Reviewerly/exHarmony huggingface dataset:
Construction of a Corpus of Rhetorical Devices in Slogans and Structural Analysis of Antitheses

An advertising slogan is a sentence that expresses a product or a work of art in a straightforward manner and is used for advertising and publicity. Moving the consumer's mind and attracting their interest can significantly influence sales. Although rhetorical techniques in a slogan are known to improve the effectiveness of advertising, not much attention has been devoted to analyze or automatically generate sentences with the techniques. Therefore, we constructed a large corpus of slogans and revealed the linguistic characteristics of the basic statistics and rhetorical devices. Another point of focus was antitheses, of which the usage rates are relatively high and which have a specific sentence structure and lexical constraints. The generation of a slogan that contains an antithesis necessitates the structure of sentences, known as templates, to be extracted and also requires knowledge of word pairs with semantic contrast. Thus, the next step involved analysis of the structure to extract the sentence structure and lexical knowledge about the antithesis. Despite its simple architecture, the proposed method exceeds the prediction accuracy and efficiency of a comparable method. Lexical knowledge that is not available in existing dictionaries was also extracted.
Regularized One-Layer Neural Networks for Distributed and Incremental Environments

Deploying machine learning models at scale is still a major challenge; one reason is that performance degrades when they are put into production. It is therefore very important to ensure the maximum possible generalization capacity of the models and regularization plays a key role in avoiding overfitting. We describe Regularized One-Layer Artificial Neural Network (ROLANN), a novel regularized training method for one-layer neural networks. Despite its simplicity, this network model has several advantages: it is noniterative, has low complexity, and is capable of incremental and privacy-preserving distributed learning, while maintaining or improving accuracy over other state- of-the-art methods as demonstrated by the experimental study in which it has been compared with ridge regression, lasso and elastic net over several data sets.
Research on the Development Strategy of Industrial Design Industry Based on TOT Mode

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
A bad turn deserves another: linkages between terrorism, capital flight and industrialisation

This study examines how the association between terrorism and capital flight affects the process of industrialisationin 36 African countries. The empirical evidence is based on Generalised Method of Moments (GMM) and Quantile Regressions (QR). GMM-oriented findings show that capital flight interacts with terrorism to negatively affect industrialisation in ‘domestic terrorism’- and ‘total terrorism’-oriented regressions. With QR approach, the GMM results are confirmed exclusively in the 25th and 50th quantiles, in regressions pertaining to domestic terrorism, unclear terrorism and total terrorism. It follows that the negative effect from the investigated interaction is driven by bottom quantiles of the industrialisation distribution. This confirms existing literature that developed countries are more likely to limit the negative externalities from terrorism compared to their developing counterparts. Hence, the negative consequence of the association between terrorism and capital flight on industrialisation is a decreasing function of industrialisation.
Searching Architecture and Precision for U-net based Image Restoration Tasks

Manually architecting Deep Neural Networks (DNNs) has led to the success of Deep Learning in many domains. However, recent DNNs designed using Neural Architecture Search (NAS) have exceeded manually designed architectures and have significantly reduced the human effort to develop complex networks. Current works use NAS to identify a cell architecture constrained by a fixed order of operations that is then replicated throughout the network. The constraints potentially limit the effectiveness of NAS in converging on a more efficient DNN architecture. In the first part of our paper, we propose “Operation Search,” a search on an enlarged topological space for U-net and its variants that retain efficiency. The idea is to allow for custom cells (operations and their sequence) at various levels of the network to maximize image quality while being sensitive to computation cost. In the second part of our paper, we propose custom quantization at various levels resulting in a mixed-precision network. Additionally, we increase the search efficiency by constraining the search space to use the same precision for both weights and activations at any level. This does not result in computational inefficiency because it matches the operand precisions supported by Tensor Core enabled GPUs.
Laser Marking Hatch Contour Generation Algorithm

In order to calculate the laser filling path of the irregular simply connected figure, we establish the parallel filling path model and contour filling path model. According to the classification of the concave and convex points, the total path of the laser filling line is calculated by dividing the polygon. For the multi-connected laser-filled images, an optimized multi-connected contour filling model is established, and the laser path is calculated according to the new polygon segmentation method. Through the model test, the path length required by the parallel filling model to fill the simply connected graph and the multi-connected graph is 9697.1m and 8762.8m respectively, and the required time is 29.1s and 30.2s respectively. The path lengths needed to fill simply connected graphs and multi-connected graphs by contour filling model are 10005.1m and 8210.3m respectively, and the required time is 65.8s and 49.7s respectively
Investigating Semantic Augmentation in Virtual Environments for Image Segmentation Using Convolutional Neural Networks

