Reviewerly/exHarmony huggingface dataset:
Decrypting the Black Boxing of Artificial Intelligence Using Explainable Artificial Intelligence in Smart Healthcare

Artificial Intelligence (AI) is creating a revolution in the healthcare industry with its recent developments in organized and amorphous data and quick progress in analytic techniques. The usefulness of AI in healthcare is being recognised at the same time as people begin to be concerned with the possible lack of explainability and bias in the models created. This explains the concept of explainable artificial intelligence (XAI), which increases the faith held in a system, thus leading to more widespread use of AI in healthcare. In this chapter, we offer diverse ways of viewing the XAI concepts, understandability and interpretability of explainable AI systems, mainly focussing on the healthcare domain. The intention is to educate healthcare providers on the understandability and interpretability of explainable AI systems. The medical model is the root cause of life, and we should be assured adequate to treat the patient according to its rules. This chapter uses AI explainability as a way to help build trustworthiness in the medical domain and takes a look at the recent developments in the area of explainable AI which encourages creativity, and at times are necessities in practice to raise awareness.
Solving Multi-Period Financial Planning Models: Combining Monte Carlo Tree Search and Neural Networks

This paper introduces the MCTS algorithm to the financial world and focuses on solving significant multi-period financial planning models by combining a Monte Carlo Tree Search algorithm with a deep neural network. The MCTS provides an advanced start for the neural network so that the combined method outperforms either approach alone, yielding competitive results. Several innovations improve the computations, including a variant of the upper confidence bound applied to trees (UTC) and a special lookup search. We compare the two-step algorithm with employing dynamic programs/neural networks. Both approaches solve regime switching models with 50-time steps and transaction costs with twelve assets. Heretofore, these problems have been outside the range of solvable optimization models via traditional algorithms.
Scale-Train: A Scalable DNN Training Framework for a Heterogeneous GPU Cloud

In order to cope with the growing scale of deep neural network (DNN) models and training data, the use of cloud computing for distributed DNN training is becoming increasingly popular.The amount of available resources in a cloud continuously changes according to users' demands.Although distributed DNN training has a long execution time ranging from several hours to several days, existing frameworks cannot provide a dynamic scale function or have high scale in/out overhead.Therefore, it is difficult to achieve higher performance by adding graphics processing unit (GPU) nodes to a running training cluster, even when surplus GPU resources become available.In addition, the inability to dynamically reconfigure the training cluster prohibits the reform of the cluster topology when it was sub-optimally created.This paper proposes a dynamic scaling technique with which the dynamic addition and removal of new workers can be performed without suspending the ongoing training job.In addition, we propose a heterogeneityaware straggler-proof technique so that, even when the performance of the GPUs in the cloud are uneven, a performance benefit can be guaranteed through the addition of the surplus resources.The proposed scheme improved throughput by up to a factor of 17.52 during scaling out the existing cluster of five workers to ten compared to the existing checkpoint-based scheme.Furthermore, training was continued at 95.52% of the maximum performance while being stopped for 841 seconds in Elastic Horovod, which supports dynamic scaling.Finally, even when GPUs of different performances were mixed, the error between the determined batch size and the optimal batch size was 3.37% on average.
Intelligent analysis of multimedia healthcare data using natural language processing and deep-learning techniques

Healthcare data is complex, unstructured, and are collected in different modes. Analysis of healthcare data for diagnosis and prognosis has been a research challenge for decades. Natural language processing (NLP) and more recent deep-learning techniques are subfields of the artificial intelligence domain. These are used to identify patterns in the multimodal data and extract meaning from the unstructured data. The advancement in NLP methods allowed speedy analysis of huge amounts of text, which majorly benefitted the healthcare domain. This helped extract relevant information from the healthcare records that provide better decision support for clinicians to understand and help their patients. The exploration and information extraction from health records, social media, scientific literature, technical reports, and different documents will embody the strengthening of existing intelligence systems by unifying various disease diagnosis reports from multiple sources and experiences of various people over social media and other online forums. Health science and clinical analytics rely heavily on data gathered from numerous sources. Historically, a trained professional can make a patient fill out a form to get an idea to identify the medical condition. However, medical practitioners have access to several sources of knowledge and the patient’s writings on numerous media. NLP is a vital tool that will serve the health sector in decreasing the burden of health imbalance within the population through quicker identification of illness and risk factors and illness interference methods. NLP permits researchers to collect such information and examine it to extract the first means of such writings. Due to various health and medical documents available online, NLP algorithms could help the novice by drawing out related documents and providing necessary information about the underlying condition. This also allows clinics with fewer expert physicians to check other medical records of the exact nature which have been referred from expert physicians helping in the availability of good measures and diagnosis procedures. In this work, we glance at some NLP applications to the healthcare domain and how the healthcare domain has evolved over time, along with the evolution of NLP addressing the challenges.
An Eight-Channel Switching-Linear Hybrid Dynamic Regulator With Dual-Supply LDOs for Thermo-Optic Tuning

