Reviewerly/exHarmony huggingface dataset:
A privacy-aware data sharing framework for Internet of Things through edge computing platform

Due to the wide adoption and deployment of the Internet of Things (IoT), massive amounts of data are being generated and shared across various sectors. Privacy disclosure is a major threat in IoT-related applications if collected data is directly outsourced. In IoT environments with large datasets, the existing Privacy-Preserving Data Mining (PPDM) mechanisms are inefficient and not scalable. To deal with this shortcoming, we develop a novel evolutionary PPDM framework, namely GPU-Enabled PPDM for IoT (GEPI), using GPUs at the edge layer to make the PPDM both efficient and exhibiting usefulness for IoT applications. On the one hand, the evolutionary algorithm used in the PPDM can guarantee the high utility by selecting the best candidate transactions for modification, providing a shareable dataset with minimum modifications and maximum privacy. On the other hand, the evolutionary algorithm is parallelized using a developed GPU based mechanism to accelerate database scans. In our mechanism, the dataset is distributed among the GPU threads to compute the fitness value in a parallel manner. Tests with extensive benchmarks reveal that our mechanism can accelerate the fitness function step 53.7x on average. The findings also show that the developed PPDM algorithm achieves an average speedup of 43.8x and 47.3x when compared to the state-of-the-art algorithms of ABC4ARH and PACO2DT, respectively.
Local Memory Read-and-Comparator for Video Object Segmentation

Recently, the memory-based approach, which performs non-local matching between previously segmented frames and a query frame, has led to significant improvement in video object segmentation. However, the positional proximity of the target objects between the query and the local memory (previous frame), i.e. temporal smoothness, is often neglected. There are some attempts to solve the problem, but they are vulnerable and sensitive to large movements of target objects. In this paper, we propose local memory read-and-compare operations to address the problem. First, we propose local memory read and sequential local memory read modules to explore temporal smoothness between neighboring frames. Second, we propose the memory comparator to read the global memory and local memory adaptively by comparing the affinities of the global and local memories. Experimental results demonstrate that the proposed algorithm yields more strict segmentation results than the recent state-of-the-art algorithms. For example, the proposed algorithm improves the video object segmentation performance by 0.4% and 0.5% in terms of <i>J&#x0026;F</i> on the most commonly used datasets, DAVIS2016 and DAVIS2017, respectively.
<i>The Smale Collection II</i> photographed by Jeffrey A. Scovil, edited by Gloria A. Stabler with preface by Stephen Smale. Lithiographie Ltd., Arvada, Colorado. 165 pages; 2021; $60 plus shipping and handling (hardbound).

"The Smale Collection II photographed by Jeffrey A. Scovil, edited by Gloria A. Stabler with preface by Stephen Smale. Lithiographie Ltd., Arvada, Colorado. 165 pages; 2021; $60 plus shipping and handling (hardbound).." Rocks & Minerals, 97(5), p. 479
On Variable Strength Quantum ECC

Quantum error correcting codes (QECC) facilitate timely detection and correction of errors to increase the robustness of qubits. Higher expected error rates necessitate stronger (i.e., larger-distance) QECC to guarantee correct operation. With increasing strength, however, QECC overhead can easily become forbidding. Based on the observation that quantum algorithms exhibit varying spatio-temporal sensitivity to noise (hence, errors), this article explores challenges and opportunities of variable strength QECC where QECC strength gets adapted to the degree of noise tolerance, to minimize QECC overhead without compromising correctness.
Heidegger’s Fugue: Musicality and the Heraclitus Lectures

