Reviewerly/exHarmony huggingface dataset:
Fuzzy Representation Learning on Dynamic Graphs

Exploring dynamic patterns from complex and large-scale networks is a significant and challenging task in graph analysis. One of the most advanced solutions is dynamic graph representation learning, which embeds structural and temporal correlations into a representative vector for each node or subgraph. Existing models have made some successes, such as overcoming the problems of induction for unseen nodes and scalability for large-scale evolving networks. However, these models usually rely on crisp representation learning that is incapable of modeling feature fuzziness and capturing uncertainties in dynamic graphs. While real-world dynamic networks as complex systems always contain non-negligible but inestimable uncertainties in node/link attributes and network topology. These uncertainties may cause the learned representations from crisp models hard to precisely reflect network evolution. To address the issues, we propose a new dynamic graph representation learning model, called FuzzyDGL, which first incorporates fuzzy representation learning to handle the uncertainties in dynamic graphs. Through combining CDGRL with fuzzy logic, the FuzzyDGL digests both of their advantages. On the one hand, it has flexible model scalability and brilliant inductive capability. On the other hand, it can model feature fuzziness to reduce the impact of uncertainties in dynamic graphs, improving the quality of learned representations. To demonstrate its effectiveness, we conduct two important tasks of network analysis, including link prediction and node classification, over eight real-world datasets. The experimental results show the strong competitiveness and generalization of the FuzzyDGL against a number of baseline models.
An improved Arabic text classification method using word embedding

Feature selection (FS) is a widely used method for removing redundant or irrelevant features to improve classification accuracy and decrease the model’s computational cost. In this paper, we present an improved method (referred to hereafter as RARF) for Arabic text classification (ATC) that employs the term frequency-inverse document frequency (TF-IDF) and Word2Vec embedding technique to identify words that have a particular semantic relationship. In addition, we have compared our method with four benchmark FS methods namely principal component analysis (PCA), linear discriminant analysis (LDA), chi-square, and mutual information (MI). Support vector machine (SVM), k-nearest neighbors (K-NN), and naive Bayes (NB) are three machine learning based algorithms used in this work. Two different Arabic datasets are utilized to perform a comparative analysis of these algorithms. This paper also evaluates the efficiency of our method for ATC on the basis of performance metrics viz accuracy, precision, recall, and F-measure. Results revealed that the highest accuracy achieved for the SVM classifier applied to the Khaleej-2004 Arabic dataset with 94.75%, while the same classifier recorded an accuracy of 94.01% for the Watan-2004 Arabic dataset.
Predicting automobile insurance fraud using classical and machine learning models

Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases.
Data integrity aware system for executing dynamic operations on outsourced cloud data

The cloud computing platform of today handles a greater amount of real-time data, which requires dynamic data operations. The majority of the currently available cloud techniques, however, do not concentrate on dynamic data operations. Hence, this work provides a system that enables dynamic data operations to be carried out on a cloud computing platform, such as the insertion, deletion, and updating of data. The performance of the work is tested in terms of time consumption with respect to data storage, retrieval, data block insertion and deletion operations. The analysis is carried out by altering the data volume and the performance of the work is satisfactory with reasonable time consumption.
Exploiting Physical Referent Features as Input for Multidimensional Data Selection in Augmented Reality

Embedding data into the physical environment using augmented reality (AR) is a practical approach for data visualization as it offers a large and flexible display space on or around the physical referent, i.e. the physical object to which the data is related. Yet current interaction in such context is often performed using cumbersome dedicated devices, tiring mid-air gestures or awkward on-body input. In this paper, we investigate the use of the physical referent itself as a support for input interaction with an embedded space-time cube (STC) representation. Hence, we first identify the most promising mappings between the physical features of the referent (edges, faces, corners) and the STC dimensions. Then, we design three data selection techniques using the physical referent and compare them to mid-air gestures when performing selection tasks on the STC. Overall, our work demonstrates that using the physical referent to support input interaction with embedded data representations is an efficient and comfortable approach for data selection in standing and sitting situations.
Deep learning enabled blockchain based electronic heathcare data attack detection for smart health systems

