Miracle-dz/UniFAR-FTUs huggingface dataset:
POLAR: a holistic framework for the modelling of polarization and identification of polarizing topics in news media

Polarization is an alarming trend in modern societies with serious implications on social cohesion and the democratic process. Typically, polarization manifests itself in the public discourse in politics, governance and ideology. In recent years, however, polarization arises increasingly in a wider range of issues, from identity and culture to healthcare and the environment. As the public and private discourse moves online, polarization feeds in and is fed by phenomena like fake news and hate speech. The identification and analysis of online polarization is challenging because of the massive scale, diversity, and unstructured nature of online content, and the rapid and unpredictable evolution of polarizing issues. Therefore, we need effective ways to identify, quantify, and represent polarization and polarizing topics algorithmically and at scale. In this work, we introduce POLAR - an unsupervised, large-scale framework for modeling and identifying polarizing topics in any domain, without prior domain-specific knowledge. POLAR comprises a processing pipeline that analyzes a corpus of an arbitrary number of news articles to construct a hierarchical knowledge graph that models polarization and identify polarizing topics discussed in the corpus. Our evaluation shows that POLAR is able to identify and rank polarizing topics accurately and efficiently.
SentiStance: quantifying the intertwined changes of sentiment and stance in response to an event in online forums

How are the sentiment and stance of online users affected by real-world events? Previous studies have ignored the role of events in co-determining sentiment and stance and hence have failed to understand the relationship between these two important aspects of public opinion. In this paper, we develop SentiStance, a systematic framework to understand the intertwined change of sentiment and stance due to real-world events in online discussions. In our approach: (a) we customize state-of-the-art NLP techniques to overcome domain-specific constraints, and (b) we provide an efficient way to quantify the change of sentiment and stance in tandem. As a case study, we focus on the 2020 United States Election events and we analyze 7.5 million posts from 4chan, Reddit, and Parler over a span of three months from November 2020 to January 2021. We showcase our framework by describing the effect that the Jan 6 insurrection had on concepts "Pence" and "Trump. " Parler users turn significantly against Pence with (33.1% increase in Against stance and Negative sentiment), while Reddit users' opinion improves (with a drop of 7.1% in the same combination of sentiment and stance). By contrast, the effect of the same event on the concept "Trump" shows no statistically significant change. In addition, our results suggest that conditioning on significant events strengthens the correlation between sentiment and stance, which provides a new perspective on the debate around the correlation between sentiment and stance. Overall, we see our work as a fundamental building block towards a data-driven understanding of the interplay of preferences and emotions of online forum users towards a concept.
Prediction of unplanned islanding using an energy based strategy

In this study a new index is proposed for power system islanding prediction. The total energy absorbed by coherent synchronous generators during unplanned islanding conditions is formulated as an islanding predictor. Decision tree (DT) algorithm is utilised to extract the information gain of the proposed predictor over the input training samples. A comprehensive list of input scenarios, including island and non-island conditions is constructed. The rotor angles of synchronous generators are estimated by phasor measurements at generators’ terminals and used for calculating the predictor over the input samples. The results of DT algorithm are rearranged as a set of if–then rules for practical applications. The proposed method is implemented in the 10-machine 39-bus New-England test system.
DiffuScope: inferring post-specific diffusion network

Post-specific diffusion network elucidates the who-saw-from-whom paths of a post on social media. A diffusion network for a specific post can reveal trustworthy and/or incentivized connections among users. Unfortunately, such a network is not observable from available information from social media platforms; hence an inference mechanism is needed. In this paper, we propose an algorithm to infer the diffusion network of a post, exploiting temporal, textual, and network modalities. The proposed algorithm identifies the maximum likely diffusion network using a conditional point process. The algorithm can scale up to thousands of shares from a single post and can be implemented as a real-time analytical tool. We analyze inferred diffusion networks and show discernible differences in information diffusion within various user groups (i.e. verified vs. unverified, conservative vs. liberal) and across local communities (political, entrepreneurial, etc.). We discover differences in inferred networks showing disproportionate presence of automated bots, a potential way to measure the true impact of a post.
Groovy Pixels: Generating Drum Set Rhythms from Images

It is a consensus that auditory and visual information can be quite similar in terms of the expression of emotions and knowledge. To explore this relationship with machine learning, this paper proposes a feasible system to generate drum beats from images. Specifically, the model converts the input image to an embedding vector, calculates a corresponding music embedding of a 4-bar drum set performance for this image embedding, and converts it to a playable MIDI file. The training process of the model is implemented by categorising the source dataset into the same set of genres and training with different combinations of images and drum beat for each genre. This paper also includes an evaluation of the performance of the system under different configurations.
Detecting cyber security related Twitter accounts and different sub-groups: a multi-classifier approach

