Reviewerly/exHarmony huggingface dataset:
An Encrypted On-Chip Power Supply With Random Parallel Power Injection and Charge Recycling Against Power/EM Side-Channel Attacks

As hardware security becomes a crucial challenge for modern electronic devices to protect information privacy, this article presents an encrypted on-chip power supply to combat power and electromagnetic (EM) side-channel attacks (SCAs) for crypto cores. By splitting power and security into separate paths, random parallel power injection and charge recycling are realized to encrypt supply power activities for high SCA immunity while largely minimizing power and performance overheads. Despite of crypto core power variations, the proposed power supply can maintain uncorrelated input profile by adaptively modulating parallel noisy power injection. Moreover, its EM leakage is highly attenuated by spreading the spectrum energy to a wide frequency range and increasing noise floor. To achieve such, a recycled masking power stage with an encryption interface is designed to randomly inject power noise and recycle system charge with random <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> -time modulation while still retaining nominal power delivery. A silicon IC prototype of this design is fabricated using a 65 nm CMOS process with an active die area of 0.19 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Measured input power profiles are fully encrypted to improve power SCA immunity. Operating at a nominal switching frequency of 10 MHz, random parallel power encryption reduces peak EM interference noise from 64.57 dBμV to 43.12 dBμV to meet EN55032 Class B standards and verify EM spectrum profile encryption. In response to a step-up load change from zero to full load current of 200 mA, the power supply achieves 1% settling time of 0.78 μs, with measured voltage droop of 54 mV with no other performance overhead. It achieves a peak efficiency of 90.5%, only suffering the maximum power overhead of 4.9%.
SE-<i>P</i>²μm: Secure and Efficient Privacy-Preserving User-Profile Matching Protocol for Social Networks

By matching attributes in user profiles, social networking services (SNSs) are able to create certain social relationships between different users. However, user profiles often contain sensitive information about users. If data matching is done in plaintext, users’ privacy and security will face serious challenges. Most recently, the hacking incident of Ashley Madison website, which incurred more than 60 GB of data being leaked, has prompted researchers to explore the privacy of user data in social networks urgently. Existing schemes may be able to achieve privacy-preserving user profile matching, but they require high communication bandwidth and complex computational costs. To address them, we proposed a private user profile matching protocol for social networks using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$</tex-math> </inline-formula> -out-of- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula> matching servers. Applying the Chinese remainder theorem (CRT), Bloom filter technique, and skyline computation idea, the matching users are efficiently returned with the help of any <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$</tex-math> </inline-formula> matching server without disclosing their identities. In addition, our scheme allows up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$</tex-math> </inline-formula> matching servers to collude while simultaneously ensuring user profile privacy and query privacy. Finally, compared with two recent user profile matching protocols, the performance results demonstrate that our scheme outperforms them.
Group Ensemble Block: Subspace Diversity Improves Coarse-to-Fine Image Retrieval

The random subspace method is an ensemble learning technique of great potential. However, its popularity does not match its potential because of its prohibitive cost and the lack of plug-and-play reusable modules. To fill this gap, we propose the random-subspace-based group ensemble block. The proposed ensemble block increases the diversity among subspaces by masking each sampled subspace. It also utilizes the diversity among a large number of subspaces, e.g., 1024, which is facilitated by combining the advantages of both averaging and concatenating strategies for merging different subspaces' outcomes. In terms of cost, the proposed ensemble block contributes few parameters by parameter sharing, negligible computations by the linear transformation, and fast running speed by parallel processing for every subspace. In terms of reusability, it encapsulates every necessary step in one small block, and can be interchangeably used as a linear layer in neural networks. We show that our group ensemble block is complementary to existing methods for image retrieval such as class-level and instance-level discrimination, and can also be deployed for other tasks such as image classification. Experiments show that the proposed group ensemble block achieves the state-of-the-art accuracy on the CIFAR-100 and ImageNet datasets for the coarse-to-fine image retrieval problem, where the model is trained with coarse-level annotation (e.g., trees) and evaluated with fine-grained category (e.g., maple trees and oak trees).
Accelerating On-Device DNN Training Workloads via Runtime Convergence Monitor

