Reviewerly/exHarmony huggingface dataset:
Designing Optimal Key Lengths and Control Laws for Encrypted Control Systems Based on Sample Identifying Complexity and Deciphering Time

In the state-of-the-art literature on cryptography and control theory, there has been no systematic methodology of constructing cyber–physical systems that can achieve the desired control performance while being protected against eavesdropping attacks. In this article, we tackle this challenging problem. We first propose two novel notions referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sample identifying complexity</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sample deciphering time</i> in an encrypted control framework. The former explicitly captures the relation between the dynamical characteristics of control systems and the level of identifiability of the systems while the latter shows the relation between the computation time for the identification and the key length of a cryptosystem. Based on these two tractable new notions, we propose a systematic method for designing both of an optimal key length to prevent system identification with a given precision within a given life span of systems and of an optimal controller to maximize both of the control performance and the difficulty of the identification. The efficiency of the proposed method in terms of security level and realtime-ness is investigated through numerical simulations. To the best of our knowledge, this article first connects the relationship between the security of cryptography and dynamical systems from a control-theoretic perspective.
Augmenting robot intelligence via EEG signals to avoid trajectory planning mistakes of a smart wheelchair

Abstract Assistive robots operate in complex environments and in presence of human beings, but the interaction between them can be affected by several factors, which may lead to undesired outcomes: wrong sensor readings, unexpected environmental conditions, or algorithmic errors represent just a few examples of the possible scenarios. When the safety of the user is not only an option but must be guaranteed, a feasible solution is to rely on a human-in-the-loop approach, e.g., to monitor if the robot performs a wrong action during a task execution or environmental conditions affect safety during the human-robot interaction, and provide a feedback accordingly. The present paper proposes a human-in-the-loop framework to enable safe autonomous navigation of an electric powered and sensorized (smart) wheelchair. During the wheelchair navigation towards a desired destination in an indoor scenario, possible problems (e.g. obstacles) along the trajectory cause the generation of electroencephalography (EEG) potentials when noticed by the user. These potentials can be used as additional inputs to the navigation algorithm in order to modify the trajectory planning and preserve safety. The framework has been preliminarily tested by using a wheelchair simulator implemented in ROS and Gazebo environments: EEG signals from a benchmark known in the literature were classified, passed to a custom simulation node, and made available to the navigation stack to perform obstacle avoidance.
On the Ideal Shortest Vector Problem over Random Rational Primes

Any non-zero ideal in a number field can be factored into a product of prime ideals. In this paper we report a surprising connection between the complexity of the shortest vector problem (SVP) of prime ideals in number fields and their decomposition groups. When applying the result to number fields popular in lattice based cryptosystems, such as power-of-two cyclotomic fields, we show that a majority of rational primes lie under prime ideals admitting a polynomial time algorithm for SVP. Although the shortest vector problem of ideal lattices underpins the security of the Ring-LWE cryptosystem, this work does not break Ring-LWE, since the security reduction is from the worst case ideal SVP to the average case Ring-LWE, and it is one-way.
Digital image forensic using deep flower pollination with adaptive Harris hawk optimization

This paper presents a new segmentation algorithm and deep learning concept for innovative copy-move image forgery identification. In this work, the segmentation algorithm uses a new Adaptive Harris Hawk Optimization (AHHO) algorithm. The host image is segmented into irregular and non-overlapping blocks, named as Image Blocks (IB). Here the segmented image is compressed with a Discrete Cosine Transform (DCT). After that, the Zernike moments and Gabor filter-based features extraction techniques are processed to extract the Block Features (BF). At last, the tampered portions are classified with the hybrid Deep Neural Network (DNN) and Flower Pollination Algorithm (FPA) for forgery classification. This novel deep learning algorithm reduces the learning complexities and improves detection accuracy. The compression attacks are removed with anti-forensic blocking artefact removal concept. The experiments are conducted on the Benchmark dataset, CoMoFoD, and GRIP. For the benchmark dataset, precision, recall and F1 values are 91.27,100.0 and 96.07, respectively. For CoMoFoD dataset, the values of precision, recall and F1 are 92.57, 98.0 and 93.05, respectively. For GRIP dataset, the values of precision, recall and F1 are 97.02, 98.0 and 93.05, respectively. The implementation outcomes demonstrated that the proposed scheme is most effective in copy-move forgery recognition than the existing forgery detection approaches.
Explainable Online Validation of Machine Learning Models for Practical Applications

