Reviewerly/exHarmony huggingface dataset:
Making Machine Learning Forget

Machine learning models often overfit to the training data and do not learn general patterns like humans do. This allows an attacker to learn private membership or attributes about the training data, simply by having access to the machine learning model. We argue that this vulnerability of current machine learning models makes them indirect stores of the personal data used for training and therefore, corresponding data protection regulations must apply to machine learning models as well. In this position paper, we specifically analyze how the “right-to-be-forgotten” provided by the European Union General Data Protection Regulation can be implemented on current machine learning models and which techniques can be used to build future models that can forget. This document also serves as a call-to-action for researchers and policy-makers to identify other technologies that can be used for this purpose.
Continuous Authentication in Mobile Devices Using Behavioral Biometrics

In recent years, the development and use of mobile devices such as smartphones and tablets grew significantly. They are used for virtually every activity of our lives, from communication or online shopping to e-banking or gaming, just to name a few. As a consequence, these devices contribute significantly to make our lives more digital, with all the perks and risks that this encompasses. One of the most serious risk is that of an authorized individual gaining physical access to our mobile device and, potentially, to all the applications and personal data it contains. Most of mobile devices are protected using some kind of password, that can be easily spotted by unauthorized users or event guessed. In the last years, new authentication mechanisms have been proposed, such as those using traditional biometrics or behavioral biometrics. In this paper we propose a new continuous authentication mechanism for mobile devices based on behavioral biometrics that monitors user interaction behavior for classifying the identity of the user.
Locust Search Algorithm Applied to Multi-threshold Segmentation

In a computer vision one problem is image segmentation as an alternative the problem has been handled whit optimization algorithm. Most of the methods have two difficulties (1) sub-optimal results (2) the number of classes is previously known. In this chapter presented an algorithm that automatic selection of this classes for image segmentation whit a new objective function in gaussian mixture model. The optimization algorithm called Locust Search (LS), is based on behavior of locust swarms, presented a balance between exploration and exploitation. The method was tested over several images to validate the efficacy over other techniques.
Value-based cloud price modeling for segmented business to business market

Cloud price modeling is the major challenge facing many cloud computing practitioners and researchers in the field of cloud economics, which is also known as “Cloudonomics.” Previous attempts mainly focused on a uniform market and used existing price models to explain the issue of revenue maximization for cloud service providers (CSPs) from a cost or internal rationality perspective but paid less attention to the cloud market segmentation for cloud business customers from a surplus value or external rationality perspective. This study considers both aspects of the value proposition. Based on the assumptions of the customers’ utility values for different market segments, we establish a framework of value-based pricing strategy and demystify the process of modeling and optimizing cloud prices for CSP to maximize its profits. This framework is built upon the theory of value co-creation for both customers and CSPs to form a business partnership. We show how to create four cloud pricing models, namely: on-demand, bulk-selling, reserved, and bulk + reserved. We also demonstrate how to identify the optimal price point of each model to maximize CSP’s profit by genetic algorithm. We exhibit that reserved, bulk + reserved, on-demand, and bulk-selling can deliver a profit margin of 203%, 183%, 166%, and 157% for CSPs respectively. While the reserved model provides the highest profit margin, it does not necessarily mean that CSPs should adopt one model only. We provide a novel solution that allows CSPs to achieve the maximum profit by offering multiple pricing models simultaneously to various customers in the segmented market. We argue CSPs should capitalize on cloud pricing rather than price to gain more cloud market share and profit. Thus, we present state-of-the-art cloud pricing for segmented business to business cloud market.
Academic Quality Management System Audit Using Artificial Intelligence Techniques

Quality management systems are a challenge for higher education centers. Nowadays, there are different management systems, for instance: quality, environmental, information security, etc. that can be applied over education centers, but to implement all of them is not a guarantee of education quality because the educational process is very complex. However, a few years ago the Quality Management Systems for higher education centers are taking importance especially in Europe and North America, although in Latin America is an unexplored field. Higher education centers quality is a very complex problem because it is difficult to measure the quality since there are a lot of academic processes as enrollment, matriculation, teaching-learning with a lot of stakeholders as students, teachers, authorities even society; in a lot of locations as campuses, buildings, laboratories with different resources. Each process generates a lot of records and documentation. This information has a varied nature and it is present at a structured and no-structured form. In this context, artificial intelligence techniques can help us to analyze and management knowledge. Our work presents a new approach to audit academic information with machine learning and information retrieval. In our experiments, we used information about syllabus, grades, assessments and online content from a Latin American University. We conclude that using artificial intelligence techniques minimize the decision support time, it allows full data analysis instead of a data sample and it finds out patterns never seen in the case study university.
New design approaches of reversible BCD encoder using Peres and Feynman gates

