Reviewerly/exHarmony huggingface dataset:
Accelerator-Aware Pruning for Convolutional Neural Networks

Convolutional neural networks have shown tremendous performance capabilities in computer vision tasks, but their excessive amounts of weight storage and arithmetic operations prevent them from being adopted in embedded environments. One of the solutions involves pruning, where certain unimportant weights are forced to have a value of zero. Many pruning schemes have been proposed, but these have mainly focused on the number of pruned weights. Previous pruning schemes scarcely considered ASIC or FPGA accelerator architectures. When these pruned networks are run on accelerators, the lack of consideration of the architecture causes some inefficiency problems, including internal buffer misalignments and load imbalances. This paper proposes a new pruning scheme that reflects accelerator architectures. In the proposed scheme, pruning is performed so that the same number of weights remain for each weight group corresponding to activations fetched simultaneously. In this way, the pruning scheme resolves the inefficiency problems, doubling the accelerator performance. Even with this constraint, the proposed pruning scheme reached a pruning ratio similar to that of previous unconstrained pruning schemes, not only on AlexNet and VGG16 but also on state-of-the-art very deep networks such as ResNet. Furthermore, the proposed scheme demonstrated a comparable pruning ratio on compact networks such as MobileNet and on slimmed networks that were already pruned in a channel-wise manner. In addition to improving the efficiency of previous sparse accelerators, it will be also shown that the proposed pruning scheme can be used to reduce the logic complexity of sparse accelerators.The pruned models are publicly available at https://github.com/HyeongjuKang/accelerator-aware-pruning.
Calculation of the inverse kinematics solution of the 7-DOF redundant robot manipulator by the firefly algorithm and statistical analysis of the results in terms of speed and accuracy

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Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey

The number of devices connected to the Internet is increasing, exchanging large amounts of data, and turning the Internet into the 21st-century silk road for data. This road has taken machine learning to new areas of applications. However, machine learning models are not yet seen as complex systems that must run in powerful computers (i.e., Cloud). As technology, techniques, and algorithms advance, these models are implemented into more computational constrained devices. The following paper presents a study about the optimizations, algorithms, and platforms used to implement such models into the network’s end, where highly resource-scarce microcontroller units (MCUs) are found. The paper aims to provide guidelines, taxonomies, concepts, and future directions to help decentralize the network’s intelligence.
MobiSR

In recent years, convolutional networks have demonstrated unprecedented performance in the image restoration task of super-resolution (SR). SR entails the upscaling of a single low-resolution image in order to meet application-specific image quality demands and plays a key role in mobile devices. To comply with privacy regulations and reduce the overhead of cloud computing, executing SR models locally on-device constitutes a key alternative approach. Nevertheless, the excessive compute and memory requirements of SR workloads pose a challenge in mapping SR networks on resource-constrained mobile platforms. This work presents MobiSR, a novel framework for performing efficient super-resolution on-device. Given a target mobile platform, the proposed framework considers popular model compression techniques and traverses the design space to reach the highest performing trade-off between image quality and processing speed. At run time, a novel scheduler dispatches incoming image patches to the appropriate model-engine pair based on the patch's estimated upscaling difficulty in order to meet the required image quality with minimum processing latency. Quantitative evaluation shows that the proposed framework yields on-device SR designs that achieve an average speedup of 2.13x over highly-optimized parallel difficulty-unaware mappings and 4.79x over highly-optimized single compute engine implementations.
Zero-shot Text Classification via Reinforced Self-training

Zero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes with low similarity.In this situation, transferring from seen classes to unseen classes is extremely hard.To tackle this problem, in this paper we propose a self-training based method to efficiently leverage unlabeled data.Traditional self-training methods use fixed heuristics to select instances from unlabeled data, whose performance varies among different datasets.We propose a reinforcement learning framework to learn data selection strategy automatically and provide more reliable selection.Experimental results on both benchmarks and a real-world e-commerce dataset show that our approach significantly outperforms previous methods in zero-shot text classification.
Reasoning with Multimodal Sarcastic Tweets via Modeling Cross-Modality Contrast and Semantic Association