Collecting real-world data for the training of neural networks is enormously time-consuming and expensive. As such, the concept of virtualizing the domain and creating synthetic data has been analyzed in many instances. This virtualization offers many possibilities of changing the domain, and with that, enabling the relatively fast creation of data. It also offers the chance to enhance necessary augmentations with additional semantic information when compared with conventional augmentation methods. This raises the question of whether such semantic changes, which can be seen as augmentations of the virtual domain, contribute to better results for neural networks, when trained with data augmented this way. In this paper, a virtual dataset is presented, including semantic augmentations and automatically generated annotations, as well as a comparison between semantic and conventional augmentation for image data. It is determined that the results differ only marginally for neural network models trained with the two augmentation approaches.
Unsupervised Learning: Graph Vector

Graph data involves rich and complex potential relationships and plays an important role in many real-world applications, being used extensively in areas such as social networks, recommendation systems, science, and NLP. As AI continues to gain popularity, a growing number of machine learning tasks need to analyze and process graph data. One effective method for graph analysis is to map a graph’s elements to a low-dimensional vector space while retaining the graph’s structure and property information. This low-dimensional vector is called a graph vector (or “graph embedding”), which is described below.
Design of cloud learning work order system for software workshop application

In the exploration and practice of talent training mode of craftsman workshop, management is an important part, and supervising and checking the learning progress and knowledge mastery of students is an important part of management; in the cloud era of rapid development of information technology, applying cloud computing to the management of craftsman workshop is not only the embodiment of keeping pace with the times, but also a very effective and efficient management means. Based on this, this paper designs a cloud learning work order system for craftsman workshop management, and then implements it.
Application of S-transform and sub-pixel parallax for optical three-dimensional measurement without phase unwrapping

In the optical three-dimensional measurements of complex objects, the process of phase unwrapping affects the measurement accuracy and measurement speed. To avoid phase unwrapping, S-transform and sub-pixel matching are applied. Firstly, a digital fringe pattern is projected to an object and the wrapped phase can be extracted. Secondly, an initial parallax is calculated based on the deformed fringe pattern. According to the principle of stereo vision, candidate matching points can be calculated. Thirdly, phase slope function can be defined and the edge points can be confirmed from the phase slope. Finally, the sub-pixel parallax is calculated and 3D information can be obtained based on system calibration parameters. Experiments and results verify that the proposed method can provide high-speed and accurate 3D information, especially for discontinuous objects and shiny objects.
A New Code Change Prediction Dataset: A Case Study Based on HEP Software

Predicting changes proneness in software modules is an open area of research. This activity implies dealing with code changes datasets that are typically either incomplete or absent. To obtain a change dataset properly constructed, a new dictionary of software changes terms has been defined by leveraging our experience with the High Energy Physics (HEP) software. Our new dictionary includes various terms that classify a “code change” like warning, fixed bug, minor fix and optimization. Each term has been opportunely used to label each software module analyzed. The derived categories range from code development to performance improvements and refer to single pieces of the considered software. The resulting code-change dataset has been used to build a prediction model able to monitor software evolution and assess its maintainability over time. The present article gives details of the designed procedure that has been followed and presents the obtained results. The designed dictionary can be used with other, non-HEP, software as long as researchers can rely on well documented code changes. In such respect, our prediction model can be tested against these new datasets in order to improve both reliability and performance.
Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation

Abstract Assumption-based argumentation (ABA) is a central structured argumentation formalism. As shown recently, answer set programming (ASP) enables efficiently solving NP-hard reasoning tasks of ABA in practice, in particular in the commonly studied logic programming fragment of ABA. In this work, we harness recent advances in incremental ASP solving for developing effective algorithms for reasoning tasks in the logic programming fragment of ABA that are presumably hard for the second level of the polynomial hierarchy, including skeptical reasoning under preferred semantics as well as preferential reasoning. In particular, we develop non-trivial counterexample-guided abstraction refinement procedures based on incremental ASP solving for these tasks. We also show empirically that the procedures are significantly more effective than previously proposed algorithms for the tasks.
Hierarchical Embedding Guided Network for Video Object Segmentation