This paper proposes two new design approaches for decimal to binary-coded-decimal (BCD) encoder using reversible logic through Peres gate (PG) and Feynman gate (FG) which consume 10 and 11 gates respectively to realize such circuitry. Gates have been arranged properly to minimize the gate count (GC) followed by the reduction of the quantum cost (QC) and garbage outputs (GO). The proposed designs have an improvement at least up to ∼45%, ∼40%, and ∼50% of GC, GO and QC respectively from the best state of the art designs.
Hypergraph-of-entity

Abstract Modern search is heavily powered by knowledge bases, but users still query using keywords or natural language. As search becomes increasingly dependent on the integration of text and knowledge, novel approaches for a unified representation of combined data present the opportunity to unlock new ranking strategies. We have previously proposed the graph-of-entity as a purely graph-based representation and retrieval model, however this model would scale poorly. We tackle the scalability issue by adapting the model so that it can be represented as a hypergraph. This enables a significant reduction of the number of (hyper)edges, in regard to the number of nodes, while nearly capturing the same amount of information. Moreover, such a higher-order data structure, presents the ability to capture richer types of relations, including n ary connections such as synonymy, or subsumption. We present the hypergraph-of-entity as the next step in the graph-of-entity model, where we explore a ranking approach based on biased random walks. We evaluate the approaches using a subset of the INEX 2009 Wikipedia Collection. While performance is still below the state of the art, we were, in part, able to achieve a MAP score similar to TF-IDF and greatly improve indexing efficiency over the graph-of-entity.
Privacy-Preserving Distributed Machine Learning Based on Secret Sharing

Machine Learning has been widely applied in practice, such as disease diagnosis, target detection. Commonly, a good model relies on massive training data collected from different sources. However, the collected data might expose sensitive information. To solve the problem, researchers have proposed many excellent methods that combine machine learning with privacy protection technologies, such as secure multiparty computation (MPC), homomorphic encryption (HE), and differential privacy. In the meanwhile, some other researchers proposed distributed machine learning which allows the clients to store their data locally but train a model collaboratively. The first kind of methods focuses on security, but the performance and accuracy remain to be improved, while the second provides higher accuracy and better performance but weaker security, for instance, the adversary can launch membership attacks from the gradients' updates in plaintext. In this paper, we join secret sharing to distributed machine learning to achieve reliable performance, accuracy, and high-level security. Next, we design, implement, and evaluate a practical system to jointly learn an accurate model under semi-honest and servers-only malicious adversary security, respectively. And the experiments show our protocols achieve the best overall performance as well.
In-Network YouTube Performance Estimation in Light of End User Playback-Related Interactions

Recent research efforts have addressed the challenge of estimating HTTP adaptive video streaming Quality of Experience (QoE) and Key Performance Indicators (KPIs) from a network provider perspective, commonly relying on machine learning models and the analysis of features extracted solely from encrypted network traffic. This challenge is further complicated in light of realistic end user playback-related interactions, such as video skipping, pausing, and seeking. Given that user interactions impact traffic characteristics, such scenarios need to be considered when training QoE/KPI estimation models. We train models on datasets with and without user interactions (focusing on YouTube as a case study), with the aim to investigate the impact of user interaction on classification accuracy. Results motivate the need to systematically include data corresponding to various interaction scenarios when training QoE/KPI classification models that would be applicable in real-world scenarios.
Analyzing Comment-Induced Updates on Stack Overflow

Stack Overflow is home to a large number of technical questions and answers. These answers also include comments from the community and other users about the answer's validity. Such comments may point to flaws in the posted answer or may indicate deprecated code that is no longer valid due to API changes. In this paper, we explore how comments affect answer updates on Stack Overflow, using the SOTorrent dataset. Our results show that a large number of answers on Stack Overflow are not updated, even when they receive comments that warrant an update. Our results can be used to build recommender systems that automatically identify answers that require updating, or even automatically update answers as needed.
Learning ADL Daily Routines with Spatiotemporal Neural Networks