Sarcasm is a sophisticated linguistic phenomenon to express the opposite of what one really means.With the rapid growth of social media, multimodal sarcastic tweets are widely posted on various social platforms.In multimodal context, sarcasm is no longer a pure linguistic phenomenon, and due to the nature of social media short text, the opposite is more often manifested via cross-modality expressions.Thus traditional text-based methods are insufficient to detect multimodal sarcasm.To reason with multimodal sarcastic tweets, in this paper, we propose a novel method for modeling cross-modality contrast in the associated context.Our method models both cross-modality contrast and semantic association by constructing the Decomposition and Relation Network (namely D&R Net).The decomposition network represents the commonality and discrepancy between image and text, and the relation network models the semantic association in cross-modality context.Experimental results on a public dataset demonstrate the effectiveness of our model in multimodal sarcasm detection.
Trusted Academic Transcripts on the Blockchain: A Systematic Literature Review

Much of the excitement around blockchain is mainly due to promising applications in the financial sector. However, many also believe in the technology’s potential to disrupt non-financial sectors and applications, including supply chains, energy, e-voting, healthcare, and education. The application of blockchain within the education sector is expected to make improvements to academic transcripts, credentials, digital libraries, and student records. Research in this domain is rapidly increasing, and current reviews summarize the proposed improvements. On the other hand, the analysis undertaken has remained at a general level that lacks the depth required to cover diverging proposals that have emerged. This review focuses on the application of blockchain for academic transcripts. The aim is to find, among the proposed models, converging aspects that resolve common challenges and may lead to a universally accepted de-facto standard. Furthermore, since academic institutions will serve as oracles to the particular blockchain applications, a discussion on their trustworthiness will be outlined to explore if the proposed applications efficiently address the oracle problem. The outcome of this review highlights the need for a standardized approach built on a public blockchain to promote faster adoption and acceptance. Furthermore, oracles should be incentivized in order for the system to be sustainable, while their identities and activities should be known and identifiable.
Study of Retail Applications with Virtual and Augmented Reality Technologies

The rapidly emerging virtual reality (VR) and augmented reality (AR) technologies have greatly improved the digital shopping experience and retail selling environment. In terms of practical applications and academic research, fragmentation in VR and AR contributes to the technology's multidisciplinary roots in terms of applications. In this paper, the retail applications and research works that make use of VR and AR technology are compared and analyzed. The implementation, consumer acceptance, applications, issues and other related terms are compared. This study establishes a foundation for future work in the retail applications field.
Data Secure Storage Mechanism of Sensor Networks Based on Blockchain

As the number of sensor network application scenarios continues to grow, the security problems inherent in this approach have become obstacles that hinder its wide application. However, it has attracted increasing attention from industry and academia. The blockchain is based on a distributed network and has the characteristics of nontampering and traceability of block data. It is thus naturally able to solve the security problems of the sensor networks. Accordingly, this paper first analyzes the security risks associated with data storage in the sensor networks, then proposes using blockchain technology to ensure that data storage in the sensor networks is secure. In the traditional blockchain, the data layer uses a Merkle hash tree to store data; however, the Merkle hash tree cannot provide non-member proof, which makes it unable to resist the attacks of malicious nodes in networks. To solve this problem, this paper utilizes a cryptographic accumulator rather than a Merkle hash tree to provide both member proof and nonmember proof. Moreover, the number of elements in the existing accumulator is limited and unable to meet the blockchain’s expansion requirements. This paper therefore proposes a new type of unbounded accumulator and provides its definition and security model. Finally, this paper constructs an unbounded accumulator scheme using bilinear pairs and analyzes its performance.
dLSTM: a new approach for anomaly detection using deep learning with delayed prediction

