Reviewerly/exHarmony huggingface dataset:
Aspect-Based Sentiment Classification with Background Information and Syntactic Auxiliary Tasks

Aspect-based sentiment classification is the task of predicting the sentiment tendency of a text toward a given aspect. Existing works on this task mainly focus on aspect-relevant information. In contrast, we design a model (BAT) which could extract overall Background information as well as Aspect-relevant informaTion. To make the BAT model learn better semantic representation of the given text, we introduce two auxiliary tasks (dependency neighborhood prediction and part-of-speech tagging). These auxiliary tasks are used to train the model together with the main sentiment classification task. Experiments on three benchmark datasets demonstrate that our method is effective and the proposed model achieves substantial performance improvements over comparison models.
From Science Fiction to Science Fact - The Ivory Tower's Role in Developing the Law of the Future

Inaugural Lecture. While it is important to be available to our students, for teaching, and for our participation in society in general – we should jealously guard the little time of solitary, and lofty confinement, away from the bustle of the crowd and the demands of everyday life. This peaceful isolation is the most necessary evil of research. Away from the pressing needs of the real world and pressing administrative tasks. Sometimes, we need to be able to ask, "what if", - and be inspired by irrelevant and fanciful ideas from impractical and even improbable sources. Because, if we are able to dream every once in a while, those dreams turn into visions which turn into reality. The crazy "what if's" become "what whens", and eventually "what nows"? And sometime, especially when it comes to the intricate dance between law and technological innovation, it is better for lawyers to be prepared, than to always lag behind, scratching our heads, and saying "but this is not allowed".
Chain-AAFL: Chained Adversarial-Aware Federated Learning Framework

Federated learning (FL) distributes model training among multiple agents, who perform training locally but only exchange gradients due to privacy concerns. However, the threat of model poisoning attack and deep leakage from gradients still limit its practical use. We propose a novel privacy-preserving FL framework, termed as Chain-AAFL, based on a chained multi-party computing paradigm with the adversarial-aware gradient aggregation method to solve these problems. Specifically, the Chained-communication mechanism enables masked gradients to be transferred among participants. Adversarial-aware gradient aggregation could avoid model degradation caused by the model poisoning attack and gradually identify the adversary from all clients. Experimental results on CIFAR-100 demonstrate that our proposed Chain-AAFL can defense against the model poisoning attack and achieve privacy-preservation for federated learning without impairing the accuracy and training speed of the training model.
Using an LCD Monitor and a Robotic Arm to Quickly Establish Image Datasets for Object Detection

This study proposes a method for quickly establishing a large image dataset required for object detection. One way of obtaining a large image dataset is to use the green screen capturing method to collect sample images. The limitation is that the color of the object to be photographed must be different from the green background. To solve this issue, this study used an LCD monitor that can change the background color to capture the object images, and it then adopted image processing techniques to obtain the sample images and their masks for image synthesis. This study also used a robotic arm to move the camera to different locations to capture the images from various view directions to increase the diversity of the sample images. A Faster R-CNN model and a YOLOv4 model were used as the deep learning models. After training the models with 10 000 synthetic images that were created by using the proposed method, we could achieve an average mAP of 0.76 for the Faster R-CNN model and that of 0.97 for the YOLOv4 model, which were higher than the average mAP of 0.66 for the Faster R-CNN model and of 0.93 for the YOLOv4 model, both trained with 800 real images. Although more synthetic images were required to achieve better accuracy, only about 2 h were spent to prepare the 10 000 annotated synthetic images, saving about 69% of the required time to capture and label 800 real images. Another advantage of the method is that it generates instance segmentation annotations, which can be easily converted to other annotation types.
Reducing Higher-order Recursion Scheme Equivalence to Coinductive Higher-order Constrained Horn Clauses

Higher-order constrained Horn clauses (HoCHC) are a semantically-invariant system of higher-order logic modulo theories. With semi-decidable unsolvability over a semi-decidable background theory, HoCHC is suitable for safety verification. Less is known about its relation to larger classes of higher-order verification problems. Motivated by program equivalence, we introduce a coinductive version of HoCHC that enjoys a greatest model property. We define an encoding of higher-order recursion schemes (HoRS) into HoCHC logic programs. Correctness of this encoding reduces decidability of the open HoRS equivalence problem -- and, thus, the LambdaY-calculus B\"ohm tree equivalence problem -- to semi-decidability of coinductive HoCHC over a complete and decidable theory of trees.
Motion planning for nonholonomic systems with earlier destination reaching

