Reviewerly/exHarmony huggingface dataset:
LiVoAuth: Liveness Detection in Voiceprint Authentication With Random Challenges and Detection Modes

Voiceprint authentication provides great convenience to users in many application scenarios. However, it easily suffers from spoofing attacks including speech synthesis, speech conversion, and speech replay. Liveness detection is an effective way to resist these attacks. But existing methods suffer from many disadvantages, such as extra deployment costs due to precise data collection, environmental disturbance, high computational overhead, and operational complexity. A uniform platform that can offer voiceprint authentication as a service (VAaS) over the cloud is also lacked. Hence, it is imperative to design an economic and effective method for liveness detection in voiceprint authentication. In this article, we propose a novel liveness detection method named LiVoAuth for voiceprint authentication. It applies a randomly generated vector sequence as liveness detection mode (LDM), corresponding to a random challenge code used for authentication. We implement LiVoAuth and conduct a series of user studies to evaluate its performance in terms of accuracy, stability, efficiency, security, and user acceptance. Experimental results demonstrate its advantages compared with cutting-edge methods
The Effectiveness of Project-Based Learning to Students Ability in Writing Recount Text

Teaching English writing for non-native students of senior high schools is not easy. There are several things that must be considered. So that is why the students' writing skill becomes crucial. This study is aimed to know the effectiveness of Project-Based Learning to the students’ ability in writing recount text for the class 10 students of SMK Ma’arif NU Sunan Giri Mulung Driyorejo Gresik. The researcher used a quantitative research reasoning by a pre-experimental design. The population in this research is the class 10 students of the SMK Ma'arif NU Sunan Giri containing 126 students and the sample of this research is 36 students of that class. The instrument was a test that was in form of a pre-test and a post-test after given a treatment. Then data analysis used SPSS by the formula paired sample t-test. This study showed that the average obtained by the post-test was higher than the pre-test. This indicated that the treatment was successful. Paired T-test calculation researcher. T-test was calculated to determine the comparison of the two means between pre-test and post-test. The results were that the value of sig (2-tailed) is 0.001 at the critical value for the 5% level. This means that the significance value is less than 5% or (0.001 &lt;0.05). So, the null hypothesis can be rejected. It can be concluded that the writing the recount text by project-based learning method is effective. Therefore, it was found that 57% of the students underwent the grammar errors. 43% of the students suffered the mechanics errors. The organization of errors happened in 37% of the students’ writing work. 35% of the students faced the vocabulary errors in writing the recount text. So, the future researchers need to take these percentages of the errors into consideration in their research as the students’ difficulties in writing the recount text using project-based learning.
Pruning Filter via Gaussian Distribution Feature for Deep Neural Networks Acceleration

Deep learning has achieved impressive results in many areas, but the deployment of edge intelligent devices is still very slow. To solve this problem, we propose a novel compression and acceleration method based on data distribution characteristics for deep neural networks, namely Pruning Filter via Gaussian Distribution Feature (PFGDF). Compared with previous advanced pruning methods, PFGDF compresses the model by filters with insignificance in distribution, regardless of the contribution and sensitivity information of the convolution filter. PFGDF is significantly different from weight sparsification pruning because it does not require the special accelerated library to process the sparse weight matrix and introduces no more extra parameters. The pruning process of PFGDF is automated. Furthermore, the model compressed by PFGDF can restore the same performance as the uncompressed model. We evaluate PFGDF through extensive experiments, on CIFAR-10, PFGDF compresses the convolution filter on VGG-16 by 66.62% with more than 90% parameter reduced, while the inference time is accelerated by 83.73% on Huawei MATE 10.
Salsa: SGX Attestation for Live Streaming Applications

Intel SGX is a hardware-based security feature that allows executing software in enclaves that are strongly isolated from the operating system and applications. Even if an attacker gains full control over the system, it is not possible to inspect these enclaves. This makes SGX enclaves an adequate solution for storing and processing highly sensitive data, such as encryption keys. However, recent research demonstrates that enclaves are still highly vulnerable to standard software exploitation attacks. While SGX features static attestation, i.e., allowing validation of the integrity of the program code and data in the enclave, static attestation cannot cope with run-time attacks such as return-oriented programming. We present Salsa, the first solution to allow control-flow attestation of SGX enclaves. To show its applicability, we leverage Salsa to implement a video streaming service that uses an SGX enclave to decode the video stream. When a compromise of the SGX enclave is detected, the streaming of the video stops instantly. In the evaluation, we demonstrate that the performance of this setup is sufficiently efficient to attest a live video streaming service.
Research of Data Compression Using Huffman Coding and Arithmetic Coding

