Reviewerly/exHarmony huggingface dataset:
CryptInfer: Enabling Encrypted Inference on Skin Lesion Images for Melanoma Detection

Deep learning models such as Convolutional Neural Networks (CNNs) have shown the potential to classify medical images for accurate diagnosis. These techniques will face regulatory compliance challenges related to privacy of user data, especially when they are deployed as a service on a cloud platform. Fully Homomorphic Encryption (FHE) can enable CNN inference on encrypted data and help mitigate such concerns. However, encrypted CNN inference faces the fundamental challenge of optimizing the computations to achieve an acceptable trade-off between accuracy and practical computational feasibility. Current approaches for encrypted CNN inference demonstrate feasibility typically on smaller images (e.g., MNIST and CIFAR-10 datasets) and shallow neural networks. This work is the first to show encrypted inference results on a real-world dataset for melanoma detection with large-sized images of skin lesions based on the Cheon-Kim-Kim-Song (CKKS) encryption scheme available in the open-source HElib library. The practical challenges related to encrypted inference are first analyzed and inference experiments are conducted on encrypted MNIST images to evaluate different optimization strategies and their role in determining the throughput and latency of the inference process. Using these insights, a modified LeNet-like architecture is designed and implemented to achieve the end goal of enabling encrypted inference on melanoma dataset. The results demonstrate that 80% classification accuracy can be achieved on encrypted skin lesion images (security of 106 bits) with a latency of 51 seconds for single image inference and a throughput of 18,000 images per hour for batched inference, which shows that privacy-preserving machine learning as a service (MLaaS) based on encrypted data is indeed practically feasible.
Off-Policy Correction for Deep Deterministic Policy Gradient Algorithms via Batch Prioritized Experience Replay

The experience replay mechanism allows agents to use the experiences multiple times. In prior works, the sampling probability of the transitions was adjusted according to their importance. Reassigning sampling probabilities for every transition in the replay buffer after each iteration is highly inefficient. Therefore, experience replay prioritization algorithms recalculate the significance of a transition when the corresponding transition is sampled to gain computational efficiency. However, the importance level of the transitions changes dynamically as the policy and the value function of the agent are updated. In addition, experience replay stores the transitions are generated by the previous policies of the agent that may significantly deviate from the most recent policy of the agent. Higher deviation from the most recent policy of the agent leads to more off-policy updates, which is detrimental for the agent. In this paper, we develop a novel algorithm, Batch Prioritizing Experience Replay via KL Divergence (KLPER), which prioritizes batch of transitions rather than directly prioritizing each transition. Moreover, to reduce the off-policyness of the updates, our algorithm selects one batch among a certain number of batches and forces the agent to learn through the batch that is most likely generated by the most recent policy of the agent. We combine our algorithm with Deep Deterministic Policy Gradient and Twin Delayed Deep Deterministic Policy Gradient and evaluate it on various continuous control tasks. KLPER provides promising improvements for deep deterministic continuous control algorithms in terms of sample efficiency, final performance, and stability of the policy during the training.
Exploratory RegTech: Sandboxes Supporting Trust by Balancing Regulation of Algorithms with Automation of Regulations

To explore participatory and experimental engagements with algorithmic governance, we developed and tested a sandbox for near-future blockchain and satellite services (Lithopy). The article summarizes the insights from the final two 2020 workshops with 59 participants deciding on how to regulate a discriminatory blockchain smart contract. The participants expressed a preference for hybrid forms of off-chain and on-chain governance that uses industry standards and audits to prevent biases and excesses. The sandbox environment increases the trust in a system through design and policy iterations that probe issues of power, stakes, and interests without compromising on innovation.
Cross Domain Descriptor for Face Sketch-Photo Image Recognition

Face-sketch to Face-photo matching or recognition is a cross domain modelling problem which identifies the face based on the given sketch query. The sketch and photo differs in their representation so matching the different representations is a challenging task. Compared to hand-crafted image descriptors deep descriptors perform well in solving the problem. Extracting the patches from the sketches and photos can improve the efficiency of the technique. This method is implemented with the benchmarked datasets CUFS, CUFSF and IIITD. The deep descriptor is developed using pre-trained CNN with triplet loss to learn the features. The method performance is better when results are compared with similar procedures.
Google Workspace for EducationPaul T.Johnson