A novel switching-linear hybrid dynamic regulator architecture with dual-supply low dropout regulators (LDOs) is presented in this paper. This architecture leverages the intrinsic dual supplies to extend the operating range of the LDOs. Furthermore, it increases the thermo-optic tuning efficiency by reducing the LDO dropout voltage through dynamic supply modulation. This architecture is suitable for large-scale thermo-optic tuning in silicon photonics. The efficiency improvement is particularly effective when tracking signals of several adjacent channels are close to each other, e.g., wavelength tuning of a microring array. The principle of this architecture is general and can be implemented using different switching converters and LDOs. A specific design with extensive post-layout simulation results is used to verify the effectiveness of our architecture. Implemented in a 130 nm CMOS process, this design can simultaneously regulate eight output channels with an output swing of 0.8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text V_{\text {pp}}$ </tex-math></inline-formula> . Its peak efficiency when driving 100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> loads is 92% at 1 V output, and the dynamic efficiency is around 86% when tracking 50 kHz sinusoidal signals. To the best of our knowledge, this is the first time that dynamic supply modulation has been applied to multi-channel thermo-optic tuning of silicon photonic devices.
A Module Based Full Cycle Construction Method of Domain-Specific Knowledge Graph

Knowledge graph (KG) is booming since Google announced the concept about it in 2012, and its underlying semantic technologies support inference and reasoning natively, provides the possibility of artificial intelligence (AI) to develop to be explainable. Meanwhile, the knowledge graph has emerged as a major area in artificial intelligence and been widely applied to various fields along with the continuous development of intelligent service applications. Knowledge graph construction involves a variety of technologies and methods, currently researchers are focus on the accuracy of specific algorithms. However, there are still many challenges in the construction of knowledge graph for lack of the full life cycle of knowledge graph construction methods. This paper proposed a module based full cycle construction of domain-specific knowledge graph, including five sub modules. We designed a standardized graph construction process, which can realize the semi-automatic knowledge graph construction with the least manual intervention. Knowledge graph designers can not only facilitate the procedures for the construction model preparation and integration, but also can increase the speed and efficiency of the knowledge graph construction. At last, a knowledge graph is constructed by our methodology as a case study, which demonstrated to verify the superiority and practicality of the proposed method.
Comparative analysis of T5 model for abstractive text summarization on different datasets

Abstractive Text Summarization is a burgeoning natural language processing task that has seen success with the Transformer model. With the soaring amassment of unstructured text data, text summarization has found applications in many domains such as law, medicine and news. The growth in research interest in developing better summarization systems, more fluent human-like summaries, and significant, more refined datasets have offered to test natural language processing models to improve existing model performance. This paper investigates the T5 Transformer model for abstractive text summarization and analyses its performance on the CNNDM, MSMO and XSUM datasets. The proposed model compared the resultant output across the datasets to determine the proficiency of the model and the datasets with regards to ROUGE and BLEU scores.
Unified Program Generation and Verification: A Case Study on Number-Theoretic Transform

Giving correctness assurance to the generated code in the context of generative programming is a poorly explored problem. Such assurance is particularly desired for applications where correctness of the optimized code is far from obvious, such as cryptography. This work presents a unified approach to program generation and verification, and applies it to an implementation of Number-Theoretic Transform, a key building block in lattice-based cryptography. Our strategy for verification is based on problem decomposition: While we found that an attempt to prove functional correctness of the whole program all at once is intractable, low-level components in the optimized program and its high-level algorithm structure can be separately verified using procedures of appropriate levels of abstraction. We demonstrate that such a decomposition and subsequent verification of each component are naturally realized in a program-generation approach based on the tagless-final style, leading to an end-to-end functional correctness verification of a highly optimized program.
Other Applications for Cybersecurity Awareness

We characterise the main aspects of the CS-AWARE approach and intervention in two municipalities. We apply these aspects to six examples of possible domains for which our approach to cybersecurity awareness could make a significant contribution: knowledge management within organisations, collaboration between organisations and agencies, smart city applications, e-Democracy in times of COVID, cross-sector and cross-border dependencies, and, finally, autonomous robots, on the ground, in the air, or on water.
Detection and Localization of Video Transcoding From AVC to HEVC Based on Deep Representations of Decoded Frames and PU Maps

