Reviewerly/exHarmony huggingface dataset:
A Software Defect Prediction Approach Based on BiGAN Anomaly Detection

Software defect prediction usually is regarded as a classification problem, but classification models will face the class imbalance problem. Although there are many methods to solve the class imbalance problem, there is no method that can fundamentally solve the problem currently. In addition, supervised learning algorithms are always used to train defect prediction models, but obtaining a large amount of high-quality labelled data requires a lot of time and labor cost. In order to solve the class imbalance problem and eliminate the disadvantage of supervised learning, this paper attempts to predict software defects from a new perspective of anomaly detection. We propose an Anomaly Detection Model Based on BiGAN for Software Defect Prediction (ADGAN-SDP). The model proposed in this paper not only does not need to consider the class imbalance problem but also uses a semi-supervised method to train the model. Eight classification-based software defect prediction models are used as the baseline models and compared with ADGAN-SDP model. We evaluate ADGAN-SDP on 19 projects from NASA, AEEEM, and ReLink repositories. The experimental results show that the ADGAN-SDP model, which has a higher recall, outperforms all baseline models. It is suggested that the anomaly detection approach can be applied to the software defect prediction to fundamentally solve the class imbalance problem.
Entropy encoder for low-power low-resources high-quality CFA image compression

An entropy encoder for high-quality image compression in low-power, low-resources devices like wireless capsule endoscopy (WCE) or wireless camera sensor network (WCSN) is proposed. The proposed entropy encoder is optimized for encoding data resulting from transformation and quantization of images captured with a color filter array (CFA) digital camera. It efficiently exploits existing correlations among color components in CFA images to improve the performance of the image compressor in terms of power consumption, silicon area, and compression ratio. The image compressor based on the new encoder requires only 22% of the memory size needed by the JPEG-LS-based CFA compressor (for an image with 512 columns). Despite this significant reduction in memory size, the average compression ratio in relation to the image quality is superior to other state-of-the-art algorithms of similar computational complexity and memory requirements. The image compressor based on the proposed encoder has been synthesized using a low-cost UMC 180 nm CMOS process as an intellectual property (IP) core. The design requires a very small silicon area (0.54 × 0.50 mm) and achieves excellent energy efficiency (30 μJ per a single 512 × 512 image frame and 20 fps). The selected technology for the implementation is not only low-cost but also well suited to implement an RF transmitter in addition to the compressor to provide a cost effective System on Chip (SoC) for resource-constrained devices like WCE or WCSN.
A Filter Pruning Method of CNN Models Based on Feature Maps Clustering

The convolutional neural network (CNN) has been widely used in the field of self-driving cars. To satisfy the increasing demand, the deeper and wider neural network has become a general trend. However, this leads to the main problem that the deep neural network is computationally expensive and consumes a considerable amount of memory. To compress and accelerate the deep neural network, this paper proposes a filter pruning method based on feature maps clustering. The basic idea is that by clustering, one can know how many features the input images have and how many filters are enough to extract all features. This paper chooses Retinanet and WIDER FACE datasets to experiment with the proposed method. Experiments demonstrate that the hierarchical clustering algorithm is an effective method for filtering pruning, and the silhouette coefficient method can be used to determine the number of pruned filters. This work evaluates the performance change by increasing the pruning ratio. The main results are as follows: Firstly, it is effective to select pruned filters based on feature maps clustering, and its precision is higher than that of a random selection of pruned filters. Secondly, the silhouette coefficient method is a feasible method for finding the best clustering number. Thirdly, the detection speed of the pruned model improves greatly. Lastly, the method we propose can be used not only for Retinanet, but also for other CNN models. Its effect will be verified in future work.
Beyond Meditation: Everyday Mindfulness and Technology Use

Mindfulness, a practice of maintaining awareness by bringing attention to the present without judgment, has many mental and physical well-being benefits when practiced consistently. Many technologies have been invented to support mindfulness practice: mobile apps, web resources, virtual reality environments, and wearables. We present findings from a semi-structured interview study with 6 experienced mindfulness practitioners to understand their daily practice experiences and technologies they incorporate in their practice. Participants identify the benefits and challenges of developing long-term commitment to mindfulness practice, and combine informal mindfulness practice in their daily activities in addition to formal meditation. While conflicted about including and even relying on technology, they adopt and appropriate a range of technologies in their daily practice, some of which are not designed for mindfulness, such as productivity tools and media streaming. Based on our findings, we suggest that designers of mindfulness technologies be cautious about applying behavioral change design principles and go beyond meditation to better situate the tools in practitioners’ existing daily routines.
On the Importance of User Backgrounds and Impressions: Lessons Learned from Interactive AI Applications

