Reviewerly/exHarmony huggingface dataset:
Motion Planning of Redundant Manipulator With Variable Joint Velocity Limit Based on Beetle Antennae Search Algorithm

Redundant manipulators play important roles in many industrial and service applications by assisting people fulfill heavy and repetitive jobs.However, redundant manipulators are coupled highlynonlinear systems which exert difficulty of redundancy resolution computation.Conventional methods such as pseudo-inverse-based approaches obtain the resolved joint angles from joint velocity level, which may bring about more computational cost and may neglect joint velocity limits.In this work, a motion planning method based on beetle antennae search algorithm (BAS) is proposed for motion planning of redundant manipulators with the variable joint velocity limit.Such proposed work does not need to resolve the velocity kinematics equation as the conventional methods do, and the proposed method can directly deal with the forward kinematics equation to resolve the desired joint angles.The simulation and experiment on the five-link planar manipulator and the Kuka industrial manipulator system demonstrate the efficiency of the proposed method for motion planning of redundant manipulator, and reveal the reliable performance of the BAS algorithm as compared with genetic algorithm (GA), particle swarm optimization (PSO), firefly algorithm(FA) and quantum behaved particle swarm algorithm(QPSO) methods.
Face Recognition System Based on CNN

With the development of computer vision and artificial intelligence, face recognition is widely used in daily life. As one of the most concerned methods of biometric recognition, face recognition has become one of the research hotspots in the field of computer vision and artificial intelligence. However, face recognition is easily affected by internal and external differences, and it is often difficult for traditional face recognition methods to achieve ideal results. In order to further improve the recognition accuracy of current face recognition algorithms, this paper proposes a face recognition algorithm based on improved convolutional neural network. Experiments show that the improved algorithm can be effectively applied to the data set.
A Stacked Generalization U-shape network based on zoom strategy and its application in biomedical image segmentation

Background and objective: The deep neural network model can learn complex non-linear relationships in the data and has superior flexibility and adaptability. A downside of this flexibility is that they are sensitive to initial conditions, both in terms of the initial random weights and in terms of the statistical noise in the training dataset. And the disadvantage caused by adaptability is that deep convolutional networks usually have poor robustness or generalization when the models are trained using the extremely limited amount of labeled data, especially in the biomedical imaging informatics field. Methods: In this paper, we propose to develop and test a stacked generalization U-shape network (SG-UNet) based on the zoom strategy applying to biomedical image segmentation. SG-UNet is essentially a stacked generalization architecture consisting of multiple sub-modules, which takes multi-resolution images as input and uses hybrid features to segment regions of interest and detect diseases under the multi-supervision. The proposed new SG-UNet applies the zoom of multi-supervision to do optimization search in global feature space without pre-training. Besides, the zoom loss function can gradually enhance the focus training on a sparse set of hard samples. Results: We evaluated the proposed algorithm in comparison with several popular U-shape ensemble network architectures across multi-modal biomedical image segmentation tasks to segment malignant rectal cancers, polyps and glands from the three imaging modalities of computed tomography (CT), digital colonoscopy and histopathology images. Applying the proposed algorithm improves 3.116%, 2.676%, 2.356% on Dice coefficients, and 3.044%, 2.420%, 1.928% on F2-score for the three imaging modality datasets, respectively. The comparison results using different amounts of rectal cancer CT data show that the proposed algorithm has a slower tendency of diminishing marginal efficiency. And glands segmentation study results also support the feasibility of yielding comparable performance with other state-of-the-art methods. Conclusions: The proposed algorithm can be trained more efficiently by using the small image datasets without using additional techniques such as fine-tuning, and achieves higher accuracy with less computational complexity than other stacked ensemble networks for biomedical image segmentation.
The joy of Probabilistic Answer Set Programming: Semantics, complexity, expressivity, inference

Probabilistic Answer Set Programming (PASP) combines rules, facts, and independent probabilistic facts. We argue that a very useful modeling paradigm is obtained by adopting a particular semantics for PASP, where one associates a credal set with each consistent program. We examine the basic properties of PASP under this credal semantics, in particular presenting novel results on its complexity and its expressivity, and we introduce an inference algorithm to compute (upper) probabilities given a program.
Effectiveness of Using Voice Assistants in Learning: A Study at the Time of COVID-19

