Reviewerly/exHarmony huggingface dataset:
Reasoning Path Generation for Answering Multi-hop Questions Over Knowledge Graph

Multi-hop Knowledge Graph Question Answering (KGQA) aims to find the answer entity via a reasoning path consisting of multiple fact triples in the knowledge graph (KG). Most of end-to-end KGQA approaches only pay attention to answering one-hop simple questions and lack scalability and interpretability. Meanwhile, since the high cost for data annotations, the lack of intermediate supervision signals becomes a major challenge. To address these challenges, we propose a policy-based reinforcement learning model called RPGQA which converts the task of KGQA to a reasoning path generation task in the KG. Firstly, in order to improve the interpretability of the model, the agent in our model learns an effective policy to reason a path to the answer entity as the evidence for the question. Secondly, we design an algorithm for entity disambiguation during entity linking. After that, the topic entity in the question can be linked as the beginning of the reasoning path. Furthermore, we propose a reward shaping policy consisting of three parts to enhance intermediate supervision signals, which alleviates the problem of reward delay and sparsity of reward. Extensive experiments on multiple benchmark datasets have demonstrated the effectiveness of our model. RPGQA outperforms most of the state-of-art baselines on the multi-hop KGQA task.
Automatic Report Generation Method based on Multiscale Feature Extraction and Word Attention Network

A medical report is a textual description of the information presented in a medical image, which includes detailed information about different body organs and the radiologist’s diagnosis from medical images. However, when summarizing the medical image content into a complete and accurate medical report, doctors usually face problems such as time-consuming and repetitive work. Although there are many studies in the field of automatic medical report generation, a lot of challenges still exist. First, when describing multiple organs and lesions presented in medical images, the generated report based on the single-scale feature extraction method is still inadequate and inaccurate. Second, when generating reports, most existing methods encounter problems such as duplicate words or lack of key descriptions. To solve the problems mentioned above, we propose Multiscale Feature Extraction and Word Attention Network (MFWAN) which is an automatic medical report generation model. The model contains three modules. In order to focus on abnormalities in different regions, the model includes the EPSA (Efficient Pyramid Split Attention) Multiscale Feature Extraction module which utilizes spatial information at different scales of medical images. After that, the visual features are classified by a Multi-Classification Context Generation Module to generate context messages. Then, by assigning different weights to the hidden layers of word LSTM, the Word-Attention-Based Report Generation module generates more accurate words with implicit disease critical information. Experimental results on benchmark datasets, IU X-Ray, show that our proposed MFWAN outperforms previous works and generates more accurate reports.
A Task-Aware Attention-Based Method for Improved Meta-Learning

Based on massive data, deep neural networks have been proven to have a powerful learning capability of non-linear relationships. However, training deep neural networks on limited samples is still challenging, which may lead to the over-fitting problem. To alleviate this problem, meta-learning was proposed to train a model that can rapidly adapt to a new task with only a few related examples. However, existing meta-learning approaches tend to ignore the domain gap between different tasks. For a specific task, some of the features are unrelated or even disruptive, which may cause damage to the effectiveness of meta-learning. To address this issue, in this paper, we propose a novel attention-based method that can skip the useless features and highlight the task-specific information. We design two simple but effective attention modules, which take task representation as input and produce attention weights for features from two different perspectives. Experiments conducted on four benchmarks validate that our method outperforms state-of-the-art methods, and the main idea can be applied to various existing meta-learning models.
Multi-Information-Enhanced Knowledge Embedding in Hyperbolic Space

Knowledge Graph Representation Learning(KGRL) aims to map entities and relationships into a low-dimensional dense vector space. Most of the existing models focus only on the information of the triple when doing representation learning, ignoring the rich external semantic information. At the same time, these models consider entities and relations as static and single representations, so the knowledge represent ability is poor. Accordingly, we propose a novel knowledge graph representation model which enhanced knowledge graph embedding with multi-information. Firstly, our model carries out text enhancement and hyperbolic space embedding of triples in the knowledge graph respectively; Secondly, we concatenate the enhanced vector. Then, the concatenated vector through two transformation layer to fuse the semantic information and spacial information. Finally, we use the fused information to learn the context information through the Transformer coding layer, which will dynamically produce the final representation of the entity based on its context. Experimental results show that our model has a great improvement over other models. In the link prediction task, the evaluation protocol Hits@10 and MRR in the public dataset FB15k improve by 28.4% and 29.5% compared with the translation model. Compared with state-of-the-art model, the improvement is 2.5%, 6.3%.
SSCG: Spatial Subcluster Clustering Method by Grid-Connection