Martin Heidegger rarely explicitly dealt with the topic of music. The Heraclitus lectures, delivered in 1943 and 1944, offer a notable exception. Heidegger here speaks openly of the “Lied der Erde” (“Earth’s song”). Most intriguing, perhaps, though, is the use of Fügung in relation to ἁρμονία (harmonia), which he links to understanding φὑσις (physis; the “emerging” character of the world) and being. Translated by Assaiante and Ewegen as “jointure,” Fügung bears a connection with the German Fuge, which contains the double meaning of joint and fugue. Drawing on a close reading of the Heraclitus lectures, this paper will explore the dual nature of Fügung with an eye towards laying the groundwork for understanding a latent musicality within Heidegger’s larger corpus, centering around perceiving, fugue, and their implications for listening to the “Earth’s song,” which will be read in literal, environmental terms as the soundscape or, perhaps, musical manifold.
A Short Survey on Inductive Biased Graph Neural Networks

Many real-world networks including the World Wide Web and the Internet of Things are graphs in their abstract forms. Graph neural networks (GNNs) have emerged as the main solution for deep learning on graphs. Recently, tremendous effort has been made to enhance the performance and expressivity of GNNs. In this paper, we review the state-of-the-art graph neural network models and frameworks with a focus on the latest developments in graph representation learning. We propose a new taxonomy which divides general GNNs into recurrent GNNs, spectral GNNs, spatial GNNs and topology-aware GNNs. We will also discuss the inductive biases behind different categories of GNNs.
Face Synthesis With a Focus on Facial Attributes Translation Using Attention Mechanisms

Synthesis of face images by translating facial attributes is an important problem in computer vision and biometrics and has a wide range of applications in forensics, entertainment, etc. Recent advances in deep generative networks have made progress in synthesizing face images with certain target facial attributes. However, visualizing and interpreting generative adversarial networks (GANs) is a relatively unexplored area and generative models are still being employed as black-box tools. This paper takes the first step to visually interpret conditional GANs for facial attribute translation by using a gradient-based attention mechanism. Next, a key innovation is to include new learning objectives for knowledge distillation using attention in generative adversarial training, which result in improved synthesized face results, reduced visual confusions and boosted training for GANs in a positive way. Firstly, visual attentions are calculated to provide interpretations for GANs. Secondly, gradient-based visual attentions are used as knowledge to be distilled in a teacher-student paradigm for face synthesis with focus on facial attributes translation tasks in order to improve the performance of the model. Finally, it is shown how “pseudo”-attentions knowledge distillation can be employed during the training of face synthesis networks when teacher and student networks are trained to generate different facial attributes. The approach is validated on facial attribute translation and human expression synthesis with both qualitative and quantitative results being presented.
Differentially Private Auction for Federated Learning with Non-IID Data

With the increase in clients&#x2019; concerns about their privacy, federated learning, as a new model of machine learning process, was proposed to help people complete learning tasks on the basis of privacy protection. But the large-scale application of federated learning depends on the extensive participation of individual clients. This motivates the incentive mechanism design to increase clients&#x2019; willingness to participate. However, the incentive mechanism should take into account non-IID issues and privacy protection of clients&#x2019; sensitive information of data distribution. These two aspects are not well studied jointly in the existing incentive mechanism design. In this paper, we propose a differentially private auction for federated learning with non-IID data. It can not only protect clients&#x2019; private information of data distribution with differential privacy but also incentivize clients with suitable data distribution to deal with non-IID issues. Finally, we prove that the designed mechanism meets the design objective through detailed theoretical analysis.
Automatic Meter Reading Based on Bi-Fusion MSP Network and Carry-Out Rechecking

