Reviewerly/exHarmony huggingface dataset:
Between reality and non-reality

Virtual reality is all too often considered as antithetical to reality, the former being an entity fully separated from the latter. Since there has been historically no consensus among philosophers as to what constitutes reality, this article seeks to contribute to the debate on i crucial issue. It argues that reality should be considered as including non-tangible properties and that, from the first-person point of view, virtual reality is part of the reality of each and every one of us. Furthermore, grey zones between reality and virtual reality, that is to say environments in which reality blends with fantasy and highly personal perception of our surroundings are much more common than often assumed. The article claims that architecture is the most powerful foundation for virtual reality and therefore creator of grey zones. Real spaces (such as cafés or streets, and moreover cities) offer experiences more intense than any typical virtual environment and cause the blurring of awareness in which world we are. Virtual reality is an impoverished reality, and attempts to realize it have led to disastrous outcomes. On the contrary, grey zones, partially anchored on the materiality, actually enrich reality with non-tangible qualities, without threatening its authority in our souls and minds.
TunTap: A Tunisian Dataset for Topic and Polarity Extraction in Social Media

The massive usage of social networks has recently opened up new research avenues in the fields of data mining and decision-making. One of the most relevant forms of data generated by users in social media is an unstructured text that identifies their emotions on a given topic. Analyzing this new form of writing to extract valuable information is a challenging task, and could be of great interest in several fields such as healthcare, business intelligence, marketing strategies, $$\ldots $$ to name but a few. This article considers topic and polarity extraction in application to Online Social Media (OSM) analysis, in the benefit of numerous domain applications. Implementing sentiment analysis and topic extraction algorithms for the purpose of detecting the polarity of a given comment towards a certain topic requires a sophisticated machine and deep learning supervised models and, at the same time, collecting, preparing and annotating a huge amount of data to train those models. In this paper, we propose a special dataset that can be used to extract both topic and polarity features from dialectical messages used in Tunisian daily electronic writing across the most popular OSM networks. We collected our data by crawling posts and comments' text from Facebook, Twitter and YouTube using related network graph API. In this work, we describe the whole pipeline used to prepare our corpus as well as the several extensive experiments setup and results conducted to evaluate the generated dataset. Up to our knowledge, the proposed multivariate Arabic dataset (Topic and Polarity) of Tunisian dialect is a first-time introduced in the NLP community up to now, and we made it publicly available on GitHub ( https://github.com/DescoveryAmine/TunTap ).
French Object Clitics in the Interlanguage: A Linguistic Description and a Formal Analysis in the ACCG Framework

The interlanguage of second language French learners presents certain regularities that provides a formal framework within the context of the Applicative Combinatory Categorical Grammar. To illustrate an example, we examined the object clitic pronouns that appeared in a corpus of texts by scrutinizing the different facets of these pronouns. A formal analysis was then proposed. The ultimate goal of this research is to establish an automated system for processing learners' interlanguage in terms of the Applicative Combinatory Categorial Grammar.
ASPECT-ORIENTED MODELS-BASED FRAMEWORK TO SECURE INTELLIGENT SYSTEMS

Security is a high-priority issue for any software system. Emerging intelligent systems have unique security challenges that need to be addressed. In this paper, we propose a novel aspect-oriented models-based security framework to address security concerns in intelligent systems. The aspects of this framework derive from identified threats to intelligent systems. We use Unified Modeling Language (UML) diagrams to present each aspect.
Ontology-Based Cognitive Service Discovery &amp; Composition

Cloud cognitive computing has received a lot of attention lately especially for tackling real-world problems such as vision, natural language processing, fraud detection, sentiment analysis and speech recognition. This paradigm is based on cloud serverless computing and it provides machine learning based functions to end users. Part of the appeal in adopting this paradigm is its simplicity and the future promises a fast-growing serverless-native ecosystem in which service discovery and composition methods must be provided. However, serverless platforms still lack automated searching methods and the research community’s attention regarding this issue has been limited. In this paper, we propose an ontology-based approach for discovering and composing cognitive functions in serverless platforms in order to automate the searching process and semantically answer users’ requirements. We also carried a set of experiments to verify the correctness and the feasibility of our approach and discuss the influence of cognitive services nature on the outcomes of the discovery and composition approach.
Multi-label text mining method for electrical equipment defects based on improved CNN-SVM

