Reviewerly/exHarmony huggingface dataset:
Keep Moving Forward: Health Informatics and Information Management beyond the COVID-19 Pandemic

Objectives: To identify gaps and challenges in health informatics and health information management during the COVID-19 pandemic. To describe solutions and offer recommendations that can address the identified gaps and challenges. Methods: A literature review of relevant peer-reviewed and grey literature published from January 2020 to December 2020 was conducted to inform the paper. Results: The literature revealed several themes regarding health information management and health informatics challenges and gaps: information systems and information technology infrastructure; data collection, quality, and standardization; and information governance and use. These challenges and gaps were often driven by public policy and funding constraints. Conclusions: COVID-19 exposed complexities related to responding to a world-wide, fast moving, quickly spreading novel virus. Longstanding gaps and ongoing challenges in the local, national, and global health and public health information systems and data infrastructure must be addressed before we are faced with another global pandemic.
Recognition and Tracking of Objects in a Clustered Remote Scene Environment

Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision. Computer vision is one of the most active research fields that lies at the intersection of deep learning and machine vision. This paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shape moving objects while accommodating the shift and scale invariances that the object may encounter. The first part uses the Maximum Average Correlation Height (MACH) filter for object recognition and determines the bounding box coordinates. In case the correlation based MACH filter fails, the algorithms switches to a much reliable but computationally complex feature based object recognition technique i.e., affine scale invariant feature transform (ASIFT). ASIFT is used to accommodate object shift and scale object variations. ASIFT extracts certain features from the object of interest, providing invariance in up to six affine parameters, namely translation (two parameters), zoom, rotation and two camera axis orientations. However, in this paper, only the shift and scale invariances are used. The second part of the algorithm demonstrates the use of particle filters based Approximate Proximal Gradient (APG) technique to periodically update the coordinates of the object encapsulated in the bounding box. At the end, a comparison of the proposed algorithm with other state-of-the-art tracking algorithms has been presented, which demonstrates the effectiveness of the proposed algorithm with respect to the minimization of tracking errors.
Energy Anomaly Detection in Industrial Applications with Long Short-term Memory-based Autoencoders

With the goal of reducing energy costs, carbon emissions, and achieving cleaner production, manufacturing companies aim to reduce their energy consumption. In manufacturing companies, a considerable amount of energy is wasted due to plant-, process- and human-related faults. Tools and methods for detecting anomalies are widely used for fraud detection in finance or intrusion detection in cybersecurity. When it comes to anomaly detection of malicious energy consumption, the residential building sector is leading. Industrial applications are not being addressed by now. In this paper, an end-to-end solution of an anomaly detection system is presented that uses the concept of a Long Short-term Memory based Autoencoder (LSTM-AE) as an unsupervised learning model that detects anomalies without labeling the data beforehand.
Are End-to-End Systems Really Necessary for NER on Handwritten Document Images?

Named entities (NEs) are fundamental in the extraction of information from text. The recognition and classification of these entities into predefined categories is called Named Entity Recognition (NER) and plays a major role in Natural Language Processing. However, only a few works consider this task with respect to the document image domain. The approaches are either based on a two-stage or end-to-end architecture. A two-stage approach transforms the document image into a textual representation and determines the NEs using a textual NER. The end-to-end approach, on the other hand, avoids the explicit recognition step at text level and determines the NEs directly on image level. Current approaches that try to tackle the task of NER on segmented word images use end-to-end architectures. This is motivated by the assumption that handwriting recognition is too erroneous to allow for an effective application of textual NLP methods. In this work, we present a two-stage approach and compare it against state-of-the-art end-to-end approaches. Due to the lack of datasets and evaluation protocols, such a comparison is currently difficult. Therefore, we manually annotated the known IAM and George Washington datasets with NE labels and publish them along with optimized splits and an evaluation protocol. Our experiments show, contrary to the common belief, that a two-stage model can achieve higher scores on all tested datasets.
TraceVis: Towards Visualization for Deep Statistical Model Checking

With the proliferation of neural networks (NN), the need to analyze, and ideally verify, their behavior becomes more and more pressing. Significant progress has been made in the analysis of individual NN decision episodes, but the verification of NNs as part of larger systems remains a grand challenge. Deep statistical model checking (DSMC) is a recent approach addressing that challenge in the context of Markov decision processes (MDP) where a NN represents a policy taking action decisions. The NN determinizes the MDP, resulting in a Markov chain which is analyzed by statistical model checking. Initial results in a Racetrack case study (a simple abstract encoding of driving control) suggest that such a DSMC analysis can be useful for quality assurance in system approval or certification. Here we explore the use of visualization to support DSMC users (human analysts, domain engineers). We implement an interactive visualization tool, TraceVis, for the Racetrack case study. The tool allows to explore crash probabilities into particular wall segments as a function of start position and velocity. It furthermore supports the in-depth examination of the policy traces generated by DSMC, in aggregate form as well as individually. This demonstrates how visualization can foster the effective analysis of DSMC results, and it forms a first step in combining model checking and visualization in the analysis of NN behavior.
New design paradigm of distortion cost function for efficient JPEG steganography

