Reviewerly/exHarmony huggingface dataset:
Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.
Multilevel Multiobjective Particle Swarm Optimization Guided Superpixel Algorithm for Histopathology Image Detection and Segmentation

Histopathology image analysis is considered as a gold standard for the early diagnosis of serious diseases such as cancer. The advancements in the field of computer-aided diagnosis (CAD) have led to the development of several algorithms for accurately segmenting histopathology images. However, the application of swarm intelligence for segmenting histopathology images is less explored. In this study, we introduce a Multilevel Multiobjective Particle Swarm Optimization guided Superpixel algorithm (MMPSO-S) for the effective detection and segmentation of various regions of interest (ROIs) from Hematoxylin and Eosin (H&amp;E)-stained histopathology images. Several experiments are conducted on four different datasets such as TNBC, MoNuSeg, MoNuSAC, and LD to ascertain the performance of the proposed algorithm. For the TNBC dataset, the algorithm achieves a Jaccard coefficient of 0.49, a Dice coefficient of 0.65, and an F-measure of 0.65. For the MoNuSeg dataset, the algorithm achieves a Jaccard coefficient of 0.56, a Dice coefficient of 0.72, and an F-measure of 0.72. Finally, for the LD dataset, the algorithm achieves a precision of 0.96, a recall of 0.99, and an F-measure of 0.98. The comparative results demonstrate the superiority of the proposed method over the simple Particle Swarm Optimization (PSO) algorithm, its variants (Darwinian particle swarm optimization (DPSO), fractional order Darwinian particle swarm optimization (FODPSO)), Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), non-dominated sorting genetic algorithm 2 (NSGA2), and other state-of-the-art traditional image processing methods.
Mites

There is increasing interest in deploying building-scale, general-purpose, and high-fidelity sensing to drive emerging smart building applications. However, the real-world deployment of such systems is challenging due to the lack of system and architectural support. Most existing sensing systems are purpose-built, consisting of hardware that senses a limited set of environmental facets, typically at low fidelity and for short-term deployment. Furthermore, prior systems with high-fidelity sensing and machine learning fail to scale effectively and have fewer primitives, if any, for privacy and security. For these reasons, IoT deployments in buildings are generally short-lived or done as a proof of concept. We present the design of Mites, a scalable end-to-end hardware-software system for supporting and managing distributed general-purpose sensors in buildings. Our design includes robust primitives for privacy and security, essential features for scalable data management, as well as machine learning to support diverse applications in buildings. We deployed our Mites system and 314 Mites devices in Tata Consultancy Services (TCS) Hall at Carnegie Mellon University (CMU), a fully occupied, five-story university building. We present a set of comprehensive evaluations of our system using a series of microbenchmarks and end-to-end evaluations to show how we achieved our stated design goals. We include five proof-of-concept applications to demonstrate the extensibility of the Mites system to support compelling IoT applications. Finally, we discuss the real-world challenges we faced and the lessons we learned over the five-year journey of our stack's iterative design, development, and deployment.
DES-Co-RSA: A Hybrid Encryption Algorithm Based on DES and RSA

The Internet is developing rapidly, which leads to a sharp increase in information transmitted by the network. Moreover, people have a higher demand for encrypting meetings and encrypting information transmission. Currently, the data encryption technologies used in network transmission mainly include symmetric encryption algorithms, asymmetric encryption algorithms, virtual network encryption technology, SSL protocol encryption, etc. However, the widely used symmetric encryption algorithm has some problems, such as complex distribution and complicated management of keys, while the asymmetric encryption algorithm generally has the disadvantages of slow encryption and decryption speed. The research and analysis of the typical symmetric encryption algorithm DES and asymmetric encryption algorithm RSA are studied to address this issue. This paper puts forward mixed cryptogram arithmetic of DES and RSA. The principle of the algorithm is that the sender encrypts the key of DES with RSA’s public key and then encrypts the plaintext with the new longer key by DES. The receiver can decrypt the encrypted key and ciphertext to obtain plaintext. The advantage of this hybrid algorithm is that RSA only needs to encrypt the short key of DES. Therefore, the encryption speed is improved greatly, and a longer new key is obtained, which makes the encryption result of DES more secure and reliable. The hybrid algorithm shows good performance through the code implementation and analysis of the results. Not only the encryption speed is fast, but the security is also guaranteed, and it has a favorable application prospect in digital signature and other fields.
Cyclic Self-Training With Proposal Weight Modulation for Cross-Supervised Object Detection

