Reviewerly/exHarmony huggingface dataset:
A Graph-Based Indexing Technique to Enhance the Performance of Boolean AND Queries in Big Data Systems

This paper introduces a new graph-based indexing (GBI) technique for big data systems. It uses a directed graph structure that effectively captures the simultaneous occurrence of multiple keywords in the same document. The objective is to use the relationship between the search keywords captured in the graph structure to effectively retrieve all results of Boolean AND queries at once. The performance of the proposed technique is compared with the conventional inverted index-based technique. This paper highlights that, irrespective of the intersection algorithm used to evaluate Boolean AND queries, GBI always returns Boolean AND search results faster than the inverted index. This is due to the fact that GBI always performs a smaller number of intersection operations and avoids intersection if search keywords do not have a common document. A preliminary performance analysis is performed through prototyping and measurement on a system subjected to a synthetic workload. The analysis shows that GBI improves search latency when executing Boolean AND queries by an average of 69% to 99.9% in comparison to the inverted index.
Properties of a q-Analogue of Zero Forcing

Zero forcing is a combinatorial game played on a graph where the goal is to start with all vertices unfilled and to change them to filled at minimal cost. In the original variation of the game there were two options. Namely, to fill any one single vertex at the cost of a single token; or if any currently filled vertex has a unique non-filled neighbor, then the neighbor is filled for free. This paper investigates a q-analogue of zero forcing which introduces a third option involving an oracle. Basic properties of this game are established including determining all graphs which have minimal cost 1 or 2 for all possible q, and finding the zero forcing number for all trees when $$q=1$$ .
Model of knowledge portal system for higher education: Literature review

Abstract This study presents a discussion of the Tridharma-based university knowledge portal model (education, research, and community service). This study aims to ensure that lecturers activities are in accordance with the Tridharma of higher education and national standards of higher education. The analytical method used is the literature review, with the object of research being hypothetical colleges in the form of universities. Based on the results of the identification of university needs, the knowledge portal model is required to have the ability to develop lecturer competencies in accordance with the Tridharma of higher education. Knowledge portal is designed to have the ability to manage knowledge well, by providing facilities: information gathering, information categorization, information distribution, information collaboration, information retrieval, and knowledge sharing.
Expressive Numbers of Two or More Hidden Layer ReLU Neural Networks

One of the reasons why neural networks are used in machine learning is their high expressive power, that is, the ability to express functions. Expressive power of neural networks depends on its structures and is measured by some indices. In this paper, we focus on one of these measures named "expressive number", which is based on the number of data that can be expressed. Expressive numbers enable us to see whether the size of a neural network is suitable for the given training data before we conduct machine learning. However, existing works on expressive numbers mainly target single hidden layer neural networks, and little is known about those with two or more hidden layers. In this paper, we give a lower bound of the maximum expressive number of two hidden layer neural networks and an upper bound of that of multilayer neural networks with ReLU activation function. This result shows the expressive number of two hidden layer neural networks is in O(a_1a_2) where a_1 and a_2 are the numbers of each hidden layer's neurons.
Neural Collaborative Preference Learning With Pairwise Comparisons

Collaborative Ranking (CR), as an effective recommendation framework, has attracted increasing attention in recent years. Most CR methods simply adopt the inner product between user/item embeddings as the rating score function, with an assumption that the interacted items are preferred to non-interacted ones. However, such fixed score functions and assumptions might not be sufficient to capture the real preference ranking list from the complicated interactions in real-world data. To alleviate this issue, we develop a novel collaborative ranking framework that learns an arbitrary utility function for item ranking with user preference concerned. In the core of our framework, a neural network is employed to model the utility function for personalized ranking with the strength of its nonlinearity. On top of this, we further adopt a pairwise ranking loss for user-item pairs to preserve the preference order of items for users. Besides, such a utility function enables us to generate the final top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> preference list in a much easier way. Finally, extensive experiments on four real-world datasets show the validity of our proposed method.
Short‐Contact Touch‐Manipulation of Scatterplot Matrices on Wall Displays