With the growing demand for processing deep learning applications on edge devices, on-device DNN training has become a major workload to execute a variety of vision tasks suited for users. Therefore, architectures employed with algorithm co-design to accelerate the training process have been steadily studied. However, previous solutions are mostly supported by extended versions of the inference studies, such as sparsity, data flow, quantization, etc. Moreover, most works examine their schemes on from-the-scratch training that cannot tolerate inaccurate computing. Accordingly, there are still factors that hinder the overall speed of the DNN training process that has not been addressed in practical workloads. In this work, we propose a runtime convergence monitor to achieve massive computational savings in the practical on-device training workloads (i.e., transfer-learning-based task adaptation). By monitoring the network output data, we determine the training intensity of incoming tasks and adaptively detect the convergence in iteration intervals for training diverse datasets. Furthermore, we enable the computation skip of converged images determined by the monitored prediction probability to enhance the training speed within an iteration. As a result, we perform an accurate but fast convergence in model training for the task adaptation with minimal overhead. Unlike the previous approximation methods, our monitoring system enables runtime optimization and can be easily applicable to any type of accelerator attaining significant speedup. Evaluation results on various datasets show geomean of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.2\times $ </tex-math></inline-formula> speedup when applied in any systolic architectures and further enhancement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.6\times $ </tex-math></inline-formula> when applied in accelerators dedicated for on-device training.
BreastMultiNet: A Multi-Scale Feature Fusion Method Using Deep Neural Network to Detect Breast Cancer

Diagnosis of breast cancer using biopsy tissue images is cumbersome approch among doctors. Pathologists can now diagnose breast cancer more consistently and promptly because of advances in the Computer-Aided Diagnosis (CAD) system. As a result, there has been a surge in demand for CAD-based machine learning techniques. This study describes a "BreastMultiNet" framework that focuses on the transfer learning concept for identifying distinct types of breast cancer by utilizing two publicly available datasets. The suggested scheme extracts features from microscope images with the help of well-known conventional and deep learning models such as HOG, LBP, SURP, DenseNet201, and VGG19. The collected properties of transfer learning models are subsequently dispatched into the summing layer, resulting in a fused vector. Using the BreakHis dataset, the proposed approach achieves 99% accuracy and obtains 95% accuracy when used with the ICIAR dataset, where it surpasses numerous state-of-the-art approaches, proving the suggested model's supremacy.
A Tribute to Fritz Dolak, Copyright Librarian of Ball State University

The community of copyright professionals lost a champion recently, with the passing of Frank J. (“Fritz”) Dolak on July 14, 2021. Fritz was the copyright librarian at Ball State University, and he was known nationally for the series of annual copyright conferences he organized and led in Indiana, and for his leadership in addressing the use of copyrighted works for the enhancement of distance education. He came to copyright after years of music, military service, and more. A hallmark of his life was bringing clarity and humor to serious challenges, starting with his family name. He had a simple family name that was routinely mispronounced. As Fritz would tell anyone who would listen, “The name is ‘Doo-Lok,’ as in ‘Do Lock the Door.’” Fritz was a professional colleague to many editors and contributors to JCEL. Three of his copyright colleagues—Dwayne K. Buttler, Kenneth D. Crews, and Donna L. Ferullo—worked with him for many years, and they share memories of Dr. Fritz Dolak and his influence and leadership. The story of Fritz Dolak is a story of kindness, generosity, and inspiration.
Rearrangements of the Simple Random Walk

In this paper we will look at representations of the simple random walk, $$S_{n}$$ , and show how to effectively rearrange the sequence of terms $$\frac{S_{n}}{\sqrt{n}}$$ in order to achieve almost sure convergence to the standard normal on the open interval $$\left( 0,1\right) $$ . This is done via a suitable choice of permutation $$F:\left\{ 0,1\right\} ^{n}\rightarrow \left\{ 0,1\right\} ^{n}$$ . We are interested in how much rearranging of the simple random walk is optimal. We will describe how to minimize the graph-theoretic complexity of these permutations and also show that they satisfy some additional nice properties.
Independence and Domination of Chess Pieces on Triangular Boards and on the Surface of a Tetrahedron

Independence and domination results are given for six chess-like pieces on triangular boards with triangular spaces and triangular boards with hexagonal spaces. The question of independence and domination on for these same boards on the surface of a tetrahedron is introduced, and some initial results are given. ...
Energy yield optimization for micro-inverter photovoltaic systems with spare parts inventory

