Reviewerly/exHarmony huggingface dataset:
New Digital Education as the Market Solution to the Student Debt Crisis

I investigate social interest, or hype, in online learning. The two datasets of interest are derived from public search engine data for the United States over the period 2000-2020. This data is assessed for hype cycle dynamics. I find that interest in online learning and massive online open courses follow well-defined hype cycles. Interest in online learning has recovered from a low point. This indicates economic durability and positive growth outlook from an institutional point of view. I describe the history of online education as proceeding in three ages. New Digital Education is an affordable, employer-lead, hybrid learning approach to accredited higher education. New Digital Education emerges during the Second Age, from 2012-2017, and is normalized in the Third Age, from 2018 through the present. New Digital Education constitutes a sustainable market solution to the student debt crisis. A market solution to the student debt crisis undercuts the need for policy action.
Does Blockchain Patent-Development Influence Bitcoin Risk?

The paper presents a novel analysis specifically investigating as to whether stocks associated with leading blockchain patent-developments influence the price volatility of Bitcoin across multiple time frequencies. It is important to further develop our understanding of the inter-dynamics between this relatively youthful financial product and pricing sensitivities associated with corporate technological advancement. Several interesting results are presented. First, Bitcoin is identified as a volatility receiver instead of a transmitter across all of the time frequencies considered during periods of patent development. Secondly, Microsoft, Mastercard, Intel and Visa contribute the largest volatility spillovers to the Bitcoin market due to patent development. Finally, for most of the companies considered, the calculated spillover effects towards Bitcoin markets are found to increase from the short-term to the long-term. These results suggest the existence of an avenue through which large corporations can influence cryptocurrency prices through their announcements of future technological intentions. The inherent risks incorporated with blockchain and cryptocurrency patent-development should be studied in detail, with particular warnings presented to those companies with no evidence of prior exposure and market knowledge.
Conversational Query Rewriting with Self-Supervised Learning

Context modeling plays a critical role in building multi-turn dialogue systems. Conversational Query Rewriting (CQR) aims to simplify the multi-turn dialogue modeling into a single-turn problem by explicitly rewriting the conversational query into a self-contained utterance. However, existing approaches rely on massive supervised training data, which is labor-intensive to annotate. And the detection of the omitted important information from context can be further improved. Besides, intent consistency constraint between contextual query and rewritten query is also ignored. To tackle these issues, we first propose to construct a large-scale CQR dataset automatically via self-supervised learning, which does not need human annotation. Then we introduce a novel CQR model Teresa based on Transformer, which is enhanced by self-attentive keywords detection and intent consistency constraint. Finally, we conduct extensive experiments on two public datasets. Experimental results demonstrate that our proposed model outperforms existing CQR baselines significantly, and also prove the effectiveness of self-supervised learning on improving the CQR performance.
Video Synopsis for Accident Detection using Deep Learning Technique

Video synopsis is an activity-based video summarization method and the main goal is to present as many activities as possible simultaneously in the shortest time period. Nowadays, a huge amount of the multimedia dictionary makes the perusing, recovery, and conveyance of video content exceptionally moderate and even troublesome undertakings. Visual data like images and videos are easily accessible nowadays and play a critical role in many real-time applications like surveillance. but Accident detection may be a tougher task at the instant. Numbers of researches on accident detection have expanded over the past a long time. This study proposes the latest approaches in accident detection and video synopsis. We analyze existing processes in accident detection, video synopsis, and advance deep learning techniques. At last, we present the encouraging research directions on this subject.
S-Box Construction Method Based on the Combination of Quantum Chaos and PWLCM Chaotic Map

For a security system built on symmetric-key cryptography algorithms, the substitution box (S-box) plays a crucial role to resist cryptanalysis. In this article, we incorporate quantum chaos and PWLCM chaotic map into a new method of S-box design. The secret key is transformed to generate a six tuple system parameter, which is involved in the generation process of chaotic sequences of two chaotic systems. The output of one chaotic system will disturb the parameters of another chaotic system in order to improve the complexity of encryption sequence. S-box is obtained by XOR operation of the output of two chaotic systems. Over the obtained 500 key-dependent S-boxes, we test the S-box cryptographical properties on bijection, nonlinearity, SAC, BIC, differential approximation probability, respectively. Performance comparison of proposed S-box with those chaos-based one in the literature has been made. The results show that the cryptographic characteristics of proposed S-box has met our design objectives and can be applied to data encryption, user authentication and system access control.
Unimodal Cyclic Regularization For Training Multimodal Image Registration Networks