A large number of descriptive transmission and transformation equipment defect records have been accumulated in the work of power grid informatization, and the category imbalance and label symbiosis phenomenon directly affect the multi-label classification effect of the documents. There is a large amount of complex information in the existing power equipment defect record text, and the same text information corresponds to multiple data labels, so the data is highly correlated. It is necessary to comprehensively extract the text information features and to mine the correlation between the global tags from the graph structure data. To this end, this paper proposes a multi-label text classification model (CS-GAT) that fuses convolutional neural network-self-attention mechanism (CNN-SAM) and graph attention network (GAT). The scheme first considers the professional word classification method in the field of power system, and performs text preprocessing on the sentences in the defect record text. The word2vec model is used to map words to a high-dimensional feature space to form distributed word vectors; and meanwhile, the correlation between different text labels is transformed into an edge-weighted graph with global information, and the multi-layer graph attention mechanism is used to automatically learn the relationship between different labels. And then interact with the contextual semantic information of the text to obtain the global label information with the semantic relationship of the text. The example analysis shows that the multilabel classification scheme proposed in this paper has better classification effect, can achieve automatic accurate and efficient classification of power equipment defect texts, and can effectively improve the automatic classification performance of power texts.
Robot path planning based on the improved grey wolf optimization algorithm

A modified grey wolf optimization algorithm for the path planning of mobile robots is proposed, for the slow convergence speed and high search path cost. Establish two-dimensional space model of mobile robot obstacle avoidance path planning, convert linear convergence factors in the grey wolf optimization algorithm into non-linear convergence factor; add collaborative quantum optimization of grey wolf population; and use four international test functions to prove that the improved algorithm is better in terms of convergence accuracy and stability. The improved algorithm was applied to the path planning of the mobile robot and compared with the original algorithm to verify the effectiveness of the algorithm.
TAG: Pandemic Competition

Cooperation between humans and AI is an area of research explored more frequently in recent literature. Yet, environments used for this purpose are generally lacking in complexity. In this paper, we describe the first Tabletop Games Framework (TAG) competition designed around the Pandemic: a cooperative board game where players aim to cure the world of disease. We discuss the many AI challenges introduced through this environment, detail the competition setup, present baseline results for sample AI players and explore the game parameter space for interesting insights, such as the most dominant player roles: the Scientist and the Medic.
Illuminating the Space of Enemies Through MAP-Elites

Action-Adventure games have several challenges to overcome, where the most common are enemies. The enemies’ goal is to hinder the players’ progression by taking life points, and the way they hinder this progress is distinct for different kinds of enemies. In this context, this paper introduces an extended version of an evolutionary approach for procedurally generating enemies that target the enemy’s difficulty as the goal. Our approach advances the enemy generation research by incorporating a MAP-Elites population to generate diverse enemies without losing quality. The computational experiment showed the method converged most enemies in the MAP-Elites in less than a second for most cases. Besides, we experimented with players who played an Action-Adventure game prototype with enemies we generated. This experiment showed that the players enjoyed most levels they played, and we successfully created enemies perceived as easy, medium, or hard to face.
Multi-goal Reinforcement Learning via Exploring Successor Matching

Multi-goal reinforcement learning (RL) agent aims at achieving and generalizing over various goals. Due to the sparsity of goal-reaching rewards, it suffers from unreliable value estimation and is thus unable to efficiently identify essential states towards specific goal-reaching. To deal with the problem, we propose Exploring Successor Matching (ESM), a framework that enables goal-conditioned policy and progressively encourages the multi-goal exploration towards the promising frontier. ESM adopts the idea of successor feature and extends it to goal-reaching successor mapping that serves as a more stable state feature under sparse rewards. After acquiring the successor mapping, it further explores intrinsic goals that are more likely to be achieved from a diverse set of states in terms of future state occupancies. Experiments on challenging manipulation tasks show that ESM deals well with sparse rewards and achieves better sample efficiency.
Optimists at Heart: Why Do We Research Game AI?

