Reviewerly/exHarmony huggingface dataset:
PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection

With ever-increasing dataset sizes, subset selection techniques are becoming increasingly important for a plethora of tasks. It is often necessary to guide the subset selection to achieve certain desiderata, which includes focusing or targeting certain data points, while avoiding others. Examples of such problems include: i)targeted learning, where the goal is to find subsets with rare classes or rare attributes on which the model is under performing, and ii)guided summarization, where data (e.g., image collection, text, document or video) is summarized for quicker human consumption with specific additional user intent. Motivated by such applications, we present PRISM, a rich class of PaRameterIzed Submodular information Measures. Through novel functions and their parameterizations, PRISM offers a variety of modeling capabilities that enable a trade-off between desired qualities of a subset like diversity or representation and similarity/dissimilarity with a set of data points. We demonstrate how PRISM can be applied to the two real-world problems mentioned above, which require guided subset selection. In doing so, we show that PRISM interestingly generalizes some past work, therein reinforcing its broad utility. Through extensive experiments on diverse datasets, we demonstrate the superiority of PRISM over the state-of-the-art in targeted learning and in guided image-collection summarization. PRISM is available as a part of the SUBMODLIB (https://github.com/decile-team/submodlib) and TRUST (https://github.com/decile-team/trust) toolkits.
Exploring Relational Semantics for Inductive Knowledge Graph Completion

Knowledge graph completion (KGC) aims to infer missing information in incomplete knowledge graphs (KGs). Most previous works only consider the transductive scenario where entities are existing in KGs, which cannot work effectively for the inductive scenario containing emerging entities. Recently some graph neural network-based methods have been proposed for inductive KGC by aggregating neighborhood information to capture some uncertainty semantics from the neighboring auxiliary triples. But these methods ignore the more general relational semantics underlying all the known triples that can provide richer information to represent emerging entities so as to satisfy the inductive scenario. In this paper, we propose a novel model called CFAG, which utilizes two granularity levels of relational semantics in a coarse-grained aggregator (CG-AGG) and a fine-grained generative adversarial net (FG-GAN), for inductive KGC. The CG-AGG firstly generates entity representations with multiple semantics through a hypergraph neural network-based global aggregator and a graph neural network-based local aggregator, and the FG-GAN further enhances entity representations with specific semantics through conditional generative adversarial nets. Experimental results on benchmark datasets show that our model outperforms state-of-the-art models for inductive KGC.
Acceleration-based Activity Recognition of Repetitive Works with Lightweight Ordered-work Segmentation Network

This study presents a new neural network model for recognizing manual works using body-worn accelerometers in industrial settings, named Lightweight Ordered-work Segmentation Network (LOS-Net). In industrial domains, a human worker typically repetitively performs a set of predefined processes, with each process consisting of a sequence of activities in a predefined order. State-of-the-art activity recognition models, such as encoder-decoder models, have numerous trainable parameters, making their training difficult in industrial domains because of the consequent substantial cost for preparing a large amount of labeled data. In contrast, the LOS-Net is designed to be trained on a limited amount of training data. Specifically, the decoder in the LOS-Net has few trainable parameters and is designed to capture only the necessary information for precise recognition of ordered works. These are (i) the boundary information between consecutive activities, because a transition in the performed activities is generally associated with the trend change of the sensor data collected during the manual works and (ii) long-term context regarding the ordered works, e.g., information about the previous and next activity, which is useful for recognizing the current activity. This information is obtained by introducing a module that can collect it at distant time steps using few trainable parameters. Moreover, the LOS-Net can refine the activity estimation by the decoder by incorporating prior knowledge regarding the order of activities. We demonstrate the effectiveness of the LOS-Net using sensor data collected from workers in actual factories and a logistics center, and show that it can achieve state-of-the-art performance.
Power Transformations and Feature Alignment Guided Network for SAR Ship Detection

Due to the capacity of full-time and full-weather working, synthetic aperture radar (SAR) images have been frequently applied to ship detection. However, the interference of speckle noise and shores poses enormous challenges to the accuracy of detection. Extracting multi-scale features is regarded as a good way to detect ships of different sizes, but features at different scales are not strictly aligned, which may further affect the detection results. Therefore, this letter proposes an anchor-free method, namely power transformations and feature alignment guided network (Pow-FAN), to solve the above problems. In Pow-FAN, we first utilize a power-based convolution block called PCB to extract features, which can suppress speckle noise and shores and enhance the ship targets. Furthermore, a novel feature alignment block named FAB is put forward to avoid the dislocation problems when integrating features of different scales. Compared with other state-of-the-art methods, Pow-FAN can achieve competitive detection results on the SAR ship detection dataset (SSDD) and the High-Resolution SAR Images Dataset (HRSID). The ablation study and discussion on the backbone and different situations further demonstrate the superiority of our network structure.
Edge Machine Learning Techniques Applied to RFID for Device-Free Hand Gesture Recognition