The loss function of an unsupervised multimodal image registration framework has two terms, i.e., a metric for similarity measure and regularization. In the deep learning era, researchers proposed many approaches to automatically learn the similarity metric, which has been shown effective in improving registration performance. However, for the regularization term, most existing multimodal registration approaches still use a hand-crafted formula to impose artificial properties on the estimated deformation field. In this work, we propose a unimodal cyclic regularization training pipeline, which learns task-specific prior knowledge from simpler unimodal registration, to constrain the deformation field of multimodal registration. In the experiment of abdominal CT-MR registration, the proposed method yields better results over conventional regularization methods, especially for severely deformed local regions.
A Universal Law of Robustness via Isoperimetry

Classically, data interpolation with a parametrized model class is possible as long as the number of parameters is larger than the number of equations to be satisfied. A puzzling phenomenon in deep learning is that models are trained with many more parameters than what this classical theory would suggest. We propose a theoretical explanation for this phenomenon. We prove that for a broad class of data distributions and model classes, overparametrization is necessary if one wants to interpolate the data smoothly. Namely we show that smooth interpolation requires $d$ times more parameters than mere interpolation, where $d$ is the ambient data dimension. We prove this universal law of robustness for any smoothly parametrized function class with polynomial size weights, and any covariate distribution verifying isoperimetry. In the case of two-layers neural networks and Gaussian covariates, this law was conjectured in prior work by Bubeck, Li and Nagaraj. We also give an interpretation of our result as an improved generalization bound for model classes consisting of smooth functions.
Incremental Schema Discovery at Scale for RDF Data

The lack of a descriptive schema for an RDF dataset has motivated several research works addressing the problem of automatic schema discovery. The goal of these approaches is to generate a structural schema of a given RDF dataset from its instances. However, as new instances are added, the generated schema may become inconsistent with the dataset.In this paper, we propose an incremental schema discovery approach for massive RDF datasets. It is based on a scalable and incremental density-based clustering algorithm which propagates the changes occurring in the dataset into the clusters corresponding to the classes of the schema. Our approach is implemented using big data technology to scale up schema discovery while providing a high quality clustering result. We present some experiments which demonstrate the efficiency of our proposal on both synthetic and real datasets.
Inter-Series Attention Model for COVID-19 Forecasting

COVID-19 pandemic has an unprecedented impact all over the world since early 2020. During this public health crisis, reliable forecasting of the disease becomes critical for resource allocation and administrative planning. The results from compartmental models such as SIR and SEIR are popularly referred by CDC and news media. With more and more COVID-19 data becoming available, we examine the following question: Can a direct data-driven approach without modeling the disease spreading dynamics outperform the well referred compartmental models and their variants? In this paper, we show the possibility. It is observed that as COVID-19 spreads at different speed and scale in different geographic regions, it is highly likely that similar progression patterns are shared among these regions within different time periods. This intuition lead us to develop a new neural forecasting model, called Attention Crossing Time Series (\textbf{ACTS}), that makes forecasts via comparing patterns across time series obtained from multiple regions. The attention mechanism originally developed for natural language processing can be leveraged and generalized to materialize this idea. Among 13 out of 18 testings including forecasting newly confirmed cases, hospitalizations and deaths, \textbf{ACTS} outperforms all the leading COVID-19 forecasters highlighted by CDC.
Sharf: Shape-conditioned Radiance Fields from a Single View

We present a method for estimating neural scenes representations of objects given only a single image. The core of our method is the estimation of a geometric scaffold for the object and its use as a guide for the reconstruction of the underlying radiance field. Our formulation is based on a generative process that first maps a latent code to a voxelized shape, and then renders it to an image, with the object appearance being controlled by a second latent code. During inference, we optimize both the latent codes and the networks to fit a test image of a new object. The explicit disentanglement of shape and appearance allows our model to be fine-tuned given a single image. We can then render new views in a geometrically consistent manner and they represent faithfully the input object. Additionally, our method is able to generalize to images outside of the training domain (more realistic renderings and even real photographs). Finally, the inferred geometric scaffold is itself an accurate estimate of the object's 3D shape. We demonstrate in several experiments the effectiveness of our approach in both synthetic and real images.
Chances and Risks of Artificial Intelligence—A Concept of Developing and Exploiting Machine Intelligence for Future Societies

