Reviewerly/exHarmony huggingface dataset:
The Applications of the Quantum Computer and the Exascale Computer [Commentary] [Commentary]

Supercomputers are making rapid progress. For the world's fastest supercomputer “Frontier,” the computing speed has reached 10 trillion floating-point instruction cycles per second (10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> , exaFLOPS), making many existing complex problems, including weather forecasting and disaster prevention, more accurate than ever. However, the calculation of real systems with a large number of electrons is still difficult. Exascale computers can simulate some systems with dual quantum states, but in the case of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> dual quantum states, the exponential growth of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> makes the calculation to simulate real system impossible. If we want to understand the dynamic quantum state evolution of a real matter, it is even more difficult to track the dynamics of “every possible” configuration of a huge number of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> quantum states. If n is 20, it will take about 128 KB of memory to store with the current computer's memory, which can be done by ordinary computers. When there are hundreds of electrons, the memory required to store this system exceeds the number of particles in the entire universe. Unfortunately, almost all materials and drugs have at least hundreds of electrons, which means that direct use of Exascale computers to develop and design any new drugs and materials also becomes impossible. At the same time, after the popularization of artificial intelligence and deep learning, it is also found that the computing power, bandwidth, and memory space of classical computers are not enough, while quantum computing has at least the following three advantages and has the opportunity to provide solutions for complex systems.
Review of Determination of Heavy Metals and Total Petroleum Hydrocarbon Concentrations in Remediated Crude Oil Contaminated Soil

The discharge of crude oil products to the environment (soil, water and air) has great negative impacts on the organisms living therein. Most of these effects are from heavy metals, total petroleum hydrocarbons (TPH), polyaromatic hydrocarbons (PAH) among others. This research adopts an extensive review of literatures such as conference papers, journal articles, internet sources, books to find out the amount of total metal concentrations and the total petroleum hydrocarbons (TPH) that are likely to be found on crude oil contaminated soil even after remediation or cleanup activities have been carried out. The driving force behind this research is the fact that the people living in most petroleum-contaminated areas such as the Niger Delta Region of Nigeria still experience some negative effects (such as health and agricultural effects) associated with the crude oil pollutants even after remediation. The study concludes that even though heavy metals were still present in the remediated soil, they are not bioavailable for plants uptake; hence their presence is negligible harmless. On the other hand, the total petroleum hydrocarbons (TPH) that remains after cleanup is still well above the background level. As such, it would impose negative impacts on the inhabitants of such areas. The study further recommends that more effective methods of remediation should be carried out, or a combination of more than one method should be adopted in other to obtain absolute cleanup of the polluted area..
Embracing Deepfakes and AI-generated images in Neuroscience Research

The rise of deepfakes and AI-generated images has raised concerns regarding their potential misuse in society. However, this commentary highlights the valuable opportunities these technologies offer for neuroscience research. Deepfakes provide accessible, realistic, and customisable dynamic face stimuli, while generative adversarial networks (GANs) can generate and modify diverse and high-quality static content. These advancements enhance the variability and ecological validity of research methods, and enable the creation of previously unattainable stimuli. When AI-generated images are informed by brain responses, they provide unique insights into the structure and function of visual systems. The authors encourage experimental psychologists and cognitive neuroscientists to stay informed about these emerging tools and embrace their potential to advance visual neuroscience.
Effects of the covid-19 pandemic on the mental health of hotel housekeeping staff

This research aimed to understand the effects of the Covid-19 pandemic on the mental health of workers of hotel housekeeping departments in the city of João Pessoa/PB. A quantitative analysis was carried out, to measure the information gathered from workers of three medium-sized hotels in the capital of the state of Paraíba, using a questionnaire to gather data, and descriptive analysis for the data analysis. It was found that the Covid-19 pandemic had caused impacts on the mental health of workers in this area, mainly in terms of financial worries; fear of losing their jobs; fear of contracting the disease while at work; and fear that if infected, they might pass it on to other members of the family. Added to this were physical fatigue, obsession with cleaning, and stress.
A Multi-Dictionary Approach to Abstractness/Concreteness-Based Authorship Attribution

