Reviewerly/exHarmony huggingface dataset:
QR Code Labels and Audio Commentaries for Commonly Used Chemistry Laboratory Apparatus: An Assisted Learning Experience for Visually Impaired Students

An instructional technology application of QR (quick response) codes for introducing commonly used chemistry laboratory apparatus for visually impaired students is presented. Audio commentaries describing commonly used apparatus were recorded. The commentaries consisted of concise introductions and applications of various apparatus used in high school and undergraduate chemistry laboratories. The QR code for each apparatus was produced and linked to the corresponding audio commentary via a unique URL. The QR code labels were attached to the apparatus. The QR codes were scanned on a mobile device and an audio commentary was played. Sixteen visually impaired students participated in testing the activity. The results of the testing activity underscored the efficacy of the technology offering an assisted physical learning experience. Technology presented in this article does not give visually impaired students an independent learning experience, rather it gives them an assisted learning experience which enhances their sense of inclusion in a laboratory environment.
Fs-DSM: Few-Shot Diagram-Sentence Matching via Cross-Modal Attention Graph Model

Diagram-sentence matching is a valuable academic research because it can help learners effectively understand the diagrams with the assisted by sentences. However, there are many uncommon objects, i.e. few-shot contents in diagrams and sentences. The existing methods for image-sentence matching have great limitations when applied to diagrams. Because they focus on the high-frequency objects during training and ignore the uncommon objects. In addition, the specialty leads to the semantic non-intuition of the diagram itself. In this work, we propose a cross-modal attention graph model for the few-shot diagram-sentence matching task named Fs-DSM. Specifically, it is composed of three modules. The graph initialization module regards the region-level diagram features and word-level sentence features as the nodes of Fs-DSM, and edges are represented as similarity between nodes. The information propagation module is a key point of Fs-DSM, in which the few-shot contents are recognized by an uncommon object recognition strategy, and then the nodes are updated by a neighborhood aggregation procedure with cross-modal propagation between all visual and textual nodes, while the edges are recomputed based on the new node features. The global association module integrates the features of regions and words to represent the global diagrams and sentences. By conducting comprehensive experiments in terms of few-shot and conventional image-sentence matching, we demonstrate that Fs-DSM achieves superior performances over the competitors on the AI2D# diagram dataset and two public benchmark datasets with nature images.
Playing with invisible animals: An interactive system of floor-projected footprints to encourage children’s imagination

Playing is essential for a child's development, and it has various positive effects. In particular, playing with toys enhances children's imagination. In recent years, children have been tending to play with smart toys equipped with built-in computers, which present various advantages that leverage their digital characteristics. For example, smart toys can provide augmented reality functionalities through which digital information can be added to physical reality. These characteristics offer an important benefit of stimulating the interest and presence of children. However, wearable devices may be required to realize this. Wearable devices are ineffective when people do not own or cannot wear them. Therefore, we developed a system that stimulates the imagination and interest of children in playing without any wearable devices. Our system projects animal footprints onto the floor, encouraging children to play with invisible animals based on their imagination from the footprints. The footprints respond to the children's movements. To evaluate the developed system, we conducted several experiments with children. First, we investigated the fundamental effects of the system on elementary school students. Then, we investigated the different effects of various interactions. Our experimental results showed that the developed system encourages the imagination of children while playing.
A Multi-Classification Sentiment Analysis Model of Chinese Short Text Based on Gated Linear Units and Attention Mechanism

Sentiment analysis of social media texts has become a research hotspot in information processing. Sentiment analysis methods based on the combination of machine learning and sentiment lexicon need to select features. Selected emotional features are often subjective, which can easily lead to overfitted models and poor generalization ability. Sentiment analysis models based on deep learning can automatically extract effective text emotional features, which will greatly improve the accuracy of text sentiment analysis. However, due to the lack of a multi-classification emotional corpus, it cannot accurately express the emotional polarity. Therefore, we propose a multi-classification sentiment analysis model, GLU-RCNN, based on Gated Linear Units and attention mechanism. Our model uses the Gated Linear Units based attention mechanism to integrate the local features extracted by CNN with the semantic features extracted by the LSTM. The local features of short text are extracted and concatenated by using multi-size convolution kernels. At the classification layer, the emotional features extracted by CNN and LSTM are respectively concatenated to express the emotional features of the text. The detailed evaluation on two benchmark datasets shows that the proposed model outperforms state-of-the-art approaches.
IBNet: Interactive Branch Network for salient object detection

