Reviewerly/exHarmony huggingface dataset:
Spherical U-Net on Cortical Surfaces: Methods and Applications

Convolutional Neural Networks (CNNs) have been providing the state-of-the-art performance for learning-related problems involving 2D/3D images in Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have a spherical topology in a manifold space, e.g., brain cortical or subcortical surfaces represented by triangular meshes, with large inter-subject and intra-subject variations in vertex number and local connectivity. Hence, there is no consistent neighborhood definition and thus no straightforward convolution/transposed convolution operations for cortical/subcortical surface data. In this paper, by leveraging the regular and consistent geometric structure of the resampled cortical surface mapped onto the spherical space, we propose a novel convolution filter analogous to the standard convolution on the image grid. Accordingly, we develop corresponding operations for convolution, pooling, and transposed convolution for spherical surface data and thus construct spherical CNNs. Specifically, we propose the Spherical U-Net architecture by replacing all operations in the standard U-Net with their spherical operation counterparts. We then apply the Spherical U-Net to two challenging and neuroscientifically important tasks in infant brains: cortical surface parcellation and cortical attribute map development prediction. Both applications demonstrate the competitive performance in the accuracy, computational efficiency, and effectiveness of our proposed Spherical U-Net, in comparison with the state-of-the-art methods.
Graph Kernels: A Survey

Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years, the considerable research activity that occurred in the field resulted in the development of dozens of graph kernels, each focusing on specific structural properties of graphs. Graph kernels have proven successful in a wide range of domains, ranging from social networks to bioinformatics. The goal of this survey is to provide a unifying view of the literature on graph kernels. In particular, we present a comprehensive overview of a wide range of graph kernels. Furthermore, we perform an experimental evaluation of several of those kernels on publicly available datasets, and provide a comparative study. Finally, we discuss key applications of graph kernels, and outline some challenges that remain to be addressed.
Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method

For the problem of mobile robot’s path planning under the known environment, a path planning method of mixed artificial potential field (APF) and ant colony optimization (ACO) based on grid map is proposed. First, based on the grid model, APF is improved in three ways: the attraction field, the direction of resultant force, and jumping out the infinite loop. Then, the hybrid strategy combined global updating with local updating is developed to design updating method of the ACO pheromone. The process of optimization of ACO is divided into two phases. In the prophase, the direction of the resultant force obtained by the improved APF is used as the inspired factors, which leads ant colony to move in a directional manner. In the anaphase, the inspired factors are canceled, and ant colony transition is completely based on pheromone updating, which can overcome the inertia of the ant colony and force them to explore a new and better path. Finally, some simulation experiments and mobile robot environment experiments are done. The experiment results verify that the method has stronger stability and environmental adaptability.
Phase-space-simulation method for quantum computation with magic states on qubits

We propose a method for classical simulation of finite-dimensional quantum systems, based on sampling from a quasiprobability distribution, i.e., a generalized Wigner function. Our construction applies to all finite dimensions, with the most interesting case being that of qubits. For multiple qubits, we find that quantum computation by Clifford gates and Pauli measurements on magic states can be efficiently classically simulated if the quasiprobability distribution of the magic states is non-negative. This provides the so far missing qubit counterpart of the corresponding result [V. Veitch et al., New J. Phys. 14, 113011 (2012)] applying only to odd dimension. Our approach is more general than previous ones based on mixtures of stabilizer states. Namely, all mixtures of stabilizer states can be efficiently simulated, but for any number of qubits there also exist efficiently simulable states outside the stabilizer polytope. Further, our simulation method extends to negative quasiprobability distributions, where it provides probability estimation. The simulation cost is then proportional to a robustness measure squared. For all quantum states, this robustness is smaller than or equal to robustness of magic.
CLIK on PLCs! Attacking Control Logic with Decompilation and Virtual PLC

This paper presents CLIK, a new remote attack on the control logic of a programmable logic controller (PLC) in industrial control systems.The control logic defines how a PLC controls a physical process such as a nuclear plant.A full control logic attack faces two critical challenges: 1) infecting the control logic in a PLC at a field site and, 2) hiding the infection from engineering software at a control center since the software can obtain the infected logic from the PLC and reveal it to a control engineer.The existing academic efforts only (partially) address the former.CLIK is a first practical control-logic attack that deals with both challenges successfully.It modifies the control logic running in a remote target PLC automatically to disrupt a physical process.CLIK also employs a new virtual PLC approach that hides the malicious modifications by engaging the engineering software with a captured network traffic of the original (uninfected) control logic.It is fully implemented on real hardware/software used in industrial settings and is made publicly available for academic research on control logic attacks 1 .CLIK consists of four phases and takes less than a minute to complete an attack cycle.As part of the implementation, we found a critical (zero-day) vulnerability in the password authentication mechanism of a target PLC, which allows the attacker to overwrite password hash in the PLC during the authentication process and gain access to the (protected) control logic.We have disclosed the vulnerability responsibly to the PLC vendor who has already patched the vulnerability 2 .
The intrinsically linked future for human and Artificial Intelligence interaction

