Grover on SPECK: Quantum Resource Estimates

Grover search algorithm reduces the security level of symmetric key cryptography with $n$-bit secret key to $O(2^{n/2})$. In order to evaluate the Grover search algorithm, the target block cipher should be implemented in quantum circuits. Recently, many research works evaluated required quantum resources of AES block ciphers by optimizing the expensive substitute layer. However, only few works devoted to ARX-based lightweight block ciphers, which are active research area. In this paper, we present optimized implementations of SPECK 32/64 and SPECK 64/128 block ciphers for quantum computers. To the best of our knowledge, this is the first implementation of SPECK in quantum circuits. Primitive operations, including addition, rotation, and exclusive-or, for SPECK block cipher are finely optimized to achieve the optimal quantum circuit, in terms of qubits, Toffoli gate, CNOT gate, and X gate. The proposed method can be applied to other ARX-based lightweight block ciphers, such as LEA, HIGHT and CHAM block ciphers

Opportunism and Opportunity Cost as Antecedents of Participatory Behavior

Despite its positive impacts, public participation often begets a representativeness problem due to participants’ opportunism and opportunity cost. Using the survey on 2,000 citizens in South Korea, the research results show that: (1) citizens’ opportunism in terms of self-interest or free-riding may significantly influence their participatory behaviors and (2) citizens’ opportunity costs may act as a mediating factor, i.e., a higher opportunity cost lessens the impact of opportunism on participation. The findings imply that a desirably represented citizen participation can be supported by considering and mobilizing (not manipulating) citizens’ sense of opportunism and opportunity cost

FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning

Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data privacy, existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via mobile devices. We propose FedTherapist, a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning. We explore multiple model designs by comparing their performance and overhead for FedTherapist to overcome the complex nature of on-device language model training on smartphones. We further propose a Context-Aware Language Learning (CALL) methodology to effectively utilize smartphones' large and noisy text for mental health signal sensing. Our IRB-approved evaluation of the prediction of self-reported depression, stress, anxiety, and mood from 46 participants shows higher accuracy of FedTherapist compared with the performance with non-language features, achieving 0.15 AUROC improvement and 8.21% MAE reduction.Comment: Accepted to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023

Representativeness in the Eyes of the Citizen: Impact of Balanced Citizenship on the Perceived Representativeness in Participatory Governance

Representativeness is one of the keys to the legitimacy of participatory governance. However, such representativeness might be in the eye of the beholder due to various definitions of, and criteria for, representativeness. This study aims to explore how citizens perceive the representativeness of their representatives and the reasons behind those perceptions. Based on a survey of 2,000 citizens in South Korea, the findings indicate: (1) the maturity of citizenship (i.e., balancing tolerance and participation) significantly influences citizens’ perceptions of the representativeness of public affairs participants, and (2) this pattern of perceived representativeness does not vary according to the representativeness type (i.e., stake, stance, service, specialty, sovereign, and socio-econ) and domain (i.e., community, corporates, and government). The findings imply (1) the existence of four distinct groups of citizens–considerate reformer, reserved endurer, silent groaner, and active grumbler–and (2) the four groups of citizens are predictors of perceived representativeness.1

Multi-Stage Approach Using Convolutional Triplet Network and Ensemble Model for Fault Diagnosis in Oil Plant Rotary Machines

Ensuring the operational safety and reliability of rotary machinery systems, especially in oil plants, has become a focal point in both academic and industry arenas. Specifically, in terms of key rotary machinery components such as shafts, the diagnosis of these systems is paramount for achieving enhanced generalization capabilities in fault diagnosis, encompassing multiple sensor-derived variables with their respective fault patterns. This study introduces a multi-stage approach to generalize capabilities for fault diagnosis that considers multiple sensor-derived variables and their fault patterns. This method combines the Convolutional Triplet Network for feature extraction with an ensemble model for fault classification. Initially, vibration signals are processed to yield the most representative temporal and spatial features. Then, an ensemble approach is used to maximize both diversity and accuracy by balancing the contributions of the individual classifiers. The approach can detect three representative types of shaft faults more accurately than traditional single-stage machine learning models. Comprehensive experiments, detailed within, showcase the method’s efficacy in diagnosing rotary machine faults across diverse operational scenarios