Reviewerly/exHarmony huggingface dataset:
Decision Anticipation for Driving Assistance Systems

Anticipating the correctness of imminent driver decisions is a crucial challenge in advanced driving assistance systems and has the potential to lead to more reliable and safer human-robot interactions. In this paper, we address the task of decision correctness prediction in a driver-in-the-loop simulated environment using unobtrusive physiological signals, namely, eye gaze and head pose. We introduce a sequence-to-sequence based deep learning model to infer the driver's likelihood of making correct/wrong decisions based on the corresponding cognitive state. We provide extensive experimental studies over multiple baseline classification models on an eye gaze pattern and head pose dataset collected from simulated driving. Our results show strong correlates between the physiological data and decision correctness, and that the proposed sequential model reliably predicts decision correctness from the driver with 80% precision and 72% recall. We also demonstrate that our sequential model performs well in scenarios where early anticipation of correctness is critical, with accurate predictions up to two seconds before a decision is performed.
Model Poisoning Defense on Federated Learning: A Validation Based Approach

Federated learning is an improved distributed machine learning approach for privacy preservation. All clients collaboratively train the model using on-device data, and the centralized server only aggregates clients’ training results instead of collecting their data. However, there is a serious shortcoming for federated learning that the centralized server cannot detect the validity of clients’ training data and correctness of training results due to its limitation on monitoring clients’ training processes. Federated learning is vulnerable to some attacks when attackers maliciously manipulate training data or updates, such as model poisoning attacks. Attackers who execute model poisoning attacks can negatively affect the global models’ performance on a targeted class by manipulating the label of this class at one or more clients. Currently, there is a gap in the defense methods against model poisoning attacks in federated learning. To address the above shortcoming, we propose an effective defense method against model poisoning attack in federated learning in this paper. We validate each client’s local model with a validation set. The server will only receive updates from well-performing clients to protect against model poisoning attacks. We consider the following two cases: all clients have a very similar distribution of training data and all clients have a very different distribution of training data, and design our methods and experiments for both cases. The experimental results show that our defense method can significantly reduce the success rate of model poisoning attacks in both cases in a federated learning setting.
The Use of National Strategic Reference Framework Data in Knowledge Graphs and Data Mining to Identify Red Flags

Red Flags in fiscal projects are warning signs that may indicate underlying problems with their implementation. In this paper, we present how National Strategic Reference Framework Open Data can be used to take full advantage of semantic web technologies and data mining techniques to build a knowledge-based system that identifies Red Flags. We collected the data from the Open Data API provided by the Greek Ministry of Economy and Finance. Data modeling consist of two ontologies; the Vocabulary of Fiscal Projects, describing the fiscal projects and the National Strategic Reference Framework Greece Vocabulary, illustrating the Greek National Strategic Reference Framework data. We transformed the data into RDF triples and uploaded them onto an OpenLink Virtuoso Server, so that we could retrieve them via SPARQL queries. Performance indicators were defined to assess the state of the project and Density-Based Spatial Clustering of Applications with Noise, (DBSCAN) was used to identify Red Flags. User’s demands is that rejected projects should raise Red Flags, to avoid project failure and assist the auditor to organize the monitoring process efficiently, by avoiding to examine most of the non-problematic projects. We performed a use case scenario in which an auditor has to examine NSRF projects, approximately 12 months before the end of the programming period. The system retrieved the fiscal information, calculated the performance indicators and identified the Red Flags. The last update of the projects status after the end of the programming period was retrieved and extracted the number of rejected projects, to test whether the user requirements are satisfied. Rejected projects consist of 3.8% of the total projects. The results of the use case scenario show that RedFlags platform is more likely to identify project failures and not raise Red Flags on not rejected projects. Therefore, the RedFlags platform using open data, assists the auditor to organize the monitoring process better.
Incorporating Domain Knowledge Into Language Models by Using Graph Convolutional Networks for Assessing Semantic Textual Similarity: Model Development and Performance Comparison

