Reviewerly/exHarmony huggingface dataset:
Learning from few examples with nonlinear feature maps

In this work we consider the problem of data classification in post-classical settings were the number of training examples consists of mere few data points. We explore the phenomenon and reveal key relationships between dimensionality of AI model's feature space, non-degeneracy of data distributions, and the model's generalisation capabilities. The main thrust of our present analysis is on the influence of nonlinear feature transformations mapping original data into higher- and possibly infinite-dimensional spaces on the resulting model's generalisation capabilities. Subject to appropriate assumptions, we establish new relationships between intrinsic dimensions of the transformed data and the probabilities to learn successfully from few presentations.
Empirical Evaluation of Deep Learning Models for Knowledge Tracing: Of Hyperparameters and Metrics on Performance and Replicability

We review and evaluate a body of deep learning knowledge tracing (DLKT) models with openly available and widely-used data sets, and with a novel data set of students learning to program. The evaluated knowledge tracing models include Vanilla-DKT, two Long Short-Term Memory Deep Knowledge Tracing (LSTM-DKT) variants, two Dynamic Key-Value Memory Network (DKVMN) variants, and Self-Attentive Knowledge Tracing (SAKT). As baselines, we evaluate simple non-learning models, logistic regression and Bayesian Knowledge Tracing (BKT). To evaluate how different aspects of DLKT models influence model performance, we test input and output layer variations found in the compared models that are independent of the main architectures. We study maximum attempt count options, including filtering out long attempt sequences, that have been implicitly and explicitly used in prior studies. We contrast the observed performance variations against variations from non-model properties such as randomness and hardware. Performance of models is assessed using multiple metrics, whereby we also contrast the impact of the choice of metric on model performance. The key contributions of this work are: Evidence that DLKT models generally outperform more traditional models, but not necessarily by much and not always; Evidence that even simple baselines with little to no predictive value may outperform DLKT models, especially in terms of accuracy -- highlighting importance of selecting proper baselines for comparison; Disambiguation of properties that affect performance in DLKT models including metric choice, input and output layer variations, common hyperparameters, random seeding and hardware; Discussion of issues in replicability when evaluating DLKT models, including discrepancies in prior reported results and methodology. Model implementations, evaluation code, and data are published as a part of this work.
Set-based comprehensive learning and particle swarm optimization with memory for discrete optimization problem

This paper describes a novel algorithm of set-based comprehensive learning and particle swarm optimization with memory for discrete optimization problem. Our algorithm is an approach of searching for the best position in a set by referring to the current position when updating the solution. In order to show the validity of our algorithm, we examine numerical experiments compared to existing algorithms.
Machine Learning Mega-Analysis Applied to the Response Time Concealed Information Test: No Evidence for Advantage of Model-Based Predictors Over Baseline

The response time Concealed Information Test (RT-CIT) can help to reveal whether a person is concealing the knowledge of a certain information detail. During the RT-CIT, the examinee is repeatedly presented with a probe, the detail in question (e.g., murder weapon), and several irrelevants, other details that are similar to the probe (e.g., other weapons). These items all require the same keypress response, while one further item, the target, requires a different keypress response. Examinees tend to respond to the probe slower than to irrelevants, when they recognize the former as the relevant detail. To classify examinees as having or not having recognized the probe, RT-CIT studies have almost always used the averaged difference between probe and irrelevant RTs as the single predictor variable. In the present study, we tested whether we can improve classification accuracy (recognized the probe: yes or no) by incorporating the average RTs, the accuracy rates, and the SDs of each item type (probe, irrelevant, and target). Using the data from 1,871 individual tests and incorporating various combinations of the additional variables, we built logistic regression, linear discriminant analysis, and extra trees machine learning models (altogether 26), and we compared the classification accuracy of each of the model-based predictors to that of the sole probe-irrelevant RT difference predictor as baseline. None of the models provided significant improvement over the baseline. Nominal gains in classification accuracy ranged between –1.5% and 3.1%. In each of the models, machine learning captured the probe-irrelevant RT difference as the most important contributor to successful predictions, or, when included separately, the probe RT and the irrelevant RT as the first and second most important contributors, respectively.
Actions over Core-closed Knowledge Bases

