Reviewerly/exHarmony huggingface dataset:
Non-negative Matrix Decomposition-Based Power Package Recommendation Method

It is a common way for price comparison websites to recommend electricity sales packages based on users’ evaluation information. However, due to the wide variety of electricity sales packages and the limited number of users of electricity sales packages, the user's evaluation information on electricity sales packages will be missing, which will lead to a large deviation in the recommendation effect. In this paper, a non-negative matrix decomposition-based power package recommendation method is proposed. Firstly, the scoring data of original users on their used electricity sales packages are collected, and an original scoring matrix for the users can be obtained; Secondly, the non-negative matrix decomposition method is used to decompose the original scoring matrix into user feature matrix and package feature matrix; Thirdly, under the condition that the distance between the original matrix and the two decomposed matrices’ product is the minimum, the elements of the two matrices are iterated to obtain the users’ predictions on the packages; Then, the obtained prediction matrix is recommended in full order, which realizes the accurate filling of the original scoring matrix and helps the electricity sales company to achieve the accurate recommendation of the electricity sales package. Finally, numerical studies are conducted, and the results show that the non-negative matrix decomposition-based power package recommendation method has a high accuracy, and can provide technical support for electricity sales companies to recommend electricity sales packages.
An improved method for reasonable segmentation based on Post-DAE

The shape prior can be added to refine the segmentation edge when the shape of object is regular. Post-DAE based on denoising autoencoder has provided a post-processing method that uses shape prior. To make the segmentation result more consistent with the real label in the case of small objects, discontinuity in the same object and other adverse factors, this paper adds morphological processing on the basis of Post-DAE. Specifically, we first perform close operation and dilation to enlarge small objects, then input them into Post-DAE, finally we carry out erosion to restore the size of objects. We performed experiments in hole segmentation of renal endoscope images, and compared the prediction results using quantitative evaluation indicators. The mean Dice and mean IOU of the improved method with morphological processing were 0.8412 and 0.7678, which significantly improved the segmentation performance.
Research on Self-Learning and Self-Correcting Technology of Sampling Deviation of Substation Secondary Equipment

With the increasing demand for stable power supply in economic development, the reliability requirements of secondary equipment in substation are also increasingly strengthened. At present, the secondary equipment involved in the collection of primary equipment operation status mainly includes the merging unit and the measurement and control device, whose function is mainly to collect the voltage, current and position signals of the primary transformer. Substations may be built in areas with high temperature, low temperature, high humidity, high altitude and other harsh climate, especially the merging units, in some substations are installed in outdoor cabinets, the operating environment is harsh. Climate change, especially temperature change, often has a bad effect on the sampling accuracy, which leads to the wrong action of the follow-up protection device and threatens the safe operation of the substation. The technology proposed in this paper can complete the statistics of the influence data of environmental factors on the sampling accuracy through secondary equipment self-learning, and then form the accuracy deviation curve for different sampling channels through big data analysis and fitting. Finally, the accuracy deviation curve is applied to the substation secondary equipment in mass production. The sampling accuracy can be automatically corrected by sensing the changes of the environment in real time and comparing with the accuracy deviation curve during the field operation. This technology improves the sampling accuracy of the substation secondary equipment and enhances the stability of the substation operation.
Research on data anomaly discrimination method for multi-station fusion measurement system

Under the condition of multi station integration, the anomaly detection of data streams with different characteristics has high requirements for accuracy and real-time, and the traditional threshold detection method can not meet the application. Based on the data communication architecture of multi station fusion, a data flow anomaly detection method under the condition of multi station fusion based on improved density clustering algorithm and single class support vector machine algorithm is proposed. The density clustering algorithm is used to preprocess the normal samples, and then the single class support vector machine algorithm is used to establish the data flow detection model. Finally, a simulation example is used and compared with the traditional algorithm to verify the effectiveness of this method.
Research on Bird Nest Image Recognition and Detection Technology of Transmission Lines Based on Improved Faster-RCNN Algorithm

