Reviewerly/exHarmony huggingface dataset:
An In-Loop Filter Based on Low-Complexity CNN using Residuals in Intra Video Coding

Neural network-based filters have shown their potential in removing video compression artifacts. However, previously studied neural networks have achieved boosted filtering performance by continuously increasing network complexity, causing heavy burden on memory cost and computation speed. In this paper, we firstly analyze properties of original residuals which are the difference between original and predicted pixel values. Then an in-loop filter based on low-complexity CNN using residuals(CNNF-R), which are generated after compression and reconstruction from original residuals, is proposed for intra video coding. Insights of designing the network are also demonstrated. Compared with the state-of-the-art video coding standard HEVC, CNNF-R achieves up to 6.8% BD-rate reduction and 4.8% on average under all intra configuration, and 2.3% on average under random access configuration. Meanwhile, CNNF-R outperforms the previous network VRCNN in terms of nearly 70% decrease in computation complexity, considerable decrease in memory consumption and 1.2% increase in BD-rate reduction.
Human Pose Estimation Using Convolutional Neural Networks

Human pose estimation has always been a challenging problem that holds great attention, it has the widespread and extensive variety of uses from the classification of images to activity acknowledgment, main challenge is the detection and localization of the key points in the variation of several body poses. To resolve this issue, substantial research work have been done in this area. This paper discusses the issues in human pose estimation and gives the overview of considerable research work in pose estimation, including deep learning approach and customary image-based techniques. After analyzing several results and detecting the restrictions, the author has reconstructed a simple model using convolutional neural network that estimates the poses and demonstrates the potential of CNN's. The author concludes with a few promising bearings and directions that have to be explored for future research.
Image Steganography Based on Foreground Object Generation by Generative Adversarial Networks in Mobile Edge Computing With Internet of Things

Mobile edge computing provides low-latency service computing for the Internet of Things (IoT). Considering the computational cost of high-quality image steganography in practical mobile applications, we believe that mobile edge computing could provide real-time service computing for covert communications. As a mainstream approach to convert communication, image steganographic algorithms prefer to hide secret data in well-textured regions in order to reduce the possibility of being detected. Recently, the generative adversarial networks (GAN) has become one of the most popular architectures for image steganography. However, the GAN-based image steganographic algorithms directly conduct the secret data embedding on the entire cover images and do not sufficiently take the regional texture complexity into account, which will compromise the anti-detection ability. To address this issue, we propose a novel image steganographic algorithm on the generated foreground object region with rich textures. More specifically, the foreground object region is generated onto a given cover image by the GAN, and the secret data is embedded in the foreground object region simultaneously during the generation of the region. The experimental results show that the proposed method can resist steganalysis effectively without significant degradation of image quality and achieve real-time processing.
Cost-Sensitive Pattern-Based classification for Class Imbalance problems

In several problems, contrast pattern-based classifiers produce high accuracy and provide an explanation of the result in terms of the patterns used for classification. However, class imbalance problems are a great challenge for these classifiers because there exist significantly fewer objects belonging to a class regarding the remaining classes and this biases the classification to the majority class. Therefore, in this paper, we propose an algorithm for discovering cost-sensitive patterns in class imbalance problems and a pattern-based classifier which uses these patterns for classification. Our proposal follows the idea of fusing pattern discovery with the cost-sensitive approach for class imbalance problems. Our experiments show that our proposal obtains cost-sensitive patterns, which allow attaining significantly lower misclassification cost than using patterns mined by other well-known state-of-the-art pattern miners. Also, we show that our proposed pattern-based classifier is suitable for working with cost-sensitive patterns.
Towards a Responsible Innovation Agenda for HCI

In recent years responsible innovation has gained significant traction and can be seen to adorn a myriad of research platforms, education programs, and policy frameworks. In this workshop, we invite HCI researchers to discuss the relations between the CHI community and responsible innovation. This workshop looks to build provocations and principles for and with HCI researchers who are or wish to become responsible innovators. The workshop looks to do this by asking attendees to think about the social, environmental, and economic impacts of ICT and HCI and explore how research innovation frameworks speak to responsible HCI innovation. Through the workshop we look to examine 5 questions to develop a set of provocations and principles, which will help encourage HCI and computer science researchers, educators, and innovators to reflect on the impact of their research and innovation.
Bipolar fuzzy relation equations systems based on the product t‐norm

