Reviewerly/exHarmony huggingface dataset:
Comprehensive Comparisons Of Uniform Quantizers For Deep Image Compression

Deep image compression is formulated as a joint rate-distortion optimization problem using an auto-encoder architecture. Latent representations obtained by an encoder are quantized by a quantizer and fed to a decoder and an entropy model to reconstruct the images and estimate probabilities for entropy coding, respectively. Existing methods presented several methods to approximate the quantization for optimization because the gradient of a naive quantizer is zero almost everywhere. Although quantization is a fundamental operation in image compression, there are few comparisons between these quantization methods and the best approximation among them remains unexplored. To address this problem, we comprehensively compare existing approximations of the uniform quantization. Furthermore, focusing on the fact that a decoder and an entropy model have different compatibility with the approximation of quantization, we also evaluate different combinations of approximations for the decoder and the entropy model. Through experiments, we find that the approximations by adding noise are better than rounding and that the best combination of approximations among what we explored outperforms existing approximations.
Can Robots Be Bullied? A Crowdsourced Feasibility Study for Using Social Robots in Anti-Bullying Interventions

Bullying in schools is a serious issue with severe and long-term consequences. We explore using social robots in anti-bullying programs to encourage children to intervene in bullying of their peers. To that end, we have conducted a crowdsourced study to explore the feasibility of using robots in the context of bullying (i.e., to investigate whether robots are perceived as entities that can be bullied). We present qualitative and quantitative results from a between-subjects video study, comparing robot bullying (robots being bullied) to human bullying (humans being bullied). Our findings suggest that while the majority of participants describe both instances with connotations of wrongness and immorality, they use different cognitive mechanisms for moral disengagement with robot bullying vs human bullying. We also found significant differences in participants’ perceptions of each scenario, including associating robot mistreatment with bullying less strongly, and being less willing to intervene in it. This work contributes insights for understanding how people perceive bullying of robots, designing intelligent behaviors to discourage bullying of robots, and to our long-term goal of developing anti-bullying pedagogical programs that use social robots.
Albert-based sentiment analysis of movie review

Movie reviews include the real evaluation of the movie by the public. Through these reviews, the audience can better judge whether the movie is worth watching. However, as the amount of data on movie reviews continues to grow, it takes a lot of manpower and material resources to manually analyze the emotional tendency of each movie review. As an important research field of machine learning, sentiment analysis focuses on extracting topic information from text reviews. The field of sentiment analysis is closely related to natural language processing and text mining. It can be successfully used to determine the reviewer's attitude towards various topics or the overall polarity of the review. As far as movie reviews are concerned, in addition to scoring movies digitally, they can also quantitatively enlighten us on the advantages and disadvantages of watching movies. This article uses the Albert model to build a classifier, and uses the "movie review dataset" issued by Stanford University for network training. Experiments show that the trained Albert model can reach an accuracy of 89.05% when performing sentiment analysis of movie reviews. Compared with the traditional LSTM and GRU, the accuracy of the Albert model is improved by 3%.
Word embeddings and semantic shifts in historical Spanish: Methodological considerations

Abstract Word embeddings have recently been applied to detect and explore changes in word meaning on large historical corpora. While word embeddings are useful in many Natural Language Processing tasks, there are a number of questions that need to be addressed concerning accuracy and applicability of these methods for historical data. There is a scarce literature on the stability and replicability of these embeddings, especially on small corpora, which are common in historical work. It also remains unclear whether methods used to evaluate embeddings in contemporary data can also be used for historical data sets. Our overarching goal is to use word embeddings for investigating semantic shifts in the history of Spanish. In the work presented here, we focus on methodological questions that arise. We first examine the stability and applicability of three commonly used word embeddings models on a small corpus of medieval and classical Spanish. Comparing our results with a study on the word algo as a test case, we show that a rank-averaging method can produce more stable results from the embeddings. We corroborate previous theoretical work while demonstrating the applicability of our method when training word embeddings on small corpora for the analysis of semantic change. Second, we investigate how best to evaluate different embeddings models. We show that an existing analogy test cannot be used without modification. Our new analogy test, consisting of roughly 10,000 questions for medieval and classical Spanish, will be released with the article.
Improving Attribute-Based Person Retrieval By Using A Calibrated, Weighted, And Distribution-Based Distance Metric

