Reviewerly/exHarmony huggingface dataset:
Multi-View Bayesian Generative Model for Multi-Subject FMRI Data on Brain Decoding of Viewed Image Categories

Brain decoding studies have demonstrated that viewed image categories can be estimated from human functional magnetic resonance imaging (fMRI) activity. However, there are still limitations with the estimation performance because of the characteristics of fMRI data and the employment of only one modality extracted from viewed images. In this paper, we propose a multi-view Bayesian generative model for multi-subject fMRI data to estimate viewed image categories from fMRI activity. The proposed method derives effective representations of fMRI activity by utilizing multi-subject fMRI data. In addition, we associate fMRI activity with multiple modalities, i.e., visual features and semantic features extracted from viewed images. Experimental results show that the proposed method outperforms existing state-of-the-art methods of brain decoding.
Towards Deep Learning on Resource-Constrained Robots: A Crowdsourcing Approach with Model Partition

Deep learning has shown great potential in promoting robot intelligence. However, a major obstacle to put it into practice is the huge computational complexity and memory consumption of neural networks. The onboard computers inside robots are usually of limited capability and cannot support the inference of large-scale neural networks with reasonable latency, a property essential to many tasks in the physical world. Moreover, because of the unstable or unacceptable network latency, it is hard to offload computation to powerful backend infrastructure (e.g., data centers) in many cases as well. To address this challenge, this paper proposes a novel "crowdsourcing" approach to enable efficient inference of large-scale deep learning models on a group of resource-constrained robots. "Crowdsourcing" means that the deep learning tasks on a single robot can exploit the idle computing power of robots in the proximity. This goal is achieved by splitting a network into several parts and distributing them to different robots with the help of the proposed latency prediction and optimal partition selection algorithms. The experiments show that our approach achieves on average 6.01X and up to 46.59X latency speedup in contrast with the approaches in which the models are executed locally on the robot or remotely on the cloud.
Divide and conquer anomaly detection: A case study predicting defective engines

The goal of this paper is to test if data partitioning based on domain knowledge improves the performance of unsupervised anomaly detection algorithms for the detection of faulty internal combustion engines. We test three common anomaly detection algorithms to predict defective engines at the end of an assembly line, both with and without data partitioning. The algorithms are trained on high-dimensional vibration data. To evaluate and compare the detection performance of partitioned and unpartitioned approaches, we use a labeled collection of known anomalies. Using domain knowledge to partition the data improves the anomaly detection performance of all tested algorithms. A divide and conquer strategy based on data partitioning, thus, appears to be a viable anomaly detection approach in cases where abundant unlabeled data is available.
Smart Distributed Ledger Technologies in Industry 4.0: Challenges and Opportunities in Supply Chain Management

The rise of new digital economies and data-driven supply-chains seeks to revolutionalise the ways information is transferred, processed and analysed across different industry segments in the value-creation. This data-driven manufacturing revolution promises to increase productivity, democratise data sharing capabilities and foster industrial growth in scales never seen before. The traditional transactional models are to be re-visited, and distributed data storage architectures are to be re-designed to accommodate for optimised data flows across different organisation units. Data is increasingly becoming a strategic business resource that through innovation in existing sharing and processing approaches can decompose business bottlenecks in existing production lines and processes and disrupt traditional supply-chain models. This work seeks to articulate a state-of-the-art review of the application and impact of ML techniques and distributed Ledger technologies to further disrupt supply-chain capabilities with regards to data accuracy and completeness.
P4UIoT: Pay-Per-Piece Patch Update Delivery for IoT Using Gradual Release

P 4 UIoT-pay-per-piece patch update delivery for IoT using gradual release-introduces a distributed framework for delivering patch updates to IoT devices. The framework facilitates distribution via peer-to-peer delivery networks and incentivizes the distribution operation. The peer-to-peer delivery network reduces load by delegating the patch distribution to the nodes of the network, thereby protecting against a single point of failure and reducing costs. Distributed file-sharing solutions currently available in the literature are limited to sharing popular files among peers. In contrast, the proposed protocol incentivizes peers to distribute patch updates, which might be relevant only to IoT devices, using a blockchain-based lightning network. A manufacturer/owner named vendor of the IoT device commits a bid on the blockchain, which can be publicly verified by the members of the network. The nodes, called distributors, interested in delivering the patch update, compete among each other to exchange a piece of patch update with cryptocurrency payment. The pay-per-piece payments protocol addresses the problem of misbehavior between IoT devices and distributors as either of them may try to take advantage of the other. The pay-per-piece protocol is a form of a gradual release of a commodity like a patch update, where the commodity can be divided into small pieces and exchanged between the sender and the receiver building trust at each step as the transactions progress into rounds. The permissionless nature of the framework enables the proposal to scale as it incentivizes the participation of individual distributors. Thus, compared to the previous solutions, the proposed framework can scale better without any overhead and with reduced costs. A combination of the Bitcoin lightning network for cryptocurrency incentives with the BitTorrent delivery network is used to present a prototype of the proposed framework. Finally, a financial and scalability evaluation of the proposed framework is presented.
Spoken Language Acquisition Based on Reinforcement Learning and Word Unit Segmentation

