Reviewerly/exHarmony huggingface dataset:
PAGE—Practical AES-GCM Encryption for Low-End Microcontrollers

An optimized AES (Advanced Encryption Standard) implementation of Galois Counter Mode of operation (GCM) on low-end microcontrollers is presented in this paper. Two optimization methods are applied to proposed implementations. First, the AES counter (CTR) mode of operation is speed-optimized and ensures constant timing. The main idea is replacing expensive AES operations, including AddRound Key, SubBytes, ShiftRows, and MixColumns, into simple look-up table access. Unlike previous works, the look-up table does not require look-up table updates during the entire encryption life-cycle. Second, the core operation of Galois Counter Mode (GCM) is optimized further by using Karatsuba algorithm, compact register utilization, and pre-computed operands. With above optimization techniques, proposed AES-GCM on 8-bit AVR (Alf and Vegard’s RISC processor) architecture from short-term, middle-term to long-term security levels achieved 415, 466, and 477 clock cycles per byte, respectively.
The changing role of Geological Surveys: introduction

Abstract This volume is a collection of papers authored by senior managers and heads of Geological Survey organizations (GSOs) from around the world in an attempt to provide a benchmark on how GSOs are responding to national and international needs in a rapidly changing world. GSOs face an uncertain future and need to understand global trends. Whereas population trends are somewhat predictable, societal responses to change are much less so and technological change is fundamentally disruptive and chaotic. As countries adopt sustainable development principles and the public becomes increasingly (but not necessarily reliably) informed about environmental issues using social media, the integration of resource development and environmental stewardship becomes increasingly important. GSOs will continue to provide key information about Earth systems, natural hazards and climate change in this context. This introduction comprises a short review of the global trends affecting GSOs, a snapshot of the state of GSOs, examples of how GSOs are adapting their activities to the modern world, including the growing use of big data, and an examination of international collaboration between GSOs. The time is perhaps ripe to reinforce international collaborations through a global network of GSOs. To achieve this will require leadership and a focus on the big picture of global sustainability.
Sparse probabilistic K-means

The goal of clustering is to partition a set of data points into groups of similar data points, called clusters. Clustering algorithms can be classified into two categories: hard and soft clustering. Hard clustering assigns each data point to one cluster exclusively. On the other hand, soft clustering allows probabilistic assignments to clusters. In this paper, we propose a new model which combines the benefits of these two models: clarity of hard clustering and probabilistic assignments of soft clustering. Since the majority of data usually have a clear association, only a few points may require a probabilistic interpretation. Thus, we apply the ℓ1 norm constraint to impose sparsity on probabilistic assignments. Moreover, we also incorporate outlier detection in our clustering model to simultaneously detect outliers which can cause serious problems in statistical analyses. To optimize the model, we introduce an alternating minimization method and prove its convergence. Numerical experiments and comparisons with existing models show the soundness and effectiveness of the proposed model.
Conditioning optimization of extreme learning machine by multitask beetle antennae swarm algorithm

Extreme learning machine (ELM) as a simple and rapid neural network has been shown its good performance in various areas. Different from the general single hidden layer feedforward neural network (SLFN), the input weights and biases in hidden layer of ELM are generated randomly, so that it only takes a little computational overhead to train the model. However, the strategy of selecting input weights and biases at random may result in ill-conditioned problems. Aiming to optimize the conditioning of ELM, we propose an effective particle swarm heuristic algorithm called Multitask Beetle Antennae Swarm Algorithm (MBAS), which is inspired by the structures of artificial bee colony (ABC) algorithm and Beetle Antennae Search (BAS) algorithm. Then, the proposed MBAS is applied for optimizing the input weights and biases of ELM to solve its ill-conditioned problems. Experiment results show that the proposed method is capable of simultaneously reducing the condition number and regression error, and achieving good generalization performance.
Light Field Editing Propagation using 4D Convolutional Neural Networks

