Reviewerly/exHarmony huggingface dataset:
Stage-Wise Neural Architecture Search

Modern convolutional networks such as ResNet and NASNet have achieved state-of-the-art results in many computer vision applications. These architectures consist of stages, which are sets of layers that operate on representations in the same resolution. It has been demonstrated that increasing the number of layers in each stage improves the prediction ability of the network. However, the resulting architecture becomes computationally expensive in terms of floating point operations, memory requirements and inference time. Thus, significant human effort is necessary to evaluate different trade-offs between depth and performance. To handle this problem, recent works have proposed to automatically design high-performance architectures, mainly by means of neural architecture search (NAS). Current NAS strategies analyze a large set of possible candidate architectures and, hence, require vast computational resources and take many GPUs days. Motivated by this, we propose a NAS approach to efficiently design accurate and low-cost convolutional architectures and demonstrate that an efficient strategy for designing these architectures is to learn the depth stage-by-stage. For this purpose, our approach increases depth incrementally in each stage taking into account its importance, such that stages with low importance are kept shallow while stages with high importance become deeper. We conduct experiments on the CIFAR and different versions of ImageNet datasets, where we show that architectures discovered by our approach achieve better accuracy and efficiency than human-designed architectures. Additionally, we show that architectures discovered on CIFAR-10 can be successfully transferred to large datasets. Compared to previous NAS approaches, our method is substantially more efficient, as it evaluates one order of magnitude fewer models and yields architectures on par with the state-of-the-art.
A Discriminant Information Approach to Deep Neural Network Pruning

Network pruning has become the de facto tool to accelerate deep neural networks for mobile and edge applications. Recently, feature-map discriminant based channel pruning has shown promising results, as it aligns well with the CNN's objective of differentiating multiple classes and offers better interpretability of the pruning decision. However, existing discriminant-based methods are challenged by computation inefficiency, as there is a lack of theoretical guidance on quantifying the feature-map discriminant power. In this paper, we develop a mathematical formulation to accurately and efficiently quantify the feature-map discriminativeness, which gives rise to a novel criterion, Discriminant Information (DI). We analyze the theoretical property of DI, specifically the non-decreasing property, that makes DI a valid channel selection criterion. By measuring the differential discriminant, we can identify and remove those channels with minimum influence to the discriminant power. The versatility of DI criterion also enables an intra-layer mixed precision quantization to further compress the network. Moreover, we propose a DI-based greedy pruning algorithm and structure distillation technique to automatically decide the pruned structure that satisfies certain resource budget, which is a common requirement in reality. Extensive experiments demonstrate the effectiveness of our method: our pruned ResNet50 on ImageNet achieves 44% FLOPs reduction without any Top-1 accuracy loss compared to unpruned model.
Review of Image Classification Method Based on Deep Transfer Learning

With the continuous development of deep learning technology, neural networks such as convolutional neural network (CNN) have shown good performance in many fields, such as image processing. Meanwhile, the relevant algorithm has made great progress. But the experiment results show that the deeper the network layers, the more the number of parameters that need to be trained in neural network, and the massive computing resources will be consumed to reconstruct and train the deep convolutional neural network (DCNN) model. These parameters often need to be trained in large dataset. But in many practical applications, the effective sample dataset that can be collected are usually small and lack of annotated samples. It is a pity that models that perform well on large datasets often have overfitting problems when applied to small datasets. And transfer learning can recognize and apply knowledge and skills learned in previous domains/tasks to novel domains/tasks. Combining deep convolutional neural network learning with transfer learning can make full use of existing models with good performance to solve problems in new fields, so has received considerable attentions due to its high research value and wide application prospect. This paper focuses on the combination of CNN and transfer learning, analyzes their characteristics, summarizes the relevant models, methods and applications, so as to promote their effective fusion in image classification.
Multiple-Hypothesis CTC-Based Semi-Supervised Adaptation of End-to-End Speech Recognition

This paper proposes an adaptation method for end-to-end speech recognition. In this method, multiple automatic speech recognition (ASR) 1-best hypotheses are integrated in the computation of the connectionist temporal classification (CTC) loss function. The integration of multiple ASR hypotheses helps alleviating the impact of errors in the ASR hypotheses to the computation of the CTC loss when ASR hypotheses are used. When being applied in semi-supervised adaptation scenarios where part of the adaptation data do not have labels, the CTC loss of the proposed method is computed from different ASR 1-best hypotheses obtained by decoding the unlabeled adaptation data. Experiments are performed in clean and multi-condition training scenarios where the CTC-based end-to-end ASR systems are trained on Wall Street Journal (WSJ) clean training data and CHiME-4 multi-condition training data, respectively, and tested on Aurora-4 test data. The proposed adaptation method yields 6.6% and 5.8% relative word error rate (WER) reductions in clean and multi-condition training scenarios, respectively, compared to a baseline system which is adapted with part of the adaptation data having manual transcriptions using back-propagation fine-tuning.
GTA-Net: Gradual Temporal Aggregation Network for Fast Video Deraining