No sleep! No sick days! No Holidays! alongside 24/7 around the clock tireless work, welcome to the world of Artificial Intelligence, how can humans match the phenomena of AI. Humanity is in the grip of an unstoppable, and exciting future with the future development of AI, humans must decide whether to ride on the coat-tails of AI or resist the inevitable change of the world as we know it. The real game changer will be the commercial availability of Quantum Computing. Humans must learn to work, live, learn and interact with AI or become second class citizens. Big data is the lifeblood of AI and Quantum Computing processing power will enable future computers to process incredible amounts of big data. AI is taking over the job’s human do, receptionists, drivers, chefs and in the future doctors and accountants. Quantum Supremacy will soon be achieved, and AI will soon reach and far exceed the “Singularity”. Will humans grasp the opportunity to develop with AI or resist? humans have the chance to develop with AI through brain computer interface (BCI) technology. Security and regulations have to be put in place, with questions of “Who is responsible for AI security and regulations”? and “can AI be trusted as an autonomous entity” also the ethical use of AI has to be addressed, “What about the rights and ethics of AI”?. The human race is on an inevitable path of AI dominance the question is “will humans and AI be friends or adversaries”?
Use of Blockchain in Healthcare: A Systematic Literature Review

Blockchain is an emerging field which works on the concept of a digitally distributed ledger and consensus algorithm removing all the threats of intermediaries.Its early applications were related to the finance sector but now this concept has been extended to almost all the major areas of research including education, IoT, banking, supplychain, defense, governance, healthcare, etc.In the field of healthcare, stakeholders (provider, patient, payer, research organizations, and supply chain bearers) demand interoperability, security, authenticity, transparency, and streamlined transactions.Blockchain technology, built over the internet, has the potential to use the current healthcare data into peer to peer and interoperable manner by using a patient-centric approach eliminating the third party.Using this technology, applications can be built to manage and share secure, transparent and immutable audit trails with reduced systematic fraud.This study reviews existing literature in order to identify the major issues of various healthcare stakeholders and to explore the features of blockchain technology that could resolve identified issues.However, there are some challenges and limitations of this technology which are needed to be focused on future research.
Generative model benchmarks for superconducting qubits

In this work we experimentally demonstrate how generative model training can be used as a benchmark for small ($<5$ qubits) quantum devices. Performance is quantified using three data analytic metrics: the Kullbeck-Leiber divergence, and two adaptations of the F1 score. Using the $2\times2$ Bars and Stripes dataset, we determine optimal circuit constructions for generative model training on superconducting qubits by including hardware connectivity constraints into circuit design. We show that on noisy hardware sparsely connected, shallow circuits out-perform denser counterparts.
NodeSketch

Embeddings have become a key paradigm to learn graph representations and facilitate downstream graph analysis tasks. Existing graph embedding techniques either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization, or factorize a high-order proximity/adjacency matrix of the graph via expensive matrix factorization. However, these techniques usually require significant computational resources for the learning process, which hinders their applications on large-scale graphs. Moreover, the cosine similarity preserved by these techniques shows suboptimal efficiency in downstream graph analysis tasks, compared to Hamming similarity, for example. To address these issues, we propose NodeSketch, a highly-efficient graph embedding technique preserving high-order node proximity via recursive sketching. Specifically, built on top of an efficient data-independent hashing/sketching technique, NodeSketch generates node embeddings in Hamming space. For an input graph, it starts by sketching the self-loop-augmented adjacency matrix of the graph to output low-order node embeddings, and then recursively generates k-order node embeddings based on the self-loop-augmented adjacency matrix and (k-1)-order node embeddings. Our extensive evaluation compares NodeSketch against a sizable collection of state-of-the-art techniques using five real-world graphs on two graph analysis tasks. The results show that NodeSketch achieves state-of-the-art performance compared to these techniques, while showing significant speedup of 9x-372x in the embedding learning process and 1.19x-1.68x speedup when performing downstream graph analysis tasks.
Dense Procedure Captioning in Narrated Instructional Videos

