Reviewerly/exHarmony huggingface dataset:
Towards a Low-Cost Companion Robot for Helping Elderly Well-Being

The use of robot assistants and companion robots has been shown to improve the well-being of the elderly, enabling them to improve their quality of life. Nowadays, many older people live alone and do not have the resources to acquire specialized systems to monitor and accompany them. But at the same time, they are under the supervision of specialized personnel that make them feel safe. Loneliness can be a crucial element that affects many people's lives; not having someone to talk to or hang out with is a current concern. This work explores this issue by developing a small companion robot and a heart signal monitoring system (ECG). The presented approach is inexpensive, easy to maintain, and integrates machine learning (ML) models and the Alexa voice assistant.
Graph Structure Learning based on Mistakenly Predicted Edges from Reconstructed Graph Representation

Recently, Graph Neural Networks (GNNs) have been focused because they are suitable for considering complex interactions. Such a real-world problem containing complex interactions can be represented by graph-structured data. However, this graph-structured data may contain task-irrelevant edges or missing edges due to human error. These noisy or incomplete edges can degrade the performance of trained GNNs. To tackle this problem, Graph Structure Learning (GSL), which focuses on finding a better graph representation of a given original graph based on the main learning objectives, has been studied. In this research, we propose a framework called Confusion-GSL that includes conventional graph neural network for node classification and edge selection for finding better graph representation. We focus on the fact that the probability matrix can be used to reconstruct graph representation like learned node embedding in Graph Auto Encoder (GAE). After training node classification, we calculate Gram matrix of the probability matrix and use this Gram matrix as an approximation of adjacency matrix. By applying Gumbel-Softmax to difference matrix between approximated adjacency matrix and original adjacency matrix, we select the connections likely to exist or not exist. After that we re-train GNN with this modified graph representation. We evaluate our proposed framework on two citation network datasets commonly used: Cora, Citeseer and Pubmed.
A systematic literature review of clone evolution

Code clones are identical or nearly similar code fragments often introduced into software systems by programmers with software modification and maintenance. During the evolution of the software system, code clones may experience multiple changes, such as the increase in number, disappearance, location change, etc. These changes increase the difficulty of clone management and possibly introduce bugs into the software, leading to the high price of clone management and maintenance. Therefore, it is necessary to study the clone evolution. In this paper, we summarize the research works in code clone evolution in recent decades. Based on the previous review and survey, we found a total of 47 relevant papers and divided them into five categories with the help of the LDA model. We present our analysis of the current research and discussion about the possible future progress in this paper. The final result of the debate is that we believe the future work will divide into two aspects. On the one hand, developing clone management tools based on the current results become a possible direction; on the other hand, development and improvement may appear in existing tools with more theoretical support due to more knowledge of the evolutionary characteristics of clones.
GPSAttack: A Unified Glyphs, Phonetics and Semantics Multi-Modal Attack against Chinese Text Classification Models

Deep learning models are vulnerable to adversarial examples that add small perturbations to original inputs, which can help to evaluate the robustness and expose deficiencies of deep learning models. Current research mainly focus on English text adversarial attacks. Due to the differences between Chinese and English, the attack methods can not directly be used in Chinese attacks. In the mean time, in the Chinese text adversarial examples generation field, existing methods can't corporate the multi-modal characteristics of the Chinese language automatically. Therefore, in this paper, we present GPSAttack, a unified glyphs, phonetics, and semantics multi-modal attack method that can generate deceitful texts efficiently against Chinese text classification models. We conducted exhaustive attack experiments on convolutional, recurrent, and BERT networks to evaluate our method. The results show that our method achieves up to 90% attack success rate while modifying less than 4 characters per sentence and remaining readable to humans.
Ranked Similarity Weighting and Top-nk Sampling in Deep Metric Learning

Deep metric learning has been widely used in many visual tasks. Its key idea is to increase the similarity of positive samples and decrease the similarity of negative samples through network training. To achieve this purpose, many studies excessively extend the distance between the query sample and hard negative samples. This may compress the distance between similar samples of other classes, causing these samples to cluster together. We call this phenomenon Negative Sample Aggregation. To address this problem, first, we propose a weighting method based on the Ranking Similarity of sample pairs, short for RS. The proposed weighting method can not only enlarge the distance between the query sample and hard negative samples, but also maintain the embedding distribution of proximal negative samples. Second, we propose a Top-nk sampling method, which can dynamically adjust the sampling strategy according to the distribution of a dataset. It solves the problem that the descent direction of the network gradient is inconsistent with the optimization target. The effectiveness of our methods is evaluated by extensive experiments on four public datasets and compared with that of other state-of-the-art methods. The results show that the proposed method obtains excellent performance, reaching 67.8% on CUB-200-2011 and 85.2% on Cars-196 at Recall@1.
Kiwrious AR: Exploring AR for Scientific Inquiry and Scaffolded Learning

