Reviewerly/exHarmony huggingface dataset:
Human Imperceptible Attacks and Applications to Improve Fairness

Modern neural networks are able to perform at least as well as humans in numerous tasks involving object classification and image generation. However, small perturbations which are imperceptible to humans may significantly degrade the performance of well-trained deep neural networks. We provide a Distributionally Robust Optimization (DRO) framework which integrates human-based image quality assessment methods to design optimal attacks that are imperceptible to humans but significantly damaging to deep neural networks. Through extensive experiments, we show that our attack algorithm generates better-quality (less perceptible to humans) attacks than other state-of-the-art human imperceptible attack methods. Moreover, we demonstrate that DRO training using our optimally designed human imperceptible attacks can improve group fairness in image classification. Towards the end, we provide an algorithmic implementation to speed up DRO training significantly, which could be of independent interest.
A Dynamic Theory of Security Free-Riding by Firms in the WFH Age

The COVID-19 pandemic has radically transformed the work-from-home (WFH) paradigm, and expanded an organization's cyber-vulnerability space. We propose a novel strategic method to quantify the degree of sub-optimal cybersecurity in an organization of employees, all of whom work in heterogeneous WFH “siloes”. Specifically, we model the per-unit cost of asymmetric WFH employees to invest in security-improving effort units as time-discounted exponential martingales over time, and derive as benchmark - the centrally-planned socially optimal aggregate employee effort at any given time instant. We then derive the time-varying strategic Nash equilibrium amount of aggregate employee effort in cybersecurity in a distributed setting. The time-varying ratio of these centralized and distributed estimates quantifies the free riding dynamics, i.e., security sub-optimality, within an organization. Rigorous estimates of the degree of sub-optimal cybersecurity will drive organizational policy makers to design appropriate (customized) solutions that voluntarily incentivize WFH employees to invest in required cybersecurity best practices.
Analysis of Measure-Valued Derivatives in a Reinforcement Learning Actor-Critic Framework

Policy gradient methods are successful for a wide range of reinforcement learning tasks. Traditionally, such methods utilize the score function as stochastic gradient estimator. We investigate the effect of replacing the score function with a measure-valued derivative within an on-policy actor-critic algorithm. The hypothesis is that measure-valued derivatives reduce the need for score function variance reduction techniques that are common in policy gradient algorithms. We adapt the actor-critic to measure-valued derivatives and develop a novel algorithm. This method keeps the computational complexity of the measure-valued derivative within bounds by using a parameterized state-value function approximation. We show empirically that measure-valued derivatives have comparable performance to score functions on the environments Pendulum and MountainCar. The empirical results of this study suggest that measure-valued derivatives can serve as low-variance alternative to score functions in on-policy actor-critic and indeed reduce the need for variance reduction techniques.
Simulation Teaching During the Pandemic: Report of an Experience in a Higher Education Private Institution

The COVID-19 pandemic has impacted virtually every sector of our society, including the educational arena. This article reports the experience of discrete-event simulation teaching as part of the industrial engineering curriculum of a higher education private institution in three time instances: the pre-pandemic period (2019), where teaching was in person; the main pandemic period (2020 and the first half of 2021), where teaching was 100% remote, and the hybrid pandemic period (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> half of 2021). We conducted comparisons of the teaching process along these instances regarding several points, by performing both qualitative and quantitative analyses. This article concludes that, despite some pedagogical difficulties, it was possible to maintain high quality in the teaching-learning process, compatible with the pre-pandemic period. The article also makes a forecast of how the teaching process of this type of discipline will be in the near future, after having been influenced by the pandemic period.
Scalable In-Memory Computing Architectures for Sparse Matrix Multiplication

We present two novel memory-dense and fully parallel architectures for analog sparse matrix multiplication: one based on memristive nanowires, and the other based on 3D lithographic memristors, both realized on a CMOS-integrated chip, Cumulus. We experimentally demonstrate accurate generation of deterministic chaotic signals via an echo state network on Cumulus.
Blockchain-based Reputation System For IoT Data Ecosystem: A Utility Maximization Approach