Understanding narrated instructional videos is important for both research and real-world web applications.Motivated by video dense captioning, we propose a model to generate procedure captions from narrated instructional videos which are a sequence of stepwise clips with description.Previous works on video dense captioning learn video segments and generate captions without considering transcripts.We argue that transcripts in narrated instructional videos can enhance video representation by providing fine-grained complimentary and semantic textual information.In this paper, we introduce a framework to (1) extract procedures by a cross-modality module, which fuses video content with the entire transcript; and (2) generate captions by encoding video frames as well as a snippet of transcripts within each extracted procedure.Experiments show that our model can achieve state-of-the-art performance in procedure extraction and captioning, and the ablation studies demonstrate that both the video frames and the transcripts are important for the task.
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking

We characterize a prevalent weakness of deep neural networks (DNNs)---overthinking---which occurs when a DNN can reach correct predictions before its final layer. Overthinking is computationally wasteful, and it can also be destructive when, by the final layer, a correct prediction changes into a misclassification. Understanding overthinking requires studying how each prediction evolves during a DNN's forward pass, which conventionally is opaque. For prediction transparency, we propose the Shallow-Deep Network (SDN), a generic modification to off-the-shelf DNNs that introduces internal classifiers. We apply SDN to four modern architectures, trained on three image classification tasks, to characterize the overthinking problem. We show that SDNs can mitigate the wasteful effect of overthinking with confidence-based early exits, which reduce the average inference cost by more than 50% and preserve the accuracy. We also find that the destructive effect occurs for 50% of misclassifications on natural inputs and that it can be induced, adversarially, with a recent backdooring attack. To mitigate this effect, we propose a new confusion metric to quantify the internal disagreements that will likely lead to misclassifications.
When Geo-Text Meets Security: Privacy-Preserving Boolean Spatial Keyword Queries

In recent years, spatial keyword query has attracted wide-spread research attention due to the popularity of the location-based services. To efficiently support the online spatial keyword query processing, the data owners need to outsource their data and the query processing service to cloud platforms. However, the outsourcing services may raise privacy leaking issues because the cloud server on the platforms may not be trusted for both data owners and query users. Therefore, in this work, we first propose and formalize the problem of privacy-preserving boolean spatial keyword query under the widely accepted Known Background Thread Model. And then, we devise a novel privacy-preserving spatial-textual Bloom Filter encoding structure and an encrypted R-tree index. They can maintain both spatial and text information together in a secure way while answering the encrypted spatial keyword queries without the need for data decryption. To further accelerate the query processing, a compressed encrypted index is provided to deal with the challenges of the large dimension expansion and the expensive space consumption in the encrypted R-tree index. In addition, we develop the corresponding algorithms based on the designed index, and present the in-depth security analysis to show our work's satisfaction meeting the strong secure scheme. Finally, we demonstrate the performance of our proposed index and algorithms by conducting extensive experiments on four datasets under various system settings.
Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis

Attention based deep learning systems have been demonstrated to be the state of the art approach for aspect-level sentiment analysis, however, end-to-end deep neural networks lack flexibility as one can not easily adjust the network to fix an obvious problem, especially when more training data is not available: e.g. when it always predicts positive when seeing the word disappointed.Meanwhile, it is less stressed that attention mechanism is likely to "over-focus" on particular parts of a sentence, while ignoring positions which provide key information for judging the polarity.In this paper, we describe a simple yet effective approach to leverage lexicon information so that the model becomes more flexible and robust.We also explore the effect of regularizing attention vectors to allow the network to have a broader "focus" on different parts of the sentence.The experimental results demonstrate the effectiveness of our approach.
Knowledge Base Question Answering With a Matching-Aggregation Model and Question-Specific Contextual Relations

Making use of knowledge bases to answer questions (KBQA) is a key direction in question answering systems. Researchers have developed a diverse range of methods to address this problem, but there are still some limitations with the existing methods. Specifically, the existing neural network-based methods for KBQA have not taken advantage of the recent “matching-aggregation” framework for the sequence matching, and when representing a candidate answer entity, they may not choose the most useful context of the candidate for matching. In this paper, we explore the use of a “matching-aggregation” framework to match candidate answers with questions. We further make use of question-specific contextual relations to enhance the representations of candidate answer entities. Our complete method is able to achieve state-of-the-art performance on two benchmark datasets: WebQuestions and SimpleQuestions.
Thermal Infrared Pedestrian Segmentation Based on Conditional GAN

