Reviewerly/exHarmony huggingface dataset:
Bridging worlds

The digitalisation of Indigenous knowledge has been challenging considering epistemological differences and the lack of involvement of indigenous people. Drawing from our community projects in Namibia we share approaches of co-designing technologies and digital presentations of indigenous knowledge.
Negative pattern discovery with individual support

Negative sequential pattern (NSP) discovery is crucial, and sometimes it carries more enlightening information than positive sequential pattern (PSP) mining in data science. Owing to its computational complexity and exponential search space, the task of discovering NSPs is often more difficult and challenging than that for PSPs. To date, a few NSP mining algorithms have been proposed. Particularly, most algorithms only consider a single support for mining, thus they cannot present good results in many special real-world applications. To solve this problem and achieve better efficiency on a long sequence database or a large-scale database, we propose a novel algorithm called Negative Sequential Patterns with Individual Support (NSPIS) in this paper. The projection mechanism is adopted to NSPIS, which allows greatly reduce the search space and simultaneously improve the efficiency. Finally, detailed results of the experiments show that NSPIS can achieve better performance, and it costs less memory on large-scale datasets compared to the state-of-the-art algorithm.
Deep Learning-Based Context-Aware Video Content Analysis on IoT Devices

Integrating machine learning with the Internet of Things (IoT) enables many useful applications. For IoT applications that incorporate video content analysis (VCA), deep learning models are usually used due to their capacity to encode the high-dimensional spatial and temporal representations of videos. However, limited energy and computation resources present a major challenge. Video captioning is one type of VCA that describes a video with a sentence or a set of sentences. This work proposes an IoT-based deep learning-based framework for video captioning that can (1) Mine large open-domain video-to-text datasets to extract video-caption pairs that belong to a particular domain. (2) Preprocess the selected video-caption pairs including reducing the complexity of the captions’ language model to improve performance. (3) Propose two deep learning models: A transformer-based model and an LSTM-based model. Hyperparameter tuning is performed to select the best hyperparameters. Models are evaluated in terms of accuracy and inference time on different platforms. The presented framework generates captions in standard sentence templates to facilitate extracting information in later stages of the analysis. The two developed deep learning models offer a trade-off between accuracy and speed. While the transformer-based model yields a high accuracy of 97%, the LSTM-based model achieves near real-time inference.
Social influence for societal interest: a pro-ethical framework for improving human decision making through multi-stakeholder recommender systems

Abstract In the contemporary digital age, recommender systems (RSs) play a fundamental role in managing information on online platforms: from social media to e-commerce, from travels to cultural consumptions, automated recommendations influence the everyday choices of users at an unprecedented scale. RSs are trained on users’ data to make targeted suggestions to individuals according to their expected preference, but their ultimate impact concerns all the multiple stakeholders involved in the recommendation process. Therefore, whilst RSs are useful to reduce information overload, their deployment comes with significant ethical challenges, which are still largely unaddressed because of proprietary constraints and regulatory gaps that limit the effects of standard approaches to explainability and transparency. In this context, I address the ethical and social implications of automated recommendations by proposing a pro-ethical design framework aimed at reorienting the influence of RSs towards societal interest. In particular, after highlighting the problem of explanation for RSs, I discuss the application of beneficent informational nudging to the case of conversational recommender systems (CRSs), which rely on user-system dialogic interactions. Subsequently, through a comparison with standard recommendations, I outline the incentives for platforms and providers in adopting this approach and its benefits for both individual users and society.
DARSegNet: A Real-Time Semantic Segmentation Method Based on Dual Attention Fusion Module and Encoder-Decoder Network

The convolutional neural network achieves excellent semantic segmentation results in artificially annotated datasets with complex scenes. However, semantic segmentation methods still suffer from several problems such as low use rate of the features, high computational complexity, and being far from practical real-time application, which bring about challenges for the image semantic segmentation. Two factors are very critical to semantic segmentation task: global context and multilevel semantics. However, generating these two factors will always lead to high complexity. In order to solve this, we propose a novel structure, dual attention fusion module (DAFM), by eliminating structural redundancy. Unlike most of the existing algorithms, we combine the attention mechanism with the depth pyramid pool module (DPPM) to extract accurate dense features for pixel labeling rather than complex expansion convolution. Specifically, we introduce a DPPM to execute the spatial pyramid structure in output and combine the global pool method. The DAFM is introduced in each decoder layer. Finally, the low-level features and high-level features are fused to obtain semantic segmentation result. The experiments and visualization results on Cityscapes and CamVid datasets show that, in real-time semantic segmentation, we have achieved a satisfactory balance between accuracy and speed, which proves the effectiveness of the proposed algorithm. In particular, on a single 1080ti GPU computer, ResNet-18 produces 75.53% MIoU at 70 FPS on Cityscapes and 73.96% MIoU at 109 FPS on CamVid.
Heterogeneous representation learning and matching for few-shot relation prediction