Recently, blockchain technology has been used to build private and secure internet of things (IoT) data markets and trading systems. Reputation of the trading parties is an important attribute that affect their profitability and trading prosperity. However, current reputation systems are prone to malicious manipulation. Such an issue can be avoided by registering all reputation scores in the blockchain which guarantees trans-parency, encryption, privacy, and security. To this end, this paper proposes a blockchain-based reputation system where market players can maximize their utility while engaged in the IoT data ecosystem. We also propose an adaptive learning mechanism that allows the data providers and consumers to enhance their reputation and review credibility scores. We provide extensive experiments to ensure our system satisfies the feasibility, usability, and availability properties. These properties are essential for functional deployment in real IoT data markets.
Josephson parametric amplifiers for rapid, high-fidelity measurement of solid-state qubits

Josephson parametric amplifiers (JPAs) are crucial devices for readout of solid-state qubits. We review how to optimize the compression power and bandwidth of JPAs, necessary for realizing large-scale quantum information processors. We also discuss recent work to realize JPAs with a Josephson Junction Field Effect Transistor made from InAs-Al heterostructures.
Quantum Internet: A Revolutionary Disruption

In the wake of unprecedented technological disruptions witnessed in the recent past, “quantum internet” has emerged as a promising candidate which has a potential to revolutionize both the existing wired and wireless communications. Quantum internet is basically a network of quantum devices that operates within an environment that harnesses the peculiar laws of quantum mechanics, namely superposition and entanglement. Unfortunately, however, the constituent quantum processing nodes as well as the transmission media are susceptible to noise, which is a major impediment to the realization of a global quantum Internet. Quantum error correction (QEC) is a set of techniques to protect quantum states from the effects of environmental noise, or de-coherence, and are thus envisaged to be very promising for future Quantum internet. Aikin to the classical internet, Quantum internet is envisaged to have following building blocks: quantum processors, quantum channel, quantum switch, quantum router and quantum repeater. However, considering the quantum mechanical constraints, different network hardware and software design solutions are essentially required. Typical hardware includes: quantum information source, repeaters, quantum memories and quantum detectors; which are predominantly optical/photonic devices and systems. Hence photonic technology is likely to be the key technology for the future quantum internet. In this paper, after describing the basic quantum mechanical principles on which the quantum computing and quantum internet is based, we conduct an intensive study of the major building blocks of a quantum internet and enumerate some critical challenges. Next, we highlight their strengths and weaknesses and some technological challenges. In the end we discuss some future research directions.
Crime Classification Using Machine Learning and Data Analytic

Crimes have both short-term and long-term effects on individuals and on society as a whole. Modern-day law enforcement agencies are making use of data analytics and machine learning algorithms for predictive policing in order to prevent crimes. There is a lack of crime reporting systems in developing countries like Pakistan. This needs improvement because the crime reporting systems serve as a backbone of generating crime data for analysis. To build a crime prediction system, it is advisable to analyze the minimalistic requirements in advance and use similar data sets from other countries for analysis and prediction. This can serve to provide the government and other authorities with insights into the benefits of correcting crime reporting systems and deploying crime prediction systems in the future. This study implements three machine learning algorithms namely Naïve Bayesian Classifier, Decision Tree Classifier, and Random Forest Classifier for predicting the primary crime type, to highlight potential data problems such as a high number of distinct values for features, class imbalance, and lack of independent features, which can be expected in Pakistan crime data in order to build crime prediction systems. The performance of the algorithms to predict crime type is analyzed on the datasets, where, the Random Forest Classifier performed outperformed Naïve Bayes and Decision tree with an accuracy of 55.03% respectively. In its entirety, this study demonstrates that the limitation of the feature set is the primary cause of the low accuracy of predictions and suggests the inclusion of more features to be extracted for improving the accuracy of predictions. Finally, this research work highlights the lack of structured crime data in the country and provides suggestions for the application of this study in Pakistan.
Life is probabilistic — why should all our computers be deterministic? Computing with p-bits: Ising Solvers and Beyond

Many problems in life are probabilistic; even deterministic problems are often solved using probabilistic algorithms. Anytime conventional computers are used to process uncertain data or to implement algorithms that leverage randomness there is a mismatch between software and hardware that can be avoided using a probabilistic processor built from probabilistic (p-)bits. In this paper, we present the general applications space for computing with p-bits and show results for learning, image segmentation, Bayesian networks and optimization run on a probabilistic (p-)computer.
Independent Optimization for Robot Path Planning and Dynamic Obstacle Avoidance

