Reviewerly/exHarmony huggingface dataset:
MBA: Mini-Batch AUC Optimization

Area under the receiver operating characteristics curve (AUC) is an important metric for a wide range of machine-learning problems, and scalable methods for optimizing AUC have recently been proposed. However, handling very large data sets remains an open challenge for this problem. This article proposes a novel approach to AUC maximization based on sampling mini-batches of positive/negative instance pairs and computing U-statistics to approximate a global risk minimization problem. The resulting algorithm is simple, fast, and learning-rate free. We show that the number of samples required for good performance is independent of the number of pairs available, which is a quadratic function of the positive and negative instances. Extensive experiments show the practical utility of the proposed method.
PathBot: An Intelligent Chatbot for Guiding Visitors and Locating Venues

This article reports the development of an intelligent Chatbot called PathBot. PathBot is used to guide visitors to locate venues on the University of Johannesburg's Bunting Road campus. The conversation of PathBot happens through a Mobile Application client. PathBot processes user's text using the DialogFlow API. We have designed a Finite Automaton that represents a sub-map of the campus. Extracted tokens from the DialogFlow API is sent to this Automaton, and an algorithm is used to generate descriptions of paths from where a user is, to where they want to go. An evaluation of the user's opinion about this tool and its userability was conducted and most users agreed that this Automaton-driven tool is helpful in locating venues on the mapped campus.
A Rule-Based Holistic Approach for Turkish Aspect-Based Sentiment Analysis

In this study, a holistic method which uses statistical, linguistic and rule-based approaches for Turkish aspect-based sentiment analysis is proposed. The proposed method has been tested on the Turkish restaurant dataset created within the scope of SemEval Aspect Based Sentiment Analysis (ABSA) 2016. Firstly, candidate aspect terms were acquired employing LDA, C-value and WSBFE. Afterwards, aspect terms were found by rule-based approach and aspect-sentiment pairs were determined. In aspect term extraction 56,28% f-score was obtained while in aspect-sentiment matching phase 52,05% accuracy was achieved.
Using Natural Language Processing to Predict Item Response Times and Improve Test Construction

Abstract In this article, it is shown how item text can be represented by (a) 113 features quantifying the text's linguistic characteristics, (b) 16 measures of the extent to which an information‐retrieval‐based automatic question‐answering system finds an item challenging, and (c) through dense word representations (word embeddings). Using a random forests algorithm, these data then are used to train a prediction model for item response times and predicted response times then are used to assemble test forms. Using empirical data from the United States Medical Licensing Examination, we show that timing demands are more consistent across these specially assembled forms than across forms comprising randomly‐selected items. Because an exam's timing conditions affect examinee performance, this result has implications for exam fairness whenever examinees are compared with each other or against a common standard.
Computer Classification of Linear Codes

We present algorithms for classification of linear codes over finite fields, based on canonical augmentation and on lattice point enumeration. We apply these algorithms to obtain classification results over fields with 2, 3 and 4 elements. We validate a correct implementation of the algorithms with known classification results from the literature, which we partially extend to larger ranges of parameters.
Error analysis of a stereo-vision-based tube measurement system

To ensure the accuracy of tube measurement systems based on multi-stereo vision, we defined error sources and their modes of transmission through the system, and developed an error propagation model. In this paper, we first discuss system processes including camera calibration, image processing, point-to-point matching, three-dimensional reconstruction, and error sources identification. Next, we develop a theoretical error calculation equation based on our proposed error propagation model. Experimental verification results showed a practical measurement error of 0.17 mm; the error associated with our proposed theoretical error calculations was 0.192 mm, verifying the utility of the model. The proposed model is expected to help researchers rapidly establish accurate measurement systems that are capable of accommodating processing challenges.
An Overview of Data Visualization

Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software. Data visualization is the presentation of quantitative information in a graphical form. In other words, data visualizations turn large and small data-sets into visuals that are easier for the human brain to understand and process. Data visualizations are surprisingly common in our everyday life, but they often appear in the form of well-known charts and graphs. It can be used to discover unknown facts and trends. Good data visualizations are created when communication, data science, and design collide. Data visualizations done right offer key insights into complicated data-sets in ways that are meaningful and intuitive. In this article, we would like to discuss about data visualization, importance of data visualization, data visualization tools etc.
Principle Of Organizing Database Identification On Mobile Devices By Imei