Abstract This paper presents a short‐contact multitouch vocabulary for interacting with scatterplot matrices (SPLOMs) on wall‐sized displays. Fling‐based gestures overcome central interaction challenges of such large displays by avoiding long swipes on the typically blunt surfaces, frequent physical navigation by walking for accessing screen areas beyond arm's reach in the horizontal direction and uncomfortable postures for accessing screen areas in the vertical direction. Furthermore, we make use of the display's high resolution and large size by supporting the efficient specification of two‐tiered focus + context regions which are consistently propagated across the SPLOM. These techniques are complemented by axis‐centered and lasso‐based selection techniques for specifying subsets of the data. An expert review as well as a user study confirmed the potential and general usability of our seamlessly integrated multitouch interaction techniques for SPLOMs on large vertical displays.
Segmentation of Areas of Interest Inside a Virtual Reality Store

In consumer behavior studies, several signals like head position and eye-tracking, which are mostly unstructured, are recorded. Hence, the first step in these studies is to extract structured features. In feature extraction, segmenting the space into several Areas of Interests (AOI) can be beneficial. In this regard, these features are computed when the shopper is inside a specific zone or interacting with or looking at a specific area. One of the difficulties of this approach is defining AOIs. In this study, positional and eye-tracking data of 57 subjects were recorded in a virtual reality store using a Head Mounted Display. Each subject performed a free navigation task and the objective of the study was to classify the shoppers based on their genders. For this purpose, some AOI-based features were extracted from the behavioral data. The AOIs were cubic and defined with rectangles in zenithal perspective and the shelves levels in virtual store. Sizes of horizontal rectangles were then optimized using Genetic Algorithm (GA). In optimization, a cost function based on Fisher criterion is defined to maximize the linear separability between classes. After optimization, the features extracted with the optimized and several arbitrary AOIs are classified with Support Vector Machine method. The results show that gender classification accuracy with optimized AOIs is 85% and outperforms that of the other AOIs. Along with the outstanding results in this study, this methodology is capable of tuning other hyperparameters like navigational thresholds in classification problems.
A Perpetual Learning Algorithm That Incrementally Improves Performance With Deliberation

During recent years, several different proposals for continuous learning (lifelong learning, never-ending learning, perpetual learning) have attracted much attention from researchers in the field of machine learning. In this paper, a perpetual learning algorithm, which is augmented with a deliberation mechanism that is geared toward incrementally improving performance on all tasks learned thus far, is described. The algorithm maintains a prototype library where each prototype in the library is a model parameters vector for a representative task learned, and produces the model for a new task as a linear combination of the prototypes. The deliberation process ensues based on two criteria (difference in loss and cosine similarity between single task model and encoded model) for the newly learned task model. If the prototypes in the library represent a new task well, then the library only needs to be tuned. Otherwise, if the prototypes in the library cannot represent a new task well, then the algorithm further determines if replacing a prototype in the library makes the library better suited for all learned tasks. If so, the prototype library will be reconstructed. Compared with other existing methods, the proposed method has several salient features. Firstly, by selecting the best prototypes to form the library to represent all learned tasks, the algorithm provided in the paper has better classification and regression performance than, say, Efficient Lifelong Learning Algorithm (ELLA) in circumstances in which only the training set for the current task can be buffered. Secondly, without the need to buffer training sets for multiple tasks, the proposed algorithm shows competitive performance when compared with curriculum learning.
Towards Generating Personalized Country Recommendation

The rise in international migration over the past decades has given more audience to this crucial issue of human life. According to reports by United Nations, more than 243 million people live in a country that is not their place of birth. People decide to immigrate, based on a range of reasons, and choose the country of destination with the hope to begin a new life. However, such a risky decision may not necessarily lead to an improvement of life and in many cases could result in complete dissatisfaction of the emigrating person, and in the extreme cases, cause human catastrophe.
A Study of Community Surveillance System Improvement based on ResNet Person Re-identification

Abstract Most existing community surveillance systems rely on face recognition to identify pedestrian. The face image captured by community surveillance camera are not always clear enough. Thus, Person re-identification (ReID) imply critical applications in surveillance as it has more image information of pedestrian. In this paper, we refine a ResNet50 based reID model which only adds a Linear layer, a Batch Norm layer and a reLU layer in front of the classifier. The refined model is simple to build on the surveillance system, and we have tested in our demo surveillance system and Market1501 data-set. The experiment result shows it can work well on real-time. On Market1501, our results are competitive that rank-1=0.875000, rank-5=0.944477, and mAP=0.706647 since its low complexity. Our demo community surveillance system shows the refined ReID model is competent and practical for identification tasks.
A Case Study of Graphical Passwords in a Chinese University