Under certain circumstances, the branches, iron wire, and firewood used by birds in the transmission line will reduce the insulation level of the line, cause the discharge of the wire and cause the trip of the line, which poses a serious threat to the safe and stable operation of the power grid. The traditional bird nest identification and detection algorithm has poor generalization ability, and classification detection accuracy is low. In order to improve the above problems, this paper uses the Inception-v3 feature extraction network to replace the original Faster-RCNN feature extraction network, improves some branches in the Inception-v3 module, and introduces a dual attention detection model between the essential network layers. The model will focus on more information channels to realize the lightweight improvement of the Faster-RCNN algorithm. The experimental results show that compared with the original feature extraction network VGG16, the improved Faster-RCNN algorithm mAP increases by 5.24%, and the recall rate increases by 4.61%. This method effectively improves the recognition rate and detection accuracy of transmission line bird nest detection.
A BiLSTM-Based Method for Detecting Time Series Data Anomalies in Power IoT Sensing Terminals

With the development of power IoT, all kinds of sensing devices in the sensing layer have increased, and a large amount of time-series data is collected every moment. However, the data will inevitably be abnormal due to the external environment or equipment, etc. To ensure that the anomalous data collected by the sensing terminal in the power IoT can be detected, a BiLSTM-based anomaly detection model for time series data of the sensing terminal in the power IoT is proposed. Firstly, the Bi-LSTM can capture bi-directional timing information to build a prediction model. Secondly, multiple thresholds are set up, and the predicted value and the data collected by the sensing terminal are calculated as residuals and then compared with multiple thresholds, and the majority result is taken to determine whether the data is abnormal or not, avoiding the misjudgment of a single threshold.
Breaking Goppa-based McEliece with hints

We consider the McEliece cryptosystem with a binary Goppa code C⊂F2n specified by an irreducible Goppa polynomial g(x)∈F2m[X] and Goppa points (α1,…,αn)∈F2mn. Since g(x) together with the αi's allow for efficient decoding, these parameters form McEliece secret keys. Such a Goppa code C is an (n−tm)-dimensional subspace of F2n, and therefore C has co-dimension tm. For typical McEliece instantiations we have tm≈n4. We show that given more than tm elements of the Goppa points allows to recover the Goppa polynomial g(x) and the remaining entries in polynomial time. Hence, in case tm≈n4, roughly a fourth of a McEliece secret key is sufficient to recover the full key efficiently. Let us give an illustrative numerical example. For ClassicMcEliece with (n,t,m)=(3488,64,12) on input 64⋅12+1=769 Goppa points, we recover the remaining 3488−769=2719 Goppa points in F212 and the degree-64 Goppa polynomial g(x)∈F212[x] in 60 secs. Our results also extend to the case of erroneous Goppa points, but in this case our algorithms are no longer polynomial time.
A controllable delegation scheme for the subscription to multimedia platforms

With the development of the Internet, various multimedia platforms have sprung up like mushrooms after rain. Through subscription, people can enjoy the rich resources provided by the multimedia platform. To attract more subscribers, the multimedia platform will give users the ability to delegate access rights. Because in this way, the subscriber can share resources with his/her family or friends. However, the delegation ability should be controllable. Otherwise, there will be a risk of delegation abuse and damage to the interests of the multimedia platform. Therefore, we propose a new Key-Policy Attribute-Based Encryption (KP-ABE) scheme supporting key delegation ability, traceability, and single-layer delegation, which can work as a controllable delegation scheme for the subscription to multimedia platforms. Firstly, KP-ABE can realize access control to resources and protect the security of resources through encryption. Secondly, the key delegation ability can meet the requirement of users to share their access rights. Lastly, traceability and single-layer delegation can limit the key delegation ability of users, so as to resist key delegation abuse. In addition, the proposed scheme supports large universe and tree access structures and is established on prime-order groups, which makes it scalable, expressive, and efficient.
Feature selection based on correlation label and B-R belief function (FSCLBF) in multi-label data

In many real-world problems, each sample has more than one label, which may also exist semantic correlations among labels. However, in multi-label datasets, there are many irrelevant features that impact the accuracy of multi-label learning. A solution to this problem is feature selection. Although many papers have presented new methods to select features, more effort needs to be put into improving their accuracy. This paper presents a novel feature selection method based on correlation labels using a belief function called FSCLBF. Our proposed method increases the accuracy of multi-label learning by considering the correlation labels and the data uncertainty. The proposed method’s first step is to select the essential features for each label using a belief function based on a rough set theory (B-R belief function). The next step is to identify equivalent labels based on overlapping their essential features. Then, the tag of samples is determined in the set of equivalent labels. In the final step, our proposed method selects the essential features for each equivalent label set, using the B-R belief function by considering the sample differentiation to label. The experimental results show the superiority of FSCLBF to state-of-the-art methods in terms of Hamming Loss, One error, and Accuracy measures.
Class-specific and self-learning local manifold structure for domain adaptation

