

Abstract In the process of target cooperative tracking of the distributed multi-imaging detection system, aiming at the problem of asynchronous measurement information from different detectors, a distributed Asynchronous and Heterogeneous Information Matrix Fusion algorithm based on the Cubature Kalman Filter (CKF-AHIMF) is proposed. According to the Local Detector Driven Communication (LDDC) method and CKF nonlinear mapping, IMF distributed fusion algorithm is generalized into nonlinear, asynchronous, and heterogeneous cases to integrate the information from a multi-imaging detection system and to obtain global high-precision target trajectory. Through the digital simulation, it is verified that the CKF-AHIMF algorithm under the LDDC method proposed in this paper is more effective and has higher tracking accuracy than other distributed fusion algorithms in dealing with the problem of target cooperative tracking under asynchronous information from local imaging detectors, which is also valid to implement in precision detection and guidance system.
Using YouTube Video Vincent Van Gogh’s the Starry Night: Great Art Explained in Teaching Art and Language Appreciation Classroom

In the currently predominant online teaching environment, teachers use materials that are available on the Internet for their classes, be they paid content or otherwise.YouTube, as the most popular free video-sharing site, has been one of the sources of instructional material in the form of online videos.One very popular YouTube video, titled Vincent van Gogh's The Starry Night: Great Art Explained was used in the teaching of Art and Language Appreciation (Apresiasi Bahasa dan Seni) class in Universitas Negeri Malang in November 2021, and its use provides insight into using free video content in online teaching.This study intends to analyze the students' perceptions of the video's characteristics and values that make it as a good learning material for the teaching of the Art and Language Criticism (Apresiasi Bahasa dan Seni), as seen in the positive response of the class' students when they were asked to watch the video in class.The data of this study is the students' responses, in the form of essays of the summary and their opinion of the video.This study uses a qualitative approach to analyze the data and relate the findings of the students' responses to identify the good qualities of the video as instructional material.Students recognized the use of the video as a useful media as they learned new knowledge and life values.The video itself was reported to be easy enough to understand, it triggered their curiosity, and it was interesting to them.
Organizational Conflicts in the Adoption of Continuous Software Engineering

Abstract Software is a critical component of nearly every product or service. Improvements in software can lead to substantial competitive advantages. At the same time, software and surrounding engineering teams have become increasingly complex. The adoption of continuous integration and delivery is a recent trend to radically improve software release speed. However, its adoption is far from straightforward. Specifically, rethinking processes, organizational culture, ways of working, and business models require buy-in from diverse stakeholders that may have conflicting objectives. Such situations are explored by organizational conflict research. This paper reports on early lessons from an ongoing research project in continuous software engineering, specifically investigating adoption challenges from an organizational conflict perspective. We identify catalysts, symptoms, and outcomes of organizational conflicts hindering the adoption process. We conclude that predictable conflicts emerge when adopting continuous engineering. Engineers, managers, and other teams can proactively prepare for and allocate resources to resolve them. Proper analysis and management can help avoid wasted time, impeding processes, and frustration.
Pattern segmentation based on PoolNet and boundary connectivity

Traditional image segmentation algorithms need to design features manually. Therefore, based on the advantage of PoolNet in object detection, this paper proposes a clothing pattern segmentation algorithm combining PoolNet detection and boundary connectivity. This algorithm has more fine edges than traditional segmentation. Especially in the case of similar background before and after, we can get more complete pattern elements, and have better results in segmentation accuracy.