The strength and memorability of picture passwords, that corelate with user's visual behavior, not only diverse between their cognitive styles but also within the culture predispositions. As part of the works investigating this topic, this paper reports a case study investigating visual behavior of Chinese students (N=36) on establishing graphical password. To examine cognitive and visual behavior, we have provided our participants with two sets of images: the first set illustrated images highly related to their daily-life experiences (culture-internal), while the second set illustrated images presenting daily-life experiences in a different sociocultural context (culture-external). Our results have indicated that users spent more time exploring culture-internal, rather than culture-external photos before they made graphical password selection. Different content of our pictures also affected the percentage of password gestures chosen by participants with culture-external image type falling higher on hot-spots segments. Our study sustains previous findings that promote individual approach towards users' sociocultural experiences in the design of personalized graphical password schemes.
Cartesian Tracking for Advanced Driver Assistance Imaging Radar Systems

This paper presents an approach of using the Kalman Filter to track automotive radar detections in Cartesian space, useful for Radar-Vision fusion. A typical automotive radar would extract, for every detection, its respective range, radial velocity and Direction of Arrival. The problem at hand is that having no information on the Direction of Displacement, one cannot uniquely map the measurements to cartesian coordinates, which is required for the prediction step of the Kalman Filter. The proposed method uses Recursive Error Division filters and a stability-controlled feedback loop to iteratively estimate the DoD, making Cartesian tracking possible.
Measuring the (perceived and effective) use of mobile applications by students in higher education classes : A pilot study

This paper was developed within the scope of a PhD thesis that pretends to characterize the use of mobile applications by University of Aveiro students during class time. The main purpose of this paper is to detail the processes used in an initial pilot study that aimed to fine-tune data collection methods in order to gather data that reflected the practices of the use of mobile applications by students in a higher education institution during classes. In this study we detail all the decisions and procedures carried out to gather mobile applications usage data logs from students of a Multimedia Laboratory course belonging to an undergraduate degree on Communication Technologies. The preliminary findings suggest that the use of apps during theoretical classes of the Department of Communication and Art is quite high and the procedures to develop this kind of studies should be very carefully planned, with deeper thought of categorization of data logs, so that the research can be more valid from a scientific and academic point of view..
A validation of a cloud migration readiness assessment instrument: case studies

Higher education institutions could introduce enormous cloud-based educational services that satisfy their stakeholders’ IT needs. Migration to these pools of cloud services is multiple decision-making processes that are impacted by different organizational and technological obstacles and drivers. Investigating the preparedness of the internal technological and organizational capabilities of a university still lacks investigation particularly in Saudi Arabia. In this study, the practicality of a research instrument called Cloud Migration Readiness Assessment (CMRA) is assessed by using three case studies. The development of the instrument was published earlier. In this study, the validation of the instrument outputs is investigated by assessing the readiness of the three cases that already have planned to adopt the cloud computing services. Subsequently, the practicality and usefulness of the CMRA were assessed using a questionnaire and interviews with IT specialists in the three selected cases. The main contribution of this study is the validation and evaluation of the CMRA tool in real-world context using three different case studies in Saudi Universities.
Gaining traction: on the convergence of an inner approximation scheme for probability maximization

Abstract We analyze an inner approximation scheme for probability maximization. The approach was proposed in Fábián et al. (Acta Polytech Hung 15:105–125, 2018), as an analogue of a classic dual approach in the handling of probabilistic constraints. Even a basic implementation of the maximization scheme proved usable and endured noise in gradient computations without any special effort. Moreover, the speed of convergence was not affected by approximate computation of test points. This robustness was then explained in an idealized setting. Here we work out convergence proofs and efficiency arguments for a nondegenerate normal distribution. The main message of the present paper is that the procedure gains traction as an optimal solution is approached.
Efficient Graph Classification via Graph Encoding Networks

Graph neural networks have been attracted attention in recent years due to their superior performance in many tasks including graph classification. Unlike deep networks for image data, in which deep networks can work directly on raw data, graph neural networks usually require to be thoughtfully designed to integrate topological information (via adjacency matrices) to the networks, rendering high-computational cost and complicating the networks. This paper introduces a novel framework, which first employs an effective process to convert adjacency matrices into more convenient Column Ordering Free (COF) matrices and then builds a deep network to learn high-level representations from these matrices for graph classification. Our framework is not only permutation invariant but also has computational efficiency. In particular, we show that our network achieves comparable performance with other existing graph neural networks but at least 3 times faster in speed on a set of both biological and social network benchmark datasets.
Research on real-time distance measurement of mobile eye tracking system based on neural network

