Reviewerly/exHarmony huggingface dataset:
Random Drift Modeling and Compensation for MEMS-Based Gyroscopes and Its Application in Handwriting Trajectory Reconstruction

Microelectromechanical system (MEMS)-based gyroscopes have been widely applied to various inertial-sensing-based human-computer interaction (HCI) devices. However, the random drift of MEMS-based gyroscopes limits their applications. Hence, studies pay attention to develop various models to model and compensate the random drift for improving the performance of the MEMS-based gyroscopes. This paper presents a self-constructing Wiener-type recurrent neural network (SCWRNN) with its false nearest-neighbors-based self-constructing strategy and recursive recurrent learning algorithm to model the random drift of the MEMS-based gyroscopes and then compensate them from the calibrated gyroscope measurement. Subsequently, the proposed random drift modeling and compensation algorithm is integrated into the handwriting trajectory reconstruction algorithm of the inertial-sensing-based HCI device, called IMUPEN, for accurately obtaining the reconstructed handwriting trajectory. Users can hold the IMUPEN, which is composed of an accelerometer, two gyroscopes, a microcontroller, and an RF wireless transmission module, to write numerals at normal speed. The accelerations and angular velocities measured by the accelerometer and gyroscopes are transmitted to a personal computer through the RF module for further reconstructing the handwriting trajectory via the handwriting trajectory reconstruction algorithm. In addition, we have developed the SCWRNN-based random drift modeling and compensation algorithm to eliminate the cumulative errors caused by the random drift of the MEMS-based gyroscopes for further increasing the accuracy of handwriting trajectory reconstruction. Our experimental results have successfully validated the effectiveness of the proposed random drift modeling and compensation algorithm and its application in handwriting trajectory reconstruction.
A Survey of the Concept of Disturbance in Quantum Mechanics

The concept of disturbance is of transcendental importance in Quantum Mechanics (QM). This key concept has been described in two different ways, the first one considering that the disturbance affects observables like x and p, as in the Heisenberg’s analysis of the measurement process and the other one takes into consideration that disturbance affects the state of the system instead. Entropic information measures have provided a path for studying disturbance in these both approaches; in fact, we found that initially it was studied by employing these entropic measures. In addition, in the last decade, there was an extensive amount of analyses and several new definitions of the disturbance concept emerged. Many crucial factors like this have inspired this concise paper which gathers the different concepts and definitions that have emerged through time for the better understanding of this topic.
Study on the Effect of Preprocessing Methods for Spam Email Detection

The use of email as a communication technology is now increasingly being exploited. Along with its progress, email spam problem becomes quite disturbing to email user. The resulting negative impacts make effective spam email detection techniques indispensable. A spam email detection algorithm or spam classifier will work effectively if supported by proper preprocessing steps (noise removal, stop words removal, stemming, lemmatization, term frequency). This research studies the effect of preprocessing steps on the performance of supervised spam classifier algorithms. Experiments were conducted on two widely used supervised spam classifier algorithms: Naïve Bayes and Support Vector Machine. The evaluation is performed on the Ling-spam corpus dataset and uses evaluation metrics: accuracy. The experimental results show that different preprocessing steps give different effects to different classifier.
Model slicing for supporting complex analytics with elastic inference cost and resource constraints

Deep learning models have been used to support analytics beyond simple aggregation, where deeper and wider models have been shown to yield great results. These models consume a huge amount of memory and computational operations. However, most of the large-scale industrial applications are often computational budget constrained. In practice, the peak workload of inference service could be 10x higher than the average cases, with the presence of unpredictable extreme cases. Lots of computational resources could be wasted during off-peak hours and the system may crash when the workload exceeds system capacity. How to support deep learning services with a dynamic workload cost-efficiently remains a challenging problem. In this paper, we address the challenge with a general and novel training scheme called model slicing, which enables deep learning models to provide predictions within the prescribed computational resource budget dynamically. Model slicing could be viewed as an elastic computation solution without requiring more computational resources. Succinctly, each layer in the model is divided into groups of a contiguous block of basic components (i.e. neurons in dense layers and channels in convolutional layers), and then partially ordered relation is introduced to these groups by enforcing that groups participated in each forward pass always starts from the first group to the dynamically-determined rightmost group. Trained by dynamically indexing the rightmost group with a single parameter slice rate, the network is engendered to build up group-wise and residual representation. Then during inference, a sub-model with fewer groups can be readily deployed for efficiency whose computation is roughly quadratic to the width controlled by the slice rate. Extensive experiments show that models trained with model slicing can effectively support on-demand workload with elastic inference cost.
Improving Eye Movement Biometrics Using Remote Registration of Eye Blinking Patterns

