Reviewerly/exHarmony huggingface dataset:
Fuzzy linguistic descriptions for execution trace comprehension and their application in an introductory course in artificial intelligence

Execution traces comprehension is an important topic in computer science since it allows software engineers to get a better understanding of the system behavior. However, traces are usually very large and hence they are difficult to interpret. Parallel, execution traces comprehension is a very impo rtant topic into the algorithms learning courses since it allows students to get a better understanding of the algorithm behavior. Therefore, there is a need to investigate ways to help students (and teachers) find and understand important information conveyed in a trace despite the trace being massive. In this paper, we propose a new approximation for execution traces comprehension based on fuzzy linguistic descriptions. A new methodology and a data-driven architecture based on linguistic modelling of complex phenomenon are presented and explained. In particular, they are applied to automatically generate linguistic reports from execution traces generated during the execution of algorithm implemented by the students of an introductory course of artificial intelligence. To the best of our knowledge, it is the first time that linguistic modelling of complex phenomenon is applied to execution traces comprehension. Throughout the article, it is shown how this kind of technology can be employed as a useful computer-assisted assessment tool that provides students and teachers with technical, immediate and personalised feedback about the algorithms that are being studied and implemented. At the same time, they provide us with two useful applications: they are an indispensable pedagogical resource for improving comprehension of execution traces, and they play an important role in the process of measuring and evaluating the “believability” of the agents implemented. To show and explore the possibilities of this new technology, a web platform has been designed and implemented by one of the authors, and it has been incorporated into the process of assessment of an introductory artificial intelligence course. Finally, an empirical evaluation to confirm our hypothesis was performed and a survey directed to the students was carried out to measure the quality of the learning-teaching process by using this methodology enriched with fuzzy linguistic descriptions.
ProSeqo: Projection Sequence Networks for On-Device Text Classification

We propose a novel on-device sequence model for text classification using recurrent projections.Our model ProSeqo uses dynamic recurrent projections without the need to store or look up any pre-trained embeddings.This results in fast and compact neural networks that can perform on-device inference for complex short and long text classification tasks.We conducted exhaustive evaluation on multiple text classification tasks.Results show that ProSeqo outperformed state-of-the-art neural and on-device approaches for short text classification tasks such as dialog act and intent prediction.To the best of our knowledge, ProSeqo is the first on-device long text classification neural model.It achieved comparable results to previous neural approaches for news article, answers and product categorization, while preserving small memory footprint and maintaining high accuracy.
Quantum state transmission over partially corrupted quantum information network

This paper derives the limit of the quantum network transmission rate when some of the quantum network channels are corrupted. The authors propose a quantum code that works efficiently even in the one-shot setting when the network operations are Clifford operations.
Trace of Products in Finite Fields from a Combinatorial Point of View

The notion of digits in finite fields was introduced a few years ago as an attempt to deliver insight in related yet unresolved questions over the natural numbers. Several such intractable questions are related to the sum of digits function, which assigns to every natural number the sum of its digits. Its analogue over finite fields, which is a map from $\mathbb{F}_q$ to $\mathbb{F}_p$, $q=p^h$ where $p$ prime and $h \geq 2$, has been studied by several authors. In particular, Cathy Swaenepoel [J. Number Theory, 189 (2018), pp. 97--114] investigated the sum of digits of products of field elements. The main techniques involved were estimates on certain character sums and Gaussian sums over $\mathbb{F}_q$ and $\mathbb{F}_p$. In this paper, we extend and generalize these results using a different approach, based on spectral graph theory, without any reference to character theory.
Visual Odometry with Deep Bidirectional Recurrent Neural Networks

We propose a novel architecture for learning camera poses from image sequences with an extended 2D LSTM (Long Short-Term Memory). Unlike most of the previous deep learning based VO (Visual Odometry) methods, our model predicts the pose per frame with temporal information from image sequences by adopting a forward-backward process. In addition, we use 3D tensors as basic structures to generate spatial information. The network learns poses in a bottom-up manner by coupling local and global constraints. Experiments demonstrate that on the public KITTI benchmark dataset, our architecture outperforms the state-of-the-art end-to-end methods in term of camera motion prediction and is comparable with model-based methods. The network generalizes well on the Málaga dataset without extra training or fine-tuning.
Geochemical dispersion processes in deep cover and neotectonics in Coompana, Nullarbor Plain, South Australia