The recent explosive development of knowledge graphs (KGs) in artificial intelligence tasks coupled with incomplete or partial information has triggered considerable research interest in relation prediction. However, many challenges still remain unsolved: (i) the previous relation prediction methods require a significant amount of training instances (i.e., head-tail entity pairs) for every relation, which is infeasible in practical scenarios; and (ii) the representation learning of entities and relations always assumes that all local neighbors and their features contribute equally to the embedding, not sufficiently considering the heterogeneity of the information; and (iii) the state-of-the-art methods usually require a lot of training time, resulting in a high cost in real-world applications. To overcome these challenges, we propose a heterogeneous representation learning and matching approach, Multi-metric Feature Extraction Network (MFEN for short), for few-shot relation prediction in KGs. Our method focuses on knowledge graphs to sufficiently explore the topological structure and node content in graphs. Rather than taking the average of the embeddings of all relational neighbors, a heterogeneity-aware representation learning method is proposed to generate high-expressive embeddings, which capture the heterogenous roles of the relational neighbors of given entity and all of their features via a convolutional encoder. To learn the expressive representations efficiently, a single-layer CNN architecture with multi-scale filters is devised. In addition, multiple heuristic metrics are combined to efficiently improve the accuracy of similarity calculation. The proposed MFEN model is evaluated on two representative benchmark datasets NELL and Wiki. Extensive experiments have demonstrated that our method gets more than 5% accuracy improvement and three times speedup to state-of-the-art models. Code is available on https://github.com/summer-funny/MFEN.
Engaging in Scientific Publishing: Benefits and Norms to Follow as Authors and Reviewers [Young Professionals]

The engagement of young researchers in the process of scholarly publishing is essential to their career development as well as to advancing science. It signals active involvement with the discourse in a field and contribution to the body of knowledge. Writing and reviewing scientific articles can facilitate a deep understanding of the scientific process and the nuances of effective experimental design as well as the acquirement of appropriate scientific communication skills; notably, these skills are central to the competitiveness and success of young professionals (YPs) in today’s and tomorrow’s workplace, be it in academia or industry. A number of research studies have explored the benefits of engaging young researchers in scientific publishing. As highlighted in some of these studies, despite the importance of the early exposure of graduate students and young researchers to scientific writing and peer review, relevant activities are often not prioritized within the framework of educational programs, thus leading to the limited development of key skills—critical to the success of YPs across different fields—such as communicating research to a general audience, structuring ideas through effective scientific writing, and identifying the strengths and weaknesses of experimental design <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . Thus, it is of paramount importance that YPs are aware of the benefits of becoming actively involved in the publication process, both as authors and reviewers, and are also provided—and pursue themselves—relevant opportunities toward augmenting their technical and communication skills, which can prove valuable in their current and future careers. The IEEE Antennas and Propagation Society (AP-S) offers a range of journals, covering the entire spectrum of the antennas and propagation field, that represent excellent opportunities for YPs to advance their career through publishing and reviewing <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> – <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref6" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref7" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[7]</xref> .
Multi-Label Annotation and Classification of Arabic Texts Based on Extracted Seed Keyphrases and Bi-Gram Alphabet Feed Forward Neural Networks Model

In natural language processing, text classification is a fundamental problem. Multi-label classification of textual data is a challenging topic in text classification where an instance can be associated with more than one label. This paper presents a multi-label annotation and classification methodology for Arabic text data that is not currently classified as multi-label, aiming to analyze and compare the performance of various multi-label learning approaches. The current work includes two phases: The first involves automatic annotation of hotel reviews with more than one label based on the aspects found in the reviews. In this phase, review data instances were automatically annotated as multi-label based on the extracted seed keyphrases clusters. The second phase involves experiments to compare the performance of various multi-label classification learning methods. In this phase, we introduced different models including a feed-forward networks model that learns a vector representation based on the bi-gram alphabet rather than the commonly used bag-of-words model. The bi-gram alphabet vector representation model has the advantage of having reduced feature dimensions and not requiring natural language processing tools. The results indicated that employing the bi-gram alphabet vector representation feed forward neural network is a competitive solution for the multi-label text classification problem. It has achieved an accuracy of about 75.2%, and standard deviation (0.062).
Implementation of Privacy and Security for a Genomic Information System Based on Standards

