OpenRaiser/Intern-Atlas huggingface dataset:
A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13: Climate Action
Venue: arXiv cs.DL 2022
Year: 2022

As sustainability becomes an increasing priority throughout global society, academic and research institutions are assessed on their contribution to relevant research publications. This study compares four methods of identifying research publications related to United Nations Sustainable Development Goal 13: climate action. The four methods, Elsevier, STRINGS, SIRIS, and Dimensions have each developed search strings with the help of subject matter experts which are then enhanced through distinct methods to produce a final set of publications. Our analysis showed that the methods produced comparable quantities of publications but with little overlap between them. We visualised some difference in topic focus between the methods and drew links with the search strategies used. Differences between publications retrieved are likely to come from subjective interpretation of the goals, keyword selection, operationalising search strategies, AI enhancements, and selection of bibliographic database. Each of the elements warrants deeper investigation to understand their role in identifying SDG-related research. Before choosing any method to assess the research contribution to SDGs, end users of SDG data should carefully consider their interpretation of the goal and determine which of the available methods produces the closest dataset. Meanwhile data providers might customise their methods for varying interpretations of the SDGs.
An unambiguous cloudiness index for nonwovens
Venue: arXiv cs.CV 2022
Year: 2022

Cloudiness or formation is a concept routinely used in industry to address deviations from homogeneity in nonwovens and papers. Measuring a cloudiness index based on image data is a common task in industrial quality assurance. The two most popular ways of quantifying cloudiness are based on power spectrum or correlation function on the one hand or the Laplacian pyramid on the other hand. Here, we recall the mathematical basis of the first approach comprehensively, derive a cloudiness index, and demonstrate its practical estimation. We prove that the Laplacian pyramid as well as other quantities characterizing cloudiness like the range of interaction and the intensity of small-angle scattering are very closely related to the power spectrum. Finally, we show that the power spectrum is easy to be measured image analytically and carries more information than the alternatives.
Deep Fusion of Lead-lag Graphs: Application to Cryptocurrencies
Venue: arXiv cs.LG 2022
Year: 2022

The study of time series has motivated many researchers, particularly on the area of multivariate-analysis. The study of co-movements and dependency between random variables leads us to develop metrics to describe existing connection between assets. The most commonly used are correlation and causality. Despite the growing literature, some connections remained still undetected. The objective of this paper is to propose a new representation learning algorithm capable to integrate synchronous and asynchronous relationships.
Non-associative and projective linear logics
Venue: arXiv cs.LO 2022
Year: 2022

A non-commutative, non-associative weakening of Girard's linear logic is developed for multiplicative and additive connectives. Additional assumptions capture the logic of quantic measurements.
Forming Predictive Features of Tweets for Decision-Making Support
Venue: arXiv cs.CL 2022
Year: 2022

The article describes the approaches for forming different predictive features of tweet data sets and using them in the predictive analysis for decision-making support. The graph theory as well as frequent itemsets and association rules theory is used for forming and retrieving different features from these datasests. The use of these approaches makes it possible to reveal a semantic structure in tweets related to a specified entity. It is shown that quantitative characteristics of semantic frequent itemsets can be used in predictive regression models with specified target variables.
A Unified Framework for Attention-Based Few-Shot Object Detection
Venue: arXiv cs.CV 2022
Year: 2022

Few-Shot Object Detection (FSOD) is a rapidly growing field in computer vision. It consists in finding all occurrences of a given set of classes with only a few annotated examples for each class. Numerous methods have been proposed to address this challenge and most of them are based on attention mechanisms. However, the great variety of classic object detection frameworks and training strategies makes performance comparison between methods difficult. In particular, for attention-based FSOD methods, it is laborious to compare the impact of the different attention mechanisms on performance. This paper aims at filling this shortcoming. To do so, a flexible framework is proposed to allow the implementation of most of the attention techniques available in the literature. To properly introduce such a framework, a detailed review of the existing FSOD methods is firstly provided. Some different attention mechanisms are then reimplemented within the framework and compared with all other parameters fixed.
BERN2: an advanced neural biomedical named entity recognition and normalization tool
Venue: arXiv cs.CL 2022
Year: 2022

In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e.g. diseases and drugs) from the ever-growing biomedical literature. In this article, we present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by employing a multi-task NER model and neural network-based NEN models to achieve much faster and more accurate inference. We hope that our tool can help annotate large-scale biomedical texts for various tasks such as biomedical knowledge graph construction.
HyperionSolarNet: Solar Panel Detection from Aerial Images
Venue: arXiv cs.CV 2022
Year: 2022