A novel thermal infrared pedestrian segmentation algorithm based on conditional generative adversarial network (IPS-cGAN) is proposed for intelligent vehicular applications. The convolution backbone architecture of the generator is based on the improved U-Net with residual blocks for well utilizing regional semantic information. Moreover, cross entropy loss for segmentation is introduced as the condition for the generator. SandwichNet, a novel convolutional network with symmetrical input, is proposed as the discriminator for real-fake segmented images. Based on the c-GAN framework, good segmentation performance could be achieved for thermal infrared pedestrians. Compared to some supervised and unsupervised segmentation algorithms, the proposed algorithm achieves higher accuracy with better robustness, especially for complex scenes.
Towards a Blockchain-Based Secure Electronic Medical Record for Healthcare Applications

Electronic medical records (EMRs) are highly sensitive information shared among peers to keep up-to-date patient history. Providing security, privacy, and availability to these sensitive data is a challenge because, typically, after data publication the patient loses control over them. In this paper, we propose a blockchain-based approach to secure EMR for healthcare applications, where access control is patient-centric. Our proposal keeps encrypted EMRs in the blockchain, and the patient shares the decryption key only with healthcare professionals in which he&#x002F;she trusts. Blockchain allows untrusted node, in a distributed peer-to-peer network to correctly and verifiably interact with each other, without any reliable intermediary. We investigate the scalability of our approach through simulations. Results show that it scales well since increasing the number of nodes in the network implies a linear increase in the size of the stored chain. Results also reveal that the time for inserting a new EMR in the blockchain remains low even when the number of nodes in the network increases.
A hybrid improved kernel LDA and PNN algorithm for efficient face recognition

This paper proposes a hybrid approach to face recognition based on a combination of probabilistic neural networks (PNNs) and improved kernel linear discriminant analysis (IKLDA). The dimensions of a sample's features are first of all reduced, whilst retaining its relevant information, A PNN method is then adopted to solve face recognition problems. The proposed IKLDA+PNN method not only improves the overall computing efficiency, but also its precision. Face recognition experiments conducted on the ORL, YALE and AR datasets, which contain a wide variety of facial expressions, facial details, and degrees of scale, were used to validate the feasibility of the IKLDA+PNN method. The results showed that it can obtain an average recognition accuracy of 97.22%, 83.8% and 99.12%, across the three datasets, respectively.
A Task in a Suit and a Tie: Paraphrase Generation with Semantic Augmentation

Paraphrasing is rooted in semantics. We show the effectiveness of transformers (Vaswani et al. 2017) for paraphrase generation and further improvements by incorporating PropBank labels via a multi-encoder. Evaluating on MSCOCO and WikiAnswers, we find that transformers are fast and effective, and that semantic augmentation for both transformers and LSTMs leads to sizable 2-3 point gains in BLEU, METEOR and TER. More importantly, we find surprisingly large gains on human evaluations compared to previous models. Nevertheless, manual inspection of generated paraphrases reveals ample room for improvement: even our best model produces human-acceptable paraphrases for only 28% of captions from the CHIA dataset (Sharma et al. 2018), and it fails spectacularly on sentences from Wikipedia. Overall, these results point to the potential for incorporating semantics in the task while highlighting the need for stronger evaluation.
Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition

Unsupervised domain adaptation aims to transfer knowledge from a source domain to a target domain so that the target domain data can be recognized without any explicit labelling information for this domain. One limitation of the problem setting is that testing data (despite no labels) from the target domain is needed during training, which prevents the trained model being directly applied to classify newly arrived test instances. We formulate a new cross-domain classification problem arising from real-world scenarios where labelled data are available for a subset of classes (known classes) in the target domain, and we expect to recognize new samples belonging to any class (known and unseen classes) once the model is learned. This is a generalized zero-shot learning problem where the side information comes from the source domain in the form of labelled samples instead of class-level semantic representations commonly used in traditional zero-shot learning. We present a unified domain adaptation framework for both unsupervised and zero-shot learning conditions. Our approach learns a joint subspace from source and target domains so that the projections of both data in the subspace can be domain invariant and easily separable. We use the supervised locality preserving projection (SLPP) as the enabling technique and conduct experiments under both unsupervised and zero-shot learning conditions, achieving state-of-the-art results on three domain adaptation benchmark datasets: Office-Caltech, Office31 and Office-Home.