Reviewerly/exHarmony huggingface dataset:
Shuttle-Exploiting Attacks and Their Defenses in Trapped-Ion Quantum Computers

Trapped-ion (TI) quantum bits are a front-runner technology for quantum computing. TI systems with multiple interconnected traps can overcome the hardware connectivity issue inherent in superconducting qubits and can solve practical problems at scale. With a sufficient number of qubits on the horizon, the multi-programming model for Quantum Computers (QC) has been proposed where multiple users share the same QC for their computing. Multi-programming is enticing for quantum cloud providers as it can maximize device utilization, throughput, and profit for clouds. Users can also benefit from the short wait queue. However, shared access to quantum computers can create new security issues. This paper presents one such vulnerability in shared TI systems that require <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">shuttle</i> operations for communication among traps. Repeated shuttle operations increase quantum bit energy and degrade the reliability of computations (fidelity). We show adversarial program design approaches requiring numerous shuttles. We propose a random and systematic methodology for adversary program generation. Our analysis shows shuttle-exploiting attacks can substantially degrade the fidelities of victim programs by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 2 \times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 63 \times $ </tex-math></inline-formula> . Finally, we present several countermeasures such as adopting a hybrid initial mapping policy, padding victim programs with dummy qubits, and capping maximum shuttles.
Data Fiduciary in Order to Alleviate Principal–Agent Problems in the Artificial Big Data Age

The classic principal–agent problem in political science and economics describes agency dilemmas or problems when one person, the agent, is put in a situation to make decisions on behalf of another entity, the principal. A dilemma occurs in situations when individual profit maximization or principal and agent are pitted against each other. This so-called moral hazard is emerging in the current artificial big data age, when big data reaping entities have to act on behalf of agents, who provide their data with trust in the principal’s integrity and responsible big data conduct. Yet, to this day, no data fiduciary has been clearly described and established to protect the agent from misusing the data. This paper introduces the agent’s predicament between utility derived from information sharing and dignity in privacy as well as hyper-hyperbolic discounting fallibilities to not clearly foresee what consequences information sharing can have over time and in groups. The principal’s predicament between secrecy and selling big data insights or using big data for manipulative purposes will be outlined. Finally, the paper draws a clear distinction between manipulation and nudging in relation to the potential social class division of those who nudge and those who are nudged.
Mitigating security attacks in kubernetes manifests for security best practices violation

Kubernetes is an open-source software system that helps practitioners in automatically deploying, scaling, and managing containerized applications. Information technology (IT) organizations, such as IBM, Spotify, and Capital One, use Kubernetes to manage their containers and reported benefits in the deployment process. However, recent security breaches and survey results among practitioners suggest that Kubernetes deployment can be vulnerable to attacks due to misconfiguration and not following security best practices. This research explores how malicious users can perform potential security exploits from the violations of Kubernetes security best practices. We explore how attacks can be conducted such as denial of service attacks against one of the security best practices violations in Kubernetes manifests. In addition, we are exploring potential exploits in the Kubernetes cluster to propose mitigation strategies to secure the Kubernetes cluster.
An Artificial Intelligence-Enabled Pipeline for Medical Domain: Malaysian Breast Cancer Survivorship Cohort as a Case Study

Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer.
Formally Verified Simulations of State-Rich Processes Using Interaction Trees in Isabelle/HOL

Simulation and formal verification are important complementary techniques necessary in high assurance model-based systems development. In order to support coherent results, it is necessary to provide unifying semantics and automation for both activities. In this paper we apply Interaction Trees in Isabelle/HOL to produce a verification and simulation framework for state-rich process languages. We develop the core theory and verification techniques for Interaction Trees, use them to give a semantics to the CSP and Circus languages, and formally link our new semantics with the failures-divergences semantic model. We also show how the Isabelle code generator can be used to generate verified executable simulations for reactive and concurrent programs.
Collective Signature Protocols for Signing Groups based on Problem of Finding Roots Modulo Large Prime Number