The process of spoken-language acquisition has been one of the topics of greatest interest to linguists for decades. By uti-lizing modern machine learning techniques, we simulated this process on computers, which helps to understand it and develop new possibilities of applying this concept on intelligent robots, among other things. This paper proposes a new framework for simulating spoken-language acquisition by combining reinforcement learning and unsupervised learning methods. Our experiments also show that a spoken language can be acquired considerably faster by identifying potential word segments from collected ambient sounds in an unsupervised manner.
Self-Attentive Sentimental Sentence Embedding for Sentiment Analysis

We propose the use of a word-level sentiment bidirectional LSTM in tandem with the self-attention mechanism for sentence-level sentiment prediction. In addition to the proposed model, we also present a finance report dataset for sentence-level financial risk detection. Experiments conducted on the proposed dataset together with two public review datasets attest the effectiveness of our model for sentence sentiment prediction.
Depthwise-STFT Based Separable Convolutional Neural Networks

In this paper, we propose a new convolutional layer called Depthwise-STFT Separable layer that can serve as an alternative to the standard depthwise separable convolutional layer. The construction of the proposed layer is inspired by the fact that the Fourier coefficients can accurately represent important features such as edges in an image. It utilizes the Fourier coefficients computed (channelwise) in the 2D local neighborhood (e.g., 3 × 3) of each position of the input map to obtain the feature maps. The Fourier coefficients are computed using 2D Short Term Fourier Transform (STFT) at multiple fixed low frequency points in the 2D local neighborhood at each position. These feature maps at different frequency points are then linearly combined using trainable pointwise (1 × 1) convolutions. We show that the proposed layer outperforms the standard depthwise separable layer based models on the CIFAR-10 and CIFAR-100 image classification datasets with reduced space-time complexity.
Low-bit Quantization of Recurrent Neural Network Language Models Using Alternating Direction Methods of Multipliers

The high memory consumption and computational costs of Recurrent neural network language models (RNNLMs) limit their wider application on resource constrained devices. In recent years, neural network quantization techniques that are capable of producing extremely low-bit compression, for example, binarized RNNLMs, are gaining increasing research interests. Directly training of quantized neural networks is difficult. By formulating quantized RNNLMs training as an optimization problem, this paper presents a novel method to train quantized RNNLMs from scratch using alternating direction methods of multipliers (ADMM). This method can also flexibly adjust the trade-off between the compression rate and model performance using tied low-bit quantization tables. Experiments on two tasks: Penn Treebank (PTB), and Switchboard (SWBD) suggest the proposed ADMM quantization achieved a model size compression factor of up to 31 times over the full precision baseline RNNLMs. Faster convergence of 5 times in model training over the baseline binarized RNNLM quantization was also obtained.
Image Set Classification Using a Distance-Based Kernel Over Affine Grassmann Manifold

Modeling image sets or videos as linear subspaces is quite popular for classification problems in machine learning. However, affine subspace modeling has not been explored much. In this article, we address the image sets classification problem by modeling them as affine subspaces. Affine subspaces are linear subspaces shifted from origin by an offset. The collection of the same dimensional affine subspaces of RD is known as affine Grassmann manifold (AGM) or affine Grassmannian that is a smooth and noncompact manifold. The non-Euclidean geometry of AGM and the nonunique representation of an affine subspace in AGM make the classification task in AGM difficult. In this article, we propose a novel affine subspace-based kernel that maps the points in AGM to a finite-dimensional Hilbert space. For this, we embed the AGM in a higher dimensional Grassmann manifold (GM) by embedding the offset vector in the Stiefel coordinates. The projection distance between two points in AGM is the measure of similarity obtained by the kernel function. The obtained kernel-gram matrix is further diagonalized to generate low-dimensional features in the Euclidean space corresponding to the points in AGM. Distance-preserving constraint along with sparsity constraint is used for minimum residual error classification by keeping the locally Euclidean structure of AGM in mind. Experimentation performed over four data sets for gait, object, hand, and body gesture recognition shows promising results compared with state-of-the-art techniques.
Light-Field Reconstruction and Depth Estimation from Focal Stack Images Using Convolutional Neural Networks

