Reviewerly/exHarmony huggingface dataset:
A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.
Low-Complexity Probing via Finding Subnetworks

The dominant approach in probing neural networks for linguistic properties is to train a new shallow multi-layer perceptron (MLP) on top of the model's internal representations.This approach can detect properties encoded in the model, but at the cost of adding new parameters that may learn the task directly.We instead propose a subtractive pruning-based probe, where we find an existing subnetwork that performs the linguistic task of interest.Compared to an MLP, the subnetwork probe achieves both higher accuracy on pre-trained models and lower accuracy on random models, so it is both better at finding properties of interest and worse at learning on its own.Next, by varying the complexity of each probe, we show that subnetwork probing Pareto-dominates MLP probing in that it achieves higher accuracy given any budget of probe complexity.Finally, we analyze the resulting subnetworks across various tasks to locate where each task is encoded, and we find that lower-level tasks are captured in lower layers, reproducing similar findings in past work.
Wave-function engineering via conditional quantum teleportation with a non-Gaussian entanglement resource

We propose and analyze a setup to tailor the wave functions of the quantum states. Our setup is based on the quantum teleportation circuit, but instead of the usual two-mode squeezed state, two-mode non-Gaussian entangled state is used. Using this setup, we can generate various classes of quantum states such as Schr\"odinger cat states, four-component cat states, superpositions of Fock states, and cubic phase states. These results demonstrate the versatility of our system as a state generator and suggest that conditioning using homodyne measurements is an important tool in the generations of the non-Gaussian states in complementary to the photon number detection.
Markovianization with approximate unitary designs

Abstract Memoryless processes are ubiquitous in nature, in contrast with the mathematics of open systems theory, which states that non-Markovian processes should be the norm. This discrepancy is usually addressed by subjectively making the environment forgetful. Here we prove that there are physical non-Markovian processes that with high probability look highly Markovian for all orders of correlations; we call this phenomenon Markovianization. Formally, we show that when a quantum process has dynamics given by an approximate unitary design, a large deviation bound on the size of non-Markovian memory is implied. We exemplify our result employing an efficient construction of an approximate unitary circuit design using two-qubit interactions only, showing how seemingly simple systems can speedily become forgetful. Conversely, since the process is closed, it should be possible to detect the underlying non-Markovian effects. However, for these processes, observing non-Markovian signatures would require highly entangling resources and hence be a difficult task.
CPS-Det: An Anchor-Free Based Rotation Detector for Ship Detection

Ship detection is a significant and challenging task in remote sensing. At present, due to the faster speed and higher accuracy, the deep learning method has been widely applied in the field of ship detection. In ship detection, targets usually have the characteristics of arbitrary-oriented property and large aspect ratio. In order to take full advantage of these features to improve speed and accuracy on the base of deep learning methods, this article proposes an anchor-free method, which is referred as CPS-Det, on ship detection using rotatable bounding box. The main improvements of CPS-Det as well as the contributions of this article are as follows. First, an anchor-free based deep learning network was used to improve speed with fewer parameters. Second, an annotation method of oblique rectangular frame is proposed, which solves the problem that periodic angle and bounded coordinates in conjunction with the regression calculation can lead to the problem of loss anomalies. For the annotation scheme proposed in this paper, a scheme for calculating Angle Loss is proposed, which makes the loss function of angle near the boundary value more accurate and greatly improves the accuracy of angle prediction. Third, the centerness calculation of feature points is optimized in this article so that the center weight distribution of each point is suitable for the rotation detection. Finally, a scheme combining centerness and positive sample screening is proposed and its effectiveness in ship detection is proved. Experiments on remote sensing public dataset HRSC2016 show the effectiveness of our approach.
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning

Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for each triple.Knowledge bases are also often incomplete, prompting the use of embedding methods to generalize from known facts, however existing embedding methods only model triple-level uncertainty and reasoning results lack global consistency.To address these shortcomings, we propose BEUrRE , a novel uncertain knowledge graph embedding method with calibrated probabilistic semantics.BEUrRE models each entity as a box (i.e.axis-aligned hyperrectangle), and relations between two entities as affine transforms on the head and tail entity boxes.The geometry of the boxes allows for efficient calculation of intersections and volumes, endowing the model with calibrated probabilistic semantics and facilitating the incorporation of relational constraints.Extensive experiments on two benchmark datasets show that BEUrRE consistently outperforms baselines on confidence prediction and fact ranking due to it's probabilistic calibration and ability to capture high-order dependencies among facts.
SentSim: Crosslingual Semantic Evaluation of Machine Translation