SummaryMineral exploration in regions under thick cover is challenging. This study looked into vertical geochemical dispersion processes through ~550 m of cover in the Coompana Province, South Australia. We collected new data from CDP008 drill hole (~680 m), which contains the most complete record of the cover sequence in this region including: (1) mafic volcanic basement; (2) ~140 m of undifferentiated fluvial sediments (Phanerozoic); (3) overlain by ~230 m of fluvio-lacustrine to marine Mesozoic deposits; and (4) followed by ~200 m of Cenozoic carbonates. Our study revealed that REE patterns in the basalts and the overlying sandstone package show similarities. Yet, no geochemical similarities existed with the Mesozoic units and the upper stratigraphic units. Regolith mapping of this area revealed intricate landscape patterns. These include numerous linear scarps, which trend NW-SE to N-S to NE-SW and experienced <10 m of displacement. When coupled with magnetic data, this scarp trend correlates with basement structural trends at depth and hence these scarps are interpreted as neotectonic features. Based on the geochemistry we concluded that: (1) the lower sandstone package contains a geochemical footprint of the basement rocks produced by vertical and lateral geochemical dispersion; (2) the Mesozoic sediments do not record vertical dispersion related to the underlying basement; (3) the top limestone units are a chemical barrier for vertical geochemical dispersion due to lack of permeability. They are an efficient stratigraphic boundary to produce redox gradients and therefore differential geochemical horizons; and (4) basement features identified from magnetic data are mimicked by linear surface landscape features. We pose that neotectonic surface features may record vertical geochemical dispersion from lower stratigraphic units. The relation between neotectonic structures and landscape geochemistry could have critical implications in mineral exploration under cover in Australia and in other similar landscape settings in the world.
A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning

In order to balance the exploration and exploitation capabilities of the PSO algorithm to enhance its robustness, this paper presents a novel particle swarm optimization with improved learning strategies (ILSPSO). Firstly, the proposed ILSPSO algorithm uses a self-learning strategy, whereby each particle stochastically learns from any better particles in the current personal history best position ( pbest ), and the self-learning strategy is adjusted by an empirical formula which expresses the relation between the learning probability and evolution iteration number. The cognitive learning part is improved by the self-learning strategy, and the optimal individual is reserved to ensure the convergence speed. Meanwhile, based on the multilearning strategy, the global best position ( gbest ) of particles is replaced with randomly chosen from the top k of gbest and further improve the population diversity to prevent premature convergence. This strategy improves the social learning part and enhances the global exploration capability of the proposed ILSPSO algorithm. Then, the performance of the ILSPSO algorithm is compared with five representative PSO variants in the experiments. The test results on benchmark functions demonstrate that the proposed ILSPSO algorithm achieves significantly better overall performance and outperforms other tested PSO variants. Finally, the ILSPSO algorithm shows satisfactory performance in vehicle path planning and has a good result on the planned path.
The zero-error entanglement cost is highly non-additive

The Schmidt number is an entanglement measure whose logarithm quantifies the zero-error entanglement cost of generating a given quantum state using local operations and classical communication. In this paper, we show that the Schmidt number is highly nonmultiplicative in the sense that for any integer n, there exist states whose Schmidt number remains constant when taking n copies of the given state. These states also provide a rare instance in which the regularized zero-error entanglement cost can be computed exactly. We then explore the question of increasing the Schmidt number by quantum operations. We describe a class of bipartite quantum operations that preserve the Schmidt number for pure state transformations, and yet they can increase the Schmidt number by an arbitrarily large amount when generating mixed states. Our results are obtained by making connections to the resource theory of quantum coherence and generalizing the class of dephasing-covariant incoherent operations to the bipartite setting.
Confidence-Calibrated Adversarial Training and Detection: More Robust Models Generalizing Beyond the Attack Used During Training

Adversarial training is the standard to train models robust againstadversarial examples. However, especially for complex datasets, adversarialtraining incurs a significant loss in accuracy and is known to generalizepoorly to stronger attacks, e.g., larger perturbations or other threat models.In this paper, we introduce confidence-calibrated adversarial training (CCAT)where the key idea is to enforce that the confidence on adversarial examplesdecays with their distance to the attacked examples. We show that CCATpreserves better the accuracy of normal training while robustness againstadversarial examples is achieved via confidence thresholding, i.e., detectingadversarial examples based on their confidence. Most importantly, in strongcontrast to adversarial training, the robustness of CCAT generalizes to largerperturbations and other threat models, not encountered during training. Forevaluation, we extend the commonly used robust test error to our detectionsetting, present an adaptive attack with backtracking and allow the attacker toselect, per test example, the worst-case adversarial example from multipleblack- and white-box attacks. We present experimental results using $L_\infty$,$L_2$, $L_1$ and $L_0$ attacks on MNIST, SVHN and Cifar10.
Bounded ranks and Diophantine error terms

