Reviewerly/exHarmony huggingface dataset:
AttSum: A Deep Attention-Based Summarization Model for Bug Report Title Generation

Concise and precise bug report titles help software developers to capture the highlights of the bug report quickly. Unfortunately, it is common that bug reporters do not create high-quality bug report titles. Recent long short-term memory (LSTM)-based sequence-to-sequence models such as iTAPE were proposed to generate bug report titles automatically, but the text representation method and LSTM employed in such model are difficult to capture the accurate semantic information and draw the global dependencies among tokens effectively. This article proposes a deep attention-based summarization model (i.e., <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AttSum</small> ) to generate high-quality bug report titles. Specifically, the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AttSum</small> model employs the encoder.decoder framework, which utilizes the robustly optimized bidirectional-encoder-representations-from-transformers approach to encode the bug report bodies to capture contextual semantic information better, the stacked transformer decoder to automatically generate titles, and the copy mechanism to handle the rare token problem. To validate the effectiveness of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AttSum</small> , we conduct automatic and manual evaluations on 333563 “ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt; body, title&gt;$</tex-math></inline-formula> ” pairs of bug reports and perform a practical analysis of its ability to improve low-quality titles. The result shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AttSum</small> is superior to the state-of-the-art baselines by a substantial margin both on automatic evaluation metrics (e.g., by 3.4%–58.8% and 7.7%–42.3% in terms of recall-oriented understudy for gisting evaluation in F1 and bilingual evaluation understudy, separately) and three human-set modalities (e.g., by 1.9%–57.5%). Moreover, we analyze the impact of the training data size on <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AttSum</small> and the results imply that our approach is robust enough to generate much better titles.
Financial Fraud Detection on Social Networks Based on a Data Mining Approach

The article summarizes the arguments and counter-arguments within the scientific debate on the issue of researching financial frauds in the Internet. The main goal of the research is to develop methodological principles for identifying financial cyber fraud in social networks based on the analysis of comments to identify relevant text patterns that may indicate manipulation attempts and further fraud. The urgency of solving this scientific problem is due to the fact that the mass involvement of Internet users in social interactions in the virtual environment has contributed to the development of various criminal schemes, as well as personal data that is initially entered during registration and information that is published in social networks can be used by a fraudster to carry out illegal financial transactions. The study of the issue of identifying financial fraud in social networks in the article is carried out in the following logical sequence: collecting comments with a corresponding request under publications in the social network using the Instaloader tool; combining comments into groups based on content similarity; conducting preliminary processing of text data (decomposing the text into simpler components (tokens) and reducing similar word forms to their main dictionary form); determination of the level of similarity of text data using the cosine of similarity; building clusters of text data that can indicate the presence of signs of financial fraud under relevant comments in social networks. Instagram was chosen to identify fraudulent operations in social networks. The analysis of comments on the social network Instagram to identify text patterns showed that offers and appeals from specific groups of people and promoted in comments with the help of spam are dangerous. Based on the results of the study, it was concluded that national regulators need to strengthen public control of the Internet, as well as improve the security system at the technical level by using the latest machine learning methods to identify attempts to commit illegal actions with the subsequent imposition of sanctions on such users in social networks.
A whole‐slide imaging based workflow reduces the reading time of pathologists

Even though entirely digitized microscopic tissue sections (whole slide images, WSIs) are increasingly being used in histopathology diagnostics, little data is still available on the effect of this technique on pathologists' reading time. This study aimed to compare the time required to perform the microscopic assessment by pathologists between a conventional workflow (an optical microscope) and digitized WSIs. WSI was used in primary diagnostics at the Laboratory for Pathology Eastern Netherlands for several years (LabPON, Hengelo, The Netherlands). Cases were read either in a traditional workflow, with the pathologist recording the time required for diagnostics and reporting, or entirely digitally. Reading times were extracted from image management system log files, and the digitized workflow was fully integrated into the laboratory information system. The digital workflow saved time in the majority of case categories, with prostate biopsies saving the most (68% time gain). Taking into account case distribution, the digital workflow produced an average gain of 12.3%. Using WSI instead of conventional microscopy significantly reduces pathologists' reading times. Pathologists must work in a fully integrated environment to fully reap the benefits of a digital workflow.
Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots

Automatic detection of visual anomalies and changes in the environment has been a topic of recurrent attention in the fields of machine learning and computer vision over the past decades. A visual anomaly or change detection algorithm identifies regions of an image that differ from a reference image or dataset. The majority of existing approaches focus on anomaly or fault detection in a specific class of images or environments, while general-purpose visual anomaly detection algorithms are more scarce in the literature. In this paper, we propose a comprehensive deep learning framework for detecting anomalies and changes in a priori unknown environments after a reference dataset is gathered, and without need for retraining the model. We use the SuperPoint and SuperGlue feature extraction and matching methods to detect anomalies based on reference images taken from a similar location and with partial overlapping of the field of view. We also introduce a self-calibrating method for the proposed model in order to address the problem of sensitivity to feature matching thresholds and environmental conditions. To evaluate the proposed framework, we have used a ground robot system for the purpose of reference and query data collection. We show that high accuracy can be obtained using the proposed method. We also show that the calibration process enhances changes and foreign object detection performance.
Entangled-State Time Multiplexing for Multiphoton Entanglement Generation

Finding a path toward efficient multiphoton entanglement generation would boost both foundational studies and practical applications of quantum entanglement in e.g. quantum computation, metrology, and communication. The common approach has a notorious problem of exponentially decreasing success probability with increasing photon number, so another method is needed for scalability. This Letter presents a scheme for creating $2n$-photon entangled states with an exponential efficiency-enhancement factor of ${B}^{n\ensuremath{-}1}$ without spoiling the entanglement. Even with just the currently demonstrated multiplexing power, applying this method to state-of-the-art 12-photon states would yield an enhancement factor of 10${}^{4}$.
Meta-Learning a Cross-lingual Manifold for Semantic Parsing

Abstract Localizing a semantic parser to support new languages requires effective cross-lingual generalization. Recent work has found success with machine-translation or zero-shot methods, although these approaches can struggle to model how native speakers ask questions. We consider how to effectively leverage minimal annotated examples in new languages for few-shot cross-lingual semantic parsing. We introduce a first-order meta-learning algorithm to train a semantic parser with maximal sample efficiency during cross-lingual transfer. Our algorithm uses high-resource languages to train the parser and simultaneously optimizes for cross-lingual generalization to lower-resource languages. Results across six languages on ATIS demonstrate that our combination of generalization steps yields accurate semantic parsers sampling ≤10% of source training data in each new language. Our approach also trains a competitive model on Spider using English with generalization to Chinese similarly sampling ≤10% of training data.1
Individual yield nowcasting for residential PV systems

Due to the inherent uncertainty in photovoltaic (PV) energy generation, an accurate power forecasting is essential to ensure a reliable operation of PV systems and a safe electric grid. Machine learning (ML) techniques have gained popularity on the development of this task due to its increased accuracy. Most literature, however, focuses only on less than 5 PV systems during training process, which does not ensure generalization to unseen systems. When in presence of a large feet, regional forecasts are the norm. Nevertheless, none of these approaches are usable when it comes to monitoring residential PV systems. In this work, we propose a single ML model that is able to predict the individual power of a large fleet of 1102 PV systems. XGBoost algorithm was selected as the most suitable algorithm for the task of PV yield nowcasting due to its performance and ease of use. This algorithm obtains Mean Absolute Error (MAE) of 0.877 kWh (considering an average system size of 4.44 kWp) and Mean Absolute Percentage Error (MAPE) of 23% for hourly data aggregated to daily values. XGBoost predictions for individual PV systems are on average two times better than currently used commercial software. We discuss the lack of a suitable loss function that can combine absolute and relative errors for residential PV yield forecasting. We also point out the lack of an adequate metric to compute the error made on the predictions and provide hints on developing a suitable one.
A Novel Mixed Precision Distributed TPU GAN for Accelerated Learning Curve

