Reviewerly/exHarmony huggingface dataset:
Inferring End-to-End Latency in Live Videos

This paper provides an end-to-end latency model of real-time generated live videos composed of sub-models of capturing, encoding, network, decoding, rendering, and display refreshing. We build the model by deeply analyzing each sub part’s latency with parameters like video’s Spatial (frame scale <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> ), Temporal (frame rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> ), Amplitude (quantization step-size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> ) Resolutions (STAR), Compressed Frame Bit Numbers ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n_{F}$ </tex-math></inline-formula> ), and other necessary parameters. The latency of 640 combinations of STAR for eight representative video sequences is measured to fit accurate models. We primarily focus on processing procedures, whose latency is becoming the principal affection, as network latency decreases due to the its development. We then introduce a baseline video sequence set to reduce the complexity when fitting the encoding and decoding sub-models by predicting the model parameters on new hardware from parameters of a known one, and improve models’ robustness crossing different scenarios. These sub-models correlate well with the realistic latency data, with the average Pearson Correlation Coefficient (PCC) of 0.99 and the average Spearman’s Rank Correlation Coefficient (SRCC) of 0.98, according to an independent validation test. Furthermore, we validate our end-to-end latency model in a simulated end-to-end live video transmission from generating to displaying, introducing the latency as a strict condition in adaptive bit-rate selection. The simulation shows that our model could significantly increase time during which the latency can satisfy the given tolerance at a low cost.
The African tragedy: the effect of democracy on economic growth

We estimate the effect of democracy on economic growth for the countries in Sub-Saharan Africa in comparison with other countries. We find that in contrast to other countries, democracy in Africa benefits neither GDP per capita nor total GDP. We explain the former by changes in the size of the population and the latter by changes in the age structure of the population. Both demographic changes relate to the finding that unlike in other countries, democracy does not reduce child mortality in Africa. The evidence suggests that without improvements in health, democracy puts Africa on a path toward a Malthusian trap.
Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach

In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted. Such architecture usually consists of matching, pre-ranking, ranking, and re-ranking stages. In the pre-ranking stage, vector-product based models with representation-focused architecture are commonly adopted to account for system efficiency. However, it brings a significant loss to the effectiveness of the system. In this paper, a novel pre-ranking approach is proposed which supports complicated models with interaction-focused architecture. It achieves a better tradeoff between effectiveness and efficiency by utilizing the proposed learnable Feature Selection method based on feature Complexity and variational Dropout (FSCD). Evaluations in a real-world e-commerce sponsored search system for a search engine demonstrate that utilizing the proposed pre-ranking, the effectiveness of the system is significantly improved. Moreover, compared to the systems with conventional pre-ranking models, an identical amount of computational resource is consumed.
Learning Domain Semantics and Cross-Domain Correlations for Paper Recommendation

Understanding how knowledge is technically transferred across academic disciplines is very relevant for understanding and facilitating innovation. There are two challenges for this purpose, namely the semantic ambiguity and the asymmetric influence across disciplines. In this paper we investigate knowledge propagation and characterize semantic correlations for cross discipline paper recommendation. We adopt a generative model to represent a paper content as the probabilistic association with an existing hierarchically classified discipline to reduce the ambiguity of word semantics. The semantic correlation across disciplines is represented by an influence function, a correlation metric and a ranking mechanism. Then a user interest is represented as a probabilistic distribution over the target domain semantics and the correlated papers are recommended. Experimental results on real datasets show the effectiveness of our methods. We also discuss the intrinsic factors of results in an interpretable way. Compared with traditional word embedding based methods, our approach supports the evolution of domain semantics that accordingly lead to the update of semantic correlation. Another advantage of our approach is its flexibility and uniformity in supporting user interest specifications by either a list of papers or a query of key words, which is suited for practical scenarios.
Socializing the Videos: A Multimodal Approach for Social Relation Recognition