Autonomous robots can be assigned with various tasks such as moving payload, analyzing terrain, and capturing data in an environment. For an Autonomous Mobile Robot (AMR) to execute such tasks the robot (Hussarion ROSbot) will require efficient algorithms and techniques to reference its current location. The robot is relative to surrounding obstacles in its predetermined path. The conducted research study explains the coordinated method used to successfully allow a robot to identify its position in the environment (Gazebo Simulation) and avoid obstructions with increasing velocity - contingent on nearby surroundings. The results show multiple robots individually tasked with distinct roles, while incorporating an obstacle avoidance function used to avoid both static and dynamic obstacles. Such results can be used in the applications of a high-capacity warehouse environments.
Feedback is Needed for Retakes: An Explainable Poor Image Notification Framework for the Visually Impaired

We propose a simple yet effective image captioning framework that can determine the quality of an image and notify the user of the reasons for any flaws in the image. Our framework first determines the quality of images and then generates captions using only those images that are determined to be of high quality. The user is notified by the flaws feature to retake if image quality is low, and this cycle is repeated until the input image is deemed to be of high quality. As a component of the framework, we trained and evaluated a low-quality image detection model that simultaneously learns difficulty in recognizing images and individual flaws, and we demonstrated that our proposal can explain the reasons for flaws with a sufficient score. We also evaluated a dataset with low-quality images removed by our framework and found improved values for all four common metrics (e.g., BLEU-4, METEOR, ROUGE-L, CIDEr), confirming an improvement in general-purpose image captioning capability. Our framework would assist the visually impaired, who have difficulty judging image quality.
Mimicking Muscle Relaxation Through 3D Print Materials &amp; Magnetic Systems

The purpose of this study is to examine the inter-action of a magnetic system with that of an elastic 3D print design to replicate a muscle in contraction expanding back to its original relaxed state. An electromagnetic system uses a design made of soft material to maintain a compression length when an electric current is applied. When the current is removed, the system expands back to its original length due to the geometry and elastic properties of the material. Preliminary experiments on differing magnets help establish a separation distance between the magnets within the design. Mathematical models then further detailed the design using the cross-sectional area, length, and angle between supports to estimate deflection. This design was then tested using nonlinear simulations to displace the component at a distance equivalent to the estimated distance given from the calculations. The reaction force was then compared to the force in the calculations to estimate the percent difference. Once decent results were obtained, a model was 3D printed and tested to verify the findings. The secondary goals of this endeavor are to utilize common materials, reduce the complexity of production, and limit the number of components within the system to reduce failure points along with an estimated lower cost for the overall apparatus.
Sector-wise Investigation of BYOD Security Policies in Pakistan

The growing use of personal devices at workplace due to the Bring Your Own Device (BYOD) paradigm has given rise to new security challenges. Several organizations within Pakistan have recently started adopting the BYOD approach. However, limited work has been carried out to investigate the current state of BYOD security policies in Pakistan. We use a survey-based study to explore the BYOD security policies within four industrial sectors of Pakistan namely, Defence Services, Finance, IT/Telecommunications, and Medical. We highlight the BYOD security issues and currently implemented counter-measures within these sectors, and propose a BYOD security framework and a set of sector-wise recommendations for dealing with the BYOD security issues.
Enabling full fault tolerant quantum computing with silicon based VLSI technologies

Quantum computing when available will tackle life changing applications, like in energy or chemistry. Silicon has the ability to enable this full quantum advantage leveraging Very-Large-Scale Integration (VLSI) fabrication and design techniques. First scientific demonstrations have been made, it’s now up to electrical engineers in collaboration with physicist to turn these demonstrations into practical machines.
Real-Time Scheduling Based on Simulation and Deep Reinforcement Learning with Featured Action Space

In this study, real-time scheduling is narrowed to the selection of one job to be processed from the queue of a machine when the machine becomes idle. It is considered to be one kind of sequential decision-making. Deep reinforcement learning with simulation has been widely used to make such decisions for most environments where the action space is either continuous or discrete but limited in size. However, in the real-time scheduling environment, the number of actions is the number of jobs in the queue which are varying over time. Moreover, if jobs arrive randomly, it is impossible to fix the actions. The action space is dynamic and stochastic. To overcome the difficulties raised by this, the action space is transformed into a featured action space. Actions are distinguished by their features. To apply the featured action space, three innovative structures of neural networks are proposed and compared with each other.
Understanding and Characterizing Side Channels Exploiting Phase-Change Memories