&#x0D; &#x0D; &#x0D; We present some early results from a research project aimed at exploring the usefulness of abstractness/concreteness as stylistic features for authorship attribution. We conjecture that authors use abstract/concrete words and phrases in suf- ficiently unique ways, so that machine learning classifiers can learn to distinguish the individual authors’ writing styles. Our approach is based on using the abstractness rat- ings of words and phrases from texts with established au- thorship to generate training vectors for different machine learning classifiers. The combined word/phrase ratings are extracted from two separate abstractness dictionaries – an approach that yields stronger results than using single ab- stractness dictionaries. The paper describes the details of our methodology and compares the results to those obtained using traditional authorship attribution stylistic features. The limitations of our current methodology and directions for further research are outlined at the end of the paper.&#x0D; &#x0D; &#x0D;
Semi-supervised Learning of Visual Causal Macrovariables

&#x0D; &#x0D; &#x0D; Discovery of causally related concepts is one of the key challenges in extracting knowledge from observational data. Lower-dimensional “causal macrovariables” represent concepts which preserve all relevant causal information in high-dimensional systems. Existing causal macrovariable discovery algorithms are limited by assumptions about known and controllable interventions. We propose a variational autoencoder-inspired architecture with regularization terms for semi-supervised causal macrovariable discovery. These terms impose domain knowledge regarding visual causal concepts to differentiate between correlation and causation. Experiments on both synthetic and real-world datasets with known causal dynamics show that our method can discover more concise and precise causal macrovariables than unsupervised methods. &#x0D; &#x0D; &#x0D;
On Enhancing Security for Division Homomorphism with ElGamal

Secure auctions and machine learning in cloud increasingly employs multi-party and homomorphic encryption support.A modification to Elgamal public key cryptosystem was shown to enable homomorphic division using an encoding of plaintext as fractions with numerator and denominator encrypted separately. However we notice that unlike for other homomorphic cryptography schemes, the obtained division homomorphism allows for the retrieval of the input secrets from the result of the division. Since this cancels the benefit of the encryption, we propose the introduction of a masking operation based on random factors and discuss its success with operations in Zp and Q.
Towards a Temporal Probabilistic Argumentation Framework

In recent years, the notion of time has been studied in different ways in Dung-style Argumentation Frameworks. For example, time intervals of availability have been added to arguments and relations. As a result, the output of Dung semantics varies over time. In this paper, we consider the situation in which arguments hold with a certain probability distribution during a given interval. To model the uncertain character of events, we propose different notions of temporal conflict between arguments according to the type of availabilities intersection (partial, inclusive, or total). Then, we refine these notions of conflict by a defeat relation, using criterion functions that evaluate an attack’s significance according to the probability over time.
Preliminary results of a conceptual graph query language

The contribution of this article is to bring the interesting features of SPARQL to the conceptual graph model while preserving its distinctive features and especially the graphical ones.Therefore this article presents a general conceptual graph query language, called CGQL.This article also provides an experimental comparison between CGQL and SPARQL engines: it shows the interest of our implementation for querying knowledge.
Answering Student Queries with a Supervised Memory Conversational Agent

This paper describes a discussion-bot that provides answers to students’ questions about the Data Science master program at the University of Lyon 1. Based on a seq2seq architecture combined with a supervised memory module, the bot identifies the questioner’s interest and encodes relevant information from the past conversation to provide personalized answers. A dialogue generator based on hand-crafted dialogues was built to train our model on these synthetic dialogues. The agent and its memory are adaptable to another context by modifying the intention database of the generator. The model was deployed and the results show that the discussion-bot meets most students’ learning requests. We discuss further directions that might be taken to increase the model's effectiveness.
Harnessing Bullying Traces to Enhance Bullying Participant Role Identification in Multi-Party Chats