TESOL JournalVolume 12, Issue 3 e593 MATERIALS & MEDIA REVIEW Google Workspace for Education , Johnson, Paul T. https://edu.google.com/products/workspace-for-education/ Paul T. Johnson, Corresponding Author Paul T. Johnson ajwoodsum@gmail.com Woosong University, Daejeon, South KoreaSearch for more papers by this author Paul T. Johnson, Corresponding Author Paul T. Johnson ajwoodsum@gmail.com Woosong University, Daejeon, South KoreaSearch for more papers by this author First published: 12 October 2021 https://doi.org/10.1002/tesj.593Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume12, Issue3September 2021e593 RelatedInformation
Utilization of Unsigned Inputs for NAND Flash-Based Parallel and High-Density Synaptic Architecture in Binary Neural Networks

A novel design method using unsigned input is proposed for a high-density and parallel synaptic string architecture capable of bit-wise operation and bit-counting utilizing NAND flash memory. Though the NAND flash memory has a cell string structure, unsigned binary input enables analogue and parallel bit-counting in NAND flash memory, while achieving high accuracy comparable to that of signed input. Adopting unsigned input allows using current sense amplifier as neuron circuit, which reduces burden of peripheral circuits. In addition, the operation scheme for convolution layers is proposed for parallel convolution operation utilizing NAND flash memory. We show that sufficiently low device variation of 7.2 % obtained by applying 1 erase or program pulse achieves accuracy of 98.12 % and 87.12 % for MNIST and CIFAR 10 patterns, respectively.
Unsupervised Driver Behavior Profiling Leveraging Recurrent Neural Networks

In the era of intelligent transportation, driver behavior profiling has become a beneficial technology as it provides knowledge regarding the driver’s aggressiveness. Previous approaches achieved promising driver behavior profiling performance through establishing statistical heuristics rules or supervised learning-based models. Still, there exist limits that the practitioner should prepare a labeled dataset, and prior approaches could not classify aggressive behaviors which are not known a priori. In pursuit of improving the aforementioned drawbacks, we propose a novel approach to driver behavior profiling leveraging an unsupervised learning paradigm. First, we cast the driver behavior profiling problem as anomaly detection. Second, we established recurrent neural networks that predict the next feature vector given a sequence of feature vectors. We trained the model with normal driver data only. As a result, our model yields high regression error given a sequence of aggressive driver behavior and low error given at a sequence of normal driver behavior. We figured this difference of error between normal and aggressive driver behavior can be an adequate flag for driver behavior profiling and accomplished a precise performance in experiments. Lastly, we further analyzed the optimal level of sequence length for identifying each aggressive driver behavior. We expect the proposed approach to be a useful baseline for unsupervised driver behavior profiling and contribute to the efficient, intelligent transportation ecosystem.
A Rapid HMI Prototyping Based on Personality Traits and AI for Social Connected Thermostats

Residential energy efficiency decreases electricity consumption and saves energy worldwide. Moreover, 86% of the residential buildings use thermostats that control the Heating, Ventilation, and Air-Conditioning (HVAC) system. The energy consumption can decrease from 11% to 18% due to user behavior modifications accompanied by strategies such as feedback and gamification. Social products use gamification features to interact with the end-user and other devices. However, tailored gamified Human-Machine Interfaces (HMI) must consider five personality traits. As a result, implementing Artificial Neural Networks (ANN) within the HMIs can classify the type of user and deploy a tailored strategy to save energy and promote behavioral changes. Thus, this paper shows a three-step framework to develop a rapid prototype using Arduino and MATLAB/Simulink to predict which type of gamified interface is needed depending on the personality traits.
A low power and real-time hardware recurrent neural network for time series analysis on wearable devices

The research presented in this paper addresses the exploitation of Deep Learning methods on wearable devices. We propose a hardware architecture capable of analyzing time series signals through a Recurrent Neural Network implemented on FPGA technology. This architecture has been validated using a real dataset, which includes three-axial accelerometer data acquired by a wearable device used for fall detection. The experiments have been conducted considering different devices and demonstrates that the proposed hardware architecture outperforms the state of the art solutions both in terms of processing time and power consumption. Indeed, the proposed architecture is real-time compliant in the elaboration of the fall detection dataset adopted for the validation. The power consumption is in the order of dozens μW. Finally, futher functionalities could be added in the same chip since the resource usage is low.
An Expert Systems Journey—Red Pepper and Blue Martini