Bipolar fuzzy relation equations arise as a generalization of fuzzy relation equations considering unknown variables together with their logical connective negations. The occurrence of a variable and the occurrence of its negation simultaneously can give very useful information for certain frameworks where the human reasoning plays a key role. Hence, the resolution of bipolar fuzzy relation equations systems is a research topic of great interest. This paper focuses on the study of bipolar fuzzy relation equations systems based on the max‐product t‐norm composition. Specifically, the solvability and the algebraic structure of the set of solutions of these bipolar equations systems will be studied, including the case in which such systems are composed of equations whose independent term be equal to 0. As a consequence, this paper complements the contribution carried out by the authors on the solvability of bipolar max‐product fuzzy relation equations.
Deep embedding learning with adaptive large margin N-pair loss for image retrieval and clustering

Deep embedding learning becomes more attractive for discriminative feature learning, but many methods still require hard-class mining, which is computationally complex and performance-sensitive. To this end, Adaptive Large Margin N-Pair loss (ALMN) is proposed to address the aforementioned issues. First, the class center is adopted as the anchor point to avoid the difficulty on anchor selection. Then instead of exploring hard example-mining strategy, we introduce the adaptive large margin constraint, where a novel geometrical Virtual Point Generating (VPG) method is proposed to convert a fixed margin into a local-adaptive angular margin, by automatically generating a boundary training sample in feature space. The effectiveness of our method is demonstrated on fine-grained image retrieval and clustering tasks using six popular databases, including CUB, CARS, Flowers, Aircraft, Stanford Online Products and In-Shop Clothes. The results show that the proposed method achieves better performance than other state-of-the-art methods, such as N-Pair loss, Lifted loss and Triplet loss.
Multi-Factor Authentication in Cyber Physical System: A State of Art Survey

Digital Multifactor authentication is one of the best ways to make secure authentication. It covers many different areas of a Cyber-connected world, including online payments, communications, access right management, etc. Most of the time, Multifactor authentication is little complex as it require extra step from users. With two-factor authentication, along with the user-ID and password, user also needs to enter a special code which they normally receive by short message service or some special code which they got in advance. This paper will discuss the evolution from single authentication to Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). In addition, this paper presents five high-level categories of features of user authentication in the gadget-free world including security, privacy, and usability aspects. These are adapted and extended from earlier research on web authentication methods. In conclusion, this paper gives future research directions and open problems that stem from our observations.
A study of the effects of narration on comprehension and memorability of visualisations

Information visualisation researchers have posited that author-driven narratives will allow information to be conveyed efficiently and argue for the adoption of storytelling techniques in information visualisation. However, limited work has been done to date to concretely examine the effects of author-driven narratives in users’ comprehension and memorability of visualisations, and their associated benefits and limitations in relation to interactive visualisations (devoid of author narratives). Recommendations for author-driven visualisation stories are largely based on anecdotal reports and/or research from journalism, and not on factual user studies in information visualisation. To investigate these issues, we carried out a confirmatory user study that compared purely author-driven narratives with interactive visualisations devoid of author narratives, in terms of comprehension and short-term and long-term memorability. We found that the presence of narration in author-driven stories significantly aided the understanding of information but had no significant effect on the long-term recall of information from visualisations.
GUINNESS: A GUI Based Binarized Deep Neural Network Framework for Software Programmers

The GUINNESS (GUI based binarized neural network synthesizer) is an open-source tool flow for a binarized deep neural network toward FPGA implementation based on the GUI including both the training on the GPU and inference on the FPGA. Since all the operation is done on the GUI, the software designer is not necessary to write any scripts to design the neural network structure, training behavior, only specify the values for hyperparameters. After finishing the training, it automatically generates C++ codes to synthesis the bit-stream using the Xilinx SDSoC system design tool flow. Thus, our tool flow is suitable for the software programmers who are not familiar with the FPGA design. In our tool flow, we modify the training algorithms both the training and the inference for a binarized CNN hardware. Since the hardware has a limited number of bit precision, it lacks minimal bias in training. Also, for the inference on the hardware, the conventional batch normalization technique requires additional hardware. Our modifications solve these problems. We implemented the VGG-11 benchmark CNN on the Digilent Inc. Zedboard. Compared with the conventional binarized implementations on an FPGA, the classification accuracy was almost the same, the performance per power efficiency is 5.1 times better, as for the performance per area efficiency, it is 8.0 times better, and as for the performance per memory, it is 8.2 times better. We compare the proposed FPGA design with the CPU and the GPU designs. Compared with the ARM Cortex-A57, it was 1776.3 times faster, it dissipated 3.0 times lower power, and its performance per power efficiency was 5706.3 times better. Also, compared with the Maxwell GPU, it was 11.5 times faster, it dissipated 7.3 times lower power, and its performance per power efficiency was 83.0 times better. The disadvantage of our FPGA based design requires additional time to synthesize the FPGA executable codes. From the experiment, it consumed more three hours, and the total FPGA design took 75 hours. Since the training of the CNN is dominant, it is considerable.
A Software Exoskeleton to Protect and Support Citizen’s Ethics and Privacy in the Digital World