With the effects of global climate change impacting the world, collective efforts are needed to reduce greenhouse gas emissions. The energy sector is the single largest contributor to climate change and many efforts are focused on reducing dependence on carbon-emitting power plants and moving to renewable energy sources, such as solar power. A comprehensive database of the location of solar panels is important to assist analysts and policymakers in defining strategies for further expansion of solar energy. In this paper we focus on creating a world map of solar panels. We identify locations and total surface area of solar panels within a given geographic area. We use deep learning methods for automated detection of solar panel locations and their surface area using aerial imagery. The framework, which consists of a two-branch model using an image classifier in tandem with a semantic segmentation model, is trained on our created dataset of satellite images. Our work provides an efficient and scalable method for detecting solar panels, achieving an accuracy of 0.96 for classification and an IoU score of 0.82 for segmentation performance.
Treehouse: A Case For Carbon-Aware Datacenter Software
Venue: arXiv cs.DC 2022
Year: 2022

The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path. Datacenters are already a significant fraction of worldwide electricity use, with application demand scaling at a rapid rate. We argue that substantial reductions in the carbon intensity of datacenter computing are possible with a software-centric approach: by making energy and carbon visible to application developers on a fine-grained basis, by modifying system APIs to make it possible to make informed trade offs between performance and carbon emissions, and by raising the level of application programming to allow for flexible use of more energy efficient means of compute and storage. We also lay out a research agenda for systems software to reduce the carbon footprint of datacenter computing.
Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets
Venue: arXiv cs.IR 2022
Year: 2022

Social Media usage has increased to an all-time high level in today's digital world. The majority of the population uses social media tools (like Twitter, Facebook, YouTube, etc.) to share their thoughts and experiences with the community. Analysing the sentiments and opinions of the common public is very important for both the government and the business people. This is the reason behind the activeness of many media agencies during the election time for performing various kinds of opinion polls. In this paper, we have worked towards analysing the sentiments of the people of India during the Lok Sabha election of 2019 using the Twitter data of that duration. We have built an automatic tweet analyser using the Transfer Learning technique to handle the unsupervised nature of this problem. We have used the Linear Support Vector Classifiers method in our Machine Learning model, also, the Term Frequency Inverse Document Frequency (TF-IDF) methodology for handling the textual data of tweets. Further, we have increased the capability of the model to address the sarcastic tweets posted by some of the users, which has not been yet considered by the researchers in this domain.
Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets
Venue: arXiv cs.LG 2022
Year: 2022

In this paper we propose to study generalization of neural networks on small algorithmically generated datasets. In this setting, questions about data efficiency, memorization, generalization, and speed of learning can be studied in great detail. In some situations we show that neural networks learn through a process of "grokking" a pattern in the data, improving generalization performance from random chance level to perfect generalization, and that this improvement in generalization can happen well past the point of overfitting. We also study generalization as a function of dataset size and find that smaller datasets require increasing amounts of optimization for generalization. We argue that these datasets provide a fertile ground for studying a poorly understood aspect of deep learning: generalization of overparametrized neural networks beyond memorization of the finite training dataset.
wCQ: A Fast Wait-Free Queue with Bounded Memory Usage
Venue: arXiv cs.DC 2022
Year: 2022

The concurrency literature presents a number of approaches for building non-blocking, FIFO, multiple-producer and multiple-consumer (MPMC) queues. However, only a fraction of them have high performance. In addition, many queue designs, such as LCRQ, trade memory usage for better performance. The recently proposed SCQ design achieves both memory efficiency as well as excellent performance. Unfortunately, both LCRQ and SCQ are only lock-free. On the other hand, existing wait-free queues are either not very performant or suffer from potentially unbounded memory usage. Strictly described, the latter queues, such as Yang & Mellor-Crummey's (YMC) queue, forfeit wait-freedom as they are blocking when memory is exhausted. We present a wait-free queue, called wCQ. wCQ is based on SCQ and uses its own variation of fast-path-slow-path methodology to attain wait-freedom and bound memory usage. Our experimental studies on x86 and PowerPC architectures validate wCQ's great performance and memory efficiency. They also show that wCQ's performance is often on par with the best known concurrent queue designs.
FIXME: Synchronize with Database An Empirical Study of Data Access Self-Admitted Technical Debt
Venue: arXiv cs.SE 2022
Year: 2022