The use of advanced learning technologies in a learning management system (LMS) can greatly assist learning processes, especially when used in university environments, as they promote the development of Self-Regulated learning, which increases academic performance and student satisfaction towards personal learning. One of the most innovative resources that an LMS may have is an Intelligent Personal Assistant (IPA). We worked with a sample of 109 third-grade students following Health Sciences degrees. The aims were: (1) to verify whether there will be significant differences in student access to the LMS, depending on use versus non-use of an IPA. (2) To verify whether there will be significant differences in student learning outcomes depending on use versus non-use of an IPA. (3) To verify whether there will be significant differences for student satisfaction with teaching during the COVID-19 pandemic, depending on use versus non-use of an IPA. (4) To analyze student perceptions of the usefulness of an IPA in the LMS. We found greater functionality in access to the LMS and satisfaction with teaching, especially during the health crisis, in the group of students who had used an IPA. However, both the expansion of available information and the usability of the features embedded in an IPA are still challenging issues.
Domain-attention Conditional Wasserstein Distance for Multi-source Domain Adaptation

Multi-source domain adaptation has received considerable attention due to its effectiveness of leveraging the knowledge from multiple related sources with different distributions to enhance the learning performance. One of the fundamental challenges in multi-source domain adaptation is how to determine the amount of knowledge transferred from each source domain to the target domain. To address this issue, we propose a new algorithm, called Domain-attention Conditional Wasserstein Distance (DCWD), to learn transferred weights for evaluating the relatedness across the source and target domains. In DCWD, we design a new conditional Wasserstein distance objective function by taking the label information into consideration to measure the distance between a given source domain and the target domain. We also develop an attention scheme to compute the transferred weights of different source domains based on their conditional Wasserstein distances to the target domain. After that, the transferred weights can be used to reweight the source data to determine their importance in knowledge transfer. We conduct comprehensive experiments on several real-world data sets, and the results demonstrate the effectiveness and efficiency of the proposed method.
An Attack on Some Signature Schemes Constructed from Five-Pass Identification Schemes

We present a generic forgery attack on signature schemes constructed from 5-round identification schemes made non-interactive with the Fiat-Shamir transform. The attack applies to ID schemes that use parallel repetition to decrease the soundness error. The attack can be mitigated by increasing the number of parallel repetitions, and our analysis of the attack facilitates parameter selection. We apply the attack to MQDSS, a post-quantum signature scheme relying on the hardness of the MQ-problem. Concretely, forging a signature for the L1 instance of MQDSS, which should provide 128 bits of security, can be done in $$\approx $$ $$2^{95}$$ operations. We verify the validity of the attack by implementing it for round-reduced versions of MQDSS, and the designers have revised their parameter choices accordingly. We also survey other post-quantum signature algorithms and find the attack succeeds against PKP-DSS (a signature scheme based on the hardness of the permuted kernel problem) and list other schemes that may be affected. Finally, we use our analysis to choose parameters and investigate the performance of a 5-round variant of the Picnic scheme.
Continual Reinforcement Learning in 3D Non-stationary Environments

High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques.Artificial agents, nowadays, are often trained off-line in very static and controlled conditions in simulation such that training observations can be thought as sampled i.i.d.from the entire observations space.However, in real world settings, the environment is often non-stationary and subject to unpredictable, frequent changes.In this paper we propose and openly release CRLMaze, a new benchmark for learning continually through reinforcement in a complex 3D non-stationary task based on ViZDoom and subject to several environmental changes.Then, we introduce an end-to-end model-free continual reinforcement learning strategy showing competitive results with respect to four different baselines and not requiring any access to additional supervised signals, previously encountered environmental conditions or observations.
Building an Interactive Immersive Virtual Reality Crime Scene for Future Chemists to Learn Forensic Science Chemistry

Immersive technology, such as virtual reality (VR) experiences, has the ability to enhance forensic science chemistry education by allowing students to view realistic crime scenes. With the help of a WebVR software such as Uptale, we created a VR crime scene activity that students can participate in remotely with the help of their mobile device. Students were tasked to investigate a VR crime scene and collect evidence for subsequent analysis. They would then complete an online report, where they would report the evidence they had collected and subsequently perform crime scene reconstruction to describe what they think happened at the crime scene. Overall, 84% of students enjoyed the activity and would recommend others to try the VR crime scene experience.
Collecting money through blockchain technologies: first insights on the determinants of the return on Initial Coin Offerings