Domain adaptation (DA) is a powerful technology that allows a classifier trained on a well-labeled source domain to be adapted to an unlabeled target domain with different distributions. Existing DA methods aim to enhance feature transferability by reducing distribution distance, and they often rely on preserving the global discriminative (GD) structure to boost feature discriminability. However, strict GD loss may degrade transferability, and poor discriminability may result from wrongly labeled samples. To address these issues, we propose a new approach called class-specific and self-learning local manifold structure (CSSL-LM), to extract more desirable features for better DA effects. Specifically, it draws two data points close with large weight if they are from the same class and close in the original feature space. This approach is more relaxed than GD and thus mitigates the negative effect on transferability. Moreover, CSSL-LM is more robust to wrongly labeled samples than GD since two data points that are wrongly labeled as the same classes have small weight and are not required to be close. Inspired by previous adaptive local manifold learning, we utilize a self-learning mechanism to model CSSL-LM more accurately and reliably, particularly for feature-corrupted samples. Extensive experiments on four DA benchmarks verify that CSSL-LM outperforms some state-of-the-art methods. We also construct DA datasets that are randomly wrongly labeled or feature-corrupted to further evaluate CSSL-LM's robustness.
Assessor-guided learning for continual environments

This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pace of the learning process thus allowing an efficient learning of new environments while protecting against the catastrophic interference problem. The assessor is trained in a meta-learning manner with a meta-objective to boost the learning process of the base learner. It performs a soft-weighting mechanism of every sample accepting positive samples while rejecting negative samples. The training objective of a base learner is to minimize a meta-weighted combination of the cross entropy loss function, the dark experience replay (DER) loss function and the knowledge distillation loss function whose interactions are controlled in such a way to attain an improved performance. A compensated over-sampling (COS) strategy is developed to overcome the class imbalanced problem of the episodic memory due to limited memory budgets. Our approach, Assessor-Guided Learning Approach (AGLA), has been evaluated in the class-incremental and task-incremental learning problems. AGLA achieves improved performances compared to its competitors while the theoretical analysis of the COS strategy is offered. Source codes of AGLA, baseline algorithms and experimental logs are shared publicly in https://github.com/anwarmaxsum/AGLA for further study.
Beyond model splitting: Preventing label inference attacks in vertical federated learning with dispersed training

Federated learning is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data. As an important variant, vertical federated learning (VFL) deals with cases in which collaborating organizations own data of the same set of users but with disjoint features. It is generally regarded that VFL is more secure than horizontal federated learning. However, recent research (USENIX Security’22) reveals that it is still possible to conduct label inference attacks in VFL, in which attacker can acquire privately owned labels of other participants; even VFL constructed with model splitting (the kind of VFL architecture with higher security guarantee) cannot escape it. To solve this issue, in this paper, we propose the dispersed training framework. It utilizes secret sharing to break the correlations between the bottom model and the training data. Accordingly, even if the attacker receives the gradients in the training phase, he is incapable to deduce the feature representation of labels from the bottom model. Besides, we design a customized model aggregation method such that the shared model can be privately combined, and the linearity of secret sharing schemes ensures the training accuracy to be preserved. Theoretical and experimental analyses indicate the satisfactory performance and effectiveness of our framework.
Time-constrained learning

Consider a scenario in which we have a huge labeled dataset D and a limited time to train a given learner using D. Since we may not be able to use the whole dataset, how should we proceed? We propose TCT, an algorithm for this task, whose design relies on principles from Machine Teaching. We present an experimental study involving 5 different learners and 20 datasets where we show that TCT consistently outperforms alternative teaching/training methods, namely: (1) Training over batches of random samples, until the time limit is reached; (2) The state-of-the-art Machine Teaching algorithm for black-box learners proposed in [Dasgupta et al., ICML 19], and (3) Stochastic Gradient Descent (when applicable). While our work is primarily practical, we also show that a stripped-down version of TCT has provable guarantees.
Standard English and the Distortion Introduced by Social Media Short Messages