Developers sometimes choose design and implementation shortcuts due to the pressure from tight release schedules. However, shortcuts introduce technical debt that increases as the software evolves. The debt needs to be repaid as fast as possible to minimize its impact on software development and software quality. Sometimes, technical debt is admitted by developers in comments and commit messages. Such debt is known as self-admitted technical debt (SATD). In data-intensive systems, where data manipulation is a critical functionality, the presence of SATD in the data access logic could seriously harm performance and maintainability. Understanding the composition and distribution of the SATDs across software systems and their evolution could provide insights into managing technical debt efficiently. We present a large-scale empirical study on the prevalence, composition, and evolution of SATD in data-intensive systems. We analyzed 83 open-source systems relying on relational databases as well as 19 systems relying on NoSQL databases. We detected SATD in source code comments obtained from different snapshots of the subject systems. To understand the evolution dynamics of SATDs, we conducted a survival analysis. Next, we performed a manual analysis of 361 sample data-access SATDs, investigating the composition of data-access SATDs and the reasons behind their introduction and removal. We identified 15 new SATD categories, out of which 11 are specific to database access operations. We found that most of the data-access SATDs are introduced in the later stages of change history rather than at the beginning. We also observed that bug fixing and refactoring are the main reasons behind the introduction of data-access SATDs.
A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture
Venue: arXiv cs.RO 2022
Year: 2022

Currently, most social robots interact with their surroundings and humans through sensors that are integral parts of the robots, which limits the usability of the sensors, human-robot interaction, and interchangeability. A wearable sensor garment that fits many robots is needed in many applications. This article presents an affordable wearable sensor vest, and an open-source software architecture with the Internet of Things (IoT) for social humanoid robots. The vest consists of touch, temperature, gesture, distance, vision sensors, and a wireless communication module. The IoT feature allows the robot to interact with humans locally and over the Internet. The designed architecture works for any social robot that has a general-purpose graphics processing unit (GPGPU), I2C/SPI buses, Internet connection, and the Robotics Operating System (ROS). The modular design of this architecture enables developers to easily add/remove/update complex behaviors. The proposed software architecture provides IoT technology, GPGPU nodes, I2C and SPI bus mangers, audio-visual interaction nodes (speech to text, text to speech, and image understanding), and isolation between behavior nodes and other nodes. The proposed IoT solution consists of related nodes in the robot, a RESTful web service, and user interfaces. We used the HTTP protocol as a means of two-way communication with the social robot over the Internet. Developers can easily edit or add nodes in C, C++, and Python programming languages. Our architecture can be used for designing more sophisticated behaviors for social humanoid robots.
Towards Industry 5.0: Intelligent Reflecting Surface (IRS) in Smart Manufacturing
Venue: arXiv cs.NI 2022
Year: 2022

Industry 5.0 envisions close cooperation between humans and machines requiring ultra-reliable and low latency communications (URLLC). The Intelligent Reflecting Surface (IRS) has the potential to play a crucial role in realizing wireless URLLC for Industry 5.0. IRS is forecast to be a key enabler of 6G wireless communication networks as it can significantly improve wireless network performance by creating a controllable radio environment. In this paper, we first provide an overview of IRS technology and then conceptualize the potential for IRS implementation in a future smart manufacturing environment to support the emergence of Industry 5.0 with a series of applications. Finally, to stimulate future research in this area, we discuss the strength, open challenges, and opportunities of IRS technology in modern smart manufacturing.
PIEEG: Turn a Raspberry Pi into a Brain-Computer-Interface to measure biosignals
Venue: arXiv cs.HC 2022
Year: 2022

This paper presents an inexpensive, high-precision, but at the same time, easy-to-maintain PIEEG board to convert a RaspberryPI to a Brain-computer interface. This shield allows measuring and processing eight real-time EEG (Electroencephalography) signals. We used the most popular programming languages - C, C++ and Python to read the signals, recorded by the device . The process of reading EEG signals was demonstrated as completely and clearly as possible. This device can be easily used for machine learning enthusiasts to create projects for controlling robots and mechanical limbs using the power of thought. We will post use cases on GitHub (https://github.com/Ildaron/EEGwithRaspberryPI) for controlling a robotic machine, unmanned aerial vehicle, and more just using the power of thought.
Designing Social VR: A Collection of Design Choices Across Commercial and Research Applications
Venue: arXiv cs.HC 2022
Year: 2022

Social VR has experienced tremendous growth in the commercial space recently as an emerging technology for rich interactions themed around leisure, work, and relationship building. As a result, the state of social VR application design has become rapidly obfuscated, which complicates identification of design trends and uncommon features that could inform future design, and hinders inclusion of new voices in this design space. To help address this problem, we present a taxonomy of social VR application design choices as informed by 44 commercial and prototypical applications. Our taxonomy was informed by multiple discovery strategies including literature review, search of VR-themed subreddits, and autobiographical landscape research. The taxonomy elucidates various features across three design areas: the self, interaction, and the environment.
Delay Alignment Modulation: Enabling Equalization-Free Single-Carrier Communication
Venue: arXiv cs.IT 2022
Year: 2022