Genomic information is a very sensitive type of digital information as it not only applies to a person, but also to close relatives. Therefore, privacy provision is key to protecting genomic information from unauthorized access. It is worth noting that most of the current genomic information formats do not provide specific mechanisms by which to secure the stored information. In order to solve, among other things, the privacy provision issue, we proposed the GIPAMS (Genomic Information Protection And Management System) modular architecture, which is based on the use of standards such as ISO/IEC 23092 and a few GA4GH (Global Alliance for Genomics and Health) initiatives. Some of the GIPAMS modules have already been implemented, mainly based on ISO/IEC 23092 features, and we are conducting work on the complete version of the architecture, and other standards are also considered. One of the objectives of GIPAMS is to enable the management of different formats of genomic information in a unique and interoperable way, providing privacy and security for formats that do not currently support them.
Securing Big Data: A Survey on Security Solutions

Big Data (BD) is the combination of several technologies which address the gathering, analyzing and storing of massive heterogeneous data. The tremendous spurt of the Internet of Things (IoT) and different technologies are the fundamental incentive behind this enduring development. Moreover, the analysis of this data requires high-performance servers for advanced and parallel data analytics. Thus, data owners with their limited capabilities may outsource their data to a powerful but untrusted environment, i.e., the Cloud. Furthermore, data analytic techniques performed on external cloud may arise various security intimidations regarding the confidentiality and the integrity of the aforementioned; transferred, analyzed, and stored data. To countermeasure these security issues and challenges, several techniques have been addressed. This survey paper aims to summarize and emphasize the security threats within Big Data framework, in addition, it is worth mentioning research work related to Big Data Analytics (BDA).
Benchmarking Object Detection Deep Learning Models in Embedded Devices

Object detection is an essential capability for performing complex tasks in robotic applications. Today, deep learning (DL) approaches are the basis of state-of-the-art solutions in computer vision, where they provide very high accuracy albeit with high computational costs. Due to the physical limitations of robotic platforms, embedded devices are not as powerful as desktop computers, and adjustments have to be made to deep learning models before transferring them to robotic applications. This work benchmarks deep learning object detection models in embedded devices. Furthermore, some hardware selection guidelines are included, together with a description of the most relevant features of the two boards selected for this benchmark. Embedded electronic devices integrate a powerful AI co-processor to accelerate DL applications. To take advantage of these co-processors, models must be converted to a specific embedded runtime format. Five quantization levels applied to a collection of DL models are considered; two of them allow the execution of models in the embedded general-purpose CPU and are used as the baseline to assess the improvements obtained when running the same models with the three remaining quantization levels in the AI co-processors. The benchmark procedure is explained in detail, and a comprehensive analysis of the collected data is presented. Finally, the feasibility and challenges of the implementation of embedded object detection applications are discussed.
Proteus: Autonomous Adaptive Storage for Mixed Workloads

Enterprises use distributed database systems to meet the demands of mixed or hybrid transaction/analytical processing (HTAP) workloads that contain both transactional (OLTP) and analytical (OLAP) requests. Distributed HTAP systems typically maintain a complete copy of data in row-oriented storage format that is well-suited for OLTP workloads and a second complete copy in column-oriented storage format optimized for OLAP workloads. Maintaining these data copies consumes significant storage space and system resources. Conversely, if a system stores data in a single format, OLTP or OLAP workload performance suffers. This paper presents Proteus, a distributed HTAP database system that adaptively and autonomously selects and changes its storage layout to optimize for mixed workloads. Proteus generates physical execution plans that utilize storage-aware operators for efficient transaction execution. Using comprehensive HTAP workloads and state-of-the-art comparison systems, we demonstrate that Proteus delivers superior HTAP performance while providing OLTP and OLAP performance on par with designs specialized for either type of workload.
Simulating the Evolution of Clone-and-Own Projects with VEVOS

In clone-and-own development, new variants of a software system are typically created by manually copying and adapting an existing variant. This approach is flexible but suffers from various challenges such as high maintenance cost in the long term. While researchers started to address the challenges of clone-and-own, there is yet little empirical evidence on the efficiency and effectiveness of clone-and-own research. The main reason for this is the lack of appropriate benchmarks, which need to expose a multitude of different data and meta-data serving as input and ground truth for experimental evaluations. We present VEVOS, a benchmark generation framework that picks up these requirements and, given the version history of a software product line, enables the simulation of the evolution of cloned variants, and provides meta-data serving as ground truth.
Research on defect detection algorithm of complex texture ceramic tiles based on visual attention mechanism