This article traces Monte Zweben's journey in building expert systems and bringing them into the business world. He started out learning about rule-based systems at Carnegie Mellon in the 1970s and implementing constraint-based systems at NASA and MITRE in the 1980s. He built upon this early experience to co-found Red Pepper Software, a company that specialized in planning and scheduling manufacturing tasks. Following Red Pepper's success, he founded his second startup, Blue Martini, which used machine learning to enhance e-commerce businesses. Since the arrival of Big Data in the 21st century, he has watched the industry transform and was a part of the data-driven marketing revolution at Rocket Fuel. Throughout his career, his work has centered on expert systems and how they can be used to help people. This is the story of his journey and the evolution of AI in business.
Efficient On-chip Acceleration of Machine Learning Models for Detection of RF Signal Modulation

This paper presents a design methodology for efficient on-chip acceleration of deep neural network (DNN) for classification of signal modulation in Radio Frequency signals. A low complexity DNN model with ternary weights is developed to reduce computational demand. A digital chip architecture with complex multiply-and-accumulate (MAC) engines are presented to accelerate the DNN model. Simulation results in 28nm CMOS show that the low-complexity DNN model coupled with the on-chip accelerator increases allowable instantaneous bandwidth of the RF signals with minimal impact on the classification accuracy.
Characterizing Hydration of the Ocean Crust Using Shortwave Infrared Microimaging Spectroscopy of ICDP Oman Drilling Project Cores

Abstract Although ocean crust covers over 60% of Earth's surface, the processes that form, cool, and alter the ocean crust are not completely understood. We utilize shortwave infrared micro‐imaging spectroscopy of ∼1.2 km of rock cored by the International Continental Scientific Drilling Program's Oman Drilling Project to quantify hydration of basaltic dikes and gabbros from the Samail ophiolite as a function of depth, mineralogy and deformation. We develop a regression ( R 2 = 0.66) between area of the ∼1,350–1,650 nm OH/H 2 O absorption and measurements of loss on ignition of samples and apply this relationship to generate quantitative ∼250 μm/pixel hydration maps for all cores. The lowest mean hydration is observed in the most pervasively altered dike‐gabbro boundary (GT3A, H 2 O mean = 2.1 ± 1.6 wt%), consistent with the low H 2 O content of the dominant alteration minerals, amphibole and epidote. The highest H 2 O content occurs in deeper foliated and layered gabbros (GT2A, H 2 O mean = 3.2 ± 3.0 wt%) and layered gabbros (GT1A, H 2 O mean = 2.8 ± 3.1 wt%). The greater prevalence with depth of zeolite alteration as opposed to lower wt% H 2 O amphibole at shallow stratigraphic depths, as well as the occurrence of zones of intensive hydration associated with fault zones (H 2 O mean = 5.7 ± 4.0 wt%) lead to greater hydration of the lower ocean crust. This new approach provides an objective quantification of hydration in these cores, enabling an improved understanding of quantities and characteristics of ocean crust hydration. It highlights the importance of specific phases and faulting in controlling hydration, which has implications for ocean crust cooling, rheological properties, and the role of alteration in global biogeochemical cycling.
A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher

This paper presents the employment of a DPA attack on the NIST (National Institute of Standards and Technology) standardized AES (advance encryption standard) protocol for key retrieval and prevention. Towards key retrieval, we applied the DPA attack on AES to obtain a 128-bit secret key by measuring the power traces of the computations involved in the algorithm. In resistance to the DPA attack, we proposed a countermeasure, or a new modified masking scheme, comprising (i) Boolean and (ii) multiplicative masking, for linear and non-linear operations of AES, respectively. Furthermore, we improved the complexity involved in Boolean masking by introducing Rebecca’s approximation. Moreover, we provide a novel solution to tackle the zero mask problem in multiplicative masking. To evaluate the power traces, we propose our custom correlation technique, which results in a decrease in the calculation time. The synthesis results for original implementation (without countermeasure) and inclusion of countermeasure are given on a Zynq 7020 FPGA (Artix-7 device). It takes 424 FPGA slices when implemented without considering the countermeasure, whereas 714 slices are required to implement AES with the inclusion of the proposed countermeasure. Consequently, the implementation results provide the acceptability of this work for area-constrained applications that require prevention against DPA attacks.
Enhancement of photovoltaic power farms using a new power prediction approach

