Reviewerly/exHarmony huggingface dataset:
An Instruction Set Extension to Support Software-Based Masking

In both hardware and software, masking can represent an effective means of hardening an implementation against side-channel attack vectors such as Differential Power Analysis (DPA). Focusing on software, however, the use of masking can present various challenges: specifically, it often 1) requires significant effort to translate any theoretical security properties into practice, and, even then, 2) imposes a significant overhead in terms of efficiency. To address both challenges, this paper explores the use of an Instruction Set Extension (ISE) to support masking in software-based implementations of a range of (symmetric) cryptographic kernels including AES: we design, implement, and evaluate such an ISE, using RISC-V as the base ISA. Our ISE-supported first-order masked implementation of AES, for example, is an order of magnitude more efficient than a software-only alternative with respect to both execution latency and memory footprint; this renders it comparable to an unmasked implementation using the same metrics, but also first-order secure.
pyBART: Evidence-based Syntactic Transformations for IE

Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks.However, their utility can be improved.These syntactic dependencies are designed to accurately reflect syntactic relations, and they do not make semantic relations explicit.Therefore, these representations lack many explicit connections between content words, that would be useful for downstream applications.Proposals like English Enhanced UD improve the situation by extending universal dependency trees with additional explicit arcs.However, they are not available to Python users, and are also limited in coverage.We introduce a broad-coverage, datadriven and linguistically sound set of transformations, that makes event-structure and many lexical relations explicit.We present pyBART, an easy-to-use open-source Python library for converting English UD trees either to Enhanced UD graphs or to our representation.The library can work as a standalone package or be integrated within a spaCy NLP pipeline.When evaluated in a pattern-based relation extraction scenario, our representation results in higher extraction scores than Enhanced UD, while requiring fewer patterns.
Efficient Robot Planning for Achieving Multiple Independent Partially Observable Tasks That Evolve over Time

We focus on domains where a robot is required to accomplish a set of tasks that are partially observable and evolve independently of each other according to their dynamics. An example domain is a restaurant setting where a robot waiter should take care of an ongoing stream of tasks, namely serving a number of tables, including delivering food to the tables and checking on customers. An action that the robot should take next at any point of time typically depends on the duration of possible actions, the state of each table, and how these tables evolve over time, e.g., the food becomes cold after a few time steps. As most of these domains are dynamic and tasks are frequently being added and removed, the robot typically needs to plan for a short h-step horizon to quickly decide on the next action and replans at each time step. A conventional approach to deal with this problem is to combine all the tasks' states and robot actions into one large model and to compute an h-step optimal policy for this combined model. For the problems that we are interested in, the number of tasks, e.g., the number of tables in the restaurant domain, can be large making this planning approach computationally impractical. The observation that we make however is that in many domains the number of tasks that the robot can accomplish within h-steps is very limited. We present an algorithm that exploits this observation and decomposes the problem into a series of much smaller planning problems, the solution to which gives us an optimal solution. We demonstrate the efficiency of our algorithm on the restaurant domain.
– D3WA+ – A Case Study of XAIP in a Model Acquisition Task for Dialogue Planning

Recently, the D3WA system was proposed as a paradigm shift in how complex goal-oriented dialogue agents can be specified by taking a declarative view of design. However, it turns out actual users of the system have a hard time evolving their mental model and grasping the imperative consequences of declarative design. In this paper, we adopt ideas from existing works in the field of Explainable AI Planning (XAIP) to provide guidance to the dialogue designer during the model acquisition process. We will highlight in the course of this discussion how the setting presents unique challenges to the XAIP setting, including having to deal with the user persona of a domain modeler rather than the end-user of the system, and consequently having to deal with the unsolvability of models in addition to explaining generated plans.Quickview http://ibm.biz/d3wa-xaip
Mini-batch Serialization: CNN Training with Inter-layer Data Reuse

Training convolutional neural networks (CNNs) requires intense computations and high memory bandwidth. We find that bandwidth today is over-provisioned because most memory accesses in CNN training can be eliminated by rearranging computation to better utilize on-chip buffers and avoid traffic resulting from large per-layer memory footprints. We introduce the MBS CNN training approach that significantly reduces memory traffic by partially serializing mini-batch processing across groups of layers. This optimizes reuse within on-chip buffers and balances both intra-layer and inter-layer reuse. We also introduce the WaveCore CNN training accelerator that effectively trains CNNs in the MBS approach with high functional-unit utilization. Combined, WaveCore and MBS reduce DRAM traffic by 75%, improve performance by 53%, and save 26% system energy for modern deep CNN training compared to conventional training mechanisms and accelerators.
Cognitive computation on consumer’s decision making of internet financial products based on neural activity data

