Reviewerly/exHarmony huggingface dataset:
Creating a Forensic Database of Shoeprints from Online Shoe-Tread Photos

Shoe-tread impressions are one of the most common types of evidence left at crime scenes. However, the utility of such evidence is limited by the lack of databases of footwear prints that cover the large and growing number of distinct shoe models. Moreover, the database is preferred to contain the 3D shape, or depth, of shoe-tread photos so as to allow for extracting shoeprints to match a query (crime-scene) print. We propose to address this gap by leveraging shoe-tread photos collected by online retailers. The core challenge is to predict depth maps for these photos. As they do not have ground-truth 3D shapes allowing for training depth predictors, we exploit synthetic data that does. We develop a method, termed ShoeRinsics, that learns to predict depth from fully supervised synthetic data and unsupervised retail image data. In particular, we find domain adaptation and intrinsic image decomposition techniques effectively mitigate the synthetic-real domain gap and yield significantly better depth predictions. To validate our method, we introduce 2 validation sets consisting of shoe-tread image and print pairs and define a benchmarking protocol to quantify the quality of predicted depth. On this benchmark, ShoeRinsics outperforms existing methods of depth prediction and synthetic-to-real domain adaptation.
Designing Sightseeing Support System in Oku-Nikko Using BLE Beacon

We are developing a tourism support application using BLE (Bluetooth Low Energy) beacons in Senjogahara, Oku-Nikko. There are several ways to supply power to a BLE beacon. We decided to use solar-powered since the Oku-Nikko area is on a mountain, and it is not easy to visit there frequently for maintenance. So, we developed a BLE beacon with a small silicon solar cell in 2018. However, during the summer, sunlight is blocked by the leaves of trees, and it is not easy to get enough electricity to drive a BLE beacon. Therefore, we redesigned the BLE beacon using dye-sensitized solar cells to generate electricity with weaker light than ordinary silicon solar cells. We replaced BLE beacons in Senjogahara with the new beacon in 2021. We confirmed that the new BLE beacons work stably even in an environment covered with forests. In this paper, we explain the design of the new BLE beacon and how it works under weak light.
Doppelter Nutzen

Social reading networks also exist in Russia. The degree of fluctuation in these <italic>knizhnye sozial’nye seti</italic> is high. BookMix.ru and LiveLib.ru have become established online groups, and offer readers space for interactive discussion about a book. Users comment, give reviews and recommend reading material. At the same time, they provide publishing houses with useful information for targeted marketing. The publishing sector has discovered the commercial benefits of such groups and helps fund them. The users of the social reading platforms are mainly between 20 and 40 years old. Their reading requirements are not being met by either the bookshops or the libraries.
Automated Graph Neural Network Search Under Federated Learning Framework

<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> raph <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> eural <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> etwork (GNN) has achieved great success in the field of graph data processing and analysis, but the design of GNN architecture is difficult and time-consuming. To reduce the development cost of GNNs, recently, some <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GNN</u> <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> eural <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> rchitecture <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> earch (GNN NAS) techniques are presented for the automatic design of GNN architectures. These techniques bring great convenience to the use of GNN, but cannot be applied to the federated learning scenarios. They only consider the single-source graph dataset, while failing to deal with the distributed and private graph datasets, which limits their applications. To address this shortcoming, in this paper we propose FL-AGNNS, an efficient GNN NAS algorithm which enables distributed agents to cooperatively design powerful GNN models while keeping personal information on local devices. FL-AGNNS designs a novel federated evolutionary optimization strategy. This strategy can fully consider the GNN architectures favored by each client, thus recommend GNN architectures that perform well in multiple datasets. In additions, FL-AGNNS applies the GNN super-network, a weight sharing strategy, to speed up the evaluation of GNN models during the search phase. Extensive experimental results show that FL-AGNNS can recommend better GNN models in short time under the federated learning framework, surpassing the state-of-the-arts GNN models.
Blockchain Solutions, Challenges, and Opportunities for DNA Classification and Secure Storage for the E-Healthcare Sector

Everyone today wants to detect disease early on, but because there aren't many patterns for the many diseases available, it's hard to do so. Because DNA sequences contain all the genetic data about organisms, which can be utilised by researchers to discover or treat diseases early on by developing new medications, using DNA sequences to extract patterns of disease can be very advantageous. The largest global collection of genomic sequences is made available by NCBI, but today the biggest worry is how to protect this enormous amount of data. One of the options is to encrypt these genetic sequences using blockchain technology. As a result, a study of the number of studies in this area as well as the demand for blockchain in healthcare has been conducted in this chapter. Additionally, surveys about research done in the field of DNA sequence classification are suggested along with recommendations for using classification of DNA sequences to detect disease earlier.
Extending the Pre-Training of BLOOM for Improved Support of Traditional  Chinese: Models, Methods and Results