Recently, the development of intelligent technology arouses the requirements of high-quality videos. Rain streak is a frequent and inevitable factor to degrade the video. Many researchers have put their energies into eliminating the adverse effects of rainy video. Unfortunately, how to fully utilize the temporal information from rainy video is still in suspense. In this work, to effectively exploit temporal information, we develop a simple but effective network, Gradual Temporal Aggregation Network (GTA-Net for short). To be specific, according to the temporal distance between rainy frames and the reference frame, we divide the rainy frames into different groups. A multi-stream coarse temporal aggregation module is first performed to aggregate different temporal information with equal status and importance. Then we design a single-stream fine temporal aggregation module to further fuse the integrated frames that maintain the different distances with the target frame. In this way of coarse-to-fine, we not only achieve superior performance, but also gain the surprising execution speed owing to abandon the time-consuming alignment operation. Plenty of experimental results demonstrate that our GTA-Net performs favorably compared to other state-of-the-art approaches. The meticulous ablation study further indicates the effectiveness of our designed GTA-Net.
GridToPix: Training Embodied Agents with Minimal Supervision

While deep reinforcement learning (RL) promises freedom from hand-labeled data, great successes, especially for Embodied AI, require significant work to create supervision via carefully shaped rewards. Indeed, without shaped rewards, i.e., with only terminal rewards, present-day Embodied AI results degrade significantly across Embodied AI problems from single-agent Habitat-based PointGoal Navigation (SPL drops from 55 to 0) and two-agent AI2-THOR-based Furniture Moving (success drops from 58% to 1%) to three-agent Google Football-based 3 vs. 1 with Keeper (game score drops from 0.6 to 0.1). As training from shaped rewards doesn’t scale to more realistic tasks, the community needs to improve the success of training with terminal rewards. For this we propose GRIDTOPIX: 1) train agents with terminal rewards in gridworlds that generically mirror Embodied AI environments, i.e., they are independent of the task; 2) distill the learned policy into agents that reside in complex visual worlds. Despite learning from only terminal rewards with identical models and RL algorithms, GRIDTOPIX significantly improves results across tasks: from PointGoal Navigation (SPL improves from 0 to 64) and Furniture Moving (success improves from 1% to 25%) to football gameplay (game score improves from 0.1 to 0.6). GRIDTOPIX even helps to improve the results of shaped reward training.
Efficient Hardware Implementation of Decision Tree Training Accelerator

This paper proposes a serial architecture as implementation for Two Means Decision Tree. This DT algorithm exhibits lower complexity. This architecture is implemented on Field Programmable Gate Array (FPGA) running at 62 MHz. Simulation results show that the proposed hardware architecture exhibits 10× speed-up as compared to its software implementation and runs 28× faster than C4.5 algorithm. It also consumes less power as compared to complex algorithms implemented on Graphical Processor Units (GPU). Hence the architecture is suitable for simple low power high speed applications.
Cold Start Revisited: A Deep Hybrid Recommender with Cold-Warm Item Harmonization

Collaborative filtering-based recommender systems are known to suffer from the item cold-start problem. Most recent attempts to mitigate this problem presented parametric approaches, such as deep content based models. In this paper, we show that a straightforward application of parametric models may lead to discrepancies between the cold and warm items’ distributions in the CF space. As a remedy, we propose to combine parametric with non-parametric estimation for robust cold item placement. Extensive evaluation indicates that our method is competitive with other baselines, while producing cold items placement that better resembles the distribution of warm items in the collaborative filtering space.
Generalization Comparison of Deep Neural Networks via Output Sensitivity

Although recent works have brought some insights into the performance improvement of techniques used in state-of-the-art deep-learning models, more work is needed to understand their generalization properties. We shed light on this matter by linking the loss function to the output's sensitivity to its input. We find a rather strong empirical relation between the output sensitivity and the variance in the bias-variance decomposition of the loss function, which hints on using sensitivity as a metric for comparing the generalization performance of networks, without requiring labeled data. We find that sensitivity is decreased by applying popular methods which improve the generalization performance of the model, such as (1) using a deep network rather than a wide one, (2) adding convolutional layers to baseline classifiers instead of adding fully-connected layers, (3) using batch normalization, dropout and max-pooling, and (4) applying parameter initialization techniques.
Challenges in Multilingual and Mixed Script Sentiment Analysis