At present, saliency detection methods have achieved gratifying progress benefiting from the development of deep learning. However, the existing methods always fail to make full use of the label information. To address this problem, we focus on the complementarity of salient body information and salient detail information within the labels and propose the Interactive Branch Network (IBNet) in this paper. Generally, IBNet contains three components of Label Redefinition Module (LRM), Information Exchange Module (IEM) and Connected Flow Loss (CFL). These three components all play an enormously important role in the complementary performance of detection. In LRM, enough useful and meaningful heuristic knowledge from the given labels is expanded for dynamic and collaborative supervised learning. In IEM, different derived branches are assigned to collect different types of features for interactive fusion. In CFL, the losses from all connected branches are merged to calculate the total loss. Extensive experiments on benchmark datasets exhibit the effectiveness and efficiency of the proposed method against the state-of-the-art approaches. The source code is publicly available at https://github.com/xianfangfx/IBNet.
Sentiment Analysis of Short Informal Text by Tuning BERT - Bi-LSTM Model

Sentiment analysis is one of the significant tasks in processing natural language by a machine. However, it is difficult for a machine to understand the feelings of a person and opinion about a topic. Many approaches have been introduced for analyzing sentiment from long text in recent past. In contrast, these approaches fail to address the small length text problem like Twitter data efficiently. Recent advances in the pre-trained contextualized embeddings like Bidirectional Encoder Representations from Transformers (BERT) show far greater accuracy than traditional embeddings. In this paper, we develop a novel architecture to tune the BERT using a Bidirectional Long Short-Term Memory (Bi-LSTM) model. A task-specific layer is incorporated along with the BERT in the proposed model. Our model extracts sentiment from short texts, especially Twitter data. The extensive experiments show the superiority of our model over state-of-the-art models in sentiment analysis task across several gold standard datasets.
Slow Storytelling and hybridity: Re-staging community storytelling as a tool for co-thinking

Since the early 2000s social media has transformed the internet into a site for the exchange of stories through the mass democratization of publishing. And yet, new forms of digital and online storytelling have at the same time compromised one of the core functions of storytelling, namely its social aspect, the ability to build community when two or more people share stories in the same space, at the same time, breathing the same air. Somewhat ironically the advent of social media may have broadened the audience for any one person’s storytelling, whilst diminishing the social intimacy of the storytelling experience. As part of its research work into storytelling as a means of engaging people in the public debate around environment, the Storytelling Academy at Loughborough University has been developing new forms and processes of digital storytelling to promote wider engagement and dissemination of environmentally driven personal stories. ‘The Reasons’, first staged in Cambridgeshire in 2016, was an attempt to create a live, community social event that provided a public forum for storytelling as a way of debating issues around drought and water governance in the Fens. Inspired by a re-staging of La Rasgioni in Sardinia in 2015, a traditional form of conflict resolution, whereby a ‘mock’ court provides the means for the community to publicly tell its stories to each other, ‘The Reasons’ was co-designed for the Fenland context and was performed twice in 2016. It was then further adapted for use in the Korogocho slum in Nairobi for an event to discuss the issue of waste management with members of the local community, as part of an initiative with UN Live. ‘The Reasons’ is an attempt to bring together the advantages of digital storytelling as a reflective process with the social intimacy of the live storytelling event. The result is a new form of hybrid storytelling that seeks to build community and establish co-thinking processes to build resilience to environmental change. This article reflects critically upon the development and evolution of this work over the past five years.
On Filtering Methods for State-Space Systems having Binary Output Measurements

In this paper we develop two filtering algorithms for state-space systems with binary outputs. We approximate the conditional probability mass function of the output signal given the state by using a Gaussian quadrature rule. This approximation naturally leads to a Gaussian Sum structure for the a posteriori density function. Our first algorithm is based on Gaussian Mixture models, and the second algorithm is based on Particle Filtering. Finally, we present numerical examples to illustrate the effectiveness of our proposal.
Feeding Hungry Models Less: Deep Transfer Learning for Embedded Memory PPA Models : Special Session

Supervised machine learning requires large amounts of labeled data for training. In power, performance and area (PPA) estimation of embedded memories, every new memory compiler version is considered independently of previous versions. Since the data of different memory compilers originate from similar domains, transfer learning may reduce the amount of supervised data required by pre-training PPA estimation neural networks on related domains. We show that provisioning times of PPA models for new compiler versions can be reduced significantly by exploiting similarities across versions and technology nodes. Through transfer learning, we shorten the time to provision PPA models for new compiler versions by 50% to 90%, which speeds up time-critical periods of the design cycle. This is achieved by requiring less than 6,500 ground truth samples for the target compiler to achieve average estimation errors of 0.35% instead of 13,000 samples. Using only 1,300 samples is sufficient to achieve an almost worst-case (98th percentile) error of approximately 3% and allows us to shorten model provisioning times from over 40 days to less than one week.
Ensemble-based Efficient Anomaly Detection for Smart Building Control Systems