We show that Lang's conjecture on error terms in Diophantine approximation implies Honda's conjecture on ranks of elliptic curves over number fields. We also show that even a very weak version of Lang's error term conjecture would be enough to deduce boundedness of ranks for quadratic twists of elliptic curves over number fields. This can be seen as evidence for boundedness of ranks not relying on probabilistic heuristics on elliptic curves.
Non-Local Attentive Temporal Network for Video-Based Person Re-Identification

Given a video containing a person, the goal of person re-identification is to identify the same person from videos captured under different cameras. A common approach for tackling this problem is to first extract image features for all frames in the video. These frame-level features are then combined (e.g. via temporal pooling) to form a video-level feature vector. The video-level features of two input videos are then compared by calculating the distance between them. More recently, attention-based learning mechanism has been proposed for this problem. In particular, recurrent neural networks have been used to generate the attention scores of frames in a video. However, the limitation of RNN-based approach is that it is difficult for RNNs to capture long-range dependencies in videos. Inspired by the success of non-local neural networks, we propose a novel non-local temporal attention model in this paper. Our model can effectively capture long-range and global dependencies among the frames of the videos. Extensive experiments on three different benchmark datasets (i.e. iLIDS-VID, PRID-2011 and SDU-VID) show that our proposed method outperforms other state-of-the-art approaches.
Person ReIDentification for Detection of Pedestrians in Blind Spots through V2V Communications

Traffic accidents with pedestrians jumping out from a blind spot are expected to be prevented as much as possible. For this sake, sharing pedestrian information among nearby vehicles is useful for a vehicle to be aware of pedestrians in blind spots. Any pedestrian perceived by other vehicles but unperceived by a certain vehicle itself can be regarded to be basically out of the vehicle's view or be behind obstacles even if in its sight. In order to confirm this judgment more certainly, in this paper, we propose a classification method using image-based person Re-Identification (ReID) based on deep neural network (DNN) to classify whether a pedestrian is in a blind spot or not based on its corresponding information gathered from nearby vehicles. Further, since there is no suitable dataset to evaluate our method, we newly constructed it, and then evaluated our classification method by using our dataset and the existing dataset. Our experiments showed a possibility that pedestrian ReID can achieve accurate classification thanks to a newly constructed dataset that reflects the resolution and illumination differences among pedestrians in a real environment.
Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation

Road markings provide guidance to traffic participants and enforce safe driving behaviour, understanding their semantic meaning is therefore paramount in (automated) driving. However, producing the vast quantities of road marking labels required for training state-of-the-art deep networks is costly, time-consuming, and simply infeasible for every domain and condition. In addition, training data retrieved from virtual worlds often lack the richness and complexity of the real world and consequently cannot be used directly. In this paper, we provide an alternative approach in which new road marking training pairs are automatically generated. To this end, we apply principles of domain randomization to the road layout and synthesize new images from altered semantic labels. We demonstrate that training on these synthetic pairs improves mIoU of the segmentation of rare road marking classes during real-world deployment in complex urban environments by more than 12 percentage points, while performance for other classes is retained. This framework can easily be scaled to all domains and conditions to generate large-scale road marking datasets, while avoiding manual labelling effort.
Empirical model development for the estimation of clearness index usingmeteorological parameters

The clearness index is an indispensable parameter required for the design and analysis of solar energy systems. In the absence of measured values for a specific location, the clearness index can be estimated from other measured meteorological variables. In this study three meteorological parameters, sunshine hours, monthly mean values of the temperature difference ($\Delta$T), and cloudiness, are used to develop empirical models for the estimation of clearness index. The empirical models are developed for five major cities in Pakistan (Karachi, Multan, Lahore, Islamabad, and Quetta). For empirical model development, long-term data (1991 to 2010) of monthly average clearness index, sunshine hours, average daily minimum and maximum temperatures, and cloudiness have been used. The accuracy of the models has been tested by statistical indicators that include mean percentage error (MPE), coefficient of determination (R$^2$), mean absolute relative error (MARE), mean bias error (MBE), and root mean square error (RMSE). The error analysis revealed that the proposed models are suitable for the estimation of the clearness index. It is also concluded that multiple regression models give better estimates of clearness index for all the stations (0.80 $\leq$ R$^{2}$ $\leq$ 0.86) compared to single parameter model and therefore are recommended. The study indicated that clear sky conditions prevail throughout the months at all the investigated sites (0.58 $\leq$ K$_T$ $\leq$ 0.68), which is a good indicator for solar energy utilization. The statistical indicators also suggest that multilinear regression model M-3 gives a better representation of the climate system and using three parameters reduces the uncertainties in the developed model.
Words of Estimative Correlation: Studying Verbalizations of Scatterplots

