Reviewerly/exHarmony huggingface dataset:
Interference Classification Using Deep Neural Networks

The recent success in implementing supervised learning to classify modulation types suggests that other problems akin to modulation classification would eventually benefit from that implementation. One of these problems is classifying the interference type added to a signal-of-interest, also known as interference classification. In this paper, we propose an interference-classification method using a deep neural network. We generate six distinct types of interfering signals then use both the power-spectral density (PSD) and the cyclic spectrum of the received signal as input features to the network. The computer experiments reveal that using the received signal PSD outperforms using its cyclic spectrum in terms of accuracy. In addition, the same experiments show that the feed-forward networks yield better accuracy than classic methods. The proposed classifier aids the subsequent stage in the receiver chain with choosing the appropriate mitigation algorithm and also can coexist with modulation-classification methods to further improve the classifier accuracy.
Quaternion Generative Adversarial Networks for Inscription Detection in Byzantine Monuments

In this work, we introduce and discuss Quaternion Generative Adversarial Networks, a variant of generative adversarial networks that uses quaternion-valued inputs, weights and intermediate network representations. Quaternionic representation has the advantage of treating cross-channel information carried by multichannel signals (e.g. color images) holistically, while quaternionic convolution has been shown to be less resource-demanding. Standard convolutional and deconvolutional layers are replaced by their quaternionic variants, in both generator and discriminator nets, while activations and loss functions are adapted accordingly. We have succesfully tested the model on the task of detecting byzantine inscriptions in the wild, where the proposed model is on par with a vanilla conditional generative adversarial network, but is significantly less expensive in terms of model size (requires \(4{\times }\) less parameters). Code is available at https://github.com/sfikas/quaternion-gan.
Using Human Centered Design to Improve Socio-economic Livelihoods with Modernized Irrigation Systems

The agro-sector contributes significantly to the Indian economy, however there has been a decline in the agricultural practices over the years. One of the major reasons for that is the inefficient irrigation practices followed. This predicament was endured by the farming community of Dewgain, Jharkhand whose challenges have been studied in this paper. The study revealed that water scarcity was prevalent in Dewgain during the summers. Poor water management has led to decline of agricultural activities during summer resulting in seasonal unemployment. In order to address this challenge, the study focused on understanding the current irrigation practices followed and its impact on agricultural sustenance. The study was conducted using Human Centered Design. The outcome of the study was analysed through qualitative and quantitative methods. As a result, an IoT enabled solution for efficient irrigation management in the form of Automated Irrigation Management System (AIMS) was proposed and experimented.
Commercial Versus Volunteer: Comparing User Perceptions of Toxicity and Transparency in Content Moderation Across Social Media Platforms

Content moderation is a critical service performed by a variety of people on social media, protecting users from offensive or harmful content by reviewing and removing either the content or the perpetrator. These moderators fall into one of two categories: employees or volunteers. Prior research has suggested that there are differences in the effectiveness of these two types of moderators, with the more transparent user-based moderation being useful for educating users. However, direct comparisons between commercially-moderated and user-moderated platforms are rare, and apart from the difference in transparency, we still know little about what other disparities in user experience these two moderator types may create. To explore this, we conducted cross-platform surveys of over 900 users of commercially-moderated (Facebook, Instagram, Twitter, and YouTube) and user-moderated (Reddit and Twitch) social media platforms. Our results indicated that although user-moderated platforms did seem to be more transparent than commercially-moderated ones, this did not lead to user-moderated platforms being perceived as less toxic. In addition, commercially-moderated platform users want companies to take more responsibility for content moderation than they currently do, while user-moderated platform users want designated moderators and those who post on the site to take more responsibility. Across platforms, users seem to feel powerless and want to be taken care of when it comes to content moderation as opposed to engaging themselves.
HU‐PageScan: a fully convolutional neural network for document page crop

November The offer of online, automated, and impersonal services demand users to upload scanned copies of their documents to the organisations. As a consequence of this decentralisation, the documents present more challenges to the already complex process of image processing and information extraction. To address this problem, the authors presented an optimised fully convolutional neural network model for document segmentation that works on mobile devices to detect the region of the document in the captured image. They performed experiments in three representative datasets comparing the proposed method with the Geodesic object Proposals, U-net, Mask R-CNN, and OctHU-PageScan algorithms. They also compared the proposed model with all competitors of the ICDAR2015 Competition on smartphone document capture. Furthermore, they performed a qualitative and comparative analysis with the CamScanner software, a popular app for Android and iOS smartphones used for more than 100 million users in over 200 countries. The proposed approach achieved a significant performance compared with the current state-of-the-art methods, providing a powerful approach for document segmentation in photos and scanned images.
Online Explanation Generation for Planning Tasks in Human-Robot Teaming