As a crucial task for video analysis, social relation recognition for characters not only provides semantically rich description of video content but also supports intelligent applications, e.g., video retrieval and visual question answering. Unfortunately, due to the semantic gap between visual and semantic features, traditional solutions may fail to reveal the accurate relations among characters. At the same time, the development of social media platforms has now promoted the emergence of crowdsourced comments, which may enhance the recognition task with semantic and descriptive cues. To that end, in this article, we propose a novel multimodal-based solution to deal with the character relation recognition task. Specifically, we capture the target character pairs via a search module and then design a multistream architecture for jointly embedding the visual and textual information, in which feature fusion and attention mechanism are adapted for better integrating the multimodal inputs. Finally, supervised learning is applied to classify character relations. Experiments on real-world data sets validate that our solution outperforms several competitive baselines.
Privileged Graph Distillation for Cold Start Recommendation

The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction records. In these recommendation systems, warm users (items) have privileged collaborative signals of interaction records compared to cold start users (items), and these Collaborative Filtering (CF) signals are shown to have competing performance for recommendation. Many researchers proposed to learn the correlation between collaborative signal embedding space and the attribute embedding space to improve the cold start recommendation, in which user and item categorical attributes are available in many online platforms. However, the cold start recommendation is still limited by two embedding spaces modeling and simple assumptions of space transformation. As user-item interaction behaviors and user (item) attributes naturally form a heterogeneous graph structure, in this paper, we propose a privileged graph distillation model (PGD). The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links. The student model is composed of an entity-attribute graph without CF links. Specifically, the teacher model can learn better embeddings of each entity by injecting complex higher-order relationships from the constructed heterogeneous graph. The student model can learn the distilled output with privileged CF embeddings from the teacher embeddings. Our proposed model is generally applicable to different cold start scenarios with new user, new item, or new user-new item. Finally, extensive experimental results on the real-world datasets clearly show the effectiveness of our proposed model on different types of cold start problems, with average 6.6%, 5.6%, and 17.1% improvement over state-of-the-art baselines on three datasets, respectively.
Medley: Predicting Social Trust in Time-Varying Online Social Networks

Social media, such as Reddit, has become a norm in our daily lives, where users routinely express their attitude using upvotes (likes) or downvotes. These social interactions may encourage users to interact frequently and form strong ties of trust between one another. It is therefore important to predict social trust from these interactions, as they facilitate routine features in social media, such as online recommendation and advertising.Conventional methods for predicting social trust often accept static graphs as input, oblivious of the fact that social interactions are time-dependent. In this work, we propose Medley, to explicitly model users' time-varying latent factors and to predict social trust that varies over time. We propose to use functional time encoding to capture continuous-time features and employ attention mechanisms to assign higher importance weights to social interactions that are more recent. By incorporating topological structures that evolve over time, our framework can infer pairwise social trust based on past interactions. Our experiments on benchmarking datasets show that Medley is able to utilize time-varying interactions effectively for predicting social trust, and achieves an accuracy that is up to 26% higher over its alternatives.
Small-scale image recognition based on Cascaded Convolutional Neural Network

The advent of the era of big data and the rapid improvement of computer computing capabilities have promoted the development of image recognition technology in a more advanced direction. Image recognition technology based on deep learning has become a current research hotspot in the field of artificial intelligence. As one of the algorithms of deep learning, convolutional neural network has been widely used in the field of image recognition due to its superior performance. This paper designs a convolutional neural network that can take into account model size and accuracy. It is suitable for small-scale image recognition and is convenient for deployment in some environments with low hardware platforms. Based on the improvement of the classic ALexNet network, a parallel convolutional structure is used to design a parallel cascaded convolutional network based on jumper connections for small-scale image recognition. Parallel convolution uses convolution kernels of different scales to extract features in parallel, and cascade fusion the features extracted from different scales. In order to further improve the accuracy, the network was optimized by adding a layer connection, and the network performance was evaluated on the Caltech-256 and Food-101 data sets. The results show that compared with the classic AlexNet network, the network RPCNet constructed based on the layer connection has improved accuracy by 6.12% and 12.28%, respectively, and the network scale is only 1/15 of the ALexNet network.
Quantum Circuit Design of Toom 3-Way Multiplication