Automatic meter reading is important for power billing in a smart city.Most SoTA (State-ofthe-Art) vision-based methods can read only cyclometers and fail to handle dial meters due to their in-between problem and ambiguous patterns to interpret a digit and are not light enough to be run on an embedded platform.This paper focuses on the design and development of an Internet of Things (IoT)-assisted real-time Automatic Meter Reading (AMR) system for utility billing in a smart city.To enhance the accuracy of object detection, most SoTA methods use a very deep CNN-based architecture to create rich feature maps.However, this backbone also makes small objects in the last layer become one pixel or less.This paper proposes a novel BI-Fusion Mixed Stage Partial (BIF-MSP) network to hold the spatial information of a smaller object at the end of network architecture and also increase the efficiency while operating on an embedded system.It can accurately detect small digits not only from cyclometers but also from dial meters.It can automatically decide a rule (anticlockwise or clockwise) to accurately read digits on a dial-type meter.After that, a carryout re-checking module is proposed to further improve the accuracy of this AMR system.The experiments show the superiorities of our ARM system in terms of accuracy and efficiency.
Secure Offloading of User-level IDS with VM-compatible OS Emulation Layers for Intel SGX

Since virtual machines (VMs) provided by Infrastructure-as-a-Service clouds often suffer from attacks, they need to be monitored using intrusion detection systems (IDS). For secure execution of host-based IDS (HIDS), IDS offloading is used to run IDS outside target VMs, but offloaded IDS can still be attacked. To address this issue, secure IDS offloading using Intel SGX has been proposed. However, IDS development requires kernel-level programming, which is difficult for most IDS developers. This paper proposes SCwatcher for enabling user-level HIDS running on top of the operating system (OS) to be securely offloaded using VM-compatible OS emulation layers for SGX. SCwatcher provides the standard OS interface used in a target VM to in-enclave IDS. Especially, the virtual proc filesystem called vProcFS analyzes OS data using VM introspection and returns the system information inside the target VM. We have implemented SCwatcher using Xen supporting SGX virtualization and two types of OS emulation layers for SGX called SCONE and Occlum. Then, we confirmed that SCwatcher could offload legacy HIDS and showed that the performance could be comparable to insecure IDS offloading.
An Open-source Repository Retrieval Service Using Functional Semantics for Software Developers

Software developers are encouraged to reuse mature third-party code repositories to accelerate their developing. The first step is to find some code repositories which satisfy developers&#x2019; demand for specific functionality. Repository hosting platforms, such as GitHub, NPM, etc. are good search databases. However, these platforms provide little retrieval function based on repositories&#x2019; functional semantics, which is not conducive for developers to get desired code repository by claing what functionality they require.To solve this problem, we propose and implement an open- source repository retrieval service. First, to extract structured functional semantics of open-source repositories, we design a NER (Named Entity Recognition) model to extract features of six different dimensions related to software development. Next, we construct a HIN (Heterogeneous Information Network) to represent nonstructured semantic information of repositories. Finally, by combining two modules preceding, we implement an open-source repository retrieval service, in which a software developer only needs to tell the service what functionality he (or she) wants and will get a ranked list of repositories as return. We further conduct experiments and the results demonstrate the effectiveness of the service.
Constellation Shaping for Phase Noise Channels with Deep Learning Approach

Constellation shaping can significantly enhance the capacity of communication systems without incurring extra bandwidth. The statistics of the noise in the system dictate the appropriate constellation geometry. While additive white Gaussian noise (AWGN) channels have been widely studied, the more challenging phase noise channels are rarely investigated. To fill in this gap, in this paper we optimize the constellation shaping for phase noise channels using a deep neural network (DNN) end-to-end learning approach called autoencoder. The proposed DNN method is compared with state-of-the-art baseline algorithms in terms of symbol error rate (SER). It is shown via simulation that our method provides improved SER for all considered phase noise values, with significant improvement at the high phase noise regime.
A Novel Science and Technology Resource Recommendation Service based on Knowledege Graph and Collaborative Filtering