Light-field (LF) reconstruction from focal stack images has diverse applications including face recognition, autonomous driving, and 3D reconstruction in virtual reality. It is a large-scale ill-conditioned inverse problem and typically requires regularized iterative algorithms to solve, which can be slow. This paper proposes a non-iterative LF reconstruction and depth estimation method based on three sequential convolutional neural networks (CNNs). The first CNN estimates an all-in-focus image from focal stack images. The second CNN estimates 4D ray depth from the estimated all-in-focus image via the first CNN, and focal stack images. The third CNN refines a Lambertian LF that is rendered using the all-in-focus image and ray depth estimated by the first and second CNNs, respectively. Numerical experiments show that the proposed CNN-based method achieves significantly more accurate and/or faster LF reconstruction, compared to a state-of-the-art sequential CNN using a single image, conventional model-based image reconstruction from a focal stack, and direct regression CNN from a focal stack.
Design-Gan: Cross-Category Fashion Translation Driven By Landmark Attention

The rise of generative adversarial networks has boosted a vast interest in the field of fashion image-to-image translation. However, previous methods do not perform well in cross-category translation tasks, e.g., translating jeans to skirts in fashion images. The translated skirts are easier to lose the detail texture of the jeans, and the generated legs or arms often look unnatural. In this paper, we propose a novel approach, called DesignGAN, that utilizes the landmark guided attention and a similarity constraint mechanism to achieve fashion cross-category translation. Moreover, we can achieve texture editing on any customized input, which can even be used as an effective way to empower fashion designers. Experiments on fashion datasets verify that DesignGAN is superior to other image-to-image translation methods.
Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation

Matrix factorization has now become a dominant solution for personalized recommendation on the Social Web. To alleviate the cold start problem, previous approaches have incorporated various additional sources of information into traditional matrix factorization models. These upgraded models, however, achieve only "marginal" enhancements on the performance of personalized recommendation. Therefore, inspired by the recent development of deep-semantic modeling, we propose a hybrid deep-semantic matrix factorization (HDMF) model to further improve the performance of tag-aware personalized recommendation by integrating the techniques of deep-semantic modeling, hybrid learning, and matrix factorization. Experimental results show that HDMF significantly outperforms the state-of-the-art baselines in tag-aware personalized recommendation, in terms of all evaluation metrics.
MicroScope: Enabling Microarchitectural Replay Attacks

A microarchitectural replay attack is a novel class of attack where an adversary can denoise nearly arbitrary microarchitectural side channels in a single run of the victim. The idea is to cause the victim to repeatedly replay by inducing pipeline flushes. In this article, we design, implement, and demonstrate our ideas in a framework, called MicroScope, that causes repeated pipeline flushes by inducing page faults. Our main result shows that MicroScope can denoise the port contention channel of execution units. Specifically, we show how MicroScope can reliably detect the presence or absence of as few as two divide instructions in a single logical run of the victim program. We also discuss the broader implications of microarchitectural replay attacks.
The Best Selection of Programmers in Generation 4.0 Using AHP and ELECTRE Elimination Methods

Abstract The quality of data processing in information systems in the 4.0 industrial era is expected to be in the form of paperless-based digitalization, thus a number of capable and reliable users are needed in terms of data processing in the form of digitalization, the means developed can be through the internet or via smartphone-based on Android. The need for The best programmer is certainly very needed in terms of data development and processing and information delivery. This needs to be done so that the process of sending data and information can be done simply and very high speed, because the transfer of resources has been converted into digital form. The need for programmer staff must be selected consistently so that users who are accepted as the best and reliable programmers according to job requirements, from ten programmers with collaboration AHP method and ELECTRE can provide optimal decisions with the following results, programmer code P5, P6, P7 and P10 get the biggest score with weight of 3, followed by programmer code P9 with weight of 2, and followed by programmer code P1, P2, P8 with weight of 1, and there are two programmer codes which are eliminated by the ELECTRE method, P3 and P4. The collaboration in the method of Analytic Hierarchy Process (AHP) and ELECTRE Elimination which is the crystallization of the Multi-criteria Decision Making (MCDM) can be a decision support in the selection process of the best programmers to produce optimal decisions.
TCSD: Term Co-occurrence Based Sarcasm Detection from Twitter Trends