In this paper, the biometric potential of eye movement and eye blinking for human recognition task is investigated. These modalities might be useful for specific biometric applications like driver authentication for law enforcement. For this purpose, a database of 22 subjects was build where eye movements and blinks were recorded using Gazepoint GP3 while users were watching real driving sessions. Eye movement features were extracted from eye fixations and saccades separately. Eye blinking features include the blink pattern, its speed and acceleration patterns, and time delineation features. Evaluation of each modality was investigated first, then, both modalities are combined in a multi-modal setup for performance improvement. Although the employment of eye movement or eye blinking separately as a biometric trait might not be secure enough, the fusion of both traits achieves higher levels of identification which are comparable to that of other conventional biometric traits like fingerprint.
Neural Constituency Parsing of Speech Transcripts

Paria Jamshid Lou, Yufei Wang, Mark Johnson. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
It Takes a Village: Understanding the Collective Security Efficacy of Employee Groups

An organization’s ability to successfully manage information security incidents is determined by the actions of its employees, as well as the actions of various groups of employees within its organizational boundaries. To date, information security research has primarily focused on individual-level phenomena and has not yet explored group-level phenomena such as how employee groups recognize and respond to security incidents in a way that is consistent with the organization’s security goals and objectives. The current study addresses this gap, thereby answering the research call for group-level security research perspectives. The present study explores how employee groups formulate their collective security efficacy, which influences how group members recognize and respond to information security incidents. Using a case study of a large healthcare research organization (HRO), we analyze two security incidents, a malware attack, and a physical security breach, to identify a unique set of ecological and social properties of employee groups that are salient to their collective security efficacy.
Formal Verification of a Decision-Tree Ensemble Model and Detection of Its Violation Ranges

As one type of machine-learning model, a "decision-tree ensemble model" (DTEM) is represented by a set of decision trees. A DTEM is mainly known to be valid for structured data; however, like other machine-learning models, it is difficult to train so that it returns the correct output value for any input value. Accordingly, when a DTEM is used in regard to a system that requires reliability, it is important to comprehensively detect input values that lead to malfunctions of a system (failures) during development and take appropriate measures. One conceivable solution is to install an input filter that controls the input to the DTEM, and to use separate software to process input values that may lead to failures. To develop the input filter, it is necessary to specify the filtering condition of the input value that leads to the malfunction of the system. Given that necessity, in this paper, we propose a method for formally verifying a DTEM and, according to the result of the verification, if an input value leading to a failure is found, extracting the range in which such an input value exists. The proposed method can comprehensively extract the range in which the input value leading to the failure exists; therefore, by creating an input filter based on that range, it is possible to prevent the failure occurring in the system. In this paper, the algorithm of the proposed method is described, and the results of a case study using a dataset of house prices are presented. On the basis of those results, the feasibility of the proposed method is demonstrated, and its scalability is evaluated.
Exploring Effects of Conversational Fillers on User Perception of Conversational Agents

Through technological advancements in various areas of our lives, Conversational Agents progressed in their human-likeness. In the field of HCI, however, the use of conversational fillers (e.g., "um," "uh," etc.) by Conversational Agents have not been fully explored in an experimental setting. We observed the effects on user perceptions of Intelligence, Human-likeness and Likability of Conversational Agents by a 2 x 2 experimental design. From the results of 26 total participants, we concluded that 1) the use of fillers by Conversational Agents are perceived as less intelligent and less likable in task-oriented conversations, 2) and the fillers did not have any statistically significant change in perception of human-likeness. However, further examination showed that users reported filler-speaking agents as more entertaining for social-oriented conversations. With these findings, we discuss design implications for voice-based Conversational Agents.
Exquisitor at the Lifelog Search Challenge 2019

Interactive learning is an umbrella term for methods that attempt to understand the information need of the user and formulate queries that satisfy that information need. We propose to apply the state of the art in interactive multimodal learning to visual lifelog exploration and search, using the Exquisitor system. Exquisitor is a highly scalable interactive learning system, which uses semantic features extracted from visual content and text to suggest relevant media items to the user, based on user relevance feedback on previously suggested items. Findings from our initial experiments indicate that interactive multimodal learning will likely work well for some LSC tasks, but also suggest some potential enhancements.
Certificateless Short Aggregate Signature Scheme for Mobile Devices