We present a reformulation of the regression and classification, which aims to validate the result of a machine learning algorithm. Our reformulation simplifies the original problem and validates the result of the machine learning algorithm using the training data. Since the validation of machine learning algorithms must always be explainable, we perform our experiments with the kNN algorithm as well as with an algorithm based on conditional probabilities, which is proposed in this work. For the evaluation of our approach, three publicly available data sets were used and three classification and two regression problems were evaluated. The presented algorithm based on conditional probabilities is also online capable and requires only a fraction of memory compared to the kNN algorithm.
Perspectives of Bioartists and Community Lab Organizers on Working with Living Organisms

There is increasing interest within the HCI community about working with living organisms in the design of interactive systems. Bioartists and community lab participants have worked with living organisms for decades. Their motivations for doing so include artistic expression, design innovation, and activism. We interviewed 12 artists, community lab organizers, and researchers who work with or facilitate work with living organisms. Participants expressed perspectives on working with living organisms and described bioart as an effective practice for engaging and informing the public, fostering transdisciplinary collaborations, and facilitating inspiration and learning from organic processes. They also discussed questions of agency and consent, among other ethical issues in this context. Based on our findings, we present future directions to investigate the potential of hybrid living media interfaces to engage and educate human users, open up possibilities for transdisciplinary collaborations, and participate in ethical dialogues on emerging technologies in a new way.
Effective close-range accuracy comparison of Microsoft HoloLens Generation one and two using Vuforia ImageTargets

This paper analyzes the effective accuracy for close-range operations for the first and the second generation of Microsoft HoloLens in combination with Vuforia Image Targets in a black-box approach. The implementation of Augmented Reality (AR) on optical see-through (OST), head-mounted devices (HMDs) has been proven viable for a variety of tasks, such as assembly, maintenance, or educational purposes. For most of these applications, minor localization errors are tolerated since no accurate alignment between the artificial and the real parts is required. For other potential applications, these accuracy errors represent a major obstacle. The "realistically achievable" accuracy remains largely unknown for close-range usages (e.g. within "arms-reach" of a user) for both generations of Microsoft HoloLens. Thus, the authors developed a method to benchmark and compare the applicability of these devices for tasks that demand a higher accuracy like composite manufacturing or medical surgery assistance. Furthermore, the method can be used for a broad variety of devices, establishing a platform for bench-marking and comparing these and future devices. This paper analyzes the performance of test users, which were asked to pinpoint the perceived location of holographic cones. The image recognition software package "Vuforia" was used to determine the spatial transform of the predefined ImageTarget. By comparing the user-markings with the algorithmic locations, a mean deviation of 2.59 ±1.79 [mm] (HL 1) and 1.11 ±0.98 [mm] (HL 2) has been found, which means that the mean accuracy improved by 57.1% and precision by 45.4%. The highest mean accuracy of a single test user has been measured with 0.47 ±1.683 [mm] (HL 1) and 0.085 ± 0.567 [mm] (HL 2).
Dian: Duration Informed Auto-Regressive Network for Voice Cloning

In this paper, we propose a novel end-to-end speech synthesis approach, Duration Informed Auto-regressive Network (DIAN), which consists of an acoustic model and a separate duration model. Un-like other auto-regressive TTS methods, the duration information of phonemes is provided as part of the input to the acoustic model, which enables the removal of the attention mechanism between its encoder and decoder parts. This eliminates the common seen skipping and repeating issues and improves speech intelligibility while ensuring high speech quality. A Transformer-based duration model is used to predict the duration of each phoneme for the attention-free acoustic model. We developed our TTS systems for the multi-speaker multi-style voice cloning challenge (M2VoC) using the proposed DIAN approach. In our procedure, a multi-speaker attention-free acoustic model and its Transformer-based duration model are first separately trained based on the training data released by M2VoC. Next, the multi-speaker models are adapted to form the speaker-specific models with the speaker-dependent data and transfer learning. At last, a speaker-specific LPCNet is estimated and used to synthesize the speech of the corresponding speaker. The M2VoC results showed that our proposed approach achieved the 3rd-place in the speech quality ranking and the 4th-place in the speaker similarity and style similarity ranking in the Track1-a task.
Towards Low-Resource Stargan Voice Conversion Using Weight Adaptive Instance Normalization

Many-to-many voice conversion with non-parallel training data has seen significant progress in recent years. It is challenging because of lacking of ground truth parallel data. StarGAN-based models have gained attentions because of their efficiency and effective. However, most of the StarGAN-based works only focused on small number of speakers and large amount of training data. In this work, we aim at improving the data efficiency of the model and achieving a many-to-many non-parallel StarGAN-based voice conversion for a relatively large number of speakers with limited training samples. In order to improve data efficiency, the proposed model uses a speaker encoder for extracting speaker embeddings and weight adaptive instance normalization (W-AdaIN) layers. Experiments are conducted with 109 speakers under two low-resource situations, where the number of training samples is 20 and 5 per speaker. An objective evaluation shows the proposed model outperforms baseline methods significantly. Furthermore, a subjective evaluation shows that, for both naturalness and similarity, the proposed model outperforms baseline method.
A peer-to-peer service approach in academic libraries

