Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of the Use of Pre-Trained Models to Profile Authors’ Ages and Genders

Many researchers have contributed to the field of authorship profiling and offered a variety of Artificial Intelligence methods, including machine and deep learning. The advent and application of transformer-based pre-trained models such as BERT and other successive models enabled researchers to improve the accuracy of applied models in different natural language processing domains. A crucial domain that attracted our attention is authorship profiling, wherein anonymous authors’ demographic information (e.g., age and gender) is determined. In this paper, we present a comparative study wherein we examine which of the selected pre-trained models perform the best when determining authors’ ages and genders; we employ the BERT, RoBERTa, and BERTweet pre-trained models and experiment with different hyperparameter values to observe the effect of each associated variable. We find that the selection of certain hyperparameters allow the models to successfully converge; we conclude that with specific hyperparameter values, the BERT model yielded the highest result for the age- and gender-profiling tasks.
Learning Neural Networks under Input-Output Specifications

In this paper, we examine an important problem of learning neural networks that certifiably meet certain specifications on input-output behaviors. Our strategy is to find an inner approximation of the set of admissible policy parameters, which is convex in a transformed space. To this end, we address the key technical challenge of convexifying the verification condition for neural networks, which is derived by abstracting the nonlinear specifications and activation functions with quadratic constraints. In particular, we propose a reparametrization scheme of the original neural network based on loop transformation, which leads to a convex condition that can be enforced during learning. This theoretical construction is validated in an experiment that specifies reachable sets for different regions of inputs.
Evaluations on Uncertainty Characterization of an SE(3) Variational Filter

Completely understanding an agent’s pose is crucial to resilient autonomous operations particular in relative dynamic scenarios. Proper characterization of uncertainty is imperative to completely understanding an agent’s pose and plays a significant role in the safe navigation of autonomous actors. Within an autonomous relative, proximity operations, and docking framework, this work considers covariance handling of the recently developed Lie Group Variational Filtering scheme. Results from Monte-Carlo, Extended and Unscented Kalman Filters, and Baker-Campbell-Hausdorff approaches are presented using the LGVF as a stand-alone estimator. In addition, model-based approaches are developed for the Baker-Campbell-Hausdorff and Extended Kalman Filter schemes using the LGVF output as the measurement.
A Novel DNN Accelerator for Light-weight Neural Networks: Concept and Design

Convolution Neural Network (CNN) has achieved great successes and has been widely applied in various visual applications such as object detections and image classifications. However, large amount of data and complicated computing process lower the inference performance if all data are collected and sent to a central process node for computation. Edge AI, which directly process the convolution on the edge device itself, has attracted huge attentions. Nevertheless, limited computation power and memory makes it impractical to process entire CNN computation. To overcome the challenge, light-weight neural networks have been proposed to reduce computation complexity with the accuracy drops as a cost. Recently, two light-weight neural networks, MobileNet and ShuffleNet, are widely discussed with acceptable accuracy degradation. However, most of the state-of-the-art DNN accelerators are not suitable for MobileNet and ShuffleNet. Therefore, in this paper, we propose a novel CNN accelerator which can support both depthwise separable convolution and channel shuffle for these light-weight neural networks. Experimental results show that our design can successfully compute MobileNet and ShuffleNet. Moreover, compare with previous works, we can achieve at most 20% area reduction while maintaining performance.
Contrast Agent Removal for Brain CT Angiography Using Switchable CycleGAN with AdaIN and Histogram Equalization

Computed tomography angiography uses an injection of contrast agent into the blood vessels, and helps to diagnose diseases that occur in the vessels and soft tissues. This requires contrast-enhanced CT (CECT) and non-enhanced CT (NECT) images, and the rigid registration and bone subtraction techniques are applied for better vessel visualization. However, the visualization process needs additional radiation exposure for obtaining two scans for the CECT and NECT. Also, it has limitations in that some vessels can be partially deleted due to insufficient registration. In this work, we propose a method to synthesize NECT from CECT images, using cycle-consistent generative adversarial network (cycleGAN) with adaptive instance normalization. Especially, a single generator of our framework utilizes the histogram equalization of CECT and NECT images so that the generator to effectively learn the image contrast. The experimental results demonstrate that the proposed method provides synthetic NECT images from CECT with high quality than the original cycleGAN, which reduces not only the radiation exposure for patients but also computational cost for the vessel visualization.
Graphic User Interface for Hausa Text-to-Speech System

Natural language processing and Digital signal processing are broadly used methods used to enable systems to understand commands and manipulate speech or text. Most of the Text-to-speech done was for major languages such as English, French and others, with no or little for African languages like Hausa, which are termed under resource languages. In this paper, we developed a graphical user interface for the Hausa Text-to-Speech system. This system converts Hausa text to Hausa audio sound, by processing and analyzing it using natural language processing and Digital Signal Processing. Our graphical user interface, aid in converting entered Hausa language text into Hausa speech.
Musical Interface Agendas. Musical Appropriation via Technological Pre-configuration

