Reviewerly/exHarmony huggingface dataset:
Using Blackbox ML Techniques to Diagnose QoE Problems for an IPTV Service

IPTV services continue to disrupt the traditional cable-based TV delivery and grow in popularity. However, they also face challenges related to the delivery of stable and problem free video streams to the end users while minimizing downtime due to the inherent complexity and uncertainty associated with the IP based networks. In this work, we share our experience on addressing this service assurance problem for a commercial IPTV service provider. The key focus here is on the root cause inference aspects of the service assurance aimed at providing QoE problem hints with localization in the delivery network to the technicians of network service provider. To solve this problem, we investigate various ML-based model interpretability approaches to extract insights from predictive models built for the alarm generation. Key focus is on Blackbox model interpretability approaches namely LIME and SHAP with their associated pros and cons while at the same time comparing these techniques to the popular model agnostic (i.e., Whitebox) way of explainability based on XGB. One of the findings is that SHAP Blackbox technique is most suitable for diagnosing IPTV QoE problems. However, having the downside of long computational time, for cases where the predictive model is not tree-based such as XGB or Random Forest.
Requirement Prioritization Based on Non-Functional Requirement Classification Using Hierarchy AHP

Abstract Requirement prioritization is a process in requirement engineering, which is a part of software development life cycle (SDLC). Requirement is prioritized due to constraints such as budget, time and resource allocation. Requirements of software is often classified as functional requirements (FR), and non-functional requirement (NFR). In order to produce a high-quality software, both requirement must be considered during requirement prioritization process. Various prioritization techniques have been invented, and Analytical Hierarchical Prioritization (AHP) is the most popular technique that has been cited. However, AHP does not support the NFR and unscalable. Meanwhile, Hierarchy-AHP has been introduced unto increase the scalability of AHP by using hierarchical requirements as input. Nevertheless, hierarchy-AHP does not meant for NFR and experimental result for increasing the scalability is not received significant attention. Thus, we intend to use NFR with large dataset on hierarchy-AHP. Aim of this paper is an exploration of hierarchy-AHP experimenting on RALIC dataset. Our major findings are: (i) NFR can be used hierarchy-AHP with minor process amendment, and (ii) hierarchy-AHP able to reduce pairwise comparison which is up to 97.33% for 403 number of requirements, compared to original AHP.
Agent Dialogue: A Platform for Conversational Information Seeking Experimentation

Conversational Information Seeking (CIS) is an emerging area of Information Retrieval focused on interactive search systems. As a result there is a need for new benchmark datasets and tools to enable their creation. In this demo we present the Agent Dialogue (AD) platform, an open-source system developed for researchers to perform Wizard-of-Oz CIS experiments. AD is a scalable cloud-native platform developed with Docker and Kubernetes with a flexible and modular micro-service architecture built on production-grade state-of-the-art open-source tools (Kubernetes, gRPC streaming, React, and Firebase). It supports varied front-ends and has the ability to interface with multiple existing agent systems, including Google Assistant and open-source search libraries. It includes support for centralized structure logging as well as offline relevance annotation.
Srnmfrb: A Deep Light-Weight Super Resolution Network Using Multi-Receptive Field Feature Generation Residual Blocks

Deep neural networks use a nonlinear end-to-end mapping in order to transform a low resolution image to the high resolution one. Residual blocks facilitate the flow of the information in deep neural networks and enhance the network performance. In this paper, a new residual block that enhances the representational capability of a super resolution network is proposed. The proposed residual block combines the features generated in various receptive fields using different hierarchical levels of convolution operations or convolution operations in conjunction with the space-to-depth and depth-to-space operations in order to provide a rich set of residual features. The experimental results demonstrate the superiority of the super resolution network using the proposed residual block over the state-of-the-art light-weight super resolution networks in terms of objective and subjective metrics.
Demo Abstract: Human Activity Detection with Loose-Fitting Smart Jacket

We demonstrate a human activity detection with casual loose-fitting smart garment system. By employing a new type of highly sensitive, stretchable, optical transparent and low-cost strain sensor and a deep learning model enabled by CNN-LSTM, the loose-fitting jacket is able to recognize 5 activities with 90.9% accuracy when the system is trained with the user data, and 73.5% accuracy when an unseen user wears the smart jacket, which is comparable with tight-fitting smart garment system. In the demonstration, we will showcase activity recognition of three activities: walk, sit, and stand.
Two Polynomial Time Graph Labeling Algorithms Optimizing Max-Norm-Based Objective Functions