Solar energy power prediction is a vital process for designing and operating solar power plants to evaluate the system performance and reduce the cost of grid management as it decreases the grid imbalance for a more stable grid. This study introduces a new power prediction approach to enhance the power prediction quality by combining different solar models. The study investigates the performance of 4.5 MWp solar field located at the California Polytechnic State University at San Luis Obispo, California, United States to validate this new approach. The system is real-time monitored for 1 year to study its performance. A representative identical model for the actual farm is then designed and built on a solar performance modeling software to validate the software. The actual energy generation of the field is compared with the predicted energy. The model is optimized by selecting different transposition and decomposition models to achieve the most accurate energy prediction. After that, the model is used in a computational framework to predict the energy injected into the grid for photovoltaic solar farms in Egypt based on the new approach methodology. Egypt is considered a solar belt country. Following the validation, a pilot model is used to predict energy generation for the selected locations in Egypt. Egypt's case study is then used as a pilot project to optimize some design parameters to study its effect and the suitability of the power generation in Egypt's weather. The results show that the relative error using the new approach for the whole year when using the proposed approach of combining the solar models is 2.9%. The performance ratio of the actual farm is 79% for the whole year, and the total energy produced is 10.92 GWh/year. The power prediction for the Egyptian sites concludes that 14 GWh/year energy potential can be achieved from the Red Sea location from the suggested model and the most suitable modules are polycrystalline. Furthermore, predicting a 13% increase in power generation due to using tracking technology compared with the usage of polycrystalline with fixed mounts.
Low-light Image Enhancement Method by Soft-closing Using Local Histogram

Recently, various low-light image enhancement methods are proposed. LIME is one of the low-light image enhancement methods based on Retinex theory. Retinex theory assumes that the image is decomposed into the illumination component and the reflectance component. In LIME, the illumination map is estimated by solving optimization problem for edge-aware smoothing and the output image is obtained by adjusting the illumination map. Though LIME significantly enhances the contrast in dark regions, it tends to cause clipping in the bright regions. In this paper, we propose a new method of low-light image enhancement. In the proposed method, firstly the illumination map is estimated by the soft-closing operation using smoothed local histogram. Then, the output image is obtained by adjusting the illumination in the same way as LIME. The proposed method can improve the contrast of the image overall while suppressing clipping in bright regions. The effectiveness of the proposed method is verified by experiments using various images.
Environment Model based Requirements Consistency Verification: An Example

Nowadays formal methods have shown their ability in the requirements consistency verification, at least for safety-critical systems. But most requirements verification researches only focus on software requirements specification, without considering the software’s interactive environment. In this paper, we propose the environmental properties should be included into the specification verification. They should be considered as inherent constraints that must be satisfied. We extract environmental property constraints from interactive scenarios and state transitions of the environment entities, and transform them into formal models for verification. We use a running example to illustrate the role of environment models in requirements verification.
A Robust Segmentation of Malaria Parasites Detection using Fast k-Means and Enhanced k-Means Clustering Algorithms

Image segmentation is the crucial stage in image analysis since it represents the first step towards extracting important information from the image. In summary, this paper presents several clustering approach to obtain fully malaria parasite cells segmented images of Plasmodium Falciparum and Plasmodium Vivax species on thick smear images. Despite k-means is a renowned clustering approach, its effectiveness is still unreliable due to some vulnerabilities which leads to the need of a better approach. To be specific, fast k-means and enhanced k-means are the adaptation of existing k-means. Fast k-means eliminates the requirement to retraining cluster centres, thus reducing the amount of time it takes to train image cluster centres. While, enhanced k-means introduces the idea of variance and a revised edition of the transferring method for clustered members to aid the distribution of data to the appropriate centre throughout the clustering action. Hence, the goal of this study is to explore the efficacy of k-means, fast k-means and enhanced k-means algorithms in order to achieve a clean segmented image with ability to correctly segment whole region of parasites on thick smear images. Practically, about 100 thick blood smear images were analyzed, and the verdict demonstrate that segmentation via fast k-means clustering algorithm has splendid segmentation performance, with an accuracy of 99.91%, sensitivity of 75.75%, and specificity of 99.93%.
A blind-spot-aware optimization-based planner for safe robot navigation

Safe mobile robot navigation should consider not only collision avoidance with current obstacles but also include non-visible areas (to which we refer as blind spots) and the resulting risk of collision with hidden moving objects (e.g. people). Such capability is important for mobile robots operating in environments shared with humans - for instance a shopping assistant robot in a supermarket. This work aims to extend an existing motion planner for mobile robots (the Time Elastic Band planner) by including blind spots. As a result, the final planner does not only consider static and visible dynamic obstacles, but handles blind spots, too. To identify such blind spots, we define and use critical corners that imply them. Hence, our contributions in this paper are creating a critical corner detector, which operates on laser scan data, and the extension of a factor-graph-based path planner. We evaluate the proposed method standalone and in our simulation environment of a supermarket. It can be seen that the implementation is capable of detecting and dealing with blind spots. Finally, we provide source code for both the detector and the planner extensions.