Reviewerly/exHarmony huggingface dataset:
Deep Learning Evaluation Using Receiver Operating Curve (ROC) for Footprint Biometric Authentication

Biometric authentication compares two templates of individual biological characteristics, one of which is submitted by the user and the other is retrieved from the system. Among the comparison techniques that have been widely used in biometric systems, is deep learning. When deep learning is used for authentication, there is a conflict regarding the performance evaluation criteria of the system. The criteria that are usually used to evaluate the performance of deep learning models are accuracy and loss. It is obtained from the analysis of training and verification data submitted by an individual. In this case, the validation data comes from users, which is known by the deep learning model. In authentication terms, this measurement is known as the ‘Genuine’ score. This evaluation is still insufficient to conclude the performance of a deep learning model. The ‘Imposter’ score also needs to be considered so that the evaluation of this system can be done comprehensively. The focus of this paper is on the performance evaluation procedure of deep learning models for authentication, considering the ‘Imposter Score’ when constructing the Receiver Operating Characteristic (ROC) curve. Footprints have been used as a biometric feature in this study. The AlexNet architecture was chosen to train the system. 45 ‘Genuine’ scores and 2880 ‘Imposter’ scores were successfully generated from 15 individuals using the proposed procedure. At the end of this study, it was found that when the ‘Imposter’ score is included when evaluating the deep learning model, it can give a better conclusion about the deep learning authentication performance. The training duration of a deep learning model can also be reduced based on discriminative authentication criteria.
Performance Analysis of Multiple Linear Regression and Random Forest for an Estimate of the Price of a House

The house is a human need for boards. House prices that continue to rise every year make it difficult for some people to buy a house according to their respective financial capabilities. Many property developers in big cities continue to build housing, including the South Jakarta area with many new arrivals. In this study, we will predict house prices using a comparison of 2 methods, multiple linear regression and random forest which produces a better RMSE value at an 8:2 comparison between training data and testing data, and the multiple linear regression method produces fewer errors. The 8:2 experiment produces an RMSE 3673441811.575 of Linear Regression and 3693111743.726 of Random Forest.
Open-World Object Detection via Discriminative Class Prototype Learning

Open-world object detection (OWOD) is a challenging problem that combines object detection with incremental learning and open-set learning. Compared to standard object detection, the OWOD setting is task to: 1) detect objects seen during training while identifying unseen classes, and 2) incrementally learn the knowledge of the identified unknown objects when the corresponding annotations is available. We propose a novel and efficient OWOD solution from a prototype perspective, which we call OCPL: Open-world object detection via discriminative Class Prototype Learning, which consists of a Proposal Embedding Aggregator (PEA), an Embedding Space Compressor (ESC) and a Cosine Similarity-based Classifier (CSC). All our proposed modules aim to learn the discriminative embeddings of known classes in the feature space to minimize the overlapping distributions of known and unknown classes, which is beneficial to differentiate known and unknown classes. Extensive experiments performed on PASCAL VOC and MS-COCO benchmark demonstrate the effectiveness of our proposed method.
Elastic cloud services

Snowflake's "Data Cloud", provided as Software-as-a-Service (SaaS), enables data storage, processing, and analytic solutions in a performant, easy to use, and flexible manner. Although cloud service providers provide the foundational infrastructure to run and scale a variety of workloads, operating Snowflake on cloud infrastructure presents interesting challenges. Customers expect Snowflake to be available at all times and to run their workloads with high performance. Behind the scenes, the software that runs customer workloads needs to be serviced and managed. Additionally, failures in individual components such as Virtual Machines (VM) need to be handled without disrupting running workloads. As a result, lifecycle management of compute artifacts, their scheduling and placement, software rollout (and rollback), replication, failure detection, automatic scaling, and load balancing become extremely important.
Optimized architectures for universal quantum state transformations using photonic path and polarization