Machine translation (MT) is currently evaluated in one of two ways: in a monolingual fashion, by comparison with the system output to one or more human reference translations, or in a trained crosslingual fashion, by building a supervised model to predict quality scores from human-labeled data.In this paper, we propose a more cost-effective, yet well performing unsupervised alternative SentSim: relying on strong pretrained multilingual word and sentence representations, we directly compare the source with the machine translated sentence, thus avoiding the need for both reference translations and labelled training data.The metric builds on state-of-the-art embedding-based approachesnamely BERTScore and Word Mover's Distance -by incorporating a notion of sentence semantic similarity.By doing so, it achieves better correlation with human scores on different datasets.We show that it outperforms these and other metrics in the standard monolingual setting (MT-reference translation), a well as in the source-MT bilingual setting, where it performs on par with glass-box approaches to quality estimation that rely on MT model information.
PROTOTYPE-TO-STYLE: Dialogue Generation With Style-Aware Editing on Retrieval Memory

The ability of dialogue systems to express pre-specified style during conversations has a direct, positive impact on their usability and user satisfaction. While it has attracted much research interest, existing methods often generate stylistic responses at the cost of content quality. In this work, we introduce a prototype-to-style (PS) framework to tackle the challenge of stylistic dialogue generation. The proposed framework first exploits an Information Retrieval (IR) system and extracts a response prototype from the retrieved response. A stylistic response generator then takes the response prototype and the desired style as input to produce a high-quality and stylistic response. To effectively train the proposed model and imitate the real testing environment, we introduce a new style-aware learning objective and a denoising learning strategy. Results on three benchmark datasets (gender, emotion, and sentiment) from two languages demonstrate that the proposed approach significantly outperforms existing baselines both in terms of in-domain and cross-domain evaluations.
Sustainable Education Using Augmented Reality in Vocational Certification Courses

In the field of vocational education, no studies have examined the use of augmented reality (AR) learning technologies in the teaching of cosmetology. However, this study proposed the “Makeup AR” learning approach and conducted a quasi-experiment to examine the interaction effect between said approach and students with active and reflective learning styles. The research participants were 70 students from a vocational senior high school. The experimental group comprised 36 participants who were taught using Makeup AR, and the control group comprised 34 participants who were taught using an e-book. The results revealed that the use of Makeup AR improved students’ learning effectiveness. The proposed approach also effectively enhanced students’ self-efficacy and reduced their cognitive load. The students with a reflective learning style had a higher cognitive load than those with an active learning style when using the e-book learning approach. No difference in learning motivation was noted between the experimental and control groups. Overall, Makeup AR is suitable for makeup design certification courses in vocational senior high schools and can enhance learning effectiveness, improve students’ self-efficacy, and reduce cognitive load. This study suggests that the implementation of AR technology in certification courses can be beneficial to learning outcomes.
Onsite Early Prediction of PGA Using CNN With Multi-Scale and Multi-Domain P-Waves as Input

Although convolutional neural networks (CNN) have been applied successfully to many fields, the onsite earthquake early warning by CNN remains unexplored. This study aims to predict the peak ground acceleration (PGA) of the incoming seismic waves using CNN, which is achieved by analyzing the first 3 s of P-wave data collected from a single site. Because the amplitude of P-wave data of large and small earthquakes can differ, the multi-scale input of P-wave data is proposed in this study in order to let the CNN observe the input data in different scales. Both the time and frequency domains of the P-wave data are combined into multi-domain input, and therefore the CNN can observe the data from different aspects. Since only the maximum absolute acceleration value of the time history of seismic waves is the target output of the CNN, the absolute value of the P-wave time history data is used instead of the raw value. The proposed arrangement of the input data shows its superiority to the one directly inputting the raw P-wave data into the CNN. Moreover, the predicted PGA accuracy using the proposed CNN approach is higher than the one using the support vector regression approach that employed the extracted P-wave features as its input. The proposed CNN approach also shows that the accuracy of the predicted PGA and the alert performances are acceptable based on data from two independent and damaging earthquakes.
Paradigm of Geological Mapping of the Adıyaman Fault Zone of Eastern Turkey Using Landsat 8 Remotely Sensed Data Coupled with PCA, ICA, and MNFA Techniques