The role of the student worker is expanding in academic libraries. Their unique perspectives add valuable insights to the design and delivery of library services, project development, outreach activities, and operational planning. Student-workers gain internal and external benefits from working in peer-to-peer learning environments which also inform library managers of ways to improve library operations. The peer-to-peer service model is a framework that library managers can use to facilitate peer-to-peer learning. Lessons learned in expanding the role of the student worker through a peer-to-peer service model are shared and should be considered when creating experiential and operational support for library services.
Pauli error estimation via Population Recovery

Motivated by estimation of quantum noise models, we study the problem of learning a Pauli channel, or more generally the Pauli error rates of an arbitrary channel. By employing a novel reduction to the "Population Recovery" problem, we give an extremely simple algorithm that learns the Pauli error rates of an $n$-qubit channel to precision $\epsilon$ in $\ell_\infty$ using just $O(1/\epsilon^2) \log(n/\epsilon)$ applications of the channel. This is optimal up to the logarithmic factors. Our algorithm uses only unentangled state preparation and measurements, and the post-measurement classical runtime is just an $O(1/\epsilon)$ factor larger than the measurement data size. It is also impervious to a limited model of measurement noise where heralded measurement failures occur independently with probability $\le 1/4$. We then consider the case where the noise channel is close to the identity, meaning that the no-error outcome occurs with probability $1-\eta$. In the regime of small $\eta$ we extend our algorithm to achieve multiplicative precision $1 \pm \epsilon$ (i.e., additive precision $\epsilon \eta$) using just $O\bigl(\frac{1}{\epsilon^2 \eta}\bigr) \log(n/\epsilon)$ applications of the channel.
Prediction of Public Trust in Politicians Using a Multimodal Fusion Approach

This paper explores the automatic prediction of public trust in politicians through the use of speech, text, and visual modalities. It evaluates the effectiveness of each modality individually, and it investigates fusion approaches for integrating information from each modality for prediction using a multimodal setting. A database was created consisting of speech recordings, twitter messages, and images representing fifteen American politicians, and labeling was carried out per a publicly available ranking system. The data were distributed into three trust categories, i.e., the low-trust category, mid-trust category, and high-trust category. First, unimodal prediction using each of the three modalities individually was performed using the database; then, using the outputs of the unimodal predictions, a multimodal prediction was later performed. Unimodal prediction was performed by training three independent logistic regression (LR) classifiers, one each for speech, text, and images. The prediction vectors from the individual modalities were then concatenated before being used to train a multimodal decision-making LR classifier. We report that the best performing modality was speech, which achieved a classification accuracy of 92.81%, followed by the images, achieving an accuracy of 77.96%, whereas the best performing model for text-modality achieved a 72.26% accuracy. With the multimodal approach, the highest classification accuracy of 97.53% was obtained when all three modalities were used for trust prediction. Meanwhile, in a bimodal setup, the best performing combination was that combining the speech and image visual modalities by achieving an accuracy of 95.07%, followed by the speech and text combination, showing an accuracy of 94.40%, whereas the text and images visual modal combination resulted in an accuracy of 83.20%.
A Novel Approach for Solving TSP Problem Using Genetic Algorithm Problem

Abstract TSP is a well-known optimization problem. It comes in the category of NP-Hard problems. The solution of TSP problem is not possible using traditional algorithms. It is having many application areas in science and engineering. Genetic algorithm is a well-known AI technique which performs well to solve optimization problems. The performance of genetic algorithm depends upon its operators. In this paper a novel genetic cross over is proposed to solve TSP problem. The performance of proposed algorithm is better as compared to other techniques to solve TSP.
Analysis of English Idiomatic Learning Behaviors of an Audio-Visual Mobile Application