As AI becomes an integral part of our lives, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to justify its behavior is one of the key requirements of explainable agency. Prior work on explanation generation has been focused on supporting the rationale behind the robot's decision or behavior. These approaches, however, fail to consider the mental demand for understanding the received explanation. In other words, the human teammate is expected to understand an explanation no matter how much information is presented. In this work, we argue that explanations, especially those of a complex nature, should be made in an online fashion during the execution, which helps spread out the information to be explained and thus reduce the mental workload of humans in highly cognitive demanding tasks. However, a challenge here is that the different parts of an explanation may be dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented with three variations satisfying different "online" properties. The new explanation generation methods are based on a model reconciliation setting introduced in our prior work. We evaluated our methods both with human subjects in a simulated rover domain, using NASA Task Load Index (TLX), and synthetically with ten different problems across two standard IPC domains. Results strongly suggest that our methods generate explanations that are perceived as less cognitively demanding and much preferred over the baselines and are computationally efficient.
Phase-based thresholding schemes for segmentation of fetal thigh cross-sectional region in ultrasound images

Measurement of thigh circumference has been used as an indication of nutritional adequacy and fetal growth in the womb. Fractional thigh volume is one of the variables for estimating fetal weight that is also calculated using the fetal thigh circumference. This cross-section of the fetal thigh is an intricate part of detecting because it has a less clear boundary and sometimes attached to other tissue in the ultrasound image. This study aims to establish a detection scheme and segmentation of the fetal thigh cross-sectional area automatically on ultrasound images. We propose a segmentation framework that integrates two phase-based thresholding schemes and a gap connecting step. In the first scheme, we combine the phase congruency of the local phase features and the fuzzy entropy method. The second thresholding scheme is a combination of the phase symmetry method and the saliency visual attention model. The proposed framework has the advantage of being able to segment cross-sectional objects more precisely, even though there are other objects attached to them. This method also works well when applied to low-quality ultrasound images taken using a portable ultrasound scanner. This proposed segmentation method exhibits an average performance precision of 0.922 and a Dice similarity of 0.928.
Data Poisoning Attack on Deep Neural Network and Some Defense Methods

In recent years, Artificial Intelligence has disruptively changed information technology and software engineering with a proliferation of technologies and applications based-on it. However, recent researches show that AI models in general and the most greatest invention since sliced bread - Deep Learning models in particular, are vulnerable to being hacked and can be misused for bad purposes. In this paper, we carry out a brief review of data poisoning attack - one of the two recently dangerous emerging attacks - and the state-of-the-art defense methods for this problem. Finally, we discuss current challenges and future developments.
Collaborative Data Analysis: Non-model Sharing-Type Machine Learning for Distributed Data

This paper proposes a novel non-model sharing-type collaborative learning method for distributed data analysis, in which data are partitioned in both samples and features. Analyzing these types of distributed data are essential tasks in many applications, e.g., medical data analysis and manufacturing data analysis due to privacy and confidentiality concerns. By centralizing the intermediate representations which are individually constructed in each party, the proposed method achieves collaborative analysis without revealing the individual data, while the learning models remain distributed over local parties. Numerical experiments indicate that the proposed method achieves higher recognition performance for artificial and real-world problems than individual analysis.
Cross-Session Aware Temporal Convolutional Network for Session-based Recommendation