To address the problems of large volume of science and technology information, low information value density, and matrix sparsity of recommendation algorithms, we propose STIR-KG, a science and technology information recommendation method integrating knowledge graph, and build a science and technology information recommendation service. The main contributions are: (1) Establishing a new material knowledge graph, which has been open-sourced in GitHub (2) Combining collaborative filtering methods with knowledge graphs to solve the cold-start and matrix sparsity problems. (3) Propose the representation learning method TransAR, which enhances the representation capability compared with traditional methods, and uses the Mahalanobis distance metric score function to reduce the influence of irrelevant dimensions on the similarity calculation. (4) Based on the STIR-KG method, we use the streaming computing framework Flink to build a recommendation service for scientific and technical information, which captures user interest migration in real time and makes the recommendation results more time-efficient. And according to the experimental verification, STIR-KG has significantly improved the accuracy and recall rate compared with other algorithms.
Anomaly Detection in SACP Cell Data using Changepoint and One-Class SVM

Sacrificial Anode Cathodic Protection (SACP) is a commonly used technique for corrosion protection of under-ground steel structures. The short-circuit current flowing between the anode and the cathode is measured to model the lifetime of the sacrificial anode (SA) for its eventual replacement. We have designed a hardware system to harvest energy from an SACP cell to power an underground wireless sensor node to send data (that is, the short-circuit current) to the cloud to model the lifetime of the SA. We also measure the ambient temperature, humidity, soil temperature, and soil moisture, because those parameters affect the short-circuit current. In this paper, we propose a methodology to detect anomalous behavior of SACP cells by processing the aforementioned data collected from SACP cells. The methodology applies both changepoint detection theory and One-Class SVM (Support Vector Machine) on the same dataset. For the One-Class SVM (OCSVM) approach, the features consist of the principal components of the normalized input data. In the changepoint analysis approach: (i) the features consist of the statistical properties of the changepoints of the input data; and (ii) an optimal classifier is found among Decision Trees, Random Forests, Naive Bayesian, Logistic Regression, and SVM models. The irregular flow of current between anode and cathode, changes in the charging and discharging rate of the capacitor, or the changes in circuit operations of the SACP cell due to changes in environmental conditions indicate the anomalous behaviour of the SACP cell. In this work, we show how feature extraction improves the overall efficiency of the model to detect anomalous behavior. Moreover, comparing both the approaches proposed above shows that changepoint detection outperforms OCSVM by 7% in terms of accuracy.
Event-Argument Linking in Disaster Domain

Linking event triggers with their respective arguments is an essential component for building an event extraction system.It is challenging to link event triggers with the corresponding arguments triggers when the sentence contains multiple events and arguments triggers.The task becomes even more challenging in a low-resource setup due to the unavailability of natural language processing resource and tools.In this paper, we study the event-argument linking task based on disaster event ontology in a low resource setup.We use BERT and non-BERT-based deep learning models in both monolingual and cross-lingual eventargument linking task.We also perform an ablation study of various features like position embeddings (PE), position indicator (PI), and segment ID (SI) to understand their contribution to performance improvement in non-BERT-based models.Using three different languages viz.Hindi, Bengali, and Marathi, we compare the results with multilingual BERT-based deep neural models in both monolingual and cross-lingual scenarios.We observe that the multilingual BERT-based model outperforms the best performing non-BERT-based model in cross-lingual settings.But in monolingual settings, the performance is similar in Hindi and Bengali datasets and slightly better in Marathi dataset.We choose the disaster domain due to its social implications.Our current experiments can be helpful in mining important information related to disaster events from news articles and building event knowledge graphs in low-resource languages.
Converging Blockchain and Artificial-Intelligence Towards Healthcare: A Decentralized-Private and Intelligence Health Record System

In the current healthcare environment, they lock the patient records in multiple centralized systems which are maintained by the different healthcare institutions. So, the complete, comprehensive medical data history of the patient is locked away, making it difficult for doctors to make informed decisions. Our system aims at tackling these issues using a decentralized system to store the patient's record. The patient and the doctor/healthcare institutions use a Decentralized Application as an interface to the Blockchain network. When a patient visits the doctor, the patient can give access to his/her medical data through this Decentralized Application via an Ethereum smart contract. Once the patient gives access, the doctor can access all the patient's medical records and history in one unified interface. Artificial Intelligence and Machine Learning are used to give a tailored medical experience to the patients. With rich data that is available from the users' network, can be fed into the Machine Learning models to do various levels of analysis to give patients and doctors further insight into the medical records.
Photonic Physical Unclonable Function Based on Integrated Neuromorphic Devices

