Reviewerly/exHarmony huggingface dataset:
Domain Adaptation with Dynamic Open-Set Targets

Open-set domain adaptation aims to improve the generalization performance of a learning algorithm on a target task of interest by leveraging the label information from a relevant source task with only a subset of classes. However, most existing works are designed for the static setting, and can be hardly extended to the dynamic setting commonly seen in many real-world applications. In this paper, we focus on the more realistic open-set domain adaptation setting with a static source task and a time evolving target task where novel unknown target classes appear over time. Specifically, we show that the classification error of the new target task can be tightly bounded in terms of positive-unlabeled classification errors for historical tasks and open-set domain discrepancy across tasks. By empirically minimizing the upper bound of the target error, we propose a novel positive-unlabeled learning based algorithm named OuterAdapter for dynamic open-set domain adaptation with time evolving unknown classes. Extensive experiments on various data sets demonstrate the effectiveness and efficiency of our proposed OuterAdapter algorithm over state-of-the-art domain adaptation baselines.
Extracting Relevant Information from User's Utterances in Conversational Search and Recommendation

Conversational search and recommendation systems can ask clarifying questions through the conversation and collect valuable information from users. However, an important question remains: how can we extract relevant information from the user's utterances and use it in the retrieval or recommendation in the next turn of the conversation? Utilizing relevant information from users' utterances leads the system to better results at the end of the conversation. In this paper, we propose a model based on reinforcement learning, namely RelInCo, which takes the user's utterances and the context of the conversation and classifies each word in the user's utterances as belonging to the relevant or non-relevant class. RelInCo uses two Actors: 1) Arrangement-Actor, which finds the most relevant order of words in user's utterances, and 2) Selector-Actor, which determines which words, in the order provided by the arrangement Actor, can bring the system closer to the target of the conversation. In this way, we can find relevant information in the user's utterance and use it in the conversation. The objective function in our model is designed in such a way that it can maximize any desired retrieval and recommendation metrics (i.e., the ultimate
Type Linking for Query Understanding and Semantic Search

Huawei is currently undertaking an effort to build map and web search services using query understanding and semantic search techniques. We present our efforts to built a low-latency type mention detection and linking service for map search. In addition to latency challenges, we only had access to low quality and biased training data plus we had to support 13 languages. Consequently, our service is based mostly on unsupervised term- and vector-based methods. Nevertheless, we trained a Transformer-based query tagger which we integrated with the rest of the pipeline using a reward and penalisation approach. We present techniques that we designed in order to address challenges with the type dictionary, incompatibilities in scoring between the term-based and vector-based methods as well as over-segmentation issues in Thai, Chinese, and Japanese. We have evaluated our approach on the Huawei map search use case as well as on community Question Answering benchmarks.
HyperLogLogLog

We present HyperLogLogLog, a practical compression of the HyperLogLog sketch that compresses the sketch from $O(młogłog n)$ bits down to $m łog_2łog_2łog_2 m + O(m+łogłog n)$ bits for estimating the number of distinct elements~n using m~registers. The algorithm works as a drop-in replacement that preserves all estimation properties of the HyperLogLog sketch, it is possible to convert back and forth between the compressed and uncompressed representations, and the compressed sketch maintains mergeability in the compressed domain. The compressed sketch can be updated in amortized constant time, assuming n is sufficiently larger than m. We provide a C++ implementation of the sketch, and show by experimental evaluation against well-known implementations by Google and Apache that our implementation provides small sketches while maintaining competitive update and merge times. Concretely, we observed approximately a 40% reduction in the sketch size. Furthermore, we obtain as a corollary a theoretical algorithm that compresses the sketch down to $młog_2łog_2łog_2łog_2 m+O(młogłogłog m/łogłog m+łogłog n)$ bits.
On-Device Learning for Model Personalization with Large-Scale Cloud-Coordinated Domain Adaption

Cloud-based learning is currently the mainstream in both academia and industry. However, the global data distribution, as a mixture of all the users' data distributions, for training a global model may deviate from each user's local distribution for inference, making the global model non-optimal for each individual user. To mitigate distribution discrepancy, on-device training over local data for model personalization is a potential solution, but suffers from serious overfitting. In this work, we propose a new device-cloud collaborative learning framework under the paradigm of domain adaption, called MPDA, to break the dilemmas of purely cloud-based learning and on-device training. From the perspective of a certain user, the general idea of MPDA is to retrieve some similar data from the cloud's global pool, which functions as large-scale source domains, to augment the user's local data as the target domain. The key principle of choosing which outside data depends on whether the model trained over these data can generalize well over the local data. We theoretically analyze that MPDA can reduce distribution discrepancy and overfitting risk. We also extensively evaluate over the public MovieLens 20M and Amazon Electronics datasets, as well as an industrial dataset collected from Mobile Taobao over a period of 30 days. We finally build a device-tunnel-cloud system pipeline, deploy MPDA in the icon area of Mobile Taobao for click-through rate prediction, and conduct online A/B testing. Both offline and online results demonstrate that MPDA outperforms the baselines of cloud-based learning and on-device training only over local data, from multiple offline and online metrics.
Importance Prioritized Policy Distillation