A principal and independent component analysis (PCA and ICA) and a minimum noise fraction analysis (MNFA) were applied in this study to Landsat 8 Operational Land Imager (OLI) images along the Adıyaman fault zone in Eastern Turkey. These analyses indicated that the lithologic units, fault patterns, and the morphological and structural features can be mapped highly accurately by using spectral-matching techniques in regions where rocks are well exposed. An inspection of all possible band combinations indicated that the PCA 134 and 231 and the ICA 132 band combinations give the best false color composite images for identifying the lithological units and contacts. The findings of the MNFA band combinations show that the MNFA 521 band combination also is robust for discriminating the lithological units, particularly Quaternary clastic units (colluvium/alluvium). MNFA band 1 alone provides the best image for tracing the tectonic and structural elements in the study area. The new up-to-date lithologic map of the Adıyaman fault zone we produced upon the interpretation of the processed OLI images reveals several river channels that are offset and beheaded by the Adıyaman fault, which verifies its Quaternary activity. This study demonstrated that, when used with the OLI data, the PCA, ICA, and MNFA are very powerful for lithological and structural mapping in actively deforming tectonic zones and hence can be applied to other regions elsewhere in the world where the climate is arid to semiarid, and the vegetation cover is scarce.
A Package for Learning on Tabular and Text Data with Transformers

Recent progress in natural language processing has led to Transformer architectures becoming the predominant model used for natural language tasks. However, in many real- world datasets, additional modalities are included which the Transformer does not directly leverage. We present Multimodal- Toolkit, an open-source Python package to incorporate text and tabular (categorical and numerical) data with Transformers for downstream applications. Our toolkit integrates well with Hugging Face's existing API such as tokenization and the model hub which allows easy download of different pre-trained models.
Design of a Computer Aided System for Self-Learning Vocal Music Singing with the Help of Mobile Streaming Media Technology

With the rapid development of computer graphics and image technology and three-dimensional modeling, great progress has also been made in the field of three-dimensional digital sculpture art.Digital sculpture art is generated through computer-aided modeling, which overcomes the shortcomings of traditional manual sculpture art, which is low in production efficiency and difficult to modify the product, and is easy to save.This article is dedicated to the study of non-contact depth-of-field-based three-dimensional measurement methods to recover the three-dimensional shape from the focused image (depth from focus), which is characterized by using an ordinary microscope to shoot a series of images with different focus depths to calculate the three-dimensional object.Without increasing the hardware cost, the shape can obtain the approximate three-dimensional shape through the analysis and processing of the digital image, which has the advantages of simple hardware, non-contact and measurable sub-microscopic objects.According to the actual measurement requirements, the hardware frame part was designed, a complete prototype was assembled, and the software system for stepping motor control and image data acquisition was compiled.On the basis of comparing the current main computer-aided modeling methods, starting from the analysis of traditional hand-sculpted modeling methods, in view of the actual characteristics of conceptual design and artistic modeling design, a sculpture method modeling idea based on Polygon modeling is proposed, and specific details are given.The modeling steps and the evaluation method of reasonable wiring are verified by examples through the modeling realization process of folk crafts.Practical application has proved that the computer sculpture modeling method has strong practicability for the 3D software modeling concept of artistic design, can fully follow the designer's conception and design, and is easier to be understood and understood by the modeling designer for creative design.
JPEG XS—A New Standard for Visually Lossless Low-Latency Lightweight Image Coding

Joint Photographic Experts Group (JPEG) XS is a new International Standard from the JPEG Committee (formally known as ISO/International Electrotechnical Commission (IEC) JTC1/SC29/WG1). It defines an interoperable, visually lossless low-latency lightweight image coding that can be used for mezzanine compression within any AV market. Among the targeted use cases, one can cite video transport over professional video links (serial digital interface (SDI), internet protocol (IP), and Ethernet), real-time video storage, memory buffers, omnidirectional video capture and rendering, and sensor compression (for example, in cameras and the automotive industry). The core coding system is composed of an optional color transform, a wavelet transform, and a novel entropy encoder, processing groups of coefficients by coding their magnitude level and packing the magnitude refinement. Such a design allows for visually transparent quality at moderate compression ratios, scalable end-to-end latency that ranges from less than one line to a maximum of 32 lines of the image, and a low-complexity real-time implementation in application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), central processing unit (CPU), and graphics processing unit (GPU). This article details the key features of this new standard and the profiles and formats that have been defined so far for the various applications. It also gives a technical description of the core coding system. Finally, the latest performance evaluation results of recent implementations of the standard are presented, followed by the current status of the ongoing standardization process and future milestones.
Optical Implementation of 2 × 2 Universal Unitary Matrix Transformations