Existing clustering methods rely on prior knowledge of the data set to cluster it, so the quality of the clustering effect is entirely dependent on the user’s familiarity with the data set. Furthermore, when extracting the information from a data set, existing clustering algorithms frequently ignore the geometric distribution of data, making it difficult to identify data objects in their entirety and detect local spatial structures. To address these issues, this paper proposes a spatial subcluster clustering method by grid-connection, which automatically obtains subclusters by iterative local labeling without requiring a priori knowledge of the data set and efficiently extracts correlations between data by establishing relationships between subclusters by grid-connecting. Experiments are conducted to validate the proposed algorithm against existing state-of-the-art algorithms on 9 synthetic and 4 real data sets. The results show SSCG can efficiently utilize the information on the grid space without relying on a priori knowledge, and the overall performance is better than the existing advanced algorithms.
Transformer-Based Representation Learning on Temporal Heterogeneous Graphs

Temporal heterogeneous graphs can model lots of complex systems in the real world, such as social networks and e-commerce applications, which are naturally time-varying and heterogeneous. As most existing graph representation learning methods cannot efficiently handle both of these characteristics, we propose a Transformer-like representation learning model, named THAN, to learn low-dimensional node embeddings preserving the topological structure features, heterogeneous semantics, and dynamic evolutionary patterns of temporal heterogeneous graphs simultaneously. Specifically, THAN first samples heterogeneous neighbors with temporal constraints and projects node features into the same vector space, then encodes time information and aggregates the neighborhood influence in different weights via type-aware self-attention. Experiments on three real-world datasets demonstrate that THAN outperforms the state-of-the-arts in terms of effectiveness with respect to the temporal link prediction task.
AOPSS: A Joint Learning Framework for Aspect-Opinion Pair Extraction as Semantic Segmentation

Aspect-opinion pair extraction (AOPE) task, aiming at extracting aspect terms and their corresponding opinion terms in pairs, has caused widespread attention in recent years. Most studies focus on incorporating external knowledge, such as syntactic information. However, they are limited by the inadequate ability to capture long-distance information, and the utilization of external knowledge is more costly. In this paper, we propose AOPSS, a joint learning framework, to explore the AOPE task as semantic segmentation. As in most prior studies, we divide the AOPE task into two subtasks: entity recognition and relation detection. Specifically, AOPSS can synchronously capture task-invariant and task-specific features for the two subtasks without integrating any additional knowledge. Furthermore, we consider the interaction between entity and relation feature representations, which can improve the mutual heuristic effect for the two subtasks. Experimental results illustrate that our method achieves state-of-the-art performance on four public datasets, and we take further analysis to demonstrate the effectiveness of our approach.
Privacy-preserving cloud-edge collaborative learning without trusted third-party coordinator

Abstract Cloud-edge collaborative learning has received considerable attention recently, which is an emerging distributed machine learning (ML) architecture for improving the performance of model training among cloud center and edge nodes. However, existing cloud-edge collaborative learning schemes cannot efficiently train high-performance models on large-scale sparse samples, and have the potential risk of revealing the privacy of sensitive data. In this paper, adopting homomorphic encryption (HE) cryptographic technique, we present a privacy-preserving cloud-edge collaborative learning over vertically partitioned data, which allows cloud center and edge node to securely train a shared model without a third-party coordinator, and thus greatly reduces the system complexity. Furthermore, the proposed scheme adopts the batching technique and single instruction multiple data (SIMD) to achieve parallel processing. Finally, the evaluation results show that the proposed scheme improves the model performance and reduces the training time compared with the existing methods; the security analysis indicates that our scheme can guarantee the security in semi-honest model.
A Multiplicative-Additive Chaotic-Address Steganography

In this study, Multiple-Chaotic maps were merged by using multiplicative-additive form to generate the&#x0D; chaotic sequences which are used to track the addresses of shuffled bits in steganography. Three techniques are&#x0D; introduced for image steganography in the spatial domain. The first system is based on the well-known LSB&#x0D; technique, the second system is based on looking for the identical bits between the secret message and the cover&#x0D; image and the third system is based on the concept of LSB substitution, it is employed the mapping of secret data bits&#x0D; with cover pixel bits. It was tested and evaluated security levels for the proposed techniques by using the Peak&#x0D; Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), histogram analysis and correlative analysis and tested the&#x0D; Chaotic sequences generated by using correlation, Lypaunov exponents, Poincaré section and 0-1Test. The results&#x0D; show that the proposed methods perform better than existed systems.
Graph-Based Sequential Interpolation Recommender for Cold-Start Users