The idea of networked personal medical devices is a component of contemporary Smart Health Systems (SHS). These gadgets offer remote observing and the exchange of wellbeing information, which enormously further develop the patient's personal satisfaction while at the same time reducing treatment expenses for both the patient and the medical care suppliers (telemedicine). For individuals' wellbeing, a cutting edge individual wellbeing record framework is fundamental. Here, there are still difficulties with information mix from different EHRs, information interoperability, and guaranteeing that admittance to information is totally under the power of the patient. Some security issues are caused by the Network. To settle these issues, we propose a novel profound learning-based framework that circuits state of the art decentralized innovations like IPFS and blockchain with wellbeing information interoperability principles and advances like FHIR's APIs. In this review, we show that correspondence between private clinical gadgets is as a matter of fact powerless to different cyber attacks. We show how an outer assailant could involve man-in-the-center, replay, bogus information infusion, and refusal of-administration assaults to block delicate wellbeing information stream by capturing the correspondence of the individual clinical gadget. We likewise suggest an Interruption Recognition Framework (IDS), GAN, to additional screen traffic on private clinical gear and spot attacks against them. Our extensive investigation shows that GAN, with an F1-score of 98 % and an accuracy of 98.7 %, can successfully and accurately recognise numerous assaults on personal medical equipment.
Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom\'s epistemic taxonomy based on textual comments in educational discussion forums. Our proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom\'s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis.
Professional and personal traits of the teacher and the relationship with didactic strategies

&lt;span lang="EN-US"&gt;During the context of virtual classes due to the pandemic, it was evidenced that many teachers had to face accelerated adaptation stages, which is why teachers presented difficulties in designing their teaching strategies and students showed certain difficulties in their learning. In this sense, this article aims to determine how the professional and personal traits of the teacher are related to their teaching strategies, in the context of COVID-19, from the perspective of the students. For which an investigation was developed with a quantitative approach, correlational level and non-experimental design. It was determined that there is a moderate direct relationship between the professional and personal traits of the teacher with his didactic strategies in the context of virtual teaching. Thus, it was also identified that teachers do not pour their professional experiences into the development of the subject, additionally it does not favor the solution of problems related to real life applications; this leads teacher to reflect on the importance of the construction of student knowledge, especially if it is about subjects that are taught in the last cycles, since the student is waiting for how and in which processes their knowledge will be applied theoretical knowledge.&lt;/span&gt;
DGTRL: Deep graph transfer reinforcement learning method based on fusion of knowledge and data

Deep reinforcement learning has shown promising application effects in many fields. However, issues such as low sample efficiency and weak knowledge transfer and generalization capabilities constrain its in-depth development. This paper proposes a deep graph transfer reinforcement learning method based on knowledge and data fusion. Through knowledge transfer between tasks and data training in exploration, efficient learning driven by knowledge and data fusion is realized. First, we map the trajectories of agents in the source and target domains into graph structures to represent knowledge and environmental models and use a graph matching method to match the knowledge adaptation domain based on similarity. Second, real-time transfer of source domain knowledge is carried out in a similar mapping domain, and policy mapping, verification, and adaptive transfer mechanisms based on knowledge reuse frequency are designed to effectively improve the transfer effect. Finally, we evaluate and validate the proposed algorithm, mechanism, and their application effectiveness in diverse and multitask knowledge transfer scenarios using three indicators in environments such as maze. The results show that the method improves the target task time indicators by 84.7% and 70% under the conditions of no verification exploration and a standard transfer setting, respectively, achieving significant knowledge reuse effects.
Teaching speaking in Kampung Inggris: the tutors’ challenges and solutions