In this paper, we propose delayed Long Short-Term Memory (dLSTM), an anomaly detection method for time-series data. We first build a predictive model from normal (non-anomalous) training data, then perform anomaly detection based on the prediction error for observed data. However, there are multiple states in the waveforms of normal data, which may lower prediction accuracy. To deal with this problem, we utilize multiple prediction models based on LSTM for anomaly detection. In this scheme, the prediction accuracy strongly depends on the method of selecting a proper predictive model from multiple possible models. We propose a novel method to determine the proper predictive model for anomaly detection. Our approach provides multiple predicted value candidates in advance and selects the one that is closest to the measured value. We delay the model selection until the corresponding measured values are acquired. Using this concept for anomaly detection, dLSTM selects the proper predictive model to enhance prediction accuracy. In our experimental evaluation using real and artificial data, dLSTM detects anomalies more accurately than methods in comparison.
Benchmarking the noise sensitivity of different parametric two-qubit gates in a single superconducting quantum computing platform

The possibility to utilize different types of two-qubit gates on a single quantum computing platform adds flexibility in the decomposition of quantum algorithms. A larger hardware-native gate set may decrease the number of required gates, provided that all gates are realized with high fidelity. Here, we benchmark both controlled-Z (CZ) and exchange-type (iSWAP) gates using a parametrically driven tunable coupler that mediates the interaction between two superconducting qubits. Using randomized benchmarking protocols we estimate an error per gate of 0.9±0.03 and 1.3±0.4% for the CZ and the iSWAP gate, respectively. We argue that spurious ZZ-type couplings are the dominant error source for the iSWAP gate, and that phase stability of all microwave drives is of utmost importance. Such differences in the achievable fidelities for different two-qubit gates have to be taken into account when mapping quantum algorithms to real hardware.15 MoreReceived 20 May 2020Accepted 11 August 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.033447Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement productionQuantum algorithmsQuantum benchmarkingQuantum controlQuantum gatesQuantum information architectures & platformsQuantum information with solid state qubitsQuantum Information
Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN

Channel estimation is a challenging task, especially in a massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADC). Traditional deep learning (DL) methods, that learn the mapping from inputs to real channels, have significant difficulties in estimating accurate channels because their loss functions are not well designed and investigated. In this paper, a conditional generative adversarial networks (cGAN) is developed to predict more realistic channels by adversarially training two DL networks. cGANs not only learn the mapping from quantized observations to real channels but also learn an adaptive loss function to correctly train the networks. Numerical results show that the proposed cGAN based approach outperforms existing DL methods and achieves high robustness in massive MIMO systems.
Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference

Membership inference (MI) attacks exploit the fact that machine learning algorithms sometimes leak information about their training data through the learned model. In this work, we study membership inference in the white-box setting in order to exploit the internals of a model, which have not been effectively utilized by previous work. Leveraging new insights about how overfitting occurs in deep neural networks, we show how a model's idiosyncratic use of features can provide evidence for membership to white-box attackers---even when the model's black-box behavior appears to generalize well---and demonstrate that this attack outperforms prior black-box methods. Taking the position that an effective attack should have the ability to provide confident positive inferences, we find that previous attacks do not often provide a meaningful basis for confidently inferring membership, whereas our attack can be effectively calibrated for high precision. Finally, we examine popular defenses against MI attacks, finding that (1) smaller generalization error is not sufficient to prevent attacks on real models, and (2) while small-$\epsilon$-differential privacy reduces the attack's effectiveness, this often comes at a significant cost to the model's accuracy; and for larger $\epsilon$ that are sometimes used in practice (e.g., $\epsilon=16$), the attack can achieve nearly the same accuracy as on the unprotected model.
Prevention of cybercrimes in smart cities of India: from a citizen’s perspective