Social Media network content processing is one of the important tasks in Natural Language processing. Variety of languages and its forms are used by people to express themselves, in Blogs, Twitter, Facebook and social networking sites. Processing Multilingual and Mixed script contents in heterogenous set of multilingual documents. Language Identification, Normalization, Emoticon's processing, and sentiment classification are important blocks of Multilingual and Mixed script text processing. Many online resources such as lexicon dictionaries and classifiers are available to process such complex text reviews. This paper showcases different challenges for Indian Social media contents like language Identification, Processing and Sentiment analysis methods available for Sentiment analysis task.
Visual Privacy Protection via Mapping Distortion

Privacy protection is an important research area, which is especially critical in this big data era. To a large extent, the privacy of visual classification data is mainly in the mapping between the image and its corresponding label, since this relation provides a great amount of information and can be used in other scenarios. In this paper, we propose the mapping distortion based protection (MDP) and its augmentation-based extension (AugMDP) to protect the data privacy by modifying the original dataset. In the modified dataset generated by MDP, the image and its label are not consistent (e.g., a cat-like image is labeled as the dog), whereas the DNNs trained on it can still achieve good performance on benign testing set. As such, this method can protect privacy when the dataset is leaked. Extensive experiments are conducted, which verify the effectiveness and feasibility of our method. The code for reproducing main results is available at https://github.com/PerdonLiu/Visual-Privacy-Protection-via-Mapping-Distortion.
Facial parts swapping with generative adversarial networks

In this paper, we present a novel deep generative facial parts swapping method: parts-swapping generative adversarial network (PSGAN). PSGAN independently handles facial parts, such as eyes (left eye and right eye), nose, mouth and jaw, which achieves facial parts swapping by replacing the target facial parts with source facial parts and reconstructing the entire face image with these parts. By separately modeling the facial parts in the form of region inpainting, the proposed method can successfully achieve highly photorealistic face swapping results, enabling users to freely manipulate facial parts. In addition, the proposed method is able to perform jaw editing based on sketch guidance information. Experimental results on the CelebA dataset suggest that our method achieves superior performance for facial parts swapping and provides higher user control flexibility.
Blockchain-based Fair and Decentralized Data Trading Model

Abstract Data is a kind of important asset in the digital economy and is driving the rise of data markets. Meanwhile, data markets promote data trading efficiently and improve the utilization of data. However, several challenges about data trading need to be addressed. Here, we resolve these challenges via our blockchain-based fair and decentralized data trading model. Disputes about data correctness is settled by the decentralized arbitration mechanism in our model. To ensure the fairness of data trading, we integrate a sale contract and a deterministic public-key encryption algorithm. The decentralization feature of blockchain cuts off the single-point failure for the data trading platform. In addition, we prove that the proposed protocol achieves the desirable security properties that a secure data trading protocol should have. Moreover, utilizing the smart contract in Solidity and program in Java, we implement our model and then evaluate its performance.
Improved Step-Size Schedules for Noisy Gradient Methods

Noise is inherited in many optimization methods such as stochastic gradient methods, zeroth-order methods and compressed gradient methods. For such methods to converge toward a global optimum, it is intuitive to use large step-sizes in the initial iterations when the noise is typically small compared to the algorithm-steps, and reduce the step-sizes as the algorithm progresses. This intuition has been con-firmed in theory and practice for stochastic gradient methods, but similar results are lacking for other methods using approximate gradients. This paper shows that the diminishing step-size strategies can indeed be applied for a broad class of noisy gradient methods. Unlike previous works, our analysis framework shows that such step-size schedules enable these methods to enjoy an optimal $\mathcal{O}(1/k)$ rate. We exemplify our results on zeroth-order methods and stochastic compression methods. Our experiments validate fast convergence of these methods with the step decay schedules.
Body As Starting Point 4: Inbodied Interaction Design for Health Ownership.