Many cyber security experts, organizations, and cyber criminals are active users on online social networks (OSNs). Therefore, detecting cyber security related accounts on OSNs and monitoring their activities can be very useful for different purposes such as cyber threat intelligence, detecting and preventing cyber attacks and online harms on OSNs, and evaluating the effectiveness of cyber security awareness activities on OSNs. In this paper, we report our work on developing several machine learning based classifiers for detecting cyber security related accounts on Twitter, including a base-line classifier for detecting cyber security related accounts in general, and three sub-classifiers for detecting three subsets of cyber security related accounts (individuals, hackers, and academia). To train and test the classifiers, we followed a more systemic approach (based on a cyber security taxonomy, real-time sampling of tweets, and crowdsourcing) to construct a dataset of cyber security related accounts with multiple tags assigned to each account. For each classifier, we considered a richer set of features than those used in past studies. Among five machine learning models tested, the Random Forest model achieved the best performance: 93% for the baseline classifier, 88-91% for the three sub-classifiers. We also studied feature reduction of the base-line classifier and showed that using just six features we can already achieve the same performance.
GPT-2C: a parser for honeypot logs using large pre-trained language models

Deception technologies like honeypots generate large volumes of log data, which include illegal Unix shell commands used by latent intruders. Several prior works have reported promising results in overcoming the weaknesses of network-level and program-level Intrusion Detection Systems (IDSs) by fussing network traffic with data from honeypots. However, because honeypots lack the plug-in infrastructure to enable real-time parsing of log outputs, it remains technically challenging to feed illegal Unix commands into downstream predictive analytics. As a result, advances on honeypot-based user-level IDSs remain greatly hindered. This article presents a run-time system (GPT-2C) that leverages a large pre-trained language model (GPT-2) to parse dynamic logs generated by a live Cowrie SSH honeypot instance. After fine-tuning the GPT-2 model on an existing corpus of illegal Unix commands, the model achieved 89% inference accuracy in parsing Unix commands with acceptable execution latency.
SABER-GPU: A Response-Based Cryptography Algorithm for SABER on the GPU

The Internet of Things (IoTs) contain many low-powered devices that require secure communication. Post-quantum cryptography (PQC) is needed to address quantum computers that eventually will be able to break current encryption schemes. However, IoT devices are low-powered and often do not have the computational power to carry out computationally expensive error correction schemes. To address this limitation, we propose leveraging response-based cryptography (RBC) to secure IoT devices. Physical unclonable functions (PUFs) can replace random number generators that are used in the public key creation procedures. In this scheme, a server authenticates a client by generating the same seed from the image of the client's PUF stored in the server in a secure environment, thus replacing static public keys with dynamically generated key pairs. In this paper, we focus on generating the public key from the SABER PQC algorithm. Due to the inherent bit error rates existing in PUF technology, the protocol requires that the server recognizes the erratic keys. This error correction requires a massive parallel search over a key space bounded by the expected PUF error rate. This paper examines the use of parallel computing technologies to rapidly find a client's public key within reasonable time constraints. In particular, we examine using multi-core CPUs and many-core Graphics Processing Units (GPUs) in shared-memory environments. The design space for the SABER PQC algorithm is large. Therefore, we focus on performance engineering several CUDA kernels used in the RBC search that systematically explore this space. Our RBC search algorithms are highly scalable: the multi-core CPU algorithm achieves a speedup of 61.82 x on 64 CPU cores, and our multi-GPU algorithm achieves a near-perfect speedup of 2.93 x on 3 GPUs. Using a typical PUF error rate that requires searching 1.75 × 108 keys, we find that our GPU algorithm can authenticate a user within 6 seconds, which is well below our authentication time threshold.
Comparative Phenotyping of Two Commonly Used Chlamydomonas reinhardtii Background Strains: CC-1690 (21gr) and CC-5325 (The CLiP Mutant Library Background)

