Reviewerly/exHarmony huggingface dataset:
Understanding the Extent to which Content Quality Metrics Measure the Information Quality of Summaries

Reference-based metrics such as ROUGE or BERTScore evaluate the content quality of a summary by comparing the summary to a reference.Ideally, this comparison should measure the summary's information quality by calculating how much information the summaries have in common.In this work, we analyze the token alignments used by ROUGE and BERTScore to compare summaries and argue that their scores largely cannot be interpreted as measuring information overlap.Rather, they are better estimates of the extent to which the summaries discuss the same topics.Further, we provide evidence that this result holds true for many other summarization evaluation metrics.The consequence of this result is that the most frequently used summarization evaluation metrics do not align with the community's research goal, to generate summaries with high-quality information.However, we conclude by demonstrating that a recently proposed metric, QAEval, which scores summaries using question-answering, appears to better capture information quality than current evaluations, highlighting a direction for future research. 1
Microtargeted propaganda by foreign actors: An interdisciplinary exploration

This article discusses a problem that has received scant attention in literature: microtargeted propaganda by foreign actors. Microtargeting involves collecting information about people, and using that information to show them targeted political advertisements. Such microtargeting enables advertisers to target ads to specific groups of people, for instance people who visit certain websites, forums, or Facebook groups. This article focuses on one type of microtargeting: microtargeting by foreign actors. For example, Russia has targeted certain groups in the US with ads, aiming to sow discord. Foreign actors could also try to influence European elections, for instance by advertising in favour of a certain political party. Foreign propaganda possibilities existed before microtargeting. This article explores two questions. In what ways, if any, is microtargeted propaganda by foreign actors different from other foreign propaganda? What could lawmakers in Europe do to mitigate the risks of microtargeted propaganda?
Tight and Scalable Side-Channel Attack Evaluations through Asymptotically Optimal Massey-like Inequalities on Guessing Entropy

The bounds presented at CHES 2017 based on Massey's guessing entropy represent the most scalable side-channel security evaluation method to date. In this paper, we present an improvement of this method, by determining the asymptotically optimal Massey-like inequality and then further refining it for finite support distributions. The impact of these results is highlighted for side-channel attack evaluations, demonstrating the improvements over the CHES 2017 bounds.
Integer-Only CNNs with 4 Bit Weights and Bit-Shift Quantization Scales at Full-Precision Accuracy

Quantization of neural networks has been one of the most popular techniques to compress models for embedded (IoT) hardware platforms with highly constrained latency, storage, memory-bandwidth, and energy specifications. Limiting the number of bits per weight and activation has been the main focus in the literature. To avoid major degradation of accuracy, common quantization methods introduce additional scale factors to adapt the quantized values to the diverse data ranges, present in full-precision (floating-point) neural networks. These scales are usually kept in high precision, requiring the target compute engine to support a few high-precision multiplications, which is not desirable due to the larger hardware cost. Little effort has yet been invested in trying to avoid high-precision multipliers altogether, especially in combination with 4 bit weights. This work proposes a new quantization scheme, based on power-of-two quantization scales, that works on-par compared to uniform per-channel quantization with full-precision 32 bit quantization scales when using only 4 bit weights. This is done through the addition of a low-precision lookup-table that translates stored 4 bit weights into nonuniformly distributed 8 bit weights for internal computation. All our quantized ImageNet CNNs achieved or even exceeded the Top-1 accuracy of their full-precision counterparts, with ResNet18 exceeding its full-precision model by 0.35%. Our MobileNetV2 model achieved state-of-the-art performance with only a slight drop in accuracy of 0.51%.
Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules

Objective. To investigate the application value of a deep convolutional neural network (CNN) model for cytological assessment of thyroid nodules. Methods. 117 patients with thyroid nodules who underwent thyroid cytology examination in the Affiliated People’s Hospital of Ningbo University between January 2017 and December 2019 were included in this study. 100 papillary thyroid cancer samples and 100 nonmalignant samples were collected respectively. The sample images were translated vertically and horizontally. Thus, 900 images were separately created in the vertical and horizontal directions. The sample images were randomly divided into training samples (n = 1260) and test samples (n = 540) at the ratio of 7 : 3 per the training sample to test sample. According to the training samples, the pretrained deep convolutional neural network architecture Resnet50 was trained and fine-tuned. A convolutional neural network-based computer-aided detection (CNN-CAD) system was constructed to perform full-length scan of the test sample slices. The ability of CNN-CAD to screen malignant tumors was analyzed using the threshold setting method. Eighty pathological images were collected from patients who received treatment between January 2020 and May 2020 and used to verify the value of CNN in the screening of malignant thyroid nodules as verification set. Results. With the number of iterations increasing, the training and verification loss of CNN model gradually decreased and tended to be stable, and the training and verification accuracy of CNN model gradually increased and tended to be stable. The average loss rate of training samples determined by the CNN model was (22.35 ± 0.62) %, and the average loss rate of test samples determined by the CNN model was (26.41 ± 3.37) %. The average accuracy rate of training samples determined by the CNN model was (91.04 ± 2.11) %, and the average accuracy rate of test samples determined by the CNN model was (91.26 ± 1.02)%. Conclusion. A CNN model exhibits a high value in the cytological diagnosis of thyroid diseases which can be used for the cytological diagnosis of malignant thyroid tumor in the clinic.
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks

Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells.In this paper, we propose a new task of code documentation generation (CDG) for computational notebooks.In contrast to the previous CDG tasks which focus on generating documentation for single code snippets, in a computational notebook, one documentation in a markdown cell often corresponds to multiple code cells, and these code cells have an inherent structure.We proposed a new model (HAConvGNN) that uses a hierarchical attention mechanism to consider the relevant code cells and the relevant code tokens information when generating the documentation.Tested on a new corpus constructed from well-documented Kaggle notebooks, we show that our model outperforms other baseline models.
Broadband complex two-mode quadratures for quantum optics

In their seminal paper, Caves and Schumaker presented a new formalism for quantum optics, intended to serve as a building block for describing two-photon processes, in terms of new, generalized qudratures. The important, revolutionary concept in their formalism was that it was fundamentally two-mode, i.e. the related observables could not be attributed to any single one of the comprising modes, but rather to a generalized complex quadrature that could only be attributed to both of them. Here, we propose a subtle, but fundamentally meaningful modification to their important work. Unlike the above proposal, we deliberately choose a frequency-agnostic definition of the two-mode quadrature, that we motivate on physical grounds. This simple modification has far-reaching implications to the formalism – the real and imaginary parts of the quadratures now coincide with the famous EPR variables, and our two-mode operators transform trivially under two-mode and single-mode squeezing operations. Their quadratic forms, which we call the “quadrature powers” are shown to succinctly generate the SU (1, 1) algebra of squeezing Hamiltonians, and correspond directly to important, broadband physical observables, that have been directly measured in experiment and are explicitly related to properties like squeezing and entanglement. This new point of view gives a fresh perspective on two-mode processes that is completely agnostic to the bandwidth, and reveals intriguing new ways for understanding and measuring broadband two-mode squeezing.
An Extended Account of Trace-relating Compiler Correctness and Secure Compilation

Compiler correctness, in its simplest form, is defined as the inclusion of the set of traces of the compiled program in the set of traces of the original program. This is equivalent to the preservation of all trace properties. Here, traces collect, for instance, the externally observable events of each execution. However, this definition requires the set of traces of the source and target languages to be the same, which is not the case when the languages are far apart or when observations are fine-grained. To overcome this issue, we study a generalized compiler correctness definition, which uses source and target traces drawn from potentially different sets and connected by an arbitrary relation. We set out to understand what guarantees this generalized compiler correctness definition gives us when instantiated with a non-trivial relation on traces. When this trace relation is not equality, it is no longer possible to preserve the trace properties of the source program unchanged. Instead, we provide a generic characterization of the target trace property ensured by correctly compiling a program that satisfies a given source property, and dually, of the source trace property one is required to show to obtain a certain target property for the compiled code. We show that this view on compiler correctness can naturally account for undefined behavior, resource exhaustion, different source and target values, side channels, and various abstraction mismatches. Finally, we show that the same generalization also applies to many definitions of secure compilation, which characterize the protection of a compiled program linked against adversarial code.
Quantifying Rowhammer Vulnerability for DRAM Security