Teaching scientific inquiry to schoolkids using electronic sensors enables them to learn and observe their surrounding environment. In this work we demonstrate Kiwrious AR, an app integrating mobile-based AR with electronic sensors to scaffold learning experiences. The app suggests which part of the surrounding environment can be measured or observed using the connected sensor. This will enrich the scientific learning experience at schools.
The Fun Facets of Mario: Multifaceted Experience-Driven PCG via Reinforcement Learning

The recently introduced EDRL framework approaches the experience-driven (ED) procedural generation of game content via a reinforcement learning (RL) perspective. EDRL has so far shown its effectiveness in generating novel platformer game levels endlessly in an online fashion. This paper extends the framework by integrating multiple facets of game creativity in the ED generation process. In particular, we employ EDRL on the creative facets of game level and gameplay design in Super Mario Bros. Inspired by Koster’s theory of fun, we formulate fun as moderate degrees of level or gameplay divergence and equip the algorithm with such reward functions. Moreover, we enable faster and more efficient game content generation through an episodic generative soft actor-critic algorithm. The resulting multifaceted EDRL is not only capable of generating fun levels efficiently, but it is also robust with respect to dissimilar playing styles and initial game level conditions.
An Exploration of Prompt-Based Zero-Shot Relation Extraction Method

Zero-shot relation extraction is an important method for dealing with the newly emerging relations in the real world which lacks labeled data. However, the mainstream two-tower zero-shot methods usually rely on large-scale and in-domain labeled data of predefined relations. In this work, we view zero-shot relation extraction as a semantic matching task optimized by prompt-tuning, which still maintains superior generalization performance when the labeled data of predefined relations are extremely scarce. To maximize the efficiency of data exploitation, instead of directly fine-tuning, we introduce a prompt-tuning technique to elicit the existing relational knowledge in pre-trained language model (PLMs). In addition, very few relation descriptions are exposed to the model during training, which we argue is the performance bottleneck of two-tower methods. To break through the bottleneck, we model the semantic interaction between relational instances and their descriptions directly during encoding. Experiment results on two academic datasets show that (1) our method outperforms the previous state-of-the-art method by a large margin with different samples of predefined relations; (2) this advantage will be further amplified in the low-resource scenario.
A JSON Web Signature Based Adaptive Authentication Modality for Healthcare Applications

In the era of fast internet-centric systems, the importance of security cannot be stressed more. However, stringent and multiple layers of security measures tend to be a hindrance to usability. This even prompts users to bypass multi-factor authentication schemes recommended by enterprises. The need to balance security and usability gave rise to Adaptive authentication. This system of utilizing the user's behavioral context and earlier access patterns is gaining popularity. Continuously analyzing the user's request patterns and attributes against an established contextual profile helps maintain security while challenging the user only when required. This paper proposes an Open standards based authentication modality that can seamlessly integrate with an Adaptive Authentication system. The proposed authentication modality uses JavaScript Object Notation(JSON), JSON Web Signature(JWS) and supports a means of verifying the authenticity of the requesting client. The proposed authentication modality has been formally verified using Scyther and all the claims have been validated.
A Survey of Deep learning-based Image caption

Image caption is to use computer vision technology to extract the semantic content information contained in the image, and use natural language processing technology to generate a reasonable text caption. This paper takes the development of image description technology based on deep learning as the main line, and introduces the typical image description methods in each period. Image description generally adopts an encoding-decoding structure, which is the most commonly used method at present. By improving the encoder or decoder, increasing the attention mechanism, introducing Transformer technology, the advantages and disadvantages of different methods are discussed and summarized. Data sets and evaluation indicators such as MS-COCO and Flicker commonly used in this field are introduced in detail.
Self-Supervised Learning of Neural Speech Representations From Unlabeled Intracranial Signals