To further enhance energy production and reduce the Levelized cost of energy (LCOE) it is essential to plan spare parts to reduce the downtime of the photovoltaic (PV) systems with micro-inverter (MI) caused by the failure. The present investigations only consider the deterministic effects of spare parts of PV products on energy yield. The spares optimization, especially considering the stochastic inventory policy, for the PV system has not been reported yet. In this paper, an optimization model of generating capacity incorporating inventory policies of spare parts is constructed for the MI PV system combining the Continuous time Markov chain (CTMC) with stochastic inventory theory. The range of the installation scale from 100 to 2000 is investigated to analyze the effect of spare parts on the energy yield. We find that the installation scale being more than 200 for a 500 W PV system should reserve spare parts. At the installation scale approaching 2000 PV systems, the LCOE decreases by 0.0063 $/kWh, about 15 %. Compared to not stocking the spare parts of policy (−1,1), the cost saves 8.3 × 10^5 $, more than 14 %. The optimal profits deduced by the proposed methodology can enhance the competitiveness of the utilities and the PV manufacturer.
Challenges in Measuring Utility for Fully Synthetic Data

Evaluating the utility of the generated data is a pivotal step in any synthetic data project. Most projects start by exploring various synthesis approaches trying to identify the most suitable synthesis strategy for the data at hand. Utility evaluations are also always necessary to decide whether the data are of sufficient quality to be released. Various utility measures have been proposed for this purpose in the literature. However, as I will show in this paper, some of these measures can be misleading when considered in isolation while others seem to be inappropriate to assess whether the synthetic data are suitable to be released. This illustrates that a detailed validity assessment looking at various dimensions of utility will always be inevitable to find the optimal synthesis strategy.
Tit-for-Tat Disclosure of a Binding Sequence of User Analyses in Safe Data Access Centers

Safe access centers, a.k.a. trusted research environments, and digital marketplaces allow a user to submit her own statistical analyses on the data stored by controllers. As a responsible organization for the data, the controller may wish to enforce several ethical controls on the user's code. If data are personal or otherwise confidential, the controller needs to make sure the outputs of user analyses do not leak any personal information. Additionally, the data controller may require other ethical properties of the user analyses, like fairness or explainability in case an analysis consists of training a decision model. If a user analysis fails to meet the controller's ethical requirements, the controller stops running the user's code on his data. On the other side, the user would like to achieve fair disclosure of her code, that is, stop disclosing the remainder of her code to the controller in case the controller refuses to return the result of the current analysis. In this concept paper, we present a framework allowing the user: i) to bind herself to a sequence of analyses before receiving any actual output, which precludes disclosure attacks via adaptive analyses; ii) to disclose her next instruction/analysis to the controller only after the controller has returned the output of the current instruction/analysis, which ensures fair disclosure. Our aim is to protect both the controller's and the user's interests.
Automatic Evaluation of Disclosure Risks of Text Anonymization Methods

The standard approach to evaluate text anonymization methods consists of comparing their outcomes with the anonymization performed by human experts. The degree of privacy protection attained is then measured with the IR-based recall metric, which expresses the proportion of re-identifying terms that were correctly detected by the anonymization method. However, the use of recall to estimate the degree of privacy protection suffers from several limitations. The first is that it assigns a uniform weight to each re-identifying term, thereby ignoring the fact that some missed re-identifying terms may have a larger influence on the disclosure risk than others. Furthermore, IR-based metrics assume the existence of a single gold standard annotation. This assumption does not hold for text anonymization, where several maskings (each one encompassing a different combination of terms) could be equally valid to prevent disclosure. Finally, those metrics rely on manually anonymized datasets, which are inherently subjective and may be prone to various errors, omissions and inconsistencies. To tackle these issues, we propose an automatic re-identification attack for (anonymized) texts that provides a realistic assessment of disclosure risks. Our method follows a similar premise as the well-known record linkage methods employed to evaluate anonymized structured data, and leverages state-of-the-art deep learning language models to exploit the background knowledge available to potential attackers. We also report empirical evaluations of several well-known methods and tools for text anonymization. Results show significant re-identification risks for all methods, including also manual anonymization efforts.
Perspectives for Tabular Data Protection – How About Synthetic Data?

