Reviewerly/exHarmony huggingface dataset:
Do Product Lines have Sweet Spots?

Abstract Creating a Product Line should not be undertaken lightly. There are many complexities to consider relative to development of individual products or cloning of existing designs. However, in today's business environment, development of multiple, similar products as individual or clone projects is unsustainable, both from the viewpoint of cost and of availability of suitably skilled engineers. This paper considers approaches to development of Product Lines in the context of gas turbine engine full authority digital electronic control systems. The Product Line in this paper was created for an existing engine, with new Applications and a new control system for multiple helicopters. The paper explores the challenges with developing the Product Line and the benefits relative to development of individual products. The benefits are illustrated by an investigation of software cost and schedule metrics for Product Line builds, Application Builds and non‐Product Line builds.
Division and Fusion: Rethink Convolutional Kernels for 3D Medical Image Segmentation

There has been a debate of using 2D and 3D convolution on volumetric medical image segmentation. The problem is that 2D convolution loses 3D spatial relationship of image features, while 3D convolution layers are hard to train from scratch due to the limited size of medical image dataset. Employing more trainable parameters and complicated connections may improve the performance of 3D CNN, however, inducing extra computational burden at the same time. It is meaningful to improve performance of current 3D medical image processing without requiring extra inference computation and memory resources. In this paper, we propose a general solution, Division-Fusion (DF)-CNN for free performance improvement on any available 3D medical image segmentation approach. During the division phase, different view-based kernels are divided from a single 3D kernel to extract multi-view context information that strengthens the spatial information of feature maps. During the fusion phase, all kernels are fused into one 3D kernel to reduce the parameters of deployed model. We extensively evaluated our DF mechanism on prostate ultrasound volume segmentation. The results demonstrate a consistent improvement over different benchmark models with a clear margin.
How to Improve the Recommendation’s Accuracy in POI Domains?

Nowadays, Recommender Systems (RS) have been applied in most of Location-Based Social Networks (LBSNs). In general, these RSs aim to provide the best points-of-interest (POIs) to users, encouraging them to visit new places or explore more of their preferences. Despite the researches advances in this scenario, there is an opportunity for improvements in the recommendation task. The main reason behind it is related to specific characteristics of this scenario, such as the geolocation of users. In general, most users are not interested in POIs located far from their home or work area. In this sense, we address a new research perspective in the POI Recommendation field, proposing a re-ordering method to be applied after any RS and improve the POIs located nearby from the users’ geolocation. Our assumption is that POIs located on the sub-areas with more activity of a user are more interesting than POIs from new sub-areas. For this reason, we propose to measure the activity level of users in subareas of a city and use it to re-order the POIs recommended before. We evaluate our proposal considering six traditional RSs and three datasets from Yelp, achieving gains up to 15% of precision.
A Generative Self-Ensemble Approach To Simulated+Unsupervised Learning

In this paper, we consider Simulated and Unsupervised (S+U) learning which is a problem of learning from labeled synthetic and unlabeled real images. After translating the synthetic images to real ones, existing S+U learning methods use only the labeled synthetic images for training a predictor (e.g., a regression function) and ignore the target real images, which may result in unsatisfactory prediction performance. Our approach utilizes both synthetic and real images to train the predictor. The main idea of ours is to involve a self-ensemble learning framework into S+U learning. More specifically, we require the prediction results for an unlabeled real image to be consistent between “teacher” and “student” predictors, even after some perturbations are added to the image. Furthermore, aiming at generating diverse perturbations along the underlying data manifold, we introduce one-to-many image translation between synthetic and real images. Evaluation experiments on an appearance-based gaze estimation task demonstrate that the proposed ideas can improve the prediction accuracy and our full method can outperform existing S+U learning methods.
Hanging by the thread

Open source plays a critical role in our software infrastructure. It is used in the creation of almost every product and makes it increasingly easy to create powerful software cheaply and quickly, which many companies benefit from. However, its importance and our dependence on it, are often not recognized [2]. Like all software projects, open source needs maintenance to fix bugs and adapt code to evolving technologies [12]. With increasing popularity, demands for maintenance and support work also rise, resulting in many requests and reported issues. How to supply all of the needed maintenance and development work is an open and sometimes controversial question.
Enhancement of the Accuracy and QoS in Clustering of Data

Clustering is an important data mining and tool for examining data. The paper compares the two techniques of clustering, in the first technique only Cosine similarity is used and in the second technique Improved Rank Similarity (Cosine Similarity + Gaussian Similarity) is used. The results are compared with various parameters constituting the Accuracy in NetBeans and QoS parameters using AODV routing protocol. The simulation is done on MATLAB, a network is created and communication from source node to target node is noted.
Embodied Conversation