Purpose The purpose of this paper is to identify the factors influencing the citizens of India to prevent cybercrimes in the proposed Smart Cities of India. Design/methodology/approach A conceptual model has been developed for identifying factors preventing cybercrimes. The conceptual model was validated empirically with a sample size of 315 participants from India. Data were analyzed using structural equation modeling with SPSS and AMOS softwares. Findings The study reveals that the “awareness of cybercrimes” significantly influences the actual usage of technology to prevent cybercrimes in Smart Cities of India. The study reveals that government initiative (GI) and legal awareness are less influential in spreading of the awareness of cybercrimes (AOC) to the citizens of the proposed smart cities. Research limitations/implications The conceptual model utilizes two constructs from the technology adoption model, namely, perceived usefulness and ease of use. The study employs other factors such as social media, word of mouth, GIs, legal awareness and organizations constituting entities spreading awareness from different related literature works. Thereby, a comprehensive theoretical conceptual model has been proposed which helps to identify the factors that may help in preventing cybercrimes. Practical implications This study provides an insight to the policy maker to understand several factors influencing the AOC of the citizens of the proposed Smart Cities of India for the prevention of cybercrimes. Originality/value There are few existing studies analyzing the effect of AOC to mitigate cybercrimes. Thus, this study offers a novel contribution.
Visual Distortions in 360° Videos

Omnidirectional (or 360-degree) images and videos are emergent signals in many areas such as robotics and virtual/augmented reality. In particular, for virtual reality, they allow an immersive experience in which the user is provided with a 360-degree field of view and can navigate throughout a scene, e.g., through the use of Head Mounted Displays. Since it represents the full 360-degree field of view from one point of the scene, omnidirectional content is naturally represented as spherical visual signals. Current approaches for capturing, processing, delivering, and displaying 360-degree content, however, present many open technical challenges and introduce several types of distortions in these visual signals. Some of the distortions are specific to the nature of 360-degree images, and often different from those encountered in the classical image communication framework. This paper provides a first comprehensive review of the most common visual distortions that alter 360-degree signals undergoing state of the art processing in common applications. While their impact on viewers' visual perception and on the immersive experience at large is still unknown ---thus, it stays an open research topic--- this review serves the purpose of identifying the main causes of visual distortions in the end-to-end 360-degree content distribution pipeline. It is essential as a basis for benchmarking different processing techniques, allowing the effective design of new algorithms and applications. It is also necessary to the deployment of proper psychovisual studies to characterise the human perception of these new images in interactive and immersive applications.
Smart Campus: Fostering the Community Awareness Through an Intelligent Environment

Interconnected computational devices in the Internet of Things (IoT) context make possible to collect real-time data about a specific environment. The IoT paradigm can be exploited together with data visualization techniques to put into effect intelligent environments, where pervasive technologies enable people to experience and interact with the generated data. In this paper, we present a case study where these emerging areas and related technologies have been explored to benefit communities, making their members actively involved as central players of such an intelligent environment. To give practical effect to our approach, we designed and developed a system, named Smart Campus, composed of: i) an infrastructure made of sensors to collect real-time data in a University Campus, and ii) a rich web-based application to interact with spatio-temporal data, available in a public interactive touch monitor. To validate the system and grasp insights, we involved 135 students through a survey, and we extracted meaningful data from the interactive sessions with the public display. Results show that this Campus community understood the potential of the system and students are willing to actively contribute to it, pushing us to better investigate future scenarios where students can participate with ideas, visualizations/services to integrate into the web-based system, as well as sensors to plug into the infrastructure.
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies

State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical dependencies with some success.Here we investigate whether supervision with hierarchical structure enhances learning of a range of grammatical dependencies, a question that has previously been addressed only for subject-verb agreement.Using controlled experimental methods from psycholinguistics, we compare the performance of word-based LSTM models versus two models that represent hierarchical structure and deploy it in left-to-right processing: Recurrent Neural Network Grammars (RNNGs) (Dyer et al., 2016) and a incrementalized version of the Parsing-as-Language-Modeling configuration from Charniak et al. (2016).Models are tested on a diverse range of configurations for two classes of non-local grammatical dependencies in English-Negative Polarity licensing and Filler-Gap Dependencies.Using the same training data across models, we find that structurally-supervised models outperform the LSTM, with the RNNG demonstrating best results on both types of grammatical dependencies and even learning many of the Island Constraints on the filler-gap dependency.Structural supervision thus provides data efficiency advantages over purely stringbased training of neural language models in acquiring human-like generalizations about non-local grammatical dependencies.