In this paper, we consider the possibility of creating a Unified IMEI mobile device identification database information system, which will be able to analyze, monitor and control imported mobile devices, which will help to reduce crimes related to theft by blocking stolen mobile devices in Uzbekistan.
Sparse BD-Net

In this work, we propose a multiplication-less binarized depthwise-separable convolution neural network, called BD-Net. BD-Net is designed to use binarized depthwise separable convolution block as the drop-in replacement of conventional spatial-convolution in deep convolution neural network (DNN). In BD-Net, the computation-expensive convolution operations (i.e., Multiplication and Accumulation) are converted into energy-efficient Addition/Subtraction operations. For further compressing the model size while maintaining the dominant computation in addition/subtraction, we propose a brand-new sparse binarization method with a hardware-oriented structured sparsity pattern. To successfully train such sparse BD-Net, we propose and leverage two techniques: (1) a modified group-lasso regularization whose group size is identical to the capacity of basic computing core in accelerator and (2) a weight penalty clipping technique to solve the disharmony issue between weight binarization and lasso regularization. The experiment results show that the proposed sparse BD-Net can achieve comparable or even better inference accuracy, in comparison to the full precision CNN baseline. Beyond that, a BD-Net customized process-in-memory accelerator is designed using SOT-MRAM, which owns characteristics of high channel expansion flexibility and computation parallelism. Through the detailed analysis from both software and hardware perspectives, we provide an intuitive design guidance for software/hardware co-design of DNN acceleration on mobile embedded systems. Note that this journal submission is the extended version of our previous published paper in ISVLSI 2018 [24].
Machine learning approaches for classifying lunar soils

We examine the ability of machine learning (ML) techniques to determine the physical and mineralogical properties of lunar soil using reflectance spectra. We use the Lunar Soil Characterization Consortium (LSCC) dataset to train and assess the predictive power of classification models based on their type (Mare soil and Highland soil), particle size, maturity, and the dominant type of pyroxene (High-Ca and Low-Ca). Nine ML algorithms including linear methods, non-linear methods, and rule-based methods (three from each) were selected, representing a range of characteristics such as simplicity, flexibility, computational complexity, and interpretability along with their ability to handle different types of data. Fifteen spectral parameters were initially introduced to the models as input features and a maximum of four features was selected as the best feature combinations to classify lunar soils based on their types, particle size, maturity, and the type of pyroxene. The Support Vector Machine with radial basis function (svmRadial) and the penalized logistic regression model (glmnet) performed well for all target variables with high accuracies. Band depths and Integrated band depths at 1 μm, 1.25 μm and 2 μm, band position of the 1 μm band, along with four band ratios (band tilt, band strength, band curvature, and olivine/pyroxene) are important features for classifying soil type, grain size, maturity, and type of pyroxene from reflectance spectra. This study shows that proper preprocessing and feature engineering techniques are crucial for high performance of the predictive models.
An eye gaze-driven metric for estimating the strength of graphical passwords based on image hotspots

In this paper, we propose an eye gaze-driven metric based on hotspot vs. non-hotspot segments of images for unobtrusively estimating the strength of user-created graphical passwords by analyzing the users' eye gaze behavior during password creation. To examine the feasibility of this method, i.e., the existence of correlation between the proposed metric and the strength of users' generated passwords, we conducted an eye-tracking study (n=42), in which users created a graphical password with a personalized image that triggers declarative memory of users (familiar image) vs. an image illustrating generic content unfamiliar to the users' episodic and semantic memory (generic image). Results revealed a strong positive correlation between the password strength and the proposed eye gaze-driven metric, pointing towards a new direction for the design of intelligent eye gaze-driven graphical password schemes for unobtrusively assisting users in making better password choices.
Live Dance Performance Investigating the Feminine Cyborg Metaphor with a Motion-activated Wearable Robot

This paper presents artistic work that comments on the exploitation of feminine gender performance in technology, from the point of view of a particular artist, enabled by a wearable robotic device that creates an onstage cyborg character. The piece, entitled "Babyface'' was created in residence in a robotics lab and has catalyzed the development of a motion-activated wearable robot. This paper reports on the creation of this piece, its accompanying device, along with initial responses that have been shared informally. Iterations of multiple flexible attachment structures, a clear plastic dress that references the hyperbolic representation of the female sex, as well as the electronic subsystems are presented alongside discussion of the somatic and choreographic investigations that accompanied prototype development. These fabricated elements alongside the actions of the performer and a soundscape that quotes statements made by real "female'' robots create an otherwordly, sad cyborg character that causes viewers to question their assumptions about and pressures on the female ideal. This work is an important first step in the development of a wearable robot with embodied connection to the performer and, eventually, audience members.
Multiple Instance Learning with Genetic Pooling for medical data analysis