Motion planning for nonholonomic systems with earlier destination reaching
The Impact of Artificial Intelligence on Work and Human Value: Views from Social Researchers

The rise of artificial intelligence (AI) and related new technologies have been widely discussed in recent years, especially on its benefits and threats to society. This study aims to explore the impact of AI on work and human value at different levels of AI capability. Adopting a qualitative approach, we conducted a focus group discussion with six scholars who have AI research experience in different social disciplines and in various global regions. Four main topics were discussed in the focus group, which are: (1) attitude toward AI, (2) types of businesses, industries, or workers that benefit or are threatened the most by AI, (3) willingness to work with a robot with different levels of intelligence, and (4) how to find human’s value and get prepared for future workplace. The discussion was recorded under the consent of participants and transcribed verbatim. ATLAS.ti version 8 software was used for the textual data analysis. The findings reveal that scholars: (1) are optimistic toward AI in general, (2) believe that most industries will benefit from AI, (3) are divided in attitude toward robots with empathetic intelligence, (4) argue that humans need to get prepared for the future workplace. Implications and future research suggestions are provided.
Cross-Domain Sequential Recommendation Based on Self-Attention and Transfer Learning

Abstract Cross-domain recommendation provides effective solutions to the problems of cold start and data sparsity by transferring information from one or more source domains to another target domain. However, people’s interactive behaviour usually occurs in a sequence, but most cross-domain recommendation models often ignore the sequential information. At the same time, most sequential recommendation models only consider the case of single-domain. In this paper, we propose a new cross-domain sequential recommendation model (SATLR) based on self-attention and transfer learning, which make recommendations based on user preferences and sequential dependencies learn from cross-domain. The experiment results demonstrate that on the three groups of datasets from amazon, our model is better than other recommendation models.
Kinship Verification via Reference List Comparison

Kinship verification based on facial images has attracted the attention of pattern recognition and computer vision community. Most of existing methods belong to supervised mode, in which they need to know the labels of training samples. In this paper, we adapt an unsupervised method via Reference List Comparison (RLC) for kinship verification task, which does not use external data or data augmentation. Specifically, we obtain a reference list by calculating the similarities of a probe image and all the images in the reference set. Given two probe face images, their similarity is reflected by the similarity of the two ordered reference lists. Experimental results on the KinFaceW-I and KinFaceW-II datasets show the effectiveness of RLC approach for kinship verification.
Bilingual Self-Attention Network: Generating Headlines for Online Linguistic Questions

Beginners of foreign languages tend to pose their linguistic questions online. To describe the confusion, they usually use several example sentences in a foreign language, which makes the linguistic problem tedious long, and composed of two different languages. Therefore, generating a brief headline that covers the main points of the linguist question is important. We propose a new task: Generating Headlines for online Linguistic Questions (GHLQ). The task differs from the previous headline generation task. The main point is that the linguistic question and the headline contain two languages with specific corresponding morphology and syntax. For this reason, we propose a bilingual self-attention network, which can jointly model the information in two different languages effectively. Besides, we construct two datasets, JapLQ and GerLQ, from the real world, and we conduct experiments on these datasets. By the ways of automatic evaluation and human evolution, results show the effectiveness of our model.
A robust quadruple adaptation network in few-shot scenarios

In few-shot domain adaptation (FDA), classifiers for target domain are trained with clean labeled data from source domain and few labeled data from target domain. However, in the source domain, it is not easy to acquire a large amount of clean labeled data in the wild world. Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from source domain and few labeled data from target domain, named wildly FDA (WFDA). We show that WFDA ruins existing FDA methods if taking no account of label noise in source domain. Therefore, we propose a robust quadruple adaptation network (QAN), a concise but effective solution to WFDA. QAN trains two models (each model consists of one encoder and one classifier) simultaneously, where one model samples data as the input of the other one to eliminate the negative impact caused by noisy source data. Experiments demonstrate that under WFDA, QAN outperforms existing baselines.
A new chaotic binary gravitational search algorithm and its algorithm test

A new chaotic binary gravitational search algorithm (CBGSA) is proposed to accelerate the convergence speed and improve the searching precision of binary gravitational search algorithm. Firstly, the randomness, ergodicity and regularity of chaotic sequence are used to search the kbest set in order to enhance the global searching ability. Then the dynamic adjustment strategy of gravitational constant is introduced to increase the diversity of particles and improve the convergence speed and accuracy of the algorithm. Finally, by comparing the simulation test results of each algorithm in six typical functions, it is shown that CBGSA can effectively overcome the shortcomings of premature convergence, and has obvious advantages in convergence accuracy, convergence speed and stability.
Prenominal French and Uninflected Dutch Adjectives*