Employment of idioms is essential to reach higher English expressive levels, especially for English as Foreign Language (EFL) learners. However, English idioms are challenging for both instructors and learners because the complex content of idioms depends on understanding their cultural context. Most mobile language applications are for vocabulary acquisition. Therefore, the purpose of this study was to develop an animation/video-based application, “My English Idiom Learning Assistant” (MEILA), to explore the different idiom learning behaviors, as well as the relationships of their learning behaviors to MEILA. To explore the relationship between the learning outcomes and the learning behaviors, the researcher used logs from the MEILA database. The participants consisted of 59 freshmen from two English conversation classes in one private university in central Taiwan. Students experienced the learning activities over 3 weeks. The researcher adopted idiomatic understanding pre- and posttests for the study as well as in-depth interviews. The results revealed that MEILA significantly enhanced idiomatic learning outcomes. The sequential analysis used provides language instructors an example of monitoring learning behaviors to improve teaching materials and methods. The findings may stimulate more mobile-assist language learning (MALL) researchers, English instructors, and app designers to create innovative mobile environments for English idiomatic learning.
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework

To build machine learning-based applications for sensitive domains like medical, legal, etc. where the digitized text contains private information, anonymization of text is required for preserving privacy.Sequence tagging, e.g. as used for Named Entity Recognition (NER), can help to detect private information.However, to train sequence tagging models, a sufficient amount of labeled data are required but for privacy-sensitive domains, such labeled data also can not be shared directly.In this paper, we investigate the applicability of a privacy-preserving framework for sequence tagging tasks, specifically NER.Hence, we analyze a framework for the NER task, which incorporates two levels of privacy protection.Firstly, we deploy a federated learning (FL) framework where the labeled data are neither shared with the centralized server nor with the peer clients.Secondly, we apply differential privacy (DP) while the models are being trained in each client instance.While both privacy measures are suitable for privacy-aware models, their combination results in unstable models.To our knowledge, this is the first study of its kind on privacy-aware sequence tagging models.
Scalable Multi-grained Cross-modal Similarity Query with Interpretability

Abstract Cross-modal similarity query has become a highlighted research topic for managing multimodal datasets such as images and texts. Existing researches generally focus on query accuracy by designing complex deep neural network models and hardly consider query efficiency and interpretability simultaneously, which are vital properties of cross-modal semantic query processing system on large-scale datasets. In this work, we investigate multi-grained common semantic embedding representations of images and texts and integrate interpretable query index into the deep neural network by developing a novel Multi-grained Cross-modal Query with Interpretability (MCQI) framework. The main contributions are as follows: (1) By integrating coarse-grained and fine-grained semantic learning models, a multi-grained cross-modal query processing architecture is proposed to ensure the adaptability and generality of query processing. (2) In order to capture the latent semantic relation between images and texts, the framework combines LSTM and attention mode, which enhances query accuracy for the cross-modal query and constructs the foundation for interpretable query processing. (3) Index structure and corresponding nearest neighbor query algorithm are proposed to boost the efficiency of interpretable queries. (4) A distributed query algorithm is proposed to improve the scalability of our framework. Comparing with state-of-the-art methods on widely used cross-modal datasets, the experimental results show the effectiveness of our MCQI approach.
REFINE: Prediction Fusion Network for Panoptic Segmentation

Panoptic segmentation aims at generating pixel-wise class and instance predictions for each pixel in the input image, which is a challenging task and far more complicated than naively fusing the semantic and instance segmentation results. Prediction fusion is therefore important to achieve accurate panoptic segmentation. In this paper, we present REFINE, pREdiction FusIon NEtwork for panoptic segmentation, to achieve high-quality panoptic segmentation by improving cross-task prediction fusion, and within-task prediction fusion. Our single-model ResNeXt-101 with DCN achieves PQ=51.5 on the COCO dataset, surpassing state-of-the-art performance by a convincing margin and is comparable with ensembled models. Our smaller model with a ResNet-50 backbone achieves PQ=44.9, which is comparable with state-of-the-art methods with larger backbones.
SQL Ledger: Cryptographically Verifiable Data in Azure SQL Database

SQL Ledger is a new technology that allows cryptographically verifying the integrity of relational data stored in Azure SQL Database and SQL Server. This is achieved by maintaining all historical data in the database and persisting its cryptographic (SHA-256) digests in an immutable, tamper-evident ledger. Digests representing the overall state of the ledger can then be extracted and stored outside of the RDBMS to protect the data from any attacker or high privileged user, including DBAs, system and cloud administrators. The ledger and the historical data are managed transparently, offering protection without any application changes. Historical data is maintained in a relational form to support SQL queries for auditing, forensics and other purposes. SQL Ledger provides cryptographic data integrity guarantees while maintaining the power, flexibility and performance of a commercial RDBMS. In contrast to Blockchain solutions that aim for full integrity, SQL Ledger offers a form of integrity protection known as Forward Integrity. The proposed technology is significantly cheaper and more secure than traditional solutions that establish trust based on audits or mediators, but also has substantial advantages over Blockchain solutions that are complex to deploy, lack data management capabilities and suffer in terms of performance due to their decentralized nature.