Multiple Instance Learning is a weakly supervised learning technique which is particularly well suited for medical data analysis as the class labels are often not available at desired granularity. Multiple Instance Learning through Deep Neural Networks is relatively a new paradigm in machine learning. The most important part of Multiple Instance Learning through Deep Neural Networks is designing a trainable pooling function which determines the instance-to bag relationship. In this paper, we propose a Multiple Instance pooling technique based on Genetic Algorithm called Genetic Pooling. In this technique, instance labels inside a bag are optimized by minimizing bag-level losses. The main contribution of the paper is that the bag level pooling layer for generating attention weights for bag instances are replaced by random initialization of attention weights and finding the optimized attention weights through Genetic Algorithm.
Update now or later? Effects of experience, cost, and risk preference on update decisions

Abstract Installing software updates is one of the most important security actions that people can take to protect their computer systems. However, people often delay installing updates. Why would people delay installation of security updates, knowing that these updates may reduce the risk of information loss from attacks? In a laboratory experiment, we studied how people learn to make update decisions from past experiences. In a simulated “work” environment, participants could defend against low probability and high impact losses, by installing a security update. The cost of updates was variable; participants could update immediately for a high cost or wait to update for free, risking increased exposure to attacks and losses. Thus, the optimal decision was to update immediately when the update was made available. The results from our experiment indicate people learn from experience to delay security updates. The cost of the update and individual risk preference both significantly predicted the tendency to delay the update; people with higher willingness to take risks may be more likely to neglect to update, keeping the status quo even when it may be sub-optimal. We discuss the implications of these findings for the design of interventions to reduce delays in update installations.
Schemes, Critical Questions, and Complete Argument Evaluation

Abstract According to the argument scheme approach, to evaluate a given scheme-saturating instance completely does entail asking all critical questions (CQs) relevant to it. Although this is a central task for argumentation theorists, the field currently lacks a method for providing a complete argument evaluation. Approaching this task at the meta-level, we combine a logical with a substantive approach to the argument schemes by starting from Toulmin’s schema: ‘data, warrant, so claim’. For the yet more general schema: ‘premise(s); if premise(s), then conclusion; so conclusion’, we forward a meta-level CQ-list that is arguably both complete and applicable. This list should inform ongoing theoretical efforts at generating appropriate object-level CQs for specific argument schemes.
A passage-based approach to learning to rank documents

According to common relevance-judgments regimes, such as TREC's, a document can be deemed relevant to a query even if it contains a very short passage of text with pertinent information. This fact has motivated work on passage-based document retrieval: document ranking methods that induce information from the document's passages. However, the main source of passage-based information utilized was passage-query similarities. In this paper, we address the challenge of utilizing richer sources of passage-based information to improve document retrieval effectiveness. Specifically, we devise a suite of learning-to-rank-based document retrieval methods that utilize an effective ranking of passages produced in response to the query. Some of the methods quantify the ranking of the passages of a document. Others utilize the feature-based representation of the document's passages. Empirical evaluation attests to the clear merits of our methods with respect to highly effective baselines. Our best performing method is based on learning a document ranking function using document-query features and passage-query features of the document's passage most highly ranked; the passage-query features are those used to learn a highly effective passage ranker.
Faster RCNN Detection Based OpenCV CSRT Tracker Using Drone Data

Object tracking is one of the well-known topic in Computer Vision sphere with challenging and problematic tasks that frames come with problems like overlapping, camera motion blur, changing object appearance, environmental variations and so on. Using OpenCV based CSRT (Channel and Spatial Reliability Tracking) tracker itself, without integration object detection part may face problems which mentioned above. To overcome that challenges we integrated deep learning based object detection with CSRT tracker and got better tracking results.
Fault tolerance of neural networks in adversarial settings