Citizens of the digital world are threatened. The digital systems that surround them are increasingly able to make autonomous decisions over and above them and on their behalf. They feel that their moral rights, as well as the social, economic, and political spheres, can be affected by the behavior of such systems. Although unavoidable, the digital world is becoming uncomfortable and potentially hostile to its users as human beings and as citizens. Notwithstanding the introduction of the GDPR and of initiatives to establish criteria on software transparency and accountability, users feel vulnerable and unprotected. In this paper, we present EXOSOUL, an overarching research framework that aims at building a software a personalized exoskeleton that enhances and protects users by mediating their interactions with the digital world according to their own ethics of actions and privacy of data. The exoskeleton disallows or adapts the interactions that would result in unacceptable or morally wrong behaviors according to the ethics and privacy preferences of the users. With their software shield, users will feel empowered and in control, and more in the balance of forces with the other actors of the digital world. To reach the breakthrough result of automatically building a personalized exoskeleton, EXOSOUL identifies multidisciplinary challenges never touched before: 1) defining the scope for and inferring citizen's ethical preferences; 2) treating privacy as an ethical dimension managed through the disruptive notion of active data; and 3) automatically synthesizing ethical actuators, i.e., connector components that mediate the interaction between the user and the digital world to enforce her ethical preferences. In this paper, we discuss the research challenges of EXOSOUL in terms of their feasibility and risks.
Matching Image and Sentence with Multi-faceted Representations

In this paper, we propose a novel multimodal matching model for the image and sentence based on their multiple representations. Each representation of the image or sentence undergoes an independent neural network, consisting of multiple layers of nonlinear mappings to yield the corresponding embedding. Besides exploiting the image and sentence relationship based on their embeddings, we propose one novel loss to further exploit the relationship within each single modality, namely, image and sentence based on the yielded multiple embeddings, which is used to train the neural networks simultaneously. The experimental results demonstrate that multiple representations can help to capture the image contents and the sentence semantic meaning more precisely, thus making comprehensive exploitations of the complicated image and sentence matching relationship. More concretely, the proposed matching model significantly outperforms the state-of-the-art approaches in bidirectional image-sentence retrieval on the Flickr8K, Flickr30K, and Microsoft COCO datasets.
Activity Recognition Using Binary Sensors for Elderly People Living Alone: Scanpath Trend Analysis Approach

Unobtrusive activity recognition is known to be the most preferred solution for monitoring daily activities of elderly people. In this paper, Scanpath Trend Analysis (STA) is employed for unobtrusive activity recognition of elderly people living alone. Binary sensor data are used and each activity is considered as a sequence of sensor points for this purpose. The real-world long-term fully annotated Aruba open dataset collected by binary sensors is used for the verification of accuracy and the efficacy of the proposed approach. With the STA, the F1-score of 0.758 is obtained, and furthermore, by adding some extra semantic information through an activity transition matrix, it is possible to have F1-score as 0.863. This F1-score is superior to all the related works that use binary sensor data for activity prediction, while computationally the approach presented is advantageous since long periods of training process can be avoided.
A systematic literature review of test breakage prevention and repair techniques

When an application evolves, some of the developed test cases break. Discarding broken test cases causes a significant waste of effort and leads to test suites that are less effective and have lower coverage. Test repair approaches evolve test suites along with applications by repairing the broken test cases. Numerous studies are published on test repair approaches every year. It is important to summarise and consolidate the existing knowledge in the area to provide directions to researchers and practitioners. This research work provides a systematic literature review in the area of test case repair and breakage prevention, aiming to guide researchers and practitioners in the field of software testing. We followed the standard protocol for conducting a systematic literature review. First, research goals were defined using the Goal Question Metric (GQM). Then we formulate research questions corresponding to each goal. Finally, metrics are extracted from the included papers. Based on the defined selection criteria a final set of 41 primary studies are included for analysis. The selection process resulted in 5 journal papers, and 36 conference papers. We present a taxonomy that lists the causes of test case breakages extracted from the literature. We found that only four proposed test repair tools are publicly available. Most studies evaluated their approaches on open-source case studies. There is significant room for future research on test repair techniques. Despite the positive trend of evaluating approaches on large scale open source studies, there is a clear lack of results from studies done in a real industrial context. Few tools are publicly available which lowers the potential of adaption by industry practitioners.
Text mining and determinants of sentiments: Twitter social media usage by traditional media houses in Uganda