Typically, person re-identification systems use so-called query images of a person to find occurrences of a person in surveillance footage. In real-world scenarios, however, often only witness descriptions and no images of a person-of-interest are available. In such cases, attribute-based retrieval can be performed based on pedestrian attribute recognition approaches. Current methods rely on calculating the Euclidean distance between query attributes and attribute predictions of gallery samples to compute ranking result lists. However, these methods do not consider the output distributions of the attribute classifier. We propose to do so by introducing a distance computation that remedies the effects of unbalanced distributions of attribute predictions. Moreover, we adapt a calibration technique to reduce the negative influences further. We also propose to weight the attributes during retrieval based on their prediction errors. In total, our approach surpasses the retrieval performance achieved by the Euclidean distance by a large margin on several datasets. In figures, we were able to increase the mAP on the RAP-2.0 and Market-1501 datasets by 4.2% and 2.4% points, respectively.
Studying Bitcoin Privacy Attacks and Their Impact on Bitcoin-Based Identity Methods

The Bitcoin blockchain was the first publicly verifiable, and distributed ledger, where it is possible for everyone to download and check the full history of all data records from the genesis block. These properties lead to the emergence of new types of applications and the redesign of traditional systems that no longer respond to current business needs (e.g., transparency, protection against censorship, decentralization). One particular application is the use of blockchain technology to enable decentralized and self-sovereign identities including new mechanisms for creating, resolving, and revoking them. The public availability of data records has, in turn, paved the way for new kinds of attacks that combine sophisticated heuristics with auxiliary information to compromise users’ privacy and deanonymize their identities. In this paper, we review and categorize Bitcoin privacy attacks, investigate their impact on one of the Bitcoin-based identity methods namely did:btcr, and analyze and discuss its privacy properties.
Multispectral background subtraction with deep learning

In this paper, we follow the trend of deep learning and make an attempt to investigate the potential benefit of using multispectral images via convolutional neural networks for background subtraction task. The major contributions of this work lie in two aspects, based on the impressive algorithm FgSegNet_v2. Firstly, we extract three channels out of the seven of the FluxData FD-1665 multispectral dataset to match the number of input channels of the VGG16 deep model. Some combinations of three-channel based multispectral images perform better than RGB images. Secondly, a new convolutional encoder is designed to use all the multispectral channels available to further explore the information of multispectral images. The results outperform the RGB images and also other approaches using the same multispectral dataset.
Design of a Blockchain-based Decentralized Architecture for Sustainable Agriculture : Research-in-Progress

Exchange of information, financial transactions, and involvement of intermediaries play an important role in agriculture. However, current ICT-based agriculture systems are centralized and fail to address problems such as information asymmetry and reliability. Besides, consumers are keen to know the origin of food (traceability), intermediaries focus on transparency of shareable data and tracking of transactions are the areas to be studied in depth for smart and sustainable farming. Disruptive technology like blockchain is suitable to effectively manage smart farming. Blockchain promises reliability and authenticity of source of information as it can track the origin of a transaction. Several researchers have proposed conceptual frameworks and models. However, there is a limited study on real-time architecture that uses blockchain and decentralized applications to address the issues for smart farming. This research in progress paper presents the current gaps and focuses on incorporating blockchain to a smart and sustainable farming by developing an effective architecture. The research outcomes of the proposed study will benefit the agriculture value chains, intermediaries, farmers, and food suppliers in Australia.
Reverse guidance butterfly optimization algorithm integrated with information cross-sharing

Aiming at the disadvantages of slow convergence and the premature phenomenon of the butterfly optimization algorithm (BOA), this paper proposes a modified BOA (MBOA) called reverse guidance butterfly optimization algorithm integrated with information cross-sharing. First, the quasi-opposition concept is employed in the global search phase that lacks local exploitation capabilities to broaden the search space. Second, the neighborhood search weight factor is added in the local search stage to balance exploration and exploitation. Finally, the information cross-sharing mechanism is introduced to enhance the ability of the algorithm to jump out of the local optima. The proposed MBOA is tested in fourteen benchmark functions and three constrained engineering problems. The series of experimental results indicate that MBOA shows better performance in terms of convergence speed, convergence accuracy, stability as well as robustness.
How far can Wearable Augmented Reality Influence Customer Shopping Behavior