When transmitting and storing pictures or information, we hope that the picture information can occupy less space without losing the original information. Entropy coding is a lossless data compression coding that can encode according to the frequency of elements without losing information. Common entropy codes include Shannon coding, Huffman coding, arithmetic coding and so on. This paper will make a comparative analysis of two common coding methods, Huffman coding and arithmetic coding. Considering the correlation between coded symbol sequences, the probability value of the symbol sequence is used to replace the smaller probability value of a single symbol. The principle is applied to binary arithmetic coding to form a more effective method than traditional Huffman coding, which can shorten the average code length and make the amount of information of the code approach the entropy rate of the symbol, so as to significantly improve the data compression ratio of binary arithmetic coding experimental tests on different types of data show that the compression coding effect is good. The experimental results demonstrate the efficiency of the optimization method described in this paper.
Entity-level Attention Pooling and Information Gating for Document-level Relation Extraction

Document-level relation extraction intends to extract relation facts among different entity pairs in the entire document. Previously proposed methods make numerous efforts for this task, however, these researches neglect to treat the two entities in an entity pair as an organic unity for relation extraction, which leads to the lack of valuable information about the entity pairs and insufficient information interaction. To tackle the above problem, we propose a framework, named Entity-level Attention Pooling and Information Gating (EAPIG), for document-level relation extraction. Specifically, we first utilize an encoder module to capture the long-distance dependencies of entities in the document, and then we propose two modules: the Entity-level Attention Pooling module obtains the local information of entity pairs, and the Information Gating module introduces the global information of entity pairs and promotes sufficient interaction between the local information and the global information. Experimental results on the benchmark dataset DocRED show that our approach can efficiently capture and combine the abundant information from the entity pairs to achieve better performance than the previous baselines.
PRAG: Periodic Regularized Action Gradient for Efficient Continuous Control

For actor-critic methods in reinforcement learning, it is vital to learn a useful critic such that the actor can be guided efficiently and properly. Previous methods mainly seek to estimate more accurate Q-values. However, in continuous control scenario where the actor is updated via deterministic policy gradient, only the action gradient (AG) is useful for updating the actor. It is thus a promising way to achieve higher sample efficiency by leveraging the action gradient of Q functions for policy guidance. Nevertheless, we empirically find that directly incorporating action gradient into the critics downgrades the performance of the agent, as it can be easily trapped in the local maxima. To fully utilize the benefits of action gradient and escape from the local optima, we propose Periodic Regularized Action Gradient (PRAG), which periodically involves action gradient for critic learning and additionally maximizes the target value. On a set of MuJoCo continuous control tasks, we show that PRAG can achieve higher sample efficiency and better final performance without much extra training cost, comparing to common model-free baselines. Our code is available at: https://github.com/Galaxy-Li/PRAG .
Adversarial Audio Detection Method Based on Transformer

Speech recognition technology has been applied to all aspects of our daily life, but it faces many security issues. One of the major threats is the adversarial audio examples, which may tamper the recognition results of the acoustic speech recognition system (ASR). In this paper, we propose an adversarial detection framework to detect adversarial audio examples. The method is based on the transformer self-attention mechanism. Spectrogram features are extracted from the audio and divided into patches. Position information are embedded and then fed into transformer encoder. Experimental results show that the method achieves good performance with the detection accuracy of above 96.5% under the white-box attacks and blackbox attacks, and noisy circumstances. Even when detecting adversarial examples generated by the unknown attacks, it also achieves satisfactory results.
Physics-Guided Machine Learning Identifies 5 Optimum Test Locations to Predict Global PV Energy Yield for Arbitrary Farm Topologies

The photovoltaics (PV) technology landscape is evolving rapidly. To gauge the relative merit of emerging PV technologies and their scalable deployability, the global performance of these systems must be understood. Historically, most experimental and computational studies have focused on PV performance in specific regional climatic conditions; however, it has been difficult to translate these isolated regional studies to a global scale. Here, we present a physics-guided machine learning (PG-ML) scheme to demonstrate that: (a) analogous to Köppen–Geiger classification, the world can be divided into just a handful of PV-specific climate zones, and (b) the monthly energy yield (YM) data from a few locations (only 5!) is sufficient to predict the yearly energy yield (EY) of over 250,000 locations with a high spatial resolution ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.5^{\circ}\times 0.5^{\circ}$</tex> ) and accuracy with root mean square error (RMSE) less than just 8 kW·h·m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> . The map reveals that physically relevant meteorological conditions are shared across continents allowing pan-continental geographical extrapolation. Moreover, the scheme is agnostic to PV technology and farm topology, and thus, can be extended to novel PV technology/farm topology. Our results will lead to data-driven collaboration between national policymakers and research organizations to build efficient decision support systems for accelerated PV qualification and deployment across the world.
Coupling Deep Imputation with Multitask Learning for Downstream Tasks on Omics Data

