Reviewerly/exHarmony huggingface dataset:
Explainable artificial intelligence for mental health through transparency and interpretability for understandability

The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what "explainability" means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability meaning model-agnostic techniques that augment a complex model (with internal mechanics intractable for human understanding) with a simpler model argued to deliver results that humans can comprehend. Given the differing usage and intended meaning of the term "explainability" in AI and ML, we propose instead to approximate model/algorithm explainability by understandability defined as a function of transparency and interpretability. These concepts are easier to articulate, to "ground" in our understanding of how algorithms and models operate and are used more consistently in the literature. We describe the TIFU (Transparency and Interpretability For Understandability) framework and examine how this applies to the landscape of AI/ML in mental health research. We argue that the need for understandablity is heightened in psychiatry because data describing the syndromes, outcomes, disorders and signs/symptoms possess probabilistic relationships to each other-as do the tentative aetiologies and multifactorial social- and psychological-determinants of disorders. If we develop and deploy AI/ML models, ensuring human understandability of the inputs, processes and outputs of these models is essential to develop trustworthy systems fit for deployment.
Blockchain Integration in the Era of Industrial Metaverse

Blockchain can be realized as a distributed and decentralized database, also known as a “distributed ledger,” that is shared among the nodes of a computer network. Blockchain is a form of democratized and distributed database for storing information electronically in a digital format. Under the framework of Industry 4.0, the digitization and digitalization of manufacturing and production systems and networks have been focused, thus Big Data sets are a necessity for any manufacturing activity. Big Data sets are becoming a useful resource as well as a byproduct of the activities/processes taking place. However, there is an imminent risk of cyberattacks. The contribution of blockchain technology to intelligent manufacturing can be summarized as (i) data validity protection, (ii) inter- and intra-organizational communication organization, and (iii) efficiency improvement of manufacturing processes. Furthermore, the need for increased cybersecurity is magnified as the world is heading towards a super smart and intelligent societal model, also known as “Society 5.0,” and the industrial metaverse will become the new reality in manufacturing. Blockchain is a cutting-edge, secure information technology that promotes business and industrial innovation. However, blockchain technologies are bound by existing limitations regarding scalability, flexibility, and cybersecurity. Therefore, in this literature review, the implications of blockchain technology for addressing the emerging cybersecurity barriers toward safe and intelligent manufacturing in Industry 5.0 as a subset of Society 5.0 are presented.
A systematic threat analysis and defense strategies for the metaverse and extended reality systems

With the rapid development and evolution of immersive technologies there are growing concerns of security and privacy threats to the metaverse and extended reality (XR) systems. Immersive reality solutions are a combination of multiple vulnerable technologies allowing attackers to easily undermine security. Furthermore the deployment of appropriate security controls and defensive mechanisms for resource constrained proprietary XR products has been limited. In this paper, we provide a comprehensive overview of extended reality systems and the metaverse with emphasis on technology weaknesses, cyber security challenges and users’ safety concerns. Five major taxonomies have been presented in this research with an aim of identifying privacy inference vectors and potential cyber threats; determining the impact on human health and the extent to which cyberstalking, and digital currency scam activities proliferate when using XR. This research also proposes strategies for primary lines of defense and provides recommendations on the adoption of safety measures.
An enhanced distributed differential evolution algorithm for portfolio optimization problems

The population structure of differential evolution (DE) algorithm cannot maintain the diversity of the population to the greatest extent and help the population avoid to fall into the local optima in time. In this paper, a co-evolutionary multi-swarm adaptive differential evolution algorithm, namely ECMADE is proposed to solve the premature convergence and search stagnation. First of all, in terms of population structure, based on the parallel distributed framework, ECMADE randomly and evenly divides the population into exploration subpopulation, development subpopulation, and auxiliary subpopulation, and introduces an adaptive information exchange mechanism so that subpopulations can escape local optima in time. Then, a multi-operator parallel search strategy is proposed to keep population diversity and meet the optimization needs of different problems. Finally, an adaptive adjustment mechanism of control parameters is developed, through recent elite parameter archive and weight distribution to fully mine successful parameter information, and generate control parameters with a high success rate for the current evolutionary stage. In order to prove the effectiveness of the ECMADE, 10 test functions and portfolio optimization problem are selected in here. The experiment results show that the ECMADE can effectively solve these test functions, the accuracy and efficiency is superior to those of two classical DE algorithms. The actual application results show that the ECMADE can significantly improve the ability of portfolio to resist extreme losses, which proves the effectiveness and feasibility of the ECMADE once again. The ECMADE has better optimization performance by comparing with some well-known algorithms in term of the solution quality, robustness and space distribution. It provides a new algorithm for solving complex optimization problems.
OneFormer: One Transformer to Rule Universal Image Segmentation

Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation include scene parsing, panoptic segmentation, and, more recently, new panoptic architectures. However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance. Ideally, a truly universal framework should be trained only once and achieve SOTA performance across all three image segmentation tasks. To that end, we propose OneFormer, a universal image segmentation framework that unifies segmentation with a multi-task train-once design. We first propose a task-conditioned joint training strategy that enables training on ground truths of each domain (semantic, instance, and panoptic segmentation) within a single multi-task training process. Secondly, we introduce a task token to condition our model on the task at hand, making our model task-dynamic to support multi-task training and inference. Thirdly, we propose using a query-text contrastive loss during training to establish better intertask and inter-class distinctions. Notably, our single OneFormer model outperforms specialized Mask2Former models across all three segmentation tasks on ADE20k, Cityscapes, and COCO, despite the latter being trained on each task individually. We believe OneFormer is a significant step towards making image segmentation more universal and accessible.
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation

In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version. Intriguingly, we observe that such a simple pipeline already achieves competitive results against recent advanced works, when transferred to our segmentation scenario. Its success heavily relies on the manual design of strong data augmentations, however, which may be limited and inadequate to explore a broader perturbation space. Motivated by this, we propose an auxiliary feature perturbation stream as a supplement, leading to an expanded perturbation space. On the other, to sufficiently probe original image-level augmentations, we present a dual-stream perturbation technique, enabling two strong views to be simultaneously guided by a common weak view. Consequently, our overall Unified Dual-Stream Perturbations approach (UniMatch) surpasses all existing methods significantly across all evaluation protocols on the Pascal, Cityscapes, and COCO benchmarks. Its superiority is also demonstrated in remote sensing interpretation and medical image analysis. We hope our reproduced FixMatch and our results can inspire more future works.
Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study

Artificial intelligence (AI) as an independent reader of screening mammograms has shown promise, but there are few prospective studies. Our aim was to conduct a prospective clinical trial to examine how AI affects cancer detection and false positive findings in a real-world setting.ScreenTrustCAD was a prospective, population-based, paired-reader, non-inferiority study done at the Capio Sankt Göran Hospital in Stockholm, Sweden. Consecutive women without breast implants aged 40-74 years participating in population-based screening in the geographical uptake area of the study hospital were included. The primary outcome was screen-detected breast cancer within 3 months of mammography, and the primary analysis was to assess non-inferiority (non-inferiority margin of 0·15 relative reduction in breast cancer diagnoses) of double reading by one radiologist plus AI compared with standard-of-care double reading by two radiologists. We also assessed single reading by AI alone and triple reading by two radiologists plus AI compared with standard-of-care double reading by two radiologists. This study is registered with ClinicalTrials.gov, NCT04778670.From April 1, 2021, to June 9, 2022, 58 344 women aged 40-74 years underwent regular mammography screening, of whom 55 581 were included in the study. 269 (0·5%) women were diagnosed with screen-detected breast cancer based on an initial positive read: double reading by one radiologist plus AI was non-inferior for cancer detection compared with double reading by two radiologists (261 [0·5%] vs 250 [0·4%] detected cases; relative proportion 1·04 [95% CI 1·00-1·09]). Single reading by AI (246 [0·4%] vs 250 [0·4%] detected cases; relative proportion 0·98 [0·93-1·04]) and triple reading by two radiologists plus AI (269 [0·5%] vs 250 [0·4%] detected cases; relative proportion 1·08 [1·04-1·11]) were also non-inferior to double reading by two radiologists.Replacing one radiologist with AI for independent reading of screening mammograms resulted in a 4% higher non-inferior cancer detection rate compared with radiologist double reading. Our study suggests that AI in the study setting has potential for controlled implementation, which would include risk management and real-world follow-up of performance.Swedish Research Council, Swedish Cancer Society, Region Stockholm, and Lunit.
Glaive: A State-Space Narrative Planner Supporting Intentionality and Conflict