Generally, digital signature algorithms are based on a single difficult computational problem like prime factorization problem, discrete logarithm problem, elliptic curve problem. There are also many other algorithms which are based on the hybrid combination of prime factorization problem and discrete logarithm problem. Both are true for different types of digital signatures like single digital signature, group digital signature, collective digital signature etc. In this paper we propose collective signature protocols for signing groups based on difficulty of problem of finding roots modulo large prime number. The proposed collective signatures protocols have significant merits one of which is connected with possibility of their practical using on the base of the existing public key infrastructures.
Automated Machine Learning

Over the past few decades, machine learning has been the subject of extensive research and application.
A novel robust zero-watermarking algorithm for audio based on sparse representation

Behind the prevalence of multimedia technology, digital copyright disputes are becoming increasingly serious. The digital watermarking prevention technique against the copyright infringement needs to be improved urgently. Among the proposed technologies, zero-watermarking has been favored recently. In order to improve the robustness of the zero-watermarking, a novel robust audio zerowatermarking method based on sparse representation is proposed. The proposed scheme is mainly based on the K-singular value decomposition (K-SVD) algorithm to construct an optimal over complete dictionary from the background audio signal. After that, the orthogonal matching pursuit (OMP) algorithm is used to calculate the sparse coefficient of the segmented test audio and generate the corresponding sparse coefficient matrix. Then, the mean value of absolute sparse coefficients in the sparse matrix of segmented speech is calculated and selected, and then comparing the mean absolute coefficient of segmented speech with the average value of the selected coefficients to realize the embedding of zero-watermarking. Experimental results show that the proposed audio zero-watermarking algorithm based on sparse representation performs effectively in resisting various common attacks. Compared with the baseline works, the proposed method has better robustness.
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness

In this paper, we present a strategy for training convolutional neural networks to effectively resolve interference arising from competing hypotheses relating to inter-categorical information throughout the network. In this work, this is accomplished for the task of dense image labelling by blending images based on (i) categorical clustering or (ii) the co-occurrence likelihood of categories. We then train a source separation network which simultaneously segments and separates the blended images. Subsequent feature denoising to suppress noisy activations reveals additional desirable properties and high degrees of successful predictions. Through this process, we reveal a general mechanism, distinct from any prior methods, for boosting the performance of the base segmentation and salient object detection network while simultaneously increasing robustness to adversarial attacks.
Application value of different dimension ZnO nano materials in sports rehabilitation of basketball players with limb fracture injury

The process of basketball is often accompanied by physical injury, serious fractures of limbs will also occur. At present, the most common treatment for limb fracture is surgery, which can repair the fracture site and achieve the ideal treatment effect through the postoperative sports rehabilitation training. But even so, there are still some difficult problems such as high postoperative infection, most complications, loosening of fixed points, etc. the main solution is to add zinc oxide nano material coating in the implant to reduce infection. However, the current research on the application of different dimensions of ZnO nanomaterials in postoperative sports rehabilitation is almost zero. To solve this problem, this paper will take this as the main research purpose. Through the establishment of the corresponding experimental program, this paper first screened the experimental samples, and randomly divided 30 patients who met the experimental conditions. And divided into zero dimensional group, one-dimensional group and two-dimensional group. Through different groups, the members of each group were coated with different dimensions of ZnO nano materials. This paper unifies the operation standard, the postoperative rehabilitation program, and the corresponding evaluation indicators. Through the way of quantitative indicators, we try to analyze the impact of different dimensions of zinc oxide nano material coating on the postoperative rehabilitation of fracture surgery. According to the experimental data, different dimensions of ZnO nanomaterials have little effect on the success rate of the operation, but there are significant differences in the aspect of anti-infection. Zero dimensional materials performed well in the experiment. Compared with one and two-dimensional groups, the rehabilitation time was shortened and complications were effectively reduced. In this paper, it is suggested that the use of zero dimensional ZnO nanomaterials in fracture surgery is a treatment plan that can be popularized.
Lossy codecs for digital image signatures

Most applications in recent times make use of images one way or the other. As physical devices for capturing images improve, the quality and sizes of images also increase. This causes a significant footprint of images on storage devices. There is ongoing research to reduce the footprint of images on storage. Since storage is a finite resource, the goal is to reduce the sizes of images while maintaining enough quality pleasant to the human eye. In this paper, the design of two lossy codecs for compressing grayscale digital signature images has been presented. The algorithms used either simple thresholding or transform coding to introduce controlled losses into the image coding chain. This was to reduce, to a great extent, the average number of bits per pixel required to represent the images. The codecs were implemented in MATLAB and experiments were conducted with test images to study the performances of the algorithms.
A High-Fidelity Model to Predict Length of Stay in the Neonatal Intensive Care Unit