In this paper we present the multilingual language model BLOOM-zh that features enhanced support for Traditional Chinese. BLOOM-zh has its origins in the open-source BLOOM models presented by BigScience in 2022. Starting from released models, we extended the pre-training of BLOOM by additional 7.4 billion tokens in Traditional Chinese and English covering a variety of domains such as news articles, books, encyclopedias, educational materials as well as spoken language. In order to show the properties of BLOOM-zh, both existing and newly created benchmark scenarios are used for evaluating the performance. BLOOM-zh outperforms its predecessor on most Traditional Chinese benchmarks while maintaining its English capability. We release all our models to the research community.
Towards Robust Image-in-Audio Deep Steganography

The field of steganography has experienced a surge of interest due to the recent advancements in AI-powered techniques, particularly in the context of multimodal setups that enable the concealment of signals within signals of a different nature. The primary objectives of all steganographic methods are to achieve perceptual transparency, robustness, and large embedding capacity - which often present conflicting goals that classical methods have struggled to reconcile. This paper extends and enhances an existing image-in-audio deep steganography method by focusing on improving its robustness. The proposed enhancements include modifications to the loss function, utilization of the Short-Time Fourier Transform (STFT), introduction of redundancy in the encoding process for error correction, and buffering of additional information in the pixel subconvolution operation. The results demonstrate that our approach outperforms the existing method in terms of robustness and perceptual transparency.
Gene expression data analysis: a statistical and machine learning perspective. PankajBarah, DhrubaKumar Bhattacharyya, JugalKumar Kalita (2022). Boca Raton, Florida and London. CRC Press; Taylor and Francis. https://doi.org/10.1201/9780429322655

BiometricsVolume 79, Issue 1 p. 526-528 BOOK REVIEWS Gene expression data analysis: a statistical and machine learning perspective. Pankaj Barah, Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita (2022). Boca Raton, Florida and London. CRC Press; Taylor and Francis. https://doi.org/10.1201/9780429322655 Amrita Chattopadhyay, Corresponding Author Amrita Chattopadhyay [email protected] orcid.org/0000-0001-5591-7674 Center for Translational Genomics and Regenerative Medicine, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan Correspondence Amrita Chattopadhyay, Assistant Research Fellow, Center for Translational Genomics and Regenerative Medicine, Department of Medical Research, China Medical University Hospital, No. 2, Yude Road, North District, Taichung City, 404, Taiwan. Email: [email protected]Search for more papers by this author Amrita Chattopadhyay, Corresponding Author Amrita Chattopadhyay [email protected] orcid.org/0000-0001-5591-7674 Center for Translational Genomics and Regenerative Medicine, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan Correspondence Amrita Chattopadhyay, Assistant Research Fellow, Center for Translational Genomics and Regenerative Medicine, Department of Medical Research, China Medical University Hospital, No. 2, Yude Road, North District, Taichung City, 404, Taiwan. Email: [email protected]Search for more papers by this author First published: 22 March 2023 https://doi.org/10.1111/biom.13817Read 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 No abstract is available for this article. REFERENCES Hong, M., Tao, S., Zhang, L., Diao, L.-T., Huang, X., Huang, S., et al. (2020) RNA sequencing: new technologies and applications in cancer research. Journal of Hematology and Oncology, 13(1), 1– 16. Jazayeri, S.M., Melgarejo Munoz, L.M., Romero, H.M. (2015) RNA-Seq: a glance at technologies and methodologies. Acta Biológica Colombiana, 20(2), 23– 35. Stark, R., Grzelak, M., Hadfield, J. (2019) RNA sequencing: the teenage years. Nature Reviews Genetics, 20(11), 631– 656. Volume79, Issue1March 2023Pages 526-528 ReferencesRelatedInformation
Covert Communication over Two Types of Additive Noise Channels

We extend previous results on covert communication over the additive white Gaussian noise channel to two other types of additive noise channels. The first is the Gaussian channel with memory, where the noise sequence is a Gaussian vector with an arbitrary invertible covariance matrix. We show that the fundamental limit for covert communication over such a channel is the same as over the channel with white, i.e., memoryless, Gaussian noise. The second type of channel we consider is one with memoryless generalized Gaussian noise. For such a channel we prove a general upper bound on the dominant term in the maximum number of nats that can be covertly communicated over n channel uses. When the shape parameter p of the generalized Gaussian noise distribution is in the interval (0, 1], we also prove a matching lower bound.
Runtime Verification of Self-Adaptive Systems with Changing Requirements