Weakly-supervised object detection (WSOD), which requires only image-level annotations for training detectors, has gained enormous attention. Despite recent rapid advance in WSOD, there remains a large performance gap compared with fully-supervised object detection. To narrow the performance gap, we study cross-supervised object detection (CSOD), where existing classes (base classes) have instance-level annotations while newly added classes (novel classes) only need image-level annotations. For improving localization accuracy, we propose a Cyclic Self-Training (CST) method to introduce instance-level supervision into a commonly used WSOD method, online instance classifier refinement (OICR). Our proposed CST consists of forward pseudo labeling and backward pseudo labeling. Specifically, OICR exploits the forward pseudo labeling to generate pseudo ground-truth bounding-boxes for all classes, thus enabling instance classifier training. Then, the backward pseudo labeling is designed to generate pseudo ground-truth bounding-boxes of higher quality for novel classes by fusing the predictions of the instance classifiers. As a result, both novel and base classes will have bounding-box annotations for training, alleviating the supervision inconsistency between base and novel classes. In the forward pseudo labeling, the generated pseudo ground-truths may be misaligned with objects and thus introduce poor-quality examples for training the ICs. To reduce the impacts of these poor-quality training examples, we propose a Proposal Weight Modulation (PWM) module learned in a class-agnostic and contrastive manner by exploiting bounding-box annotations of base classes. Experiments on PASCAL VOC and MS COCO datasets demonstrate the superiority of our proposed method.
Is there more to bitcoin mining than carbon emissions?

Critics decry cryptocurrency mining as a huge waste of energy, while proponents insist on claiming that it is a green industry. Is Bitcoin mining really worth the energy it consumes? The high power consumption of cryptocurrency mining has become the latest global flashpoint. In this paper, we define the Mining Domestic Production (MDP) as a method to account for the final outcome of the Bitcoin mining industry’s production activities in a certain period time, calculate the carbon emission per unit output value of the Bitcoin mining industry in China, and compare it with three other traditional industries. The results show that Bitcoin mining does not always have the highest when compared with others. The contribution of this paper is that we give a new perspective on thinking whether Bitcoin mining is more efficient to make more profit, in terms of the same amount of carbon emissions per unit compared to other industries. Moreover, it could even be argued that Bitcoin may present an opportunity for some developing countries to build out their electrical capacity and generate revenue.
Hey Alexa … Examining Factors Influencing the Educational Use of AI-Enabled Voice Assistants During the COVID-19 Pandemic

The COVID-19 pandemic has resulted in a rapid growth of online learning. While majority of the current research focus on different learning management systems, massive open online courses, or even specific softwares like Zoom and Microsoft Teams, the use of artificial-intelligence (AI) based voice assistants (VAs) for the purpose of online education is very rare. In this work we propose, validate, and test a research model that explains the continuance usage of VAs by students for learning purpose during their home quarantine period. We consider novel pandemic-specific psychological factors like loneliness and self-quarantine, together with anthropomorphic factors like voice attractiveness of the VAs for proposing the research model. The factors of satisfaction and continuance usage are borrowed from Expectation Confirmation Theory. Partial Least Squares Structural Equation Modelling is used for testing the proposed model.
One-Stage Shifted Laplacian Refining for Multiple Kernel Clustering

Graph learning can effectively characterize the similarity structure of sample pairs, hence multiple kernel clustering based on graph learning (MKC-GL) achieves promising results on nonlinear clustering tasks. However, previous methods confine to a "three-stage" scheme, that is, affinity graph learning, Laplacian construction, and clustering indicator extracting, which results in the information distortion in the step alternating. Meanwhile, the energy of Laplacian reconstruction and the necessary cluster information cannot be preserved simultaneously. To address these problems, we propose a one-stage shifted Laplacian refining (OSLR) method for multiple kernel clustering (MKC), where using the "one-stage" scheme focuses on Laplacian learning rather than traditional graph learning. Concretely, our method treats each kernel matrix as an affinity graph rather than ordinary data and constructs its corresponding Laplacian matrix in advance. Compared to the traditional Laplacian methods, we transform each Laplacian to an approximately shifted Laplacian (ASL) for refining a consensus Laplacian. Then, we project the consensus Laplacian onto a Fantope space to ensure that reconstruction information and clustering information concentrate on larger eigenvalues. Theoretically, our OSLR reduces the memory complexity and computation complexity to O(n) and O(n2) , respectively. Moreover, experimental results have shown that it outperforms state-of-the-art MKC methods on multiple benchmark datasets.
Dimension reduction method of high-dimensional fault datasets based on C_M_t-SNE under unsupervised background

