Reviewerly/exHarmony huggingface dataset:
An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-level Structural Information

In this paper, we focus on the problem of unsupervised image-sentence matching. Existing research explores to utilize document-level structural information to sample positive and negative instances for model training. Although the approach achieves positive results, it introduces a sampling bias and fails to distinguish instances with high semantic similarity. To alleviate the bias, we propose a new sampling strategy to select additional intra-document image-sentence pairs as positive or negative samples. Furthermore, to recognize the complex pattern in intra-document samples, we propose a Transformer based model to capture fine-grained features and implicitly construct a graph for each document, where concepts in a document are introduced to bridge the representation learning of images and sentences in the context of a document. Experimental results show the effectiveness of our approach to alleviate the bias and learn well-aligned multimodal representations.
Outsourced Secure Face Recognition Based on CKKS Homomorphic Encryption in Cloud Computing

With the enhancement of the performance of cloud servers, face recognition applications are becoming more and more popular, but it also has some security problems, such as user privacy data leakage. This article proposes a face recognition scheme based on homomorphic encryption in cloud environment. The article first uses the MTCNN algorithm to detect face and correct the data and extracts the face feature vector through the FaceNet algorithm. Then, the article encrypts the facial features with the CKKS homomorphic encryption scheme and builds a database of the encrypted facial feature in the cloud server. The process of face recognition is as follows: calculate the distance between the encrypted feature vectors and the maximum value of the ciphertext result, decrypt it, and compare the threshold to determine whether it is a person. The experimental results show that when the scheme is based on the LFW data set, the threshold is 1.1236, and the recognition accuracy in the ciphertext is 94.8837%, which proves the reliability of the proposed scheme.
A Virtual Reality Library Space for Health Centered Education and Well-Being

The use of virtual reality (VR) applications in health sciences education and, by extension, in health sciences libraries, is growing as VR equipment has become more affordable and the desire to au...
Efficient SAT-based Proof Search in Intuitionistic Propositional Logic

Abstract We present an efficient proof search procedure for Intuitionistic Propositional Logic which involves the use of an incremental SAT-solver. Basically, it is obtained by adding a restart operation to the system by Claessen and Rosén, thus we call our implementation . We gain some remarkable advantages: derivations have a simple structure; countermodels are in general small; using a standard benchmarks suite, we outperform and other state-of-the-art provers.
Four by four MDS matrices with the fewest XOR gates based on words

MDS matrices play an important role in the design of block ciphers, and constructing MDS matrices with fewer xor gates is of significant interest for lightweight ciphers. For this topic, Duval and Leurent proposed an approach to construct MDS matrices by using three linear operations in ToSC 2018. Taking words as elements, they found $ 16\times16 $ and $ 32\times 32 $ MDS matrices over $ \mathbb{F}_2 $ with only $ 35 $ xor gates and $ 67 $ xor gates respectively, which are also the best known implementations up to now. Based on the same observation as their work, we consider three linear operations as three kinds of elementary linear operations of matrices, and obtain more MDS matrices with $ 35 $ and $ 67 $ xor gates. In addition, some $ 16\times16 $ or $ 32\times32 $ involutory MDS matrices with only $ 36 $ or $ 72 $ xor gates over $ \mathbb{F}_2 $ are also proposed, which are better than previous results. Moreover, our method can be extended to general linear groups, and we prove that the lower bound of the sequential xor count based on words for $ 4 \times 4 $ MDS matrix over general linear groups is $ 8n+2 $.
FANS: Fusing ASR and NLU for On-Device SLU

Spoken language understanding (SLU) systems translate voice input commands to semantics which are encoded as an intent and pairs of slot tags and values. Most current SLU systems deploy a cascade of two neural models where the first one maps the input audio to a transcript (ASR) and the second predicts the intent and slots from the transcript (NLU). In this paper, we introduce FANS, a new end-to-end SLU model that fuses an ASR audio encoder to a multi-task NLU decoder to infer the intent, slot tags, and slot values directly from a given input audio, obviating the need for transcription. FANS consists of a shared audio encoder and three decoders, two of which are seq-to-seq decoders that predict non null slot tags and slot values in parallel and in an auto-regressive manner. FANS neural encoder and decoders architectures are flexible which allows us to leverage different combinations of LSTM, self-attention, and attenders. Our experiments show compared to the state-of-the-art end-to-end SLU models, FANS reduces ICER and IRER errors relatively by 30 % and 7 %, respectively, when tested on an in-house SLU dataset and by 0.86 % and 2 % absolute when tested on a public SLU dataset.
Challenges and Recommendations in Big Data Indexing Strategies