Neuroprosthetics have demonstrated the potential to decode speech from intracranial brain signals, and hold promise for one day returning the ability to speak to those who have lost it.However, data in this domain is scarce, highly variable, and costly to label for supervised modeling.In order to address these constraints, we present brain2vec, a transformer-based approach for learning feature representations from intracranial electroencephalogram data.Brain2vec combines a self-supervised learning methodology, neuroanatomical positional embeddings, and the contextual representations of transformers to achieve three novelties: (1) learning from unlabeled intracranial brain signals, (2) learning from multiple participants simultaneously, all while (3) utilizing only raw unprocessed data.To assess our approach, we use a leave-one-participant-out validation procedure to separate brain2vec's feature learning from the holdout participant's speech-related supervised classification tasks.With only two linear layers, we achieve 90% accuracy on a canonical speech detection task, 42% accuracy on a more challenging 4-class speech-related behavior recognition, and 53% accuracy when applied to a 10-class, few-shot word classification task.Combined with visualizations of unsupervised class separation in the learned features, our results evidence brain2vec's ability to learn highly generalized representations of neural activity without the need for labels or consistent sensor location.
Swin-CasUNet: Cascaded U-Net with Swin Transformer for Masked Face Restoration

Masked face restoration is one of the most valuable challenges in the computer vision community. With the in-depth study of u-shaped architectures, also known as U-Net, great progress has been achieved in the development of masked face restoration during the past few years. However, previous restoration methods fail to fully model the long-range dependency due to the locality of convolution layers of the U-Net. To address this problem, we propose a shifted windows Transformer (Swin Transformer) based cascaded U-Net framework called Swin-CasUNet, which incorporates the long-range dependency merit of Transformer into the cascaded U-Net architecture to effectively enhance the functionality and generalization of U-shaped architecture. Specifically, we design a two-stage cascaded U-Net architecture to implement the coarse-to-fine restoration of the masked face. Swin Transformers is adopted to extract global self-attention contexts for the feature map produced by the encoder part of the U-Net. An improved face structure loss is proposed to supervise structure learning. To evaluate the robustness of our masked face restoration model, we collect 3800 pairs of full face images and corresponding masked face images from the real-world and web. Experiments on the datasets demonstrate that our proposed method can generate high quality restoration results. In order to quantitatively compare with previous face restoration methods, we modify the input of our system by manually adding regular and irregular white masks on CelebA face datasets, and then retrain our network. Experiments show that our Swin-CasUNet outperforms previous methods on benchmark datasets.
Modernizing CPS with Blockchain: Applications, Challenges &amp; Future Directions

The necessity for ameliorating management and secure process is imperiously required in this tech-driven world with the digitized working environment. The increased usage of the internet and the burgeoning connectivity through IoT have provided a significant opportunity for Cyber-Physical Systems (CPS) to preponderate. These are advanced computerized devices that function together to perform specific tasks, respond to human stimuli, and control the physical segments. While this technology is already used in automatic pilot avionics, robotics systems, medical monitoring, industrial control systems, etc. yet the advancement of these systems must comprehend incontrovertible focus on making them efficient and secure. To improve the resilience, dependability, and security of these systems, researchers can incorporate Blockchain technology which has an inbuilt blend of consensual algorithms, secure protocols, and distributed data storage, with the CPS. Moreover, with the advancement of IoT in the upgraded version of CPS, such as autonomous driving, there’s a major need to manage big data with low latency and high accuracy. Therefore, deep learning can be applied for systematic analysis of big data, which supports vivid analytic ability. While these emerging technologies set some security standards and enable users to authenticate their digital information, these technologies have the potential to significantly influence the current trend in the field of CyberPhysical Systems. This article focuses on a comprehensive analysis of Blockchain applications synthesized with CPS. Furthermore, this article briefly presented the BCPS (Blockchain-enabled CPS) framework for addressing issues in the Industry 4.0 manufacturing system while adopting Blockchain (BC) in CPS.
Split Computing Video Analytics Performance Enhancement With Auction-based Resource Management

Recently, computer vision applications based on deep neural networks (DNN) have developed rapidly. They are expected to be used in Internet-of-Things (IoT) systems such as smart cities, factories of the future, and security surveillance cameras. However, resource-limited IoT devices cannot execute such computationally intensive inference tasks locally within a reasonable time. Edge computing and the split computing technique provide such systems with a solution that reduces the inference latency and improves the system performance by enabling collaborative inference between the devices and the nearby edge server and alleviating the large uplink transmission with the split point carefully selected. This article proposes an incentive-compatible mechanism to configure the frame rate and the input resolution of the camera network for video analytics using layer-level split computing. Specifically, we evaluate the performance of the system at the video level, where the frame rate and the input resolution are both configuration knobs that contribute to accuracy. We also address the joint resource allocation and the split point decision problem for split computing that underlies the configuration. We show that the proposed mechanism achieves optimal configuration and guarantees truthfulness, individual rationality, and weak budget balance.
SHPIA: A Low-Cost Multi-purpose Smart Home Platform for Intelligent Applications