Abstract Many problems in applied computer science can be expressed in a graph setting and solved by finding an appropriate vertex labeling of the associated graph. It is also common to identify the term “appropriate labeling” with a labeling that optimizes some application-motivated objective function. The goal of this work is to present two algorithms that, for the objective functions in a general format motivated by image processing tasks, find such optimal labelings. Specifically, we consider a problem of finding an optimal binary labeling for the objective function defined as the max-norm over a set of local costs of a form that naturally appears in image processing. It is well known that for a limited subclass of such problems, globally optimal solutions can be found via watershed cuts , that is, by the cuts associated with the optimal spanning forests of a graph. Here, we propose two new algorithms for optimizing a broader class of such problems. The first algorithm, that works for all considered objective functions, returns a globally optimal labeling in quadratic time with respect to the size of the graph (i.e., the number of its vertices and edges) or, for an image associated graph, the size of the image. The second algorithm is more efficient, with quasi-linear time complexity, and returns a globally optimal labeling provided that the objective function satisfies certain given conditions. These conditions are analogous to the submodularity conditions encountered in max-flow/min-cut optimization, where the objective function is defined as sum of all local costs. We will also consider a refinement of the max-norm measure, defined in terms of the lexicographical order, and examine the algorithms that could find minimal labelings with respect to this refined measure.
Bare Quantum simultaneity versus classical interactivity in communication complexity

A relational bipartite communication problem is presented that has an efficient quantum simultaneous-messages protocol, but no efficient classical two-way protocol.
Towards Blockchain-Based Architecture for Smart Cities Cyber-Security

The rapid development of urbanization poses many economic, social and environmental problems. The smart city brings smart solutions that are effective and sustainable in different areas: transportation, environment, energy and government affairs. The Internet of Things (IoT) is the basis of the structure of a smart city to interconnect all devices together. At present, and despite the potential benefits presented, IoT is still not secure by design, making its applications particularly vulnerable to security threats such as: Confidentiality and data integrity. However, there is the risk of creating urban environments with compromised data. The Blockchain has recently been proposed as a revolutionary technology that can be integrated and resolves many vulnerabilities in IoT applications. In this work, we analyze the applicability of the Blockchain to ensure the security of the data transmitted and received by the nodes of an IoT network. The purpose of this article is to integrate the Blockchain with IoT to create a secure decentralized architecture to provide a secure communication platform in a smart city.
A Study Of Parking-Slot Detection With The Aid Of Pixel-Level Domain Adaptation

The self-parking system is an important component of self-driving vehicles. Such a system needs to detect and locate the parking-slots from surround-view images, and then guide the vehicle to the designated parking-slot. In the real world, the appearances and environmental conditions of parking-slots can be rich and varied. Thus, to train the parking-slot detection model, it is necessary to collect and label a huge quantity of surround-view images covering as many real cases as possible. Such a process is cumbersome and costly, and will be repeated whenever encountering an unseen parking condition that is quite different from the ones covered by existing training set. To this end, in this paper we propose an extensible pipeline, namely FakePS, to assist parking-slot detection model training by making use of synthetic data. Specifically, with FakePS, we can first build various simulated parking scenes and collect labeled surround-view images automatically. Besides, we resort to pixel-level domain adaptation strategies to enhance the realism of the synthetic images using unlabeled real images while preserving their label information. The efficacy of FakePS has been corroborated by experimental results.
Investigation of solar tracking performance using isotropic and anisotropic models

This work presents a study in the theoretical and experimental performance of PV cells and tracking surfaces using the isotropic and anisotropic (HDKR) models’ simulations for single and dual axis systems. The solar spatial and temporal coordinates are obtained for the experiment site during Julian days throughout the year. From there, further simulations are performed using Typical Meteorological Year (TMY) data to evaluate various incident irradiance parameters on tracking surfaces. Experiments with different systems including fixed, single-axis, and dual-axis systems are conducted and compared with simulation results. The simulation outcomes show a gain in PV power generation of around 6% in the anisotropic system compared to the isotropic model prototype. However, experiments show no difference between both models in dual-axis trackers. By using TMY data for optimization of tracking angles, the dual-axis trackers performance gain is 8.5% in PV power generation compared to single-axis system, and a 37% gain is observed compared to fixed-tilted PV cell. Finally, the tracking systems’ optimized angles are not a function of the model used (isotropic/anisotropic) during summer months.
Multi-Similarity Semantic Correctional Hashing For Cross Modal Retrieval

