Reviewerly/exHarmony huggingface dataset:
Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data

Procedures are inherently hierarchical.To make videos, one may need to purchase a camera, which in turn may require one to set a budget.While such hierarchical knowledge is critical for reasoning about complex procedures, most existing work has treated procedures as shallow structures without modeling the parent-child relation.In this work, we attempt to construct an open-domain hierarchical knowledge-base (KB) of procedures based on wikiHow, a website containing more than 110k instructional articles, each documenting the steps to carry out a complex procedure.To this end, we develop a simple and efficient method that links steps (e.g., purchase a camera) in an article to other articles with similar goals (e.g., how to choose a camera), recursively constructing the KB.Our method significantly outperforms several strong baselines according to automatic evaluation, human judgment, and application to downstream tasks such as instructional video retrieval.
Machine/Deep Learning for Software Engineering: A Systematic Literature Review

Since 2009, the deep learning revolution, which was triggered by the introduction of ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine Learning (ML)/Deep Learning (DL). Meanwhile, critical reviews have emerged that suggest that ML/DL should be used cautiously. To improve the applicability and generalizability of ML/DL-related SE studies, we conducted a 12-year Systematic Literature Review (SLR) on 1,428 ML/DL-related SE papers published between 2009 and 2020. Our trend analysis demonstrated the impacts that ML/DL brought to SE. We examined the complexity of applying ML/DL solutions to SE problems and how such complexity led to issues concerning the reproducibility and replicability of ML/DL studies in SE. Specifically, we investigated how ML and DL differ in data preprocessing, model training, and evaluation when applied to SE tasks, and what details need to be provided to ensure that a study can be reproduced or replicated. By categorizing the rationales behind the selection of ML/DL techniques into five themes, we analyzed how model performance, robustness, interpretability, complexity, and data simplicity affected the choices of ML/DL models.
Forensic Analysis of Synthetically Generated Western Blot Images

The widespread diffusion of synthetically generated content is a serious threat that needs urgent countermeasures.As a matter of fact, the generation of synthetic content is not restricted to multimedia data like videos, photographs or audio sequences, but covers a significantly vast area that can include biological images as well, such as western blot and microscopic images.In this paper, we focus on the detection of synthetically generated western blot images.These images are largely explored in the biomedical literature and it has been already shown they can be easily counterfeited with few hopes to spot manipulations by visual inspection or by using standard forensics detectors.To overcome the absence of publicly available data for this task, we create a new dataset comprising more than 14K original western blot images and 24K synthetic western blot images, generated using four different state-of-the-art generation methods.We investigate different strategies to detect synthetic western blots, exploring binary classification methods as well as one-class detectors.In both scenarios, we never exploit synthetic western blot images at training stage.The achieved results show that synthetically generated western blot images can be spot with good accuracy, even though the exploited detectors are not optimized over synthetic versions of these scientific images.We also test the robustness of the developed detectors against post-processing operations commonly performed on scientific images, showing that we can be robust to JPEG compression and that some generative models are easily recognizable, despite the application of editing might alter the artifacts they leave.
Secure Infectious Diseases Detection System With IoT-Based e-Health Platforms

In a traditional health system, it merely depends on doctors’ initiative reports to discover infectious diseases, which causes late responses from the Center for Disease Control (CDC) and therefore may result in snowballed loss of lives and economy. Sometimes, the disease has spread when doctors realize it is infectious, and the CDC has to invest more human and material resources to control it. In this article, we propose a new secure infectious diseases detection system with the help of the IoT-based e-health platform. In our system model, the hospitals collect patients’ electronic health records (EHRs) and outsource the encrypted EHRs to the contracted cloud. The CDC can regularly send a test query to the cloud server to check whether there are patients who have similar symptoms or some increasing signs, which are regarded as signs of infectious diseases. With this system, the CDC can find the small signs of infectious diseases so that it can make appropriate and timely measures to save more lives. To enable the cloud server to perform the required test, we propose a new cryptographic notion, called public-key encryption with DFET (PKE-DFET), with which we can check whether the underlying messages of two ciphertexts are equal or not after ignoring the bits on designated positions without decryption. The cloud server can utilize the PKE-DFET to flexibly count the number of patients with similar symptoms following the CDC’s instructions. We first instantiate the PKE-DFET into a concrete construction, where anyone can be a tester to perform the DFET on ciphertexts. Finally, we extend our PKE-DFET construction to enable it to be flexible in different actual application scenarios.
Vocational Training With Microlearning—How Low-Immersive 360-Degree Learning Environments Support Work-Process-Integrated Learning