Having an interpretable dynamic length-of-stay (LOS) model can help hospital administrators and clinicians make better decisions and improve the quality of care. The widespread implementation of electronic medical record (EMR) systems has enabled hospitals to collect massive amounts of health data. However, how to integrate this deluge of data into healthcare operations remains unclear. We propose a framework grounded in established clinical knowledge to model patients' lengths-of-stay. In particular, we impose expert knowledge when grouping raw clinical data into medically meaningful variables, which summarize patients' health trajectories. We use dynamic predictive models to output patients' remaining lengths-of-stay (RLOS), future discharges, and census probability distributions based on their health trajectories up to the current stay. Evaluated with large-scale EMR data, the dynamic model significantly improves predictive power over the performance of any model in previous literature and remains medically interpretable.
An Electro-Photonic System for Accelerating Deep Neural Networks

The number of parameters in deep neural networks (DNNs) is scaling at about 5× the rate of Moore’s Law. To sustain this growth, photonic computing is a promising avenue, as it enables higher throughput in dominant general matrix-matrix multiplication (GEMM) operations in DNNs than their electrical counterpart. However, purely photonic systems face several challenges including lack of photonic memory and accumulation of noise. In this article, we present an electro-photonic accelerator, ADEPT, which leverages a photonic computing unit for performing GEMM operations, a vectorized digital electronic application-specific integrated circuits for performing non-GEMM operations, and SRAM arrays for storing DNN parameters and activations. In contrast to prior works in photonic DNN accelerators, we adopt a system-level perspective and show that the gains while large are tempered relative to prior expectations. Our goal is to encourage architects to explore photonic technology in a more pragmatic way considering the system as a whole to understand its general applicability in accelerating today’s DNNs. Our evaluation shows that ADEPT can provide, on average, 5.73× higher throughput per watt compared to the traditional systolic arrays in a full-system, and at least 6.8× and 2.5× better throughput per watt, compared to state-of-the-art electronic and photonic accelerators, respectively.
Synthetic data generation for optical flow evaluation in the neurosurgical domain

Abstract Towards computer-assisted neurosurgery, scene understanding algorithms for microscope video data are required. Previous work utilizes optical flow to extract spatiotemporal context from neurosurgical video sequences. However, to select an appropriate optical flow method, we need to analyze which algorithm yields the highest accuracy for the neurosurgical domain. Currently, there are no benchmark datasets available for neurosurgery. In our work, we present an approach to generate synthetic data for optical flow evaluation on the neurosurgical domain. We simulate image sequences and thereby take into account domainspecific visual conditions such as surgical instrument motion. Then, we evaluate two optical flow algorithms, Farneback and PWC-Net, on our synthetic data. Qualitative and quantitative assessments confirm that our data can be used to evaluate optical flow for the neurosurgical domain. Future work will concentrate on extending the method by modeling additional effects in neurosurgery such as elastic background motion.
A Noise-aware Method with Type Constraint Pattern for Neural Relation Extraction

Distant supervision is an efficient way to generate large-scale training data for relation extraction without human efforts. However, the accompanying challenges have been plaguing the advance of the extractor: (1) the automatically annotated labels for training data contain much noisy data; (2) the annotations, based on bag-level (cluster of sentences) instead of sentence-level (single sentence), are too coarse to train an accurate extractor; (3) hetergeneous sentences are hard for a denoising model to capture the underlying commonness among valid relational expressions. To address these issues, we bulid a novel sentence representation and craft reinforcement learning to select the expressive sentence for each relation mentioned in a bag. More specifically, we introduce entity-free sentence pattern incorporated with attentive type information. Furthermore, multiple interactions between entity-specific and entity-free representation are proposed to generate complementary sentence features (for challenge 3). Then we design a fine-grained reward function, and model the sentence selection process as an auction where different relations for a bag need to compete together to achieve the possession of a specific sentence based on its expressiveness(for challenge 1 and 2). The experimental results on two public datasets demonstrate the superiority of our model for distantly supervised relation extraction.
A Comparative Study of Linear Regression and Regression Tree