The chapter presents an overview of human-computer interfaces, which are a crucial element of an ambient intelligence solution. The focus is given to the embodied conversational agents, which are needed to communicate with users in a most natural way. Different input and output modalities, with supporting methods, to process the captured information (e.g., automatic speech recognition, gesture recognition, natural language processing, dialog processing, text to speech synthesis, etc.), have the crucial role to provide the high level of quality of experience to the user. As an example, usage of embodied conversational agent for e-Health domain is proposed.
Sliding Hierarchical Recurrent Neural Networks for Sequence Classification

Hierarchical Recurrent Neural Networks (HRNN) is an important advance in improving efficiency and performance of sequence classification in recent years. The intuition behind this approach is to slice long sequences into many short sub-sequences and process them in parallel, then capturing the long-term dependencies between those sub-sequences by deeper layers of the networks. In this paper, we propose a novel architecture called Sliding Hierarchical Recurrent Neural Network (SHRNN). We introduce a new sliding mechanism on the input sequence of each layer, named recursive block, so that SHRNN can process the input sequence effectively. We also introduce layer-wise attention and multi-layer regularization for further improvements. We perform large-scale experiments in sequence classification task of both text and image on 8 datasets. As result, we not only achieve new start-of-the-art performance on all datasets by SHRNN, but also investigate effects of different components of SHRNN systematically and thoroughly, which provides best practice for the usage of SHRNN.
Notary

Notary is a new design for a hardware wallet, a device that is used to perform sensitive transactional operations like cryptocurrency transfers. Notary aims to be more secure than past hardware wallets by eliminating classes of bugs by design and by formally proving the correctness of the key operation used in its implementation. We built a physical prototype of Notary and showed that it achieves functionality similar to existing hardware wallets while avoiding many bugs that affect them.
The Impact of the Solar Radiation Profile on Sizing and Performance of Photovoltaic Systems, Case Study Tamanrasset, Algeria

The sizing of photovoltaic installations requires a correct control of the solar radiation profile; incorrect sizing leads to significant additional costs. To overcome this problem, a comparative conceptual analysis of two design techniques based on different solar radiation profiles was required. In this paper, we have defined two different sizing techniques based on two solar radiation profiles; the first one is based on hourly step and the second with daily step model. The sizing results were established and analyzed in detail, and followed by a comparative analysis of the performance of the photovoltaic systems under study. This analysis procedure is applied to Tamanrasset region located in the south of Algeria.
DDSL: Efficient Subgraph Listing on Distributed and Dynamic Graphs

Subgraph listing is a fundamental problem in graph theory and has wide applications in many areas. Modern graphs can usually be large-scale and highly dynamic, which challenges the efficiency of existing subgraph listing algorithms. In this paper, we propose an efficient join-based approach, called Distributed and Dynamic Subgraph Listing (DDSL), which can incrementally update the results instead of running from scratch. Extensive experiments are conducted on real-world datasets. The results show that DDSL outperforms existing methods in dealing with both static and dynamic graphs in terms of the responding time.
Real-Time Recommendation Engine for Readers

In today's world, every reader or social media user has different choices/hobbies in terms of reading. For example, if any social media user is searching for a book to read without any specific idea of what s/he wants, s/he wastes a lot of time browsing around on the internet and crawling/trawling through various sites hoping that s/he might get good book. To avoid confusion, the authors are building a recommendation system for every reader/user that helps to recommend a book based on his choices, hobbies, or what s/he had read previously that will be massive help for users instead wasting time on various sites. Data from social media is the powerful fuel that can be used to helps in decision making and building a recommendation engine. Social media data in the different format is biggest challenge for the business to ingest data at the reasonable speed and further process. In social media data, it is difficult to detect and capture data. Real-time recommendation engine for users, which includes data ingestion methods, challenges, metadata problem, analysis, and consumption, is discussed here.
Loss Rescaling by Uncertainty Inference For Single-Stage Object Detection

In this paper, we propose a novel loss rescaling method for single-stage convolutional object detection by predicted uncertainty scores. In object detection tasks, the difficulty of each object differs, and most of the background samples are easier to distinguish than others. Equally treating such samples causes data imbalance, resulting in underperforming accuracy and low efficiency in training. To resolve this issue, our method relates prediction difficulty to Bayesian uncertainty estimates and prediction correctness. Subsequently, we modify the architecture of a single-stage object detector to propagate Bayesian uncertainty and use the uncertainty estimates as anchor wise weights to re-scale the training loss. The proposed method is evaluated on both image classification and object detection tasks. The experiment results demonstrate that our method achieves better accuracy than existing loss rescaling methods, and verifies the effectiveness of Bayesian uncertainty inference in improving model training.
Communication of Design Space Relationships Learned by Bayesian Networks