Recently, with the introduction of JPEG (Joint Photographic Experts Group) phase-aware steganalysis features, e.g., GFR, the design of JPEG steganographic distortion cost function turns to maintain not only the statistical undetectability in DCT (Discrete Cosine Transform) domain but also in spatial domain. To tackle this issue, this paper presents a novel paradigm for the design of JPEG steganographic distortion cost function, which calculates the distortion cost via a generalized Distortion Cost Domain Transformation (DCDT) function. The proposed function comprises the decompressed pixel block embedding changes and their corresponding embedding distortion costs for unit change, where the pixel embedding distortion costs are represented in a more general exponential model, aiming to flexibly allocate the embedding data. In this way, the JPEG steganography could be formulated as the optimization problem of minimizing the overall distortion cost in its decompressed spatial domain, which is equivalent to maximizing its statistical undetectability against JPEG phase-aware steganalysis features. Experimental results show that the proposed DCDT equipped with HiLL (a spatial steganographic distortion cost function) is superior to other state-of-the-art JPEG steganographic schemes, e.g., UERD, J-UNIWARD, and GUED in resisting the detection of JPEG phase-aware feature-based steganalyzers GFR and SCA-GFR, and rivals BET-HiLL with one order of magnitude lower computational complexity, along with the possibility of being further improved by considering the mutually dependent embedding interactions. In addition, the proposed DCDT is also verified to be effective for different image databases and quality factors.
How do investors react to the data breaches news? Empirical evidence from Facebook Inc. during the years 2016–2019

We examine in this paper the effect of data breaches on Facebook share prices from 2016 until 2019. This analysis revealed counterintuitive results regarding stock price using a sample of multiple firms in the extant literature. The findings indicate that there is no significant effect of data breaches on the share price of Facebook. In comparison, data breaches of other publicly traded U.S. companies resulted in a cumulative average abnormal return on the event day and the following day. In addition, the other factors found in the literature about data breaches do not seem to hold for Facebook's case. This paper proposes the explanation of the possibility that the stock prices have already factored in data breaches. Hence, the reactions are only found in the context of surprisingly new data breaches.
The mental model theory of free choice permissions and paradoxical disjunctive inferences

Inferences of the sort: A or B; therefore A, are invalid. Yet, the paradoxes of free choice are acceptable: You can have sole or lobster; so, you can have sole. Pragmatic theories attempt to save logic. A semantic theory of human reasoning is founded on mental models of possibilities. “Or” refers to a conjunction of possibilities that each hold in default of knowledge to the contrary. A disjunction: it is permissible to do A or to do B, yields a deontic interpretation of the possibilities, and elicits mental models of a conjunction of default permissions. They yield or-deletions, such as: therefore, it’s permissible to do A. The theory predicts the paradoxes and new phenomena, which four experiments corroborated. For example, exclusive disjunctions such as: Few of the artists are brutalists or else cubists, have an intuitive model that yields or-deletions, but deliberation can construct models that refute them.
DFTNet: Deep Fish Tracker With Attention Mechanism in Unconstrained Marine Environments

Multiple fish tracking in an unconstrained marine videos is a highly challenging task. Trajectories of fishes convey critical information for the analysis of fish behaviour. In this paper, we have proposed DFTNet which incorporates Siamese network for encoding the appearance similarity and attention-long short term memory network to capture the motion similarity across subsequent frames. Finally, intersection over union matching score is computed to amalgamate spatial similarity cue in the final score. The proposed framework can provide joint optimization score to maintain the tracklet information encoding appearance, motion and spatial similarity cues. We perform exhaustive experiments and compare the proposed approach with competing techniques over Fish4knowledge videos and achieve significant average reduction in ID switches by 60.9%. The source code is made publicly available at: https://github.com/hemanth-s17/Deep-Fish-Tracker-Network.
It’s not Rocket Science: Interpreting Figurative Language in Narratives