Internet finance has become a popular business in today?s society. However, different from the physical objects or services sold online, Internet financial products are actually contracts defined by financial terms which make customers bear the possibility of capital losses and liquidity restrictions, but they can obtain profits in the future with some uncertainties. This paper takes consumer?s cognition in the decision making of Internet financial products as research circumstances, studies the above issue by conducting an EEG-fNIRS experiment, and proposes an effective cognitive computation method based on neural activity data through BP-GA algorithm. On this basis, a new recommendation approach of Internet financial products is explored according to consumer?s typical shared mental model. The computing and testing results indicate that researches of this paper provide promising new ideas and novel methods for the cognitive computation of artificial intelligence and the recommendation of Internet financial products.
Confident Classification Using a Hybrid Between Deterministic and Probabilistic Convolutional Neural Networks

Traditional neural networks trained using point-based maximum likelihood estimation are deterministic models and have exhibited near-human performance in many image classification tasks. However, their insistence on representing network parameters with point-estimates renders them incapable of capturing all possible combinations of the weights; consequently, resulting in a biased predictor towards their initialisation. Most importantly, these deterministic networks are inherently unable to provide any uncertainty estimate for their prediction which is highly sought after in many critical application areas. On the other hand, Bayesian neural networks place a probability distribution on network weights and give a built-in regularisation effect making these models able to learn well from small datasets without overfitting. These networks provide a way of generating posterior distribution which can be used for model's uncertainty estimation. However, Bayesian estimation is computationally very expensive since it greatly widens the parameter space. This paper proposes a hybrid convolutional neural network which combines high accuracy of deterministic models with posterior distribution approximation of Bayesian neural networks. This hybrid architecture is validated on 13 publicly available benchmark classification datasets from a wide range of domains and different modalities like natural scene images, medical images, and time-series. Our results show that the proposed hybrid approach performs better than both deterministic and Bayesian methods in terms of classification accuracy and also provides an estimate of uncertainty for every prediction. We further employ this uncertainty to filter out unconfident predictions and achieve significant additional gain in accuracy for the remaining predictions.
Tracking the Evolution of Written Language Competence in L2 Spanish Learners

In this paper we present an NLP-based approach for tracking the evolution of written language competence in L2 Spanish learners using a wide range of linguistic features automatically extracted from students' written productions.Beyond reporting classification results for different scenarios, we explore the connection between the most predictive features and the teaching curriculum, finding that our set of linguistic features often reflects the explicit instruction that students receive during each course.
I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance (Student Abstract)

Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.
Using Learning Techniques to Observe Elderly’s Behavior Changes over Time in Smart Home

Smart environments and technology used for elder care, increases independent living time and cuts long-term care costs. A key requirement for these systems consists in detecting and informing about abnormal behavior in users'routines. In this paper, our objective is to automatically observe the elderly behavior over time and detect anomalies that may occur on the long term. Therefore, we propose a learning method to formalize a normal behavior pattern for each elderly people related to his Activities of Daily Living (ADL). We also adopt a temporal similarity score between activities that allows to detect behavior changes over time. In change behavior period we focus on each activity to detect anomalies. A use case with real datasets are promising.
Context-based Automated Scoring of Complex Mathematical Responses

The tasks of automatically scoring either textual or algebraic responses to mathematical questions have both been well-studied, albeit separately.In this paper we propose a method for automatically scoring responses that contain both text and algebraic expressions.Our method not only achieves high agreement with human raters, but also links explicitly to the scoring rubric -essentially providing explainable models and a way to potentially provide feedback to students in the future.
The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2