Modern ship design often involves the automated creation of thousands of design alternatives; even when provided a Pareto front of optimal solutions designers may struggle to understand the differences in designs and the relationships of design variables. Machine learning Bayesian networks from automatically developed design data can allow us to analyze the designs, understand the variable relationships that drive their differences and optimalities, and lead engineers to better designs. However, the information about variable relationships in Bayesian network are encoded in difficult to interpret conditional probability tables (CPTs). Translation of a Bayesian network’s CPTs into simpler edge weights defining the strength of relationship between nodes allows engineers to more easily interpret and use the complex information encoded in the network through standard network analysis techniques. Bayesian networks developed from a multi-objective bulk carrier design problem developed by Sen are transformed to network adjacency matrices for such analysis in this work.
Low-Complexity Real-Time Light Field Compression using 4-D Approximate DCT

A low-complexity codec and a hardware architecture are proposed for achieving real-time compression of four-dimensional (4-D) light field (LF) signals captured from camera/lenslet arrays. The proposed system employs the 4-D extension of the two-dimensional (2-D) 8×8 approximate discrete cosine transform (ADCT) that has recently appeared in the literature. Motivated by the partial separability of the multidimensional spectrum of LFs, the proposed 4-D ADCT is obtained by cascading 2-D inter-view and 2-D intra-view transform stages. Software simulations are provided to confirm the performance of the 4-D ADCT based compression and comparisons are made with respect to 2-D inter-view only and 2-D intra-view only ADCT-based compression. Proposed digital architectures are validated using stepped hardware co-simulation on a Xilinx Virtex-7 VC-707 FPGA platform verifying 597 MHz maximum possible clock frequency, implying an ideal throughput of 18×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> LFs/sec for performing 4-D ADCT on (8× 8×432×624×3) size LFs. When 10% of the ADCT coefficients per each (8×8×8×8) hypercube are retained sub aperture images show 38 dB average PSNR and 0.95 average SSIM.
ELVMC: A Predictive Energy-Aware Algorithm for Virtual Machine Consolidation in Cloud Computing

Virtual machine consolidation (VMC) is a technology that aggregates virtual machines distributed on multiple physical machines into a small number of physical machines to improve resource utilization and energy efficiency of data center. However, excessive virtual machine aggregation and migration can also have a significant negative impact on performance. In this paper, an algorithm named ELVMC with multiple resource prediction is proposed for optimal virtual machine consolidation. It applies a modified Best-Fit Decreasing (BFD) algorithm for resource optimization at both overloaded hosts and underloaded hosts with consideration of load balancing. Different from current research, ELVMC aims to obtain an optimal virtual machine (VM) placement during each consolidation process by simultaneously optimizing multiple system performance metrics in terms of energy consumption, VM migrations and QoS guarantees while keeping the load balanced. Simulation results show that ELVMC is superior to the state of the arts, including the traditional BFD and SABFD-HS algorithms as well as recent research VMCUP-M and MUC-MBFD.
Web Text Feature Extraction with Bean optimization Algorithm

The Internet continues to grow at a phenomenal rate and the amount of information on the web is overwhelming. It provides us a great deal of information resource. Web text feature extraction is considered as the main problem in text mining. We use Vector Space Model (VSM) as the description of web text and present a novel feature extraction algorithm which is based on the improved Bean optimization Algorithm (BOA). This algorithm will greatly improve the efficiency of web texts processing.
Weakly Supervised Object Localization Using Self-Paced Pyramid Adversarial Learning

Weakly Supervised Object Localization (WSOL) has increasingly attracted interests for only using image-level supervision instead of location annotations. Some common challenges for existing methods are that they cover only the most discriminative part of the object. And a substantial amount of noise in training causes ambiguities for learning in a robust manner. In this paper, we propose to address these drawbacks by Self-Paced Pyramid Adversarial Learning(SPAL). Specifically, for suppressing noise, we use self-paced learning (SPL) to training data from simple to complex and from coarse to fine. And our network divides two subnetworks: 1) coarse pyramid network(CPN), 2) fine pyramid network (FPN). In CPN, we aim to utilize pyramid adversarial erase mechanism to process the feature maps of different scale. Consequently, CPN can cover the entire object to generate initial object proposals. Then CPN builds the relevance score as pseudo labels of proposals. In FPN, object proposals and pseudo labels can be trained to locate precise object boundaries. Finally, We also propose adversarial loss function to fit our network. Detailed experimental results on the PASCAL VOC dataset demonstrate that SPAL performs promising against the state-of-the-art methods.
Fundamental Limits Of Steganographic Capacity For Multivariate-Quantized-Gaussian-Distributed Multimedia

