Reviewerly/exHarmony huggingface dataset:
A multi-instance support vector machine with incomplete data for clinical outcome prediction of COVID-19

In order to manage the public health crisis associated with COVID-19, it is critically important that healthcare workers can quickly identify high-risk patients in order to provide effective treatment with limited resources. Statistical learning tools have the potential to help predict serious infection early-on in the progression of the disease. However, many of these techniques are unable to take full advantage of temporal data on a per-patient basis as they handle the problem as a single-instance classification. Furthermore, these algorithms rely on complete data to make their predictions. In this work, we present a novel approach to handle the temporal and missing data problems, simultaneously; our proposed Simultaneous Imputation-Multi Instance Support Vector Machine method illustrates how multiple instance learning techniques and low-rank data imputation can be utilized to accurately predict clinical outcomes of COVID-19 patients. We compare our approach against recent methods used to predict outcomes on a public dataset with a cohort of 361 COVID-19 positive patients. In addition to improved prediction performance early on in the progression of the disease, our method identifies a collection of biomarkers associated with the liver, immune system, and blood, that deserve additional study and may provide additional insight into causes of patient mortality due to COVID-19. We publish the source code for our method online.1
Making progress on causal inference in economics

Enormous progress has been made on causal inference and modelling in areas outside of economics. We now have a full semantics for causality in a number of empirically relevant situations. This semantics is provided by causal graphs and allows provable precise formulation of causal relations and testable deductions from them. The semantics also allows provable rules for sufficient and biasing covariate adjustment and algorithms for deducing causal structure from data. I outline these developments, show how they describe three basic kinds of causal inference situations that standard multiple regression practice in econometrics frequently gets wrong, and show how these errors can be remedied. I also show that instrumental variables, despite claims to the contrary, do not solve these potential errors and are subject to the same morals. I argue both from the logic of elemental causal situations and from simulated data with nice statistical properties and known causal models. I apply these general points to a reanalysis of the Sachs and Warner model and data on resource abundance and growth. I finish with open potentially fruitful questions.
Detection of Inflatable Boats and People in Thermal Infrared with Deep Learning Methods

Smuggling of drugs and cigarettes in small inflatable boats across border rivers is a serious threat to the EU’s financial interests. Early detection of such threats is challenging due to difficult and changing environmental conditions. This study reports on the automatic detection of small inflatable boats and people in a rough wild terrain in the infrared thermal domain. Three acquisition campaigns were carried out during spring, summer, and fall under various weather conditions. Three deep learning algorithms, namely, YOLOv2, YOLOv3, and Faster R-CNN working with six different feature extraction neural networks were trained and evaluated in terms of performance and processing time. The best performance was achieved with Faster R-CNN with ResNet101, however, processing requires a long time and a powerful graphics processing unit.
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data

We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. This is a common scenario in many important applications. Existing related methods either exclusively fit the limited anomaly examples that typically do not span the entire set of anomalies, or proceed with unsupervised learning from the unlabeled data. We propose here instead a deep reinforcement learning-based approach that enables an end-to-end optimization of the detection of both labeled and unlabeled anomalies. This approach learns the known abnormality by automatically interacting with an anomaly-biased simulation environment, while continuously extending the learned abnormality to novel classes of anomaly (i.e., unknown anomalies) by actively exploring possible anomalies in the unlabeled data. This is achieved by jointly optimizing the exploitation of the small labeled anomaly data and the exploration of the rare unlabeled anomalies. Extensive experiments on 48 real-world datasets show that our model significantly outperforms five state-of-the-art competing methods.
Graph Edit Distance Learning via Modeling Optimum Matchings with Constraints

Graph edit distance (GED) is a fundamental measure for graph similarity analysis in many real applications. GED computation has known to be NP-hard and many heuristic methods are proposed. GED has two inherent characteristics: multiple optimum node matchings and one-to-one node matching constraints. However, these two characteristics have not been well considered in the existing learning-based methods, which leads to suboptimal models. In this paper, we propose a novel GED-specific loss function that simultaneously encodes the two characteristics. First, we propose an optimal partial node matching-based regularizer to encode multiple optimum node matchings. Second, we propose a plane intersection-based regularizer to impose the one-to-one constraints for the encoded node matchings. We use the graph neural network on the association graph of the two input graphs to learn the cross-graph representation. Our experiments show that our method is 4.2x-103.8x more accurate than the state-of-the-art methods on real-world benchmark graphs.
A Prompt-Independent and Interpretable Automated Essay Scoring Method for Chinese Second Language Writing