The scope of social media platforms like twitter, enabling the target audience to surge their opinion about the vivid domains like products, entertainment, promotions of a concept, government related decisions, individual celebrities and many more. Sentiment analysis is the potential strategy of data science and data mining, which allows notifying the opinion of the target audience. The opinions projected in social media platforms often adapt sarcasm to portray the opinion with negative sentiment polarity. In such context, the computer aided methods of sentiment analysis prone to considerable level of false alarming. The contributions of contemporary research in sentiment analysis evincing that the detection methods of sarcasm in user opinions is a crucial task of the sentiment analysis. In the context of this argument, a novel machine learning based method is derived to identify the sarcasm in the user tweets. The simulation study shows the proposal importance, which measured by comparing the performance of the proposed model with another contemporary model.
Design and Development of Unsupervised Stemmer for Sindhi Language

Stemmer is a fundamental NLP tool which performs the task of normalization (i.e. to remove suffixes) of inflected word. This paper presents a stemmer, design and developed for Sindhi Language, using unsupervised approach. Suffixes are extracted using "Linguistica 5 "[22] a tool for unsupervised learning of morphology. The raw corpus of 10000 sentences of Sindhi Language is used for extraction of suffixes. Unsupervised stemmer is evaluated using Direct approach. Results are compared with existing rule-based, stemmer [32] and Lemmatizer[33], 1000 words are extracted from Sindhi Dictionary for evaluation.
First-order rewritability of ontology-mediated queries in linear temporal logic

We investigate ontology-based data access to temporal data. We consider temporal ontologies given in linear temporal logic LTL interpreted over discrete time (Z,<). Queries are given in LTL or MFO(<), monadic first-order logic with a built-in linear order. Our concern is first-order rewritability of ontology-mediated queries (OMQs) consisting of a temporal ontology and a query. By taking account of the temporal operators used in the ontology and distinguishing between ontologies given in full LTL and its core, Krom and Horn fragments, we identify a hierarchy of OMQs with atomic queries by proving rewritability into either FO(<), first-order logic with the built-in linear order, or FO(<,E), which extends FO(<) with the standard arithmetic predicates saying that "x is equivalent to 0 modulo n", for any fixed n > 1, or FO(RPR), which extends FO(<) with relational primitive recursion. In terms of circuit complexity, FO(<,E)- and FO(RPR)-rewritability guarantee OMQ answering in uniform AC0 and, respectively, NC1. We obtain similar hierarchies for more expressive types of queries: positive LTL-formulas, monotone MFO(<)- and arbitrary MFO(<)-formulas. Our results are directly applicable if the temporal data to be accessed is one-dimensional; moreover, they lay foundations for investigating ontology-based access using combinations of temporal and description logics over two-dimensional temporal data.
TwinPeaks: An approach for certificateless public key distribution for the internet and internet of things

The current public key infrastructure (PKI) has thorny issues like the overhead of certificate revocations and the consequence of fraudulent certificates. To address such issues, we propose TwinPeaks, which is an infrastructure to distribute public keys of named entities on the Internet and the Internet of Things (IoT). TwinPeaks leverages certificateless public key cryptography (CL-PKC), where a key generation center (KGC) cannot know the private key of its member, and hence its compromise will not result in member key leakage. By extending CL-PKC, the public key of an entity becomes dependent on any combination of its networking parameters; thus TwinPeaks can thwart spoofing attacks systematically. With TwinPeaks, the public key of every named entity is distributed online while addressing the PKI's vulnerabilities. TwinPeaks has public key servers, which constitute the domain name system (DNS)-like hierarchical tree structure. For each parent-child link in the tree, the parent node serves as a key generation center (KGC), and its child nodes set up their own public/secret key pairs by interacting with the KGC as proposed in CL-PKC. In this way, every named entity (e.g., a domain name) has its own public/secret key pair. Thus, a public key of an entity will be provided to a user by its key server as the DNS response is returned to the user by its DNS server. TwinPeaks removes certificates and hence has no revocation overhead. Instead, each named entity should keep/update its networking parameters and public key up-to-date in its DNS server and key server, respectively. By making its public key depend on both its Internet protocol (IP) address and domain name, the compromise of a single entity (e.g., a DNS or key server) cannot lead to successful impersonation. TwinPeaks achieves scalable distribution of public keys since public keys can be cached long term. We also show that TwinPeaks can be applied to the IoT environments by extending the naming scheme.