This Fourth Body as a Starting Point workshop investigates how to design interactive health technologies that assist users in developing insourcing abilities and then assist users in letting go of the same technology—in other words, supporting a transition from health technology dependence to independence. By making explicit two inbodied design continua of (1) ownership, from "outsourcing" to "insourcing" and (2) engagement period, from "single", to" cycle", to "permanent", to prototype and reflect on interactive technology that takes the body as a starting point.
Automated Billing Smart Trolley and Stock Monitoring

In this modern world, all people like to use products which is of high technology. People do not want to waste time and energy by using conventional systems. Rather they prefer advanced devices which is automated, smart, to finish their work soon. Smart trolley is one such advanced devices which is more flexible and a easy process to complete shopping without any delay. Customers in the store do not want to wait for long time to pay their bill. In the smart trolley the bill can be paid simultaneously without waiting in the counter. Once the shopping is over payment is done through online or offline based on the customer. This flexibility is not provided by the existing trolley. To overcome this, Smart trolley is preferred. The newly designed smart trolley consists of Arduino UNO, RFID reader and tag, Wi-Fi module. Apart from this payment feature, smart trolley allows the admin to view the stock details also. Each product’s stock can be monitored and planned accordingly without any extra manual work.
Re-Entering Computing through Emerging Technology

introduction Re-Entering Computing through Emerging Technology: Current State and Special Issue Introduction Share on Authors: Farzana Rahman Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USAView Profile , Elodie Billionniere School of Engineering & Technology, Miami Dade College, Miami, FL, USA School of Engineering & Technology, Miami Dade College, Miami, FL, USAView Profile Authors Info & Claims ACM Transactions on Computing EducationVolume 21Issue 2June 2021 Article No.: 9pp 1–5https://doi.org/10.1145/3446840Online:23 April 2021Publication History 1citation76DownloadsMetricsTotal Citations1Total Downloads76Last 12 Months76Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Video Object Detection Using Object's Motion Context and Spatio-Temporal Feature Aggregation

The deep learning technique has recently led to significant improvement in object detection accuracy. In many applications, object detection is performed on video data consisting of a sequence of two-dimensional (2D) image frames. Numerous object detection schemes have been designed to detect objects independently in each video frame. Though temporal information within adjacent image frames can be exploited in subsequent object tracking stage, it has been shown that the object detection accuracy can be significantly improved by exploiting the temporal structure in the image sequence in the object detection stage. In this paper, we propose a novel video object detection method that exploits both the motion context inferred from the adjacent frames and the spatio-temporal features aggregated over the image sequence. First, correlation between the spatial feature maps over two adjacent frames are computed and the embedding vector, representing the motion context, is obtained by encoding the N correlation maps using long short term memory (LSTM). In addition to utilizing the motion context, the spatial feature maps for ( N+1) consecutive frames are aggregated to boost the quality of the feature map. The gated attention network is employed to selectively combine the temporal feature maps based on their relevance to the feature map in the present image frame. While most video object detectors have been developed for two-stage object detectors, our proposed idea applies to one-stage detectors with the advantage of low computational complexity in practical real-time applications. Our numerical evaluation conducted on the ImageNet object detection from video (VID) dataset demonstrates that our proposed network achieves significant performance gain over the baseline algorithms and outperforms the existing one-stage video object detectors.
A Base-Derivative Framework for Cross-Modality RGB-Infrared Person Re-Identification

Cross-modality RGB-infrared (RGB-IR) person reidentification (Re-ID) is a challenging research topic due to the heterogeneity of RGB and infrared images. In this paper, we aim to find some auxiliary modalities, which are homologous with the visible or infrared modalities, to help reduce the modality discrepancy caused by heterogeneous images. Accordingly, a new base-derivative framework is proposed, where base refers to the original visible and infrared modalities, and derivative refers to the two auxiliary modalities that are derived from base. In the proposed framework, the double-modality cross-modal learning problem is reformulated as a four-modality one. After that, the images of all the base and derivative modalities are fed into the feature learning network. With the doubled input images, the learned person features become more discriminative. Furthermore, the proposed framework is optimized by the enhanced intra- and cross-modality constraints with the assistance of two derivative modalities. Experimental results on two publicly available datasets SYSU-MM and RegDB show that the proposed method outperforms the other state-of-the-art methods. For instance, we achieve a gain of over 13 % in terms of both Rank- and mAP on RegDB dataset.
Recent Advances in Arabic Syntactic Diacritics Restoration

Restoring Arabic syntactic diacritics based on Long Short-Term Memory (LSTM) networks leads to state-of-the-art performance. These LSTM networks are commonly augmented with Maximum Entropy (MaxEnt) sparse direct connections between the input and the output layers of the tagger. One way to improve such tagger performance is to use an ensemble of taggers. However, an ensemble of taggers may require huge computational and memory resources. In this paper, we implement a knowledge distillation technique where an ensemble of teachers/taggers is used to train a single student tagger. On the other hand, Arabic is a morphologically rich language and has a high Out-Of-Vocabulary (OOV) rate. In addition to word embeddings, we propose to use character embeddings encoded using LSTMs for each word to overcome this problem. On the Arabic tree bank task, our hybrid LSTM/MaxEnt tagger achieves 1.0% absolute WER improvement over a strong baseline using the proposed two techniques.