As online content continues to grow, so does the spread of online hate, especially on social media. Most research efforts conducted on the task of bullying participant role identification are directed towards social networks such as Twitter and Instagram. However, private instant messaging platforms and channels were pinpointed in recent studies as the most prominent grounds for cyberbullying, especially among teens. Since data collection from major social media platforms is strictly limited, very few studies have investigated this task in a multi-party setting. However, the recent release of resources mimicking online aggression situations that may occur among teens on private instant messaging platforms contributes to filling this gap. In this study, we introduce a full pipeline aiming at automating the identification of bullying participant roles (bully and victim) in multi-party chats. Leveraging pre-trained language models and different learning frameworks, we perform hateful content classification of exchanged messages according to a binary scheme (online hate or no online hate). Then, - from these bullying traces - bullying behavioural cues (repetition and intention to harm) are derived and formalised into a role scoring function. As a result, the proposed pipeline identifies the bully and the victim among chat participants. Evaluated against state-of-the-art methods, the proposed pipeline achieves better performances considering all the datasets and roles to predict. In addition, the error analysis confirms that deriving bullying behavioural cues is beneficial to the task of participant role identification.
A Study of Compressed Language Models in Social Media Domain

Transfer learning from large-scale language models is witnessing incredible growth and popularity in natural language processing (NLP). However, operating these large models always requires a huge amount of computational power and training effort. Many applications leveraging these large models are not very feasible for industrial products since applying them into power-scarce devices, such as mobile phone, is extremely challenging. In this case, model compression, i.e. transform deep and large networks to shallow and small ones, is becoming a popular research trend in NLP community. Currently, there are many techniques available, such as weight pruning and knowledge distillation. The primary concern regarding these techniques is how much of the language understanding capabilities will be retained by the compressed models in a particular domain? In this paper, we conducted a comparative analyses between several popular large-scale language models, such as BERT, RoBERTa, XLNet-Large and their compressed variants, e.g. Distilled BERT, Distilled RoBERTa and etc, and evaluated their performances on three datasets in the social media domain. Experimental results demonstrate that the compressed language models, though consume less computational resources, are able to achieve approximately the same level of language understanding capabilities as the large-scale language models in the social media domain.
Enforcing Grammar in Code Synthesis with Transformers

Even more so than natural language, code is extremely sensitive to syntax; a small error could make an entire snippet invalid. It is therefore important to explore methods for ensuring syntactic correctness in generated code. Existing methods to resolve this issue often rely on the complex architecture of syntax-guided decoders. In this work, we present the grammar enforcement method, which introduces a separate layer that constrains the decisions of the transformer during fine-tuning according to syntactic constructs present both in the target language grammar and the given training set. We experiment with the Hearthstone dataset to study its effects on result programs and compare it with the existing state-of-art syntax-guided decoders. We demonstrate a statistically significant positive effect of grammar enforcement on the quality of generated programs in terms of exact match accuracy and grammatically correct percent of samples. At the same time, we observe lower values for text-based metrics, chrF, and BLEU, potentially indicating their inability to represent the quality of generated abstract syntax sequences.
TinderAI: Support System for Matching AI Algorithms and Embedded Devices

&#x0D; &#x0D; &#x0D; Artificial Intelligence (AI) is becoming increasingly important and pervasive in the modern world. The widespread adoption of AI algorithms is reflected in the extensive range of HW devices on which they can be deployed, from high-performance computing nodes to low-power embedded devices. Given the large set of heterogeneous resources where AI algorithms can be deployed, finding the most suitable device and its con- figuration is challenging, even for experts.&#x0D; We propose a data-driven approach to assist AI adopters and developers in choosing the optimal HW resource. Our approach is based on three key elements: i) fair benchmarking of target AI algorithms on a set of hetero- geneous platforms, ii) creation of ML models to learn the behaviour of these AI algorithms, and iii) support guidelines to help identify the best deployment option for a given AI algorithm. We demonstrate our approach on a specific (and relevant) use case: Deep Neural Net- work (DNN) inference on embedded devices.&#x0D; &#x0D; &#x0D;
Multiple View Summarization Framework for Social Media