Nowadays, smart devices have invaded the market and consequently our daily life. Their use in smart home contexts, to improve the quality of life, specially for elderly and people with special needs, is getting stronger and stronger. Therefore, many systems based on smart applications and intelligent devices have been developed, for example, to monitor people's environmental contexts, help in daily life activities, and analyze their health status. However, most of the existing solutions present disadvantages regarding accessibility, as they are costly, and applicability, due to lack of generality and interoperability. This paper is intended to tackle such drawbacks by presenting SHPIA, a multi-purpose smart home platform for intelligent applications. It is based on the use of a low-cost Bluetooth Low Energy (BLE)-based devices, which "transforms" objects of daily life into smart objects. The devices allow collecting and automatically labelling different type of data to provide indoor monitoring and assistance. SHPIA is intended, in particular, to be adaptable to different home-based application scenarios, like for example, human activity recognition, coaching systems, and occupancy detection and counting. The SHPIA platform is open source and freely available to the scientific and industrial community.
Robust Video Watermarking Based on Residual Synchronization in the DCT Domain

Designing an efficient synchronization method for video watermarking under complex hybrid attacks is an urgent task due to the development of social networks and multimedia software. However, few existing watermarking methods can resist various complex attacks on the social network, such as scaling, frame rate conversion, and other strong hybrid attacks. To address this issue, we first analyze the relationship between the spatial and discrete cosine transform (DCT) domain and propose a robust video watermarking based on residual synchronization (RS) mechanism. In the RS mechanism, the spread-spectrum encoded RS unit and sync frames are generated according to the change rules obtained from the analysis to achieve robustness. On the other hand, the high-frequency energy average distribution (HFEAD) mechanism is designed by analyzing the effects of distortion drifts and texture features for watermark embedding to achieve good visual quality. Extensive experimental results show that the RS mechanism can effectively synchronize watermarks under various attacks, e.g., asymmetric cropping, resizing, scaling, etc., hybridized with strong recompression attacks. Moreover, the proposed scheme is resistant to screenshots and social platform transcoding.
Adaptive Clustering Quasi-Line Search Path Planning Algorithm Based On Sampling

In this paper, the adaptive clustering quasi-line search path planning algorithm (ACPP) is proposed from the viewpoint of non-convex optimization in an artificial potential field (APF). In ACPP, the initial path is the trajectory of the optimization process from the initial point to the target one. In a complex environment, the drivable area is divided by the edges into many relatively isolated regions, which leads to a non-convex path planning problem. Therefore, the clustering based on path nodes is set adaptively to make the potential function of each isolated region convex in the potential field. In each isolated region, the environment can be perceived by updating the parameters with sampling based on probability distribution functions, and these functions have similar influence to the cost function in optimization. Inspired by convex optimization, the quasi-line search algorithm is proposed for sampling points. Therefore, ACPP reduces the number of samples, dramatically, and has advantages of both sampling-based and APF-based algorithms in path planning. Based on the line segment based map which can be obtained from sensors readily, a gridding strategy is used to further reduce the time complexity. A series of simulation and experiential results validate the effectiveness of ACPP in virtual and real-world environments, respectively.
Homomorphic Encryption in Manufacturing Compliance Checks

Compliance data consists of manufacturing quality measures collected in the production process. Quality checks are most of the times computationally expensive to perform mainly due to the amount of collected data. Having trusted solutions for outsourcing analyses to the Cloud is an opportunity for reducing costs of operation. However, the adoption of the Cloud computation paradigm is delayed for the many security risks associated with it. In the use case we consider in this paper, compliance data is very sensitive, because it may contain IP-critical information, or it may be related to safety-critical operations or products. While the technological solutions that protect data in-transit or at rest has reached a satisfying level of maturity, there is a huge demand for securing data in-use. Homomorphic Encryption (HE) is one of the main technological enablers for secure computation outsourcing. In the last decade, HE has reached maturity with remarkable pace. However, using HE is still far from being an automated process and each use case introduces different challenges. In this paper, we investigate application of HE to the described scenario highlighting, in particular, the main operations of the comparison algorithm, we identify the challenges that HE technology introduces and we propose a solution per challenge. Finally, we evaluate our proposals using one of the open source HE libraries, SEAL, for our implementations.