In-company training is facing new challenges in preparing employees for the demands of digitalized and automated manufacturing. New training concepts like microlearning are necessary to support work-process-related learning. To handle the limitations of microlearning, we develop a 360-degree learning system to demonstrate a realistic work environment and overcome the lack of design knowledge supporting the motivation and performance of employees using such systems. Based on a systematic literature review and semistructured interviews, we have developed design requirements for interactive 360-degree learning environments. We used a workshop-based mixed-method approach with interviews, concept maps, and video analysis to evaluate the motivation and performance of precision mechanics within a prototypical work-process-oriented learning environment in an intercompany vocational training center. The results show a positive effect on learning outcomes and motivation. In addition, the ease of use and sense of presence while using the learning environment are rated as high. We contribute to theory by shedding new light on learners’ motivation and performance within work-process-oriented interactive 360-degree learning environments. Furthermore, we offer guidelines for developing interactive 360-degree learning environments against the background of the current metaverse discussion.
Efficient Multi-view K-means Clustering with Multiple Anchor Graphs

Multi-view clustering has attracted a lot of attention due to its ability to integrate information from distinct views, but how to improve efficiency is still a hot research topic. Anchor graph-based methods and k-means-based methods are two current popular efficient methods, however, both have limitations. Clustering on the derived anchor graph takes a while for anchor graph-based methods, and the efficiency of k-means-based methods drops significantly when the data dimension is large. To emphasize these issues, we developed an efficient multi-view k-means clustering method with multiple anchor graphs (EMKMC). It first constructs anchor graphs for each view and then integrates these anchor graphs using an improved k-means strategy to obtain sample categories without any extra post-processing. Since EMKMC combines the high-efficiency portions of anchor graph-based methods and k-means-based methods, its efficiency is substantially higher than current fast methods, especially when dealing with large-scale high-dimensional multi-view data. Extensive experiments demonstrate that, compared to other state-of-the-art methods, EMKMC can boost clustering efficiency by several to thousands of times while maintaining comparable or even exceeding clustering effectiveness.
A Feasibility Study of Answer-Unaware Question Generation for Education

We conduct a feasibility study into the applicability of answer-agnostic question generation models to textbook passages.We show that a significant portion of errors in such systems arise from asking irrelevant or uninterpretable questions and that such errors can be ameliorated by providing summarized input.We find that giving these models human-written summaries instead of the original text results in a significant increase in acceptability of generated questions (33% → 83%) as determined by expert annotators.We also find that, in the absence of human-written summaries, automatic summarization can serve as a good middle ground.
Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training

Nested named entity recognition (Nested NER) aims to identify named entities which may overlap.Span-based approaches regard nested NER as a two-stage task-span extraction and classification, thus having the innate ability to handle this task.However, they face the problems of error propagation, ignorance of span boundary, difficulty in long entity recognition and requirement on largescale annotated data.In this paper, we propose Extract-Select, a span selection framework for nested NER, to tackle these problems.Firstly, we introduce a span selection framework in which nested entities with different entity categories would be separately extracted by the extractor, thus naturally avoiding error propagation in prior two-stage approaches.In the inference phase, the trained extractor selects final results specific to the given entity category.Secondly, we propose a hybrid selection strategy in the extractor, which not only makes full use of both span boundary and span content, but also improves the ability of long entity recognition.Thirdly, we design a discriminator to evaluate the extraction result, and train both extractor and discriminator with generative adversarial training (GAT).The use of GAT greatly alleviates the stress on the dataset size.Experimental results on four benchmark datasets demonstrate that Extract-Select outperforms competitive nested NER models, obtaining state-of-the-art results.The proposed model also performs well with less labeled data, proving the effectiveness of GAT.
DKNet: Deep Kuzushiji Characters Recognition Network