Policy distillation (PD) has been widely studied in deep reinforcement learning (RL), while existing PD approaches assume that the demonstration data (i.e., state-action pairs in frames) in a decision making sequence is uniformly distributed. This may bring in unwanted bias since RL is a reward maximizing process instead of simple label matching. Given such an issue, we denote the frame importance as its contribution to the expected reward on a particular frame, and hypothesize that adapting such frame importance could benefit the performance of the distilled student policy. To verify our hypothesis, we analyze why and how frame importance matters in RL settings. Based on the analysis, we propose an importance prioritized PD framework that highlights the training on important frames, so as to learn efficiently. Particularly, the frame importance is measured by the reciprocal of weighted Shannon entropy from a teacher policy's action prescriptions. Experiments on Atari games and policy compression tasks show that capturing the frame importance significantly boosts the performance of the distilled policies.
Core-periphery Partitioning and Quantum Annealing

We propose a new kernel that quantifies success for the task of computing a core-periphery partition for an undirected network. Finding the associated optimal partitioning may be expressed in the form of a quadratic unconstrained binary optimization (QUBO) problem, to which a state-of-the-art quantum annealer may be applied. We therefore make use of the new objective function to (a) judge the performance of a quantum annealer, and (b) compare this approach with existing heuristic core-periphery partitioning methods. The quantum annealing is performed on a commercially available D-Wave machine. The QUBO problem involves a full matrix even when the underlying network is sparse. Hence, we develop and test a sparsified version of the original QUBO which increases the available problem dimension for the quantum annealer. Results are provided on both synthetic and real data sets, and we conclude that the QUBO/quantum annealing approach offers benefits in terms of optimizing this new quantity of interest.
MetaV

Protecting the intellectual property (IP) of deep neural networks (DNN) becomes an urgent concern for IT corporations. For model piracy forensics, previous model fingerprinting schemes are commonly based on adversarial examples constructed for the owner's model as the fingerprint, and verify whether a suspect model is indeed pirated from the original model by matching the behavioral pattern on the fingerprint examples between one another. However, these methods heavily rely on the characteristics of classification tasks which inhibits their application to more general scenarios. To address this issue, we present MetaV, the first task-agnostic model fingerprinting framework which enables fingerprinting on a much wider range of DNNs independent from the downstream learning task, and exhibits strong robustness against a variety of ownership obfuscation techniques. Specifically, we generalize previous schemes into two critical design components in MetaV: the adaptive fingerprint and the meta-verifier, which are jointly optimized such that the meta-verifier learns to determine whether a suspect model is stolen based on the concatenated outputs of the suspect model on the adaptive fingerprint. As a key of being task-agnostic, the full process makes no assumption on the model internals in the ensemble only if they have the same input and output dimensions. Spanning classification, regression and generative modeling, extensive experimental results validate the substantially improved performance of MetaV over the state-of-the-art fingerprinting schemes and demonstrate the enhanced generality of MetaV for providing task-agnostic fingerprinting. For example, on fingerprinting ResNet-18 trained for skin cancer diagnosis, MetaV achieves simultaneously 100% true positives and 100% true negatives on a diverse test set of 70 suspect models, achieving an about 220% relative improvement in ARUC over the optimal baseline.
Open-Domain Aspect-Opinion Co-Mining with Double-Layer Span Extraction

The aspect-opinion extraction tasks extract aspect terms and opinion terms from reviews. The supervised extraction methods achieve state-of-the-art performance but require large-scale human-annotated training data. Thus, they are restricted for open-domain tasks due to the lack of training data. This work addresses this challenge and simultaneously mines aspect terms, opinion terms, and their correspondence in a joint model. We propose an Open-Domain Aspect-Opinion Co-Mining (ODAO) method with a Double-Layer span extraction framework. Instead of acquiring human annotations, ODAO first generates weak labels for unannotated corpus by employing rules-based on universal dependency parsing. Then, ODAO utilizes this weak supervision to train a double-layer span extraction framework to extract aspect terms (ATE), opinion terms (OTE), and aspect-opinion pairs (AOPE). ODAO applies canonical correlation analysis as an early stopping indicator to avoid the model over-fitting to the noise to tackle the noisy weak supervision. ODAO applies a self-training process to gradually enrich the training data to tackle the weak supervision bias issue. We conduct extensive experiments and demonstrate the power of the proposed ODAO. The results on four benchmark datasets for aspect-opinion co-extraction and pair extraction tasks show that ODAO can achieve competitive or even better performance compared with the state-of-the-art fully supervised methods.
RCAD: Real-time Collaborative Anomaly Detection System for Mobile Broadband Networks