In classical computation, Toom–Cook is one of the multiplication methods for large numbers which offers faster execution time compared to other algorithms such as schoolbook and Karatsuba multiplication. For the use in quantum computation, prior work considered the Toom-2.5 variant rather than the classically faster and more prominent Toom-3, primarily to avoid the nontrivial division operations inherent in the latter circuit. In this paper, we investigate the quantum circuit for Toom-3 multiplication, which is expected to give an asymptotically lower depth than the Toom-2.5 circuit. In particular, we designed the corresponding quantum circuit and adopted the sequence proposed by Bodrato to yield a lower number of operations, especially in terms of nontrivial division, which is reduced to only one exact division by 3 circuit per iteration. Moreover, to further minimize the cost of the remaining division, we utilize the unique property of the particular division circuit, replacing it with a constant multiplication by reciprocal circuit and the corresponding swap operations. Our numerical analysis shows that the resulting circuit indeed gives a lower asymptotic complexity in terms of Toffoli depth and qubit count compared to Toom-2.5 but with a large number of Toffoli gates that mainly come from realizing the division operation.
A Computational Analysis of Vagueness in Revisions of Instructional Texts

WikiHow is an open-domain repository of instructional articles for a variety of tasks, which can be revised by users.In this paper, we extract pairwise versions of an instruction before and after a revision was made.Starting from a noisy dataset of revision histories, we specifically extract and analyze edits that involve cases of vagueness in instructions.We further investigate the ability of a neural model to distinguish between two versions of an instruction in our data by adopting a pairwise ranking task from previous work and showing improvements over existing baselines.
Exclusive Feature Constrained Class Activation Mapping for Better Visual Explanation

Whereas Deep Neural Network(DNN) shows wonderful performance on large scale data, lacking interpretability limits their usage in scenarios relevant to security. To make visual explanations less noisy and more class-discriminative, in this work, we propose a visual explanation method of DNN, named Exclusive Feature Constrained Class Activation Mapping(EFC-CAM). A new exclusive feature constraint is introduced to optimize the weight calculated from Grad-CAM or initialized from a constant vector. To better measure visual explanation methods, we design an effective evaluation metric which does not need bounding boxes as auxiliary information. Extensive quantitative experiments and visual inspection on ImageNet and Fashion validation set show the effectiveness of the proposed method.
The Automatic Detection of Pedestrians under the High-Density Conditions by Deep Learning Techniques

The automatic detection and tracking of pedestrians under high-density conditions is a challenging task for both computer vision fields and pedestrian flow studies. Collecting pedestrian data is a fundamental task for the modeling and practical implementations of crowd management. Although there are many methods for detecting pedestrians, they may not be easily adopted in the high-density situations. Therefore, we utilized one emerging method based on the deep learning algorithm. Based on the top-view video data of some pedestrian flow experiments recorded by an unmanned aerial vehicle (UAV), we produce our own training datasets. We train the detection model by using Yolo v3, a very popular deep learning model among many available detection models in recent years. We find the detection results are good; e.g., the precisions, recalls, and F1 scores could be larger than 0.95 even when the pedestrian density is as high as <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>9.0</mn> <mtext> </mtext> <mrow> <mtext>ped</mtext> </mrow> <mo>/</mo> <mrow> <msup> <mrow> <mtext>m</mtext> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </math> . We think this approach could be used for the other pedestrian flow experiments or field data which have similar configurations and can also be useful for automatic crowd density estimation.
A Study of the Applicability of Ideal Lattice-Based Fully Homomorphic Encryption Scheme to Ethereum Blockchain

Without the requirement of trusted third parties (TTPs), Blockchain builds an environment where mutual trust among valid members is established. Nevertheless, the associated transparency property caused a hazard to real-world applications because every on-chain information is exposed to the public. That implies those applications involving sensitive data or personal information, which require security and privacy protection, are not suitable to be implemented in Blockchain, directly. Fully homomorphic encryption (FHE) schemes have been treated as one of the end-to-end solutions to data security and privacy protection problems. In this article, we examine the possibility (or cost) of embedding the ideal lattice-based FHE into the Ethereum Blockchain for building up a new trustworthy framework with security and privacy protection capability. Due to the limitations of current Blockchain, the execution of FHE is conducted off-chain; at the same time, on-chain members can call FHE-based functions to directly compute the ciphertext domain operations after their Smart Contracts have been deployed to the Blockchain. To illustrate and benchmark the examining results of our framework, an FHE and Blockchain-based Vickrey auction system is also developed, in which the online bidding prices are kept in secret. Simultaneously, the determination of the winner and the transferring of payments are conducted autonomously by Smart Contracts.
Generative adversarial network for Table-to-Text generation