We describe the NYU-CUBoulder systems for the SIGMORPHON 2020 Task 0 on typologically diverse morphological inflection and Task 2 on unsupervised morphological paradigm completion.The former consists of generating morphological inflections from a lemma and a set of morphosyntactic features describing the target form.The latter requires generating entire paradigms for a set of given lemmas from raw text alone.We model morphological inflection as a sequenceto-sequence problem, where the input is the sequence of the lemma's characters with morphological tags, and the output is the sequence of the inflected form's characters.First, we apply a transformer model to the task.Second, as inflected forms share most characters with the lemma, we further propose a pointer-generator transformer model to allow easy copying of input characters.Our best performing system for Task 0 is placed 6th out of 23 systems.We further use our inflection systems as subcomponents of approaches for Task 2. Our best performing system for Task 2 is the 2nd best out of 7 submissions.
Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation

Simultaneous machine translation systems rely on a policy to schedule read and write operations in order to begin translating a source sentence before it is complete.In this paper, we demonstrate the use of Adaptive Computation Time (ACT) as an adaptive, learned policy for simultaneous machine translation using the transformer model and as a more numerically stable alternative to Monotonic Infinite Lookback Attention (MILk).We achieve stateof-the-art results in terms of latency-quality tradeoffs.We also propose a method to use our model on unsegmented input, i. e. without sentence boundaries, simulating the condition of translating output from automatic speech recognition.We present first benchmark results on this task.
OPPORTUNITIES AND CHALLENGES FOR AUGMENTED REALITY SITUATED GEOGRAPHICAL VISUALIZATION

Abstract. Augmented reality (AR) enables to display situated geographical visualizations, i.e visualizations that use virtual elements that are displayed in a geographical location. The place where the data is displayed complements the visualization. Many applications that take advantage of AR and situated visualizations exist, but they differ in the visualizations they present, their relationship to the geographic locations and goals. To better understand why and how AR based situated geovisualization is used, we review 45 papers coming from Human Computer Interaction, Visualization and Geographical Information Science venues that present such applications. Inspired by existing classifications, we characterize these papers according to the data they visualize and the geographical distance between the visualization and the data the visualization represents. This analysis reveals existing opportunities for situated geovisualization applications using AR.
HIBoost: A hubness-aware ensemble learning algorithm for high-dimensional imbalanced data classification

Learning from high-dimensional imbalanced data is prevalent in many vital real-world applications, which poses a severe challenge to traditional data mining and machine learning algorithms. The existing works generally use dimension reduction methods to deal with the curse of dimensionality, then a pply traditional imbalance learning techniques to combat the problem of class imbalance. However, dimensionality reduction may cause the loss of useful information, especially for the minority classes. This paper introduces an ensemble-based method, HIBoost, to directly handle the imbalanced learning problem in high dimensional space. HIBoost takes into account the inherent high-dimensional hubness phenomenon, i.e., high-dimensional data tends to contain the singular points (hubs and anti-hubs) which frequently or rarely occur in k-nearest neighbors of other points. For the singular hubs and anti-hubs induced by high dimension, HIBoost introduces a discount factor to restrict the weight growth of them in the process of updating weight, so that the risk of over fitting can be reduced when training component classifiers. For class imbalance problem, HIBoost uses SMOTE to balance the training data in each iteration so as to alleviate the prediction bias of component classifiers. Experimental results based on sixteen high-dimensional imbalanced data sets demonstrate the effectiveness of HIBoost.
Absolute phase recovery based on two-wavelength fringes using a defocusing technique in fringe projection profilometry

We present an approach that combines binary structured patterns generated by tripolar pulse width modulation technique with a two-wavelength phase-shifting unwrapping method to realize absolute phase recovery at the same defocusing level. Optimized binary fringe patterns projected by a small defocusing degree projector can significantly alleviate the phase error based on fringe projection profilometry, providing the binary patterns with a similar performance to standard sinusoidal fringe patterns. With this method, a set of images using only two-wavelength fringe patterns is captured by the camera, and it is not obliged to obtain the equivalent wavelengths and their corresponding phase maps. In consequence, the error propagation is avoided, which leads to improved accuracy in the continuous phase map retrieval. Furthermore, The effectiveness of the presented phase recovery technique that can be utilized to measure discontinuous and multiple objects was also verified by experiments. And it can reach high-precision and fast three-dimensional shape measurement.
Combining Machine Learning Models Using combo Library