Background Although electronic health record systems have facilitated clinical documentation in health care, they have also introduced new challenges, such as the proliferation of redundant information through the use of copy and paste commands or templates. One approach to trimming down bloated clinical documentation and improving clinical summarization is to identify highly similar text snippets with the goal of removing such text. Objective We developed a natural language processing system for the task of assessing clinical semantic textual similarity. The system assigns scores to pairs of clinical text snippets based on their clinical semantic similarity. Methods We leveraged recent advances in natural language processing and graph representation learning to create a model that combines linguistic and domain knowledge information from the MedSTS data set to assess clinical semantic textual similarity. We used bidirectional encoder representation from transformers (BERT)–based models as text encoders for the sentence pairs in the data set and graph convolutional networks (GCNs) as graph encoders for corresponding concept graphs that were constructed based on the sentences. We also explored techniques, including data augmentation, ensembling, and knowledge distillation, to improve the model’s performance, as measured by the Pearson correlation coefficient (r). Results Fine-tuning the BERT_base and ClinicalBERT models on the MedSTS data set provided a strong baseline (Pearson correlation coefficients: 0.842 and 0.848, respectively) compared to those of the previous year’s submissions. Our data augmentation techniques yielded moderate gains in performance, and adding a GCN-based graph encoder to incorporate the concept graphs also boosted performance, especially when the node features were initialized with pretrained knowledge graph embeddings of the concepts (r=0.868). As expected, ensembling improved performance, and performing multisource ensembling by using different language model variants, conducting knowledge distillation with the multisource ensemble model, and taking a final ensemble of the distilled models further improved the system’s performance (Pearson correlation coefficients: 0.875, 0.878, and 0.882, respectively). Conclusions This study presents a system for the MedSTS clinical semantic textual similarity benchmark task, which was created by combining BERT-based text encoders and GCN-based graph encoders in order to incorporate domain knowledge into the natural language processing pipeline. We also experimented with other techniques involving data augmentation, pretrained concept embeddings, ensembling, and knowledge distillation to further increase our system’s performance. Although the task and its benchmark data set are in the early stages of development, this study, as well as the results of the competition, demonstrates the potential of modern language model–based systems to detect redundant information in clinical notes.
Hybrid SMS Spam Filtering System Using Machine Learning Techniques

Due to the massive proliferation of Short Message Service (SMS), Spammers got the interest to dig their way into it in the hope to reach more targets. Spam SMS can trick mobile users into giving away their confidential information which can result in severe consequences. The seriousness of this problem has raised the need to develop an accurate Spam filtration solution. Machine learning algorithms have emerged as a great tool to classify data into labels. This description fits our case perfectly as it classifies SMS into two labels: spam or ham. This paper will tackle the SMS spam filtration solutions by introducing a hybrid system using two types of machine learning techniques: supervised & unsupervised machine learning algorithms. The new hybrid system is designed to achieve better spam filtration accuracy and F-measures.
CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation

This work proposes the continuous conditional generative adversarial network (CcGAN), the first generative model for image generation conditional on continuous, scalar conditions (termed regression labels). Existing conditional GANs (cGANs) are mainly designed for categorical conditions (e.g., class labels); conditioning on a continuous label is mathematically distinct and raises two fundamental problems: (P1) Since there may be very few (even zero) real images for some regression labels, minimizing existing empirical versions of cGAN losses (a.k.a. empirical cGAN losses) often fails in practice; (P2) Since regression labels are scalar and infinitely many, conventional label input methods (e.g., combining a hidden map of the generator/discriminator with a one-hot encoded label) are not applicable. The proposed CcGAN solves the above problems, respectively, by (S1) reformulating existing empirical cGAN losses to be appropriate for the continuous scenario; and (S2) proposing a novel method to incorporate regression labels into the generator and the discriminator. The reformulation in (S1) leads to two novel empirical discriminator losses, termed the hard vicinal discriminator loss (HVDL) and the soft vicinal discriminator loss (SVDL) respectively, and a novel empirical generator loss. The error bounds of a discriminator trained with HVDL and SVDL are derived under mild assumptions in this work. A new benchmark dataset, RC-49, is also proposed for generative image modeling conditional on regression labels. Our experiments on the Circular 2-D Gaussians, RC-49, and UTKFace datasets show that CcGAN is able to generate diverse, high-quality samples from the image distribution conditional on a given regression label. Moreover, in these experiments, CcGAN substantially outperforms cGAN both visually and quantitatively.
Encrypted internet traffic classification using a supervised spiking neural network