Abstract Unitary operations are a specific class of linear transformations that have become an essential ingredient for the realization of classical and quantum information processing. The ability of implementing any n ‐dimensional unitary signal transformation by using a reconfigurable optical hardware has recently led to the pioneering concept of programmable linear optical processor, whose basic building block (BB) must be correctly designed to guarantee that the whole system is able to perform universal (i.e., arbitrary) unitary matrix transformations. Here, it is demonstrated that the present architectures of the BB do not fulfil the universal unitary property (at least) in optical processors, limiting the number of unitary matrix transformations that may be generated. Aiming to solve this fundamental constraint, the theoretical tools required to analyze and design universal unitary optical circuits and their corresponding BBs are presented. The consequences of this mathematical framework are explored, obtaining a simple route to implement different BB architectures, all of them guaranteeing a true universal unitary functionality in the resulting optical processors. These findings may pave the way to revisit the design of high‐dimensional unitary optical processors, unleashing the potential of programmable integrated photonics technology.
Improving the Faithfulness of Attention-based Explanations with Task-specific Information for Text Classification

Neural network architectures in natural language processing often use attention mechanisms to produce probability distributions over input token representations.Attention has empirically been demonstrated to improve performance in various tasks, while its weights have been extensively used as explanations for model predictions.Recent studies (Jain and Wallace, 2019; Serrano and Smith, 2019; Wiegreffe and Pinter, 2019) have showed that it cannot generally be considered as a faithful explanation (Jacovi and Goldberg, 2020) across encoders and tasks.In this paper, we seek to improve the faithfulness of attention-based explanations for text classification.We achieve this by proposing a new family of Task-Scaling (TaSc) mechanisms that learn task-specific non-contextualised information to scale the original attention weights.Evaluation tests for explanation faithfulness, show that the three proposed variants of TaSc improve attentionbased explanations across two attention mechanisms, five encoders and five text classification datasets without sacrificing predictive performance.Finally, we demonstrate that TaSc consistently provides more faithful attentionbased explanations compared to three widelyused interpretability techniques. 1
Benchmarking Neural Topic Models: An Empirical Study

Neural topic modeling approach has been attracting much attention recently as it is able to leverage the advantages of both neural networks and probabilistic topic models.Previous works have proposed several models that are based on this framework and obtained impressive experimental results compared to traditional probabilistic models.However, the reported result is not consistent across the works, making them hard for gaining a rigorous assessment of these approaches.This work aims to address this issue by offering an extensive empirical evaluation of typical neural topic models in different aspects using large, diverse datasets as well as a thorough set of metrics.Precisely, we examine the performance of these models in three tasks, namely uncovering cohesive topics, modeling the input documents, and representing them for downstream classification.Our results show that while the neural topic models are better in the first and the third tasks, the traditional probabilistic models are still a strong baseline and are better in the second task in many cases.These findings give us more insights for choosing offthe-shelf topic modeling toolboxes in different contexts, as well as for designing more comprehensive evaluation for neural topic models.
P-Stance: A Large Dataset for Stance Detection in Political Domain

Stance detection determines whether the author of a text is in favor of, against or neutral to a specific target and provides valuable insights into important events such as presidential election.However, progress on stance detection has been hampered by the absence of large annotated datasets.In this paper, we present P-STANCE, a large stance detection dataset in the political domain, which contains 21,574 labeled tweets.We provide a detailed description of the newly created dataset and develop deep learning models on it.Our best model achieves a macro-average F1-score of 80.53%, which we improve further by using semi-supervised learning.Moreover, our P-STANCE dataset can facilitate research in the fields of cross-domain stance detection such as cross-target stance detection where a classifier is adapted from a different but related target.We publicly release our dataset and code. 1