Artificial Intelligence (AI): Boon or Bane for societies? AI technologies and solutions—as most revolutionary technologies have done in the past—offer negative implications on the one hand and considerable positive potential on the other. Avoiding the former and fostering the latter will require substantial investments in future societal concepts, research and development, and control of AI-based solutions in AI security while avoiding abuse. Preparation for the future role of AI in societies should strive towards the implementation of related methods and tools for risk management, models of complementary human–machine cooperation, strategies for the optimization of production and administration, and innovative concepts for the distribution of the economic value created. Two extreme possible “end states” of AI impact (if there is ever an end state) that are being discussed at present may manifest as (a) uncontrolled substitution by AI of major aspects of production, services, and administrative and decision-making processes, leading to unprecedented risks such as high unemployment, and devaluation and the underpayment of people in paid work, resulting in inequality in the distribution of wealth and employment, diminishing social peace, social cohesion, solidarity, security, etc., or, on the contrary, (b) the freeing of people from routine labor through increased automation in production, administration and services, and changing the constitution of politics and societies into constituencies with high ethical standards, personal self-determination, and the general dominance of humane principles, as opposed to pure materialism. Any mix of these two extremes could develop, and these combinations may vary among different societies and political systems.
Collaborative Co-Attention Network for Session-Based Recommendation

Session-based recommendation aims to model a user’s intent and predict an item that the user may interact with in the next step based on an ongoing session. Existing session-based recommender systems mainly aim to model the sequential signals based on Recurrent Neural Network (RNN) structures or the item transition relations between items with Graph Neural Network (GNN) based frameworks to identify a user’s intent for recommendation. However, in real scenarios, there may be strong sequential signals existing in users’ adjacent behaviors or multi-step transition relations among different items. Thus, either RNN- or GNN-based methods can only capture limited information for modeling complex user behavior patterns. RNNs pay attention to the sequential relations among consecutive items, while GNNs focus on structural information, i.e., how to enrich the item embedding with its adjacent items. In this paper, we propose a Collaborative Co-attention Network for Session-based Recommendation (CCN-SR) to incorporate both sequential and structural information, as well as capture the co-relations between them for obtaining an accurate session representation. To be specific, we first model the ongoing session with an RNN structure to capture the sequential information among items. Meanwhile, we also construct a session graph to learn the item representations with a GNN structure. Then, we design a co-attention network upon these two structures to capture the mutual information between them. The designed co-attention network can enrich the representation of each node in the session with both sequential and structural information, and thus generate a more comprehensive representation for each session. Extensive experiments are conducted on two public e-commerce datasets, and the results demonstrate that our proposed model outperforms state-of-the-art baseline model for session based recommendation in terms of both Recall and MRR. We also investigate different combination strategies and the experimental results verify the effectiveness of our proposed co-attention mechanism. Besides, our CCN-SR model achieves better performance than baseline models with different session lengths.
Cold Start Problem For Automated Live Video Comments

Live video comments, or "danmu", are an emerging feature on Asian online video platforms.Danmu are time-synchronous comments that are overlaid on a video playback.These comments uniquely enrich the experience and engagement of their users, and have become a determining factor in the popularity of videos on these platforms.Similar to the "cold start problem" in recommender systems, a video will only start to attract attention when sufficient danmu comments have been posted on it.We study this video cold start problem and examine how new comments can be generated automatically on less-commented videos.We propose to predict danmu comments to promote user engagement, by exploiting a multi-modal combination of the video visual content, subtitles, audio signals, and any surrounding comments (when they exist).Our method fuses these multiple modalities in a transformer network which is then trained for different comment density scenarios.We evaluate our proposed system through both a retrieval based evaluation method, as well as human judgement.Results show that our proposed system improves significantly over stateof-the-art methods.
The persuasive language of online advertisements featuring social media influencers on Instagram: A multimodal analysis

The popularity of online advertising is increasing alongside the rising number of internet users, and one of the online platforms to advertise one’s products or services is Instagram. On Instagram, many advertisers opt for one viable alternative way of advertising: social media influencers. They typically hire popular figures to endorse their products, and the influencers will then post an image and a caption, persuading their followers to buy the products. It is intriguing to investigate how they make use of images and texts in Instagram to strengthen their persuasive act. Therefore, this paper aims to present a multimodality analysis where both the linguistic features and the images of Instagram advertisements of influencers are explored in relation to their communicative intent. The sample was built up from 24 Instagram ads posted by 13 Indonesian influencers. Several salient lexicogrammatical features were pinpointed, and the images were analyzed following the social semiotic approach called visual grammar. The findings revealed that the Instagram ads demonstrated vast amount of colloquial and informal language use, which was intended to establish familiarity with the audience. The image analysis also showed that influencers had roles to display themselves in the image to build credibility and trust as credible reviewers. The study has contributed to explicating how diverse language use on the internet is vis-à-vis traditional media, particularly the discourse of influencer advertising.
Tourism safety monitoring information service system based on internet of things and block-chain