Index structures are one of the main strategies for effective data access applied for indexing data. According to expansion of data, traditional indexing strategies on big data meets several challenges that lead to weak performance; they haven't abilities to handle the rapid increase of data in terms of accurate retrieval results and processing time. So, it is necessary to substitute traditional index with another efficient index structure called learned index. Learned index goes to use machine learning models to tackle such issues and achieve more enhancements of processing time and accurate results. In this research, the authors discuss different indexing strategies on big data both traditional and learned indexes, demonstrate the main features of them, perform comparison in terms of its performance, and present big data indexing challenges and solutions. Consequently, the research suggests replacing traditional indexes by dynamic index models, which lead to less processing time and more accurate results taking into consideration specification of hardware used.
Regulation of the Financial Components of the Crypto-Economy

The emergence into the public discourse in 2009 of the novel peer-to-peer Bitcoin crypto-currency phenomenon caught many regulators unawares. We now know that Bitcoin and its underlying ‘blockchain’ technology represented a transformational vanguard of a new ‘trustless’ method of sharing data and processes and contracting in a decentralized, traceable and secure manner and, in many cases, without the need for intermediaries. The family of blockchains and its analogues are now known as distributed ledger technologies (DLTs).Whatever their form or function, ‘crypto’-derived and focused products and services based on DLT are here to stay. They are transformational not just of their utilitarian function in making data transfer and storage more efficient, but notably we argue also in their potential to democratize access to financial products and services, a change not seen the dawn of the commercial Internet in the 1990s. There are still many hurdles and risks to overcome though before they become mainstream, not least of which is regulatory certainty.The ‘crypto-economy’, as we dub it, has many avenues for transformation by DLTs. This may be through DLT’s novel utilitarian function of new data sharing and storage techniques that are secure, tamper-evident and distributed. Or the introduction of new financial products and trading techniques through the production and use of new ‘crypto-assets’ that feature at their core malleable crypto ‘tokens’ used as ‘programmable money.’ The financial sector in particular is seeing the release of these new asset classes that democratize access to financial products through tokenization, catalyzing and enabling fractional ownership of legacy and new crypto-inspired asset classes. Some of these crypto-assets have attached profit or governance rights, others providing some consumption value. They may also act as payment tool as a crypto-currency, enabling the buying and selling of goods and services. The crypto-economy may also support capital raising through a now controversial method called an Initial Coin Offering.
Cultural Algorithms for Optimization

This chapter provides the use of cultural algorithms for both single- and multi-objective optimization. Ravichandran proposed a cultural algorithm based on decomposition to decompose a dynamic multi-objective optimization problem into several sub-problems that are then optimized using information shared by neighboring problems. Although this is clearly a multi-objective optimization algorithm, the author used it to solve single-objective optimization problems. In this case, the authors used all of the previously designed knowledge sources, and they investigated the role of the belief space in the different stages of a dynamic optimization process. The topics covered in this chapter include static and dynamic single-objective optimization as well as multi-objective optimization. Cultural algorithms have been adopted in a wide variety of applications in which they have been used to solve both single- and multi-objective optimization problems. The cultural algorithm is able to produce competitive results while performing a lower number of objective function evaluations than the other algorithms.
Prediksi Gelombang Corona Dengan Metode Neural Network

Until recently the spread of COVID-19 is unstoppable. COVID-19 is caused by RNA Virus that spread widely between humans, mammals, and birds which cause respiratory, enteric, heart and neurologic diseases. Although it is known for respiratory infection, the virus through plasma or serum is also happen often. Therefore, there is still theoretical risk of the virus spreading through blood transfusion. Because there are more cases that shown no symptoms, worries about the spread of COVID-19 is increasing. Several attempts have been done for alleviate mortality rates like mask usage and lockdown quarantine. Neural network adapt on how a human brain works. One of neural network techniques is Multilayer Perceptron (MLP). In MLP, input data is received through one dimension and spread through network until an output is achieved. Every neuron connection on two neighboring layers have one dimensional value that determine the quality of that node. On every input data at each layer calculation is done by the weight of the layer, and then the result will be transformed by using non-linear formula that called as activation function. The result of this research is found by the help of two cross validation technic: GridSearchCV and KFold Cross Validation which gave each 0.943887 and 0.911341 score. The score is achieved using r2 which the best parameter of the model is determined as: relu, 0.1, (10,10), invscaling and lbfgs. Result showns that the proposed model can do the prediction well against the mortality rate of corona.
Semantic framework of event detection in emergency situations for smart buildings