Gesture recognition is a novel technology that aims to change the way people interact with machines. Existing solutions typically recognize gestures using camera vision, wearable sensors, or specialized signals (e.g., WiFi, sound, and visible light), but they have limitations such as high-power consumption or low signal-to-noise ratio (SNR) in comparison to their surroundings, making it difficult to accurately detect finger movements. In this research, we propose a device-free gesture identification system that recognizes different hand movements by processing through Edge Machine Learning (EML) algorithms the received signal strength indication (RSSI) and phase values from backscattered signals of a collection of Radio Frequency IDentification (RFID) tags mounted on a plastic plate. The performances of three algorithms, the Random Forest Classifier, the Support Vector Machine, and the Decision Tree Classifier, were compared giving very encouraging results with accuracy up to 99.4%.
A SINS/GNSS/VDM Integrated Navigation Fault-Tolerant Mechanism Based on Adaptive Information Sharing Factor

Autonomous driving vehicle is a kind of unmanned vehicle on the ground. Navigation and positioning technology is becoming an indispensable part of practical applications, especially in intelligent transportation systems and military fields. In this study, multi-source information fusion technology plays a necessary role in enhancing the quality and reliability of navigation and positioning. In the actual complex environment, the accuracy requirements of vehicle navigation and positioning in the case of nonlinear systems and non-Gaussian distribution are fully considered. In this paper, a new adaptive sharing factor integrated navigation information fusion technology scheme is designed. From a geometric point of view, the VDM is studied, and a new VDM observation information that fully considers the turning process of the vehicle is established. It combines SINS, GNSS and VDM multi-source sensors, a VDM and GNSS measurement model based on non-integrity constraints is developed. The performance of the local filter is adaptively reflected by the F-norm adaptive information sharing factor algorithm established based on the variance. At the same time, the development process of a federated filter fault diagnosis algorithm based on Unscented Kalman Filter (UKF) is reported, which has fault diagnosis and information recovery functions. The independent characteristics of the sub-filters are considered, and a fault-tolerant mechanism for state chi-square detection is established. Both GNSS and VDM are used as observation sources to improve the accuracy of low-cost SINS. This strategy is implemented on the basis of Carsim professional vehicle dynamics software. The evaluation test indicated that the algorithm has high navigation accuracy, high fault tolerance and guaranteed system reliability, which is verified in this paper.
Does ICT diffusion contribute to women's political empowerment in Africa?

Abstract In this article we investigate the effects of information and communication technologies (ICT) on women's political empowerment (WPE) from a sample of 51 African countries. We specify and estimate a panel data model by using the ordinary least squares (OLS) and two‐stage least squares (2SLS) over the period 1996–2017. The results show that ICT diffusion, measured by mobile phone and internet penetration, significantly increases WPE. This suggests an improvement of the institutional framework governing the use of ICT for a better WPE.
Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention

Most of today's AI systems focus on using self-attention mechanisms and transformer architectures on large amounts of diverse data to achieve impressive performance gains. In this paper, we propose to augment the transformer architecture with an external attention mechanism to bring external knowledge and context to bear. By integrating external information into the prediction process, we hope to reduce the need for ever-larger models and increase the democratization of AI systems. We find that the proposed external attention mechanism can significantly improve the performance of existing AI systems, allowing practitioners to easily customize foundation AI models to many diverse downstream applications. In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and significantly improve the model's reasoning capabilities. The proposed system, Knowledgeable External Attention for commonsense Reasoning (KEAR), reaches human parity on the open CommonsenseQA research benchmark with an accuracy of 89.4% in comparison to the human accuracy of 88.9%.
A survey on solutions to support developers in privacy-preserving IoT development