With the increasing popularity of learning Chinese as a second language (L2), the development of an automated essay scoring (AES) method specially for Chinese L2 essays has become an important task. To build a robust model that could easily adapt to prompt changes, we propose 90 linguistic features with consideration of both language complexity and correctness, and introduce the Ordinal Logistic Regression model that explicitly combines these linguistic features and low-level textual representations. Our model obtains a high QWK of 0.714, a low RMSE of 1.516 and a considerable Pearson correlation of 0.734. With a simple linear model, we further analyze the contribution of the linguistic features to score prediction, revealing the model’s interpretability and its potential to give writing feedback to users. This work provides insights and establishes a solid baseline for Chinese L2 AES studies.
Glitching Demystified: Analyzing Control-flow-based Glitching Attacks and Defenses

Hardware fault injection, or glitching, attacks can compromise the security of devices even when no software vulnerabilities exist. Attempts to analyze the hardware effects of glitching are subject to the Heisenberg effect and there is typically a disconnect between what people “think” is possible and what is actually possible with respect to these attacks. In this work, we attempt to provide some clarity to the impacts of attacks and defenses for control-flow modification through glitching. First, we introduce a glitching emulation framework, which provides a scalable playground to test the effects of bit flips on specific instruction set architectures (ISAs) (i.e., the fault tolerance of the instruction encoding). Next, we examine real glitching experiments using the ChipWhisperer, a popular microcontroller using open-source glitching hardware. These real-world experiments provide novel insights into how glitching attacks are realized and might be defended against in practice. Finally, we present GLITCHRESISTOR, an open-source, software-based glitching defense tool that can automatically insert glitching defenses into any existing source code, in an architecture-independent way. We evaluated GLITCHRESISTOR, which integrates numerous software-only defenses against powerful and real-world glitching attacks. Our findings indicate that software-only defenses can be implemented with acceptable run-time and size overheads, while completely mitigating some single-glitch attacks, minimizing the likelihood of a successful multi-glitch attack (i.e., a success rate of 0.000306%), and detecting failed glitching attempts at a high rate (between 79.2% and 100%).
Digital Forensic Methodology for Detection of Abnormal Flight of Drones

When a drone accident has occurred, it is difficult to decide whether it is due to a crime, malfunction, mistake, or external force. Although the cause of the accident is elucidated through analysis of artifacts or flight data, there are many limitations. In this study, we present a method for detecting an abnormal flight using the motor current values and controller direction values of a drone. The experimental result revealed that, in the case of a normal flight, the current values of four motors were similar in hovering state and the current value of rear motors were increased when the drone was flying forwards. In the case of an abnormal flight, when the drone moved rightwards due to external force in hovering state, the current values of the two motors on the right side were increased greatly. After a period of time following the movement to the right side, the current values of all the motors converged to 0. In the future, motor current values and controller direction values may be used to determine whether an abnormal flight in a drone accident has occurred because of external force by wind, birds, persons, or the like.
A Non-Volatile Optical Memory in Silicon Photonics

We demonstrate a non-volatile optical memory element integrated in silicon photonics for low-power reconfigurable photonic circuits and neural networks. Stable transmission states are set by manipulating ferroelectric domains in BaTiO3 films embedded in photonic waveguides.
Quantum Artificial Intelligence for the Science of Climate Change

Climate change has become one of the biggest global problems increasingly compromising the Earth’s habitability. Recent developments such as the extraordinary heat waves in California and Canada and the devastating floods in Germany point to the role of climate change in the ever-increasing frequency of extreme weather. Numerical modelling of weather and climate has seen tremendous improvements in the last five decades, yet stringent limitations remain to be overcome. Spatially and temporally localized forecasting is the need of the hour for effective adaptation measures towards minimizing the loss of life and property. Artificial Intelligence based methods are demonstrating promising results in improving predictions, but are still limited by the availability of requisite hardware and software required to process the vast deluge of data at a scale of the planet Earth. Quantum computing is an emerging paradigm that has found potential applicability in several fields. In this opinion piece, we argue that new developments in artificial intelligence algorithms designed for quantum computers – also known as Quantum Artificial Intelligence (QAI) – may provide the key breakthroughs necessary to furthering the science of climate change. The resultant improvements in weather and climate forecasts are expected to cascade to numerous societal benefits.
Difficulty-Controllable Visual Question Generation