Modern building control systems integrate the internet of things (IoT) for real-time monitoring of the building’s demand and manage the heating, ventilation, and air conditioning (HVAC) cost-efficiently and reliably. However, adversarial alterations of the sensor data can disrupt the occupants’ comfort or increase energy consumption. Several intrusion detection systems (IDSs) are proposed to detect the tempering of the sensor measurements. However, these approaches either demonstrate a high false alarm rate or fail to detect anomalies, putting the HVAC control or the building occupants in a vulnerable condition. This paper proposes a novel intrusion detection technique amalgamating two unsupervised machine learning techniques, namely autoencoder(AE) and one-class support vector machine (OCSVM), for identifying abnormality in smart building sensor measurements. Our experimental analysis shows that the AE model-based anomaly detector demonstrates satisfactory performance for lowering false alarms but fails to detect a number of anomalous samples. In contrast, the OCSVM-based anomaly detection model performs significantly well for anomaly detection while raises a lot of false alarms. Our proposed ensembled AE-OCSVM model combines both models’ benefits, resulting in significant reductions of false positive and false negative rates compared to the existing smart building IDSs. We evaluate the proposed intrusion detection system on the commercial occupancy dataset (COD) and find that the proposed IDS model can achieve a 99.6% F1-score.
Classifying Memory Bugs Using Bugs Framework Approach

In this work, we present an orthogonal classification of memory corruption bugs, allowing precise structured descriptions of related software vulnerabilities. The Common Weakness Enumeration (CWE) is a well-known and used list of software weaknesses. However, it’s exhaustive list approach is prone to gaps and overlaps in coverage. Instead, we utilize the Bugs Framework (BF) approach to define language-independent classes that cover all possible kinds of memory corruption bugs. Each class is a taxonomic category of a weakness type, defined by sets of operations, cause→consequence relations, and attributes. A BF description of a bug or a weakness is an instance of a taxonomic BF class, with one operation, one cause, one consequence, and their attributes. Any memory vulnerability then can be described as a chain of such instances and their consequence–cause transitions. We showcase that BF is a classification system that extends the CWE, providing a structured way to precisely describe real world vulnerabilities. It allows clear communication about software bugs and weaknesses and can help identify exploit mitigation techniques.
On Quotient Structures of Fuzzy Multiset Finite Automata

The objective of this paper is to characterize the quotient structures of fuzzy multiset finite automata.We introduce different congruence relation on multiset associated with fuzzy multiset finite automata and show that each congruence relation associates a semigroup with the fuzzy multiset finite automata.We also discuss the charaterization of fuzzy multiset finite automata by defining an admissible relation.
Application Analysis of Virtual Reality VR Technology in Art Design Teaching

Abstract The rapid growing of Internet promotes the emergence and application of teaching methods and new teaching media. When multimedia is widely used in teaching, teaching technology is updated very quickly, and virtual reality technology, a new teaching media, has emerged. Traditional environmental art design creation and expression methods cannot fully satisfy the development requirements of the digital period, and new technical forces are needed to enrich and expand the content and tension of art design, the art design industry also needs VR technology, which should have more new vitality. This article will study virtual reality technology from the perspective of educational technology, as a new educational technology, and on this basis, put forward a new research field related to educational technology-virtual reality VR technology teaching. After studying the types of VR technology characteristics and actual teaching cases, the concept of desktop VR technology intervening in experimental model making courses is proposed, a virtual learning environment is constructed, and the objective reality generated by the use of knowledge is reproduced, thereby imparting knowledge points It is provided to students and guides them to use their own senses such as vision and hearing to receive information in the virtual environment, which enhances students’ interest in learning and their own sense of innovation, enhances students’ ability to actively exert their own imagination, and greatly reduces The hardware cost improves the learning effect of users.
Decentralizing Identity Management and Vehicle Rights Delegation through Self-Sovereign Identities and Blockchain

With smart vehicles interconnected with multiple systems and other entities, whether they are people or IoT devices, the importance of a digital identity for them has emerged. We present in this paper how a Self-Sovereign Identities combined with blockchain can provide a solution to this end, in order to decentralize the identity management and provide them with capabilities to identify the other entities they interact with. Such entities can be the owners of the vehicles, other drivers and workshops that act as service providers. Two use cases are examined along with the interactions between the participants, to demonstrate how a decentralized identity management solution can take care of the necessary authentication and authorization processes. Finally, we test the system and provide the measurements to prove its feasibility in real-life deployments.
Sentiment Analysis of Public Reaction to COVID19 in Twitter Media using Naïve Bayes Classifier