Omics data such as RNA gene expression, methylation and micro RNA expression are valuable sources of information for various clinical predictive tasks. For example, predicting survival outcomes, response to drugs, cancer histology type and other patients related information is possible using not only clinical data but molecular data as well. Moreover, using these data sources together, for example in multitask learning, might boost the performance. However, in practice, there are many missing data points which leads to significantly lower patient numbers when analysing full cases, which in our setting refers to all modalities being present. In this paper we investigate how imputing data with missing values using deep learning coupled with multitask learning can help to reach state-of-the-art performance results using combined omics modalities - RNA, micro RNA and methylation. We propose a generalised deep imputation method to impute values where a patient has data for one modality missing. Interestingly, deep imputation by itself outperforms multitask learning in classification and regression tasks across most combinations of modalities. In contrast, when using all available modalities for survival prediction we observe that multitask learning by itself significantly outperforms deep imputation (adjusted p-value of 0.03). Thus, both approaches are complementary when optimising performance for downstream predictive tasks.
Quantitative Evaluation of Cloud Elasticity based on Fuzzy Analytic Hierarchy Process

Elasticity is one of the most important cloud computing characteristics, which enables deployed applications to dynamically adapt to workload-changing demands by acquiring and releasing shared computing resources at runtime. However, the existing cloud elasticity metrics are either oversimplified or hard to use, thereby lacking a comprehensive evaluation mech-anism to properly compare the elastic feature among different cloud providers. To address this gap, we propose an assessment method for cloud elasticity based on fuzzy hierarchical analysis. We use a fuzzy hierarchical model to quantitatively assess the qualitative metrics with a unified standard model. We compare three public cloud providers (Ali Cloud, HUAWEI Cloud, Tencent Cloud) as case studies and measure their cloud elasticity based on the proposed model on a cluster. To verify the effectiveness of our method, we also measure three cloud platforms using auto scaling performance metrics proposed by SPEC Cloud Group. The results show that our proposed elasticity quantification method is feasible.
Multimodal Breast Cancer Diagnosis Based on Multi-level Fusion Network

With the widespread application of artificial intelligence technology, deep learning algorithms have been extensively applied to the diagnosis and screening of breast cancer. However, the classification of breast cancer with the data from a single modality is still not accurate enough to meet clinical needs. This paper proposes a Multimodal breast cancer diagnosis based on Multi-level fusion network which integrates pathological images, structured data and medical description text. Specifically, we first construct a fully connected graph to extract the node and graph level feature representation of pathological images with graph attention layers. Second, we use the BERT model to extract the text features from the medical records. At last, the features of the above three modal data are fused using a multimodal adaption gate (MAG) for diagnosis. Experimental results indicate that the proposed method obtains superior performance (accuracy 93.62%) to most baseline methods on PathoEMR dataset.
Post-quantum Privacy-Preserving Aggregation in Federated Learning Based on Lattice

With the fast growth of quantum computers in recent years, the use of post-quantum cryptography has steadily become vital. In the privacy-preserving aggregation of federated learning, different cryptographic primitives, such as encryption, key agreement, digital signature, secret-sharing, and pseudorandom generator, may be used to protect users’ privacy. However, in the original privacy-preserving aggregation protocol, the classical cryptographic primitives are vulnerable to quantum attacks. Bonawitz et al. presented the SecAgg (Secure Aggregation) protocol, which is based on double-masking MPC (Multi-Party Computation). On this basis, we took advantage of the structure of the original protocol and the properties of lattice cryptography to apply lattice cryptography schemes to the protocol and presented a lattice-based post-quantum privacy-preserving aggregation protocol, which can withstand quantum attacks.
Self-supervised Approach for a Fully Assistive Esophageal Surveillance: Quality, Anatomy and Neoplasia Guidance