The unicellular green alga Chlamydomonas reinhardtii is an excellent model organism to investigate many essential cellular processes in photosynthetic eukaryotes. Two commonly used background strains of Chlamydomonas are CC-1690 and CC-5325. CC-1690, also called 21gr, has been used for the Chlamydomonas genome project and several transcriptome analyses. CC-5325 is the background strain for the Chlamydomonas Library Project (CLiP). Photosynthetic performance in CC-5325 has not been evaluated in comparison with CC-1690. Additionally, CC-5325 is often considered to be cell-wall deficient, although detailed analysis is missing. The circadian rhythms in CC-5325 are also unclear. To fill these knowledge gaps and facilitate the use of the CLiP mutant library for various screens, we performed phenotypic comparisons between CC-1690 and CC-5325. Our results showed that CC-5325 grew faster heterotrophically in dark and equally well in mixotrophic liquid medium as compared to CC-1690. CC-5325 had lower photosynthetic efficiency and was more sensitive to heat than CC-1690. Furthermore, CC-5325 had an intact cell wall with comparable integrity to that in CC-1690, though appears to have reduced thickness. Finally, CC-5325 could perform phototaxis, but could not maintain a sustained circadian rhythm of phototaxis as CC160 did. Our results will be useful for researchers in the Chlamydomonas community to choose suitable background strains for mutant analysis and employ the CLiP mutant library for genome-wide mutant screens under appropriate conditions, especially in the areas of photosynthesis, thermotolerance, cell wall, and circadian rhythms.
Task-specific neural processes underlying conflict resolution during cognitive control

Cognitive control involves flexibly combining multiple sensory inputs with task-dependent goals during decision making. Several tasks have been proposed to examine cognitive control, including Stroop, Eriksen-Flanker, and the Multi-source interference task. Because these tasks have been studied independently, it remains unclear whether the neural signatures of cognitive control reflect abstract control mechanisms or specific combinations of sensory and behavioral aspects of each task. To address this question, here we recorded invasive neurophysiological signals from 16 subjects and directly compared the three tasks against each other. Neural activity patterns in the theta and high-gamma frequency bands differed between incongruent and congruent conditions, revealing strong modulation by conflicting task demands. These neural signals were specific to each task, generalizing within a task but not across tasks. These results highlight the complex interplay between sensory inputs, motor outputs, and task demands and argue against a universal and abstract representation of conflict.
Extended ω-Regular Languages and Interval Temporal Logic

Some extensions of ω-regular languages have been proposed in the literature to express asymptotic properties of ω-words which are not captured by ω-regular languages. Formal definitions of extended ωregular languages have been given in terms of both suitable classes of automata and extended ω-regular expressions. On the contrary, satisfactory temporal logic counterparts are still missing. In this paper, we give a characterization of them in terms of interval temporal logics.
Behavioural Plasticity Can Help Evolving Agents in Dynamic Environments but at the Cost of Volatility

Neural networks have been widely used in agent learning architectures; however, learnings for one task might nullify learnings for another. Behavioural plasticity enables humans and animals alike to respond to environmental changes without degrading learned knowledge; this can be achieved by regulating behaviour with neuromodulation—a biological process found in the brain. We demonstrate that by modulating activity-propagating signals, neurally trained agents evolving to solve tasks in dynamic environments that are prone to change can expect a significantly higher fitness than non-modulatory agents and also achieve their goals more often. Further, we show that while behavioural plasticity can help agents to achieve goals in these variable environments, this ability to overcome environmental changes with greater success comes at the cost of highly volatile evolution.
Implementation of Harmonized Food Consumption Data Collection in the Balkan Region According to the EFSA EU Menu Methodology Standards

Initiatives in the Capacity Development in Nutrition Research in the Balkan region in the last decade have been toward the creation of contemporary, harmonized Research Infrastructure (RI) compliant with European standards. This study describes the process of creation and implementation of the European Food Safety Authority (EFSA) EU Menu methodology in the Balkan region during the EFSA support projects for food consumption data collection in four countries (Serbia, Montenegro, Bosnia and Herzegovina, and North Macedonia). This process entailed the application and improvement of an innovative tool, the DIET ASSESS and PLAN (DAP), a platform for standardized food consumption data collection and dietary intake assessment. DAP comprises computerized food consumption, anthropometric measurements, and physical activity questionnaires, validated food picture book, and FoodEx2 exposure hierarchy with sets of facet descriptors of the interest. It hosts the Balkan food platform with a Serbian food composition database (FCDB) and Regional FCDB, compliant with European Food Information Resource (EuroFIR™) standards. The implementation of the DAP platform in national dietary surveys conducted with the support of the EFSA EU Menu project in Balkan countries enabled harmonized food consumption data compilation and reporting. Application of the methodology entailed the development of study protocol and extensive education and training of study personnel. The entire data collection process was managed by internal and external survey coordinators. A pilot study was conducted to test the entire data collection and control process and was afterward used to make necessary improvements and adjustments to meet EU Menu requirements. Data collected are internationally comparable with food consumption data in other European countries within the framework of the EU Menu program. The existence of such data in the Balkan region will catalyze research activities in emerging topics, such as identification of dietary patterns, the establishment of national nutrient reference values and food-based dietary guidelines (not only in Serbia, but in the whole Balkan region), dietary exposure assessments, the endorsement and evaluation of new food legislations, the environmental and other effects of diet on the food system. The developed and implemented methodology underpins evidence-based policy-making processes lacking in the field of public health nutrition in the region.
Multiscale Recursive Feedback Network for Image Super-Resolution

