Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography-Based Cognitive Workload Detection

This article summarizes a systematic review of the electroencephalography (EEG)-based cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate experimental paradigms used for reliably eliciting discreet and quantifiable levels of cognitive load and the specific nature and representational structure of the commonly used input formulations in deep neural networks (DNNs) used for signal classification. The analysis revealed a number of studies using EEG signals in its native representation of a two-dimensional matrix for offline classification of CWL. However, only a few studies adopted an online or pseudo-online classification strategy for real-time CWL estimation. Further, only a couple of interpretable DNNs and a single generative model were employed for cognitive load detection till date during this review. More often than not, researchers were using DNNs as black-box type models. In conclusion, DNNs prove to be valuable tools for classifying EEG signals, primarily due to the substantial modeling power provided by the depth of their network architecture. It is further suggested that interpretable and explainable DNN models must be employed for cognitive workload estimation since existing methods are limited in the face of the non-stationary nature of the signal.
Almost sure one-endedness of a random graph model of distributed ledgers

Blockchain and other decentralized databases, known as distributed ledgers, are designed to store information online where all trusted network members can update the data with transparency. The dynamics of ledger's development can be mathematically represented by a directed acyclic graph (DAG). One essential property of a properly functioning shared ledger is that all network members holding a copy of the ledger agree on a sequence of information added to the ledger, which is referred to as consensus and is known to be related to a structural property of DAG called one-endedness. In this paper, we consider a model of distributed ledger with sequential stochastic arrivals that mimic attachment rules from the IOTA cryptocurrency. We first prove that the number of leaves in the random DAG is bounded by a constant infinitely often through the identification of a suitable martingale, and then prove that a sequence of specific events happens infinitely often. Combining those results we establish that, as time goes to infinity, the IOTA DAG is almost surely one-ended.
Safe and Accelerated Deep Reinforcement Learning-Based O-RAN Slicing: A Hybrid Transfer Learning Approach

The open radio access network (O-RAN) architecture supports intelligent network control algorithms as one of its core capabilities. Data-driven applications incorporate such algorithms to optimize radio access network (RAN) functions via RAN intelligent controllers (RICs). Deep reinforcement learning (DRL) algorithms are among the main approaches adopted in the O-RAN literature to solve dynamic radio resource management problems. However, despite the benefits introduced by the O-RAN RICs, the practical adoption of DRL algorithms in real network deployments falls behind. This is primarily due to the slow convergence and unstable performance exhibited by DRL agents upon deployment and when encountering previously unseen network conditions. In this paper, we address these challenges by proposing transfer learning (TL) as a core component of the training and deployment workflows for the DRL-based closed-loop control of O-RAN functionalities. To this end, we propose and design a hybrid TL-aided approach that leverages the advantages of both policy reuse and distillation TL methods to provide safe and accelerated convergence in DRL-based O-RAN slicing. We conduct a thorough experiment that accommodates multiple services, including real VR gaming traffic to reflect practical scenarios of O-RAN slicing. We also propose and implement policy reuse and distillation-aided DRL and non-TL-aided DRL as three separate baselines. The proposed hybrid approach shows at least: 7.7% and 20.7% improvements in the average initial reward value and the percentage of converged scenarios, and a 64.6% decrease in reward variance while maintaining fast convergence and enhancing the generalizability compared with the baselines.
Do Not Give Away My Secrets: Uncovering the Privacy Issue of Neural Code Completion Tools

Neural Code Completion Tools (NCCTs) have reshaped the field of software development, which accurately suggest contextually-relevant code snippets benefiting from language modeling techniques. However, language models may emit the training data verbatim during inference with appropriate prompts. This memorization property raises privacy concerns of commercial NCCTs about the hard-coded credential leakage, leading to unauthorized access to systems. Therefore, to answer whether NCCTs will inadvertently emit the hard-coded credential, we propose an evaluation tool called Hard-coded Credential Revealer (HCR). HCR effectively constructs test prompts from GitHub code files with credentials to trigger memorization phenomenon of commercial NCCTs. Then, HCR extracts credentials with pre-defined format from the responses by four designed filters. We apply HCR to evaluate two representative commercial NCCTs: GitHub Copilot and Amazon CodeWhisperer and successfully extracted 2,702 hard-coded credentials from Copilot and 129 secrets from CodeWhisper under the black-box setting, among which at least 3.6% and 5.4% secrets are real strings from GitHub repositories. Moreover, two operational credentials were identified. The experimental results raise the severe privacy concern of the potential leakage of hard-coded credentials in the training data of commercial NCCTs.
The Dynamical Principles of Storytelling