Initial Coin Offerings (ICOs), i.e. the initial offer of a crypto-token, represent an increasingly popular method to raise money. However, the determinants of ICOs’ returns for investors are still overlooked. Following this cue, the empirical outcomes of our study based on crypto-tokens issued between 2017 and 2018 evidence the main determinants of ICOs’ returns: first, crypto-tokens returns are positively associated to Ether’s returns; second, ICO price is negatively associated to later price increases; third, crypto-tokens returns are lower when they are offered in presale; finally, the more a crypto-token price increases between the ICO and the listing-date on the secondary-market and the more its price rises in the following month. In so doing, we contribute to a better understanding of the ICO phenomenon and highlight which aspects may benefit fund raising, which are relevant for the establishment of new startups and more broadly for the economic development of a country.
A Mixed-Precision RISC-V Processor for Extreme-Edge DNN Inference

Low bit-width Quantized Neural Networks (QNNs) enable deployment of complex machine learning models on constrained devices such as microcontrollers (MCUs) by reducing their memory footprint. Fine-grained asymmetric quantization (i.e., different bit-widths assigned to weights and activations on a tensor-by-tensor basis) is a particularly interesting scheme to maximize accuracy under a tight memory constraint. However, the lack of sub-byte instruction set architecture (ISA) support in SoA microprocessors makes it hard to fully exploit this extreme quantization paradigm in embedded MCUs. Support for sub-byte and asymmetric QNNs would require many precision formats and an exorbitant amount of opcode space. In this work, we attack this problem with status-based SIMD instructions: rather than encoding precision explicitly, each operand's precision is set dynamically in a core status register. We propose a novel RISC-V ISA core MPIC (Mixed Precision Inference Core) based on the open-source RI5CY core. Our approach enables full support for mixed-precision QNN inference with 292 different combinations of operands at 16-, 8-, 4- and 2-bit precision, without adding any extra opcode or increasing the complexity of the decode stage. Our results show that MPIC improves both performance and energy efficiency by a factor of 1.1-4.9x when compared to software-based mixed-precision on RI5CY; with respect to commercially available Cortex-M4 and M7 microcontrollers, it delivers 3.6-11.7x better performance and 41-155x higher efficiency.
An Efficient Framework for Secure Image Archival and Retrieval System Using Multiple Secret Share Creation Scheme

Due to the advanced growth in multimedia data and Cloud Computing (CC), Secure Image Archival and Retrieval System (SIARS) on cloud has gained more interest in recent times. Content based image retrieval (CBIR) systems generally retrieve the images relevant to the query image (QI) from massive databases. However, the secure image retrieval process is needed to ensure data confidentiality and secure data transmission between cloud storage and users. Existing secure image retrieval models faces difficulties like minimum retrieval performance, which fails to adapt with the large-scale IR in cloud platform. To resolve this issue, this article presents a SIARS using deep learning (DL) and multiple share creation schemes. The proposed SIARS model involves Adagrad based convolutional neural network (AG-CNN) based feature extractor to extract the useful set of features from the input images. At the same time, secure multiple share creation (SMSC) schemes are executed to generate multiple shares of the input images. The resultant shares and the feature vectors are stored in the cloud database with the respective image identification number. Upon retrieval, the user provides a query image and reconstructs the received shared image to attain the related images from the database. An elaborate experimentation analysis is carried out on benchmark Corel10K dataset and the results are examined in terms of retrieval efficiency and image quality. The attained results ensured the superior performance of the SIARS model on all the applied test images.
Optimal Neighborhood Multiple Kernel Clustering with Adaptive Local Kernels

Multiple kernel clustering (MKC) algorithm aims to group data into different categories by optimally integrating information from a group of pre-specified kernels. Though demonstrating superiorities in various applications, we observe that existing MKC algorithms usually <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">do not sufficiently consider the local density around individual data samples</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">excessively limit the representation capacity of the learned optimal kernel</i> , leading to unsatisfying performance. In this paper, we propose an algorithm, called optimal neighborhood MKC with adaptive local kernels (ON-ALK), to address the two issues. In specific, we construct adaptive local kernels to sufficiently consider the local density around individual data samples, where different numbers of neighbors are discriminatingly selected on each sample. Further, the proposed ON-ALK algorithm boosts the representation of the learned optimal kernel via relaxing it into the neighborhood area of weighted combination of the pre-specified kernels. To solve the resultant optimization problem, a three-step iterative algorithm is designed and theoretically proven to be convergent. After that, we also study the generalization bound of the proposed algorithm. Extensive experiments have been conducted to evaluate the clustering performance. As indicated, the algorithm significantly outperforms state-of-the-art methods in recent literatures on six challenging benchmark datasets, verifying its advantages and effectiveness.
Optical Character Recognition using Ensemble of SVM, MLP and Extra Trees Classifier

