Reviewerly/exHarmony huggingface dataset:
Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation

Real-world integrated personalized recommendation systems usually deal with millions of heterogeneous items. It is extremely challenging to conduct full corpus retrieval with complicated models due to the tremendous computation costs. Hence, most large-scale recommendation systems consist of two modules: a multi-channel matching module to efficiently retrieve a small subset of candidates, and a ranking module for precise personalized recommendation. However, multi-channel matching usually suffers from cold-start problems when adding new channels or new data sources. To solve this issue, we propose a novel Internal and contextual attention network (ICAN), which highlights channel-specific contextual information and feature field interactions between multiple channels. In experiments, we conduct both offline and online evaluations with case studies on a real-world integrated recommendation system. The significant improvements confirm the effectiveness and robustness of ICAN, especially for cold-start channels. Currently, ICAN has been deployed on WeChat Top Stories used by millions of users. The source code can be obtained from https://github.com/zhijieqiu/ICAN.
How does BERT’s attention change when you fine-tune? An analysis methodology and a case study in negation scope

Large pretrained language models like BERT, after fine-tuning to a downstream task, have achieved high performance on a variety of NLP problems.Yet explaining their decisions is difficult despite recent work probing their internal representations.We propose a procedure and analysis methods that take a hypothesis of how a transformer-based model might encode a linguistic phenomenon, and test the validity of that hypothesis based on a comparison between knowledge-related downstream tasks with downstream control tasks, and measurement of cross-dataset consistency.We apply this methodology to test BERT and RoBERTa on a hypothesis that some attention heads will consistently attend from a word in negation scope to the negation cue.We find that after fine-tuning BERT and RoBERTa on a negation scope task, the average attention head improves its sensitivity to negation and its attention consistency across negation datasets compared to the pre-trained models.However, only the base models (not the large models) improve compared to a control task, indicating there is evidence for a shallow encoding of negation only in the base models.
RETRACTED: Hybrid blockchain–based privacy-preserving electronic medical records sharing scheme across medical information control system

With the development of big data and medical information control system, electronic medical records sharing across organizations for better medical treatment and advancement has attracted much attention both from academic and industrial areas. However, the source of big data, personal privacy concern, inherent trust issues across organizations and complicated regulation hinder the great progress of healthcare intelligence. Blockchain, as a novel technique, has been used widely to resolve the privacy and security issues in electronic medical records sharing process. In this paper, we propose a hybrid blockchain–based electronic medical records sharing scheme to address the privacy and trust issues across the medical information control systems, rendering the electronic medical records sharing process secure, effective, relatively transparent, immutable, traceable and auditable. Considering the above confidential issues, we use different sharing methods for different parts of medical big data. We share privacy-sensitive couples on the consortium blockchain, while sharing the non-sensitive parts on the public blockchain. In this way, authorized medical information control systems within the consortium can access the data on it for precise medical diagnosis. Institutions such as universities and research institutes can get access to the non-sensitive parts of medical big data for scientific research on symptoms to evolve medical technologies. A working prototype is implemented to demonstrate how the hybrid blockchain facilitates the pharmaceutical operations in a healthcare information control ecosystem. A blockchain benchmark tool Hyperledger Caliper is used to evaluate the performance of hybrid blockchain–based electronic medical records sharing scheme on throughput and average latency which proves to be practicable and excellent.
Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation

The masked language model has received remarkable attention due to its effectiveness on various natural language processing tasks. However, few works have adopted this technique in the sequence-to-sequence models. In this work, we introduce a jointly masked sequence-to-sequence model and explore its application on non-autoregressive neural machine translation~(NAT). Specifically, we first empirically study the functionalities of the encoder and the decoder in NAT models, and find that the encoder takes a more important role than the decoder regarding the translation quality. Therefore, we propose to train the encoder more rigorously by masking the encoder input while training. As for the decoder, we propose to train it based on the consecutive masking of the decoder input with an n-gram loss function to alleviate the problem of translating duplicate words. The two types of masks are applied to the model jointly at the training stage. We conduct experiments on five benchmark machine translation tasks, and our model can achieve 27.69/32.24 BLEU scores on WMT14 English-German/German-English tasks with 5+ times speed up compared with an autoregressive model.
A Non-PCP Approach to Succinct Quantum-Safe Zero-Knowledge