Recent advancements in Graph Neural Networks (GNN) have achieved promising results for the session-based recommendation, which aims to predict a user's actions based on anonymous sessions. However, existing graph-structured recommendation methods only focus on the internals of a session and neglect cross-session effect which contains valuable complement information for more accurately learning the taste of the user in the current session. Meanwhile, the graph structure lacks the sequential position information so that different sequential sessions can be constructed as the same graph, inevitably limiting its capacity of obtaining an accurate vector of a session representation. In order to solve the above limitations, we propose Cross-session Aware Temporal Convolutional Network (CA-TCN) model. For the cross-session aware aspect, CA-TCN builds a global-item graph and a session-context graph to model cross-session influence on both items and sessions. Global-item graph explores the global cross-session influence on items by building relevant item connections among all sessions. Session-context graph explores the complex cross-session influence on sessions by building the connections between the current session and other sessions with similar user intents and behavioral patterns as the current session. And, we connect items and sessions with hierarchical item-level and session-level attention mechanism. Besides, compared with the GNN, TCN can perform convolution operation on multi-hops items and maintain sequence information in the process of convolution. Extensive experiments on two real-world datasets show that our method outperforms state-of-the-art methods consistently.
Development of a Chatbot Program for Follow-Up Management of Workers’ General Health Examinations in Korea: A Pilot Study

(1) Background: Follow-up management of workers’ general health examination (WGHE) is important, but it is not currently well done. Chatbot, a type of digital healthcare tool, is used in various medical fields but has never been developed for follow-up management of WGHE in Korea. (2) Methods: The database containing results and explanations related to WGHE was constructed. Then, the channel, which connects users with the database was created. A user survey regarding effectiveness was administered to 23 healthcare providers. Additionally, interviews on applicability for occupational health services were conducted with six nurses in the agency of occupational health management. (3) Results: Chatbot was implemented on a small scale on the Amazon cloud service (AWS) EC2 using KaKaoTalk and Web Chat as user channels. Regarding the effectiveness, 21 (91.30%) rated the need for chatbots as very high; however, 11 (47.83%) rated the usability as not high. Of the 23 participants, 14 (60.87%) expressed overall satisfaction. Nurses appreciated the chatbot program as a method for resolving accessibility and as an aid for explaining examination results and follow-up management. (4) Conclusions: The effectiveness of WGHE and the applicability in the occupational health service of the chatbot program for follow-up management can be confirmed.
An end-to-end framework for unconstrained monocular 3D hand pose estimation

This work addresses the challenging problem of unconstrained 3D hand pose estimation using monocular RGB images. Most of the existing approaches assume some prior knowledge of hand (such as hand locations and side information) is available for 3D hand pose estimation. This restricts their use in unconstrained environments. Therefore, we present an end-to-end framework that robustly predicts hand prior information and accurately infers 3D hand pose by learning ConvNet models while only using keypoint annotations. To enhance the hand detector's robustness, we propose a novel keypoint-based method to simultaneously predict hand regions and side labels, unlike existing methods that suffer from background color confusion caused by using segmentation or detection-based technology. Moreover, inspired by the human hand's biological structure, we introduce two geometric constraints directly into the 3D coordinates prediction that further improves its performance. Experimental results show that our proposed framework outperforms the state-of-art methods on standard benchmark datasets while providing robust predictions.
Relationship between innovation, regional institutions and firm performance: Micro-evidence from Africa

(2022). Relationship between innovation, regional institutions and firm performance: Micro-evidence from Africa. African Journal of Science, Technology, Innovation and Development: Vol. 14, No. 2, pp. 316-332.
Fractional-order quaternion exponential moments for color images

Due to their strong image description ability, in recent years, quaternion exponential moments (QEMs) have attracted wide attention from relevant researchers. However, there is a numerical instability problem with QEMs because they can only take integer orders, and this problem restricts the performance of QEMs in image reconstruction and noise resistance. In this paper, the concept of fractional order is introduced and incorporated into exponential moments (EMs) and fractional-order exponential moments (FrEMs) are proposed; then the FrEMs are extended to fractional-order quaternion exponential moments (FrQEMs) that are suitable for color images and have better performance than QEMs in noise resistance and image reconstruction; subsequently, it is proved that FrQEMs are invariant to rotation; and finally, the properties of FrQEMs are experimentally analyzed and FrQEMs are used for object recognition. The experimental results show that FrQEMs have very excellent performance in color image reconstruction, high noise resistance, good rotation invariance and object recognition in color images.
Incremental Rebalancing Learning on Evolving Data Streams

Nowadays, every device connected to the Internet generates an ever-growing (formally, unbounded) stream of data. Machine Learning on data streams is a grand challenge due to its resource constraints. Indeed, standard machine learning techniques are not able to deal with data whose statistics are subject to gradual or sudden changes (formally, concept drift) without any warning. Massive Online Analysis (MOA) is the collective name, as well as a software library, for new learners that can manage data streams. In this paper, we present a research study on streaming rebalancing. Indeed, data streams can be imbalanced as static data, but there is not a method to rebalance them incrementally. For this reason, we propose a new streaming approach able to rebalance data streams online. Our new methodology is evaluated against some synthetically generated datasets using prequential evaluation to demonstrate that it outperforms the existing approaches.
Optimization-based Path Planning for Person Following using Following Field