Glaive is a state-space planner based on Hoffmann and Nebel's Fast-Forward which solves the narrative planning problem defined by Riedl and Young — to construct a plan which achieves the author's goals out of steps which are clearly motivated and goal-oriented toward individual character goals. Glaive reasons about how characters cooperate and conflict based on causal structures and possible worlds. By leveraging the unique constraints of narrative planning, Glaive reduces its branching factor and calculates a more accurate heuristic. We evaluate it on 8 narrative planning problems and demonstrate that it can solve certain non-trivial problems in under 1 second.
Advances in Lifted Importance Sampling

We consider lifted importance sampling (LIS), a previously proposed approximate inference algorithm for statistical relational learning (SRL) models. LIS achieves substantial variance reduction over conventional importance sampling by using various lifting rules that take advantage of the symmetry in the relational representation. However, it suffers from two drawbacks. First, it does not take advantage of some important symmetries in the relational representation and may exhibit needlessly high variance on models having these symmetries. Second, it uses an uninformative proposal distribution which adversely affects its accuracy. We propose two improvements to LIS that address these limitations. First, we identify a new symmetry in SRL models and define a lifting rule for taking advantage of this symmetry. The lifting rule reduces the variance of LIS. Second, we propose a new, structured approach for constructing and dynamically updating the proposal distribution via adaptive sampling. We demonstrate experimentally that our new, improved LIS algorithm is substantially more accurate than the LIS algorithm.
Enhanced convergence of Bat Algorithm based on dimensional and inertia weight factor

Heuristic optimisation method typically hinges on the efficiency in exploitation and global diverse exploration. Previous research has shown that Bat Algorithm could provide a good exploration and exploitation of a solution. However, Bat Algorithm can be get trapped in a local minimum in some multi-dimensional functions. Thus, the phenomenon of slow convergence rate and low accuracy still exits. This paper aims to modify the exploitation of Bat Algorithm in optimising the solution by modifying dimensional size and providing inertia weight. Benchmark test function is then performed for the basic Bat Algorithm and the modified Bat Algorithm (MBA) for comparison. The result is analysed according to the number of iteration needed for a convergence toward the objective. From simulations, it is found that the modified dimension and additional inertia weight factor of Bat Algorithm proves to be more effective than the basic Bat Algorithm in terms of searching for a solution while improving quality of results in all cases or significantly improving convergence speed.
Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input

In this paper, we explore neural network models that learn to associate segments of spoken audio captions with the semantically relevant portions of natural images that they refer to. We demonstrate that these audio-visual associative localizations emerge from network-internal representations learned as a by-product of training to perform an image-audio retrieval task. Our models operate directly on the image pixels and speech waveform, and do not rely on any conventional supervision in the form of labels, segmentations, or alignments between the modalities during training. We perform analysis using the Places 205 and ADE20k datasets demonstrating that our models implicitly learn semantically coupled object and word detectors.
Influence of cognitive stance and physical perspective on subjective and autonomic reactivity to observed pain and pleasure: An immersive virtual reality study

Observing others’ pain may induce a reaction called personal distress that may be influenced by top-down (imagine self or other in pain, i.e., self- vs other-oriented stance) and bottom-up (physical perspective of those who suffer, i.e., first vs third person perspective- 1PP vs 3PP) processes. The different contributions of these processes have not been teased apart. By capitalizing on the power of Immersive Virtual Reality, we explored how behavioural (subjective ratings) and physiological reactivity (skin conductance reactivity, SCR) to pain and pleasure delivered to an avatar was influenced by Cognitive stance and Physical perspective. Taking an Other-Oriented stance leads to attributing higher congruent valence (i.e. pain rated as unpleasant and pleasure as pleasant) and intensity to the stimuli and induces reduced SCR. Ownership over the virtual limb was maximal in 1PP where physiological reactivity to the stimuli was comparable. Our results highlight different components underpinning reactivity to pain and pleasure.
An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data Mobility