2D image editing has been a well-studied problem. However, 2D image processing techniques cannot be directly applied to the emerging light field image (LFI) due to the particular structural characteristics of LFI. Without a dedicatedly designed editing scheme for LFI, users need to manually edit each sub-view of the LFI. This process is extremely time consuming, and more importantly, users have no ways to guarantee parallax consistency between sub-views. This poster proposes two different LFI editing schemes including the direct editing scheme and the deep-learning-based scheme. These schemes enable automatic propagation of the user’s edits, particularly “augmentation” editing, from central view to all the other sub-views of LFI. In particular, the learning-based scheme employs interleaved spatial-angular convolutions (4D CNN) to enable effective learning of both spatial and angular features, which are subsequently used to help the augmentation editing. We constructed a preliminary LFI dataset and compared the proposed two schemes. The experimental results show that the learning-based scheme produces higher PSNR (0.51dB) and more pleasant subjective editing results than the direct editing.
Detecting oculomotor problems using eye tracking: Comparing EyeX and TX300

Most of our daily activities are dependent on our eyes. Having oculomotor dysfunctions, i.e. problems with coordinating the muscles around each eye, means it will become difficult to focus both eyes at the same point. While a large percentage of the population may suffer from some oculomotor dysfunction, the detection of these problems is difficult and resource demanding. There are great promises from technologies supporting human experts in the detection of oculomotor problems. This paper compares the use of two different eye-tracking technologies supporting the identification of oculomotor problems. It examines the use of a portable and affordable laptop-based eye tracker (EyeX) with a more expensive eye tracker (TX300) significantly heavier. The data were collected from screening a class of 39 children in a 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> grade. The results show the applicability of low-cost eye-trackers for recognizing oculomotor problems and discuss the benefits and limitations of the used eye-trackers as support for manual screening by experts.
Facilitating Electromagnetic Side-Channel Analysis for IoT Investigation: Evaluating the EMvidence Framework

The Internet of Things (IoT) has opened up new opportunities for digital forensics by providing new sources of evidence. However, acquiring data from IoT is not a straightforward task for multiple reasons including the diversity of manufacturers, the lack of standard interfaces, the use of light-weight data encryption, e.g. elliptic curve cryptography (ECC), etc. Electromagnetic side-channel analysis (EM-SCA) has been proposed as a new approach to acquire forensically useful data from IoT devices. However, performing successful EM-SCA attacks on IoT devices requires domain knowledge and specialised equipment that are not available to most digital forensic investigators. This work presents the methodology behind and an evaluation of a framework, EMvidence, that enables forensic investigators to acquire evidence from IoT devices through EM-SCA. This framework helps to automate and perform electromagnetic side-channel evidence collection for forensic purposes. An evaluation of the framework is performed by applying it to multiple realistic digital investigation scenarios. In the case of attacking ECC cryptographic operations, the evaluation demonstrates that the volume of EM data that needs to be stored and processed can be significantly reduced using the framework's machine learning based approach.
KAEA: A Novel Three-stage Ensemble Model for Software Defect Prediction

Software defect prediction is a research hotspot in the field of software engineering. However, due to the limitations of current machine learning algorithms, we can’t achieve good effect for defect prediction by only using machine learning algorithms. In previous studies, some researchers used extreme learning machine (ELM) to conduct defect prediction. However, the initial weights and biases of the ELM are determined randomly, which reduces the prediction performance of ELM. Motivated by the idea of search based software engineering, we propose a novel software defect prediction model named KAEA based on kernel principal component analysis (KPCA), adaptive genetic algorithm, extreme learning machine and Adaboost algorithm, which has three main advantages: (1) KPCA can extract optimal representative features by leveraging a nonlinear mapping function; (2) We leverage adaptive genetic algorithm to optimize the initial weights and biases of ELM, so as to improve the generalization ability and prediction capacity of ELM; (3) We use the Adaboost algorithm to integrate multiple ELM basic predictors optimized by adaptive genetic algorithm into a strong predictor, which can further improve the effect of defect prediction. To effectively evaluate the performance of KAEA, we use eleven datasets from large open source projects, and compare the KAEA with four machine learning basic classifiers, ELM and its three variants. The experimental results show that KAEA is superior to these baseline models in most cases.
Improving the language model for low-resource ASR with online text corpora