Person following is an essential task for a robot to serve a person. In an indoor environment, however, the following task can be failed due to the occlusion of the target by structures, e.g., walls or pillars. To address this problem, we propose a method that helps the robot follow the target well and rapidly re-detect the target after missing. The proposed method is an optimization-based path planning which uses a Following Field that we propose in this paper. The following field consists of two sub-fields: the repulsion field getting the robot out of the occluded area, and the target attraction field pushing the robot toward the target. We introduce how to construct the fields and how to integrate the field into a path optimization process. We show that our method works properly for following the target well in a maze consisting of various in-door features.
MONet: heterogeneous Memory over Optical Network for large-scale data center resource disaggregation

The memory over optical network (MONet) system is a disaggregated data center architecture where serial (HMC)/parallel (DDR4) memory resources can be accessed over optically switched interconnects within and between racks. An FPGA/ASIC-based custom hardware IP (ReMAT) supports heterogeneous memory pools, accommodates optical-to-electrical conversion for remote access, performs the required serial/parallel conversion, and hosts the necessary local memory controller. An optically interconnected HMC-based (serial I/O type) memory card is accessed by a memory controller embedded in the compute card, simplifying the hardware near the memory modules. This substantially reduces overheads on latency, cost, power consumption, and space. We characterize CPU–memory performance by experimentally demonstrating the impact of distance, number of switching hops, transceivers, channel bonding, and bit rate per transceiver on the bit error rate, power consumption, additional latency, sustained remote memory bandwidth/throughput (using industry standard benchmark STREAMS), and cloud workload performance (such as operations per second, average added latency, and retired instructions per second memcached with YCSB cloud workloads). MONet pushes the CPU–memory operational limit from a few centimeters to tens of meters, yet applications can experience as low as 10% performance penalty (at 36 m) compared to a direct-attached equivalent. Using the proposed parallel topology, a system can support up to 100,000 disaggregated cards.
Monitoring Social Media for Vulnerability-Threat Prediction and Topic Analysis

Publicly available software vulnerabilities and exploit code are often abused by malicious actors to launch cyberattacks to vulnerable targets. Organizations not only have to update their software to the latest versions, but do effective patch management and prioritize security-related patching as well. In addition to intelligence sources such as Computer Emergency Response Team (CERT) alerts, cybersecurity news, national vulnerability database (NBD), and commercial cybersecurity vendors, social media is another valuable source that facilitates early stage intelligence gathering. To early detect future cyber threats based on publicly available resources on the Internet, we propose a dynamic vulnerability-threat assessment model to predict the tendency to be exploited for vulnerability entries listed in Common Vulnerability Exposures, and also to analyze social media contents such as Twitter to extract meaningful information. The model takes multiple aspects of vulnerabilities gathered from different sources into consideration. Features range from profile information to contextual information about these vulnerabilities. For the social media data, this study leverages machine learning techniques specially for Twitter which helps to filter out non-cybersecurity-related tweets and also label the topic categories of each tweet. When applied to predict the vulnerabilities exploitation and analyzed the real-world social media discussion data, it showed promising prediction accuracy with purified social media intelligence. Moreover, the AI-enabling modules have been deployed into a threat intelligence platform for further applications.
Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace

Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations. Due to the lack of supervision, confident and consistent predictions are usually hard to obtain. Typically, people handle these problems by either adopting an auxiliary task with the well-labeled dataset or incorporating a graphical model with additional requirements on scribble annotations. Instead, this work aims to achieve semantic segmentation by scribble annotations directly without extra information and other limitations. Specifically, we propose holistic operations, including minimizing entropy and a network embedded random walk on the neural representation to reduce uncertainty. Given the probabilistic transition matrix of a random walk, we further train the network with self-supervision on its neural eigenspace to impose consistency on predictions between related images. Comprehensive experiments and ablation studies verify the proposed approach, which demonstrates superiority over others; it is even comparable to some full-label supervised ones and works well when scribbles are randomly shrunk or dropped.