Teaching English speaking has become a centre of attention among scholars and teaching English to speakers of other languages (TESOL) practitioners for a few decades. However, the issue of teaching English speaking in Kampung Inggris/English Village remains under-researched, notably viewed from the tutors’ challenges and solutions in the Indonesian English as a foreign language (EFL) milieu. This study addressed this gap. Two English tutors from Kampung Inggris/English Village got involved as the participants. The data were collected through semi-structured interviews and analyzed with thematic analysis. The findings reported that there were five challenges encountered by tutors when teaching English speaking, namely tutors' inability of exploring English language teaching materials, tutors' insufficient English vocabulary, tutors' ineffective time management during language teaching practices, demotivated tutees to learn English, and perceived teaching anxiety. In addition, the tutors stipulated four solutions to cope with such challenges, namely building a good rapport between tutor and tutees, tutors' self-motivation to English vocabulary enrichment, selecting appropriate and providing updated English language teaching materials, and motivating English language tutees. Therefore, teaching English speaking should not only rely on immersing students in fluency-based activities but also manage tutees' psychological factors to attain more effective teaching English speaking objectives.
Multimodal Detection and Classification of Robot Manipulation Failures

An autonomous service robot should be able to interact with its environment safely and robustly without requiring human assistance. Unstructured environments are challenging for robots since the exact prediction of outcomes is not always possible. Even when the robot behaviors are well-designed, the unpredictable nature of the physical robot-object interaction may lead to failures in object manipulation. In this work, we focus on detecting and classifying both manipulation and post-manipulation phase failures using the same exteroception setup. We cover a diverse set of failure types for primary tabletop manipulation actions. In order to detect these failures, we propose FINO-Net [1], a deep multimodal sensor fusion-based classifier network architecture. FINO-Net accurately detects and classifies failures from raw sensory data without any additional information on task description and scene state. In this work, we use our extended FAILURE dataset [1] with 99 new multimodal manipulation recordings and annotate them with their corresponding failure types. FINO-Net achieves 0.87 failure detection and 0.80 failure classification F1 scores. Experimental results show that FINO-Net is also appropriate for real-time use.
Precision in spatial working memory examined with mouse pointing

The capacity of visuospatial working memory (VSWM) is limited. However, there is continued debate surrounding the nature of this capacity limitation. The resource model (Bays et al., 2009) proposes that VSWM capacity is limited by the precision with which visuospatial features can be retained. In one of the few studies of spatial working memory, Schneegans and Bays (2016) report that memory guided pointing responses show a monotonic decrease in precision as set size increases, consistent with resource models. Here we report two conceptual replications of this study that use mouse responses rather than pointing responses. Overall results are consistent with the resource model, as there was an exponential increase in localisation error and monotonic increases in the probability of misbinding and guessing with increases in set size. However, an unexpected result of Experiment One was that, unlike Schneegans and Bays (2016), imprecision did not increase between set sizes of 2 and 8. Experiment Two replicated this effect and ruled out the possibility that the invariance of imprecision at set sizes greater than 2 was a product of oculomotor strategies during recall. We speculate that differences in imprecision are related to additional visuomotor transformations required for memory-guided mouse localisation compared to memory-guided manual pointing localisation. These data demonstrate the importance of considering the nature of the response modality when interpreting VSWM data.
Customer sentiment recognition in conversation based on bidirectional LSTM and self-attention mechanism

In the E-commerce environment, conversations between customers and businesses contain lots of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. For conversational sentiment analysis, most existing approaches take contextual information into account. On this basis, we focus on the degree of association between utterances, which can more effectively capture overall and useful sentiment information in conversation. For this purpose, we propose a hybrid model to recognize customer sentiment in conversation. The model obtains utterance vectors with sentiment information through Sentiment Knowledge Enhanced Pre-training (SKEP), then uses the bidirectional long short-term memory network (BiLSTM) to generate contextual semantic information, and further obtains customer sentiment information by applying the self-attention mechanism to focus on the degree of association between utterances. The experimental results on the JD Dialog dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation.
Scalable and resilient 360-degree-video adaptive streaming over HTTP/2 against sudden network drops