Kuzushiji, a cursive writing style, had been extensively utilized in Japan for over a thousand years starting from the 8<sup><i>th</i></sup> century. In 1900, Kuzushiji was not included in regular school curricula due to the change in the Japanese writing system. Nowadays Japanese natives are unable to read historical books that were written using Kuzushiji language. Therefore, libraries and museums have decided to build digital copies of the documents and books that were written in Kuzushiji language. Due to a limited number of trained experts, researchers have utilized machine and deep learning models to convert historical documents and books into a modern script that can be easily read by human beings. However, the existing deep learning techniques suffer from over-fitting and gradient vanishing problems. To overcome these problems, an efficient deep Kuzushiji characters recognition network (DKNet) is proposed. Initially, to remove noise from the training images, a trilateral joint filter is applied. Contrast limited adaptive histogram equalization (CLAHE) is then applied to enhance the visibility of filtered images. Thereafter, a pre-trained MobileNet is utilized to extract the features of Kuzushiji characters. MobileNet&#x2019;s final layers are removed, including the fully connected layer and softmax. The flatten layer is then applied to the input. A fully connected classification layer is then used with Rectified linear units (ReLUs) and dropouts. Dropouts are used to generalize the model, thus preventing the over-fitting problem. Finally, the softmax activation function is employed to provide the recognition results. To test the proposed model, actual documents are first segmented by using the proposed Maximally stable extremal regions (MSERs) and convexhull-based segmentation approach. Segmented characters are then recognized using the trained DKNet. Extensive comparative analyses reveal that DKNet achieves better performance than the competitive models in terms of various performance metrics. An efficient Application Programming Interface (API) is also designed for Japanese Kuzushiji ancient heritage character recognition to help the end-users.
A Probabilistic Code Balance Constraint with Compactness and Informativeness Enhancement for Deep Supervised Hashing

Building on deep representation learning, deep supervised hashing has achieved promising performance in tasks like similarity retrieval. However, conventional code balance constraints (i.e., bit balance and bit uncorrelation) imposed on avoiding overfitting and improving hash code quality are unsuitable for deep supervised hashing owing to their inefficiency and impracticality of simultaneously learning deep data representations and hash functions. To address this issue, we propose probabilistic code balance constraints on deep supervised hashing to force each hash code to conform to a discrete uniform distribution. Accordingly, a Wasserstein regularizer aligns the distribution of generated hash codes to a uniform distribution. Theoretical analyses reveal that the proposed constraints form a general deep hashing framework for both bit balance and bit uncorrelation and maximizing the mutual information between data input and their corresponding hash codes. Extensive empirical analyses on two benchmark datasets further demonstrate the enhancement of compactness and informativeness of hash codes for deep supervised hash to improve retrieval performance (code available at: https://github.com/mumuxi/dshwr).
Boosting the Transformer with the BERT Supervision in Low-Resource Machine Translation

Previous works trained the Transformer and its variants end-to-end and achieved remarkable translation performance when there are huge parallel sentences available. However, these models suffer from the data scarcity problem in low-resource machine translation tasks. To deal with the mismatch problem between the big model capacity of the Transformer and the small parallel training data set, this paper adds the BERT supervision on the latent representation between the encoder and the decoder of the Transformer and designs a multi-step training algorithm to boost the Transformer on such a basis. The algorithm includes three stages: (1) encoder training, (2) decoder training, and (3) joint optimization. We introduce the BERT of the target language in the encoder and the decoder training and alleviate the data starvation problem of the Transformer. After the training stage, the BERT will not further attend the inference section explicitly. Another merit of our training algorithm is that it can further enhance the Transformer in the task where there are limited parallel sentence pairs but large amounts of monolingual corpus of the target language. The evaluation results on six low-resource translation tasks suggest that the Transformer trained by our algorithm significantly outperforms the baselines which were trained end-to-end in previous works.
An Internet of Things (IoT) Based Block Chain Technology to Enhance the Quality of Supply Chain Management (SCM)

Recent technological developments indicate possible advancements in supply chain management (SCM). These innovations have attracted a lot of interest from industries including logistics, manufacturing, packaging, and transportation. The conventional systems, however, use centralised servers to control all operations, including the exchange of raw materials, making orders, dealing with buyers and sellers, and updating orders. The network’s supply chain may thus be insecure as a result of every activity being routed via centralised servers. The danger is additionally increased by a number of difficulties, including scalability, data integrity, security, and availability. Block chain technology may be used in these circumstances to decentralise transaction processing and eliminate the need for a centralised controller. In this approach, the performance of the resource-constrained supply chain network is improved by the effective use of edge computing and priority data access. The Intelligent K-Means (IKM) clustering algorithm is suggested across the edge nodes in the current research to categorise the priority level of each piece of data. This classifier determines if the edge node has received data that is high priority or low priority. Low priority data is recorded in the log files for future data analysis. Then, to allow safe data flow in the open block chain while excluding outside parties, the High Priority Access based Smart Contract (HPASC) technique is deployed. The whole experiment was conducted in a Python environment, and variables including scalability, reaction time, throughput, and accuracy were studied. Current systems’ constrained block sizes and fork creation lengthen the time transactions must wait before being processed. The suggested methodology is quicker and uses less storage space than current block chain systems. The results show that the suggested approach works better than current blockchain technology to raise the standard of supply chain management.
Does stress reduce violation intention? Insights from eustress and distress processes on employee reaction to information security policies