Given the benefits of their low storage requirements and high retrieval efficiency, hashing methods have attracted considerable attention for large scale cross-modal retrieval and significant progress has been made recently. However, the existing methods generally use the label-guided similarity matrix to measure the similarities of sample pairs, which limits their semantic representation capability. Moreover, the sample imbalance of different classes would bias the learning process toward majority classes and affect the retrieval performance. To boost the semantic representation, to alleviate the impact of data imbalance, and to obtain a high-ranking correlation of hash code pairs, we propose a novel hashing method that uses a semantic correctional similarity matrix to enhance the embedded representation of sample pairs. Furthermore, we propose a novel cross-modal multi-similarity loss based on the general pair weighting framework to collect and weight informative pairs efficiently and accurately, thus improving the retrieval performance. Our analysis and experimental results demonstrate that, compared with recent cross-modal retrieval methods, our methods achieve greater retrieval performance on two datasets MIRFlickr-25K and NUS-WIDE.
Beyond Without Forgetting: Multi-Task Learning for Classification with Disjoint Datasets

Multi-task Learning (MTL) for classification with disjoint datasets aims to explore MTL when one task only has one labeled dataset. In existing methods, for each task, the unlabeled datasets are not fully exploited to facilitate this task. Inspired by semi-supervised learning, we use unlabeled datasets with pseudo labels to facilitate each task. However, there are two major issues: 1) the pseudo labels are very noisy; 2) the unlabeled datasets and the labeled dataset for each task has considerable data distribution mismatch. To address these issues, we propose our MTL with Selective Augmentation (MTL-SA) method to select the training samples in unlabeled datasets with confident pseudo labels and close data distribution to the labeled dataset. Then, we use the selected training samples to add information and use the remaining training samples to preserve information. Extensive experiments on face-centric and human-centric applications demonstrate the effectiveness of our MTL-SA method.
Semantic Pixel Distances for Image Editing

Many image editing techniques make processing decisions based on measures of similarity between pairs of pixels. Traditionally, pixel similarity is measured using a simple L2 distance on RGB or luminance values. In this work, we explore a richer notion of similarity based on feature embeddings learned by convolutional neural networks. We propose to measure pixel similarity by combining distance in a semantically-meaningful feature embedding with traditional color difference. Using semantic features from the penultimate layer of an off-the-shelf semantic segmentation model, we evaluate our distance measure in two image editing applications. A user study shows that incorporating semantic distances into content-aware resizing via seam carving [2] produces improved results. Off-the-shelf semantic features are found to have mixed effectiveness in content-based range masking, suggesting that training better general-purpose pixel embeddings presents a promising future direction for creating semantically-meaningful feature spaces that can be used in a variety of applications.
A Fast Image Mosaicking Method Based on Iteratively Minimizing Cloud Coverage Areas

There are many areas with cloud coverage in remote sensing images frequently, and they will shelter the ground objects under clouds. When images with clouds are used in mosaicking, cloud coverage areas may still exist in mosaics, which constrain the application of mosaics. Therefore, a simple and fast image mosaicking method based iteratively minimizing cloud coverage areas is proposed in this letter. In the proposed method, both cloud-free images and images with clouds are used to generate a mosaic with minimal cloud coverage areas. After the generation of initial effective mosaic polygons (EMPs) for images, a quadtree iterative procedure is presented to optimize each EMP by excluding the residual cloud coverage areas in each EMP. The procedure fully takes into account the spatial distribution of cloud coverage areas, and each area with cloud coverage is minimized by as few images as possible. The experimental results show that the mosaic generated by the proposed method can achieve a better radiometric continuity and higher efficiency than the pixel-based mosaic method. Besides, the proposed method can make the full use of the cloud-free areas in images, which can improve the utilization of images and the currency of mosaics.
Fuzzy Logic Controller for Classroom Air Conditioner