Deep learning-based networks have achieved great success in the field of image super-resolution. However, many networks do not fully combine high-level and low-level information, and fuse local and global information. A multiscale recursive feedback network (MSRFN) for image super-resolution is proposed. First, multiscale convolution is integrated into the feedback network to propose multiscale projection units that adaptively capture image features of different scales by driving a multipath information flow. Next, recursive learning is applied to multiscale projection groups composed of up- and down-multiscale projection units to construct a feedback module that exploits high-level information to correct the low-level representation and refines the features in the early layers. Then, global residual learning and local residual feedback were combined to provide more contextual information for the final reconstruction. Experimental results demonstrate that MSRFN can predict more high-frequency details and alleviate the ringing effect and checkerboard artifacts inherently in CNN-based models. Even when the training datasets are relatively small, MSRFN is still superior to most state-of-the-art methods, especially for large scaling factors ( $\times 8$ ).
Real-Time Simulation Modeling Method of Multiphase Converters Based on High-Order Approximation in Micro-grid

As more and more distributed energy resources accessing in a micro-grid, the electromagnetic transient simulation of the whole system becomes extremely complicated due to the increase of multiphase converters. The traditional real-time simulation models of multiphase converters are low in efficiency for their large number of electronic switches and power system nodes. To solve this problem, an efficient real-time simulation modeling method based on high-order approximation is proposed. Starting with the symmetry of structure, the twelve-phase converter and induction motor are equivalent to a three-phase converter and induction motor and thus reducing the order of the model. The switching function model of the three-phase converter is derived, and the mathematical expressions of switching functions are calculated by high-order approximation expansion of the Fourier series; then, the dc input current of the three-phase converter is formulated theoretically. Counteracting the harmonics by means of phase-shifting, the high-order approximation model of the twelve-phase converter is constructed. Finally, the real-time simulation platform and experimental platform of the twelve-phase frequency converter and induction motor are built; the simulation results verify the high efficiency and accuracy of the proposed real-time simulation model compared with traditional models of the multiphase frequency converter.
Planktonic Tintinnid Community Structure Variations in Different Water Masses of the Arctic Basin

Information on tintinnid community structure variations in different water masses in the Arctic Basin is scarce. During the summer of 2020, tintinnid diversity and vertical distribution were investigated in the Arctic Ocean. A total of 21 tintinnid species were found in five water masses and each water mass had a unique tintinnid community structure. In the Pacific Summer Water (PSW), Salpingella sp.1 occupied the top abundance proportion (61.8%) and originated from the North Pacific. In the Remnant Winter Water (RWW), Acanthostomella norvegica occupied the top abundance proportion (85.9%) and decreased northward. In the Mixed Layer Water, Pacific Winter Water, and Atlantic-origin Water, Ptychocylis urnula had the highest abundance proportion (67.1, 54.9, and 52.2%, respectively). The high abundance distribution area of Salpingella sp.1 and A. norvegica were separated by the boundary of the Beaufort Gyre and Transpolar Drift. The above species could be indicator species of each water masses. The highest abundance proportion of Salpingella sp.1 contributes 81.9% to the dominance of 12–16 μm lorica oral diameter in the PSW, which indicated that the preferred food items of tintinnid were also getting smaller. The occurrence of North Pacific tintinnid in the PSW might be due to the increasing Pacific Inflow Water. Further studies are needed to explore the lasting period of this species and whether it can establish a local population under rapid Arctic warming progress.
Limitations of link deletion for suppressing real information diffusion on social media

Although beneficial information abounds on social media, the dissemination of harmful information such as so-called "fake news" has become a serious issue. Therefore, many researchers have devoted considerable effort to limiting the diffusion of harmful information. A promising approach to limiting diffusion of such information is link deletion methods in social networks. Link deletion methods have been shown to be effective in reducing the size of information diffusion cascades generated by synthetic models on a given social network. In this study, we evaluate the effectiveness of link deletion methods on Twitter by using actual logs of retweet cascades, rather than by using synthetic diffusion models. Our results show that even after deleting 50% of links detected by the NetMelt method from a Twitter social network, the size of tweet cascades after link deletion is estimated to be only 50% the original size, which suggests that the effectiveness of the link deletion strategy for suppressing information diffusion on Twitter is limited. Moreover, our results also show that there is a considerable number of cascades with many seed users, which renders link deletion methods inefficient.