Abstract This paper proposes a uniform analysis of canonical and noncanonical French and Dutch attributive adjectives. Canonically, French adjectives are postnominal and Dutch adjectives are inflected. The noncanonical counterparts are prenominal, for French, and lacking inflection, for Dutch. A review of the literature indicates several proposed locations in the noun phrase for adjectives, and this analysis utilizes three: Spec‐Subj in the D‐layer, Spec‐Agr, and NP‐internal. Adjectives in Spec‐Agr produce a canonical reading, functioning like predicates of the noun phrase. The subjective nature of the D‐layer and the proximity of NP‐internal adjectives to N accounts for the noncanonical interpretations. The formal distinction in canonicity arises because Dutch adjective inflection is the exponent of the Agr‐head, so that only Spec‐Agr adjectives are inflected, and because French NP movement crosses the adjective in Spec‐Agr, so that only Spec‐Agr adjectives are postnominal.
Approaching authorship attribution as a multi-view supervised learning task

Authorship attribution is the problem of identifying the author of texts based on the author's writing style. It is usually assumed that the writing style contains traits inaccessible to conscious manipulation and can thus be safely used to identify the author of a text. Several style markers have been proposed in the literature, nevertheless, there is still no consensus on which best represent the choices of authors. Here we assume an agnostic viewpoint on the dispute for the best set of features that represents an author's writing style. We rather investigate how different sources of information may unveil different aspects of an author's style, complementing each other to improve the overall process of authorship attribution. For this we model authorship attribution as a multi-view learning task. We assess the effectiveness of our proposal applying it to a set of well-studied corpora. We compare the performance of our proposal to the state-of-the-art approaches for authorship attribution. We thoroughly analyze how the multi-view approach improves on methods that use a single data source. We confirm that our approach improves both in accuracy and consistency of the methods and discuss how these improvements are beneficial for linguists and domain specialists.
Balancing Speed and Accuracy in Neural-Enhanced Phonetic Name Matching

Automatic co-text free name matching has a variety of important real-world applications, ranging from fiscal compliance to border control. Name matching systems use a variety of engines to compare two names for similarity, with one of the most critical being phonetic name similarity. In this work, we re-frame existing work on neural sequence-to-sequence transliteration such that it can be applied to name matching. Subsequently, for performance reasons, we then build upon this work to utilize an alternative, non-recurrent neural encoder module. This ultimately yields a model which is 63% faster while still maintaining a 16% improvement in averaged precision over our baseline model.
Spectral Embedding Approximation and Descriptor Learning for Craniofacial Volumetric Image Correspondence

Deformable image correspondence is crucial in various medical image research. Existing deep learning-based registration and correspondence models mostly learn a nonlinear voxel-wise mapping function between volumetric images by metric space alignments in the spatial domain, without addressing the intrinsic structure correspondence. Thus, the registration requires prior affine transformation or landmark annotations to handle high-frequency perturbations due to pose and structural variations. This paper presents a novel and efficient correspondence framework via low-dimensional spectral mapping to handle the intrinsic correspondence of anatomical structures. We devise a novel multi-path graph convolutional network (GCN)-based embedding approximation module, relieving the time complexity in the eigendecomposition-based spectral embedding of volumetric images. We present a descriptor learning module and surpass the descriptor selection or hand-crafted descriptors. Experimental results demonstrate the efficacy of the core modules, i.e., the image embedding approximation and descriptor learning, for volumetric image correspondence and the atlas-based registration of craniofacial anatomical structures. The proposed approach achieves comparable corresponding accuracies with the state-of-the-art deep registration models, being resilient to pose and shape perturbations.
High Throughput Pipelined Architecture for AES Cipher

There has been a significant rise in internet and wireless device users, raising the need for security to protect consumer data shared over open networks. Field Programmable Gate Arrays (FPGAs) are particularly desirable alternative for hardware implementation of cryptographic algorithm. This paper discusses hardware implementation of AES algorithm on a Field Programmable Gate Array (FPGA) platform with focus on high throughput and low area constraint. In the proposed design, an efficient pipelined architecture for AES encryption/decryption is realized using unrolled and external pipelining techniques. The proposed design is implemented on different families Virtex-5 and Spartan-6 of FPGA platform. This design obtained maximum throughput of 37.57 Gbps and 32.44 Gbps on Virtex-5 and Spartan-6, respectively. Along with throughput, it also got maximum frequency and high throughput per slice with significant number of slices.
Text removal network based on comprehensive loss evaluation and its application