Multimedia Sensor Networks (MSNs) have enhanced the ability to analyze the environment and provide responses based on its current status. Generally, MSNs are composed of scalar and multimedia sensors that have fixed locations. However, given the advancement of smart mobile device technologies, it is currently possible to dynamically integrate mobile sensors into MSNs. In this paper, we propose a formal platform to manage MSNs and the data gathered from them to detect complex events. Our main contributions include: M2SSN ​− ​Onto, a Mobile and Multimedia Semantic Sensor Networks Ontology; Py-CEMiD, an engine for detecting complex events and generate reactions to them; a mobile device location engine to locate mobile sensors; and a proof-of-concept in the context of detecting emergency situations in smart buildings. Several scenarios are validated for emergency events, combining simulated sensor measurements with real measurements of mobile devices. Results show complex events can be detected in near real time (less than 1 ​s).
Classification of Imbalanced Fetal Health Data by PSO Based Ensemble Recursive Feature Elimination ANN

Electrocardiogram (CTG) is a simple and low-cost option to assess the health of the fetus. However, the number of normal fetuses is larger than the number of abnormal fetuses, leading to imbalances in CTG data. Existing studies have attempted to optimize the data processing or model training process by integrating machine learning methods with optimization algorithms. However, the effectiveness of features and appropriate selection of machine learning method creates new challenges. This study proposed an comprehensive method that considers the feature effectiveness and data imbalance issue. The proposed method uses the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the Edited Nearest Neighbours (ENN), Recursive Feature Elimination (RFE), and Artificial Neural Network (ANN) algorithms to find the optimal combination of the parameters of the three algorithms to further improve the accuracy of the fetal health prediction and reduce the cost of tuning. Experimental results show that the algorithm proposed in this paper can effectively solve the imbalance of CTG data, with a classification accuracy of 0.9942 and a kappa measure of 0.9783, which can effectively assist doctors in diagnosing fetal health and improve the quality of hospital visits.
Innovative approaches to information modeling of placer deposits

Summary The paper is devoted to the problem of information support for geological research and works aimed at developing the mineral resources base of priority sectors of the national industry The purpose of this publication is to present innovative approaches to information modeling of placer deposits of heavy minerals, cassiterite and rare-metal mineralization, amber. The tasks are the coverage of the methodology and methods, demonstration of modeling examples for typical objects, designation of a strategy for further research in the field of information modeling. The methodological basis of targeted information modeling is the formation analysis combined with the approaches of info-geology. Methodological and methodic development is predictive and reconstructive complex for modeling geological objects of various scales. The methodological complex of information support for research and works includes two blocks: a predictive-paleoreconstructive retrospective-static model and an integrated ecological-geological model of a local geological object (CEGM), including a digital structural-lithological (subordinate rank) model (DSLM) and other functional submodels. The examples of modelling of placers are shown. Prospects for the further development of information modeling are suggested.
General Paradigm of Edge-Based Internet of Things Data Mining for Geohazard Prevention

Geological hazards (geohazards) are geological processes or phenomena formed under external-induced factors causing losses to human life and property. Geohazards are sudden, cause great harm, and have broad ranges of influence, which bring considerable challenges to geohazard prevention. Monitoring and early warning are the most common strategies to prevent geohazards. With the development of the internet of things (IoT), IoT-based monitoring devices provide rich and fine data, making geohazard monitoring and early warning more accurate and effective. IoT-based monitoring data can be transmitted to a cloud center for processing to provide credible data references for geohazard early warning. However, the massive numbers of IoT devices occupy most resources of the cloud center, which increases the data processing delay. Moreover, limited bandwidth restricts the transmission of large amounts of geohazard monitoring data. Thus, in some cases, cloud computing is not able to meet the real-time requirements of geohazard early warning. Edge computing technology processes data closer to the data source than to the cloud center, which provides the opportunity for the rapid processing of monitoring data. This article presents the general paradigm of edge-based IoT data mining for geohazard prevention, especially monitoring and early warning. The paradigm mainly includes data acquisition, data mining and analysis, and data interpretation. Moreover, a real case is used to illustrate the details of the presented general paradigm. Finally, this article discusses several key problems for the general paradigm of edge-based IoT data mining for geohazard prevention.
A Unifying Framework for Observer-Aware Planning and its Complexity

Being aware of observers and the inferences they make about an agent's behavior is crucial for successful multi-agent interaction. Existing works on observer-aware planning use different assumptions and techniques to produce observer-aware behaviors. We argue that observer-aware planning, in its most general form, can be modeled as an interactive POMDP (I-POMDP), which is very hard to solve. Hence, we introduce a more efficient framework for producing observer-aware behaviors called Observer-Aware MDP (OAMDP) and analyze its relationship to I-POMDP. We illustrate that OAMDPs can be used to improve interpretability of agent behaviors in several scenarios and establish complexity results for OAMDPs.
Combining Open Domain Question Answering with a Task-Oriented Dialog System

We apply the modular dialog system framework to combine open-domain question answering with a task-oriented dialog system.This meta dialog system can answer questions from Wikipedia and at the same time act as a personal assistant.The aim of this system is to combine the strength of an open-domain question answering system with the conversational power of task-oriented dialog systems.After explaining the technical details of the system, we combined a new dataset out of standard datasets to evaluate the system.We further introduce an evaluation method for this system.Using this method, we compare the performance of the non-modular system with the performance of the modular system and show that the modular dialog system framework is very suitable for this combination of conversational agents and that the performance of each agent decreases only marginally through the modular setting.
An Efficient Coding Theorem via Probabilistic Representations and Its Applications.