Aiming at the difficulty of detecting the surface defects of complex texture tiles, a salient target detection method based on the human visual attention mechanism is proposed and used for the detection of tile surface defects. Firstly, the image of ceramic tile surface is pretreated using the single-scale SSR light correction method and bilateral filtering method; Secondly, according to the principle of contrast and high-frequency suppression in the visual attention mechanism, aiming at the "imaging" and "aggregation" characteristics of complex background textures, a detection model based on the visual attention mechanism is established to determine and mark defects.According to the contrast principle and high-frequency suppression principle in visual attention mechanism, feature extraction of ceramic tile surface is carried out. Then, the image color patch weight salient map and image feature fused salient map are obtained, and the two maps are fused according to the image saliency criteria.Finally, the marked ceramic tile defects are determined and marked.Finally the marked ceramic tile defects are obtained. This defect detection algorithm and the other two algorithms are applied to three kinds of randomly selected complex texture ceramic tiles. The experimental results show that compared with other algorithms, our algorithm can achieve a comprehensive detection rate of more than 96% for complex texture ceramic tiles, and can obtain a good effect of ceramic tile defect detection as well.
Multi-Modal Fusion Sign Language Recognition Based on Residual Network and Attention Mechanism

Sign language recognition (SLR) is a useful tool for the deaf-mute to communicate with the outside world. Although many SLR methods have been proposed and have demonstrated good performance, continuous SLR (CSLR) is still challenging. Meanwhile, due to the heavy occlusions and closely interacting motions, there is a higher requirement for the real-time efficiency of CSLR. Therefore, the performance of CSLR needs further improvement. The highlights include: (1) to overcome these challenges, this paper proposes a novel video-based CSLR framework. This framework consists of three components: an OpenPose-based skeleton stream extraction module, a RGB stream extraction module, and a combination module of the BiLSTM network and the conditional hidden Markov model (CHMM) for CSLR. (2) A new residual network with Squeeze-and-Excitation blocks (SEResNet50) for video sequence feature extraction. (3) This paper combines the SEResNet50 module with the BiLSTM network to extract the feature information from video streams with different modalities. To evaluate the effectiveness of our proposed framework, experiments are conducted on two CSL datasets. The experimental results indicate that our method is superior to the methods in the literature.
Despeckling of OCT images using DT-CWT based fusion technique

Optical Coherence Tomography (OCT) is a non-invasive diagnostic method used to identify and manage a variety of retinal illnesses. However, the OCT image formation method gives rise to speckle noise that can drastically degrade the quality of OCT images. These low-quality images have a detrimental effect on subsequent illness diagnoses. There are numerous software-based speckle reduction techniques, each with its own advantages. Hence, combining even some of them maximizes the benefits. We propose a fusion approach based on spatial domain filtering and a sparsity-based denoising method for speckle noise removal in OCT images. Our hybrid methodology employs the Dual-Tree Complex Wavelet Transform (DT-CWT) to integrate both spatial domain filtering and the sparsity-based denoising method and uses a multi-scale fusion technique to merge the two transformed output images. Experimental findings indicate that the proposed algorithm could successfully combine two images, resulting in an output OCT image with significant speckle reduction and image structure preservation.
My home is my secret: concealing sensitive locations by context-aware trajectory truncation

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BLENDED LEARNING TECHNOLOGY AS ONE OF THE FOREIGN LANGUAGE TEACHING METHODS IN THE CONDITIONS OF EDUCATION DURING WARTIME

The day after the start of a full-scale Russian invasion of Ukraine on February 24, the Ministry of Education and Science of Ukraine recommended stopping the educational process in educational institutions at all levels and sending students and educators on a two-week vacation. On March 14, the educational process in the areas where the security situation allowed it began to resume. Teachers have a task to ensure the learning process so that as many students as possible can gain knowledge, acquire skills and experience psychological renewal. The purpose of the article is to study some methods and technologies of teaching foreign languages in wartime conditions. Distance learning systems have been known to university teachers for a long time. The article pays special attention to Moodle and Zoom electronic platforms. Of particular importance is the use of the method of Blended Learning, as the most relevant in our opinion in conditions of wartime.