Sequential recommendation systems aim to capture the users' dynamic preferences from users' historical interactions to provide more accurate recommendations. However, in many scenarios, there are a large number of cold-start users with limited user-item interactions. To address this challenge, some studies utilize auxiliary information to infer users' interests. But with the increasing awareness of personal privacy protection, it is difficult to obtain detailed descriptions of users. Therefore, we propose a model called GitRec (Graph-based sequential interpolation Recommender for cold-start users) to address this issue. Our proposed model GitRec captures users' latent preferences and provides more proper recommendations for cold-start users without any help from external information. Specifically, (i) GitRec constructs the global graph based on all sequences to obtain the core interests of the warm-start users. (ii) Then, GitRec selects content-rich sequences that are most similar to the short sequences and constructs the local graph to find out the potential preferences of cold-start users. (iii) Finally, GitRec can find suitable candidates to enrich the information of the cold-start users by using graph neural networks, and provide better recommendations with the help of core interests and potential preferences. We conducted extensive experiments on three real-world datasets. The experimental results demonstrate that GitRec has significant improvement over state-of-the-art methods.
Research Skills as the Student Learning Achievement

This article focuses on the theoretical underpinning of the effectiveness of the conditions for research skills development in primary school students. The study used the following methods: theoretical analysis, survey, teaching observation, analysis of educational and teaching products, pedagogical experiment, and statistical method. The authors substantiated the hypothesis that effective research skill development requires technology development and the creation of necessary pedagogical conditions. As a result, the following distinguish research skills typical of primary students were identified: ability to organize own activities, work with information, conduct educational and research work, prepare and present the results of the study, analyze, and evaluate the research activity. To date, the criteria and levels of research skill development in primary students are not sufficiently developed, which complicates the diagnostics of research skills of primary students. This problem remains relevant and underdeveloped, it should be given more attention in the next phase of this research.
Self-guided Contrastive Learning for Sequential Recommendation

Sequential recommendation has injected plenty of vitality into online marketing and retail industry. Existing contrastive learning-based models usually resolve data sparsity issue of sequential recommendation with data augmentations. However, the semantic structure of sequences is typically corrupted by data augmentations, resulting in low-quality views. To tackle this issue, we propose Self-guided contrastive learning enhanced BERT for sequential recommendation (Self-BERT). We devise a self-guided mechanism to conduct contrastive learning under the guidance of BERT encoder itself. We utilize two identically initialized BERT encoders as view generators to pass bi-directional messages. One of the BERT encoders is parameter-fixed, and we use the all Transformer layers’ output as a series of views. We employ these views to guide the training of the other trainable BERT encoder. Moreover, we modify the contrastive learning objective function to accommodate one-to-many positive views constraints. Experiments on four real-world datasets demonstrate the effectiveness and robustness of Self-BERT.
A2TN: Aesthetic-Based Adversarial Transfer Network for Cross-Domain Recommendation

To address the long-standing data sparsity problem in recommender systems, Cross-Domain Recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain. Therefore, enhancing the transferability of features in different domains is crucial for improving the recommendation performance. However, existing methods are usually confronted with negative transfer due to the data sparsity problem. To this end, we propose an Aesthetic-based Adversarial Transfer Network (A2TN) for CDR, which takes advantage of the transferability of aesthetic preferences to ensure the effect of feature transfer. Specifically, we first utilize an aesthetic network to extract aesthetic features and a general feature layer to embed general features, which can collaboratively capture user’s comprehensive preferences. Then, we adopt the adversarial transfer network to generate domain-dependent features for avoiding negative transfer and domain-independent features for effective knowledge transfer. Moreover, an attention mechanism is used to fuse different preferences with different weights to reflect their importance in better portraying user’s preferences. Finally, the experimental results on real-world datasets demonstrate that our proposed model outperforms the benchmark recommendation methods.
Recording and analysing physical control variables used in clarinet playing: A musical instrument performance capture and analysis toolbox (MIPCAT)

Measuring fine-grained physical interaction between the human player and the musical instrument can significantly improve our understanding of music performance. This article presents a Musical Instrument Performance Capture and Analysis Toolbox (MIPCAT) that can be used to capture and to process the physical control variables used by a musician while performing music. This includes both a measurement apparatus with sensors and a software toolbox for analysis. Several of the components used here can also be applied in other musical contexts. The system is here applied to the clarinet, where the instrument sensors record blowing pressure, reed position, tongue contact, and sound pressures in the mouth, mouthpiece, and barrel. Radiated sound and multiple videos are also recorded to allow details of the embouchure and the instrument’s motion to be determined. The software toolbox can synchronise measurements from different devices, including video sources, extract time-variable descriptors, segment by notes and excerpts, and summarise descriptors per note, phrase, or excerpt. An example of its application shows how to compare performances from different musicians.
Ignorance as a Method: Rethinking Academic Burden in an Accelerated Society