Rowhammer is a memory-based attack that leverages capacitive-coupling to induce faults in modern dynamic random-access memory (DRAM). Over the last decade, a significant number of Rowhammer attacks have been presented to reveal that it is a severe security issue capable of causing privilege escalations, launching distributed denial-of-service (DDoS) attacks, and even runtime attack such as control flow hijacking. Moreover, the Rowhammer vulnerability has also been identified and validated in both cloud computing and data center environments, threatening data security and privacy at a large scale. Various solutions have been proposed to counter Rowhammer attacks but existing methods lack a circuit-level explanation of the capacitive-coupling phenomenon in modern DRAMs, the key cause of Rowhammer attacks.In this paper, we develop an analytical model of capacitive-coupling vulnerabilities in DRAMs. We thoroughly analyze all parameters in the mathematical model contributing to the Rowhammer vulnerability and quantify them through real DRAM measurements. We validate the model with different attributions on a wide range of DRAM brands from various manufacturers. Through our model we re-evaluate existing Rowhammer attacks on both DDR3 and DDR4 memory, including the recently developed TRRespass attack. Our analysis presents a new Rowhammer attack insight and will guide future research in this area.
Design of a seniors and Alzheimer's disease caring service platform

To meet the needs of aging and dementia patients in Taiwan, this study designed a nursing system that includes communication, location tracking, and fall detection, and early warning services. The main purpose of this research is to provide timely services to the elderly and patients and hope to reduce the burden when the number of nursing staff decreases. This article is a remote disease care service platform with the Internet of Things (IoT) devices to monitor the location of the elderly and whether they have dropped warning alerts.The device is connected to the patient's waist and chest, monitors the patient's movement and behavior, and transmits messages to the back-end system, and informs caregivers through mobile phone applications when unexpected or shocking events occur. The system can identify whether the patient has fallen, accidentally, or long-term inactivity. The device is equipped with sensors that enable it to monitor the patient's location and behavior data through Bluetooth and GPS technology. Finally, we proposed a basic model and an integrated model that will industrialize the system and is expected to play a role in a larger patient population.The system developed in this research has passed the Activities of Daily Living (ADL) test and verification, and is expected to provide appropriate safety care services for nursing homes and elderly residences.
Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization

Modern semantic parsers suffer from two principal limitations.First, training requires expensive collection of utterance-program pairs.Second, semantic parsers fail to generalize at test time to new compositions/structures that have not been observed during training.Recent research has shown that automatic generation of synthetic utterance-program pairs can alleviate the first problem, but its potential for the second has thus far been under-explored.In this work, we investigate automatic generation of synthetic utterance-program pairs for improving compositional generalization in semantic parsing.Given a small training set of annotated examples and an "infinite" pool of synthetic examples, we select a subset of synthetic examples that are structurally-diverse and use them to improve compositional generalization.We evaluate our approach on a new split of the schema2QA dataset, and show that it leads to dramatic improvements in compositional generalization as well as moderate improvements in the traditional i.i.d setup.Moreover, structurally-diverse sampling achieves these improvements with as few as 5K examples, compared to 1M examples when sampling uniformly at random -a 200x improvement in data efficiency.
Abstractive Document Summarization with Word Embedding Reconstruction

Neural sequence-to-sequence (Seq2Seq) models and BERT have achieved substantial improvements in abstractive document summarization without and with pre-training, respectively.However, they sometimes repeatedly attend to unimportant source phrases while mistakenly ignore important ones.We present new reconstruction mechanisms on two levels to alleviate this issue.The sequence-level reconstructor reconstructs the whole source document from the hidden layer of the target summary, while the word embedding-level one rebuilds the average of word embeddings of the source at the target side to guarantee that as much critical information is included in the summary as possible.Based on the assumption that inverse document frequency (IDF) measures how important a word is, we further leverage the IDF weights in our embedding-level reconstructor.The proposed frameworks lead to promising improvements for ROUGE metrics and human rating on both the CNN/Daily Mail and Newsroom summarization datasets.
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods

Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about the security vulnerabilities that might disrupt their intended behaviour.We study data poisoning attacks against KGE models for link prediction.These attacks craft adversarial additions or deletions at training time to cause model failure at test time.To select adversarial deletions, we propose to use the model-agnostic instance attribution methods from Interpretable Machine Learning, which identify the training instances that are most influential to a neural model's predictions on test instances.We use these influential triples as adversarial deletions.We further propose a heuristic method to replace one of the two entities in each influential triple to generate adversarial additions.Our experiments show that the proposed strategies outperform the state-ofart data poisoning attacks on KGE models and improve the MRR degradation due to the attacks by up to 62% over the baselines.
A Blockchain-based Untact Education System for the Post-COVID-19 Era