An arbitrary lossless transformation in high-dimensional quantum space can be decomposed into elementary operations which are easy to implement, and an effective decomposition algorithm is important for constructing high-dimensional systems. Here, we present two optimized architectures to effectively realize an arbitrary unitary transformation by using the photonic path and polarization based on the existing decomposition algorithm. In the first architecture, the number of required interferometers is reduced by half compared with previous works. In the second architecture, by using the high-dimensional X gate, all the elementary operations are transferred to the operations which act locally on the photonic polarization in the same path. Such an architecture could be of significance in polarization-based applications. Both architectures maintain the symmetric layout. Our work facilitates the optical implementation of high-dimensional transformations and could have potential applications in high-dimensional quantum computation and quantum communication.
Surprise machines

Abstract Surprise Machines is a project of experimental museology that sets out to visualize the entire image collection of the Harvard Art Museums, with a view to opening up unexpected vistas on more than 200,000 objects usually inaccessible to visitors. The project is part of the exhibition organized by metaLAB (at) Harvard entitled Curatorial A(i)gents and explores the limits of artificial intelligence to display a large set of images and create surprise among visitors. To achieve this feeling of surprise, a choreographic interface was designed to connect the audience’s movement with several unique views of the collection.
Functional Split of In-Network Deep Learning for 6G: A Feasibility Study

In existing mobile network systems, the data plane (DP) is mainly considered a pipeline consisting of network elements end-to-end forwarding user data traffics. With the rapid maturity of programmable network devices, however, mobile network infrastructure mutates toward a programmable computing platform. Therefore, such a programmable DP can provide in-network computing capability for many application services. In this article, we plan to enhance the data plane with in-network deep learning (DL) capability. However, in-network intelligence can be a significant load for network devices. Then the paradigm of the functional split is applied so that the deep neural network (DNN) is decomposed into sub-elements of the data plane for making machine learning inference jobs more efficient. As a proof-of-concept, we take a Blind Source Separation (BSS) problem as an example to exhibit the benefits of such an approach. We implement the proposed enhancement in a full-stack emulator and we provide a quantitative evaluation with professional datasets. As an initial trial, our study provides insightful guidelines for the design of the future mobile network system, employing in-network intelligence (e.g., 6G).
A Large-scale Empirical Assessment of Web API Size Evolution

Like any other type of software, also Web Application Programming Interfaces (APIs) evolve over time. In the case of widely used API, introducing changes is never a trivial task, because of the risk of breaking thousands of clients relying on the API. In this paper we conduct an empirical study over a large collection of OpenAPI descriptions obtained by mining open source repositories. We measure the speed at which Web APIs change and how changes affect their size, simply defined as the number of operations. The dataset of API descriptions was collected over a period of one year and includes APIs with histories spanning across up to 7 years of commits. The main finding is that APIs tend to grow, although some do reduce their size, as shown in the case study examples included in the appendix.
Technology behind text to image generators

Purpose This column aims to provides an overview of the techniques that make text-to-image generators possible. Design/methodology/approach This column will be the first in a two-part series, exploring the impacts of text-to-image software with an emphasis on libraries. Findings It shows how advances in machine learning and the particular technique of diffusion are critical building blocks for artificial intelligence. Originality/value Understanding these technologies and large data sets, which are often scraped by public users, help information professionals understand how this software may further develop, as well as how to help guide their patrons.
Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications

Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures.
A Gunshot Recognition Method Based on Multi-Scale Spectrum Shift Module

In view of the issues such as the larger network model and lower recognition accuracy of the current gunshot recognition networks, a neural network based on a multi-scale spectrum shift module is proposed in this paper to fully mine the relevant information among the gunshot spectrums. This network employs the architecture of a densely connected convolutional network and uses a multi-scale spectrum shift module on the branch to realize the interaction among spectrum information. This spectrum shift replaces the under-sampling operation among the spectrums, realizes the globalized feature extraction of the spectrum, avoids the loss of information during the under-sampling process, and further improves the quality of the spectrum feature map. Experiments were conducted based on the NIJ Grant 2016-DN-BX-0183 gunshot dataset and YouTube dataset on gunshots that have been open to the public, both of whose classification accuracy reached 83.2% and 95.1%, respectively, with the size of the network model being controlled at around 16 MB. The experimental results indicate that, compared with other existing methods for convolutional neural network, the proposed network can mine globalized time-frequency information better and effectively, and has a higher accuracy of gunshot recognition.
Improving topic disentanglement via contrastive learning