Semi-supervised video object segmentation is to segment the target objects given the ground truth annotation of the first frame. Previous successful methods mostly rely on online learning or static image pre-train to improve accuracy. However, online learning methods require huge time costs at inference time, thus restrict their practical use. Methods with static image pre-train require heavy data augmentation that is complicated and time-consuming. This paper presents a fast Hierarchical Embedding Guided Network (HEGNet) which is only trained on Video Object Segmentation (VOS) datasets and does not utilize online learning. Our HEGNet integrates propagation-based and matching-based methods. It propagates the predicted mask of the previous frame as a soft cue and extracts hierarchical embedding at both deep and shallow layers to do feature matching. The produced label map of the deep layer is also used to guide the matching of the shallow layer. We evaluated our method on the DAVIS-2016 and DAVIS-2017 validation sets and achieved overall scores of 84.9% and 71.9% respectively. Our method surpasses the methods without online learning and static image pre-train and runs at 0.08 seconds per frame.
Design of Service Mode of Smart Library based on “Field” Synergy Theory

Abstract This article through the integration of field synergy theory and the theory of cohesion between wisdom library service point, through the field of the connection between the three layers of meaning and wisdom library, in order to prove the feasibility of wisdom library application field synergy theory, through the design wisdom library within the field of collaborative service function between the field and puts forward the service mode.
On Optimal Estimate of the Block Orthogonal Greedy Algorithm for g-Frames

We obtain an optimal approximation of the block orthogonal greedy algorithm with regard to g-frames in Hilbert spaces and prove an optimal approximation result analogous to the result obtained by E. D. Livshitz [15] using dictionaries.
Speciality Group's Grouping Logic and Application of Vocational Colleges of Business in China

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Effect of the Temperature Difference between Land and Lake on Photovoltaic Power Generation

The rapid development of photovoltaic plays an important role in achieving the carbon-neutral goal. How to improve the conversion efficiency and power generation of solar photovoltaic has always been a focus issue. However, more attention is paid to the impact of photovoltaic panel working temperature on the performance of photovoltaic power generation, and how air temperature affects photovoltaic power generation has been ignored. This paper compared and analyzed the impact of the difference in air temperature between lake and land on the revenue of photovoltaic power generation, and established the functional equation between photovoltaic power generation, air temperature and solar radiation, and revealed the relationship between air temperature, solar radiation and photovoltaic power generation.
MixedNet: Network Design Strategies for Cost-Effective Quantized CNNs

This paper proposes design strategies for a low-cost quantized neural network. To prevent the classification accuracy from being degraded by quantization, a structure-design strategy that utilizes a large number of channels rather than deep layers is proposed. In addition, a squeeze-and-excitation (SE) layer is adopted to enhance the performance of the quantized network. Through a quantitative analysis and simulations of the quantized key convolution layers of ResNet and MobileNets, a low-cost layer-design strategy for use when building a neural network is proposed. With this strategy, a low-cost network referred to as a MixedNet is constructed. A 4-bit quantized MixedNet example achieves an on-chip memory size reduction of 60% and fewer memory access by 53% with negligible classification accuracy degradation in comparison with conventional networks while also showing classification accuracy rates of approximately 73% for Cifar-100 and 93% for Cifar-10.
Heralded Quantum Random Access Memory in a Scalable Photonic Integrated Circuit Platform

We propose a scalable photonic integrated circuit implementation of a heralded quantum random access memory and analyze its theoretical operation fidelity and efficiency.
Research on UAV cooperative formation and path planning algorithm

Unmanned ship technology is a derivative of the development of robotics, which has been gradually applied in maritime search and rescue, water quality monitoring and management, military reconnaissance and other fields. There are two core problems in the research of unmanned ship technology: cooperative formation and path planning, that is, how to keep the relative geometry and avoid obstacles in real time when the unmanned ship moves on the global optimal path. To solve the above problems, this paper proposes the base leader-follower method and the improved A* algorithm for the cooperative formation and global path planning of unmanned ships.
Sequential Emotion-Aware Recommendation: A Preliminary Study on Data Acquisition and Modeling