Unstructured data generated from sources such as the social media and traditional text documents are increasing and form a larger proportion of unanalysed data especially in the developing countries. In this study, we analysed data received from the major print and non-print media houses in Uganda through the Twitter platform to generate non-trivial knowledge by using text mining analytics. We also explored the determinants of derived sentiments in Twitter messaging. The results show that sentiments generated from tweets derived from the main print media houses (Daily Monitor and New Vision) were positively correlated, so were the sentiments from the non-print media (NBS TV and NTV) for the study period. Most of the sentiments on topics of security, politics and economics were found to be negative, while those on sports were positive. Furthermore, the tweet sentiment statistical logistic model revealed that negative sentiments were determined by the retweet status, retweet count and source of the tweets. Moreover, the positive sentiments were determined by the topic of discussion, type of media house and other sources of tweets ( $$p<0.05$$ ). Therefore, we recommend further extensions on the predictive statistical models to classify sentiments from social media based on the concept of big data analytics.
Performance Evaluation of IoT Encryption Algorithms: Memory, Timing, and Energy

Security in the Internet of Things is a crucial aspect and a lot of studies are focused on modular and scalable encryption algorithms. Resource constraints at the edge nodes of an IoT system require lightweight encryption algorithms. A comparative study of AES with and without hardware accelerators and XTEA is performed to analyze the performance of the algorithms in terms of memory, power and execution time and assess the feasibility of using XTEA in low resource embedded platforms. Although the hardware accelerated AES was fastest (0.5 ms) and consequently required the least amount of energy (0.01 mJ) out of the three, the execution time (1.25 ms) and energy consumed (0.024 mJ) by XTEA was comparatively close and can be a feasible encryption algorithm for low resource microcontrollers that do not have the resources to support AES implementation in software or lack a hardware accelerator. Software implementation of AES on 8-bit PIC architecture required 7538 bytes whereas XTEA required only 1184 bytes of program memory, leaving enough space for application firmware.
The new division of labor between human and machine and its educational implications

The aim of the article is to clarify the future division of labor between human and machine and its educational implications. Recent studies on future employment discuss whether growth in computer power and advances in artificial intelligence enable the automation of non-routine tasks, including tasks that require creativity and innovation, which are among the most unique human capacities. However, this discussion generally lacks research-based concepts of creativity and innovation. In order to clarify the discussion on the division of labor, relevant theories of creativity and innovation are introduced. The inquiry shows that creativity and innovation consist in making new connections between domains or practices. While computers and robotics are capable of operating only within specific domains, in which rules apply or data are available, creativity and innovation operate beyond the boundaries of such domains and must be guided by emotions and driven by moods. This suggests that computers and robotics make rapid change possible, but cannot solve the task of creating change themselves. The educational implications are that it is vital to cultivate the unique human capacities of sensing, understanding, handling and creating change, and to teach students and employees to collaborate with computers and robotics.
An improved clustering algorithm and its application in IoT data analysis

With the popularization of the Internet of Things(IoT), the data are exploding. Data analysis is foundation of IoT based applications, and clustering is an important tool for data analysis. In clustering, determining the number of clusters is an important issue, which can be either designated artificially or determined automatically. The artificial methods have many disadvantages. And the automatic methods have distinct advantages, whose critical task is to design an appropriate clusters number updating algorithm. Although many researches have been made, most of them are not effective or cannot guarantee the unique clustering results and the good clustering accuracy rate. Meanwhile, considering that IoT based applications always involved both numerical data and nonnumeric data, and treating all the nonnumeric data in the same way is unpractical, we try to further classify the nonnumeric attributes according to their natures and explore the corresponding similarity metrics respectively. Based on it, an algorithm for determining the initial clustering centers is put forward by the dissimilarities and the densities of data objects. And then, an improved clustering algorithm is designed on a revised inter-cluster entropy for mixed data. The experiments on the 3 datasets in University of California at Irvine(UCI) show that the improved clustering algorithm is a deterministic clustering algorithm with good performance.