To accurately make adaptation decisions, a self-adaptive system needs precise means to analyze itself at runtime. To this end, runtime verification can be used in the feedback loop to check that the managed system satisfies its requirements formalized as temporal-logic properties. These requirements, however, may change due to system evolution or uncertainty in the environment, managed system, and requirements themselves. Thus, the properties under investigation by the runtime verification have to be dynamically adapted to represent the changing requirements while preserving the knowledge about requirements satisfaction gathered thus far, all with minimal latency. To address this need, we present a runtime verification approach for self-adaptive systems with changing requirements. Our approach uses property specification patterns to automatically obtain automata with precise semantics that are the basis for runtime verification. The automata can be safely adapted during runtime verification while preserving intermediate verification results to seamlessly reflect requirement changes and enable continuous verification. We evaluate our approach on an Arduino prototype of the Body Sensor Network and the Timescales benchmark. Results show that our approach is over five times faster than the typical approach of redeploying and restarting runtime monitors to reflect requirements changes, while improving the system's trustworthiness by avoiding interruptions of verification.
An Analysis of the Effects and Interaction of Hyper Parameters in Convolutional Neural Networks

Deep Neural Network (DNN) hyper parameters are currently physically tuned, so robotized AI methods are being developed to find the best combination of hyper parameters. Finding the right design is difficult since the results of AutoML calculations heavily rely on the underlying frameworks. As a result, applying a visual analytics method is considered as a viable solution. As a solution, HyperTendril, an internet data discovery system is developed to enable user-driven hyperparameter tuning procedures in a model-independent configuration. HyperTendril encompasses a progressive approach to successfully execute the AutoML framework through a repetitive tuning process to assist clients in the tuning and execution of the AutoML framework based on a client's knowledge on the obtained outcome. Users can use HyperTendril to diagnose the configurations of various hyperparameter search methods and gain insights into their complex behaviour. Furthermore, HyperTendril provides various feature analysis to help users narrow their search areas based on the relative relevance of various hyperparameters and the consequences of their interactions.
A Descriptive Analysis of Reddit Comments Using Data Analytics Approach

We can use data from social media to examine and uncover links between positive and negative emotions. The findings of this article can be applied to a variety of subjects, including psychology and sociology. The goal was to provide insight into various observations made during our analysis. For this, we used Reddit comments as a reference point and data analytics to obtain various results. Our contributions to this paper are to grasp the concept of emotions and appropriately evaluate the data and analyzing Reddit comments and attempting to answer questions regarding the data acquired and discussing potential improvements to our analysis for future works in the field. We selected Reddit since it includes roughly 50 default subreddit themes that are accessible on the home page, including news, gaming, movies, music, and many others. Furthermore, redditors have the option of creating their own subreddit. Moreover, Reddit is one of the world's most prominent social networking websites. This makes it appropriate for our needs because it has a large number of user comments, which we can simply retrieve using the easy to use Pushshift API. The acquired comments data was then cleaned and analyzed using R in Rstudio and results were generated. Based on the results, conclusions were generated like correlations and mean values.
Multi-instance Secure Public-Key Encryption

Mass surveillance targets many users at the same time with the goal of learning as much as possible. Intuitively, breaking many users’ cryptography simultaneously should be at least as hard as that of only breaking a single one, but ideally security degradation is gradual: an adversary ought to work harder to break more. Bellare, Ristenpart and Tessaro (Crypto’12) introduced the notion of multi-instance security to capture the related concept for password hashing with salts. Auerbach, Giacon and Kiltz (Eurocrypt’20) motivated the study of public key encryption (PKE) in the multi-instance setting, yet their technical results are exclusively stated in terms of key encapsulation mechanisms (KEMs), leaving a considerable gap. We investigate the multi-instance security of public key encryption. Our contributions are twofold. Firstly, we define and compare possible security notions for multi-instance PKE, where we include PKE schemes whose correctness is not perfect. Secondly, we observe that, in general, a hybrid encryption scheme of a multi-instance secure KEM and an arbitrary data encapsulation mechanism (DEM) is unlikely to inherit the KEM’s multi-instance security. Yet, we show how with a suitable information-theoretic DEM, and a computationally secure key derivation function if need be, inheritance is possible. As far as we are aware, ours is the first inheritance result in the challenging multi-bit scenario.
Understanding Players and Play Through Game Analytics