This paper proposes a novel broadband transmission technology, termed delay alignment modulation (DAM), which enables the low-complexity equalization-free single-carrier communication, yet without suffering from inter-symbol interference (ISI). The key idea of DAM is to deliberately introduce appropriate delays for information-bearing symbols at the transmitter side, so that after propagating over the time-dispersive channel, all multi-path signal components will arrive at the receiver simultaneously and constructively. We first show that by applying DAM for the basic multiple-input single-output (MISO) communication system, an ISI-free additive white Gaussian noise (AWGN) system can be obtained with the simple zero-forcing (ZF) beamforming. Furthermore, the more general DAM scheme is studied with the ISI-maximal-ratio transmission (MRT) and the ISI-minimum mean-square error (MMSE) beamforming. Simulation results are provided to show that when the channel is sparse and/or the antenna dimension is large, DAM not only resolves the notorious practical issues suffered by orthogonal frequency-division multiplexing (OFDM) such as high peak-to-average-power ratio (PAPR), severe out-of-band (OOB) emission, and vulnerability to carrier frequency offset (CFO), with low complexity, but also achieves higher spectral efficiency due to the saving of guard interval overhead.
The Study of Peer Assessment Impact on Group Learning Activities
Venue: arXiv cs.CY 2022
Year: 2022

Comparing with lecturer marked assessments, peer assessment is a more comprehensive learning process and many of the associated problems have occurred. In this research work, we study the peer-assessment impact on group learning activities in order to provide a complete and systematic review, increase the practice and quality of the peer assessment process. Pilot studies were conducted and took the form of surveys, focus group interviews, and questionnaires. Prelimi-nary surveys were conducted with 582 students and 276 responses were received, giving a response rate of 47.4%. The results show 37% student will choose individual work over group work if given the choice. In the case study, 82.1% of the total of 28 students have en-joyed working in a group using Facebook as communication tools. 89.3% of the students can demonstrate their skills through group-working and most importantly, 82.1% of them agree that peer assess-ment is an impartial method of assessment with the help of Facebook as proof of self-contribution. Our suggestions to make group work a pleasant experience are by identifying and taking action against the freeloader, giving credit to the deserving students, educating students on how to give constructive feedback and making the assessment pro-cess transparent to all.
Deep Generative Framework for Interactive 3D Terrain Authoring and Manipulation
Venue: arXiv cs.CV 2022
Year: 2022

Automated generation and (user) authoring of the realistic virtual terrain is most sought for by the multimedia applications like VR models and gaming. The most common representation adopted for terrain is Digital Elevation Model (DEM). Existing terrain authoring and modeling techniques have addressed some of these and can be broadly categorized as: procedural modeling, simulation method, and example-based methods. In this paper, we propose a novel realistic terrain authoring framework powered by a combination of VAE and generative conditional GAN model. Our framework is an example-based method that attempts to overcome the limitations of existing methods by learning a latent space from a real-world terrain dataset. This latent space allows us to generate multiple variants of terrain from a single input as well as interpolate between terrains while keeping the generated terrains close to real-world data distribution. We also developed an interactive tool, that lets the user generate diverse terrains with minimalist inputs. We perform thorough qualitative and quantitative analysis and provide comparisons with other SOTA methods. We intend to release our code/tool to the academic community.
Unwinding Rotations Improves User Comfort with Immersive Telepresence Robots
Venue: arXiv cs.RO 2022
Year: 2022

We propose unwinding the rotations experienced by the user of an immersive telepresence robot to improve comfort and reduce VR sickness of the user. By immersive telepresence we refer to a situation where a 360\textdegree~camera on top of a mobile robot is streaming video and audio into a head-mounted display worn by a remote user possibly far away. Thus, it enables the user to be present at the robot's location, look around by turning the head and communicate with people near the robot. By unwinding the rotations of the camera frame, the user's viewpoint is not changed when the robot rotates. The user can change her viewpoint only by physically rotating in her local setting; as visual rotation without the corresponding vestibular stimulation is a major source of VR sickness, physical rotation by the user is expected to reduce VR sickness. We implemented unwinding the rotations for a simulated robot traversing a virtual environment and ran a user study (N=34) comparing unwinding rotations to user's viewpoint turning when the robot turns. Our results show that the users found unwound rotations more preferable and comfortable and that it reduced their level of VR sickness. We also present further results about the users' path integration capabilities, viewing directions, and subjective observations of the robot's speed and distances to simulated people and objects.