In general, manipulated videos will eventually undergo recompression. Video transcoding will occur when the standard of recompression is different from the prior standard. Therefore, as a special sign of recompression, video transcoding can also be considered evidence of forgery in video forensics. In this paper, we focus on the detection and localization of video transcoding from AVC to HEVC (AVC-HEVC). There are two probable cases of AVC-HEVC transcoding - whole video transcoding and partial frame transcoding. However, the existing forensic methods only consider the detection of whole video transcoding, and they do not consider partial frame transcoding localization. In view of this, we propose a framewise scheme based on a convolutional neural network. First, we analyze that the essential difference between AVC-HEVC and HEVC is reflected in the high-frequency components of decoded frames. Then, the partition and location information of prediction units (PUs) are introduced to generate frame-level PU maps to make full use of the local artifacts of PUs. Finally, taking the decoded frames and PU maps as inputs, a dual-path network including specific convolutional modules and an adaptive fusion module is proposed. Through it, the artifacts on a single frame can be better extracted, and the transcoded frames can be detected and localized. Coupled with a simple voting strategy, the results of whole transcoding detection can be easily obtained. A large number of experiments are conducted to verify the performances. The results show that the proposed scheme outperforms or rivals the state-of-the-art methods in AVC-HEVC transcoding detection and localization.
Social Media Moderation and Content Generation: Evidence from User Bans

The rise of inappropriate content (e.g., misinformation, spam, hate speech, etc.) has become a major concern for social media platforms. To deal with such challenges, platforms adopt various strategies to moderate the content on their websites. This study focuses on user bans, a common but controversial moderation strategy that suspends rule-violating users from further participation on the platform for a predetermined period. Specifically, we investigate the impacts of user bans on banned users’ content-generating behavior (both quantity and quality). Leveraging the reactance theory, we formalize our hypotheses relating users’ behavioral reactions to such content moderation strategy. We implement multiple empirical designs to analyze data from a major social media platform. Our results show that users provide more answers on average after bans are lifted. In contrast, we find that the quality of the content (measured by the linguistic features as well as content appropriateness) decreases after user bans. Furthermore, we find that platform recognitions, such as badges and recommendations, alleviate individuals’ reactance toward bans. Specifically, users who have received platform recognition would reduce inappropriate postings and improve the quality of their content after bans. Lastly, we explore the heterogeneous effects of user bans for different banning causes and repeated bans. Our research is among one of the first to evaluate the effectiveness of user bans and could have important implications for content moderation on social media.
A Local Spatial–Temporal Synchronous Network to Dynamic Gesture Recognition

Dynamic skeletal data contain advanced information represented by complex compound actions and have been extensively studied in human motion recognition. Previous works usually depend on designing the traversal rules made by hand or using two separate components to obtain spatial and temporal information based on skeleton graph, respectively, which still have generalization difficulties and significant challenges in learning effective spatial–temporal information. This article proposes a local spatial–temporal synchronous network (LSTSN) for skeleton-based dynamic gestures recognition, which can simultaneously deal with spatial and temporal information in gestures. Specifically, to fully obtain the spatial–temporal information of the skeleton data, a coupled position embedding and a three-neighbor local spatial–temporal graph (LSTG) are constructed. An adaptive threshold method is designed to describe the joint connections of LSTG, and then, an attention mechanism is used to efficiently capture complex local spatial–temporal correlations. Meanwhile, multiple spatial–temporal attention modules of different periods are designed to effectively capture the LSTG’s heterogeneity. Besides, from the data aspect, a hand motion vector (HMV) is also introduced into the model to express subtle finger motions. Extensive experiment results on DHG-14/28 and SHREC’17 Track datasets demonstrate that the proposed LSTSN is competitive with state-of-the-art methods.
Single-Byte Error-Based Practical Differential Fault Attack on Bit-Sliced Lightweight Block Cipher PIPO