Abstract Figurative language is ubiquitous in English. Yet, the vast majority of NLP research focuses on literal language. Existing text representations by design rely on compositionality, while figurative language is often non- compositional. In this paper, we study the interpretation of two non-compositional figurative languages (idioms and similes). We collected datasets of fictional narratives containing a figurative expression along with crowd-sourced plausible and implausible continuations relying on the correct interpretation of the expression. We then trained models to choose or generate the plausible continuation. Our experiments show that models based solely on pre-trained language models perform substantially worse than humans on these tasks. We additionally propose knowledge-enhanced models, adopting human strategies for interpreting figurative language types: inferring meaning from the context and relying on the constituent words’ literal meanings. The knowledge-enhanced models improve the performance on both the discriminative and generative tasks, further bridging the gap from human performance.
Robustness and Generalization via Generative Adversarial Training

While deep neural networks have achieved remarkable success in various computer vision tasks, they often fail to generalize to new domains and subtle variations of input images. Several defenses have been proposed to improve the robustness against these variations. However, current defenses can only withstand the specific attack used in training, and the models often remain vulnerable to other input variations. Moreover, these methods often degrade performance of the model on clean images and do not generalize to out-of-domain samples. In this paper we present Generative Adversarial Training, an approach to simultaneously improve the model’s generalization to the test set and out-of-domain samples as well as its robustness to unseen adversarial attacks. Instead of altering a low-level pre-defined aspect of images, we generate a spectrum of low-level, mid-level and high-level changes using generative models with a disentangled latent space. Adversarial training with these examples enable the model to withstand a wide range of attacks by observing a variety of input alterations during training. We show that our approach not only improves performance of the model on clean images and out-of-domain samples but also makes it robust against unforeseen attacks and outperforms prior work. We validate effectiveness of our method by demonstrating results on various tasks such as classification, segmentation and object detection.
The AI Methods, Capabilities and Criticality Grid

Abstract Many artificial intelligence (AI) technologies developed over the past decades have reached market maturity and are now being commercially distributed in digital products and services. Therefore, national and international AI standards are currently being developed in order to achieve technical interoperability as well as reliability and transparency. To this end, we propose to classify AI applications in terms of the algorithmic methods used, the capabilities to be achieved and the level of criticality. The resulting three-dimensional classification scheme, termed the AI Methods, Capabilities and Criticality (AI- $$\hbox {MC}^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mtext>MC</mml:mtext><mml:mn>2</mml:mn></mml:msup></mml:math> ) Grid, combines current recommendations of the EU Commission with an ethical dimension proposed by the Data Ethics Commission of the German Federal Government (Datenethikkommission der Bundesregierung: Gutachten. Berlin, 2019). As a whole, the AI- $$\hbox {MC}^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mtext>MC</mml:mtext><mml:mn>2</mml:mn></mml:msup></mml:math> Grid allows not only to gain an overview of the implications of a given AI application as well as to compare efficiently different AI applications within a given market or implemented by different AI technologies. It is designed as a core tool to define and manage norms, standards and compliance of AI applications, but helps to manage AI solutions in general as well.
PrivKVM*: Revisiting Key-Value Statistics Estimation With Local Differential Privacy

A key factor in big data analytics and artificial intelligence is the collection of user data from a large population. However, the collection of user data comes at the price of privacy risks, not only for users but also for businesses who are vulnerable to internal and external data breaches. To address privacy issues, local differential privacy (LDP) has been proposed to enable an untrusted collector to obtain accurate statistical estimation on sensitive user data (e.g., location, health, and financial data) without actually accessing the true records. As key-value data is an extremely popular NoSQL data model, there are a few works in the literature that study LDP-based statistical estimation on key-value data. However, these works have some major limitations, including supporting small key space only, fixed key collection range, difficulty in choosing an appropriate padding length, and high communication cost. In this article, we propose a two-phase mechanism <inline-formula><tex-math notation="LaTeX">$PrivKVM^*$</tex-math></inline-formula> as an optimized and highly-complete solution to LDP-based key-value data collection and statistics estimation. We verify its correctness and effectiveness through rigorous theoretical analysis and extensive experimental results.
Sequential Gesture Learning for Continuous Labanotation Generation Based on the Fusion of Graph Neural Networks