In this paper we survey the motivations behind contemporary game AI research by analysing individual publications, the researchers themselves, and the institutions that influence them. In doing so, we identify some negative effects on our field, caused both by external forces outside of our control as well as institutionalised behaviours that are easily overlooked. We suggest how we might begin to address some of these issues as a community, and reassert ourselves as the primary driving force behind the field.
Proficiency Testing Scheme of Conducted disturbance at telecommunication ports

The paper introduces a proficiency testing scheme of conducted disturbance at telecommunication ports, includes the items, homogeneity, stability, statistical analysis, result evaluation and unacceptable reason analysis. Through this scheme, we understand the ability of participants to carry out conducted disturbance test at telecommunications ports, find the problems and help them improve their testing capabilities.
Semantic multimodal violence detection based on local-to-global embedding

Automatic violence detection has received continuous attention due to its broad application prospects. However, most previous work prefers building a generalized pipeline while ignoring the complexity and diversity of violent scenes. In most cases, people judge violence by a variety of sub-concepts, such as blood, fighting, screams, explosions, etc., which may show certain co-occurrence trends. Therefore, we argue that parsing abstract violence into specific semantics helps to obtain the essential representation of violence. In this paper, we propose a semantic multimodal violence detection framework based on local-to-global embedding. The local semantic detection is designed to capture fine-grained violent elements in the video via a set of local semantic detectors, which is generated from a variety of external word embeddings. Also, we introduce a global semantic alignment branch to mitigate the intra-class variance of violence, in which violent video embeddings are guided to form a compact cluster while keeping a semantic gap with non-violent embeddings. Furthermore, we construct a multimodal cross-fusion network (MCN) for multimodal feature fusion, which consists of a cross-adaptive module and a cross-perceptual module. The former aims to eliminate inter-modal heterogeneity, while the latter suppresses task-irrelevant redundancies to obtain robust video representations. Extensive experiments demonstrate the effectiveness of the proposed method, which has a superior generalization capacity and achieves competitive performance on five violence datasets.
HARNet: Hierarchical adaptive regression with location recovery for crowd counting

Researchers have started to utilize the structures like Feature Pyramid Network (FPN) for crowd counting to extract multi-scale features, which can solve the scale variation problem in traditional density map regression-based methods. However, many downsampling operations in FPN inevitably induce loss of location information, resulting in pixel mismatch of the predicted density map. In this paper, we propose the Location Recovery Module (LRM) to recover the loss of location information. Unlike the basic feature fusion module in FPN, the proposed LRM builds an offset map to modify the position of the downsampled features under the guidance of low-level features. Meanwhile, we design a novel regression strategy to fully utilize the multi-scale information without upsampling operations in the fusion process. Considering that the highest-level feature cannot represent all density-level features well, different-scale features focus more on their related regions in our regression strategy. The proposed HARNet has been tested on four datasets and achieved state-of-the-art on two datasets. The performance metric such as MAE arrives at 52.4, 5.9 and 181.7 on ShanghaiTech A/B and UCF_CC_50 respectively.
DEFEAT: Decoupled feature attack across deep neural networks

Adversarial attacks pose a security challenge for deep neural networks, motivating researchers to build various defense methods. Consequently, the performance of black-box attacks turns down under defense scenarios. A significant observation is that some feature-level attacks achieve an excellent success rate to fool undefended models, while their transferability is severely degraded when encountering defenses, which give a false sense of security. In this paper, we explain one possible reason caused this phenomenon is the domain-overfitting effect, which degrades the capabilities of feature perturbed images and makes them hardly fool adversarially trained defenses. To this end, we study a novel feature-level method, referred to as Decoupled Feature Attack (DEFEAT). Unlike the current attacks that use a round-robin procedure to estimate gradient estimation and update perturbation, DEFEAT decouples adversarial example generation from the optimization process. In the first stage, DEFEAT learns an distribution full of perturbations with high adversarial effects. And it then iteratively samples the noises from learned distribution to assemble adversarial examples. On top of that, we can apply transformations of existing methods into the DEFEAT framework to produce more robust perturbations. We also provide insights into the relationship between transferability and latent features that helps the community to understand the intrinsic mechanism of adversarial attacks. Extensive experiments evaluated on a variety of black-box models suggest the superiority of DEFEAT, i.e., our method fools defenses at an average success rate of 88.4%, remarkably outperforming state-of-the-art transferable attacks by a large margin of 11.5%. The code is publicly available at https://github.com/mesunhlf/DEFEAT.
Subgraph-based feature fusion models for semantic similarity computation in heterogeneous knowledge graphs