In data mining, prediction modeling is a technique used for finding a mathematical correlation between a dependent variable and various independent variables or predictor variables. Generally, the linear regression describes the correlation between multiple independent or predictor variables and one response or dependent variable. In machine Learning, the CART model builds for classification and regression purpose when the decision tree is applied for classification purpose then it refers to classification tree and if it is applied for regression then it may refer to regression tree. A decision tree is also known as the Regression tree. In this paper, we are concentrated on the concepts of Linear regression and Regression tree. A dataset is incorporated from UCI (Machine learning Repository) for this research work. The objective of the study is to distinguish the results obtained from Linear regression and regression tree. In the end, the decision tree gives better results than linear regression and the final predictive model is selected on a minimum mean sum of the square.
Controllable Context-Aware Conversational Speech Synthesis

In spoken conversations, spontaneous behaviors like filled pause and prolongations always happen. Conversational partner tends to align features of their speech with their interlocutor which is known as entrainment. To produce human-like conversations, we propose a unified controllable spontaneous conversational speech synthesis framework to model the above two phenomena. Specifically, we use explicit labels to represent two typical spontaneous behaviors filled-pause and prolongation in the acoustic model and develop a neural network based predictor to predict the occurrences of the two behaviors from text. We subsequently develop an algorithm based on the predictor to control the occurrence frequency of the behaviors, making the synthesized speech vary from less disfluent to more disfluent. To model the speech entrainment at acoustic level, we utilize a context acoustic encoder to extract a global style embedding from the previous speech conditioning on the synthesizing of current speech. Furthermore, since the current and previous utterances belong to the different speakers in a conversation, we add a domain adversarial training module to eliminate the speaker-related information in the acoustic encoder while maintaining the style-related information. Experiments show that our proposed approach can synthesize realistic conversations and control the occurrences of the spontaneous behaviors naturally.
Blockchain-Based Master Data Management in Supply Chains: A Design Science Study

Master data management is an essential task for organizations and even more critical when collaborations are pursued. Using centralized platforms to manage master data across business partners is straightforward but also entails risks. Besides the dependency on intermediaries, data sovereignty is limited and a single point of failure persists. With new decentralization trends and the uprising blockchain technology, there is potential for optimized and sovereign master data management across entities. We conduct a design science study to assess blockchain technology’s suitability to store and share master data in supply chains. The developed artifact was quantitatively evaluated, focusing on costs and transaction time to further contribute insights to blockchain technology’s economic suitability. The Ethereum-based application was implemented in evan.network and Ropsten test networks. The results substantiate the previous theoretical statements in the literature with reliable numerical data, which indicate that permissioned blockchain networks are more scalable and low-cost than permissionless networks. We also highlight further research opportunities.
Virtual Reality and Augmented Reality in Psychiatric Disorders

In this study, studies conducted in the last 10 years in psychiatric disorders with virtual reality and augmented reality application were examined. Studies done so far; The psychiatric disorder studied, the equipment used, the number of people participating in the study, the database in which the study was scanned, and the results of the studies were evaluated according to the criteria. Studies using disease-specific questionnaires include 30% of all studies. In some studies, it is seen that physiological parameters are also used. Studies using physiological parameters correspond to only 10% of all studies. The practices carried out are to reduce the symptoms brought by the disease and to increase the quality of life by helping to eliminate the disease conditions of the people. Approximately 77% of the studies were carried out in the field of virtual reality and 23% in the field of augmented reality. Based on the articles examined within the scope of the study, some deficiencies in virtual reality and/or augmented reality applications in psychiatric diseases were identified. For example, whether the applications made have a disturbing effect on the users should be determined by using the SSQ questionnaire. In addition, the auditory parameters as well as the visual parameters that make up the virtual environment should be used effectively in this process. It has been observed that among the studies in the literature, the positive or negative effects of auditory parameters on users are not mentioned. It is considered that it will be beneficial for studies to examine the effect of auditory elements in the use of virtual reality environment.