We investigate if providing shoppers with Augmented reality (AR) as a shopping tool can lead to an increase in purchase rate compared with conventional shopping applications. In a "simulated shopping" study with two groups with a total number of 20 participants, a test group used a wearable AR device (HoloLens) as a primary shopping method while a control group used a tablet as a conventional 2D shopping device. According to the surveys collected from participants, 19 participants (95%) commented that the AR method was more joyful than conventional 2D method. Furthermore, all participants said that their experience with AR technology was more realistic. 16 participants (80%) believed that the AR method was more influential than the conventional method, but the other four participants (20%) said that neither of the methods had any impact on their buying intentions. Although the mean number of products added to the basket by each person in HoloLens experience and tablet experience was not significantly different (p=0.675), the mean number of bought items was significantly higher in HoloLens experience compared to the tablet experience (p=0.004). In conclusion, AR increased customer's interest in shopping. While it had no significant impact on adding products to the shopping cart, it affected the rate of purchase.
SIKE on GPU: Accelerating Supersingular Isogeny-Based Key Encapsulation Mechanism on Graphic Processing Units

Since the introduction of the Supersingular isogeny Diffie-Hellman (SIDH) key exchange protocol by Jao and de Feo in 2011, it and its variation (SIKE) have gained significant attention as a promising candidate for post-quantum cryptography (PQC).Until now, even though several implementations of the state-of-the-art SIKE mechanism were presented on CPUs and embedded MCUs, there was no consideration of implementing SIKE on parallel graphic processing units (GPUs).With the advent of the IoT era, a number of IT devices will communicate with application servers.Thus, developing efficient instance of SIKE on server sides is also important.GPUs have been considered as a promising candidate for a cryptographic accelerator.In this paper, we present an efficient implementation of Supersingular Isogeny Key Encapsulation (SIKE) mechanism on GPUs.Even though SIKE has fascinating advantages of much smaller key and ciphertext sizes compared with other NIST PQC candidates, its computational overhead is extremely high.Until now, a large amount of research has been conducted for enhancing the performance of SIKE with respect to software on typical CPU and embedded MCUs and hardware optimization on ASIC and FPGA.However, generic software optimization utilizing GPUs has not been considered yet.We target the GPU implementation of SIKEp503 security parameters which provides the security level 2 (At least as difficult to break as SHA256).For efficiency, we optimize the underlying field arithmetic, especially field multiplication and reduction over p503 = 2 250 3 159 -1 and take full advantage of the properties of GPU architecture including memory hierarchy.The proposed GPU software based on RTX2080Ti provides around 36376.61 KeyGens/s, 25603.72 Encaps/s, and 22211.61Decaps/s.These are about 140.64, 157.66, and 146.81 times of improvements to the SIKE CPU Software on Intel i9-10900K CPU, respectively.As far as we know, this is the first efficient implementation of SIKE software on GPU side.
Hybrid Pipeline Hardware Architecture Based on Error Detection and Correction for AES

Currently, cryptographic algorithms are widely applied to communications systems to guarantee data security. For instance, in an emerging automotive environment where connectivity is a core part of autonomous and connected cars, it is essential to guarantee secure communications both inside and outside the vehicle. The AES algorithm has been widely applied to protect communications in onboard networks and outside the vehicle. Hardware implementations use techniques such as iterative, parallel, unrolled, and pipeline architectures. Nevertheless, the use of AES does not guarantee secure communication, because previous works have proved that implementations of secret key cryptosystems, such as AES, in hardware are sensitive to differential fault analysis. Moreover, it has been demonstrated that even a single fault during encryption or decryption could cause a large number of errors in encrypted or decrypted data. Although techniques such as iterative and parallel architectures have been explored for fault detection to protect AES encryption and decryption, it is necessary to explore other techniques such as pipelining. Furthermore, balancing a high throughput, reducing low power consumption, and using fewer hardware resources in the pipeline design are great challenges, and they are more difficult when considering fault detection and correction. In this research, we propose a novel hybrid pipeline hardware architecture focusing on error and fault detection for the AES cryptographic algorithm. The architecture is hybrid because it combines hardware and time redundancy through a pipeline structure, analyzing and balancing the critical path and distributing the processing elements within each stage. The main contribution is to present a pipeline structure for ciphering five times on the same data blocks, implementing a voting module to verify when an error occurs or when output has correct cipher data, optimizing the process, and using a decision tree to reduce the complexity of all combinations required for evaluating. The architecture is analyzed and implemented on several FPGA technologies, and it reports a throughput of 0.479 Gbps and an efficiency of 0.336 Mbps/LUT when a Virtex-7 is used.
Sentiment Classification Algorithm Based on the Cascade of BERT Model and Adaptive Sentiment Dictionary