With the recent development of the Internet of Things (IoT), related device use is increasing rapidly. As a result, accessing and hijacking the devices is an increasing security threat. The challenges of side-channel security of IoT devices are having a way of coming to the surface due to this physical accessibility. Accordingly, there is active research on lightweight block ciphers to provide security even in resource-scarce environments situations such as IoT. The bit-sliced structure increases memory and time efficiency using an implementation method that replaces a lookup table with a bit-wise operation. Therefore, it is a widely-used design technique for lightweight block ciphers. In this paper, we show a differential fault attack study, a type of side-channel analysis, targeting bit-sliced block ciphers. In particular, it proposes a novel attack rationale on the recently proposed lightweight block cipher PIPO and shows that it applies sufficiently to other bit-sliced block ciphers. The proposed attack is based on a more alleviated attacker&#x2019;s assumption than the previously proposed attack, and it shows that less than 32 fewer fault ciphertext may fully recover the 128-bit of the PIPO 64/128 secret key. It proves that the attack is practical by verifying the attack through the actual electromagnetic fault injection. It also discusses the applicability of various bit-sliced block ciphers and shows how redundancy-based countermeasures might improve fault-robustness. Therefore, when using the bit-sliced block ciphers on IoT devices, we recommend applying appropriate countermeasures against fault injection attacks.
Experiences in the Design of Localized eHealth Tools for Users Facing Inequality of Access to Healthcare

In Colombia, making healthcare accessible to all as a basic human right poses a myriad of challenges. The rising prevalence of chronic non-communicable conditions in the growing population of elderly patients, have led to a healthcare paradigm where home-based care and self-management have become cornerstones. At the same time, as a geographically diverse and tropical country with a high prevalence of vector-borne and infectious diseases, there's a large amount of population at risk that still faces access barriers to healthcare. Thus, Colombia's health profile encompasses a series of scenarios in need of innovative solutions to deliver healthcare to its population regardless of access, literacy and/or economic barriers. New approaches such as Human-Computer Interaction for Development (HCI4D), have highlighted the need for a localized way of building digital solutions to properly function in a resource constrained context. This paper focuses on sharing reflections on the design methodology and implementation of two eHealth prototypes in different settings within Colombia using a localized approach and user-centered analysis of patient's context in urban and rural settings. We have identified that although a highly situated analysis is a valuable design practice for health technologies for the Global South, there should be also considered how contextual and individual factors are intertwined to maximize the use of human resources, relationships and social structures in the communities that the technologies are intended to serve. We aim to extend the limited body of knowledge about the in-the-wild development of patient-centric personal health technologies for vulnerable communities in Latin America.
Formal Methods for Socio-technical Security

Software engineers and analysts traditionally focus on cyber systems as technical systems, which are built only from software processes, communication protocols, crypto algorithms, etc. They often neglect, or choose not, to consider the human user as a component of the system’s security as they lack the expertise to fully understand human factors and how they affect security. However, humans should not be designed out of the security loop. Instead, we must deal with security assurance as a true socio-technical problem rather than a mere technical one, and consider cyber systems as socio-technical systems with people at their hearts. The main goal of this short paper, which accompanies my keynote talk at the $$24^\mathrm {th}$$ International Conference on Coordination Models and Languages (COORDINATION 2022), is to advocate the use of formal methods to establish the security of socio-technical systems, and to discuss some of the most promising approaches, including those that I have helped develop.
Designing a pragmatic explanation for the XAI system based on the user's context and background knowledge

Explainable artificial intelligence (XAI) has emerged due to the black box characteristics of deep learning and artificial intelligence algorithms. Researchers have suggested several technical and user-centric solutions, but have not provided a clear explanation of which factors make the explanation better for the user under the rigorous theoretical perspective. Thus, we first review the literature on the XAI, and then discuss from the most basic and necessary considerations for the explanation of the XAI system to the contextual factors which influence the user's understanding. Lastly, this chapter introduces a new explanation approach based on pragmatism which supports understanding in the user's context. The pragmatic explanation and its structure can provide a theoretical foundation and practical design insights for the XAI system designers.
Uncertainty-Aware Variational Inference for Target Tracking

In the low Earth orbit, target tracking with ground based assets in the context of situational awareness is particularly difficult. Because of the nonlinear state propagation between the moments of measurement arrivals, the inevitably accumulated errors will make the target state prediction and the measurement likelihood inaccurate and uncertain. In this article, optimizable models with learned parameters are constructed to model the state and measurement prediction uncertainties. A closed-loop variational iterative framework is proposed to jointly achieve parameter inference and state estimation, which comprises an uncertainty-aware variational filter (UnAVF). The theoretical expression of the evidence lower bound and the maximization of the variational lower bound are derived without the need for the true states, which reflect the awareness and reduction of uncertainties. The evidence lower bound can also evaluate the estimation performance of other Gaussian density filters, not only the UnAVF. Moreover, two rules, estimation consistency and lower bound consistency, are proposed to conduct the initialization of hyperparameters. Finally, the superior performance of UnAVF is demonstrated over an orbit state estimation problem.