Recent advances in nonvolatile memory (NVM), together with their performance-optimized architectural schemes, position NVMs as promising building blocks for future main memory. However, the security of such techniques has not been explored. This article performs the first study on information leakage threats in phase-change memories (PCM). We propose an attack framework, read-saw (R-SAW), that systematically investigates side channel vulnerabilities in representative read techniques under interline and intraline interleaving for multilevel cells. Our evaluation shows that the new side channels can accurately leak program secrets (e.g., crypto keys) and are extremely robust to noise. Our work highlights the need to understand microarchitecture security for emerging memory devices.
Analysis of influencing factors of space-based super-resolution imaging

Due to the diffraction limit of optical system and detector fabrication technology, super-resolution imaging has been an important means to achieve high spatial resolution in remote sensing. One super-resolution imaging method based on sub-pixels is explored in the paper, the mechanism and image reconstruction of the method are mainly studied. Taking one remote sensor as a sample, registration accuracy of homonymous points, which matters to super-resolution image reconstruction, and its influencing factors, are analyzed, showing that, the image resolution can be improved about 1.5 times when the registration accuracy of homonymous points is better than 0.12 pixels.
Circuits for Security and Secure Circuits: Implementation of cryptographic algorithms

It is an honor for me to write this overview article and share with the reader the topic of circuits and security. This topic has triggered my curiosity since I started as a Ph.D. student.
Modeling Reviews for Few-Shot Recommendation via Enhanced Prototypical Network

Although some existing models are proposed to exploit reviews for improving performance for recommender systems, few of them can handle the following issues led by the insufficient review data: (i) The regular training process does not exactly fit the scenario of preference prediction with few historical behaviors. (ii) Extracting informative and sufficient semantic features from limited review texts is a challenging work. To alleviate these issues, this paper proposes an enhanced prototypical network, FS-EPN, that leverages reviews for recommendation under the few-shot setting. FS-EPN consists of an attentional prototypical network being the basic architecture, a sentiment encoder and a memory collector cooperating to capture the extra sentimental and collaborative information from both user and item perspectives for semantic information supplement. We train FS-EPN under the meta-learning framework, which models the training process in the episodic manner to mimic the few-shot test environment. Extensive experiments conducted on six publicly available datasets demonstrate the superior capability of FS-EPN over several state-of-the-art models in few-shot recommendation.
Visual Navigation Subject to Embodied Mismatch

In the embodied visual navigation task, the agent navigates to a target location based on the visual observation it collects during the interaction with the environment. And, various approaches have been proposed to learn robust navigation strategies for this task. However, existing approaches assume that the action spaces in the training and testing phases are the same, which is usually not the case in reality. And, thus, it is difficult to directly apply these approaches on practical scenarios. In this article, we consider the situation where the action spaces in the training and testing phases are different, and a novel task of visual navigation subject to embodied mismatch is proposed. To solve the proposed task, we establish a two-stage robust adversary learning framework which can learn a robust policy to adapt the learned model to a new action space. In the first stage, an adversary training mechanism is used to learn a robust feature representation of the state. In the second stage, an adaptation training is used to transfer the learned strategy to a new action space with fewer training samples. Experiments of three types of embodied visual navigation tasks are conducted in 3-D indoor scenes demonstrating the effectiveness of the proposed approach.
‘The tears don’t give you funding’: data neocolonialism in development in the Global South

This paper examines the knowledge politics and cultures that shape data relations between international aid organisations and Global South public institutions, taking African libraries as an example. International organisations increasingly rely on data from the Global South, purportedly as a resource for development, which has raised valid concerns about the emergence of new practices of data colonialism. One proposed solution is to expand the capacity of Global South institutions to control their own data processes, so they can likewise control the politico-economic relationships that draw on their data. A pan-African library organisation representing 34 countries is exploring this possibility though a multiyear research project to increase library capacity to use data to partner with development aid organisations. However, this work revealed that data colonialism precedes practices of value extraction. In focus groups, a survey of library systems and interviews with aid organisations, aspects of the data cycle are epistemically framed by aid organisations to undercut Global South control, and subtle neocolonial mechanisms encourage libraries to shape their own data cultures according to desires of aid organisations. This underscores the need to expand data neocolonialism as a frame for confronting epistemic injustice by highlighting Western rationalities embedded in data relations.