Visual Question Generation (VQG) aims to generate questions from images. Existing studies on this topic focus on generating questions solely based on images while neglecting the difficulty of questions. However, to engage users, an automated question generator should produce questions with a level of difficulty that are tailored to a user’s capabilities and experience. In this paper, we propose a Difficulty-controllable Generation Network (DGN) to alleviate this limitation. We borrow difficulty index from education area to define a difficulty variable for representing the difficulty of questions, and fuse it into our model to guide the difficulty-controllable question generation. Experimental results demonstrate that our proposed model not only achieves significant improvements on several automatic evaluation metrics, but also can generate difficulty-controllable questions.
Research on the Cultivation of College Students’ Innovative and Entrepreneurship Ability Based on the Internet Technology

Abstract At present, China’s economy is in an important stage of transformation and upgrading. In order to provide continuous driving force for development, we must attach importance to the improvement of the comprehensive quality of college students and effectively solve the existing employment problems. Especially in the rapid development of information technology today, in addition to mastering the basic professional literacy, college students also need to have a certain ability to innovate and start a business, so as to better meet the needs of the market development for the full range of talent types, and more in line with the requirements of the phased development of our country. Based on this, this paper will study how to cultivate the innovative and entrepreneurial ability of college students in the computer Internet era, hoping to provide some help to the actual university education, so that colleges and universities can really cultivate generation after generation of high-quality innovative talents for the rapid development of society [1].
A Cross-Country Study of Innovation and Religiosity

We look at the relationship between religion and innovation at the country level. Innovation is captured by the Global Innovation Index and certain innovation outputs. Religiosity is measured across 141 countries in terms of religious belonging, belief, and intolerance. In addition, we look at the effect of religion on patent applications and university-industry collaboration. Across all specifications, greater religiosity is negatively associated with innovation, with the strongest effects observed for religious intolerance. We find the negative relationship is robust to all religions, except for the Church of Sweden, where we see a strong position association.
Criticality-enhanced quantum sensor at finite temperature

Conventional criticality-based quantum metrological schemes work only at zero or very low temperature because the quantum uncertainty around the quantum phase-transition point is generally erased by thermal fluctuations with the increase of temperature. Such an ultralow-temperature requirement severely restricts the development of quantum critical metrology. In this paper, we propose a thermodynamic-criticality-enhanced quantum sensing scenario at finite temperature. In our scheme, a qubit is employed as a quantum sensor to estimate parameters of interest in the Dicke model which experiences a thermodynamic phase transition. It is revealed that the thermodynamic criticality of the Dicke model can significantly improve the sensing precision. Enriching the scope of quantum critical metrology, our finding provides a possibility to realize highly sensitive quantum sensing without cooling.
DiLOS

Memory disaggregation places computing and memory in physically separate nodes and achieves improved memory utilization in datacenters. Kernel-based approaches for memory disaggregation offer transparent virtual memory by using paging schemes but suffer from expensive page fault handling. As an alternative, library-based approaches incorporate application semantics to memory disaggregation and can even eliminate page fault handling on its data path. However, its lack of compatibility harms generality and obstruct wide adoption.
Robust 2-bit Quantization of Weights in Neural Network Modeled by Laplacian Distribution

Significant efforts are constantly involved in finding manners to decrease the number of bits required for quantization of neural network parameters. Although in addition to compressio ...
An index theorem for one-dimensional gapless non-unitary quantum walks

Recent developments in the index theory of discrete-time quantum walks allow us to assign a certain well-defined supersymmetric index to a pair of a unitary time-evolution $U$ and a $\mathbb{Z}_2$-grading operator $\varGamma$ satisfying the chiral symmetry condition $U^* = \varGamma U \varGamma.$ In this paper, this index theory will be extended to encompass non-unitary $U$. The existing literature for unitary $U$ makes use of the indispensable assumption that $U$ is essentially gapped; that is, we require that the essential spectrum of $U$ contains neither $-1$ nor $+1$ to define the associated index. It turns out that this assumption is no longer necessary, if the given time-evolution $U$ is non-unitary. As a concrete example, we shall consider a well-known non-unitary quantum walk model on the one-dimensional integer lattice, introduced by Mochizuki-Kim-Obuse.
OSS Effort Estimation Using Software Features Similarity and Developer Activity-Based Metrics