Early pre-cancerous malignant condition such as Barrett's esophagus (BE) and quantification of associated dysplastic changes is critical for early diagnosis and treatment. While endoscopic videos are corrupted with multiple artefacts and procedure require investigating extended areas such as stomach, it is inevitable that there is risk of missing areas that may potentially harbour neoplastic changes and require immediate attention. A complete guidance assisting navigation is thus vital. Visually obvious neoplasia and suspected areas both needs be flagged for biopsies. Due to the thin demarcation between BE and early neoplasia, it is often challenging to identify subtle changes even by experts. We propose a self-supervised learning technique for a fully assistive esophageal endoscopy surveillance system. The self-supervision step allows to learn complex representations using a pretext task of solving a jigsaw puzzle. Here, the idea is to enable network to distinctly learn inconspicuous features that are characteristics of neoplasia and other classes. In order to enable an optimal decision boundary we propose to incorporated angular margin in our fine-tuning process. Our proposed framework showed a boost of 3% on overall accuracy compared to fully supervised approach with similar backbone.
Epistemic Logic via Distance and Similarity

A weighted graph extends a standard Kripke frame for modal logic with a weight on each of its edges. Distance and similarity measures can be imposed so that the edges stand for the dissimilairty/similarity relation between nodes (in particular, we focus on the distance and similarity metrics introduced in [5]). Models based on these types of weighted graphs give a simple and flexible way of formally interpreting knowledge. We study proof systems and computational complexity of the resulting logics, partially by correspondence to normal modal logics interpreted in Kripke semantics.
Refined Attention Module for WSI Cancer Diagnosis

In clinical pathology, the accurate and precise classification of cancer is one of the critical challenges due to the complex pattern of cancer cells. This study proposes an effective attention module to highlight the most important parts of cancer whole slide images (WSIs). Our attention module improves the significance of learnable features and overcomes the noisy features while training. Conventional attention modules use only feature extraction capability to learn information but we merge noisy removal capability within our attention module to leverage and randomly discard the noise during the training of the model which enhances the performance of the WSI classification task. We evaluated the performance of our module on our biopsy needle WSIs dataset, named bnWSIs. Our dataset contains a total of 24,613 labeled patches extracted from 21 WSIs. The dataset is split into two types of classification categories, with different variants of magnifications, and classes. The key is to improve the existing state-of-the-art (SoTA) performance by using the attention module, For binary classification, the achieved accuracies are improved up to 7%, whereas in multi-class classification are 6% with three magnification levels.
Logistics and Domestic Delivery Services Performance in Covid-19 Era: A Sentiment Analysis Approach

The Covid-19 pandemic has hurt the business sector as badly as the health sector, including the logistics sector and delivery services. This sector is entering a period of uncertainty both in terms of government policies and demand which can affect the performance of the services provided. Evaluation needs to be done to see whether the logistics and shipping services sector, especially in Indonesia, is ready to face a pandemic. The availability of official service accounts of each service provider on Twitter social media as a forum for complaints and public aspirations can be used to evaluate service performance by measuring customer satisfaction levels through sentiment analysis before and during the pandemic. Various kinds of research on sentiment analysis have been carried out, but the time window sentiment analysis especially Time-Window Lexicon TF-IDF SVM integrating model approach has not been widely used. The data were obtained by scrapping the entire data for October 2019 until September 2020. ± 10.000 random stratified training samples data per month per service provider were taken and labeled using lexical approach for classification model creation The classification model was done with 89,01% accuracy, which is then deployed to predict the sentiment label of the whole data. This study provides the results that: (1) the Covid-19 pandemic significantly increase the number of tweet that indicate more people use the service, (2) the Covid-19 pandemic have decreased the performance of logistics and delivery services especially at the first three months of the pandemic period, and (3) the most frequent negative opinion that significantly affects service performance is late in delivery.
Mental technology of digital transformation of education

A simple transfer of traditional educational technologies to the process of digital transformation of education does not give the expected efficiency. In many cases, the unreasonable development of means and methods of electronic and distance learning, ill-preparedness of teaching staff for professional activities in digital environment generate high material and intellectual costs. In this regard, innovative educational technologies are of interest, which make it possible to achieve greater efficiency in the digitalization of education. The work is devoted to the substantiation of the mental approach to the creation of mental technologies that automate and provide the solution of educational problems according to the “White box” model. The mental approach is built on the foundation of mental schemes that define reality and a person from the position of their mentality, formed as a result of their individual and collective life experience. The mechanism of thinking based on mental schemes makes it possible to use the “glass box” model to solve many didactic and organizational and pedagogical problems. With the help of mental technologies, it is convenient to define new goals of education, in particular, the formation and development of computational, structural, intuitive, algorithmic thinking in a person. The basis of mental technologies is various subject mental structures, concept and mind maps, knowledge mental schemes. They provide a real opportunity to implement the principles of person-centered learning, approaches to assessing the effectiveness of learning, as well as the quality of complex systems, for example, digital educational environments, etc. Mental technologies make it possible to formalize and automate many didactic problems, facilitate the achievement of cognitive goals of subject learning.