Deep neural networks are gaining importance and popularity in applications and services.Due to the enormous number of learnable parameters and datasets, the training of neural networks is computationally costly.Parallel and distributed computation-based strategies are used to accelerate this training process.Generative Adversarial Networks (GAN) are a recent technological achievement in deep learning.These generative models are computationally expensive because a GAN consists of two neural networks and trains on enormous datasets.Typically, a GAN is trained on a single server.Conventional deep learning accelerator designs are challenged by the unique properties of GAN, like the enormous computation stages with non-traditional convolution layers.This work addresses the issue of distributing GANs so that they can train on datasets distributed over many TPUs (Tensor Processing Unit).Distributed learning training accelerates the learning process and decreases computation time.In this paper, the Generative Adversarial Network is accelerated using the distributed multi-core TPU in distributed data-parallel synchronous model.For adequate acceleration of the GAN network, the data parallel SGD (Stochastic Gradient Descent) model is implemented in multi-core TPU using distributed TensorFlow with mixed precision, bfloat16, and XLA (Accelerated Linear Algebra).The study was conducted on the MNIST dataset for varying batch sizes from 64 to 512 for 30 epochs in distributed SGD in TPU v3 with 128 × 128 systolic array.An extensive batch technique is implemented in bfloat16 to decrease the storage cost and speed up floating-point computations.The accelerated learning curve for the generator and discriminator network is obtained.The training time was reduced by 79% by varying the batch size from 64 to 512 in multi-core TPU.
An Immutable Framework for Smart Healthcare Using Blockchain Technology

The advancements in sensing technologies, information processing, and communication schemes have revolutionized the healthcare sector.Electronic Healthcare Records (EHR) facilitate the patients, doctors, hospitals, and other stakeholders to maintain valuable data and medical records.The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service (DoS) attack can compromise the complete healthcare system.This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things (IoMT) to address the stated challenges.The proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy manner.The efficacy of the proposed framework is evaluated in the context of service execution time and throughput.The experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.
Multimodal Spatiotemporal Feature Map for Dynamic Gesture Recognition

Gesture recognition technology enables machines to read human gestures and has significant application prospects in the fields of human-computer interaction and sign language translation.Existing researches usually use convolutional neural networks to extract features directly from raw gesture data for gesture recognition, but the networks are affected by much interference information in the input data and thus fit to some unimportant features.In this paper, we proposed a novel method for encoding spatio-temporal information, which can enhance the key features required for gesture recognition, such as shape, structure, contour, position and hand motion of gestures, thereby improving the accuracy of gesture recognition.This encoding method can encode arbitrarily multiple frames of gesture data into a single frame of the spatio-temporal feature map and use the spatio-temporal feature map as the input to the neural network.This can guide the model to fit important features while avoiding the use of complex recurrent network structures to extract temporal features.In addition, we designed two sub-networks and trained the model using a sub-network pre-training strategy that trains the sub-networks first and then the entire network, so as to avoid the subnetworks focusing too much on the information of a single category feature and being overly influenced by each other's features.Experimental results on two public gesture datasets show that the proposed spatio-temporal information encoding method achieves advanced accuracy.
Association of the Big Five Personality Traits with Training Effectiveness, Sense of Presence, and Cybersickness in Virtual Reality

Virtual reality (VR) presents numerous opportunities for training skills and abilities through the technology’s capacity to simulate realistic training scenarios and environments. This can be seen in how newer research has emphasized how VR can be used for creating adaptable training scenarios. Nevertheless, a limited number of studies have examined how personality traits can influence the training effectiveness of participants within VR. To assess individual preferences in a virtual environment, the current study examines the associations of Big Five personality traits with training effectiveness from VR, as well as sense of presence and cybersickness. Our results show that traits of high agreeableness and low conscientiousness are predictors of training transferability in the VR environment in relation to the real world. Furthermore, the results also showed that trainees experiencing higher levels of cybersickness incurred worse training outcomes.
TailorFL

Federated learning (FL) enables distributed mobile devices to collaboratively learn a shared model without exposing their raw data. However, heterogeneous devices usually have limited and different available resources, i.e., system heterogeneity, for model training and communicating, while the diverse data distribution among devices, i.e., data heterogeneity, may result in significant performance degradation. In this paper, we propose TailorFL, a dual-personalized FL framework, which tailors a submodel for each device with personalized structure for training and personalized parameters for local inference. To achieve this, we first excavate the personalization principle for data heterogeneous FL via in-depth empirical studies, and based on which, we propose a resource-aware and data-directed pruning strategy that makes each device's submodel structure match its resource capability and correlate with its local data distribution. To aggregate the submodels while preserving their dual personalization properties, we design a scaling-based aggregation strategy that scales parameters with the pruning rate of submodels and aggregates the overlapped parameters. Moreover, to further promote beneficial and restrain detrimental collaborations among devices, we propose a server-assisted model-tuning mechanism, which dynamically tunes device's submodel structure at the server side with the global view of device's data distribution similarities. Extensive experiments demonstrate that compared to the status quo approaches, TailorFL achieves an average of 22% increase in inference accuracy, and reduces the memory, computation, and communication costs for model training simultaneously.
On Secure Ratcheting with Immediate Decryption