The unlabeled fault datasets often contain much non-sensitive redundant, and uncertain information. This study designs a novel interpretable and unsupervised dimension reduction method for unlabeled data containing redundancy and uncertainty. Firstly, a fuzzy-based way for pseudo-label generation is given, and feature cloud models under pseudo labels are established; Secondly, this study takes the expectation, entropy, and hyper entropy of the cloud models representing uncertainty in features as spatial vectors. The difference degree between vectors is treated as the evaluation standard to filter out non-sensitive features based on the maximum initial difference; Moreover, redundant elements are fused by t-SNE, and lower dimensional feature components conducive for fault classification are obtained; Finally, the effectiveness of the method is demonstrated by comparative experiments. The results show that this method has a higher factor, which means that the method can better mine the difference among different faults and improve the performance of fault identification.
Adaptive soft sensor design using a regression neural network and bias update strategy for non-linear industrial processes

Abstract Soft sensing of quality parameters in process industries has been an active area of research for the past two decades. To improve the performance of soft sensors in the scenario of time varying process states, adaptation capability is incorporated into the soft sensor model. In this work, recursive (R), sliding window (SW) and just-in-time learning (JITL) frameworks are used for adaptive soft sensor design. A rarely explored modeling technique in the adaptation framework, the generalized regression neural network (GRNN) is used as a local modeling strategy. A bias update procedure is applied during the model adaptation activity to improve the prediction accuracy. Further, the performances of the developed models are tested against input–output data dimension mismatch along with various concept drift phenomena by considering a different number of labeled samples for inputs and outputs. The proposed adaptation strategy is applied on two benchmark industrial processes. Simulation results show that the GRNN local modeling approach combined with the bias update strategy gives higher prediction accuracy than other adaptive soft sensors proposed in the literature. Moreover, GRNN local modeling strategy using SW adaptation mechanism has the least computation time among the three adaptation methods due to the use of a low number of samples for model development.
Privacy-Preserving and Byzantine-Robust Federated Learning

Federated learning (FL) trains a model over multiple datasets by collecting the local models rather than raw data, which can help facilitate distributed data analysis in many real-world applications. Since the model parameters can leak information about the training datasets, it is necessary to preserve the privacy of the FL participants' local models. Furthermore, FL is vulnerable to poisoning attacks which can significantly decrease the model utility. To settle the above issues, we propose a privacy-preserving and Byzantine-robust FL scheme <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Pi _{\rm{P2Brofl}}$</tex-math></inline-formula> that maintains robustness in the presence of poisoning attacks and preserves the privacy of local models simultaneously. Specifically, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Pi _{\rm{P2Brofl}}$</tex-math></inline-formula> leverages three-party computation (3PC) to securely achieve a Byzantine-robust aggregation method. To improve the efficiency of privacy-preserving local model selection and aggregation, we propose a maliciously secure top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> protocol <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Pi _{\rm{top}-k}$</tex-math></inline-formula> that has low communication overhead. Moreover, we present an efficient maliciously secure shuffling protocol <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Pi _{\rm{shuffle}}$</tex-math></inline-formula> since secure shuffling is necessary for our secure top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> protocol. The security proof of the scheme is given and experiments on real-world datasets are conducted in this paper. When the proportion of Byzantine participants is 50%, the error rate of the model only increases by 1.05% while it increases by 23.78% without using our protection.
JNLP Team: Deep Learning Approaches for Tackling Long and Ambiguous Legal Documents in COLIEE 2022