The use and implementation of software and digital-material controllers in conjunction with protocols indispensable for the process change the aesthetic experiences and capacities of musical and creative expression potentials in musical practices. What is first established as a constructed naturalness of individual music practices is challenged and disrupted by the digitality-specific multiplicity of the musical interface complex (hardware/software/protocol). The role of the empty-world hypothesis as a hidden agenda of these devices will be examined in more detail with the aid of a self-produced video documenting the use of the digital-material interface ROLI Seaboard by a musician playing in an “Arabic” idiom. Due to its dialectic framing character, the ROLI device will have to be described as a culturally expanding facilitator on the one hand and as a cultural capitalist artifact on the other.
Contextual Reranking of Search Engine Results

Search engines are one of the most crucial components of social platforms to compete with rivals on the market. Nowadays, after the invention of BERT, contextual information became vital for almost every NLP problem. ElasticSearch is a successful third-party tool for platforms that do not have any search engine however, it is not sophisticated enough to respond to today’s needs. In this study, we aimed to enhance ElasticSearch by combining it with contextual information. We implemented this combination by adding a BERT-based Cross-Encoder (CE) on top of the ElasticSearch results for reranking to get better Mean Reciprocal Rank (MRR) scores. Taking into account the contextual information dramatically increased the existing ElasticSearch results and at the same time, it is pretty feasible at the deployment stage. Our method boosted ES results dramatically both in online and offline tests, especially on poor results of ElasticSearch. In the online A/B test, we enhanced our MRR scores by implementing this study by 77% in contrast to ElasticSearch and achieved a 0.23 MRR score.
A Robust Local Texture Descriptor in the Parametric Space of the Weibull Distribution

Research in texture feature approximation is still in the embryonic stage because of difficulties in developing a sound theoretical model to express the unique pattern in the intensity-variation of pixels in the neighbourhood of the pixel-of-interest so that it can sufficiently discriminate different textures. Local texture descriptors are widely used in image segmentation as they comprise pixel-wise features. The Weber local descriptor (WLD) with differential excitation and gradient orientation components, inspired by Weber's Law, has been leveraged in the state-of-the-art iterative contraction and merging (ICM) image segmentation technique. However, WLD has inherent drawbacks in the formulation of the components that limit its discriminatory capability. This paper introduces a novel texture descriptor by directly modelling the distribution of intensity-variation in the parametric space of the Weibull distribution using its shape and scale parameters. A unified ‘joint scale’ texture property is introduced, which can discriminate textures better than the individual parameters while keeping the length of the descriptor shorter. Additionally, the accuracy of WLD's gradient orientation component is improved by using an extended Sobel operator and expressing gradients in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$[-\pi /2,\pi /2)$</tex-math></inline-formula> range. When incorporated in ICM, the proposed texture descriptor has consistently outperformed WLD and a recent enhancement with radial mean WLD (RM-WLD) on three benchmark datasets. It has also outperformed two other texture segmentation techniques and their deep learning based improvements.
Weighted Decoupling: An Effective Image Resizing Method for Binarized Neural Network

Many neuromorphic hardware uses a micro controller unit that has low computing power and small memory size. To process deep neural network applications on hardware-restricted conditions and to alleviate computational latency, binarized neural networks have been developed. Moreover, we can reduce required multiply-accumulate operation by shrinking resolution of input images. In this paper, we compare various image resizing method and propose weighted decoupling, a new resizing method for the best accuracy of ResNetB18 model, an additionally binarized version of ResNetE18. A ResNetB18 model with proposed resizing method shows the better accuracy performance than that of a same model trained with 12.25× large images.
A machine learning enhanced approximate message passing massive MIMO accelerator

Machine learning in the physical layer of communication systems currently receives much attention due to its potential to improve performance over difficult or unknown channels. Model-driven machine learning combines well-established algorithms with machine learning enhancements to realize these performance gains while keeping computational complexity within practical limits. In this work, we present the first model-driven machine-learning accelerator based on Orthogonal Approximate Message Passing (OAMP) for massive MIMO. The accelerator is configurable to support various machine learning enhancements such as those used in the OAMPNet and MMNet algorithms. The accelerator architecture is implemented as a deep pipeline to maximize throughput and we explore a range of antenna, user, and modulation configurations. Our results show the feasibility of deploying machine learning enhanced algorithms in future physical layer processors.
Image-Text Retrieval with Intra-Modal Soft Labels