Steganography is the art and science of hiding data within innocent-looking objects (cover objects). Multimedia objects such as images and videos are an attractive type of cover objects due to their high embedding rates. There exist many techniques for performing steganography in both the literature and the practical world. Meanwhile, the definition of the steganographic capacity for multimedia and how to be calculated has not taken full attention. In this paper, for multivariate quantized-Gaussian-distributed multimedia, we study the maximum achievable embedding rate with respect to the statistical properties of cover objects against the maximum achievable performance by any steganalytic detector. Toward this goal, we evaluate the maximum allowed entropy of the hidden message source subject to the maximum probability of error of the steganalytic detector which is bounded by the KL-divergence between the statistical distributions for the cover and the stego objects. We give the exact scaling constant that governs the relationship between the entropies of the hidden message and the cover object.
TransKP: Transformer based Key-Phrase Extraction

Increased connectivity has led to a sharp rise in the creation and availability of structured and unstructured text content, with millions of new documents being generated every minute. Key-phrase extraction is the process of finding the most important words and phrases which best capture the overall meaning and topics of a text document. Common techniques follow supervised or unsupervised methods for extractive or abstractive key-phrase extraction, but struggle to perform well and generalize to different datasets. In this paper, we follow a supervised, extractive approach and model the key-phrase extraction problem as a sequence labeling task. We utilize the power of transformers on sequential tasks and explore the effect of initializing the embedding layer of the model with pre-trained weights. We test our model on different standard key-phrase extraction datasets and our results significantly outperform all baselines as well as state-of-the-art scores on all the datasets.
iFree: design and evaluation of a pointing method for disabled users on mobile devices

We present the design and evaluation of a pointing method for disabled people, called iFree, which can run on mobile devices such as smartphones and tablets. The method is based on face recognition and supports target selection (click) in two different modes: by opening and closing the mouth (mouth click) and by making a sound (sound click).The method was experimented on a smartphone with 14 non-disabled participants in order to evaluate its performance and to compare the two above mentioned modes. In particular, we measured its performance using ISO 9241-411 standard task. The throughput grand mean was 0.86 bit/s. The sound click mode had a greater throughput and accuracy, a lower movement time and was preferred by participants.
Vulnerability and Mitigation Strategies for Secure Expert Systems

A group of computer programs and empirical/factual dataset that simulates a person or an organization’s decision and actions with expert knowledge and experience in a particular field constitutes an Expert System. Typically, an expert system has four components namely: The User Interface, Knowledge Base (Davis et al. in AI Mag 14(1):17–33, 1993, [1]), Inference Engine, and Explanation System. The User Interface establishes a communication channel between user and the expert systems’ problem-solving processes, while the Knowledge Base is created with the input gathered from the outside world obtained using User Interface. It is really a challenging task to conceptualize, design, and develop an expert system with the complexity of today’s environment while assuring that the systems are trustworthy and secure. At the same time, finding and knowing all potential forms of adversity, disruptions, and threats is nearly impossible (Bass et al. in Software architecture in practice, 3rd edn. Addison-Wesley,Upper Saddle River, 2012, [2]). The fundamental design and structure of the system can make itself less susceptible and flexible with strength that can be utilized by the system’s proprietors and administrators. In this paper, we present few attacks on the expert system and their mitigation techniques.
Service Composition in a Context-Aware Setting with Functionally Equivalent Services

Context-aware systems play a vital role in facilitating our daily life activities. The development of context-aware systems is an inherently complex task. These systems adapt to context change and provide a composed service to satisfy user requests. This paper aims to provide a complete sketch of a context-aware system. To illustrate the working of the proposed system, we consider a context-aware e-learning application that provides different types of study material according to the context. When context changes, a new plan is obtained to satisfy the user request. We implement and evaluate the context-aware e-learning system with the different phases of the proposed system. The experimental results show the efficacy of the approach.
Pervasive Health Monitoring System for Activity Detection

The increasing in aging population and long-life for elderly to maintaining mobility is becoming increasingly critical. However, sometimes there are situations where no one can accompany, especially if they are going out so that the person is out of control. Time limitations are often seen as the main cause of the absence of people accompanying them, so the availability of a system that can detect human activities or falls automatically becomes very important. Pervasive computing implemented by using smartphone can improve the quality of service by monitoring the user. Pervasive health monitory system provides activity detection and activity monitoring features to help user to see their activity profile, give suggestion to user, and notify the family or caretaker if the user fallen or show unusual activity. This proposed solution has a great impact for elderly to avoid more deadly risks rather than not using this solution because no one knows about the incident.