Social Media provide voluminous posts about current topics and events. When a user desires to investigate a popular topic, it is not feasible as there are many posts. Besides, posts show different biases, viewpoints, perspectives, and emotions. Thus, providing summaries of large post sets with different viewpoints is necessary. We develop a multiple view summa-rization framework to generate different view-based summar-ies of Twitter posts. Users can apply different methods to generate summaries: 1) Entity-centered, 2) Social feature-based, 3) Event-based summarization, using all triple embed-dings and 4) Sentiment-based summarization to generate summaries of positive or negative views of tweets. These summarization methods are compared with BertSum, SBert, T5, and Bart-Large-CNN with a gold standard dataset. Our results, based on Rouge scores, were better than these pub-lished extractive and abstractive summarization models.
Semantics-based Framework for Incentivized Research Data Sharing

We present a framework for incentivized research data sharing using an ontology called the Data Sharing Ontology (DSO). The DSO captures the semantics of academic research data sharing and provides an operational specification for data sharing between researchers. The DSO includes a two-part incentive mechanism to confirm citations and reward reproducible research methods. The proposed solution is demonstrated using a dataset-sharing decentralized application use case. The paper's contributions provide a scalable technique for creating, curating, publishing, and consuming web-based, structured, and reusable datasets, including semantically annotated knowledge graphs.
Biogeography-based optimization for feature selection

Data clustering has many applications in medical sciences, banking, and data mining. K-means is the most popular data clustering algorithm due to its efficiency and simplicity of implementation. However, K-means has some limitations, which may affect its effectiveness, such as all the features having the same degree of importance. To address these limitations and improve K-means accuracy, we adopt the Biogeography-Based Optimization (BBO) algorithm to select the most relevant features of datasets. Our primary idea is to reduce the intra-cluster distance while increasing the distance between clusters.
Automatic generation of paragraph templates using a Textual Energy approach:

In this paper, we present the results of preliminary experiments using the Textual Energy measure to be used in Automatic Text Generation tasks (ATG). Textual Energy calculates the similarity among the sentences of a document, using intuitive ideas coming from Mechanical statistical like the associative memories and the energy of a system. Using this approach we intend to generate a set of selected sentences having a semantic and structural coherence. In our experiment, the number of selected sentences was manually determined. In particular, the experiments were performed using sets of 4 sentences. Then, the selected sentences can be employed for paragraph generation using Canned Text-like techniques. We have performed an important number of experiments, and we found interesting results that we present in this paper. These results allow us to conclude that it is possible to generate a set of sentences, as paragraphs-like, through methods, avoiding as much as possible undesirable phenomena, such hallucination, which have been recently found in ATG models based on Deep learning Neural Networks.
Leveraging Graph Networks to Model Environments in Reinforcement Learning

This paper proposes leveraging graph neural networks (GNNs) to model an agent’s environment to construct superior policy networks in reinforcement learning (RL). To this end, we explore the effects of different combinations of GNNs and graph network pooling functions on policy performance. We also run experiments at different levels of problem complexity, which affect how easily we expect an agent to learn an optimal policy and therefore show whether or not graph networks are effective at various problem complexity levels. The efficacy of our approach is shown via experimentation in a partially-observable, non-stationary environment that parallels the highly-practical scenario of a military training exercise with human trainees, where the learning goal is to become the best sparring partner possible for human trainees. Our results present that our models can generate better-performing sparring partners by employing GNNs, as demonstrated by these experiments in the proof-of-concept environment. We also explore our model’s applicability in Multi-Agent RL scenarios. Our code is available online at https://github.com/Derposoft/GNNsAsEnvs.
Homomorphic inference of deep neural networks for zero-knowledge verification of nuclear warheads

Abstract Disarmament treaties have been the driving force towards reducing the large nuclear stockpile assembled during the Cold War. Further efforts are built around verification protocols capable of authenticating nuclear warheads while preventing the disclosure of confidential information. This type of problem falls under the scope of zero-knowledge protocols, which aim at multiple parties agreeing on a statement without conveying any information beyond the statement itself. A protocol capable of achieving all the authentication and security requirements is still not completely formulated. Here we propose a protocol that leverages the isotopic capabilities of NRF measurements and the classification abilities of neural networks. Two key elements guarantee the security of the protocol, the implementation of the template-based approach in the network’s architecture and the use of homomorphic inference. Our results demonstrate the potential of developing zero-knowledge protocols for the verification of nuclear warheads using Siamese networks on encrypted spectral data.