When considering the opening part of 1800 short stories, we find that the first dozen paragraphs of the average narrative follow an action principle as defined in arXiv:2309.06600. When the order of the paragraphs is shuffled, the average no longer exhibits this property. The findings show that there is a preferential direction we take in semantic space when starting a story, possibly related to a common Western storytelling tradition as implied by Aristotle in Poetics.
Neuro-Symbolic Recommendation Model based on Logic Query

A recommendation system assists users in finding items that are relevant to them. Existing recommendation models are primarily based on predicting relationships between users and items and use complex matching models or incorporate extensive external information to capture association patterns in data. However, recommendation is not only a problem of inductive statistics using data; it is also a cognitive task of reasoning decisions based on knowledge extracted from information. Hence, a logic system could naturally be incorporated for the reasoning in a recommendation task. However, although hard-rule approaches based on logic systems can provide powerful reasoning ability, they struggle to cope with inconsistent and incomplete knowledge in real-world tasks, especially for complex tasks such as recommendation. Therefore, in this paper, we propose a neuro-symbolic recommendation model, which transforms the user history interactions into a logic expression and then transforms the recommendation prediction into a query task based on this logic expression. The logic expressions are then computed based on the modular logic operations of the neural network. We also construct an implicit logic encoder to reasonably reduce the complexity of the logic computation. Finally, a user's interest items can be queried in the vector space based on the computation results. Experiments on three well-known datasets verified that our method performs better compared to state of the art shallow, deep, session, and reasoning models.
DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph

In this work, we present a web application named DBLPLink, which performs entity linking over the DBLP scholarly knowledge graph. DBLPLink uses text-to-text pre-trained language models, such as T5, to produce entity label spans from an input text question. Entity candidates are fetched from a database based on the labels, and an entity re-ranker sorts them based on entity embeddings, such as TransE, DistMult and ComplEx. The results are displayed so that users may compare and contrast the results between T5-small, T5-base and the different KG embeddings used. The demo can be accessed at https://ltdemos.informatik. uni-hamburg.de/dblplink/.
Exploring the User Experience of an AI-based Smartphone Navigation Assistant for People with Visual Impairments

Advancements in artificial intelligence (AI), computer vision, and smartphone technology have spurred the development of navigation assistants aiming to improve the independent mobility experience of people with visual impairments. Still, few studies have explored whether such tools meet this intention. Therefore, this study aimed to examine the perceptions of people with visual impairments on an AI-based navigation assistant and how their perceptions may influence their use of these technologies. A study involving 13 participants was conducted with DeepNAVI, an AI-based smartphone navigation assistant for people with visual impairments in both indoor and outdoor environments. The participants reported mixed reactions towards the navigation assistant, from excitement about the promise of increased independence to a lack of trust and skepticism toward the reliability of this technology. The perceptions from the participants provide some valuable insights about AI-based smartphone navigation assistants and their practicality in real life.
Decomposition of linear tensor transformations

One of the main issues in computing a tensor decomposition is how to choose the number of rank-one components, since there is no finite algorithms for determining the rank of a tensor. A commonly used approach for this purpose is to find a low-dimensional subspace by solving an optimization problem and assuming the number of components is fixed. However, even though this algorithm is efficient and easy to implement, it often converges to poor local minima and suffers from outliers and noise. The aim of this paper is to develop a mathematical framework for exact tensor decomposition that is able to represent a tensor as the sum of a finite number of low-rank tensors. In the paper three different problems will be carried out to derive: i) the decomposition of a non-negative self-adjoint tensor operator; ii) the decomposition of a linear tensor transformation; iii) the decomposition of a generic tensor.
Denoising Explicit Social Signals for Robust Recommendation

Social recommender system assumes that user’s preferences can be influenced by their social connections. However, social networks are inherently noisy and contain redundant signals that are not helpful or even harmful for the recommendation task. In this extended abstract, we classify the noise in the explicit social links into intrinsic noise and extrinsic noise. Intrinsic noises are those edges that are natural in the social network but do not have an influence on the user preference modeling; Extrinsic noises, on the other hand, are those social links that are introduced intentionally through malicious attacks such that the attackers can manipulate the social influence to bias the recommendation outcome. To tackle this issue, we first propose a self-supervised denoising framework that learns to filter out the noisy social edges. Specifically, we introduce the influence of key opinion leaders to hinder the diffusion of noisy signals and also function as an extra source to enhance user preference modeling and alleviate the data sparsity issue. Experiments will be conducted on the real-world datasets for the Top-K ranking evaluation as well as the model’s robustness to simulated social noises. Finally, we discuss the future plan about how to defend against extrinsic noise from the attacker’s perspective through adversarial training.
Reduced S-nitrosylation of TGFβ1 elevates its binding affinity towards the receptor and promotes fibrogenic signaling in the breast