Internet traffic recognition is essential for access providers since it helps them define adapted priorities in order to enhance user experience, e.g., a high priority for an audio conference and a low priority for a file transfer. As internet traffic becomes increasingly encrypted, the main classic traffic recognition technique, payload inspection, is rendered ineffective. Hence this paper uses machine learning techniques looking only at packet size and time of arrival. For the first time, Spiking neural networks (SNNs), which are inspired by biological neurons, were used for this task for two reasons. Firstly, they can recognize time-related data packet features. Secondly, they can be implemented efficiently on neuromorphic hardware. Here we used a simple feedforward SNN, with only one fully connected hidden layer, and trained in a supervised manner using the new method known as Surrogate Gradient Learning. Surprisingly, such a simple SNN reached an accuracy of 95.9% on ISCX datasets, outperforming previous approaches. Besides better accuracy, there is also a significant improvement in simplicity: input size, the number of neurons, trainable parameters are all reduced by one to four orders of magnitude. Next, we analyzed the reasons for this good performance. It turns out that, beyond spatial (i.e., packet size) features, the SNN also exploits temporal ones, mainly the nearly synchronous (i.e., within a 200 ms range) arrival times of packets with specific sizes. Taken together, these results show that SNNs are an excellent fit for encrypted internet traffic classification: they can be more accurate than conventional artificial neural networks (ANN), and they could be implemented efficiently on low-power embedded systems.
Generative Scene Graph Networks

Human perception excels at building compositional hierarchies of parts and objects from unlabeled scenes that help systematic generalization. Yet most work on generative scene modeling either ignores the part-whole relationship or assumes access to predefined parts labels. In this paper, we propose Generative Scene Graph Networks (GSGNs), the first deep generative model that learns to discover the primitive parts and infer the part-whole relationship jointly from multi-object scenes without supervision and in an end-to-end trainable way. We formulate GSGN as a variational autoencoder in which the latent representation is a tree-structured probabilistic scene graph. The leaf nodes in the latent tree correspond to primitive parts, and the edges represent the symbolic pose variables required for recursively composing the parts into whole objects and then the full scene. This allows novel objects and scenes to be generated both by sampling from the prior and by manual configuration of the pose variables, as we do with graphics engines. We evaluate GSGN on datasets of scenes containing multiple compositional objects, including a challenging compositional CLEVR dataset that we have developed. We show that GSGN is able to infer the latent scene graph, generalize out of the training regime, and improve data efficiency in downstream tasks.
Coherence of type class resolution

Elaboration-based type class resolution, as found in languages like Haskell, Mercury and PureScript, is generally nondeterministic: there can be multiple ways to satisfy a wanted constraint in terms of global instances and locally given constraints. Coherence is the key property that keeps this sane; it guarantees that, despite the nondeterminism, programs still behave predictably. Even though elaboration-based resolution is generally assumed coherent, as far as we know, there is no formal proof of this property in the presence of sources of nondeterminism, like superclasses and flexible contexts. This paper provides a formal proof to remedy the situation. The proof is non-trivial because the semantics elaborates resolution into a target language where different elaborations can be distinguished by contexts that do not have a source language counterpart. Inspired by the notion of full abstraction, we present a two-step strategy that first elaborates nondeterministically into an intermediate language that preserves contextual equivalence, and then deterministically elaborates from there into the target language. We use an approach based on logical relations to establish contextual equivalence and thus coherence for the first step of elaboration, while the second step’s determinism straightforwardly preserves this coherence property.
Discrete Polynomial Optimization with Coherent Networks of Condensates and Complex Coupling Switching

Gain-dissipative platforms consisting of lasers, optical parametric oscillators and nonequilibrium condensates operating at the condensation/coherence threshold have been recently proposed as efficient analog simulators of 2-local spin Hamiltonians with continuous or discrete degrees of freedom. We show that nonequilibrium condensates above the threshold arranged in an interacting network may realise k-local Hamiltonians with k>2 and lead to nontrivial phase configurations. The principle of the operation of such a system lays the ground for physics-inspired computing and the new efficient methods for finding solutions to the higher order binary optimization problems. We show how to facilitate the search for the global solution by invoking complex couplings in the system and demonstrate the efficiency of the method on tensors with million entries. This approach offers a highly flexible new kind of computation based on gain-dissipative simulators with complex coupling switching. g.
A dataset for accounting, finance and economics research on US data breaches

This data article describes a dataset of data breaches in US listed firms over a ten-year period. Data breaches represent major events that pose serious challenges to organisations. The number of incidents has been on the increase over the last decade and this has attracted the interest of the media, consumers and regulators. While there is a well-established literature on cybersecurity in Computer Science and Information Systems journals, studies exploring the economic and business impacts of data breaches represent a relatively recent phenomenon. There is a nascent but fast-growing literature in accounting, finance and economics that focuses on the financial impacts of data breaches and this dataset provides a useful resource for future studies in this space. By providing data on the company identifier, the type of breach, the dates of breach disclosure, and relates these dates to the company's fiscal year, the dataset can be merged quickly with existing accounting and finance datasets. The dataset includes data on 506 incidents over a ten-year period thereby enabling cross-sectional and longitudinal analyses.
On the Le Cam distance between Poisson and Gaussian experiments and the asymptotic properties of Szasz estimators