Today, data is exploding. A large amount of data needs to be processed in a timely and an efficient manner. Aggregate signatures are an efficient and secure way to handle large numbers of digital signatures. In an aggregate signature scheme, n signatures on n messages from n users can be combined into a single signature, which can make anyone believes that the n messages were indeed signed by the n corresponding users. In the recent decade, numerous certificateless aggregate signature (CLAS) schemes have been introduced. In most CLAS schemes currently known, the number of hash-to-point operations and the size of signature increase linearly with the number of signers, so they are not suitable for computing restricted devices, such as mobile devices. In this paper, a new CLAS scheme is constructed, which requires only two pairing operations and the signature contains only one point and state information. It is more efficient than the previous ones and suit for the mobile devices.
Shoplifting in mobile checkout settings: cybercrime in retail stores

Purpose Retailers are implementing technology-enabled mobile checkout processes in their stores to improve service quality, decrease labor costs and gain operational efficiency. These new checkout processes have increased customer convenience primarily by providing them autonomy in sales transactions in that store employee interventions play a reduced role. However, this autonomy has the unintended consequence of altering the checks and balances inherent in a traditional employee-assisted checkout process. Retailers, already grappling with shoplifting, with an estimated annual cost of billions of dollars, fear that the problem may be exacerbated by mobile checkout and concomitant customer autonomy. The purpose of this paper is to understand the effect of mobile checkout processes in retail stores on cybercrime in the form of shoplifting enabled by a technology transformed the retail environment. Design/methodology/approach The authors conducted an online survey of a US sample recruited from a crowdsourced platform. The authors test a research model that aims to understand the factors that influence the intention to shoplift in three different mobile checkout settings − namely, smartphone checkout settings, store-provided mobile device checkout settings, and employee-assisted mobile checkout settings − and compare it with a traditional fixed location checkout setting. Findings The authors found that, in a smartphone checkout setting, intention to shoplift was driven by experiential beliefs and peer influence, and experiential beliefs and peer influence had a stronger effect for prospective shoplifters when compared to experienced shoplifters; in a store-provided mobile devices checkout setting, experiential beliefs had a negative effect on shoplifters’ intention to shoplift and the effect was weaker for prospective shoplifters when compared to experienced shoplifters. The results also indicated that in an employee-assisted mobile checkout setting, intention to shoplift was driven by experiential beliefs and peer influence, and experiential beliefs had a stronger effect for prospective shoplifters when compared to experienced shoplifters. Originality/value This study is the among the first, if not first, to examine shoplifters’ intention to shoplift in mobile checkout settings. We provide insights into how those who may not have considered shoplifting in less favorable criminogenic settings may change their behavior due to the autonomy provided by mobile checkout settings and also provide an understanding of the shoplifting intention for both prospective and experienced shoplifters in different mobile checkout settings.
Mayflies, Breeders, and Busy Bees in Ethereum

Smart contracts on a blockchain are programs running in a distributed, transparent, and trustless environment, being one of the major assets of this new technology. They give rise to innovative applications and business models, with their potential and lasting impact still open. In this situation, it is interesting to understand what smart contracts are actually doing. While public announcements, by their nature, make promises of what smart contracts might achieve, the openly available data of blockchains provides a more balanced view on what is actually going on. In this paper, we analyze the activities of smart contracts on the Ethereum blockchain, the most prominent platform for smart contracts with all blockchain data visible. However, contracts operate behind the scenes. Their activities are only accessible by looking beyond the mere blockchain data that records external transactions. We also use all internal messages caused by contracts interacting with other addresses. In particular, we investigate the activities of smart contracts in their quantitative and temporal aspects. Based on lifespan and activity patterns, we identify particular groups like mayflies, loners, breeders, busy bees, sleepers, self-destructed and bonkers contracts and visualize their temporal characteristics. To gain insights into the purpose of these smart contracts we perform a basic analysis of code and message content including deployment code. We consider data up to Ethereum block 6900000 (end of 2018).
Dynamic DFS in Undirected Graphs: Breaking the $O(m)$ Barrier