A probabilistic representation of a string x ∈ {0,1}ⁿ is given by the code of a randomized algorithm that outputs x with high probability [Igor C. Oliveira, 2019]. We employ probabilistic representations to establish the first unconditional Coding Theorem in time-bounded Kolmogorov complexity. More precisely, we show that if a distribution ensemble 𝒟_m can be uniformly sampled in time T(m) and generates a string x ∈ {0,1}^* with probability at least δ, then x admits a time-bounded probabilistic representation of complexity O(log(1/δ) + log (T) + log(m)). Under mild assumptions, a representation of this form can be computed from x and the code of the sampler in time polynomial in n = |x|. We derive consequences of this result relevant to the study of data compression, pseudodeterministic algorithms, time hierarchies for sampling distributions, and complexity lower bounds. In particular, we describe an instance-based search-to-decision reduction for Levin’s Kt complexity [Leonid A. Levin, 1984] and its probabilistic analogue rKt [Igor C. Oliveira, 2019]. As a consequence, if a string x admits a succinct time-bounded representation, then a near-optimal representation can be generated from x with high probability in polynomial time. This partially addresses in a time-bounded setting a question from [Leonid A. Levin, 1984] on the efficiency of computing an optimal encoding of a string.
Network Bending Neural Vocoders

Network bending [1] aims to elicit interesting creative output from generativeneural networks by applying various transformations to the activations of groupsof network nodes. This paper describes the investigation of how this emergingtechnique of ‘network bending’ can be used to provide novel creative controlover sound synthesis networks based on the Magenta DDSP API [2] and howbest to provide access to the resulting sound synthesis neural networks to creative practitioners.
Non-negative sparse Laplacian regularized latent multi-view subspace clustering

Recently, in the area of artificial intelligence and machine learning, subspace clustering of multi-view data is a research hotspot. The goal is to divide data samples from different sources into different groups. We proposed a new subspace clustering method for multi-view data which termed as Non-negative Sparse Laplacian regularized Latent Multi-view Subspace Clustering (NSL2MSC) in this paper. The method proposed in this paper learns the latent space representation of multi view data samples, and performs the data reconstruction on the latent space. The algorithm can cluster data in the latent representation space and use the relationship of different views. However, the traditional representation-based method does not consider the non-linear geometry inside the data, and may lose the local and similar information between the data in the learning process. By using the graph regularization method, we can not only capture the global low dimensional structural features of data, but also fully capture the nonlinear geometric structure information of data. The experimental results show that the proposed method is effective and its performance is better than most of the existing alternatives.
Exploring Factors of an Inclusive Textbook Access Program in Computer Technology Courses

Many factors contribute to student success in college-level courses. One major factor is student access to required course materials. There is a growing number of students who, for several reasons, do not have access to materials or choose not to purchase textbooks. The negative impact of textbook cost is well known and documented and often a significant contributor to not purchasing course materials. In this paper, we explore the use of an inclusive textbook access program for several computer courses at a large community college. As part of this project, we present student feedback on this initiative and examine perceptions on textbook use and purchasing habits. The research helps gain insight into having the textbook built into the cost of the course and the impact of having access to a digital copy of the text on the very first day of class.
Traffic Sign Classification Comparison Between Various Convolution Neural Network Models

Fast detection and accurate classiï¬cation of trafï¬c signs is one of the major aspects of advance driver assistance system (ADAS) and intelligent transport systems (ITS), this paper presents a comparison between an 8-Layer convolutional neural network (CNN), and some state of the Arts model such as VGG16 and Resnet50, for trafï¬c sign classiï¬cation on The GTSRB. using a GPU to increase processing time, the design showed that with various augmentation applied to the CNN, our 8-layer Model was able to outperform the State of the Arts models with a higher test Accuracy, 50 times lesser training parameters, and faster training time our 8 -layer model was able to achieve 96% test accuracy.
Do No Harm Guide: Applying Equity Awareness in Data Visualization

In this guide and its associated toolkits, the Urban Institute focuses on how data practitioners can approach their work through a lens of diversity, equity, and inclusion. Through over a dozen int...