Competition on Legal Information Extraction/Entailment (COLIEE) is an annual competition associated with the International Workshop in Juris-Informatics. The challenge for this competition is required not only the skills in processing long documents but also the ability to resolve ambiguity in the legal domain. For lengthy documents, we proposed a document-level attention mechanism (Task 1) and passage mining (Task 3, 4). Regarding ambiguity in the legal domain, we propose 2 methods that use abstract meaning representation to remove noise in given query and candidate documents (Task 2), and the second approach is use-case identification (Task 3). By categorizing the given query, we have different approaches to solving questions. The results reflect the difficulty level and competitiveness in this competition.
CMAT: Integrating Convolution Mixer and Self-Attention for Visual Tracking

Convolutional Neural Networks (CNNs) and Transformer are two powerful representation learning techniques for visual tracking. Although CNNs can effectively reduce local redundancy via small-neighborhood convolution operations, their limited receptive fields make it difficult to capture global dependency. Self-attention in Transformer uses patches as the input representation, which can effectively capture long-range dependency. However, blind similarity comparisons between all patches can lead to high redundancy. Is there then a technique that combines well the advantages of both paradigms for visual tracking? In this work, we design a novel backbone network for feature extraction. First, we choose Depthwise Convolution and Pointwise Convolution to build a Convolution Mixer, which effectively separates spatial mixing from channel-wise mixing of information. The Convolution Mixer reduces redundancy in spatial and channel features while increasing receptive field. Then, to exploit the global modeling ability of self-attention, we construct a module by aggregating Convolution Mixer and self-attention. The module shares dominant computational complexity (the square of the channel size) in the first stage. In the second stage, the shift and summation operations are lightweight. Finally, to alleviate the overfitting of the backbone network during training, a dropout layer is added at the end of the module to improve the generalization ability of the network model. Stronger image features are provided for subsequent feature fusion and prediction. The proposed tracker (named CMAT) achieves satisfying tracking performance on ten challenging datasets. In particular, CMAT achieves a 64.1% AUC on LaSOT and a 68.9% AUC on UAV123 while running at 23 frames per second (FPS).
Deterministic freely propagating photonic qubits with negative Wigner functions

Engineering quantum states of free-propagating light is of paramount importance for quantum technologies. Coherent states ubiquitous in classical and quantum communications, squeezed states used in quantum sensing, and even highly-entangled cluster states studied in the context of quantum computing can be produced deterministically, but they obey quasi-classical optical field statistics described by Gaussian, positive Wigner functions. Fully harnessing the potential of many quantum engineering protocols requires using non-Gaussian Wigner-negative states, so far produced using intrinsically probabilistic methods. Here we describe the first fully deterministic preparation of non-Gaussian Wigner-negative free-propagating states of light, obtained by mapping the internal state of an intracavity Rydberg superatom onto an optical qubit encoded as a superposition of 0 and 1 photons. This approach allows us to reach a 60% photon generation efficiency in a well-controlled spatio-temporal mode, while maintaining a strong photon antibunching. By changing the qubit rotation angle, we observe an evolution from quadrature squeezing to Wigner negativity. Our experiment sets this new technique as a viable method to deterministically generate non-Gaussian photonic resources, lifting several major roadblocks in optical quantum engineering.
Split Learning with Differential Privacy for Integrated Terrestrial and Non-Terrestrial Networks

Integrated terrestrial and non-terrestrial networks (TNTNs) have become promising architecture for enabling ubiquitous connectivity. Smart remote sensing is one of the typical applications of TNTNs that collects and analyzes various dimensions of remote sensing data by deploying Internet of Things (IoT) sensors and edge computing in terrestrial, space, aerial, and underwater networks. To improve the analysis accuracy of remote sensing data, the owners of different networks should conduct collaborative learning on different dimensions of data, while data and label privacy should be jointly considered. However, the existing collaborative learning paradigms are difficult to meet this demand. In this article, we propose a paradigm of split learning (SL), where the data owner and the label owner train different parts of the deep learning model and only exchange the intermediate data (i.e., smashed data and cut layer gradients). We explore the potential privacy attacks that recover the raw data and label information based on the intermediate data and propose the differential privacy (DP) based defense mechanisms that inject randomly generated Laplace noise into the intermediate data to disturb the attack performance. We also conduct a simulation study based on a real-world satellite remote sensing dataset to validate that the SL paradigm with defense mechanisms can effectively balance the performance of the collaborative model training and the protection of the data and label privacy. Finally, we discuss the main challenges and potential research directions of the privacy-preserving SL paradigm for smart remote sensing over TNTNs.