The expand trend of cloud data mobility led to malicious data threats that necessitate using data protection techniques. Most cloud system applications contain valuable and confidential data, such as personal, trade, or health information. Threats on such data may put the cloud systems that hold these data at high risk. However, traditional security solutions are not capable of handling the security of big data mobility. The current security mechanisms are insufficient for big data due to their shortage of determining the data that should be protected or due to their intractable time complexity. Therefore, the demand for securing mobile big data has been increasing rapidly to avoid any potential risks. This paper proposes an integrated methodology to classify and secure big data before executing data mobility, duplication, and analysis. The necessity of securing big data mobility is determined by classifying the data according to the risk impact level of their contents into two categories; confidential and public. Based on the classification category, the impact of data security is studied and substantiated on the confidential data in the scope of Hadoop Distributed File System. It is revealed that the proposed approach can significantly improve the cloud systems data mobility.
Hybrid Models with Deep and Invertible Features

We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive property of our model is that both p(features), the density of the features, and p(targets | features), the predictive distribution, can be computed exactly in a single feed-forward pass. We show that our hybrid model, despite the invertibility constraints, achieves similar accuracy to purely predictive models. Moreover the generative component remains a good model of the input features despite the hybrid optimization objective. This offers additional capabilities such as detection of out-of-distribution inputs and enabling semi-supervised learning. The availability of the exact joint density p(targets, features) also allows us to compute many quantities readily, making our hybrid model a useful building block for downstream applications of probabilistic deep learning.
Type-2 fuzzy control for driving state and behavioral decisions of unmanned vehicle

In this paper, interval type-2 fuzzy sets, fuzzy comprehensive evaluation and the fuzzy control rules are synthesized to realize the control of unmanned vehicle in driving state and behavioral decisions. Compared to the type-1 fuzzy set, type-2 fuzzy sets have more advantages in handling the model based on uncertainties, linguistic information because the membership functions are fuzzy sets. Different membership functions are established for each factor when the unmanned vehicle is driving at different speed intervals. In addition, a new evaluation method is developed to analyze unmanned vehicleʼ s driving state. Finally, a set of dynamic fuzzy rules are sorted out, which can be applied to the unmanned vehicleʼ s behavioral decision-making and provide a new idea to related research.
A Heuristic Rapidly-Exploring Random Trees Method for Manipulator Motion Planning

In order to plan the robot path in 3D space efficiently, a modified Rapidly-exploring Random Trees based on heuristic probability bias-goal (PBG-RRT) is proposed. The algorithm combines heuristic probabilistic and bias-goal factor, which can get convergence quickly and avoid falling into a local minimum. Firstly, PBG-RRT is used to plan a path. After obtaining path points, path points are rarefied by the Douglas-Peucker algorithm while maintaining the original path characteristics. Then, a smooth trajectory suitable for the manipulator end effector is generated by Non-uniform B-spline interpolation. Finally, the effector is moving along the trajectory by inverse kinematics solving angle of joint. The above is a set of motion planning for the manipulator. Generally, 3D space obstacle avoidance simulation experiments show that the search efficiency of PBG-RRT is increased by 217%, while search time is dropped by 168% compared with P-RRT (Heuristic Probability RRT). After rarefying, the situation where the path oscillated around the obstacle is corrected effectively. And a smooth trajectory is fitted by spline interpolation. Ultimately, PBG-RRT is verified on the ROS (Robot Operating System) with the Robot-Anno manipulator. The results reveal that the validity and reliability of PBG-RRT are proofed in obstacle avoidance planning.
Restricting the Flow: Information Bottlenecks for Attribution

Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image. In this work we adapt the information bottleneck concept for attribution. By adding noise to intermediate feature maps we restrict the flow of information and can quantify (in bits) how much information image regions provide. We compare our method against ten baselines using three different metrics on VGG-16 and ResNet-50, and find that our methods outperform all baselines in five out of six settings. The method’s information-theoretic foundation provides an absolute frame of reference for attribution values (bits) and a guarantee that regions scored close to zero are not required for the network's decision.