This study assesses the negative effect of social media text messaging on the Standard Written English of Nigerian undergraduates. Data is elicited from the written English of undergraduates from the three most popularly used social media websites in Nigeria; namely, Facebook, Whatsapp and Instagram. The data is analyzed using a simple percentage. The results are presented in tabular form. The study reveals that the use of social media text messaging prevalent on social media platforms has impacted negatively, the Standard of written English of Nigerian university undergraduates. The study further observes that the major areas of deviation found in the written English language of the undergraduates include: wrong punctuation, wrong spelling, breaking of concord/agreement rules, code-mixing and code-switching, etc. The study shows that to a large extent, the use of text messages has affected adherence to Standard written English of undergraduates in Nigeria. The study, therefore, recommends that urgent pragmatic measures are needed to curb the problem. Specifically, the study recommends the following measures as checks: to awaken the consciousness of the undergraduates to the importance of the use of the Standard English language in their communication; to urge the media to use its functions as a watchdog of society to sensitize the public and condemn the profuse use of social media text at the expense of the use of Standard English and to enlighten undergraduates to use Standard English in all forms of their formal communication to uphold the continued development of Standard English usage in the universities and society at large.
First-detection-time statistics in many-body quantum transport

We study the transport of many partially distinguishable and possibly interacting particles under the action of repeated projective measurements on a target space and investigate how the particles' interference affects the mean first detection time. We contrast the detection of exactly $n$ versus at least $n$ particles, explain divergences in the mean first detection time through spectral properties of the generating evolution operator, and illustrate our findings by an example.
Evidential value of voice quality acoustics in forensic voice comparison

Voice recordings in forensic voice comparison casework typically involve speech style mismatch and are separated by days or weeks, but studies that aim to empirically validate the evidential value of speech features rarely include systematic comparisons on contemporaneous vs. non-contemporaneous recordings and match vs. mismatch in speech style. This study addresses this gap and focuses on the acoustics of laryngeal voice quality, since voice quality has been reported to be one of the most popular and useful features for forensic voice comparison. 75 male speakers aged 18–45 were selected from a forensically-oriented database of Australian English speakers in Sydney/New South Wales. The evidential strength of a number of spectral tilt and additive noise parameters were tested under the Bayesian likelihood-ratio framework. Results show that system performance using these parameters as input were stable across 50 replications. When speech style is controlled for, VQ parameters yielded promising results and better system validity was achieved when using more VQ parameters. However, they offered limited speaker-discriminatory value when speech style mismatch is involved, and non-contemporaneous recordings only led to a small decline in performance. Overall, forensic practitioners should be cautious when using spectral tilt measures and additive noise measures as speaker discriminants in forensic casework.
Research on the Application of Neural Network Classification Model in English Grammar Error Correction

The field of English grammar error correction has developed rapidly in recent years, and many excellent research results have been obtained. However, errors are generally viewed as a whole for revision, which cannot be subdivided to identify the specific error types of sentences, lacks interpretability, and requires a lot of manpower to label the data. To address this, the study uses the CoreNLP tool to output the input text as a basic form of each word, and encodes and decodes the linguistic text in the corpus using the CNN-based Seq2Seq model. And word embedding and left-right GRU operations are performed on left-right text and target words in turn, and the merged new vector is obtained using two attention mechanism operations. It is fed into the MLP classifier for classification and error correction. To further improve the model performance, two model optimizers, stochastic gradient descent (SGD) and Adam, are used to tune the parameters of the classification prediction model. The study thus constructs an English grammar classification and error correction model based on a neural network classification method. The experimental analysis shows that the average accuracy of the model constructed in the study is 96.83%, the average recall rate is 78.93%, and the average overall evaluation F0.5 is 92.67%, which can effectively and accurately classify and correct the grammatical errors that occur in the process of English language learning.
Personalized sampling graph collection with local differential privacy for link prediction

Link prediction (LP) is an attractive research problem on social network data. Yet, the link information may be leaked by the untrusted collector. As countermeasures, there are a few methods specially designed for link prediction under local differential privacy (LDP), which allow the third-party data collector collects user data for link prediction while protecting user connection privacy. In this paper, we propose a C ommunity-based graph collection with P ersonalized sampling R andomized R esponse (CPRR) which is a novel graph collection algorithm with LDP and reaches decent trade-off between sensitive link protection and link prediction performance. The proposed mechanism adopts a personalized sampling technique for each connection of a user and then utlizes the randomized response mechanism on the sampled subset. Based on the personalized sampling technique, we can reduce the injected noise in LDP. Meanwhile, considering the different edge distributions of the different regions on the original graph, we propose a community-based sampling strategy. Then, we prove that the proposed CPRR satisfies LDP. Through extensive experiments on several real-life graph datasets, we demonstrate that CPRR can achieve better results in balancing privacy protection and link prediction performance than the state-of-art baselines.