Ratcheting protocols let parties securely exchange messages in environments in which state exposure attacks are anticipated. While, unavoidably, some promises on confidentiality and authenticity cannot be upheld once the adversary obtains a copy of a party’s state, ratcheting protocols aim at confining the impact of state exposures as much as possible. In particular, such protocols provide forward security (after state exposure, past messages remain secure) and post-compromise security (after state exposure, participants auto-heal and regain security). Ratcheting protocols serve as core components in most modern instant messaging apps, with billions of users per day. Most instances, including Signal, guarantee immediate decryption (ID): Receivers recover and deliver the messages wrapped in ciphertexts immediately when they become available, even if ciphertexts arrive out-of-order and preceding ciphertexts are still missing. This ensures the continuation of sessions in unreliable communication networks, ultimately contributing to a satisfactory user experience. While initial academic treatments consider ratcheting protocols without ID, Alwen et al. (EC’19) propose the first ID-aware security model, together with a provably secure construction. Unfortunately, as we note, in their protocol a receiver state exposure allows for the decryption of all prior undelivered ciphertexts. As a consequence, from an adversary’s point of view, intentionally preventing the delivery of a fraction of the ciphertexts of a conversation, and corrupting the receiver (days) later, allows for correctly decrypting all suppressed ciphertexts. The same attack works against Signal. We argue that the level of (forward-)security realized by the protocol of Alwen et al., and mandated by their security model, is considerably lower than both intuitively expected and technically possible. The main contributions of our work are thus a careful revisit of the security notions for ratcheted communication in the ID setting, together with a provably secure proof-of-concept construction. One novel component of our model is that it reflects the progression of physical time. This allows for formally requiring that (undelivered) ciphertexts automatically expire after a configurable amount of time.
An Assessment of Entropy-Based Data Reduction for SEI Within IoT Applications

The research community remains focused on addressing Internet of Things (IoT) security concerns due to its continued proliferation and use of weak or no encryption. Specific Emitter Identification (SEI) has been introduced to combat this security vulnerability. Recently, Deep Learning (DL) has been leveraged to accelerate SEI using the signals' Time-Frequency (TF) representation. While TF representations improve DL-based SEI accuracy-over raw signal learning-these transforms generate large amounts of data that is computationally expensive to store and process by the DL network. This study investigates the use of entropy-based data reduction applied to “tiles” selected from the signals' TF representations. Our results show that entropy-based data reduction lowers the average SEI performance by as little as 0.86% while compressing the memory and training time requirements by as much as 92.65% and 80.7%, respectively.
Collusion-Resistant Functional Encryption for RAMs

In recent years, functional encryption (FE) has established itself as one of the fundamental primitives in cryptography. The choice of model of computation to represent the functions associated with the functional keys plays a critical role in the complexity of the algorithms of an FE scheme. Historically, the functions are represented as circuits. However, this results in the decryption time of the FE scheme growing proportional to not only the worst case running time of the function but also the size of the input, which in many applications can be quite large. In this work, we present the first construction of a public-key collusion-resistant FE scheme, where the functions, associated with the keys, are represented as random access machines (RAMs). We base the security of our construction on the existence of: (i) public-key collusion-resistant FE for circuits and, (ii) public-key doubly-efficient private-information retrieval [Boyle et al., Canetti et al., TCC 2017]. Our scheme enjoys many nice efficiency properties, including input-specific decryption time. We also show how to achieve FE for RAMs in the bounded-key setting with weaker efficiency guarantees from laconic oblivious transfer, which can be based on standard cryptographic assumptions. En route to achieving our result, we present conceptually simpler constructions of succinct garbling for RAMs [Canetti et al., Chen et al., ITCS 2016] from weaker assumptions.