Internet-of-Things (IoT) devices are rising in popularity and their usefulness often stems from the amount of data they collect. Data regulations such as the European General Data Protection Regulation (GDPR) require software developers to do their due diligence when it comes to privacy, as they are required to adhere to certain principles such as Privacy-by-Design (PbD). Due to the distributed and heterogeneous nature of IoT applications, privacy-preserving design is even more important in IoT environments. Studies have shown that developers are often not eager to implement privacy and generally do not see it as their duty or concern. However, developers are often left alone when it comes to engineering privacy in the realm of IoT. In this paper, we therefore survey which frameworks and tools have been developed for them, especially in the case of IoT. Our findings indicate that existing solutions are cumbersome to use, only work in certain scenarios, and are not enough to solve the privacy issues inherent IoT development. Based on our analysis, we further propose future research directions.
DeepCoFFEA: Improved Flow Correlation Attacks on Tor via Metric Learning and Amplification

End-to-end flow correlation attacks are among the oldest known attacks on low-latency anonymity networks, and are treated as a core primitive for traffic analysis of Tor. However, despite recent work showing that individual flows can be correlated with high accuracy, the impact of even these state-of-the-art attacks is questionable due to a central drawback: their pairwise nature, requiring comparison between N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> pairs of flows to deanonymize N users. This results in a combinatorial explosion in computational requirements and an asymptotically declining base rate, leading to either high numbers of false positives or vanishingly small rates of successful correlation. In this paper, we introduce a novel flow correlation attack, DeepCoFFEA, that combines two ideas to overcome these drawbacks. First, DeepCoFFEA uses deep learning to train a pair of feature embedding networks that respectively map Tor and exit flows into a single low-dimensional space where correlated flows are similar; pairs of embedded flows can be compared at lower cost than pairs of full traces. Second, DeepCoFFEA uses amplification, dividing flows into short windows and using voting across these windows to significantly reduce false positives; the same embedding networks can be used with an increasing number of windows to independently lower the false positive rate. We conduct a comprehensive experimental analysis showing that DeepCoFFEA significantly outperforms state-of-the-art flow correlation attacks on Tor, e.g. 93% true positive rate versus at most 13% when tuned for high precision, with two orders of magnitude speedup over prior work. We also consider the effects of several potential countermeasures on DeepCoFFEA, finding that existing lightweight defenses are not sufficient to secure anonymity networks from this threat.
A multi-layer soft lattice based model for Chinese clinical named entity recognition

Named entity recognition (NER) is a key and fundamental part of many medical and clinical tasks, including the establishment of a medical knowledge graph, decision-making support, and question answering systems. When extracting entities from electronic health records (EHRs), NER models mostly apply long short-term memory (LSTM) and have surprising performance in clinical NER. However, increasing the depth of the network is often required by these LSTM-based models to capture long-distance dependencies. Therefore, these LSTM-based models that have achieved high accuracy generally require long training times and extensive training data, which has obstructed the adoption of LSTM-based models in clinical scenarios with limited training time.Inspired by Transformer, we combine Transformer with Soft Term Position Lattice to form soft lattice structure Transformer, which models long-distance dependencies similarly to LSTM. Our model consists of four components: the WordPiece module, the BERT module, the soft lattice structure Transformer module, and the CRF module.Our experiments demonstrated that this approach increased the F1 by 1-5% in the CCKS NER task compared to other models based on LSTM with CRF and consumed less training time. Additional evaluations showed that lattice structure transformer shows good performance for recognizing long medical terms, abbreviations, and numbers. The proposed model achieve 91.6% f-measure in recognizing long medical terms and 90.36% f-measure in abbreviations, and numbers.By using soft lattice structure Transformer, the method proposed in this paper captured Chinese words to lattice information, making our model suitable for Chinese clinical medical records. Transformers with Mutilayer soft lattice Chinese word construction can capture potential interactions between Chinese characters and words.
Architecture reduction of a probabilistic neural network by merging k-means and k-nearest neighbour algorithms

Probabilistic neural network (PNN) has a sizable structure since it requires all training records in the activation of its hidden layer. This fact makes it suffer from the problem of the curse of dimensionality. Therefore, an hypotheses can be easily formulated that in order to manage large data classification tasks, it is recommended to minimise its inner design. In this paper, we directly address this issue: the method for the PNN’s architecture reduction is elaborated. It is organised as follows. First, a k-means data clustering is conducted and the obtained centres are stored. Next, one selects a single nearest neighbour to the determined centres considering each class separately. The pattern neurons of a PNN are then established using both (i) the cluster centres and (ii) the records closest to the obtained centroids. The algorithm is applied to the classification tasks of seven repository data sets. The utilised PNN is trained by means of four training techniques with different kernel functions in each case. A 10–fold cross validation method is applied to assess the performance of the original and reduced networks. The obtained results are also compared with those provided by existing methods in the literature. It is shown that in the majority classification cases, it is possible to achieve a higher accuracy of the reduced PNN compared to the original network and the approaches introduced in the literature.
Machine Learning for Source Code Vulnerability Detection: What Works and What Isn’t There Yet