Labanotation is a symbolic recording system for human movements, and also a powerful tool for protecting and spreading folk dances and other performing arts. State-of-the-art automatic Labanotation uses end-to-end methods with sequence-based skeleton representation, which cannot capture the relationship between joints and bones in the skeleton for accurate descriptions of continuous lower limb movements such as dance steps. In this paper, we propose a novel double-stream fusion method of directed graph neural networks (DGNN), combined with connectionist temporal classification (CTC), namely DFGNN-CTC, for sequential fine-grained motion recognition, such as the Labanotation generation of unsegmented dance movement. First, we extract double-stream directed graph feature, employing an orientation-normalized directed acyclic graph (ON-DAG) and an orientation-normalized temporal directed acyclic graph (ON-TDAG), to jointly model spatiotemporal properties of movement recorded in motion capture data. Then, we design a CTC-based fusion-pooling module to fuse the spatial and temporal streams encoded by two DGNNs. It concatenates and fuses the two streams to generate discriminative descriptions of each time step, and concentrates them to make per-time-step predictions of Laban gesture type, from which the CTC searches the optimal Laban symbol sequence, corresponding to elemental motions composing the movement. In this way, the new method enables much finer discrimination for similar Laban gestures with subtle differences in spatial and temporal properties through joint contextual spatiotemporal modeling so that it achieves much superior performance in continuous Labanotation generation to existing methods, which only have single-stream analysis either spatially or temporally. The experiments on two Labanotation-labelled motion capture datasets demonstrate the effectiveness of the components in the proposed method and its superiority comparing with the state-of-the-art methods, especially for lower limb movements.
Factorizing Perception and Policy for Interactive Instruction Following

Performing simple household tasks based on language directives is very natural to humans, yet it remains an open challenge for AI agents. The ‘interactive instruction following’ task attempts to make progress towards building agents that jointly navigate, interact, and reason in the environment at every step. To address the multifaceted problem, we propose a model that factorizes the task into interactive perception and action policy streams with enhanced components and name it as MOCA, a Modular Object-Centric Approach. We empirically validate that MOCA outperforms prior arts by significant margins on the ALFRED benchmark with improved generalization.
Multi-Level Hesitant Fuzzy Based Model for Usable-Security Assessment

Present day healthcare sector is frequently victimized by the intruders. Healthcare data industry has borne the brunt of the highest number of data breach episodes in the last few years. The key reason for this is attributed to the sensitivity of healthcare data and the high costs entailed in trading the data over the dark web. Hence, usable-security evaluation of healthcare information systems is the need of hour so as to identify the vulnerabilities and provide preventive measures as a shield against the breaches. Usable-security assessment will help the software designers and developers to prioritize usable-security attributes according to the customers’ needs and bridge the security gap. This study, in particular, evaluates the usable-security of Health Information Software Systems (HISS) in design tactics perspective by using Multi Criteria Decision Making (MCDM) problem solving techniques. Hesitant fuzzy based Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal-Solutions (TOPSIS) methods have been applied to conduct the quantitative analysis of usable-security of software. Five attributes and 12 sub-attributes with 6 HISS alternatives of Indian hospitals have been considered in this work for usable-security assessment. To draw a priority list from the analysis, the results of the study show that the selected usable-security attributes attained the following ranking order: User-Error protection, Learnability, Data validation, Robustness, Revoke access, Intrusion detection, Authentication, Encryption, Limit access, Reliability, Efficiency, Audit trail respectively. Furthermore, HISS-1 alternative achieved the highest satisfaction degree followed by HISS-2 and HISS-4. HISS-6 got the lowest score in the context of providing ideal usable-security mechanism. The present research endeavour will be helpful for the software designers as the findings of the study will facilitate in developing secure and usable software products from the initial stages of the software development process itself.
Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction

Manual annotation of labeled data for relation extraction is time-consuming and labor-intensive. Semi-supervised methods can offer helping hands for this problem and have aroused great research interests. Existing works focus on mapping the unlabeled samples to the classes to augment the labeled dataset. However, it is hard to find an overall good mapping function, especially for the samples with complicated syntactic components in one sentence. To tackle this limitation, we propose to build the connection between the unlabeled data and the labeled ones rather than directly mapping the unlabeled samples to the classes. Specifically, we first use two kinds of information to construct a reference graph, including entity reference and verb reference. The goal is to lexically connect the unlabeled sample(s) to the labeled one(s). Then, we develop a Multi-head Reference Graph (MRefG) model to exploit the reference information for better recognizing high-quality unlabeled samples. The effectiveness of our method is demonstrated by extensive comparison experiments with the state-of-the-art baselines. To tackle this limitation, we propose to build the connection between the unlabeled data and the labeled ones rather than directly mapping the unlabeled samples to the classes. Specifically, we first use two kinds of information to construct a reference graph, including entity reference and verb reference. The goal is to lexically connect the unlabeled sample(s) to the labeled one(s). Then, we develop a Multi-head Reference Graph (MRefG) model to exploit the reference information for better recognizing high-quality unlabeled samples. The effectiveness of our method is demonstrated by extensive comparison experiments with the state-of-the-art baselines.