In this paper, we prove a local limit theorem for the ratio of the Poisson distribution to the Gaussian distribution with the same mean and variance, using only elementary methods (Taylor expansions and Stirling's formula). We then apply the result to derive an upper bound on the Le Cam distance between Poisson and Gaussian experiments, which gives a complete proof of the sketch provided in the unpublished set of lecture notes by Pollard [41], who uses a different approach. We also use the local limit theorem to derive the asymptotics of the variance for Bernstein c.d.f. and density estimators with Poisson weights on the positive half-line (also called Szasz estimators). The propagation of errors in the literature due to the incorrect estimate in Lemma 2 (iv) of Leblanc [32] is addressed in the Appendix.
Field-induced entanglement in spatially superposed objects

We discuss the generation of field-induced entanglement between two objects, each in a superposition of two trajectories. The objects have currents coupled to local quantum fields, and the currents are evaluated around each trajectory of the objects. The fields have only dynamical degrees of freedom and satisfy the microcausality condition. We find that the superposed state of trajectories cannot be entangled when the objects are spacelike separated. This means that the quantum fields do not generate spacelike entanglement in the superposition of two trajectories of each object.
Efficient video face recognition based on frame selection and quality assessment

The article is considering the problem of increasing the performance and accuracy of video face identification. We examine the selection of the several best video frames using various techniques for assessing the quality of images. In contrast to traditional methods with estimation of image brightness/contrast, we propose to utilize the deep learning techniques that estimate the frame quality by using the lightweight convolutional neural network. In order to increase the effectiveness of the frame quality assessment step, we propose to distill knowledge of the cumbersome existing FaceQNet model for which there is no publicly available training dataset. The selected K-best frames are used to describe an input set of frames with a single average descriptor suitable for the nearest neighbor classifier. The proposed algorithm is compared with the traditional face feature extraction for each frame, as well as with the known clustering methods for a set of video frames.
Research on the Practice Information System of University Students’ Innovation and Entrepreneurship Based on the New Computer Technology Support

Abstract With the quick growth of computer new technology, under the background of China’s Ministry of Education’s active promotion of “my Chinese Dream” theme education activities, many universities vigorously guide college scholars to design, realize “innovative dream”, “entrepreneurial dream”, in-depth development of innovation and entrepreneurship education, and achieve the vision of a better life for college students. For foster more and better new talented person who can adapt to the challenges of the future, the Ministry of Education of our country has also put forward 30 items to improve the overall quality of university education, will run through the entire procedure of talent teaching and support students to achieve innovative entrepreneurship teaching. At present, in order to university students, the activities of innovative entrepreneurship education are widely carried out in colleges and universities, but there are still some problems such as inaccurate grasp of the goal and connotation of innovative entrepreneurship education, incomplete and incomplete teaching information, and disconnection with discipline and specialty education. Starting from science and reality, we can adapt to the changing new era and make a contribution to the growth of the new time.
DAMAD: Database, Attack, and Model Agnostic Adversarial Perturbation Detector

Adversarial perturbations have demonstrated the vulnerabilities of deep learning algorithms to adversarial attacks. Existing adversary detection algorithms attempt to detect the singularities; however, they are in general, loss-function, database, or model dependent. To mitigate this limitation, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DAMAD</i> —a generalized perturbation detection algorithm which is agnostic to model architecture, training data set, and loss function used during training. The proposed adversarial perturbation detection algorithm is based on the fusion of autoencoder embedding and statistical texture features extracted from convolutional neural networks. The performance of DAMAD is evaluated on the challenging scenarios of cross-database, cross-attack, and cross-architecture training and testing along with traditional evaluation of testing on the same database with known attack and model. Comparison with state-of-the-art perturbation detection algorithms showcase the effectiveness of the proposed algorithm on six databases: ImageNet, CIFAR-10, Multi-PIE, MEDS, point and shoot challenge (PaSC), and MNIST. Performance evaluation with nearly a quarter of a million adversarial and original images and comparison with recent algorithms show the effectiveness of the proposed algorithm.
Multi-Atlas Image Soft Segmentation via Computation of the Expected Label Value

The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible atlas-to-image transformations and compute the expected label value (ELV), thereby not relying merely on the transformation deemed “optimal” by the registration method. Moreover, we do so without actually performing deformable registration, thus avoiding the associated computational costs. We evaluate our ELV computation approach by applying it to brain, liver, and pancreas segmentation on datasets of magnetic resonance and computed tomography images.