The mobile social network contains a large amount of information in a form of commentary. Effective analysis of the sentiment in the comments would help improve the recommendations in the mobile network. With the development of well-performing pretrained language models, the performance of sentiment classification task based on deep learning has seen new breakthroughs in the past decade. However, deep learning models suffer from poor interpretability, making it difficult to integrate sentiment knowledge into the model. This paper proposes a sentiment classification model based on the cascade of the BERT model and the adaptive sentiment dictionary. First, the pretrained BERT model is used to fine-tune with the training corpus, and the probability of sentiment classification in different categories is obtained through the softmax layer. Next, to allow a more effective comparison between the probabilities for the two classes, a nonlinearity is introduced in a form of positive-negative probability ratio, using the rule method based on sentiment dictionary to deal with the probability ratio below the threshold. This method of cascading the pretrained model and the semantic rules of the sentiment dictionary allows to utilize the advantages of both models. Different sized Chnsenticorp data sets are used to train the proposed model. Experimental results show that the Dict-BERT model is better than the BERT-only model, especially when the training set is relatively small. The improvement is obvious with the accuracy increase of 0.8%.
Transfer Learning for Human Navigation and Triage Strategies Prediction in a Simulated Urban Search and Rescue Task

To build an agent providing assistance to human rescuers in an urban search and rescue task, it is crucial to understand not only human actions but also human beliefs that may influence the decision to take these actions. Developing data-driven models to predict a rescuer’s strategies for navigating the environment and triaging victims requires costly data collection and training for each new environment of interest. Transfer learning approaches can be used to mitigate this challenge, allowing a model trained on a source environment/task to generalize to a previously unseen target environment/task with few training examples. In this paper, we investigate transfer learning (a) from a source environment with smaller number of types of injured victims to one with larger number of victim injury classes and (b) from a smaller and simpler environment to a larger and more complex one for navigation strategy. Inspired by hierarchical organization of human spatial cognition, we used graph division to represent spatial knowledge, and Transfer Learning Diffusion Convo-lutional Recurrent Neural Network (TL-DCRNN), a spatial and temporal graph-based recurrent neural network suitable for transfer learning, to predict navigation. To abstract the rescue strategy from a rescuer’s field-of-view stream, we used attention-based LSTM networks. We experimented on various transfer learning scenarios and evaluated the performance using mean average error. Results indicated our assistant agent can improve predictive accuracy and learn target tasks faster when equipped with transfer learning methods.
BlockCopy: High-Resolution Video Processing with Block-Sparse Feature Propagation and Online Policies

In this paper we propose BlockCopy, a scheme that accelerates pretrained frame-based CNNs to process video more efficiently, compared to standard frame-by-frame processing. To this end, a lightweight policy network determines important regions in an image, and operations are applied on selected regions only, using custom block-sparse convolutions. Features of non-selected regions are simply copied from the preceding frame, reducing the number of computations and latency. The execution policy is trained using reinforcement learning in an online fashion without requiring ground truth annotations. Our universal framework is demonstrated on dense prediction tasks such as pedestrian detection, instance segmentation and semantic segmentation, using both state of the art (Center and Scale Predictor, MGAN, SwiftNet) and standard baseline networks (Mask-RCNN, DeepLabV3+). BlockCopy achieves significant FLOPS savings and inference speedup with minimal impact on accuracy.
A Robust and Accurate End-to-End Template Matching Method Based on the Siamese Network

Template matching is an important and challenging task in remote sensing and computer vision. Existing template matching methods often fail in the presence of complex nonrigid deformation, occlusion, and background clutter. In this letter, inspired by Siamese trackers, we propose an end-to-end template matching method that is based on the Siamese network. Different from the traditional template matching methods, our method treats the template matching task as a classification-regression task. It is more robust to background clutter, occlusion, and nonrigid deformation. Moreover, we introduce a channel-attention mechanism in the cross correlation operation and replace the commonly used intersection-over-union (IoU) with distance-IoU (DIoU) to build a new regression loss, which further improves the performance of our method. Extensive experiments on the commonly used public benchmark demonstrate that our method achieves the state-of-the-art performance.
A Keyword Analysis Study on Postpartum Obesity Using Big Data

This study selected Google and Naver, the most recognizable Internet portals in Korea, as subjects for analysis. “Postpartum obesity” and “postpartum depression” were used as keywords for data collection. This study aimed to provide basic data for solving maternal problems using big data. Keywords related to postpartum obesity were collected from the portal site Google from 1 January 2019 to 31 December 2019. The collected data were analyzed through simple frequency analysis, N-gram analysis, and keyword network. This study can be used as basic data for postpartum obesity-related programs or academic research. It is also expected to be used for research on the development of a mobile-based customized healthcare system focused on maternal health. Previous papers and data are still insufficient at solving the physical and mental problems related to postpartum obesity and depression. It is necessary to find ways to continuously integrate and collect data from mothers across the country.