We review machine learning approaches for detecting (and correcting) vulnerabilities in source code, finding that the biggest challenges ahead involve agreeing to a benchmark, increasing language and error type coverage, and using pipelines that do not flatten the code's structure.
Building Occupancy Detection and Localization Using CCTV Camera and Deep Learning

Occupancy information plays a key role in analyzing and improving building energy performance. The advances of Internet of Things (IoT) technologies have engendered a shift in measuring building occupancy with IoT sensors, in which cameras in closed-circuit television (CCTV) systems can provide richer measurements. However, existing camera-based occupancy detection approaches cannot function well when scanning videos with a number of occupants and determining occupants’ locations. This article aims to develop a novel deep-learning-based approach for better building occupancy detection based on CCTV cameras. To do so, this research proposes a deep-learning model to detect the number of occupants and determine their locations in videos. This model consists of two main modules, namely, feature extraction and three-stage occupancy detection. The first module presents a deep convolutional neural network to perform residual and multibranch convolutional calculation to extract shallow and semantic features, and constructs feature pyramids through a bidirectional feature network. The second module performs a three-stage detection procedure with three sequential and homogeneous detectors which have increasing Intersection over Union (IoU) thresholds. Empirical experiments evaluate the detection performance of the approach with CCTV videos from a university building. Experimental results show that the approach achieves the superior detection performance when compared with baseline models.
A semantic structuring of educational research using ontologies

This article is devoted to the presentation of the semantic interoperability of research and scientific results through an ontological taxonomy. To achieve this, the principles of systematization and structuration of the scientific/research results in scientometrics databases have been analysed. We use the existing cognitive IT platform Polyhedron and extend it with an ontology-based information model as main contribution. As a proof-of-concept we have modelled two ontological graphs, “Development of a rational way for utilization of methane tank waste at LLC Vasylkivska poultry farm” and “Development a method for utilization of methane tank effluent”. Also, for a demonstration of the perspective of ontological systems for a systematization of research and scientific results, the “Hypothesis test system” ontological graph has created.
Asymmetric determinants of Bitcoin's wild price movements

Purpose The present work endeavors to explore the potential nonlinear and asymmetric effects of supply fundamental properties of Bitcoin mining process (velocity, size and stock of Bitcoins, cost of production and mining revenue), DJIA, VIX, economic policy uncertainty and Google Trend on the price of Bitcoin (PB). Design/methodology/approach The authors apply the Nonlinear Autoregressive Distributed lag (NARDL) approach for the period from November 31, 2013 to December 30, 2020. Findings The asymmetric effects of inflation, the size of Bitcoin economy, reveal a positive impact on the PB in the short and long run. In the short run, Bitcoin price shows negative statistically significant sensitivity to positive (negative) changes in DJIA (VIX) index. In addition, Google Trends have an impact on Bitcoin prices indicating that the Bitcoin market is also driven by investors' sentiments. In the long run, negative policy uncertainty shocks increase the PB while in the short run, negative shocks decrease it. Originality/value The authors give credence to the best ways of understanding the existence of asymmetries in the link between the PB and a number of influential macro-finance variables to improve the appropriate asset allocation and portfolio management.
Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering

To improve the trajectory prediction accuracy of unmanned aerial vehicles (UAVs) with random behavior intentions, this paper presents a short-term four-dimensional (4D) trajectory prediction method based on spatio-temporal trajectory clustering. A spatio-temporal trajectory clustering algorithm is first designed to cluster the UAV trajectory segments divided by a fixed time window. Each trajectory segment is given a category label that represents some certain type of behavior characteristics, such as climbing, turning, descending, etc. The convolutional neural network (CNN) is used to identify the category label of a given trajectory segment by learning the behavior characteristics of different trajectory segments. Based on the long-short-term memory network (LSTM), a short-term trajectory prediction model for different categories of label trajectory segments is established. The global trajectory prediction includes several steps adopting the corresponding prediction models. Historical trajectory data of UAVs are used to validate the proposed prediction method. Experiment results indicate that the method can obtain obviously better prediction accuracy in a short prediction time range (0-3s) with acceptable efficiency compared to LSTM, GRU and velocity trend extrapolation.