Table-to-Text generation aims to generate descriptions which can be viewed as a set of field-value records for factual tables. Despite the significant progress, the state-of-the-art models suffer from two major issues: Nonfluency and Divergence. Nonfluency means descriptions generated by models are not as fluent as those generated by humans, and thus can be distinguished easily. Divergence refers to the fact that the generated sentences contain information which can not be concluded from factual tables. This could be attributed to that most neural models are trained with the Maximum Likelihood Estimation (MLE) loss and use divergence-contained references as the ground truth, which forces the models to learn what cannot be inferred from the source to some extent. Motivated by the limitations of current models, we propose a novel GAN-based model with adversarial learning mechanism, which simultaneously trains a generative model G and a discriminative model D, to address Nonfluency and Divergence issues in Table-to-Text generation. Specifically, we build the generator G as an agent of reinforcement learning with a sequence-to-sequence architecture, which takes the raw data as input and predicts the generated sentences. Meanwhile, we build the discriminator D with a Convolutional Neural Network (CNN) to calculate rewards to measure the fluency of generations. To judge the fidelity of generations with regard to the original table more accurately, we also calculate the rewards from BLEU-Table. With the fusion rewards from CNN and BLEU-Table, our methods outperform the baselines by a large margin on the WikiBio and Wiki3C benchmarks evaluated with BLEU, ROURGE, and PARENT. Specifically, our models achieve 49.0 (BLEU-4), 37.8 (ROUGE-4) and 45.4 (PARENT) on WikiBio, as well as 12.9 (BLEU-4) and 6.9 (ROUGE-4) on Wiki3C. More importantly, we construct a new Wiki3C dataset that improves the insufficiency of datasets and promote the progress in Table-to-Text generation.
Blockchain Machine: A Network-Attached Hardware Accelerator for Hyperledger Fabric

In this paper, we demonstrate how Hyperledger Fabric, one of the most popular permissioned blockchains, can benefit from network-attached acceleration. The scalability and peak performance of Fabric is primarily limited by the bottlenecks present in its block validation/commit phase. We propose Blockchain Machine, a hardware accelerator coupled with a hardware-friendly communication protocol, to act as the validator peer. It can be adapted to applications and their smart contracts, and is targeted for a server with network-attached FPGA acceleration card. The Blockchain Machine retrieves blocks and transactions in hardware directly from the network interface, which are then validated through a configurable and efficient block-level and transaction-level pipeline. The validation results are then transferred to the host CPU where non-bottleneck operations are executed. From our implementation integrated with Fabric v1.4 LTS, we observed up to 12× speedup in block validation when compared to software-only validator peer, with commit throughput of up to 68,900 tps. Our work provides an acceleration platform that will foster further research on hardware acceleration of permissioned blockchains.
Basic Education Students’ Digital Competence in the Area of Communication: The Influence of Online Communication and the Use of Social Networks

The global public health crisis unleashed by the COVID-19 pandemic has made it clear that digital competence in education is no longer an option, but a necessity. Online communication with friends using social networks is an activity in which young people very frequently and at increasingly early ages engage. This article presents the results of a study analyses digital-competence levels in the area of communication of Spanish basic-education students (aged 12–16) and establishes whether online communication with friends and the use of social media impact it. A quantitative methodology with a descriptive and cross-sectional design was used. The sample comprised 807 students, and data collection was based on a problem-solving test. Findings show that students who use online communication with their friends very often and that those who use social networks a lot have lower levels of digital competence. Education centres should reflect on this in order to implement curricular programs that may strengthen this type of competence.
Taking it too far

Abstract While teasing can cause offence, participants on television variety or game shows are generally expected to tolerate it. In this paper, we examine comments posted on YouTube in response to reports of a leaked recording of a television host in Taiwan swearing at and insulting a guest who teased the host about his “inability to take a defeat”. In so doing, we examine both the perceived limits of teasing (i.e. what is considered allowable and what goes too far), and the perceived limits of taking offence in response to teasing (i.e. what ways of indicating offence are considered allowable and what goes too far). We conclude that instances where there are disputes about whether taking offence is warranted by the teasing in question provides us with a useful lens to examine the role ideological discourses play in (re-)constituting the underlying moral fabric of social interaction.