We present new results on the application of semantic- and knowledge-based reasoning techniques to the analysis of cloud deployments. In particular, to the security of Infrastructure as Code configuration files, encoded as description logic knowledge bases. We introduce an action language to model mutating actions; that is, actions that change the structural configuration of a given deployment by adding, modifying, or deleting resources. We mainly focus on two problems: the problem of determining whether the execution of an action, no matter the parameters passed to it, will not cause the violation of some security requirement (static verification), and the problem of finding sequences of actions that would lead the deployment to a state where (un)desirable properties are (not) satisfied (plan existence and plan synthesis). For all these problems, we provide definitions, complexity results, and decision procedures.
A Novel Multi-objective UAV Trajectory Planning Method Based on Modified BSO Algorithm

Aiming at the problems of high complexity and low efficiency of optimization algorithms in the multi-UAV reconnaissance trajectory planning problem, a novel multi-objective UAV trajectory planning method based on modified brainstorming optimization algorithm (BSO) is proposed. First, the multi-objective UAV trajectory planning is transformed into a single-objective UAV trajectory planning problem by means of a clustering algorithm, which reduces the solution complexity; Second, a real number coded brainstorming algorithm is proposed to solve the UAV trajectory planning problem; Finally, the algorithm is evaluated in the internationally commonly used library of TSP (TSPLIB). The results show that the novel algorithm is superior to other algorithms in terms of global optimization ability, convergence speed and stability.
A Socially-Aware, Privacy-Preserving, and Scalable Federated Learning Protocol for Distributed Online Social Networks

Online Social Networks (OSNs) have been gaining tremendous growth by attracting billions of users from all over the world. Such massive growth leads to scalability and data privacy concerns. Decentralized solutions still are not able to solve privacy and scalability problems efficiently. Hence, recently fully Distributed Online Social Networks (DOSNs) have been proposed. However, despite solving scalability and privacy issues, fully DOSNs impose difficulties in executing data mining and machine learning services which are vital for social networks. In the fully DOSN, each user has only one feature vector and these vectors cannot move to any central storage or other users in a raw form due to privacy issues. In addition, users can directly communicate only with their immediate neighbours/friends in a social network/graph. To cope with these problems, we propose a novel Federated learning algorithm for DOSNs based on the Gossip protocol. We propose a two layer protocol in which the underlying layer is a socially-aware gossip sampling protocol and the upper layer is a push-based merging gossip protocol. The former is responsible for creating a socially-aware random overlay network while the latter, utilizing the sampling protocol, does the training model. We implement our algorithm and through extensive experiments show that the algorithm trains the model up to 88% accuracy compared to the centralized approach.
Personalized Desire2Learn Recommender System based on Collaborative Filtering and Ontology

In this century, attention has grown to recommendation systems (RS), especially in e-learning, to solve the problem of overloading information in e-learning systems.Elearning providers also play a major role in helping learners to find appropriate courses that fit their learning plan using Desire2Learn at Majmaah University.Although recommendation systems generally have a clear advantage in solving problems related to overloading information in various areas of e-business and making accurate recommendations, elearning recommendation systems still have problems with overloading information about the characteristics of the learning recipient Such as the appropriate education style, the level of skills provided and the student's level of education.In this paper, we suggest that a recommendation technique combining collaborative filtering and ontology be introduced to recommend courses for learning recipients through Desire2Learn.Ontology involves the integration of the characteristics of the learning recipient into the recommendation process as well as the classifications, while the liquidation process cooperates in the predictions and generates recommendations for e-learning.In addition, ontological knowledge is employed by the educational RS in the early stages if no assessments can be made to mitigate the cold start problem.The results of this study show that the proposed recommendation technique is distinguished and superior to the cooperative liquidation in terms of specialization and accuracy of the recommendation.
FaceFormer: Speech-Driven 3D Facial Animation with Transformers

Speech-driven 3D facial animation is challenging due to the complex geometry of human faces and the limited availability of 3D audio-visual data. Prior works typically focus on learning phoneme-level features of short audio windows with limited context, occasionally resulting in inaccurate lip movements. To tackle this limitation, we propose a Transformer-based autoregressive model, FaceFormer, which encodes the long-term audio context and autoregressively predicts a sequence of animated 3D face meshes. To cope with the data scarcity issue, we integrate the self-supervised pre-trained speech representations. Also, we devise two biased attention mechanisms well suited to this specific task, including the biased cross-modal multi-head (MH) attention and the biased causal MH self-attention with a periodic positional encoding strategy. The former effectively aligns the audio-motion modalities, whereas the latter offers abilities to generalize to longer audio sequences. Extensive experiments and a perceptual user study show that our approach outperforms the existing state-of-the-arts. The code will be made available.
RVPLAYER: Robotic Vehicle Forensics by Replay with What-if Reasoning