The rapid increase in mobile data traffic and the number of connected devices and applications in networks is putting a significant pressure on the current network management approaches that heavily rely on human operators. Consequently, an automated network management system that can efficiently predict and detect anomalies is needed. In this paper, we propose, RCAD, a novel distributed architecture for detecting anomalies in network data forwarding latency in an unsupervised fashion. RCAD employs the hierarchical temporal memory (HTM) algorithm for the online detection of anomalies. It also involves a collaborative distributed learning module that facilitates knowledge sharing across the system. We implement and evaluate RCAD on real world measurements from a commercial mobile network. RCAD achieves over 0.7 F-1 score significantly outperforming current state-of-the-art methods.
Clustering with Fair-Center Representation

We study a variant of classical clustering formulations in the context of algorithmic fairness, known as diversity-aware clustering. In this variant we are given a collection of facility subsets, and a solution must contain at least a specified number of facilities from each subset while simultaneously minimizing the clustering objective (k-median or k-means). We investigate the fixed-parameter tractability of these problems and show several negative hardness and inapproximability results, even when we afford exponential running time with respect to some parameters.
Small-layered Feed-Forward and Convolutional neural networks for efficient P wave earthquake detection

The number and efficiency of seismic networks have steadily increase over time delivering large datasets to be analyzed for earthquake occurrence. Automatic tools for accurate earthquake detection are under emerging and intense development. This paper first proposes a new windowing procedure of seismic traces that highly facilitates earthquake detection. This procedure applies regular trace filtering and normalization, but also performs a strict window alignment to P wave onset. These event-aligned windows represent the input data to our P wave detection networks, with relatively small or moderate number of layers. We then develop Feed-Forward (FFNN) and Convolutional (CNN) neural networks and explore multiple architecture configurations to find relevant hyperparameter patterns for better detection. To assess network performance, we adopt the widely used metrics of accuracy (ACC) and the area under the curve (AUC) of the Receiver Operating Characteristic function. In terms of ACC, the best FFNN and CNN reach performances of 91% and 98%, respectively. On the other hand, the best FFNN and CNN in terms of AUC achieve performances of 96% and 99%, respectively. Thus, our novel trace windowing procedure allows developing networks with few hyperparameters, for correct earthquake detection under low computational costs. Finally, we use the CNN with best AUC performance as an effective trace filtering with the purpose of P wave arrival time estimation.
Decentralized trade finance using blockchain and lightning network

Abstract A decentralized trade finance system is urgently needed, as smart contracts and blockchain can make the entire process fast, secure, and efficient while also allowing ordinary people to participate in the global economy. Decentralization will increase trust and transparency, as well as allow smaller businesses to participate in the industry, resulting in more jobs and a boost to the economy. We have also taken a decentralized approach to solving this massive problem with our project. We propose a decentralized trade finance model that is owned by the general public, such as you and me. On the one hand, a community of people like you and me give liquidity to the platform's liquidity pool via a lighting network, while on the other hand, importers and exporters can create profiles and engage in trade finance via the decentralized network. The exporter will use the platform validator to validate their invoice and then apply for trade finance. If everything goes well, the exporter will get monies from the liquidity pool, and the platform will be compensated by the importer after the trade is completed. Everyone involved in the process will receive trade tokens when each successful deal is completed, incentivizing the participants' good behavior.
A Federated Learning-Based Blockchain-Assisted Anomaly Detection Scheme to Prevent Road Accidents in Internet of Vehicles

In the modern era, the internet of vehicles (IoV) is being utilized in commercial applications and extensively explored in research. However, internal fault in IoV can cause accidents on the road. Moreover, privacy concerns can hamper the internal data sharing to build a model to detect the anomaly. Federated learning (FL) and blockchain are emerging technologies that can assist in mitigating these challenges. FL-based anomaly detection is introduced to prevent road accidents with the help of blockchain. An environment is built to conduct experiments to prove the feasibility of the proposed scheme. The performance analysis demonstrates that our presented scheme outperforms the traditional scheme while having privacy concerns.
Application of Abstractive Summarization in Multiple Choice Question Generation