Image-text retrieval attempts to find relevant images from a huge corpus of images given a textual description, and vice versa. Existing works usually denote the negative/positive image-text pair with a binary label. They assume that a text is only relevant to the paired images and irrelevant to other images in the corpus. However, using the binary labels cannot reveal the precise similarity between an image and a text. In this paper, we learn the model using soft labels, where each image-text pair is associated with a specific soft label. We generate the Intra-modal Soft Label (ISL) between the cross-modal (image, text) pair by the similarity between the intra-modal (image caption, text) pair. Systematic experiments on public benchmarks, including MSCOCO and Flickr30K, demonstrate the effectiveness of the proposed method, which achieves a recall@1 improvement of approximately 1%-3% compared to the baseline method.
A Mixed Method Study of Devops Challenges

Context: DevOps practices combine software development and IT (Information Technology) operations. The continuous needs for rapid but quality software development necessitates the adoption of high-quality DevOps tools. There is a growing number of DevOps related posts in popular online developer forum Stack Overflow (SO). While previous research analyzed SO posts related to build/release engineering, we are aware of no research that specifically focused on DevOps related discussions. Objective: This paper aims to learn the challenges developers face while using the currently available DevOps tools and techniques along with the organizational challenges in DevOps practices. Method: We conduct an empirical study by applying topic modeling on 174K SO posts that contain DevOps discussions. We then validate and extend the empirical study findings with a survey of 21 professional DevOps practitioners. Results: We find that: (1) There are 23 DevOps topics grouped into four categories: Cloud & CI/CD Tools, Infrastructure as Code, Container & Orchestration, and Quality Assurance. (2) The topic category ‘Cloud & CI/CD Tools’ contains the highest number of topics (10) which cover 48.6% of all questions in our dataset, followed by the category Infrastructure as Code (28.9%). (3) The file management is the most popular topic followed by Jenkins Pipeline, while infrastructural Exception Handling and Jenkins Distributed Architecture are the most difficult topics (with least accepted answers). (4) In the survey, developers mention that it requires hands-on experience before current DevOps tools can be considered easy. They raised the needs for better documentation and learning resources to learn the rapidly changing DevOps tools and techniques. Practitioners also emphasized on the formal training approach by the organizations for DevOps skill development. Conclusion: Architects and managers can use the findings of this research to adopt appropriate DevOps technologies, and organizations can design tool or process specific DevOps training programs.
Integrating Cybersecurity Risk Model to the Bug Bounty Program

Bug bounty program is a business activity in which firms invite white-hat hackers around the world to identify vulnerabilities in their cyber systems. The paper proposes a model to quantify the normal cybersecurity spending with respect to the importance of information systems. An upper limit of normal cybersecurity spending is provided, which can be described as a percent of the total value of reports received from the bug bounty program. Moreover, this paper presents quantitative analytical modeling for minimizing the total cybersecurity cost of a firm by optimizing its cybersecurity budget plan after implementing the bug bounty program. With the two models, firms can determine not only the amount of cybersecurity spending that should be input to an information system, but also the optimal spending allocation among the segments of the information system. Lastly, this paper analyzes requirements that make implementing bug bounty program a better choice.
Systematic Health Artificial Intelligence (SHAI) - a Pathology Based NLP Model for Improved Predictive Diagnostics in Personalised Medicine

The Systematic Health Artificial Intelligence (SHAI) model trains on data from medical records and clinical laboratory results to temporally identify disease markers with subsequent pathologies, more efficiently and accurately than is done in the current analog practice. The aim of the SHAI model is to gauge a patient’s medical prognostic status based on a conglomerate of data to predict lurking, occult or comorbid pathologies.Newfound associations and predictions would support clinicians in terms of comprehensively visualising a patient’s health profile, both in real-time and for the future. Proxy findings would also help to establish personalised references ranges for clinical pathological investigations of body fluids. The SHAI model processes EMR progress text-based notes through a NLP ‘Bag of Words’ system, which enables the neural network to train in word representation and ‘weigh’ words of proximity. Using ‘forward propagation’ of the vectors will allow for output activation from hidden and non-hidden layers of the developing neural network architecture, to then use ‘multiclass classification’ as the vector contents grow with new data. This manuscript identifies 8 key questions to be addressed by diagnostic ML models and explains SHAI’s design as it pertains to maximising human benefit and minimising bias. Despite the automaticity of this laboratory medicine solution, physician end-users remain essential to the diagnostic process and final clinical judgements.
Modular Bourgain-Tzafriri Restricted Invertibility Conjectures and Johnson-Lindenstrauss Flattening Conjecture

We recently formulated important Modular Bourgain-Tzafriri Restricted Invertibility Conjectures and Modular Johnson-Lindenstrauss Flattening Conjecture in the Appendix of \textit{[arXiv: 2207.12799.v1]}. For the sake of wide accessibility we give a self-contained treatment of them.
Time of Icsi (T0) and its Implication on Machine Learning Morphokinetic Algorithm's Accuracy