Abstract In the modernized era, the air conditioners are an integrated part of comfort living especially in hot climates. They are used to control the interior spatial temperature, relative humidity, degree of cleanliness, and speed of air streaming. The automatic controllers are the key elements of the modern air conditioning systems that ensure the reliable operation, improved quality, low operation cost, and better security. Thus, the realization, design, and application of the controller systems require the exact specifications of the involved processes. In this regard, controllers based on the fuzzy logic (FL) are prospective for air conditioners due to the easy accessibility of different output levels. Furthermore, using the FL it is possible to scale and control the users’ air processing demand depending on the temperature and relative humidity of the space. Based on these factors, this paper reports the design and performance evaluation of a FL based controller useful for air conditioners when implemented in the classroom setting. This FL based control system can reduce the complexity of programming thereby can be executed on general microcontrollers utilized in the control panels of classroom air conditioner. The results revealed the outperforming nature of the FL based controllers over other traditional controllers used to adjust the indoor temperature and relative humidity by air conditioners.
Verifiable inner product computation on outsourced database for authenticated multi-user data sharing

With the rapid development of cloud computing, the practical applications such as the machine learning based on the outsourced data have been investigated in the data sharing setting. In machine learning, the inner product is a necessary primitive to analyze the description statistics. However, the inner product computation in selective data sharing setting has not been fully considered. For this fact, we propose a verifiable inner product computation scheme based on Inner Product Functional Encryption (IPFE). IPFE is employed to preserve the outsourced data privacy and restrict the computation on the outsourced data to be inner product. To achieve the key privacy and result privacy, we transform the secret key into blinded form, which in turn results in a blinded result. With the aim of implementing access control over the data user and outsourced data, we design to let cloud server perform the authentication procedures before computing inner product. This can also eliminate most computational overhead resulting from the unauthorized data user and undesired data. As a result, only the authorized data user can obtain the inner product computed on the designated outsourced data. The proposed scheme is proved to be secure under the authentication model and the result unforgeability model. The performance evaluation shows that the proposed scheme is feasible. To achieve a better security level, the proposed scheme is extended to be secure against the corrupted cloud server.
Learning Discriminative Latent Features for Generalized Zero-and Few-Shot Learning

Generalized zero-shot learning (GZSL) for image classification is a challenging task since not only training examples from novel classes are absent, but also classification performance is judged on both seen and unseen classes. This setting is vital in realistic scenarios where the vast labeled data are not easily available. Some existing methods for GZSL utilize latent features learned through variational autoencoder (VAE) for recognizing novel classes, while few have solved the problem that image features have large intra-class variance affecting the quality of latent features. Hence we propose to match the soul samples to shorten the variance regularized by the pre-trained classifiers, which enables the VAE to generate much more discriminative latent features to train the softmax classifier. We evaluate our method on four benchmark datasets, i.e. CUB, SUN, AWAI, AWA2, and experimental results demonstrate that our model achieves the new state-of-the-art in generalized zero-shot and few-shot learning settings.
Element and Control System Simulation in CoDeSys and Unreal Engine 4 Development Environment

The issues of the effectiveness of using virtual laboratory work (VLR) in the educational process are considered The choice of the CoDeSys integrated programming environment as a VLR development platform for the cycle of disciplines on automation of technological processes is justified. The description of the VLR complex consisting of nine software modules is given. Thematically, the modules are grouped into four groups. The first group consists of two software modules that represent the processes of digitizing primary analog sensor signals and transmitting them via the communication system. The second group includes three software modules devoted to the study of methods and means of obtaining primary technological information. The third group (two modules) is devoted to the study of automatic control systems. The fourth group (two modules) models technological control objects, the composition and functions of the control system. The presented VLR complex is of particular importance for the students of correspondence and distance learning.
Color Balanced Histogram Equalization for Image Enhancement

Recovering a clear image solely from a hazy input image is a challenging task. Moreover, a hazy image can drastically impact the performance of many subsequent high-level computer vision tasks, such as object detection and recognition. In this study, we propose a novel image dehazing method: Color Balancing and Histogram Equalization (CBHE). The method is designed with an aim to merge it with existing object detector models like Faster RCNN [1] and improve the accuracy of object detection under poor visibility. In this method, color balancing and histogram equalization along with image processing techniques have been applied for dehazing. We used the dataset from the UG2+ challenge Track 2 competition called Realistic Single Image Dehazing(RESIDE) - STANDARD that comprised of a diverse set of both synthetic and real-world images. Experimental results on both indoor and outdoor test datasets demonstrate a large improvement in the object detection performance compared to existing techniques when the dehazed image is merged with a pre-trained object detector.