Video games produce terabytes of data, and game studios are investing more and more in game analytics to parse that data, understand their players, and improve their games. However, an essential step in game analytics is to present the results to shareholders who may not be as familiar with the technical details of data science. In this chapter, we present a reusable methodology for game analytics and discuss how to translate the results from analyses using this methodology in an interpretable manner. To illustrate our method, we view game analytics through the lens of player clustering with a case study on the mobile game My Singing Monsters.
Architectures for Quantum Information Processing

Quantum computing is changing the way people think about computing. Significant strides in research and development for managing and harnessing the power of quantum systems has been made in recent years, demonstrating the potential for transformative quantum technology. Quantum phenomena like superposition, entanglement, and interference can be exploited to solve issues that are difficult for traditional computers. IBM’s first public access to true quantum computers through the cloud and Google’s demonstration of quantum supremacy are among the accomplishments. Besides, a slew of other commercial, government, and academic projects are in the works to create next-generation hardware, a software stack to support the hardware ecosystem, and viable quantum algorithms. This chapter covers various quantum computing architectures including many hardware technologies that are being investigated. It also discusses a variety of challenges, including numerous errors/noises that plague the quantum computers. An overview of literature investigating noise-resilience approaches is also presented.
Explaining Machine Learning-Based Feature Selection of IDS for IoT and CPS Devices

In training machine learning and artificial intelligence models for Intrusion Detection Systems (IDS), feature selection plays a critical role in evaluating the prediction performance and explainability of the trained model. The feature selection in designing the IDS is often hindered by the volume, variety, and veracity of the data generated from Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) devices. In this paper, we explored selecting the best subset of features to reduce the feature space of high-dimensional datasets and thereby improve performance, explainability, and computing time. We incorporated the feature selection method of permutation importance in XGB models and prediction explainability methods, such as SHAP and LIME. Using two publicly available IDS datasets, NSL-KDD and CCID-V1, our feature selection-based XBG model for the NSL-KDD data reduced features from 42 to 20 with an AUC score of 0.8751 from the previous 0.8530 with 60% improvement in training time. A similar model for the CCID-V1 data reduced the features from 82 to 22 and achieved an AUC of 0.9999 with a 46% improvement in computing time. We also observed that SHAP and LIME explanations of the prediction showed consistent results in selecting important features. Our study demonstrated that the feature selection achieved an improvement in performance and explainability along with lower training time, which increases the usability of our model for the design of IDS.
Nighttime Semantic Segmentation with Instance-level Data Augmentation: a Case Study of the Dark Zurich Benchmark

Semantic segmentation has been a core learning task in the autonomous driving technology stack. However, current deep learning-based models do not perform well at nighttime due to the low illumination. In this study, we present an instance-level data augmentation method to increase the quantity and diversity for the low-resource classes to feed more instances of these classes to the training algorithm, with an aim to encourage the model to learn more features and patterns to better distinguish the low-resource classes presented in the original training set. We validate the method on the Dark Zurich dataset, a typical dataset that contains driving scene images taking at daytime e, twilight, and nighttime. We take the ``person'' class as an example and apply the instance-level data augmentation method. Experimental results have shown significant improvement compared to the SOTA, lifting the IoU by 4.52%. The results demonstrate the efficacy of the proposed method, indicating that the augmenting low-resource classes at the instance level is a promising strategy and can be an effective complement alongside other performance boosting methods.
An Improved Sign Language Translation approach using KNN in Deep Learning Environment

The deaf and dumb community’s primary mode of communication is signs. It is the only source through which deaf and dumb people can communicate with others. The goal of this paper is to invent a model for translating signs into text format. With assistance of machine learning algorithms, we will scan the signs and then convert them to understandable text. KNN (k-nearest neighbour) algorithm will be used to do so. User will get an interface where it can train the system according to their signs and meanings with respect to it, which can later be used for interaction between deaf and dumb people and common people and vice versa. The assessment of this model is conducted with 3 students using various training examples. The accuracy obtained is approximately 97%.
Contract Price Negotiation Using an AI-Based Chatbot

The contract management process is a tedious and often manually performed task in the procurement process in the industry. It includes requesting bids, negotiating, drafting the contract, etc. Within this purview, there is ample opportunity for smart automation. In this paper, we present a general framework for a negotiating bot that can bargain with the vendor for an agreement on the price in the contract negotiation process. Based on the data provided by the vendor, the bot determines a fair price as per market standards, using which the user (procurement staff) can further negotiate for a better deal as per the systematic steps described by the decision bot. This method can benefit the companies by saving a significant amount of time and money for the organization and reducing human dependency on such routine tasks.