When a time-lapse system is installed in an IVF lab, embryologists must decide the time of starting a time-lapse project (t0) as there are no recommendations about t0 and the implications of setting t0 are still unknown. The aim of our study was to analyse the impact of using different settings for the time of ICSI (t0) on the value of the biomarker as well as on Machine Learning algorithms’ performance. Four morphokinetic biomarkers were analysed on 220 embryos included in the study: 117 Live-Birth embryos and 103 aneuploid embryos. Different t0 configurations were compared in this analysis: t0 relativized for each oocyte; mid-time ICSI t0; t0 relativized on extrusion of the second Polar Body; and t0 relativized on the Pronuclear Fading. The analysis consisted of a calculus of the mean, standard deviation and the min-max range for each biomarker, outcome group and t0 configuration. Furthermore, a Machine Learning algorithm (XGB) was performed for each t0 configuration through V-Fold Cross-Validation and the Confusion Matrix’s results were obtained. Biomarkers’ mean values were very similar between t0 relativized for all oocytes and mid-time ICSI. Machine Learning XGB accuracy was 5.5% lower for mid-time ICSI compared with relativized t0 (66.5% vs 72.0%, respectively). Nonetheless, after removing long-time ICSI procedures, mid-time ICSI model accuracy improved by 2.4% while the models based on the other t0 configurations obtained decreased accuracies. Relativizing t0 for each oocyte could lead to better time-lapse predictive models’ performance especially when the ICSI procedure lasted more than 45 minutes.
Civil Liberties in a Lockdown: The Case of COVID-19

In response to the spread of COVID-19, governments across the world have, with very few exceptions, enacted sweeping restrictive lockdown policies that impede citizens’ freedom to move, work, and assemble. This paper critically responds to the central arguments for restrictive lockdown legislation. We build our critique on the following assumption: public policy that enjoys virtually unanimous support worldwide should be justified by uncontroversial moral principles. We argue that that the virtually unanimous support in favor of restrictive lockdowns is not adequately justified by the arguments given in favor of them. Importantly, this is not to say that states ought not impose restrictive lockdown measures, but rather that the extent of the acceptance of these measures is not proportionate to the strength of the arguments for lockdowns. We begin by exploring the case for restrictive lockdowns. We first argue that several of the principles that are used to justify the lockdowns yield unexpectedly revisionary implications for other political problems that many would be unwilling to accept. We then outline what we consider the strongest argument for a lockdown—namely, that its net welfare benefits are great enough to defeat the moral presumption against restricting citizens’ civil liberties to move, work, and assemble. However, we give a number of reasons for doubting that the lockdown’s net welfare benefits are, in fact, sufficiently high to defeat the presumption against it.
Automated Image Analysis (AIA)

Images are rapidly increasing in importance for business and social science research. This is evidenced by an increasing number of publications leveraging visual information. However, the scale of these data often exceed human annotation capabilities and extracting features from images automatically is a complex task. While open-source code for automated image analysis exists, implementations are often scattered across different repositories, making their use burdensome for applied researchers. To address this issue, we provide a bundling of automated image analysis tools that enables researchers to easily extract 47 visual features from images, including low-level features such as image quality and brightness as well as high-level features such as face presence and prominence. All code that we consolidate is open-source and applying our pipeline requires no programming skills. Consequently, we hope our tool contributes to making images more accessible to address meaningful, substantive research problems.
Privacy Preservation of Digital Health Records in Cloud

Abstract The variety and amount of patient healthcare digital data is rapidly expanding. The field of artificial intelligence has fast proven revolutionary for healthcare, allowing for unprecedented speed and precision in data analysis. Many hospitals have already transitioned to electronic health records (EHRs), a digital version of paper medical information. Information Technology has already aided in simplifying operations in this area, making the process far more efficient and patient-centered than in the past. A patient’s trust may disintegrate in the face of recurrent incidents if there is no precise control and safety in place. As a result, it limits the potential for digital health to promote an age of more accessible, connected, and individualized treatment. When it comes to picking a cloud solution, healthcare providers are most concerned about security and privacy. In this research, we seek to demonstrate various ways that aid patient data privacy while simultaneously allowing cloud services to be used.
COVID-19 Contact Tracing Applications in Portugal: Effectiveness and Privacy Issues

Portugal developed and adopted a contact tracing smartphone application, in order to help in the containment of the spreading of the COVID-19 pandemic. The particular technology used was adopted in order to guaranteed privacyPrivacy and security but it did not prove particularly effective, since it was downloaded by only a fraction of the population and very lightly used to report cases of contacts between infected and non-infected persons. These issues are addressed and discussed in the present text.