Semantic similarity is a fundamental task in natural language processing that determines the similarity between two concepts within a taxonomy. For example, a pair of words (e.g., car and bike) appear similar because they share the same category (e.g., vehicle). Numerous computation methods, such as distance-based and feature-based approaches, are proposed to precisely depict this similarity. As knowledge graphs become heterogeneous (e.g., DBpedia), existing methods have limitations on utilizing multi-view features (e.g., abstract, structure, and categories). On the one hand, some features are incomplete for various reasons, reducing the effectiveness of embedding methods. On the other hand, the hidden connections among multi-view features are omitted by existing approaches. To address the problems mentioned above, we first extract three subgraphs from a heterogeneous knowledge graph and then combine various embedding approaches to capture the global semantics of each concept. Next, we offer subgraph-based feature fusion models that improve concept representation by fusing multi-view features. Finally, we devise mixed computation methods to calculate the semantic similarity between the two concepts. Experiment results show that multi-view features, particularly the abstract feature, can effectively improve the performance of the proposed methods. Compared to existing approaches, our methods significantly improve the Pearson correlation coefficient by about 7%. The source code of this paper is available at: https://github.com/fiego/SubgraphSS.
Weakly supervised ship detection in remote sensing images

Ship detection in remote sensing images is important for maritime surveillance. With the rapid development of earth observation technology, high-resolution imaging satellites can provide more observational information. In the face of massive remote sensing data, object-level annotation requires a lot of time and manpower. Weakly supervised object detection is trained using only image-level annotations, thus reducing the requirement for object-level annotations. However, there are still some problems in the detection of weakly supervised ships in remote sensing images, because of the complex, dense distribution and diverse scale characteristics of the ship environment. We propose a weakly supervised object detection method that combines Transformer with weakly supervised learning for ship detection in remote sensing images. First, Proposal Clustering Learning (PCL) for weakly supervised object detection is used as the baseline to detect ships, and the network is continuously refined for better detection performance. Second, the prior location and size information is added to the features of the proposal through the transformer module. This additional information can be used as an important basis for judging whether the proposal is optimal, thereby improving the detection performance. To evaluate the effectiveness of our method, extensive experiments are conducted on a complex dataset of large-scene remote sensing ships. Experimental results show that our method achieves better detection performance than other methods.
Evading Logits-Based Detections to Audio Adversarial Examples by Logits-Traction Attack

Automatic Speech Recognition (ASR) provides a new way of human-computer interaction. However, it is vulnerable to adversarial examples, which are obtained by deliberately adding perturbations to the original audios. Thorough studies on the universal feature of adversarial examples are essential to prevent potential attacks. Previous research has shown classic adversarial examples have different logits distribution compared to normal speech. This paper proposes a logit-traction attack to eliminate this difference at the statistical level. Experiments on the LibriSpeech dataset show that the proposed attack reduces the accuracy of the LOGITS NOISE detection to 52.1%. To further verify the effectiveness of this approach in attacking detection based on logits, three different features quantifying the dispersion of logits are constructed in this paper. Furthermore, a richer target sentence is adopted for experiments. The results indicate that these features can detect baseline adversarial examples with an accuracy of about 90% but cannot effectively detect Logits-Traction adversarial examples, proving that Logits-Traction attack can evade the logits-based detection method.
Flexible privilege management for microcontroller-class RISC-V cores

The trustworthiness of microcontroller-class devices is crucial for a growing spectrum of applications involving embedded and industrial systems, ranging from robotics to avionics, from sensor networks to health. In that respect, integrating security-aware processes into design methodologies can lead to products that are more resilient to attacks and provide native protection mechanisms capable of mitigating system vulnerabilities, hence reducing damages and recovery costs. Based on the increasing interest in open-source hardware and relying on the non-proprietary RISC-V specification, in this work we aim to explore architectural extensions serving as a baseline for establishing a Trusted Execution Environment (TEE) in microcontroller-class processor cores targeted at embedded and industrial applications, especially those with some form of latency-sensitive requirements. We point out that supporting a trusted environment in this type of systems is particularly challenging and we identify a minimum set of hardware-level protection mechanisms, with limited assumptions on the available privileged modes and protection support. The reference architecture is demonstrated by extending a lightweight RISC-V core, Ibex. As shown in the last part of our work, the proof-of-concept design achieves flexible support for isolation at a modest overhead in terms of additional hardware resources and delay, thereby fully matching the constraints of latency-sensitive deeply embedded applications. • Supporting a Trusted Execution Environment is challenging for latency-sensitive IoT • We explore extensions enabling a TEE in RISC-V microcontroller cores • We identify a minimum set of hardware requirements including limited privileged modes • We demonstrate the solution by extending a lightweight RISC-V core, Ibex • We achieve flexible TEE support at a modest resource and delay overhead