Past research suggests that the demands of information security policies (ISPs) cause stress upon employees, leading them to violate the policies. It emphasises the distress process but overlooks a possible positive process that may arise from the ISP demands (i.e., the eustress process) and motivate employees to reduce ISP violations. This study explores both the distress and eustress processes. It proposes that the challenge and hindrance aspects of ISP demands induce these processes and subsequently affect ISP violations. Besides, employees’ ISP-related self-efficacy may facilitate or impede these processes. To test the research model, a survey was conducted on 375 employees in the U.S. The results show that the challenge aspect of ISP demands elicits a positive psychological response of employees, which in turn triggers their planful problem-solving to deal with these demands. In contrast, the hindrance aspect of ISP demands provokes a negative psychological response that triggers employees’ wishful thinking about ISP demands. Meanwhile, employees’ self-efficacy strengthens the effect of positive psychological response on planful problem-solving. Subsequently, planful problem-solving reduces employees’ intention to violate the ISP, while wishful thinking increases their intention. This dual-process view sheds new light on the connection between ISP demands and ISP violation intention.
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding

This paper presents the shared task on Multilingual Idiomaticity Detection and Sentence Embedding, which consists of two Subtasks: (a) a binary classification task aimed at identifying whether a sentence contains an idiomatic expression, and (b) a task based on semantic text similarity which requires the model to adequately represent potentially idiomatic expressions in context.Each Subtask includes different settings regarding the amount of training data.Besides the task description, this paper introduces the datasets in English, Portuguese, and Galician and their annotation procedure, the evaluation metrics, and a summary of the participant systems and their results.The task had close to 100 registered participants organised into twenty five teams making over 650 and 150 submissions in the practice and evaluation phases respectively.
Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog

Research on (multi-domain) task-oriented dialog (TOD) has predominantly focused on the English language, primarily due to the shortage of robust TOD datasets in other languages, preventing the systematic investigation of cross-lingual transfer for this crucial NLP application area.In this work, we introduce MULTI 2 WOZ, a new multilingual multidomain TOD dataset, derived from the wellestablished English dataset MULTIWOZ, that spans four typologically diverse languages: Chinese, German, Arabic, and Russian.In contrast to concurrent efforts (Ding et al., 2021;Zuo et al., 2021), MULTI 2 WOZ contains goldstandard dialogs in target languages that are directly comparable with development and test portions of the English dataset, enabling reliable and comparative estimates of cross-lingual transfer performance for TOD.We then introduce a new framework for multilingual conversational specialization of pretrained language models (PrLMs) that aims to facilitate crosslingual transfer for arbitrary downstream TOD tasks.Using such conversational PrLMs specialized for concrete target languages, we systematically benchmark a number of zero-shot and few-shot cross-lingual transfer approaches on two standard TOD tasks: Dialog State Tracking and Response Retrieval.Our experiments show that, in most setups, the best performance entails the combination of (i) conversational specialization in the target language and (ii) few-shot transfer for the concrete TOD task.Most importantly, we show that our conversational specialization in the target language allows for an exceptionally sample-efficient fewshot transfer for downstream TOD tasks.
An Intelligent Weighted Object Detector for Feature Extraction to Enrich Global Image Information

Object detection is a fundamental task in computer vision. To improve the detection accuracy of a detection model without increasing the model weights, this paper modifies the YOLOX model by first replacing some of the traditional convolution operations in the backbone network to reduce the parameter cost of generating feature maps. We design a local feature extraction module to chunk the feature maps to obtain local image features and a global feature extraction module to calculate the correlation between feature points to enrich the feature, and add learnable weights to the feature layers involved in the final prediction to assist the model in detection. Moreover, the idea of feature map reuse is proposed to retain more information from the high-dimensional feature maps. In comparison experiments on the dataset PASCAL VOC 2007 + 2012, the accuracy of the improved algorithm increased by 1.2% over the original algorithm and 2.2% over the popular YOLOv5.