Robotic vehicle (RV) attack forensics identifies root cause of an accident.Reproduction of accident and reasoning about its causation are critical steps in the process.Ideally, such investigation would be performed in real-world field tests by faithfully regenerating the environmental conditions and varying the different factors to understand causality.However, such analysis is extremely expensive and in many cases infeasible due to the difficulties of enforcing physical conditions.Existing RV forensics techniques focus on faithful accident reproduction in simulation and hence lack the support of causality reasoning.They also entail substantial overhead.We propose RVPLAYER, a system for RV forensics.It supports replay with what-if reasoning inside simulator (e.g., checking if an accident can be avoided by changing some control parameter, code, or vehicle states).It is a low-cost replacement of the expensive field test based forensics.It features an efficient demand-driven adaptive logging method capturing non-deterministic physical conditions, and a novel replay technique supporting various replay policies that selectively enable/disable information during replay for root cause analysis.Our evaluation on 6 RVs (4 real and 2 virtual), 5 realworld auto-driving traces, and 1194 attack instances of various kinds reported in the literature shows that it can precisely pinpoint the root causes of these attacks without false positives.It has only 6.57% of the overhead of a simple logging design.
Convolution Neural Network-Based Sensitive Security Parameter Identification and Analysis

When the logic of a pseudo-random number generator is followed by standards, the security of the generator depends on the entropy sources used in the seed construction, which are constructed through noise sources; thus, the noise sources are extremely important in cryptographic applications. The operation process of a cryptographic random number generator can be divided into an entropy collection stage and a pseudo-random number generation stage. To ensure the security, the noise sources used to construct the entropy source must be securely collected. If a cryptographic analyst physically acquires a cryptographic module in the form of a black box, it may be possible to predict the cryptographic random number to be used in the future by analyzing the random number generation process of the module. Thus, even if the cryptographic algorithms and protocols are securely designed and implemented without vulnerabilities, if the random number generation process is analyzed, the cryptographic system may be exposed. Noise sources can be identified through information generated when they are collected. If the noise sources can be identified during the stage of collecting sources that provide unpredictability to the cryptographic module during random number generation, future values may be predicted according to the data characteristics. We identify noise sources that are used as entropy sources with our convolution neural network model. Therefore, we establish attack scenarios in which random numbers can be analyzed by identifying data used in the model learned through this study when a random cryptographic module is obtained.
On quantization of convolutional neural networks for image restoration

Recently, commercial vision sensors hit the mobile market.To achieve that, computer vision networks had to be quantized.However, this topic was not studied well for Image Signal processor (ISP) challenging image restoration tasks, being crucially important for hardware implementation, as well as for deployment on hardware accelerators, e.g.Neural Processors Units (NPU).In this paper, we studied the effect of the quantization of deep learning network on image quality.We tried various quantization on raw RGBW image demosaicing.Experimental results show that 10 bit weight quantization can sustain image quality at the similar level with floating-point network.8 bit quantized network shows slight degradation in objective image quality and mild visual artifacts.Although network weight's bit-depth can be significantly reduced for computer vision tasks, our finding shows that it is not true for raw image restoration tasks: at least 10 bit weights are required to provide sufficient image quality.However, we can save some memory on feature maps bit-depth.We can conclude that network bit depth is critical for raw image restoration.
Visual Odometry for RGB-D Cameras

Visual odometry is the process of estimating the position and orientation of a camera by analyzing the images associated to it. This paper develops a quick and accurate approach to visual odometry of a moving RGB-D camera navigating on a static environment. The proposed algorithm uses SURF (Speeded Up Robust Features) as feature extractor, RANSAC (Random Sample Consensus) to filter the results and Minimum Mean Square to estimate the rigid transformation of six parameters between successive video frames. Data from a Kinect camera were used in the tests. The results show that this approach is feasible and promising, surpassing in performance the algorithms ICP (Interactive Closest Point) and SfM (Structure from Motion) in tests using a publicly available dataset.
Low-overhead quantum computing with the color code

Fault-tolerant quantum computation demands significant resources: large numbers of physical qubits must be checked for errors repeatedly to protect quantum data as logic gates are implemented in the presence of noise. We demonstrate that an approach based on the color code can lead to considerable reductions in the resource overheads compared with conventional methods, while remaining compatible with a two-dimensional layout. We propose a lattice surgery scheme that exploits the rich structure of the color-code phase to perform arbitrary pairs of commuting logical Pauli measurements in parallel while keeping the space cost low. Compared to lattice surgery schemes based on the surface code with the same code distance, our approach yields about a $3\times$ improvement in the space-time overhead, obtained from a combination of a $1.5\times$ improvement in spatial overhead together with a $2\times$ speedup due to the parallelisation of commuting logical measurements. Even when taking into account the color code's lower error threshold using current decoders, the overhead is reduced by 10\% at a physical error rate of $10^{-3}$ and by 50\% at $10^{-4}$.
Few-Shot Self-Rationalization with Natural Language Prompts