This research aims to provide a solution to automate multiple choice question generation using natural language processing supported by abstractive summarization. The text or chapter imported by the user is transformed with the help of PEGASUS for Abstractive Summarization developed by Google AI in 2020 enabling us to get important in a paraphrased manner. These sentences will be used as questions. The key-value or word which will be the answer and will be removed from the sentence is determined with the help of KeyBERT which uses Bidirectional Encoder Representations from Transformers (BERT) embeddings and was developed by Maarten Grootendorst. KeyBERT considers the semantics of a word while performing keyword extraction. To generate the incorrect options pertaining to the respective question, we use sense2vec which was trained on Reddit comments and returns distractors (word similar to our keyword/answer). We generate questions along with the options based on the text received from the user at the end of the process.
Crafting Text Adversarial Examples to Attack the Deep-Learning-based Malicious URL Detection

Detecting malicious URLs is of great significance to reduce cyber crimes and maintain Internet security. Currently, Deep Learning (DL) techniques have been widely used to improve the classical malicious URL detection models, as DL-based detection models can perform an in-depth analysis of the text information of the URL, and detect the fishing URLs of unknown cyber attack types with high accuracy. Any missed blocking of malicious URLs can potentially result in a huge loss of information and property. In this paper, we focus on the vulnerability of the existing DL-based malicious URL detection models and show that they are sensitive to adversarial samples. First, we construct URL adversarial samples based on the component-level and character-level perturbations and use them to attack mainstream DL-based detection models, resulting in obvious decreases in the detection accuracies. Meanwhile, the perturbations are under the constraints that each adversarial sample URL is hardly distinguished from the original URL with naked eyes. Furthermore, under most circumstances, the adversarial samples constructed by replacing 14 types of characters and perturbing other all components except the scheme component lead to the largest increased number of missed blocking of malicious URLs, i.e., a bigger drop in the accuracy than other constructed methods. Finally, extensive experiments demonstrate the effectiveness of our adversarial examples. Even if the adversarial training is used against our adversarial samples, the adversarial samples still work and bring oblivious decreases in their accuracy.
Application of Higher Education Management in Colleges and Universities by Deep Learning

The development of artificial intelligence (AI) has brought great convenience to people and has been widely used in the field of education. To monitor the classroom status of college students in real time and achieve the purpose of balanced distribution of educational resources, the facial expression recognition (FER) algorithm is applied to the management of higher education in universities. Firstly, the convolutional neural network (CNN) is studied in depth, and secondly, the process and method of FER are explored in detail, and an adaptive FER algorithm based on the differential convolutional neural network (DCNN) is constructed. Finally, the algorithm is applied to the CK + database and the BU-4DFE database. The results manifest that the designed algorithm has an accuracy of 99.02% for keyframe detection in the CK + database and 98.35% for the BU-4DFE database. The algorithm has a high accuracy of keyframe detection for both expression databases. It has a good effect on the automatic detection of keyframes of expression sequences and can reach a level similar to that of manual frame selection. Compared with the existing algorithms, the proposed method still has higher advantages. It can effectively eliminate the interference of individual differences and environmental noise on FER. Experiments reveal that the proposed FER algorithm DCNN-based has a good recognition effect and is suitable for monitoring students' classroom status. This research has certain reference significance for the application of AI in higher education management in colleges and universities.
Application of Artificial Intelligence within Virtual Reality for Production of Digital Media Art

As technology changes, virtual reality generates realistic images through computer graphics and provides users with an immersive experience through various interactive means. In the context of digitalization, the application of VR for digital media art creation becomes a normalized method. Today's digital media art creation is closely related to vigorous technological innovation behind it, so the influence of modern technology is inevitable. Virtual reality and artificial intelligence have gradually become the main technical means in line with the development aim for digital media art creation. This work proposes an art object detection method AODNET in virtual reality digital media art creation with AI. Aiming at the particularity of object detection in this direction, an art object detection strategy based on residual network and clustering idea is proposed. First of all, it uses ResNet50 as backbone, which deepens network depth and improves the model feature extraction ability. Second, it uses the K-means++ algorithm to perform clustering statistics on the size of the real annotated boxes in the dataset to obtain appropriate hyperparameters for preset candidate boxes, which enhances the tolerance of the algorithm to the target size. Third, it replaces the ROI pooling algorithm with ROI align to eliminate the error caused by the quantization operation on the characteristics of the candidate region. Fourth, to reduce the missed detection rate of overlapping targets, soft-NMS algorithm is used instead of the NMS algorithm to post-process the candidate boxes. Finally, this work conducts extensive experiments to verify the superiority of AODNET for object detection in virtual reality digital media art creation.