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to aggregate models and scores under various scenarios, including classification, clustering, and anomaly detection. In a nutshell, combo provides a unified and consistent way to combine both raw and pretrained models from popular machine learning libraries, e.g., scikit-learn, XGBoost, and LightGBM. With accessibility and robustness in mind, combo is designed with detailed documentation, interactive examples, continuous integration, code coverage, and maintainability check; it can be installed easily through Python Package Index (PyPI) or https://github.com/yzhao062/combo.
Adapting End-to-End Speech Recognition for Readable Subtitles

Automatic speech recognition (ASR) systems are primarily evaluated on transcription accuracy.However, in some use cases such as subtitling, verbatim transcription would reduce output readability given limited screen size and reading time.Therefore, this work focuses on ASR with output compression, a task challenging for supervised approaches due to the scarcity of training data.We first investigate a cascaded system, where an unsupervised compression model is used to post-edit the transcribed speech.We then compare several methods of end-to-end speech recognition under output length constraints.The experiments show that with limited data far less than needed for training a model from scratch, we can adapt a Transformer-based ASR model to incorporate both transcription and compression capabilities.Furthermore, the best performance in terms of WER and ROUGE scores is achieved by explicitly modeling the length constraints within the end-to-end ASR system.
IT Risk Management Based on ISO 31000 and OWASP Framework using OSINT at the Information Gathering Stage (Case Study: X Company)

The major IT developments lead to speed and mobility elevation of information access.One of them is using the website to share and gather information.Therefore, the mobility and information disclosure create a harmful vulnerability.Which is the leakage of information, whether organizational or sensitive information, such as bank accounts, phone number and many more.Security testing is necessarily needed on website usage.One of the website security testing method is penetration testing.Supporting framework that can be used in this method is OWASP Testing Guide Version 4. OTG Version 4 has 11 stages cover all aspects of website protection and security.Security testing is nicely done using tools / software.Tools with the concept of OSINT (Open Source Intelligence) are used to get better access and availability by using the characteristics of open source.The IT risk assessment analysis carried out by ISO 31000 framework and based on the results that have been obtained through penetration testing with OWASP framework.Significance & values of this research is finding the best and effective way to making IT risk management guidelines along with the combination of with OWASP & ISO 31000 framework, by doing website security assessment with penetration testing method based on OWASP framework to get the system vulnerabilities and analyze the risks that appears with the ISO 31000 framework.Also, the IT risk management guidelines consist of system improvement recommendations along with evaluation report which obtained from the collaboration analysis the OSINT concept, penetration testing methods, OWASP and ISO 31000 framework.
<b>BayesNetBP</b>: An <i>R</i> Package for Probabilistic Reasoning in Bayesian Networks

The BayesNetBP package has been developed for probabilistic reasoning and visualization in Bayesian networks with nodes that are purely discrete, continuous or mixed (discrete and continuous).Probabilistic reasoning enables a user to absorb information into a Bayesian network and make queries about how the probabilities within the network change in light of new information.The package was developed in the R programming language and is freely available from the Comprehensive R Archive Network.A shiny app with Cytoscape widgets provides an interactive interface for evidence absorption, queries, and visualizations.
Developing Critical Thinking in Younger Pupils Using ICT

A methodology for developing critical thinking in younger pupils using information and communication technologies (ICTs) has been developed based on the study of the pedagogical experience of O. Diachuk, a teacher from M. Ostrovsky comprehensive school No 1 (I-III levels) in Shepetivka (Khmelnytskyi Region), and O. Svystak, a teacher from specialized school No 30 (I level) in Khmelnytskyi.The paper determines its goals, objectives, stages (problem statement, information search, clear argumentation, decision-making) and results from using it.Also, it presents the results obtained from the study of its effectiveness based on the observation of younger pupils' educational and cognitive activities, their questionnaires, surveys of primary school teachers, as well as the study of pupils' compositions.It states that the use of the developed methodology helps to enhance the activity of primary school pupils, their thinking skills and abilities to justify their decisions, search for new information using digital devices and develops their communication skills.At the same time, the paper points out the following disadvantages and difficulties in using the developed methodology: potential problems in using it due to technical bugs and slow Internet connection; a long preparation for those stages of the lesson during which this methodology is applied; certain difficulties in supervising pupils' search activities in case there are more than 30 pupils in the class.Further research should aim to study the characteristics of implementing the methodology for developing critical thinking in younger pupils using ICTs during adaptation-and-gamerelated (Grades 1 and 2) and main (Grades 3 and 4) stages of pupils' learning.