Transforming Growth Factor β (TGFβ) is a pleiotropic cytokine closely linked to tumors. TGFβ is often elevated in precancerous breast lesions in association with epithelial-to-mesenchymal transition (EMT), indicating its contribution to precancerous progression. We previously reported that basal nitric oxide (NO) levels declined along with breast cancer progression. We then pharmacologically inhibited NO production in healthy mammary glands of wild-type mice and found that this induced precancerous progression accompanied by desmoplasia and upregulation of TGFβ activity. In the present study, we tested our hypothesis that NO directly S-nitrosylates (forms an NO-adduct at a cysteine residue) TGFβ to inhibit the activity, whereas the reduction of NO denitrosylates TGFβ and de-represses the activity. We introduced mutations to three C-terminal cysteines of TGFβ1 which were predicted to be S-nitrosylated. We found that these mutations indeed impaired S-nitrosylation of TGFβ1 and shifted the binding affinity towards the receptor from the latent complex. Furthermore, in silico structural analyses predicted that these S-nitrosylation-defective mutations strengthen the dimerization of mature protein, whereas S-nitrosylation-mimetic mutations weaken the dimerization. Such differences in dimerization dynamics of TGFβ1 by denitrosylation/S-nitrosylation likely account for the shift of the binding affinities towards the receptor vs. latent complex. Our findings, for the first time, unravel a novel mode of TGFβ regulation based on S-nitrosylation or denitrosylation of the protein. Significance statement Transforming Growth Factor β (TGFβ) is a widely studied cytokine associated with tumors. Because of its pleiotropic functions and dichotomous roles in tumorigenesis, the development of therapeutics targeted to TGFβ for cancer treatment has been challenging. In the present study, we report that TGFβ is indeed S-nitrosylated at specific sites for repressing its functions, whereas it is denitrosylated to derepress its activity. Such covalent modification-based regulation of TGFβ activity could potentially be utilized to design a new type of inhibitor or activator of the protein.
Fat body glycolysis defects inhibit mTOR and promote distant muscle disorganization through TNF-α/egr and ImpL2 signaling in Drosophila larvae

The fat body in Drosophila larvae serves as a reserve tissue and participates, through its endocrine function, in the regulation of organismal growth and homeostasis. To better understand its role in growth coordination, we induced severe fat body atrophy by knocking down in adipose cells several key enzymes of the glycolytic pathway. Our results show that impairing the last steps of glycolysis led to a drastic shrinkage in adipose cell size and lipid droplets content, and a downregulation of the mTOR pathway. Strikingly, fat body atrophy resulted in the distant disorganization of body wall muscles and the release of muscle-specific proteins in the hemolymph. Molecularly we showed that REPTOR activity was required for fat body atrophy downstream of glycolysis inhibition, and that the effect of fat body atrophy on muscles did not require upd3 secretion, but depended the production of egr/TNF-α and of the insulin pathway inhibitor ImpL2.
On the implementation and performance of the ( ; t ) protocol on Linux

This paper details the design and implementation of the ( ; t) protocol, a clustering and routing protocol for ad-hoc networks on Linux. Clustering and routing protocols that are developed, are normally tested using simulations. Without actual implementation, it is very difficult to perceive how efficient and effective the protocol would prove to be in the real world. The( ; t) Cluster framework deals with a unified approach to adapt dynamically to changing network topology. Nodes are organized into clusters depending on the ability to bound the probability of path failure due to node movement. This clustering scheme forms the basis for an adaptive routing strategy wherein routes within a cluster are maintained pro-actively and routes between clusters are managed re-actively. We conclude the paper by discussing an experimental study to evaluate the performance of the ( ; t) protocol.
Declining Inequality in Latin America in the 2000s: The Cases of Argentina, Brazil, and Mexico

Between 2000 and 2010, the Gini coefficient declined in 13 of 17 Latin American countries. The decline was statistically significant and robust to changes in the time interval, inequality measures, and data sources. In-depth country studies for Argentina, Brazil, and Mexico suggest two main phenomena underlie this trend: a fall in the premium to skilled labor and more progressive government transfers. The fall in the premium to skills resulted from a combination of supply, demand, and institutional factors. Their relative importance depends on the country.
Creating the next generation of news experience on ekstrabladet.dk with recommender systems

With the rise of algorithmic personalization, news organizations are finding it necessary to entrust traditionally held editorial values, such as prioritizing news for readers, to automated systems. In a case study conducted by Ekstra Bladet, the Platform Intelligent News project demonstrates how recommender systems successfully improved the click-through rates for various segments on ekstrabladet.dk, while still maintaining the news organization’s editorial values.
DeNovo: rethinking the memory hierarchy for disciplined parallelism