Abstract The rapid development of recommender systems makes researchers pay more and more attention to the utilization of users’ subjective factors. Among various subjective factors, emotion plays an important role that can truly reflect the temporal changes of users’ preferences. However, the acquisition of user emotion is difficult, and is obtained passively in many methods. Moreover, there are few datasets suitable for continuous emotion modeling in existing recommendation research. To address these shortcomings, this work collects and organizes a dataset of users watching movies containing their temporal emotional data, which is appropriate for recommendation tasks based on sequential emotions. In addition, we propose a sequential emotion-aware recommendation method suitable for continuous emotion modeling, which outperforms other advanced sequential modeling approaches on our dataset. The contribution of this work is to conduct preliminary study and exploration on the modeling of users’ sequential emotion, so that this research direction can be further developed in future. The Sequential EMotion-Aware Recommendation (SEMAR) dataset we collected is available at: https://github.com/LinZheng666/Sequential-Emotion-Dataset-for-Recommendation.
Developing Students’ Leadership Skills Through Cooperative Learning: An Action Research Case Study

Traditional teaching styles are not adequate to help students develop 21st-century skills. The purpose of this action research case study was to explore the teaching practices in a Romanian university and to propose a process model to improve students’ leadership skills through cooperative learning. The study is built upon Social Interdependence Theory (Johnson & Johnson, 2008), Structureprocess-outcome Theory (Johnson & Johnson, 2018), and the Empowerment Education Framework (Freire, 1993). Data was collected through observations, focus-group discussions, semistructured interviews, and document analysis. The data have been transcribed, coded, and organized by themes. The results of this action research showed a need to make traditional teaching more interactive. After implementing cooperative learning, results revealed positive effects in developing students’ leadership skills such as critical thinking, effective communication, decision making, and teamwork. The Process Model developed from this action research consists of four core components supporting cooperative learning implementation in higher education: training model, flipped classroom, interactive lecturing, and classroom management.
Research on Path Planning Method of Mobile Medical Robot Based on Laser Ranging

With the development of scientific and technological information, robots are widely used in industry, agriculture, architecture and even medical fields. It can assist or replace human beings in productive labor or high-risk activities. Reliable positioning is an essential part of any medical robot, which can be divided into position initialization and position maintenance. The surface of the environment fluctuates, and there are many kinds of obstacles in the environment. The shapes of obstacles are irregular, their distribution is random, and even they may block each other. Traditional robot positioning system adopts asymmetric bilateral bidirectional ranging technology to realize robot autonomous positioning, without considering the selection of the optimal path, which has the defects of poor flexibility and low accuracy. Lidar is a new type of sensor, which has the advantages of high precision and real-time. Combining the scanning data of laser rangefinder with the odometer from medical robot, the algorithm of simultaneous location and map generation is given.
Accelerating Depth-First Traversal by Graph Ordering

Cache efficiency is an important factor in the performance of graph processing due to the irregular memory access patterns caused by the sparse nature of graphs. To increase the cache hit rate, prior studies proposed a variety of preprocessing approaches based on the reordering, which permutes the vertexes’ labels to improve the locality of graph structures. However, the locality enhancement of existing reordering approaches does not bring much performance benefit in depth-first traversal, which is widely adopted in a majority of graph processing applications. Furthermore, the state-of-the-art reordering approach suffers from an obvious overhead on preprocessing which will greatly limit the application of their approach. In this paper, we propose SeqDFS, a depth-first graph traversal method that optimizes the cache efficiency by adjusting the order of vertexes visited and can be further extended to dynamic scenarios. We conduct extensive experiments on 16 real-world datasets and 3 representative depth-first graph applications, of which the results show that our proposal achieves a significant speed-up on both directed and undirected graphs.
“Unless One Does the Research, It May Seem as Just a Useless Battery-consuming App” – Field Notes on COVID-19 Contact Tracing Applications

Globally, countries have been developing contact tracing applications to control the spread of the coronavirus (COVID-19) disease. In this work, we present the findings of eight focus groups we conducted with participants living in France and Germany, to explore why they decided to adopt, or not adopt, a contact tracing application as well as understand how they perceived the benefits, drawbacks, and threat model of a contact tracing application.