Activities of daily living (ADLs) refer to the activities performed by individuals on a daily basis and are the indicators of a person's habits, lifestyle, and wellbeing. Consequently, learning an individual's ADL daily routines has significant value in the healthcare domain. Specifically, ADL recognition and inter-ADL pattern learning problems have been studied extensively in the past couple of decades. However, discovering the patterns of ADLs performed in a day and clustering them into ADL daily routines has been a relatively unexplored research area. In this paper, a self-organizing neural network model, called the Spatiotemporal ADL Adaptive Resonance Theory (STADLART), is proposed for learning ADL daily routines. STADLART integrates multimodal contextual information that involves the time and space wherein the ADLs are performed. By encoding spatiotemporal information explicitly as input features, STADLART enables the learning of time-sensitive knowledge. Moreover, a STADLART variation named STADLART-NC is proposed to normalize and customize ADL weighting for daily routine learning. A weighting assignment scheme is presented that facilitates the assignment of weighting according to ADL importance in specific domains. Empirical experiments using both synthetic and real-world public data sets validate the performance of STADLART and STADLART-NC when compared with alternative pattern discovery methods. The results show that STADLART could cluster ADL routines with better performance than baseline algorithms.
TutorBot: Contextual Learning Guide for Software Engineers

This document is poster submission on using conversational chat bot to guide a software engineer in their learning journey and keeping pace with the technology changes. We describe the motivation, technical approach, and experience of building, piloting such a Bot in a controlled setting and capturing the user feedback. The document also discusses future opportunities to extend and enhance the functionality.
GeCo2: An Optimized Tool for Lossless Compression and Analysis of DNA Sequences

The development of efficient DNA data compression tools is fundamental for reducing the storage, given the increasing availability of DNA sequences. The importance is also reflected for analysis purposes, given the search for optimized and new tools for anthropological and biomedical applications. In this paper, we describe the characteristics and impact of the GeCo2 tool, an improved version of the GeCo tool. In the proposed tool, we enhanced the mixture of models, where each context model or tolerant context model has now a specific decay factor. Additionally, specific cache-hash sizes and the ability to run only a context model with inverted repeats was developed. A new command line interface, twelve new pre-computed levels, and several optimizations in the code were also included. The results show a compression improvement using less computational resources (RAM and processing time). This new version permits more flexibility for compression and analysis purposes, namely a higher ability of addressing different characteristics of the DNA sequences. The decompression is performed using symmetric computational resources (RAM and time). The GeCo2 is freely available, under GPLv3 license, at https://github.com/pratas/geco2 .
Linear optics-based entanglement concentration protocols for cluster-type entangled coherent state

We proposed two linear optics-based entanglement concentration protocols (ECPs) to obtain maximally entangled 4-mode cluster-type entangled coherent state (ECS) from less (partially) entangled cluster-type ECS. The first ECP is designed using a superposition of single-mode coherent state with two unknown parameters, whereas the second ECP is obtained using a superposition of single-mode coherent state and a superposition of two-mode coherent state with four unknown parameters. The success probabilities have been calculated for both the ECPs. The necessary quantum circuits enabling future experimental realizations of the proposed ECPs are provided using linear optical elements. Further, the benefit of the proposed schemes is established in the context of long-distance quantum communication where photon loss is an obstruction.
Epistemic Debt: A Concept and Measure of Technical Ignorance in Smart Manufacturing

This paper introduces the notion of epistemic debt as an analytical tool for understanding and managing the effects of technical ignorance in smart manufacturing. Drawing on the concepts of technical and social debt from software engineering, the metaphor of epistemic debt refers to the implied long-term costs of rework (e.g., redesign, replacement, reconfiguration or systems and/or organizational structures) caused by a lack of understanding and/or means of knowing the internals of complex software-based manufacturing systems essential to the value chain and core business of an organization. After defining the concept, we identify three of its sources and propose strategies for coping with epistemic debt in manufacturing.
Total variation constraint GAN for dynamic scene deblurring

Recovering the sharp image solely from the blurry image in dynamic scene is challenging due to the ill-defined nature of the problem. Through Wasserstein distance and L1 norm of total variation combined regularization, we propose a novel TV-DRGAN optimization framework to obtain a latent sharp image from some observed blurry images. Our method benefits from two aspects: one is the improved object total variation energy to constrain the blurry image, and the other is the generator model combining (UPR)-Blocks and D-Blocks. An (UPR)-Block is composed of one upsampling layer and 3 convolution layers. Consisting of an average-pooling layer and multiple convolution layers, a D-Block comes with different kernel sizes that capture global, and local spatial information of the raw image, separately. By analyzing the information of gradient, we obtain a TV-based based on minimum lower bound of loss function of the generator. Our experiments show that the proposed method outperforms the state-of-the-art conventional algorithms significantly.
Unscented summation information‐weighted consensus filter for distributed sensor networks with incomplete information