Depth first search (DFS) tree is a fundamental data structure for solving various problems in graphs. It is well known that it takes $O(m+n)$ time to build a DFS tree for a given undirected graph $G=(V,E)$ on $n$ vertices and $m$ edges. We address the problem of maintaining a DFS tree when the graph is undergoing updates (insertion and deletion of vertices or edges). We present the following results for this problem: (1) Fault tolerant DFS tree: There exists a data structure of size $\tilde{O}(m)$ (where $\tilde{O}$ hides the polylogarithmic factors) which can be preprocessed in $\tilde{O}(m)$ time such that given any set ${\cal F}$ of failed vertices or edges, a DFS tree of the graph $G\setminus {\cal F}$ can be reported in $\tilde{O}(n|{\cal F}|)$ time. (2) Fully dynamic DFS tree: There exists a fully dynamic algorithm for maintaining a DFS tree that takes $\tilde{O}(m)$ time for preprocessing and worst case $\tilde{O}(\sqrt{mn})$ time per update for any arbitrary online sequence of updates. (3) Incremental DFS tree: There exists an incremental algorithm for maintaining a DFS tree that takes $\tilde{O}(m)$ time for preprocessing and worst case $\tilde{O}(n)$ time per update for any arbitrary online sequence of edge insertion. These are the first $o(m)$ worst case time results for maintaining a DFS tree of a dense graph in a dynamic environment. Moreover, our fully dynamic algorithm provides, in a seamless manner, the first deterministic algorithm for dense graphs with $O(1)$ query time and $o(m)$ worst case update time for connectivity, biconnectivity, and 2-edge connectivity in the dynamic subgraph model.
Learning to Remember More with Less Memorization

© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, thus they do not effectively leverage the short-term memory held in the controller. We hypothesize that this scheme of writing is suboptimal in memory utilization and introduces redundant computation. To validate our hypothesis, we derive a theoretical bound on the amount of information stored in a RAM-like system and formulate an optimization problem that maximizes the bound. The proposed solution dubbed Uniform Writing is proved to be optimal under the assumption of equal timestep contributions. To relax this assumption, we introduce modifications to the original solution, resulting in a solution termed Cached Uniform Writing. This method aims to balance between maximizing memorization and forgetting via overwriting mechanisms. Through an extensive set of experiments, we empirically demonstrate the advantages of our solutions over other recurrent architectures, claiming the state-of-the-arts in various sequential modeling tasks.
Equalization of pulse timings in an excitable microlaser system with delay

An excitable semiconductor micropillar laser with delayed optical feedback is able to regenerate pulses by the excitable response of the laser. It has been shown that almost any pulse sequence can, in principle, be excited and regenerated by this system over short periods of time. We show experimentally and numerically that this is not true anymore in the long term: rather, the system settles down to a stable periodic orbit with equalized timing between pulses. Several such attracting periodic regimes with different numbers of equalized pulse timing may coexist and we study how they can be accessed with single external optical pulses of sufficient strength that need to be timed appropriately. Since the observed timing equalization and switching characteristics are generated by excitability in combination with delayed feedback, our results will be of relevance beyond the particular case of photonics, especially in neuroscience.
The Steep Road to Happily Ever after: an Analysis of Current Visual Storytelling Models

Visual storytelling is an intriguing and complex task that only recently entered the research arena. In this work, we survey relevant work to date, and conduct a thorough error analysis of three very recent approaches to visual storytelling. We categorize and provide examples of common types of errors, and identify key shortcomings in current work. Finally, we make recommendations for addressing these limitations in the future.
Binary Classifier Inspired by Quantum Theory

Machine Learning (ML) helps us to recognize patterns from raw data. ML is used in numerous domains i.e. biomedical, agricultural, food technology, etc. Despite recent technological advancements, there is still room for substantial improvement in prediction. Current ML models are based on classical theories of probability and statistics, which can now be replaced by Quantum Theory (QT) with the aim of improving the effectiveness of ML. In this paper, we propose the Binary Classifier Inspired by Quantum Theory (BCIQT) model, which outperforms the state of the art classification in terms of recall for every category.
To the problem of the subsurface defects detection: theory and experiment

We solve the problem of normal waves diffraction in an elastic layer rigidly connected with interface on an interface crack under the action of antiplane dynamic loading. The Wiener–Hopf technique is applied for the solution. The resonance vibrations of the dynamic system on the main mode are investigated. The subtractive synchronized electronic speckle pattern interferometry technique and the approach based on estimation of the dynamic speckle motion of surface areas of multilayer composites were developed for detection of subsurface defects. Experimental breadboards realizing these techniques are created and experiments to identify test and real subsurface defects are fulfilled.
Online Semantic Object Segmentation for Vision Robot Collected Video

Video segmentation is the foundation of many senior computer vision applications. For robotic vision applications, video object segmentation is facing more difficulties. Since the videos are usually collected from those complicated scenes and the camera perspective may be static for some time, object saliency and motion clues which are widely used in traditional video segmentation approaches could be unreliable. Besides, its online processing ability is of crucial importance. In this paper, we propose an online video object segmentation method, which is organized as a segmentation-by-detection framework. It first effectively segments out meaningful objects in short video frame batches with the help of object detectors, and then the segmentation of different batches can be associated with each other via bi-directional notebook based connecting strategy. Experiments conducted on public available datasets verify the above-mentioned property and show good performance of the proposed method.