In this work, we present a physical unclonable function, implemented using an integrated photonic neuromorphic device. The physical security feature in this case relies on the complex and unpredictable relation between hardware implemented complex weights at the hidden layer of a photonic reservoir computing scheme and the digital trainable weights at the output layer. Numerical simulations confirm that the neural weights are significantly affected by inevitable fabrication related imperfections of the silicon photonic platform. These features can be utilized as a physical root of trust, suitable for authentication/cryptographic applications. The proposed neuromorphic physical unclonable function concept, can be based on different types of neural networks, thus it paves the way for a wide range of photonic devices, able to simultaneously perform efficient non von Neumann computation and security related operations.
Dynamically Relative Position Encoding-Based Transformer for Automatic Code Edit

Adapting deep learning (DL) techniques to automate nontrivial coding activities, such as code documentation and defect detection, has been intensively studied recently. Learning to predict code changes is one of the popular and essential investigations. Prior studies have shown that DL techniques, such as neural machine translation (NMT), can benefit meaningful code changes, including bug fixing and code refactoring. However, NMT models may encounter bottleneck when modeling long sequences; thus, they are limited in accurately predicting code changes. In this article, we design a Transformer-based approach, considering that the Transformer has proven effective in capturing long-term dependencies. Specifically, we propose a novel model named DTrans. For better incorporating the local structure of code, i.e., statement-level information in this article, DTrans is designed with dynamically relative position encoding in the multihead attention of the Transformer. Experiments on benchmark datasets demonstrate that DTrans can more accurately generate patches than the state-of-the-art methods, increasing the performance by at least 5.45–46.57% in terms of the exact match metric on different datasets. Moreover, DTrans can locate the lines to change with 1.75–24.21% higher accuracy than the existing methods.
Deep Learning based CP-OFDM Signal Classification with Data Augmentation

Nowadays, most Radio Access Technologies (RATs) employ one or other variants of Orthogonal Frequency Division Multiplexing (OFDM) modulation. This includes 4G, 5G, WiFi, Bluetooth, DVB-T and some Unmanned Aerial Vehicles (UAV) data links. For this reason, a classifier that differentiates among multicarrier communication signals must be an important component of any spectral monitoring solution. In this work, we present a deep learning (DL) based signal classifier able to differentiate among several Cyclic Prefix (CP) OFDM signals with different time parameters (useful and guard band times). The developed classifier employs a convolutional neural network (CNN) that operates with the FFT of the I -Q samples of the signals. With data augmentation techniques, varying the noise levels and phase offsets of the OFDM signals, the trained network can obtain classification accuracies better than 90 % at SNRs as low as 0 dB, for technologies with different OFDM symbol length.
Automated Configuration for Agile Software Environments

The increasing use of the DevOps paradigm in software systems has substantially increased the frequency of configuration parameter setting changes. Ensuring the correctness of such settings is generally a very challenging problem due to the complex interdependencies, and calls for an automated mechanism that can both run quickly and provide accurate settings. In this paper, we propose an efficient discrete combinatorial optimization technique that makes two unique contributions: (a) an improved and extended metaheuristic that exploits the application domain knowledge for fast convergence, and (b) the development and quantification of a discrete version of the classical tunneling mechanism to improve the accuracy of the solution. Our extensive evaluation using available workload traces that do include configuration information shows that the proposed technique can provide a lower-cost solution (by ~60%) with faster convergence (by ~48%) as compared to the traditional metaheuristic algorithms. Also, our solution succeeds in finding a feasible solution in approximately 30% more cases than the baseline algorithm.