This paper deals with retrieval of contents of any printed or handwritten document. Maximally Stable Extremal Regions (MSER) algorithm along with region-growing methods are used for the detection of printed regions. Histogram of Oriented Gradients (HOG features) are used for feature extraction. Various machine learning algorithms, namely Decision Trees, Random Forest, Extra Trees Classifier, MLP, and SVM along with ensemble method were used for classification, and the accuracies compared.
Your Privilege Gives Your Privacy Away: An Analysis of a Home Security Camera Service

Once considered a luxury, Home Security Cameras (HSCs) are now commonplace and constitute a growing part of the wider online video ecosystem. This paper argues that their expanding coverage and close integration with daily life may result in not only unique behavioral patterns, but also key privacy concerns. This motivates us to perform a detailed measurement study of a major HSC provider, covering 15.4M streams and 211K users. Our study takes two perspectives: (i) we explore the per-user behaviour, identifying core clusters of users; and (ii) we build on this analysis to extract and predict privacy-compromising insight. Key observations include a highly asymmetrical traffic distribution, distinct usage patterns, wasted resources and fixed viewing locations. Furthermore, we identify three privacy risks and explore them in detail. We find that paid users are more likely to be exposed to attacks due to their heavier usage patterns. We conclude by proposing simple mitigations that can alleviate these risks.
Relation classification via knowledge graph enhanced transformer encoder

Relation classification is an important task in natural language processing fields. The goal is to predict predefined relations for the marked nominal pairs in given sentences. State-of-the-art works usually focus on using deep neural networks as classifier to conduct the relation prediction. The rich semantic information of relationships in the triples of existing knowledge graph (KG) can be used as additional supervision for relation classification. However, these relationships were simply used as labels to specify the class of sentences in previous works, and their semantic information was completely ignored. In this paper, a novel approach is proposed for relation classification, which jointly uses information from textual sentences and knowledge graphs. To this end, we introduce a Transformer encoder to measure the semantic similarity between sentences and relation types. Besides, we connect the semantic information of marked nominals in sentences with that of the corresponding entities in knowledge graph to generate the semantic matching information between textual relations and KG relations. The matching information can provide additional supervision for relation classification. Since the words and entities are used interactively with each other in our work, we propose an embedding translating strategy to handle the semantic gap problem between word embeddings and entity embeddings. Experimental results on two widely used datasets, SemEval-2010 Task 8 and TACRED, show that our approach is able to efficiently use the semantic information from the knowledge graph to enhance the performance of the Transformer encoder for relation classification.
FEEL: A Federated Edge Learning System for Efficient and Privacy-Preserving Mobile Healthcare

With the prosperity of artificial intelligence, neural networks have been increasingly applied in healthcare for a variety of tasks for medical diagnosis and disease prevention. Mobile wearable devices, widely adopted by hospitals and health organizations, serve as emerging sources of medical data and participate in the training of neural network models for accurate model inference. Since the medical data are privacy-sensitive and non-shareable, federated learning has been proposed to train a model across decentralized data, which involves each mobile device running a training task with its own data in parallel. However, due to the ever-increasing size and complexity of modern neural network models, it becomes inefficient, and may even infeasible, to perform training tasks on wearable devices that are resource-constrained. In this paper, we propose a FEderated Edge Learning system, FEEL, for efficient privacy-preserving mobile healthcare. Specifically, we design an edge-based training task offloading strategy to improve the training efficiency. Further, we build our system on the basis of federated learning to make use of distributed user data to improve the inference performance. In addition, during model training, we provide a differential privacy scheme to strengthen the privacy protection. A prototype system has been implemented to evaluate the training efficiency, inference performance and noise sensitivity, respectively. And the results have demonstrated that our proposal could train models in an efficient and privacy-preserving way.
Epidemic contact tracing with smartphone sensors

Contact tracing is widely considered as an effective procedure in the fight against epidemic diseases. However, one of the challenges for technology based contact tracing is the high number of false positives, questioning its trust-worthiness and efficiency amongst the wider population for mass adoption. To this end, this paper proposes a novel, yet practical smartphone-based contact tracing approach, employing WiFi and acoustic sound for relative distance estimate, in addition to the air pressure and the magnetic field for ambient environment matching. We present a model combining six smartphone sensors, prioritising some of them when certain conditions are met. We empirically verified our approach in various realistic environments to demonstrate an achievement of up to 95% fewer false positives, and 62% more accurate than Bluetooth-only system. To the best of our knowledge, this paper was one of the first work to propose a combination of smartphone sensors for contact tracing.