Summary In order to improve network scalability and fault tolerance, the distributed sensor networks are desirable. However, the distributed state estimation becomes challenging when some sensors have insufficient information due to restricted observability, and/or have imparity information due to unequal measurement‐noise covariances. Centralized summation information‐fusion (CSI) model is presented which performs weighted least‐squares estimation for all measurement information to achieve the optimal centralized state estimation. The CSI model revises the initialization and covariance propagation in the original information‐weighted consensus filter (ICF). Since centralized information fusion is a summation mode and is approached by the average consensus protocol, all the covariances involved in the CSI model contain the information regarding the total number of nodes. The artificially preset initial values are considered as measurement information and fused in accordance with the CSI model. By combining the CSI model with unscented transform, distributed unscented summation information‐weighted consensus filter (USICF) is proposed. USICF realizes the nonlinear estimation in the context of highly incomplete information. Theoretical analysis and experimental verification showed that USICF achieves better performance than UICF that is based on ICF.
Statistical Analysis of Various Hybridization of Evolutionary Algorithm for Traveling Salesman Problem

The paper focus on statistical analysis of separate, combined and partial hybridization performance of evolutionary algorithm and neighborhood searcher with a goal to find an intelligent way of hybridization of evolutionary algorithms. On the Traveling Salesman Problem (TSP) we measured the influence of hybridization a 2-opt heuristic-based local searcher into the evolutionary algorithm. Evolutionary Algorithm gives a diversification, while 2-opt improves intensification. The TSP is nowadays already solved for small instances rather efficiently by exact algorithms (e.g concorde) and by local search heuristics as LKH by Helsgaun. Nevertheless, the paper shows statistical analysis that intelligently hybridized algorithm combines good qualities from the both applied components and outperforms each individual method and suggest what level and type of hybridization is best for a given problem to make them intelligent. In tests we applied hybridization at various percentages (level) of evolutionary algorithm iterations. The main contribution of the paper is to show statistical analysis of hybridized evolutionary algorithms. For that purpose we used well known statistical tools. Since the evaluation scores were not normally distributed, the nonparametric Kruskal-Wallis Test (KWT) was used instead of the standard one-way ANOVA. The differences were considered to be statistically significant in cases where the estimated p-values of statistical tests were less than or equal to 0.05. The analysis with KWT showed that there exist statistically significant differences in place of hybridization. The analysis revealed significant differences in all levels of the hybridization. However, intensifying the level of hybridization further increased the p-value of the KWT, which means that the place of hybridization becomes of a less importance when the level of hybridization increases.
Progressive Algebraic Soft-Decision Decoding of Reed–Solomon Codes Using Module Minimization

The algebraic soft-decision decoding (ASD) of Reed–Solomon (RS) codes yields a competent decoding performance with a polynomial-time complexity. But its complexity remains high due to the interpolation that generates the interpolation polynomial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q(x,y)$ </tex-math></inline-formula> . The progressive ASD (PASD) algorithm has been introduced to construct <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q(x,y)$ </tex-math></inline-formula> with a progressively enlarged <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> -degree, adjusting its error-correction capability and computation to the received information. However, this progressive decoding is realized at the cost of memorizing the intermediate decoding information. To overcome this challenge, this paper proposes a new PASD algorithm which is evolved from the ASD using module minimization (MM) interpolation. Polynomial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q(x,y)$ </tex-math></inline-formula> can be constructed through the image of the progressively enlarged submodule basis without the need of memorizing the intermediate decoding information, eliminating the memory cost of progressive decoding. The MM interpolation also attributes to a remarkably lower complexity than the original PASD algorithm. Furthermore, a complexity reducing variant is proposed based on assessing the degree of Lagrange interpolation polynomials. We also analyze the complexity of the proposed decoding methods and reveal their channel dependent feature. Our simulation results show their low-complexity and advanced decoding performances.
Abnormal Event Detection Method in Surveillance Video Based on Temporal CNN and Sparse Optical Flow

Abnormal event detection in surveillance video is very important for intelligent analysis of video. Most anomalous event detection methods rely on complex feature extraction to represent motion and appearance. Convolutional Neural Network (CNN) is a powerful and efficient tool that can fully meet the needs of feature extraction. In this paper, tracking CNN features over time can effectively detect local anomalies. We detect local anomalies by combining temporal CNN models with optical flow. Based on the traditional optical flow method, the foreground mask is used to improve the efficiency of optical flow calculation and the robustness of the optical flow. The method was tested and evaluated on real-life surveillance video and benchmark datasets to verify its effectiveness.