Software development effort estimation (SDEE) generally involves leveraging the information about the effort spent in developing similar software in the past. Most organizations do not have access to sufficient and reliable forms of such data from past projects. As such, the existing SDEE methods suffer from low usage and accuracy. We propose an efficient SDEE method for open source software, which provides accurate and fast effort estimates. The significant contributions of our article are (i) novel SDEE software metrics derived from developer activity information of various software repositories, (ii) an SDEE dataset comprising the SDEE metrics’ values derived from approximately 13,000 GitHub repositories from 150 different software categories, and (iii) an effort estimation tool based on SDEE metrics and a software description similarity model . Our software description similarity model is basically a machine learning model trained using the PVA on the software product descriptions of GitHub repositories. Given the software description of a newly envisioned software, our tool yields an effort estimate for developing it. Our method achieves the highest standardized accuracy score of 87.26% (with Cliff’s δ = 0.88 at 99.999% confidence level) and 42.7% with the automatically transformed linear baseline model. Our software artifacts are available at https://doi.org/10.5281/zenodo.5095723.
APIA: An Architecture for Policy-Aware Intentional Agents

This paper introduces the APIA architecture for policy-aware intentional agents. These agents, acting in changing environments, are driven by intentions and yet abide by domain-relevant policies. This work leverages the AIA architecture for intention-driven intelligent agents by Blount, Gelfond, and Balduccini. It expands AIA with notions of policy compliance for authorization and obligation policies specified in the language AOPL by Gelfond and Lobo. APIA introduces various agent behavior modes, corresponding to different levels of adherence to policies. APIA reasoning tasks are reduced to computing answer sets using the Clingo solver and its Python API.
Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition

Human activity recognition is an extensively researched topic in the last decade. Recent methods employ supervised and unsupervised deep learning techniques in which spatial and temporal dependency is modeled. This paper proposes a novel approach for human activity recognition using skeleton data. The method combines supervised and unsupervised learning algorithms in order to provide qualitative results and performance in real time. The proposed method involves a two-stage framework: the first stage applies an unsupervised clustering technique to group up activities based on their similarity, while the second stage classifies data assigned to each group using graph convolutional networks. Different clustering techniques and data augmentation strategies are explored for improving the training process. The results were compared against the state of the art methods and the proposed model achieved 90.22% Top-1 accuracy performance for NTU-RGB+D dataset (the performance was increased by approximately 9% compared with the baseline graph convolutional method). Moreover, inference time and total number of parameters stay within the same magnitude order. Extending the initial set of activities with additional classes is fast and robust, since there is no required retraining of the entire architecture but only to retrain the cluster to which the activity is assigned.
Differential Complex-Valued Convolutional Neural Network-Based Individual Recognition of Communication Radiation Sources

Data assaults from unauthorized access to the Internet of Things will induce severe intrusion and hazard to the whole network. Employing only traditional application layer password authentication approaches cannot guarantee the security of the communication system. Therefore, it is critical to develop a capable and efficient radio frequency fingerprints based physical layer authentication system. To incorporate the domain knowledge in more capable feature extracting and reduce information loss caused by converting RF baseband I/Q signals, we propose a novel differential complex-valued convolutional neural network based individual recognition approach of communication radiation sources in the paper. The proposed method can fully capture the nonlinearity of the RF baseband I/Q signals while decreasing the unfavorable impact of phase rotation induced by carrier frequency offset, which also significantly reduces the required data length of the collected steady-state data transmission section. The recognition performance evaluation on 20 wireless network card devices with duplicate batch, type, and manufacturer shows that the proposed approach has the best recognition performance compared with two conventional approaches whose recognition accuracies are lower than 95%, achieving the total recognition accuracy of 99.7%. Moreover, compared with constellation based approaches, which require at least 5,000 to 10,000 data points as input parameters, the proposed method can reduce the required data length of the collected steady-state data transmission section effectively, which is easier to implement in practical applications.
Towards the Evaluation of Information Retrieval Systems on Evolving Datasets with Pivot Systems

Evaluation of information retrieval systems follows the Cranfield paradigm, where the evaluation of several IR systems relies on a common evaluation environment (test collection and evaluation settings). The Cranfield paradigm requires the evaluation environment (EE) to be strictly identical to compare system’s performances. For those cases where such paradigm cannot be used, e.g. when we do not have access to the code of the systems, we consider an evaluation framework that allows for slight changes in the EEs, as the evolution of the document corpus or topics. To do so, we propose to compare systems evaluated on different environments using a reference system, called pivot. In this paper, we present and validate a method to select a pivot, which is used to construct a correct ranking of systems evaluated in different environments. We test our framework on the TREC-COVID test collection, which is composed of five rounds of growing topics, documents and relevance judgments. The results of our experiments show that the pivot strategy can propose a correct ranking of systems evaluated in an evolving test collection.