The advance of small-scale analysis and user demand driven tabulations bring cell suppression based SDC to its limits. Perturbative SDC strategies could be flexible alternatives. The present paper compares results obtained by first experiments with synthetic data generated by the Synthpop package [13] on one hand to those of a more traditional input perturbation approach, i.e. targeted record swapping [19], and on the other hand to those of the Cell Key Method [6, 8, 12] which adds noise directly to tabular outputs.
An Optimization-Based Decomposition Heuristic for the Microaggregation Problem

Given a set of points, the microaggregation problem aims to find a clustering with a minimum sum of squared errors (SSE), where the cardinality of each cluster is greater than or equal to k. Points in the cluster are replaced by the cluster centroid, thus satisfying k-anonymity. Microaggregation is considered one of the most effective techniques for numerical microdata protection. Traditionally, non-optimal solutions to the microaggregation problem are obtained by heuristic approaches. Recently, the authors of this paper presented a mixed integer linear optimization (MILO) approach based on column generation for computing tight solutions and lower bounds to the microaggregation problem. However, MILO can be computationally expensive for large datasets. In this work we present a new heuristic that combines three blocks: (1) a decomposition of the dataset into subsets, (2) the MILO column generation algorithm applied to each dataset in order to obtain a valid microaggregation, and (3) a local search improvement algorithm to get the final clustering. Preliminary computational results show that this approach was able to provide (and even improve upon) some of the best solutions (i.e., of smallest SSE) reported in the literature for the Tarragona and Census datasets, and $$k\in \{3,5, 10\}$$ .
Towards Integrally Private Clustering: Overlapping Clusters for High Privacy Guarantees

Privacy for re-identification, k-anonymity, and differential privacy are the main privacy models considered in the literature on data privacy. We introduced an alternative privacy model called integral privacy, that can be seen as a model for computations avoiding membership inference attacks, as well as other inferences, on aggregates and computations from data (e.g., machine learning models and statistics). In previous papers we have shown how we can compute integrally private statistics (e.g., means and variance), decision trees, and regression. In this paper we introduce clustering with overlapping clusters. The goal is to produce integrally private clusters. We formulate the problem in terms of an optimization problem, and provide a (sub-optimal) solution based on genetic algorithms.
The Biobjective Consistent Traveling Salesman Problem

This article deals with the problem of designing a route for each day of a time period to minimize the total travel cost and the discrepancy in the service time to customers visited on different days. In this paper we allow waiting times for the vehicle at customer locations. The literature already includes exact and heuristic approaches for the variant where the first objective is minimized and the second objective is constrained by a given threshold. The variant do not allow waiting times at customer locations, and it is known as Consistent Travelling Salesman Problem. We are not aware of any previous algorithm in the literature to tackle the biobjective problem, and this article describes three compact formulations for it. Each formulation is suitable for approaching the problem through the well-known weighted-sum method for multiobjective optimization, where some Pareto optimal solutions are sequentially determined by systematically changing the weights among the objective functions. We perform a computational study applying the formulations to tackle instances adapted from the TSPLIB library.
Synthetic Individual Income Tax Data: Methodology, Utility, and Privacy Implications

The United States Internal Revenue Service Statistics of Income (SOI) Division possesses invaluable administrative tax data from individual income tax returns that could vastly expand our understanding of how tax policies affect behavior and how those policies could be made more effective. However, only a small number of government analysts and researchers can access the raw data. The public use file (PUF) that SOI has produced for more than 60 years has become increasingly difficult to protect using traditional statistical disclosure control methods. The vast amount of personal information available in public and private databases combined with enormous computational power create unprecedented disclosure risks. SOI and researchers at the Urban Institute are developing synthetic data that represent the statistical properties of the administrative data without revealing any individual taxpayer information. This paper presents quality estimates of the first fully synthetic PUF and shows how it performs in tax model microsimulations as compared with the PUF and the confidential administrative data.
A Study on Learner Types in Online Classes by Q-Methodology

The aim of this research was to analyze the characteristics of each type of learner among university students in online classes. The P sample was composed of 28 students who had participated in online classes. We prepared a Q sample for advanced research, special publication, an open questionnaire, and an interview. Through this process, we suggested different learner types and applied the types through Q methodology, which is well-known for its use in subjective research. We then distributed the P sample amongst Q cards and generated the following results through principal component factor analysis. University students were categorized into four learning types: ‘self-directed’, ‘content’, ‘online’, and ‘team project’. We judged that the class attitude and class satisfaction of students change as the class types are diversified due to the coronavirus. By analyzing the types of learners created by non-face-to-face classes at universities, this study is meaningful in providing teachers with an outline of various classes that are suitable for various types of learners.