This paper proposes a text removal model, Text Remove Network (TRNet), which achieves an unprecedented clearing effect of picture text. The network uses a jump-connected U-net structure to encode and decode the generator, so as to obtain clearer and sharper texture details of the original image. To solve the problem of color distortion, the generator removes the batch normalization layer and uses ELUs as the activation layer of all convolutional layers. Through the comprehensive loss, which include reconstruction, content, style, total variation, and structural similarity (SSIM) loss, we can clear the image text and preserve the image information of the background, which solves the problem of incomplete text removal and loss of background texture. The local discriminator is used to evaluate the local consistency of the text erasure area. In a text elimination experiment on a synthetic dataset and the ICDAR 2013 dataset, this method had a good effect on foreground text erasure and background authenticity restoration. Experiments on a comprehensive dataset of real documents also showed good results. To achieve targeted removal of sensitive text information on pictures, we collected datasets based on real and synthetic documents, and experimental results were satisfactory. Compared to current and classic algorithms, our text removal algorithm performs best in Image Quality Assessment (IQA).
Biomedical Image Retrieval using Muti-Scale Local Bit-plane Arbitrary Shaped Patterns

A biomedical image retrieval technique using novel multi-scale pattern based feature is proposed. The introduced technique, in each scale, employs arbitrary shaped sampling structures in addition to a classical circular sampling structure in local bit-planes for effective texture description, and named as the multi-scale local bit-plane arbitrary-shaped pattern (MS-LBASP). The proposed feature descriptor first downsamples the input image into three different scales. Then the bit planes of each downsampled image are extracted and the corresponding bit-planes are locally encoded, characterizing the local spatial arbitrary and circular shaped structures of texture. The quantization and mean based fusion is utilized to reduce the features. Finally, the relationship between the center-pixel and the fused local bit-plane transformed values are encoded using both sign and magnitude information for better feature description. The experiments were conducted to test the performance of MS-LBASP. Two benchmark computer tomography (CT) image datasets and one magnetic resonance imaging (MRI) image dataset were used in the experiments. Results demonstrate that the MS-LBASP outperforms the existing relevant state of the art image descriptors.
Research on Abnormal Detection Method of Network Big Data Flow Based on Track Information

In order to solve this problem, a new anomaly detection method based on trajectory information is proposed. The method of sliding window is used to deal with network big data flow, and the data flow is divided into data blocks. The correlation degree of track information is analyzed to extract data flow features. According to the extracted data flow characteristics, the candidate outliers in the sliding window are verified and the detection method is designed. The experimental results show that the anomaly detection method based on trajectory information has high detection rate and low error detection rate, and its performance is superior.
The Weak Supervision Approach for Question Answering over Text Using Triplets Recovering with QA-Based Rankers

Nowadays, question answering systems have applications in many areas and deep learning models now show the best results, especially in the task of obtaining an answer based on a question and some text. To train such models, a large number of question-answer-text triplets (QAT) are required. We offer a weak-supervision approach that allows recovering QAT triplets for training from the question and answer pairs (QA) using text ranking instead of manual matching. Evaluation of the approach is performed on MS MARCO and SQuAD datasets for various parameters and models. The results show that this approach allows us to effectively recover QAT-triplets and train models: QAT-models got the drop in their quality no more than 10% while requiring only a small fraction of manually matched QAT-triplets.
Research on the Development Trend of China's Key Core Technologies of Artificial Intelligence Based on the Technology Life Cycle

To sort out the global artificial intelligence’s research hotspots and key technologies, this paper creates the main international patent classification(IPC) number co-occurrence matrix based on patents in the global artificial intelligence key core technology field in the Dawei Innojoy patent database. Based on the technology life cycle it uses a logistic model to analyze the technology life cycle characteristics of the eight key core technology areas of artificial intelligence in China. The study discovers that patent layout is important to key core artificial intelligence technologies in G06F, G06K, G06T, the A61 topic cluster and the H04 broad category. According to technology life cycle feature analysis, China's artificial intelligence is relatively active in the fields of computer vision technology, brain-computer interface technology and natural language processing technology. The next decade will be crucial for China's development of key core artificial intelligence technologies. Particularly between 2025 and 2030, most of the technologies will achieve breakthroughs and mature. In order to promote the development of key core technologies of artificial intelligence in China, different development suggestions are proposed for different technology areas of life cycle, with a view to providing reference for achieving major breakthroughs and technological leaps in key core technologies of artificial intelligence in China during the 14th Five-Year Plan period.