Artificial Intelligence systems require a through assessment of different pillars of trust, namely, fairness, interpretability, data and model privacy, reliability (safety) and robustness against against adversarial attacks. While these research problems have been extensively studied in isolation, an understanding of the trade-off between different pillars of trust is lacking. To this extent, the trade-off between fault tolerance, privacy and adversarial robustness is evaluated for the specific case of Deep Neural Networks, by considering two adversarial settings under a security and a privacy threat model. Specifically, this work studies the impact of the fault tolerance of the Neural Network on training the model by adding noise to the input (Adversarial Robustness) and noise to the gradients (Differential Privacy). While training models with noise to inputs, gradients or weights enhances fault tolerance, it is observed that adversarial robustness and fault tolerance are at odds with each other. On the other hand, ($\epsilon,\delta$)-Differentially Private models enhance the fault tolerance, measured using generalisation error, theoretically has an upper bound of $e^{\epsilon} - 1 + \delta$. This novel study of the trade-off between different elements of trust is pivotal for training a model which satisfies the requirements for different pillars of trust simultaneously.
An Explicit Construction of Quantum Stabilizer Codes From Quasi-Cyclic Codes

In this short letter, our main objective is to construct good quantum stabilizer codes over small fields, which have a more potential possibility to be employed in actual quantum processors. We firstly consider a suitable class of two-generators quasi-cyclic (QC) codes and determine their parameters. Then by virtue of their symplectic dual algebraic structure, we derive a sufficient condition to be symplectic dual-containing of these codes. Subsequently, an explicit construction of stabilizer codes from quasi-cyclic codes is presented. As for computational results, we provide many stabilizer codes with good parameters over binary, ternary and quaternary fields that all can not be deduced by the quantum Gilbert-Varshamov (GV) bound. In the binary case, our several codes strictly improve the lower bounds on the minimum distance in current records. In the ternary and quaternary occasions, numerous codes fill some gaps or have better parameters than the current results.
Extended Feature Pyramid Network with Adaptive Scale Training Strategy and Anchors for Object Detection in Aerial Images

Multi-scale object detection is a basic challenge in computer vision. Although many advanced methods based on convolutional neural networks have succeeded in natural images, the progress in aerial images has been relatively slow mainly due to the considerably huge scale variations of objects and many densely distributed small objects. In this paper, considering that the semantic information of the small objects may be weakened or even disappear in the deeper layers of neural network, we propose a new detection framework called Extended Feature Pyramid Network (EFPN) for strengthening the information extraction ability of the neural network. In the EFPN, we first design the multi-branched dilated bottleneck (MBDB) module in the lateral connections to capture much more semantic information. Then, we further devise an attention pathway for better locating the objects. Finally, an augmented bottom-up pathway is conducted for making shallow layer information easier to spread and further improving performance. Moreover, we present an adaptive scale training strategy to enable the network to better recognize multi-scale objects. Meanwhile, we present a novel clustering method to achieve adaptive anchors and make the neural network better learn data features. Experiments on the public aerial datasets indicate that the presented method obtain state-of-the-art performance.
A Novel Self-Supervised Re-labeling Approach for Training with Noisy Labels

The major driving force behind the immense success of deep learning models is the availability of large datasets along with their clean labels. This is very difficult to obtain and thus has motivated research on training deep neural networks in the presence of label noise. In this work, we build upon the seminal work in this area, Co-teaching and propose a simple, yet efficient approach termed mCT-S2R (modified co-teaching with self-supervision and relabeling) for this task. Firstly, to deal with significant amount of noise in the labels, we propose to use self-supervision to generate robust features without using any labels. Furthermore, using a parallel network architecture, an estimate of the clean labeled portion of the data is obtained. Finally, using this data, a portion of the estimated noisy labeled portion is re-labeled, before resuming the network training with the augmented data. Extensive experiments on three standard datasets show the effectiveness of the proposed framework.
PV-Tiles: Towards Closely-Coupled Photovoltaic and Digital Materials for Useful, Beautiful and Sustainable Interactive Surfaces

The interactive, digital future with its seductive vision of Internet-of-Things connected sensors, actuators and displays comes at a high cost in terms of both energy demands and the clutter it brings to the physical world. But what if such devices were made of materials that enabled them to self-power their interactive features? And, what if those materials were directly used to build aesthetically pleasing environments and objects that met practical physical needs as well as digital ones? In this paper we introduce PV-Tiles ? a novel material that closely couples photovoltaic energy harvesting and light sensing materials with digital interface components. We consider potential contexts, use-cases and light gestures surfaced through co-creation workshops; and, present initial technological designs and prototypes. The work opens a new set of opportunities and collaborations between HCI and material science, stimulating technical and design pointers to accommodate and exploit the material's properties.