Purpose This study applies “ignorance” as a theoretical lens to understand students’ academic burden in China as well as the value of free time in social acceleration. Design/Approach/Methods The article argues that students’ learning gradually became alienated in different times and spaces. It uses Rosa's social acceleration theory to analyze the logical paradox of the burden reduction policy in China and Benner's plasticity and self-activity principles to highlight the pedagogical value of leisure and free activities. Findings The findings suggest the need to maintain the “necessary ignorance” in leisure activities and intervene less in student learning. In this respect, “ignorance” can be viewed as a cognitive method to protect leisure time and rebuild a spontaneous order in learning, which can close the gap caused by the impossible synchronization of specific parties in the education system. Ignorance opens the door to learned spaces, providing more opportunities to reduce academic burden in an accelerated society and stimulate the imagination of education. Originality/Value This study reveals the plasticity and self-activity that students develop in free leisure time and space, and reworks the key concept of “inevitable ignorance” expounded by Friedrich Hayek into “necessary ignorance” with pedagogical and ethical meanings. The reconceptualization of academic burden opens up new ways of thinking about educational policy and educational practice.
Bit inclusion test: an enhanced avalanche test for stream ciphers

In this paper, we present a new test for stream ciphers named the ‘Bit Inclusion Test (BIT)’. This test provides an enhancement to the conventional avalanche test for stream ciphers. This test is efficient and includes the conventional avalanche test. Additionally, it can capture the shortcomings which escape the conventional avalanche test. We also demonstrate, with examples, how BIT is capable of going deeper into the structure of crypto-algorithms and detecting internal biases which make an algorithm susceptible to cryptanalytic attacks.
A secure framework for medical image by integrating watermarking and encryption through fuzzy based ROI selection

Security, secrecy, and authenticity problems have arisen as a result of the widespread sharing of medical images in social media. Copyright protection for online photo sharing is becoming a must. In this research, a cutting-edge method for embedding encrypted watermarks into medical images is proposed. The proposed method makes use of fuzzy-based ROI selection and wavelet-transformation to accomplish this. In the first step of the process, a fuzzy search is performed on the original picture to locate relevant places using the center region of interest (RoI) and the radial line along the final intensity. The suggested method takes a digital picture and divides it into 4×4 non-overlapping blocks, with the intent of selecting low information chunks for embedding in order to maximize invisibility. By changing the coefficients, a single watermark bit may be inserted into both the left and right singular SVD matrices. The absence of false positives means the suggested technique can successfully integrate a large amount of data. Watermarks are encrypted using a pseudorandom key before being embedded. Discrete wavelet transform saliency map, block mean method, and cosine functions are used to construct an adaptively-generated pseudo-random key from the cover picture. Images uploaded to social media platforms must have a high degree of invisibility and durability. These watermarking features, however, come with a price. The optimal scaling factor is used to strike a balance between the two in the proposed system. Furthermore, the suggested scheme’s higher performance is confirmed by comparison with the latest state-of-the-art systems.
Smart Sports Outward Bound Training Assistant System Based on WSNs

The outward-bound training has been a popular manner to exercise in daily life. However, there lacks an intelligent assistant system to monitor the real-time status of users to avoid accidents during training. In order to fill this gap, this paper established an intelligent system to monitor fatigue status during outward-bound training by using surface electromyography (sEMG) signals. The system consists of three parts: a wearable device, edge node, and cloud server. First, the wearable device collects sEMG signals. Second, the edge node processes the collected sEMG signals and sends the sEMG signal features to the cloud server. Finally, the cloud server returns the results to edge node according to a stored classification model that learnt from massive historical sEMG signals. The experimental results show the effectiveness of the proposed system.
Blockchain Maturity of Ghanaian Financial Institutions and Their Readiness to Adopt Distributed Ledger for KYC Processes

Although information technology has positively influenced operations in the Ghanaian financial sector, there is still a high operational cost in performing KYC procedures due to duplication of efforts during clients' onboarding. The decentralized nature of blockchains makes them ideal for addressing these challenges. In this paper, the blockchain maturity model was used to assess the maturity and readiness of Ghanaian banks to adopt blockchain technology for KYC processes. Using primary data obtained via questionnaires and interviews, the individual components of the blockchain maturity model were assessed. The results indicate that the network, hardware, and software components are at repeatable, defined, and managed stages, respectively, while the people component lags in the initial stage due to a lack of adequate staff training. Finally, security and privacy are at the defined stage, whereas policy and regulations are at the initial stage.