With the advent of the post-COVID-19 era, the existing educational environment is at the point of transition to an untact-based educational environment.The current online education system has a limitation in safely providing the untact education system.We proposed an untact education system based on blockchain.Through the proposed method, learning contents and results can be safely stored in the blockchain.In addition, integrity damage due to data manipulation can be prevented.In particular, the efficiency of learning can be improved through behavioral analysis in the learning process, and the privacy of learners is protected by masking face recognition data.In the untact education environment, privacy exposure to learners may occur, and various information such as facial recognition data and test evaluation data may be exposed.Blockchain can safely protect against these vulnerabilities.As an advantage of the proposed method, the teacher can take appropriate action by grasping the learner's concentration through real-time behavior analysis.It also generates warnings when learners take inappropriate actions.These advantages increase the efficiency of untact education by enabling timely education.
Automatic Accident Detection Techniques using CCTV Surveillance Videos: Methods, Data sets and Learning Strategies

Intelligent communities are utilizing different creative ideas to improve the quality of human life. Due to fast growing sizes of our cities, need of travelling is constantly increasing, which in turn has increased count of vehicles on the roads. Increasing number of vehicles on the roads has brought about numerous difficulties for Street Traffic Management Authorities. Amongst different traffic related issues, road accidents are something worth giving attention to and have to be on the priority list. This paper describes various automatic road accident detection techniques, which automatically detect accidents using surveillance videos in real-time. As these methods do not consider various lighting conditions, changing weather conditions and different traffic patterns, none of the methods are robust enough to address all the incidences of the accident. In this paper, authors have described and compared many such methods.
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework

Various temporal knowledge graph (KG) completion models have been proposed in the recent literature.The models usually contain two parts, a temporal embedding layer and a score function derived from existing static KG modeling approaches.Since the approaches differ along several dimensions, including different score functions and training strategies, the individual contributions of different temporal embedding techniques to model performance are not always clear.In this work, we systematically study six temporal embedding approaches and empirically quantify their performance across a wide range of configurations with about 4000 experiments and 19000 GPU hours.We classify the temporal embeddings into two classes: (1) timestamp embeddings and (2) time-dependent entity embeddings.Despite the common belief that the latter is more expressive, an extensive experimental study shows that timestamp embeddings can achieve on-par or even better performance with significantly fewer parameters.Moreover, we find that when trained appropriately, the relative performance differences between various temporal embeddings often shrink and sometimes even reverse when compared to prior results.For example, TTransE (Leblay and Chekol, 2018), one of the first temporal KG models, can outperform more recent architectures on ICEWS datasets.To foster further research, we provide the first unified open-source framework for temporal KG completion models with full composability, where temporal embeddings, score functions, loss functions, regularizers, and the explicit modeling of reciprocal relations can be combined arbitrarily.
Novel Natural Language Summarization of Program Code via Leveraging Multiple Input Representations

The lack of description of a given program code acts as a big hurdle to those developers new to the code base for its understanding.To tackle this problem, previous work on code summarization, the task of automatically generating code description given a piece of code reported that an auxiliary learning model trained to produce API (Application Programming Interface) embeddings showed promising results when applied to a downstream, code summarization model.However, different codes having different summaries can have the same set of API sequences.If we train a model to generate summaries given an API sequence, the model will not be able to learn effectively.Nevertheless, we note that the API sequence can still be useful and has not been actively utilized.This work proposes a novel multi-task approach that simultaneously trains two similar tasks: 1) summarizing a given code (code to summary), and 2) summarizing a given API sequence (API sequence to summary).We propose a novel code-level encoder based on BERT capable of expressing the semantics of code, and obtain representations for every line of code.Our work is the first code summarization work that utilizes a natural language-based contextual pretrained language model in its encoder.We evaluate our approach using two common datasets (Java and Python) that have been widely used in previous studies.Our experimental results show that our multi-task approach improves over the baselines and achieves the new stateof-the-art.
Plug-and-Blend: A Framework for Plug-and-Play Controllable Story Generation with Sketches

Large pre-trained neural language models (LM) have very powerful text generation capabilities. However, in practice, they are hard to control for creative purposes. We describe a Plug-and-Play controllable language generation framework, Plug-and-Blend, that allows a human user to input multiple control codes (topics). In the context of automated story generation, this allows a human user loose or fine-grained control of the topics and transitions between them that will appear in the generated story, and can even allow for overlapping, blended topics. Automated evaluations show our framework, working with different generative LMs, controls the generation towards given continuous-weighted control codes while keeping the generated sentences fluent, demonstrating strong blending capability. A human participant evaluation shows that the generated stories are observably transitioning between two topics.