While EXplainable Artificial Intelligence (XAI) approaches aim to improve human-AI collaborative decision-making by improving model transparency and mental model formations, experiential factors associated with human users can cause challenges in ways system designers do not anticipate. In this article, we first showcase a user study on how anchoring bias can potentially affect mental model formations when users initially interact with an intelligent system and the role of explanations in addressing this bias. Using a video activity recognition tool in cooking domain, we asked participants to verify whether a set of kitchen policies are being followed, with each policy focusing on a weakness or a strength. We controlled the order of the policies and the presence of explanations to test our hypotheses. Our main finding shows that those who observed system strengths early on were more prone to automation bias and made significantly more errors due to positive first impressions of the system, while they built a more accurate mental model of the system competencies. However, those who encountered weaknesses earlier made significantly fewer errors, since they tended to rely more on themselves, while they also underestimated model competencies due to having a more negative first impression of the model. Motivated by these findings and similar existing work, we formalize and present a conceptual model of user’s past experiences that examine the relations between user’s backgrounds, experiences, and human factors in XAI systems based on usage time. Our work presents strong findings and implications, aiming to raise the awareness of AI designers toward biases associated with user impressions and backgrounds.
Iterating through Feeling-with Nonhuman Things

In this paper, we explore the notion of sympathy in the context of more-than-human design to include nonhuman participation in a design iteration process in an ongoing project named the Morse Things. We explore ways in which nonhuman agency, particularly breakage, can participate in an assembly of human and nonhuman designers. Motivated by Ron Wakkary's theory of designing-with and the concept of repertoires, we propose feeling-with as a potential repertoire for increasing nonhuman participation before, during, and after the design process. Finally, we explore four instances of sympathy and how breakage as a nonhuman force can lead us to new design iterations to redesign the new set of Morse Things.
RaITIn: Radar-Based Identification for Tangible Interactions

Radar is primarily used for applications like tracking and large-scale ranging, and its use for object identification has been rarely explored. This paper introduces RaITIn, a radar-based identification (ID) method for tangible interactions. Unlike conventional radar solutions, RaITIn can track and identify objects on a tabletop scale. We use frequency modulated continuous wave (FMCW) radar sensors to classify different objects embedded with low-cost radar reflectors of varying sizes on a tabletop setup. We also introduce Stackable IDs, where different objects can be stacked and combined to produce unique IDs. The result allows RaITIn to accurately identify visually identical objects embedded with different low-cost reflector configurations. When combined with a radar's ability for tracking, it creates novel tabletop interaction modalities. We discuss possible applications and areas for future work.
A cost-sensitive Imprecise Credal Decision Tree based on Nonparametric Predictive Inference

Classifiers sometimes return a set of values of the class variable since there is not enough information to point to a single class value. These classifiers are known as imprecise classifiers. Decision Trees for Imprecise Classification were proposed and adapted to consider the error costs when classifying new instances. In this work, we present a new cost-sensitive Decision Tree for Imprecise Classification that considers the error costs by weighting instances, also considering such costs in the tree building process. Our proposed method uses the Nonparametric Predictive Inference Model, a nonparametric model that does not assume previous knowledge about the data, unlike previous imprecise probabilities models. We show that our proposal might give more informative predictions than the existing cost-sensitive Decision Tree for Imprecise Classification. Experimental results reveal that, in Imprecise Classification, our proposed cost-sensitive Decision Tree significantly outperforms the one proposed so far; even though the cost of erroneous classifications is higher with our proposal, it tends to provide more informative predictions. • Classification errors often have different costs in practical applications. • Classifiers sometimes predict a set of class values since there is not enough information to point to a single class value. • A new Decision Tree for cost-sensitive Imprecise Classification is proposed. • It is shown that our proposal presents some advantages over the existing cost-sensitive Imprecise Credal Decision Tree. • Our proposed cost-sensitive Imprecise Credal Decision Tree obtains significantly better results than the existing one.
Gravity surveys and 3D integrated model with magnetic and geological data of the granite-greenstone terrane in Pitangui Synclinorium, NW of the Quadrilátero Ferrífero (MG, Brazil)