Realistic Virtual Reality is supported by 360° video, which provides viewers with an immersive watching experience. However, 360°video is bulky in size, while the transmission system has limited ability to provide bandwidth. As such, intelligent adaptive delivery solutions play a crucial role in enabling users to stream high-quality 360°video. In this research, we propose a novel approach for 360-degree video streaming over HTTP/2 that can provide consumers with a good watching experience (QoE) even in varying network circumstances and head-eye movements over time. The proposed method deploys the so-called BBAG algorithm (Buffer and Bandwidth Allocation Algorithm) using Scalable Video Coding to choose appropriate tile layers to resolve the trade-off between network and user adaptivity. With the support of HTTP/2's stream termination capability, the delivery of late tile layers is terminated to handle abrupt interruptions. By employing multiple buffer thresholds, BBAG is able to adapt bitrates to changes in users' perspective while watching a 360-degree video. BBAG is proven to improve QoE up to about 90% by maintaining high and stable buffer level, while enhancing average viewport bitrates by about 80% compared to state-of-the-art methods in different scenarios of network bandwidths.
Sensor-based agitation prediction in institutionalized people with dementia A systematic review

Early detection of agitation in individuals with dementia can lead to timely interventions, preventing the worsening of situations and enhancing their quality of life. The emergence of multi-modal sensing and advances in artificial intelligence make it feasible to explore and apply technology for this goal. We conducted a literature review to understand the current technical developments and challenges of its integration in caregiving institutions. Our systematic review used the Pubmed and IEEE scientific databases, considering studies from 2017 onwards. We included studies focusing on linking sensor data to vocal and/or physical manifestations of agitation. Out of 1622 identified studies, 12 were selected for the final review. Analysis was conducted on study design, technology, decisional data, and data analytics. We identified a gap in the standardized semantic representation of both behavioural descriptions and system event generation configurations. This research highlighted initiatives that leverage existing information in a caregiver's routine, such as correlating electronic health records with sensor data. As predictive systems become more integrated into caregiving routines, false positive reduction needs to be addressed as those will discourage their adoption. Therefore, to ensure adaptive predictive capacity and personalized system re-configuration, we suggest future work to evaluate a framework that incorporates a human-in-the-loop approach for detecting and predicting agitation.
An Efficient Hybrid Deep Learning Accelerator for Compact and Heterogeneous CNNs

Resource-efficient Convolutional Neural Networks (CNNs) are gaining more attention. These CNNs have relatively low computational and memory requirements. A common denominator among such CNNs is having more heterogeneity than traditional CNNs. This heterogeneity is present at two levels: intra-layer type and inter-layer type. Generic accelerators do not capture these levels of heterogeneity, which harms their efficiency. Consequently, researchers have proposed model-specific accelerators with dedicated engines. When designing an accelerator with dedicated engines, one option is to dedicate one engine per CNN layer. We refer to accelerators designed with this approach as single-engine single-layer (SESL). This approach enables optimizing each engine for its specific layer. However, such accelerators are resource-demanding and unscalable. Another option is to design a minimal number of dedicated engines such that each engine handles all layers of one type. We refer to these accelerators as single-engine multiple-layer (SEML). SEML accelerators capture the inter-layer-type but not the intra-layer-type heterogeneity. We propose the Fixed Budget Hybrid CNN Accelerator (FiBHA), a hybrid accelerator composed of an SESL part and an SEML part, each processing a subset of CNN layers. FiBHA captures more heterogeneity than SEML while being more resource-aware and scalable than SESL. Moreover, we propose a novel module, Fused Inverted Residual Bottleneck (FIRB), a fine-grained and memory-light SESL architecture building block. The proposed architecture is implemented and evaluated using high-level synthesis (HLS) on different Field Programmable Gate Arrays representing various resource budgets. Our evaluation shows that FiBHA improves the throughput by up to 4 x and 2.5 x compared to state-of-the-art SESL and SEML accelerators, respectively. Moreover, FiBHA reduces memory and energy consumption compared to an SEML accelerator. The evaluation also shows that FIRB reduces the required memory by up to 54%, and energy requirements by up to 35% compared to traditional pipelining.