With the development and application of eye-tracking technology, mobile eye-tracking systems have become more widely used due to their safety and portability. We combine eye-tracking systems with real-time object detection using machine learning. We propose a method of wearing an eye tracker in daily life to obtain the distance between the eye tracking system and the gaze target in real time. During the visual interaction of the eye tracking system, in order to obtain the distance from the eyeball fixation target to the eyeball in real time, the world camera of the mobile eye tracking system pupil labs first collects the position and scale information of the detected target image in real time, and uses camera calibration principle, pinhole camera model and camera distortion model to establish a ranging equation, and then the feasibility of the real-time ranging equation is verified through a specified distance experiment. The total average relative error after de-distortion at the position of 50cm-75cm is reduced to 1.25%, and the highest accuracy-0.9182cm distance measurement can be achieved within the effective distance.
Object Miners: Acquire, Capture and Replay Objects to Track Elusive Bugs.

Elusive bugs are difficult to observe and to reproduce.They are often caused by non-deterministic or unexpected events, inputs or computations.To track elusive bugs, it is necessary to narrow down the scope of the bug investigation.This helps reproducing and observing very specific aspects of the program's state and behavior.In object-oriented programs, it often comes down to finding and debugging specific objects.However, some objects are particularly hard to find.Typical hard cases are finding a temporary object or a single particular instance of a given class.This is the case when debugging UI elements, or programs with non-deterministic state.This capability of identifying objects of interest is crucial to the debugging of objects.Yet, debuggers addressing this problem only provide manual or limited ways to find such objects.In this paper we present Object Miners: a non-intrusive object-centric debugging approach for acquiring, capturing and replaying objects.We show how the miners acquire objects at run time from the sub-results of an instrumented expression.Miners capture objects with their execution context and replay them to freeze strategic parts of the execution, eliminating non-determinism.We present a Pharo implementation of Object Miners along with a performance and memory overhead evaluation.We present a debugger built on top of Object Miners, and demonstrate through a series of examples how the application of Object Miners facilitates the tracking of elusive bugs.These examples include the fixing of a non-deterministic bug in an IOT application, tooling support for object-centric debugging, and the tracking of a bug in a real-world program.
A method of ROP decentralized layout

Return-oriented programming (ROP)is a technique used to break data execution protection(DEP). Existing ROP chain automatic construction technology cannot effectively use program controllable memory area. In order to improve the utilization of memory space, this paper proposes a method of ROP chain fragmentation layout. By searching the controllable memory area of the program, a set of layoutable space is formed, and the overall ROP chain is segmented to add jump instructions at the end of each segment, thereby achieving a fragmented layout of the ROP chain. The prototype system ROP-chip based on S2E proved the effectiveness of the fragmented layout of the ROP chain.
Augmented Reality System for the Assistance of Unmanned Aerial Vehicles

This article proposes the development of a virtual and augmented reality environment to assist in the assembly and maintenance of an unmanned aerial vehicle. Taking the lack of technological knowledge as the research gap, the study provides researchers and practitioners with new solutions through the familiarization and guided assistance of a UAV. The system is based on the user tracing to locate the position of the hands and validate in real-time if the actions performed are correct. In the interface can also access to virtualized environment so that the user define a route with the movement of his hands and immediately the UAV follow the path marked from the beginning to the end, the errors are displayed on the same screen to verify the stability of control.
Channel-Wise Predictive Filter Flow for Demosaicking

Demosaicking is an image reconstruction process to recover a full color image from color filter array (CFA) mosaic data. Recently, deep convolutional neural network (CNN)-based demosaicking methods have been explored and have achieved state-of-the-art accuracy. In the deep-CNN-based demosaicking, output pixels are affected by a large spatial region; however, the information involved in demosaicking often only exists locally. In this paper, we propose a channel-wise predictive filter flow (PFF) for demosaicking. Since the PFF is a model that predicts a space-variant linear filter that is transformed to the target image by linearly combining it with the input image, target pixels are reconstructed only from local information. To incorporate the PFF into demosaicking, the proposed network synthesizes the filter flow corresponding to each channel by different networks that are learned independently. Experimental results demonstrate that the proposed method provides better or competitive results compared with several state-of-the-art deep-CNN-based demosaicking algorithms.