The Pitangui Greenstone Belt (PGB) is a Meso to Neoarchean metavolcano-sedimentary sequence, inserted in the Pitangui Synclinorium, located in the northwest portion of the Archean-Paleoproterozoic metallogenic province of the Quadrilátero Ferrífero, southern of the São Francisco craton (Brazil). The PGB holds important gold mines like the Turmalina Complex and recently discovered deposits, as São Sebastião. The PGB is mapped in detail scale, but there is still little information about its subsurface configuration. This paper presents gravity surveys, constrained by geological, structural, boreholes, aeromagnetic and seismic data, aiming to provide a better understanding of the 3D geometry of this granite-greenstone terrane. The treatment of the available data resulted in Bouguer anomaly maps, 2D gravity modelling and a 3D geological-geophysical integrated model. The gravity Bouguer anomalies of the region present high amplitudes associated with the PGB, alternating with low zones related to the surrounding granite-gneiss rocks. The profiles and the model suggest that the maximum depth of the greenstone belt is approximately 5 km in its central portion, being strangled by granitic intrusions in its southernmost region. The PGB is about 100 km long, but most of it is covered by Neoproterozoic sediments of the Bambuí Group and Quaternary deposits, which significantly increase its metallogenic potential, especially in regions with few mineral exploration works. The 3D model contributes to the understanding of the tectonic framework and evolution of the PGB, showing the spatial relation with the surrounding granite-gneiss basement and intrusions, and younger sedimentary covers, providing valuable insights about their depth, vertical and lateral geometry. Additionally, this new data can become a valuable tool for developing strategies to select deep mineral targets, expanding the exploratory potential for this mineral province.
The Feet in Human-Centred Security: Investigating Foot-Based User Authentication for Public Displays

A large body of work investigated touch, mid-air, and gaze-based user authentication. However, little is known about authentication using other human body parts. In this paper, we investigate the idea of foot-based user authentication for public displays (e.g., ticket machines). We conducted a user study (N=13) on a virtual prototype, FeetAuth, on which participants use their dominant foot to rotate through PIN elements (0–9) that are augmented along a circular layout using augmented reality (AR) technology. We investigate FeetAuth in combination with three different layouts: Floor-based, Spatial, and Egocentric, finding that Floor-based FeetAuth resulted in the highest usability with 4-digit PIN entry as fast as M=6.71 (SD=0.67). Participants perceived foot-based authentication as socially acceptable and highlighted its accessibility. Our investigation of foot-based authentication paves the way for further studies on the use of the human body for user authentication.
Lightweight and Deep Appearance Embedding for Multiple Object Tracking

The main challenge of Multiple Object Tracking (MOT) is that there is great uncertainty in data association when using the tracked predicted values and tracked trajectories. Meanwhile, the MOT is complex and time-consuming. When equipment resources are limited or real-time requirement is high, its application is very limited. Therefore, we propose a Lightweight Deep Appearance Embedding (LDAE) to assist the association of trajectories. Firstly, in addition to motion information in data association, we also introduce more discriminative appearance features to participate in the affinity measure to effectively distinguish similar targets. Secondly, according to the idea of feature mapping, we design a lightweight deep appearance embedding module. It can help extract appearance features with less computation. Finally, we propose a simulated occlusion strategy for the training of the LDAE, which helps improve the ability to recognise different targets in dense scenes. The LDAE dramatically reduces the computational cost and improves the accuracy of data association. Extensive experiments are conducted on the MOT datasets (MOT16, MOT17 and MOT20), which prove that the LDAE outperforms several state-of-the-art trackers in the tracking accuracy and anti-occlusion performance. Furthermore, we apply the LDAE to escalators, which can achieve fast and stable tracking effect.
VR [we are] Training - Workshop on Collaborative Virtual Training for Challenging Contexts

Virtual reality provides great opportunities to simulate various environments and situations as reproducible and controllable training environments. Training is an inherently collaborative effort, with trainees and trainers working together to achieve specific goals. Recently, we have seen considerable effort to use virtual training environments (VTEs) in many demanding training contexts, e.g. police training, medical first responder training, firefighter training etc. For such contexts, trainers and trainees must undertake various roles as supervisors, adaptors, role players, and observers in training, making collaboration complex, but essential for training success. These social and multi-user aspects for collaborative VTEs have received little investigation so far. Therefore, we propose this workshop to discuss the potential and perspectives of VTEs for challenging training settings. In a one-day online workshop, researchers and practitioners will jointly develop a research agenda on how currently underrepresented aspects of social and collaborative work can be integrated into VR-supported training. This workshop will focus on two themes: (1) Multi-sensory experience: novel collaborative interfaces for VTEs (e.g. joint use of tangible devices, strategies for preventing simulator-induced negative effects); (2) Multi-user interaction: collaboration in VTEs between trainers (two trainers run a scenarios jointly), trainers and trainees /the trainer controls the scenario for a trainee), and trainees with each other (e.g. two trainees solve an exercise together)
A Multi-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation

Knowledge graph (KG) helps to improve the accuracy, diversity, and interpretability of a recommender systems. KG has been applied in recommendation systems, exploiting graph neural networks (GNNs), but most existing recommendation models based on GNNs ignore the influence of node types and the loss of information during aggregation. In this paper, we propose a new model, named AMulti-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation (MAKR), that relieves the sparsity of the network and overcomes the limitation of information loss of the traditional GNN recommendation model. Specifically, we propose a new graph, named the Improved Collaborative Knowledge Graph (ICKG), that integrates user–item interaction and a knowledge graph into a huge heterogeneous network, divides the nodes in the heterogeneous network into three categories—users, items, and entities, and connects the edges according to the similarity between the users and items so as to enhance the high-order connectivity of the graph. In addition, we used attention mechanisms, the factorization machine (FM), and transformer (Trm) algorithms to aggregate messages from multi-granularity and different types to improve the representation ability of the model. The empirical results of three public benchmarks showed that MAKR outperformed state-of-the-art methods such as Neural FM, RippleNet, and KGAT.
Toward Privacy Protection of Sensed Biometric Features with Extended Visual Cryptography

The popularity of more powerful and smarter digital devices has improved the quality of life and poses new challenges to the privacy protection of personal information. Although traditional encryption algorithms are effective in most application scenarios, they may not be suitable in integrated sensing and communication environments due to their low processing capabilities. In this paper, we develop a novel extended visual cryptography scheme to store private information in separate databases, which eliminates the complex operations in privacy-preserving schemes based on cryptography and watermarking. Since shares do not reveal any feature about private sensed information, we can transmit sensitive information among sensors and smart home servers in plain. To abate the influence of noise due to the use of visual cryptography, we leverage the generalization ability of transfer learning to train a visual cryptography-based recognition network. Experimental results show that our proposed method keeps the high accuracy of the feature recognition system when providing security.
English Speech Recognition and Evaluation of Pronunciation Quality Using Deep Learning

English is now one of the most important languages for economic exchange in various countries around the world, and it is also the most widely used language for cultural and information exchange. Like other countries, China likewise attaches highest significance to English learning, and people’s demand for applied learning is also increasing rapidly these days. However, there are significant differences between Chinese pronunciation and English pronunciation, and China lacks an English environment while teaching English language. Furthermore, the traditional education is limited by the place and time of classes, due to which it cannot meet people’s needs for learning English. With the fast progress of computer knowledge, the emergence of deep learning technology can better identify English pronunciation and evaluate the quality of English pronunciation. Additionally, deep learning can provide learners with precise, objective, and rapid pronunciation information. It can also assist learners in determining the differences between their pronunciation and conventional pronunciation through frequent listening and comparison, as well as correcting their pronunciation faults and increasing language learning efficacy. This study looks into the difficulty of using deep learning to evaluate the quality of English speech recognition and pronunciation. To evaluate English pronunciation quality, this paper selects intonation, speed, and rhythm, as the distinguishing indicators. The comparison between the results of manual evaluation and our evaluation clearly shows that English speech recognition and pronunciation quality model using deep learning established in this paper has much higher reliability. Among the 240 samples tested, only 32 samples differ by one grade, and the rest are similar.
Understanding How Group Workers Reflect on Organizational Stress with a Shared, Anonymous Heart Rate Variability Data Visualization

For a small group of office workers who share the same workspace and the task load, leveraging their social skills and awareness could further increase their mutual awareness of each other's work-related stress. This paper presents a case study of a one-week deployment of a shared, anonymous heart rate variability (HRV) data visualization system at six workplaces with 24 office workers, who were closely collaborated in four-person groups. We collected stress-related physiology data (i.e., heart-rate variability) from wearable sensors and anonymously visualized them on a shared display. Although the physiological data collection where noisy due to the practical constraints in the field settings, we found the participants still increasingly agreed with the systems and used the visualization as a reference for their subjective stress assessment. We also present and discuss how groups of office workers individually and socially reflect on their one-week experiences and then summarize takeaways for designing shared physiological data visualization systems for group stress management in the long term.