Self-rationalization models that predict task labels and generate free-text elaborations for their predictions could enable more intuitive interaction with NLP systems. These models are, however, currently trained with a large amount of human-written free-text explanations for each task which hinders their broader usage. We propose to study a more realistic setting of self-rationalization using few training examples. We present FEB -- a standardized collection of four existing English-language datasets and associated metrics. We identify the right prompting approach by extensively exploring natural language prompts on FEB. Then, by using this prompt and scaling the model size, we demonstrate that making progress on few-shot self-rationalization is possible. We show there is still ample room for improvement in this task: the average plausibility of generated explanations assessed by human annotators is at most 51% (with GPT-3), while plausibility of human explanations is 76%. We hope that FEB and our proposed approach will spur the community to take on the few-shot self-rationalization challenge.
Mixing detection on Bitcoin transactions using statistical patterns

Cryptocurrencies gained lots of attention mainly because of the anonymous way of online payment, which they suggested. Meanwhile, Bitcoin and other major cryptocurrencies have experienced severe deanonymization attacks. To address these attacks, Bitcoin contributors introduced services called mixers or tumblers. Mixing or laundry services aim to return anonymity back to the network. In this research, we tackle the problem of losing the footprint of money in Bitcoin and other cryptocurrencies networks caused by the usage of mixing services. We devise methods to track transactions and addresses of these services and the addresses of dirty and cleaned money. Because of the lack of labeled data, we had to transact with these services and prepare labeled data. Using this data, we found reliable patterns and developed an integrated algorithm to detect mixing transactions, mixing addresses, sender addresses, and receiver addresses in the Bitcoin network.
The Known Unknowns: Discovering Trade-Offs Between Heterogeneous Code Changes

Software projects must adhere to a variety of non-functional requirements, also known as software qualities. Automated evaluation with respect to such requirements can be conducted thanks to a wide array of available tools. Each tool usually focuses on a specific quality, since heterogeneous analyses are needed for each non-functional requirement. Apart from an overall index expressing the project’s performance in terms of the software quality they specialize on, many tools recommend code changes that are expected to improve the aforementioned index. Thus, a development team that cares for more than one non-functional requirement is facing the problem of unknown trade-offs; besides improving the quality on which the tool that generated each suggestion focuses, how would this code change, if implemented, affect the rest of the non-functional requirements? We present a framework for dealing with this problem. We pick energy efficiency, technical debt and software security as our qualities of interest, and use three respective tools for the analysis of several open-source projects with regard to these qualities. We develop an extensible empirical model, based on fuzzy sets, for the characterization of each suggestion’s trade-offs. Finally, we present an intuitive visualization of said trade-offs, and suggest a method of utilizing them towards reliable decision-making.
Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields

Though neural radiance fields (NeRF) have demonstrated impressive view synthesis results on objects and small bounded regions of space, they struggle on "unbounded" scenes, where the camera may point in any direction and content may exist at any distance. In this setting, existing NeRF-like models often produce blurry or low-resolution renderings (due to the unbalanced detail and scale of nearby and distant objects), are slow to train, and may exhibit artifacts due to the inherent ambiguity of the task of reconstructing a large scene from a small set of images. We present an extension of mip-NeRF (a NeRF variant that addresses sampling and aliasing) that uses a non-linear scene parameterization, online distillation, and a novel distortion-based regularizer to overcome the challenges presented by unbounded scenes. Our model, which we dub "mip-NeRF 360" as we target scenes in which the camera rotates 360 degrees around a point, reduces mean-squared error by 57% compared to mip-NeRF, and is able to produce realistic synthesized views and detailed depth maps for highly intricate, unbounded real-world scenes.
Research on named entity recognition in natural environment based on deep learning

The extraction of named entities and relationships is of great significance in the field of natural environment. However, Chinese natural environment entity corpus inventory is blank, which brings great challenges to the downstream task of natural language processing in this field. In order to study and improve the effect of natural environment data named entity recognition, an improved CNN-BiGRU-CRF model with attention mechanism is proposed. The constructed natural environment entity corpus was used as experimental data and labeled with BIOE sequence labeling method for comparative experiments. The experimental results show that the F1 value of six types of entity recognition reaches more than 80%, which is better than other traditional models and achieves better recognition effect on natural environment data.