Currently, the world's attention was focused on the disease outbreak, namely the corona virus (COVID19). World Health Organization (WHO) declare that this virus was a global pandemic in all countries. The various impacts that arise due to this virus cover various fields, namely health, social, political, religious, economic to resilience and security. Some of the services currently used were still focused on the health sector, namely in the form of treatment and information services related to the development of the spread of the virus. This research will develop a service that was used to identify social impacts in the community through observing community activities on social media, namely Twitter, in the form of an analysis of the public's reaction to COVID19. Through this Twitter, a data acquisition process will be carried out to obtain data related to COVID19 which will then be carried out a sentiment analysis using the Naïve Bayes method so that the results of the public reaction sentiment will be obtained. The experimental result shows that prediction accuracy was 0,86. Furthermore, the results of the Recall was 0,687, the precision was 0,827 and the F-Score was 0.749
A Kind of Wireless Modulation Recognition Method Based on DenseNet and BLSTM

Deep learning has achieved remarkable results in various fields, such as image recognition and classification. However, in the recognition of radio modulation methods, deep learning for different modulation methods of radio signal recognition results are not satisfactory. In this paper, we propose to use densely connected convolutional networks combined with bidirectional recurrent neural networks to identify the radios of 11 different modulation methods. The final results show that our method is more accurate than the traditional convolution neural network in modulation recognition.
Extracting Interpretable Decision Tree Ensemble from Random Forest

Machine learning predictive models are nowadays widely applied in various systems in both commercial and public areas. The need to understand and comprehend their behavior has arisen and was, through an extensive increase in research in Explainable AI (XAI), noticed in the last years. One of the problems in the aforementioned XAI spectrum is understanding the large rule sets, such as those mined from big datasets or induced through Random Forest algorithms. This work explores the possibility of tackling such an issue using multiple nonoverlapping decision trees created with incorporated knowledge provided by the set of rules. Random Forest is being used as a source of such rules. Performance evaluation conducted on commonly used datasets shows that such a model could be competitive with an equally deep Random Forest while providing explanations that can be adjusted and interpreted as a rule list.
An Optimized Scale-Invariant Feature Transform Using Chamfer Distance in Image Matching

Scale-Invariant Feature Transform is an image matching algorithm used to match objects of two images by extracting the feature points of target objects in each image. Scale-Invariant Feature Transform suffers from long processing time due to embedded calculations which reduces the overall speed of the technique. This research aims to enhance SIFT processing time by imbedding Chamfer Distance Algorithm to find the distance between image descriptors instead of using Euclidian Distance Algorithm used in SIFT. Chamfer Distance Algorithm requires less computational time than Euclidian Distance Algorithm because it selects the shortest path between any two points when the distance is computed. To validate and evaluate the enhanced algorithm, A data set with (412) images including: (100) images with different degrees of rotation, (100) images with different intensity levels, (112) images with different measurement levels and (100) distorted images to different degrees were used; these images were applied according to four different criteria. The simulation results showed that the enhanced SIFT outperforms the ORB and the original Scale-Invariant Feature Transform in term of the processing time, and it reduces the overall processing time of the classical SIFT by (41%).
Evidence of inconsistent results using current eye tracking glance and visit analysis standards

Eye tracking glance analyses have been used to study the behavior of motor vehicle drivers for at least two decades and eye tracking is now emerging as an experimental protocol in construction research. Previous studies have identified relationships between driver glance behavior and performance, so existing motor vehicle driver glance analysis methods were applied to search for analogous relationships with construction craftworkers during a model assembly task. Eye tracking analysis software vendors have yet to adopt standardized eye tracking event detection algorithms, and researchers have historically reported insufficient details to render most eye tracking analyses reproducible. As a result, the purpose of the present work is to address the gap in knowledge in understanding the challenges encountered as a result of a lack of standardization. The methodology applied existing glance analysis methods to a study incorporating mobile eye tracking glasses, which made the experimental environment less controlled than a typical glance analysis study using a screen-based eye tracking system, such as a motor vehicle driver behavior study. A new noise filtering parameter, maximum off-stimulus fixations, was also introduced. The results show that the absence of standardized eye tracking parameters, such as minimum fixation duration, can lead to different statistical results. The study's primary contribution to the body of knowledge is that is identifies the need for standardization of eye tracking analyses to better ensure that future eye tracking studies be more consistent and reproducible.