Today’s most compact zero-knowledge arguments are based on the hardness of the discrete logarithm problem and related classical assumptions. If one is interested in quantum-safe solutions, then all of the known techniques stem from the PCP-based framework of Kilian (STOC 92) which can be instantiated based on the hardness of any collision-resistant hash function. Both approaches produce asymptotically logarithmic sized arguments but, by exploiting extra algebraic structure, the discrete logarithm arguments are a few orders of magnitude more compact in practice than the generic constructions. In this work, we present the first (poly)-logarithmic, potentially post-quantum zero-knowledge arguments that deviate from the PCP approach. At the core of succinct zero-knowledge proofs are succinct commitment schemes (in which the commitment and the opening proof are sub-linear in the message size), and we propose two such constructions based on the hardness of the (Ring)-Short Integer Solution (Ring-SIS) problem, each having certain trade-offs. For commitments to N secret values, the communication complexity of our first scheme is $$\tilde{O}(N^{1/c})$$ for any positive integer c, and $$O(\log ^2 N)$$ for the second. Both of these are a significant theoretical improvement over the previously best lattice construction by Bootle et al. (CRYPTO 2018) which gave $$O(\sqrt{N})$$ -sized proofs.
A graph embedding based model for fine-grained POI recommendation

Point-of-interest (POI) recommendation is an important technique widely used in self-driving services. While POI recommendation aims to recommend unvisited POIs to self-driving users, users always expect their intended items can be suggested together with these POIs, e.g. what activities to perform at the recommended places. However, existing methods cannot well support such POI recommendation in a finer granularity. In this paper, we investigate this new problem and propose a novel POI-based item recommendation model via graph embedding. The model accurately captures the joint effect of geographical and temporal influences on both POI-level and item-level recommendation in a shared space, which can address data sparsity and cold start problems effectively. To optimize the model efficiently and accurately, a novel weighted negative sampling strategy is designed. Besides, we propose a novel fine-grained user dynamic preference modeling method, which can accurately capture dynamic user preferences in a finer granularity based on the embeddings of both POIs and items. Comprehensive experimental studies have been conducted on three datasets. Results show that our model achieves significant improvement over state-of-the-art baselines.
IAUnet: Global Context-Aware Feature Learning for Person Reidentification

Person reidentification (reID) by convolutional neural network (CNN)-based networks has achieved favorable performance in recent years. However, most of existing CNN-based methods do not take full advantage of spatial-temporal context modeling. In fact, the global spatial-temporal context can greatly clarify local distractions to enhance the target feature representation. To comprehensively leverage the spatial-temporal context information, in this work, we present a novel block, interaction-aggregation-update (IAU), for high-performance person reID. First, the spatial-temporal IAU (STIAU) module is introduced. STIAU jointly incorporates two types of contextual interactions into a CNN framework for target feature learning. Here, the spatial interactions learn to compute the contextual dependencies between different body parts of a single frame, while the temporal interactions are used to capture the contextual dependencies between the same body parts across all frames. Furthermore, a channel IAU (CIAU) module is designed to model the semantic contextual interactions between channel features to enhance the feature representation, especially for small-scale visual cues and body parts. Therefore, the IAU block enables the feature to incorporate the globally spatial, temporal, and channel context. It is lightweight, end-to-end trainable, and can be easily plugged into existing CNNs to form IAUnet. The experiments show that IAUnet performs favorably against state of the art on both image and video reID tasks and achieves compelling results on a general object categorization task. The source code is available at https://github.com/blue-blue272/ImgReID-IAnet.
An Organized Repository of Ethereum Smart Contracts’ Source Codes and Metrics

Many empirical software engineering studies show that there is a need for repositories where source codes are acquired, filtered and classified. During the last few years, Ethereum block explorer services have emerged as a popular project to explore and search for Ethereum blockchain data such as transactions, addresses, tokens, smart contracts’ source codes, prices and other activities taking place on the Ethereum blockchain. Despite the availability of this kind of service, retrieving specific information useful to empirical software engineering studies, such as the study of smart contracts’ software metrics, might require many subtasks, such as searching for specific transactions in a block, parsing files in HTML format, and filtering the smart contracts to remove duplicated code or unused smart contracts. In this paper, we afford this problem by creating Smart Corpus, a corpus of smart contracts in an organized, reasoned and up-to-date repository where Solidity source code and other metadata about Ethereum smart contracts can easily and systematically be retrieved. We present Smart Corpus’s design and its initial implementation, and we show how the data set of smart contracts’ source codes in a variety of programming languages can be queried and processed to get useful information on smart contracts and their software metrics. Smart Corpus aims to create a smart-contract repository where smart-contract data (source code, application binary interface (ABI) and byte code) are freely and immediately available and are classified based on the main software metrics identified in the scientific literature. Smart contracts’ source codes have been validated by EtherScan, and each contract comes with its own associated software metrics as computed by the freely available software PASO. Moreover, Smart Corpus can be easily extended as the number of new smart contracts increases day by day.
A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK

Abstract Solar photovoltaic (PV) is an increasingly significant fraction of electricity generation. Efficient management, and innovations such as short-term forecasting and machine vision, demand high-resolution geographic datasets of PV installations. However, official and public sources have notable deficiencies: spatial imprecision, gaps in coverage and lack of crucial meta data, especially for small-scale solar panel installations. We present the results of a major crowd-sourcing campaign to create open geographic data for over 260,000 solar PV installations across the UK, covering an estimated 86% of the capacity in the country. We focus in particular on capturing small-scale domestic solar PV, which accounts for a significant fraction of generation but was until now very poorly documented. Our dataset suggests nameplate capacities in the UK (as of September 2020) amount to a total of 10.66 GW explicitly mapped, or 13.93 GW when missing capacities are inferred. Our method is applied to the UK but applicable worldwide, and compatible with continual updating to track the rapid growth in PV deployment.
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading

Document interpretation and dialog understanding are the two major challenges for conversational machine reading.In this work, we propose DISCERN, a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding for both document and dialog.Specifically, we split the document into clause-like elementary discourse units (EDU) using a pre-trained discourse segmentation model, and we train our model in a weakly-supervised manner to predict whether each EDU is entailed by the user feedback in a conversation.Based on the learned EDU and entailment representations, we either reply to the user our final decision "yes/no/irrelevant" of the initial question, or generate a follow-up question to inquiry more information.Our experiments on the ShARC benchmark (blind, held-out test set) show that DISCERN achieves state-ofthe-art results of 78.3% macro-averaged accuracy on decision making and 64.0 BLEU1 on follow-up question generation.
Pedagogy of the Oppressed

Critical and liberating dialogue, which presupposes action, must be carried on with the oppressed at whatever the stage of their struggle for liberation. The content of that dialogue can and should vary in accordance with historical conditions and the level at which the oppressed perceive reality. The insistence that the oppressed engage in reflection on their concrete situation is not a call to armchair revolution. Libertarian action must recognize this dependence as a weak point and must attempt through reflection and action to transform it into independence. A revolutionary leadership must accordingly practice co-intentional education. Teachers and students (leadership and people) co-intent on reality are both Subjects, not only in the task of unveiling that reality, and thereby coming to know it critically, but in the task of re-creating that knowledge.
Missing data filling method based on linear interpolation and lightgbm

Abstract In the context of the rapid development of integrated energy and the digital transformation of power grids, data is playing an increasingly important role in the safe operation of power grids. To deepen the value of data application and ensure the accuracy of data application, this paper proposes a data filling method that combines linear interpolation and LightGBM (Light Gradient Boosting Machine) in response to the missing phenomenon in the source network data collection process. The process can generally be divided into 2 steps: First, linear interpolation is exploited to process short-term missing data. Then the LightGBM can be used to process long-term missing data. In the process of using LightGBM, linear interpolation is used to interpolate the independent variables of the input model. Through the above process, the data for the missing ratio could be obtained, which can then be used to complete all data filling in order from high to low. Through actual data test, this method has better data filling performance.
A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers

During the last decades, the volume of multimedia content posted in social networks has grown exponentially and such information is immediately propagated and consumed by a significant number of users. In this scenario, the disruption of fake news providers and bot accounts for spreading propaganda information as well as sensitive content throughout the network has fostered applied research to automatically measure the reliability of social networks accounts via Artificial Intelligence (AI). In this paper, we present a multilingual approach for addressing the bot identification task in Twitter via Deep learning (DL) approaches to support end-users when checking the credibility of a certain Twitter account. To do so, several experiments were conducted using state-of-the-art Multilingual Language Models to generate an encoding of the text-based features of the user account that are later on concatenated with the rest of the metadata to build a potential input vector on top of a Dense Network denoted as Bot-DenseNet. Consequently, this paper assesses the language constraint from previous studies where the encoding of the user account only considered either the metadata information or the metadata information together with some basic semantic text features. Moreover, the Bot-DenseNet produces a low-dimensional representation of the user account which can be used for any application within the Information Retrieval (IR) framework.
Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding

The prevailing framework for solving referring expression grounding is based on a two-stage process: 1) detecting proposals with an object detector and 2) grounding the referent to one of the proposals. Existing two-stage solutions mostly focus on the grounding step, which aims to align the expressions with the proposals. In this paper, we argue that these methods overlook an obvious mismatch between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i.e., expression-agnostic), hoping that the proposals contain all right instances in the expression (i.e., expression-aware). Due to this mismatch, current two-stage methods suffer from a severe performance drop between detected and ground-truth proposals. To this end, we propose Ref-NMS, which is the first method to yield expression-aware proposals at the first stage. Ref-NMS regards all nouns in the expression as critical objects, and introduces a lightweight module to predict a score for aligning each box with a critical object. These scores can guide the NMS operation to filter out the boxes irrelevant to the expression, increasing the recall of critical objects, resulting in a significantly improved grounding performance. Since Ref- NMS is agnostic to the grounding step, it can be easily integrated into any state-of-the-art two-stage method. Extensive ablation studies on several backbones, benchmarks, and tasks consistently demonstrate the superiority of Ref-NMS. Codes are available at: https://github.com/ChopinSharp/ref-nms.
BinaryBERT: Pushing the Limit of BERT Quantization

The rapid development of large pre-trained language models has greatly increased the demand for model compression techniques, among which quantization is a popular solution.In this paper, we propose Binary-BERT, which pushes BERT quantization to the limit by weight binarization.We find that a binary BERT is hard to be trained directly than a ternary counterpart due to its complex and irregular loss landscape.Therefore, we propose ternary weight splitting, which initializes BinaryBERT by equivalently splitting from a half-sized ternary network.The binary model thus inherits the good performance of the ternary one, and can be further enhanced by fine-tuning the new architecture after splitting.Empirical results show that our Binary-BERT has only a slight performance drop compared with the full-precision model while being 24× smaller, achieving the state-of-the-art compression results on the GLUE and SQuAD benchmarks.
Design and implementation of an open-Source IoT and blockchain-based peer-to-peer energy trading platform using ESP32-S2, Node-Red and, MQTT protocol

An open-source P2P energy trading platform facilitates energy trading amongst the peers. The proposed system provides real time data acquisition, monitoring and control of self-generated energy at a remote location. The trading activities are done on a web interface that uses a private Ethereum blockchain. A smart contract is deployed on the Ethereum blockchain and the trading activities performed on the web interface are recorded on a tamper-proof blockchain network. An internet of things platform is used to monitor and control the self-generated energy. Energy data is collected and processed by means of ESP32-S2 microcontrollers using field instrumentation devices which are connected to the voltage source and load. An open-source decentralized Peer-to-Peer (P2P) energy trading system, designed on the blockchain and internet of things (IoT) architecture is proposed. The hardware setup includes a relay, a current sensor, a voltage sensor, a Wi-Fi router and ESP32-S2 microcontroller. For data transfer the Message Queuing Telemetry Transport (MQTT) protocol is used over a local network. ESP32-S2 is set up as MQTT client and Node-Red IoT server is used as MQTT broker. Hypertext Transfer Protocol (http) request method is implemented to connect the Node-Red server with the web interface developed using React.JS library. The system design, implementation, testing, and results are presented in this paper.
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection

Aspect terms extraction (ATE) and aspect sentiment classification (ASC) are two fundamental and fine-grained sub-tasks in aspect-level sentiment analysis (ALSA).In the textual analysis, jointly extracting both aspect terms and sentiment polarities has been drawn much attention due to the better applications than individual sub-task.However, in the multimodal scenario, the existing studies are limited to handle each sub-task independently, which fails to model the innate connection between the above two objectives and ignores the better applications.Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA).Specifically, we first build an auxiliary text-image relation detection module to control the proper exploitation of visual information.Second, we adopt the hierarchical framework to bridge the multi-modal connection between MATE and MASC, as well as separately visual guiding for each sub module.Finally, we can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects.Extensive experiments show the effectiveness of our approach against the joint textual approaches, pipeline and collapsed multi-modal approaches.