In this paper, we expand on previous work on automatic speech recognition in a low-resource scenario typical of data collected by field linguists. We train DeepSpeech models on 35 hours of dialectal Komi speech recordings and correct the output using language models constructed from various sources. Previous experiments showed that transfer learning using DeepSpeech can improve the accuracy of a speech recognizer for Komi, though the error rate remained very high. In this paper we present further experiments with language models created using KenLM from text materials available online. These are constructed from two corpora, one containing literary texts, one for social media content, and another combining the two. We then trained the model using each language model to explore the impact of the language model data source on the speech recognition model. Our results show significant improvements of over 25% in character error rate and nearly 20% in word error rate. This offers important methodological insight into how ASR results can be improved under low-resource conditions: transfer learning can be used to compensate the lack of training data in the target language, and online texts are a very useful resource when developing language models in this context.
MemFlow: Memory-Aware Distributed Deep Learning

As the number of layers and the amount of training data increases, the trend is to train deep neural networks in parallel across devices. In such scenarios, neural network training is increasingly bottlenecked by high memory requirements posed by intermediate results, or feature maps, that are produced during the forward pass and consumed during the backward pass. We recognize that the best-performing device parallelization configurations should consider memory usage in addition to the canonical metric of computation time. Towards this we introduce MemFlow, an optimization framework for distributed deep learning that performs joint optimization over memory usage and computation time when searching for a parallelization strategy. MemFlow consists of: (i) a task graph with memory usage estimates; (ii) a memory-aware execution simulator; and (iii) a Markov Chain Monte Carlo search algorithm that considers various degrees of recomputation i.e., discarding feature maps during the forward pass and recomputing them during the backward pass. Our experiments demonstrate that under memory constraints, MemFlow can readily locate valid and superior parallelization strategies unattainable with previous frameworks.
Knowledge Graphs on the Web – An Overview

Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowledge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which are connected by edges representing the relations between those entities. While companies such as Google, Microsoft, and Facebook have their own, non-public knowledge graphs, there is also a larger body of publicly available knowledge graphs, such as DBpedia or Wikidata. In this chapter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap.
Unconditional Accumulation of Nonclassicality in a Single‐Atom Mechanical Oscillator

Abstract The robust experimental accumulation of nonclassicality of motion of a single trapped ion is reported. The nonclassicality stems from deterministic incoherent modulation of thermal phonon number distribution implemented by a laser excitation of nonlinear coupling between the ion's internal—electronic levels and external—motional states. It is demonstrated that the repetitive application of this nonlinear process monotonically accumulates the observable state nonclassicality. The output states converge to a phonon number distribution with high overlap with a particular Fock state and visible quantum non‐Gaussian aspects including corresponding negative Wigner function. The resulting oscillator states prove deterministic transition in the hierarchy of quantum non‐Gaussianity up to four phonons. This transition is very robust against experimental imperfections and produces increasing entanglement potential.
Approximation of Probabilistic Maximal Frequent Itemset Mining Over Uncertain Sensed Data

Event detection by discovering frequent itemsets is very popular in sensor network communities.However, the recorded data is often a probability rather than a determined value in a really productive environment as sensed data is often affected by noise.In this paper, we study to detect events by finding frequent patterns over probabilistic sensor data under the Possible World Semantics.This is technically challenging as probabilistic records can generate an exponential number of possible worlds.Although several efficient algorithms are proposed in the literature, it is still difficult to mine probabilistic maximal frequent items (PMFIs) in large uncertain database due to the high time complexity.To address this issue, we employ approximate idea to further reduce the time complexity from O(nlogn) to O(n) and propose a two-step solution (Aproximation Probabilistic Frequent Itemset-MAX, APFI-MAX in short) including PMFI candidates generation and PMFIs confirmation.We also provide the necessary proofs of our approximation method to make APFI-MAX more solid and convincing.Finally, extensive experiments have been conducted on synthetic and real databases, demonstrating that the proposed APFI-MAX always running faster than state-of-art methods under different parameter settings.
Clustering the vulnerability of traffic accidents in Medan city with fuzzy c-means algorithm

Abstract A traffic accident is one of the global problems that needs serious handling. Traffic accidents happens in various places at different times, this thing causes the difficulty to determine which area has traffic accidents-prone. Information about traffic accidents-prone areas is very needed for the people and regulators. That information can be a consideration for supervision or anticipation, especially for the police. Medan is the capital city of Sumatera Utara province, Indonesia. Medan is the third-largest city di Indonesia after Jakarta and Surabaya also the largest outside Java Island. Medan is one of the cities in Indonesia that has traffic accidents problems. 25,08% of all the traffic accidents in Sumatera Utara province in 2016 happened in Medan. 2018’s traffic accidents data acquired from the traffic unit in Medan Police Office shows a total of 947 traffic accidents that causes 148 died, 456 peoples heavily injured, and 616 people lightly injured. Then, this data is grouped based on the road they happened on and then the grouped data is clustered with the Fuzzy C-Means Algorithm based on the severity of the accidents, resulting in 5 roads being very vulnerable, 14 roads being quite vulnerable, and 224 roads being safe.
A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation

In this paper, we introduce a novel methodology to efficiently construct a corpus for question answering over structured data.For this, we introduce an intermediate representation that is based on the logical query plan in a database called Operation Trees (OT).This representation allows us to invert the annotation process without losing flexibility in the types of queries that we generate.Furthermore, it allows for fine-grained alignment of query tokens to OT operations.In our method, we randomly generate OTs from a context-free grammar.Afterwards, annotators have to write the appropriate natural language question that is represented by the OT.Finally, the annotators assign the tokens to the OT operations.We apply the method to create a new corpus OTTA (Operation Trees and Token Assignment), a large semantic parsing corpus for evaluating natural language interfaces to databases.We compare OTTA to Spider and LC-QuaD 2.0 and show that our methodology more than triples the annotation speed while maintaining the complexity of the queries.Finally, we train a state-of-the-art semantic parsing model on our data and show that our corpus is a challenging dataset and that the token alignment can be leveraged to increase the performance significantly.
Semi-supervised Contextual Historical Text Normalization

Goldwater, 2018;Bollmann et al., 2017;Korchagina, 2017). Yet, virtually all approaches suffer from the two limitations: 1) They consider a fully supervised setup, often with impractically large manually normalized datasets; 2) Normalization happens on words in isolation.By utilizing a simple generative normalization model and obtaining powerful contextualization from the target-side language model, we train accurate models with unlabeled historical data.In realistic training scenarios, our approach often leads to reduction in manually normalized data at the same accuracy levels.
The impact of screen resolution of HMD on perceptual quality of immersive videos

In this paper, we attempted to explore the impact of the screen resolution of future HMDs on users’ perceptual quality. A subjective experiment was elaborately designed and conducted on several HMDs and a high-resolution monitor to investigate the perceptual quality provided by existing and future HMDs. To simulate the HMDs having the screen with higher resolutions, we required subjects to view omnidirectional videos on the monitor at different distances in which case they can view the screen with different pixel densities. The experimental results demonstrated that users’ perceptual quality will increase with the screen resolution until a threshold, i.e., around 60 pixels per degree. This trend was further well described by an exponential model with root mean squared errors (RMSE) less than 2% and Spearman Rank Order Correlation Coefficient (SROCC) larger than 0.98.
Programming in Natural Language with fuSE: Synthesizing Methods from Spoken Utterances Using Deep Natural Language Understanding

The key to effortless end-user programming is natural language.We examine how to teach intelligent systems new functions, expressed in natural language.As a first step, we collected 3168 samples of teaching efforts in plain English.Then we built fu SE , a novel system that translates English function descriptions into code.Our approach is three-tiered and each task is evaluated separately.We first classify whether an intent to teach new functionality is present in the utterance (accuracy: 97.7% using BERT).Then we analyze the linguistic structure and construct a semantic model (accuracy: 97.6% using a BiLSTM).Finally, we synthesize the signature of the method, map the intermediate steps (instructions in the method body) to API calls and inject control structures (F 1 : 67.0% with information retrieval and knowledge-based methods).In an end-to-end evaluation on an unseen dataset fu SE synthesized 84.6% of the method signatures and 79.2% of the API calls correctly.
Assessing the Severity of Software Bug Using Neural Network

Bug severity defines that to what extent the bug influence the software. Bug reports are misclassified affecting the performance of prediction and lead to biasness. Multitude relevant and irrelevant bugs are reported through bug-tracking systems, and it becomes necessary for the developer to give precedence to the bugs reported. The critical bugs need to be fixed immediately, and minor bugs can be fixed later on the basis of available resources. In this paper, we define different severity levels and assort them on the basis of their severity levels. It becomes problematic to assort tons of bugs manually, so we have used text mining approach to congregate words pointing severity of the reports obtained from Bugzilla for different components of ECLIPSE. The TF-IDF method is used to extract features and make dictionary of critical words. We have adopted neural network for the classification of bug reports and measured its performance.