With the emergence and development of deep generative models, such as the variational auto-encoders (VAEs), the research on topic modeling successfully extends to a new area: neural topic modeling, which aims to learn disentangled topics to understand the data better. However, the original VAE framework had been shown to be limited in disentanglement performance, bringing their inherent defects to a neural topic model (NTM). In this paper, we put forward that the optimization objectives of contrastive learning are consistent with two important goals (alignment and uniformity) of well-disentangled topic learning. Also, the optimization objectives of contrastive learning are consistent with two key evaluation measures for topic models, topic coherence and topic diversity. So, we come to the important conclusion that alignment and uniformity of disentangled topic learning can be quantified with topic coherence and topic diversity. Accordingly, we are inspired to propose the Contrastive Disentangled Neural Topic Model (CNTM). By representing both words and topics as low-dimensional vectors in the same embedding space, we apply contrastive learning to neural topic modeling to produce factorized and disentangled topics in an interpretable manner. We compare our proposed CNTM with strong baseline models on widely-used metrics. Our model achieves the best topic coherence scores under the most general evaluation setting (100% proportion topic selected) with 25.0%, 10.9%, 24.6%, and 51.3% improvements above the second-best models’ scores reported on four datasets of 20 Newsgroups, Web Snippets, Tag My News, and Reuters, respectively. Our method also gets the second-best topic diversity scores on the dataset of 20Newsgroups and Web Snippets. Our experimental results show that CNTM can effectively leverage the disentanglement ability from contrastive learning to solve the inherent defect of neural topic modeling and obtain better topic quality.
Order Statistics of Inverse Pareto Distribution

In this paper we study the distribution of order statistics of the inverse Pareto distribution. We consider the single and product moment of order statistics from inverse Pareto distribution. Also, we establish some recurrence relation for single moments of order statistics. The exact analytical expressions of entropy, residual entropy and past residual entropy for order statistics of inverse Pareto distribution is derived.
Exhaustive Symbolic Regression

Symbolic Regression (SR) algorithms attempt to learn analytic expressions which fit data accurately and in a highly interpretable manner. Conventional SR suffers from two fundamental issues which we address here. First, these methods search the space stochastically (typically using genetic programming) and hence do not necessarily find the best function. Second, the criteria used to select the equation optimally balancing accuracy with simplicity have been variable and subjective. To address these issues we introduce Exhaustive Symbolic Regression (ESR), which systematically and efficiently considers all possible equations—made with a given basis set of operators and up to a specified maximum complexity— and is therefore guaranteed to find the true optimum (if parameters are perfectly optimised) and a complete function ranking subject to these constraints. We implement the minimum description length principle as a rigorous method for combining these preferences into a single objective. To illustrate the power of ESR we apply it to a catalogue of cosmic chronometers and the Pantheon+ sample of supernovae to learn the Hubble rate as a function of redshift, finding 40 functions (out of 5.2 million trial functions) that fit the data more economically than the Friedmann equation. These low-redshift data therefore do not uniquely prefer the expansion history of the standard model of cosmology. We make our code and full equation sets publicly available.
Differential diagnosis of thyroid nodule capsules using random forest guided selection of image features

Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology is gaining momentum providing the pathologist with additional cues to traditional routes when placing a diagnosis, therefore it is extremely important to develop new image analysis methods that can extract image features with diagnostic potential. In this work, we use histogram and texture analysis to extract features from microscopic images acquired on thin thyroid nodule capsules sections and demonstrate how they enable the differential diagnosis of thyroid nodules. Targeted thyroid nodules are benign (i.e., follicular adenoma) and malignant (i.e., papillary thyroid carcinoma and its sub-type arising within a follicular adenoma). Our results show that the considered image features can enable the quantitative characterization of the collagen capsule surrounding thyroid nodules and provide an accurate classification of the latter's type using random forest.
Quantum thermometry with a dissipative quantum Rabi system