As multicore systems become widespread, both software and hardware face a major challenge in efficiently exploiting and implementing parallelism. While shared-memory remains a popular programming model due to its global address space, it is plagued with undisciplined programming practices that allow implicit communication and unstructured non-determinism. Such “wild” shared-memory behavior not only makes it difficult to test and maintain software but also complicates hardware, preventing it from scaling in a power-efficient manner. Recent research has proposed replacing the wild shared-memory programming models with a more disciplined approach. The DeNovo project asks the following question: if software is more disciplined, can we build more power-, performance-, and complexity-efficient shared-memory hardware? Focusing on deterministic programs as a discipline to drive DeNovo, we first show that coherence and communication can be made much simpler and more efficient than the current state of the art. The resulting protocol is without transient states, invalidation traffic, directory sharer-lists, or false sharing all significant sources of inefficiencies in existing protocols. Widening the software space further, we then show how DeNovo can support software with disciplined non-determinism without giving up its benefits for deterministic programs. The remaining challenge is to support synchronization accesses that are inherently “racy” on DeNovo without writer-initiated invalidation. We show that arbitrary synchronization can be supported on DeNovo with a simple yet efficient hardware mechanism, a big step toward our eventual goal of supporting legacy programs. Finally, we explore the potential for a comprehensive coherence solution that merges all previous DeNovo coherence mechanisms and adaptively switches between them depending on the level of “discipline” of software. In summary, DeNovo shows the potential for commercially viable software-driven shared-memory systems with higher complexity-, performance-, and energy-efficiency than today’s software-oblivious hardware.
Mercure, arsenic et sélénium au Burkina Faso : bioaccumulation, transfert trophique dans les systèmes aquatiques et évaluation de bioaccessibilité chez les humains

Mining of gold is one of the human activities that have increased the inputs of trace elements into the environment. Mercury (Hg) arsenic (As) and selenium (Se) are trace elements that are of environmental importance. Under aquatic environmental conditions, they can be transformed into organic forms which can bioaccumulate through aquatic food webs to reach high concentrations in predatory fish posing harmful effects to wildlife and humans due to their toxicological properties. This study assessed mercury (Hg), arsenic (As) and selenium (Se) levels in aquatic systems and their potential health risk for humans and wildlife in African sub-Saharan region of Burkina Faso where small scale gold mining practices are widespread. Bioaccumulation and trophic transfer of Hg and Se through food webs were also assessed. Human Hg exposure from fish consumption was also assessed in vitro by measure of bioaccessibility as proxy of Hg bioavailability to improve risk assessment. Water and fish levels of these elements were relatively low and did not reveal an important impact of gold mining activities. However some fish, mainly from deepest reservoirs, exhibited Hg concentrations above the international marketing limit of 500 ngHg/g (w.w.) recommended by WHO/FAO. These levels may be harmful for these fish and their predators including human. However, when taking into account the antagonistic effect of Se on Hg toxicity, up to 99 % of all fish could be protected from Hg toxicity by their Se content. When considering both As/Se and Se/Hg antagonism, 83% instead the 99% of fish should be considered safe for consumption. Stable isotope ratios of nitrogen (δN) and carbon (δC) allowed us to draw food webs structure and pathways of Hg and Se bioaccumulation and
Novel BCI paradigm for ALS patients based on EEG and Pupillary Accommodative Response

Brain-computer interfaces (BCIs) are one of the few alternatives to enable locked-in syndrome (LIS) patients to communicate with the external world, while they are the only solution for complete locked-in syndrome (CLIS) patients, who lost the ability to control eye movements. However, successful usage of endogenous electroencephalogram(EEG)-based BCI applications is often not trivial, due to EEG variations between and within sessions and long user training required. In this work we suggest an approach to deal with this two main limitations of EEG-BCIs by inserting a progressive and expandable neurofeedback training program, able to continuously tailor the classifier to the specific user, into a multimodal BCI paradigm. We propose indeed the integration of EEG with a non-brain signal: the pupillary accommodative response (PAR). The PAR is a change in pupil size associated with gaze shifts from far to close targets; it is not governed by the somatic nervous system and is thus potentially preserved after the evolution from LIS to CLIS, which often occurs in neurodegenerative diseases, such as amyotrophic lateral sclerosis. Multimodal BCIs have been broadly investigated in literature, due to their ability to yield better overall control performances, but this would be the first attempt combining EEG and PAR. In the context of the BciPar4Sla, we are exploiting these two signals, with the aim of developing a more reliable BCI, adaptive to the extent of evolving together with the user’s ability to elicit the brain phenomena needed for optimal control, and providing support even in the transition from LIS to CLIS.