Tourism safety is the lifeblood of national tourism development, and tourism safety information monitoring is an important foundation for tourism safety management. The maturity of Internet information technology has led to the rapid rise of blockchain and the Internet of Things. In essence, the blockchain is a shared database. The Internet of Things can access all kinds of necessary information through various networks, such as heat, electricity, dynamics, chemistry, biology, realize the ubiquitous connection between things and people, and realize the recognition and intelligent perception of objects. This article aims to study a tourism safety monitoring information service system based on the Internet of Things and blockchain. This paper uses the principle of sensor ranging and electromagnetic induction, acoustic calculation, sensor calculation, and RFID tag distance to measure the real-time position of the person’s RFID tag information and continuous monitoring information of infrasound intensity to monitor and pre-warn natural disasters in the scenic area. Research results, it shows that this system can monitor disaster information in time and can effectively reduce the risk of injury by 10% –20% of tourists.
Single Storage Semi-Global Matching for Real Time Depth Processing

Depth-map is the key computation in computer vision and robotics. One of the most popular approach is via computation of disparity-map of images obtained from Stereo Camera. Semi Global Matching (SGM) method is a popular choice for good accuracy with reasonable computation time. To use such compute-intensive algorithms for real-time applications such as for autonomous aerial vehicles, blind Aid, etc. acceleration using GPU, FPGA is necessary. In this paper, we show the design and implementation of a stereo-vision system, which is based on FPGA-implementation of More Global Matching (MGM) [7]. MGM is a variant of SGM. We use 4 paths but store a single cumulative cost value for a corresponding pixel. Our stereo-vision prototype uses Zedboard containing an ARM-based Zynq-SoC [10], ZED-stereo-camera/ELP stereo-camera/Intel RealSense D435i, and VGA for visualization. The power consumption attributed to the custom FPGA-based acceleration of disparity map computation required for depth-map is just 0.72 watt. The update rate of the disparity map is realistic 10.5 fps.
Handling Problems of Credit Data for Imbalanced Classes using SMOTEXGBoost

Abstract Some researchers find data with imbalanced class conditions, where there are data with a number of minorities and a majority. SMOTE is a data approach for an imbalanced classes and XGBoost is one algorithm for an imbalanced data problems. This research uses SMOTE and XGBoost or abbreviated as SMOTEXGBoost for handling data with an imbalanced classes. The results showed almost the same accuracy value between SMOTE and SMOTEXGBoost at 99%. While the value of AUC SMOTEXBoost has a more stable value than SMOTE that is equal to 99.89% for training and 98.51% for testing.
Antitrust Enforcement and Big Tech: After the Remedy Is Ordered

Each of us—one an attorney and the other two software experts—has substantial experience monitoring the implementation of court-ordered remedies in two leading hi-tech cases: United States v. Microsoft Corp. and United States v. Bazaarvoice, Inc. We discuss challenges that attorney and expert monitors confront in overseeing company compliance with antitrust remedial decrees in cases against hi-tech companies. We first summarize the legal principles applicable to antitrust remedies. Thereafter, we discuss oversight in the Microsoft and Bazaarvoice cases. Finally, we offer takeaways on effective antitrust decree monitoring. Two takeaways are particularly noteworthy. First, expect the unexpected. During monitoring oversight, implementing the court decree’s relatively general relief provisions will likely uncover unanticipated issues that prove challenging to resolve and may often require hi-tech expertise to do so. Second—and relatedly—be skeptical of company resistance. Court-ordered relief is unlikely to align with company business interests; if it did, the company probably would have adopted the practice without being ordered to do so. Accordingly, company incentives probably will militate toward a cramped view of decree implementation. Monitors therefore should refrain from taking company protestations at face value and should be prepared to leverage hi-tech expertise to probe company systems, data, and personnel for verification or refutation of company positions. Finally, with artificial intelligence and computational law (including antitrust) continuing to develop, we call attention to opportunities to use computer-automated processes to inform compliance oversight.