Dissipative quantum Rabi system, a finite-component system composed of a single two-level atom interacting with an optical cavity field mode, exhibits a quantum phase transition, which can be exploited to greatly enhance the estimation precision of unitary parameters (frequency and coupling strength). Here, using the quantum Langevin equation, standard mean field theory and adiabatic elimination, we investigate the quantum thermometry of a thermal bath surrounding the atom with quantum optical probes. With the increase in coupling strength between the atom and the cavity field, two kinds of singularities can be observed. One type of singularity is the critical point (CP) of phase transition from the normal to superradiant phase. The other type of singularity is the exceptional point (EP) in the anti-parity-time (anti- $$\mathcal{P}\mathcal{T}$$ ) symmetrical cavity field. In many previous jobs, the optimal measurement precision can appear around EPs. However, we show that the optimal measurement precision occurs at the CP, instead of the EP. And the direct photon detection represents an excellent proxy for the optimal measurement near the CP. In the case where the thermal bath to be tested is independent of the extra thermal bath interacting with the cavity field, the estimation precision of the temperature always increases with the coupling strength. Oppositely, if the thermal bath to be tested is in equilibrium with the extra thermal bath interacting with the cavity field, noises that suppress the information of the temperature will be introduced when increasing the coupling strength unless it is close to the CP.
Cloud forensics and digital ledger investigation: a new era of forensics investigation

Nowadays, cloud computing has gained popularity because it provides a platform for pay-as-you-go services, including hardware, software, and operating environment. However, technological resources cannot only be shared, but allocated on-demand to various users. With the emerged rate of inevitable vulnerabilities and network crime activities all over the globe, cybercriminals targets cloud environments. So, the demand for digital investigation is increased drastically. These extreme challenges pose serious issues for the cloud investigation. It has an impact on the researcher community of digital forensics as well. The cloud service providers and customers have yet to establish adequate forensics capacity and support digital forensics investigations on cybercrime activities in the cloud. In this paper, we present a digital forensics-enabled cloud investigation framework. In addition, we survey previous related works based on existing cloud forensics practices, fog forensics, edge forensics, and law and highlight the significant role of cloud computing in digital forensics. Finally, we discuss the technical challenges and limitations along with the future directions.
Mapping Language Learning With Emojis: From Phatic Communication to Idioms and Flash Fiction

"Mapping Language Learning with Emojis: From Phatic Communication to Idioms and Flash Fiction. By roaming around digital resources in search of a customised learning approach, language learners often identify as netizens accustomed to a coded Internet language which is rarely juxtaposed with the language taught in the ESP classroom. To keep pace with shifting trends in online communication, teachers often need to expand the discursive membership by empowering learners to turn from users into content creators. By referring to the potential of using emojis in the framework of speech acts, the current paper aims to indicate directions of embedding emojis as social marking tokens and instruments of developing language and digital literacy skills in the foreign language class. The process of embedding emojis in language teaching stems from the integration of emoji as markers in the teaching of phatic communication, to reinforce the locutionary and illocutionary act of speech. Moreover, emojis can be used as an extension of teaching idioms, whereas the illocutionary and perlocutionary function of emoji is explored in interactive vocabulary practice tasks or within gamified sequences. Eventually, using emojis as perlocutionary acts in language learning is applied to storytelling as a mechanism of developing a multiliterate discourse, by means of which learners are introduced to writing emoji stories and flash fictions and then to translating them into words. Keywords: emojis, content creators, speech acts, multimodality, participatory culture "
Deep Learning for Breast MRI Style Transfer with Limited Training Data

In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is made possible by training our model on unlimited possibilities of simulated random medical imaging styles on the training set, making our work more computationally efficient when compared with other style transfer methods. Moreover, our method enables arbitrary style transfer: transferring images to styles unseen in training. This is useful for medical imaging, where images are acquired using different protocols and different scanner models, resulting in a variety of styles that data may need to be transferred between. Our model disentangles image content from style and can modify an image's style by simply replacing the style encoding with one extracted from a single image of the target style, with no additional optimization required. This also allows the model to distinguish between different styles of images, including among those that were unseen in training. We propose a formal description of the proposed model. Experimental results on breast magnetic resonance images indicate the effectiveness of our method for style transfer. Our style transfer method allows for the alignment of medical images taken with different scanners into a single unified style dataset, allowing for the training of other downstream tasks on such a dataset for tasks such as classification, object detection and others.