Natural language and visualization are being increasingly deployed together for supporting data analysis in different ways, from multimodal interaction to enriched data summaries and insights. Yet, researchers still lack systematic knowledge on how viewers verbalize their interpretations of visualizations, and how they interpret verbalizations of visualizations in such contexts. We describe two studies aimed at identifying characteristics of data and charts that are relevant in such tasks. The first study asks participants to verbalize what they see in scatterplots that depict various levels of correlations. The second study then asks participants to choose visualizations that match a given verbal description of correlation. We extract key concepts from responses, organize them in a taxonomy and analyze the categorized responses. We observe that participants use a wide range of vocabulary across all scatterplots, but particular concepts are preferred for higher levels of correlation. A comparison between the studies reveals the ambiguity of some of the concepts. We discuss how the results could inform the design of multimodal representations aligned with the data and analytical tasks, and present a research roadmap to deepen the understanding about visualizations and natural language.
Realistic Film Noise Generation Based on Experimental Noise Spectra

Generating 2D noise with local, space-varying spectral characteristics is vital where random noise fields with spatially heterogeneous statistical properties are observed and need to be simulated. A realistic, non-stationary noise generator relying on experimental data is presented. That generator is desired in areas such as photography and radiography. For example, before performing actual X-ray imaging in practice, output images are simulated to assess and improve setups. For that purpose, realistic film noise modelling is crucial because noise downgrades the detectability of visual signals. The presented film noise synthesiser improves the realism and value of radiographic simulations significantly, allowing more realistic assessments of radiographic test setups. The method respects space-varying spectral characteristics and probability distributions, locally simulating noise with realistic granularity and contrast. The benefits of this approach are to respect the correlation between noise and image as well as internal correlation, the fast generation of any number of unique noise samples, the exploitation of real experimental data, and its statistical non-stationarity. The combination of these benefits is not available in existing work. Validation of the new technique was undertaken in the field of industrial radiography. While applied to that field here, the technique is general and can also be utilised in any other field where the generation of 2D noise with local, space-varying statistical properties is necessary.
Euclidean matrix norm algorithm for simultaneous dual-wavelength phase-shifting interferometry with blind phase shifts

In this study, a simultaneous dual-wavelength phase-shifting interferometry system using two reference arms was built, and a simple phase retrieval method for the system with blind phase shifts was proposed using five phase-shifted interferograms. First, the unknown phase shifts were controlled independently to separate the information of single wavelength, which can be calculated directly by using the Euclidean matrix norm algorithm. Second, a wrapped phase of a single wavelength was obtained using a three-step phase-shifting algorithm. Finally, a continuous phase of a synthetic wavelength was obtained. The simulation and experimental results indicated the feasibility and greater accuracy of the proposed method. Moreover, the strict requirement of the phase shifter was eliminated because of the use of arbitrary and unknown phase shifts, thereby making experimental operation easier.
Understanding Man-in-the-middle-attack through Survey of Literature

These days cyberattack is a serious criminal offense and it is a hotly debated issue moreover. A man-in-the-middle-attack (MITM) is a kind of cyberattack where an unapproved outsider enters into an online correspondence between two users, remains escaped the two parties. The malware that is in the middle-attack often monitors and changes individual/classified information that was just realized by the two users. A man-in-the-middle-attack as a protocol is subjected to an outsider inside the system, which can access, read and change secret information without keeping any tress of manipulation. This issue is intense, and most of the cryptographic systems without having a decent authentication security are threatened to be hacked by the malware named MITM. This paper essentially includes the view of understanding the term of MITM; the current work is mainly emphasized to accumulate related data/information in a single article so that it can be a reference to conduct research further on this topic at college/undergraduate level. This paper likewise audits most cited research and survey articles on MITM recorded on 'Google Scholar'. The result showed that the MITM has correlation to the user behavior, in which this must be considered and careful understood for the way how to solve this problem. The motivation behind this paper is to help the readers for understanding and familiarizing the topic 'man-in-the-middle attack'.