Reviewerly/exHarmony huggingface dataset:
Optical Flow Techniques for Facial Expression Analysis -- a Practical Evaluation Study

Optical flow techniques are becoming increasingly performant and robust when estimating motion in a scene, but their performance has yet to be proven in the area of facial expression recognition. In this work, a variety of optical flow approaches are evaluated across multiple facial expression datasets, so as to provide a consistent performance evaluation. The aim of this work is not to propose a new expression recognition technique, but to understand better the adequacy of existing state-of-the art optical flow for encoding facial motion in the context of facial expression recognition. Our evaluations highlight the fact that motion approximation methods used to overcome motion discontinuities have a significant impact when optical flows are used to characterize facial expressions.
Semantic query-by-example speech search using visual grounding

A number of recent studies have started to investigate how speech systems can be trained on untranscribed speech by leveraging accompanying images at training time. Examples of tasks include keyword prediction and within- and across-mode retrieval. Here we consider how such models can be used for query-by-example (QbE) search, the task of retrieving utterances relevant to a given spoken query. We are particularly interested in semantic QbE, where the task is not only to retrieve utterances containing exact instances of the query, but also utterances whose meaning is relevant to the query. We follow a segmental QbE approach where variable-duration speech segments (queries, search utterances) are mapped to fixed-dimensional embedding vectors. We show that a QbE system using an embedding function trained on visually grounded speech data outperforms a purely acoustic QbE system in terms of both exact and semantic retrieval performance.
Influence of Control Parameters and the Size of Biomedical Image Datasets on the Success of Adversarial Attacks

In this paper, we study dependence of the success rate of adversarial attacks to the Deep Neural Networks on the biomedical image type, control parameters, and image dataset size. With this work, we are going to contribute towards accumulation of experimental results on adversarial attacks for the community dealing with biomedical images. The white-box Projected Gradient Descent attacks were examined based on 8 classification tasks and 13 image datasets containing a total of 605,080 chest X-ray and 317,000 histology images of malignant tumors. We concluded that: (1) An increase of the amplitude of perturbation in generating malicious adversarial images leads to a growth of the fraction of successful attacks for the majority of image types examined in this study. (2) Histology images tend to be less sensitive to the growth of amplitude of adversarial perturbations. (3) Percentage of successful attacks is growing with an increase of the number of iterations of the algorithm of generating adversarial perturbations with an asymptotic stabilization. (4) It was found that the success of attacks dropping dramatically when the original confidence of predicting image class exceeds 0.95. (5) The expected dependence of the percentage of successful attacks on the size of image training set was not confirmed.
Multi-lingual Dialogue Act Recognition with Deep Learning Methods

This paper deals with multi-lingual dialogue act (DA) recognition. The proposed approaches are based on deep neural networks and use word2vec embeddings for word representation. Two multi-lingual models are proposed for this task. The first approach uses one general model trained on the embeddings from all available languages. The second method trains the model on a single pivot language and a linear transformation method is used to project other languages onto the pivot language. The popular convolutional neural network and LSTM architectures with different set-ups are used as classifiers. To the best of our knowledge this is the first attempt at multi-lingual DA recognition using neural networks. The multi-lingual models are validated experimentally on two languages from the Verbmobil corpus.
Detecting Repeating Objects using Patch Correlation Analysis

In this paper we describe a new method for detecting and counting a repeating object in an image. While the method relies on a fairly sophisticated deformable part model, unlike existing techniques it estimates the model parameters in an unsupervised fashion thus alleviating the need for a user-annotated training data and avoiding the associated specificity. This automatic fitting process is carried out by exploiting the recurrence of small image patches associated with the repeating object and analyzing their spatial correlation. The analysis allows us to reject outlier patches, recover the visual and shape parameters of the part model, and detect the object instances efficiently. In order to achieve a practical system which is able to cope with diverse images, we describe a simple and intuitive active-learning procedure that updates the object classification by querying the user on very few carefully chosen marginal classifications. Evaluation of the new method against the state-of-the-art techniques demonstrates its ability to achieve higher accuracy through a better user experience.
Ensemble Models for Spoofing Detection in Automatic Speaker Verification

Detecting spoofing attempts of automatic speaker verification (ASV) systems is challenging, especially when using only one modeling approach. For robustness, we use both deep neural networks and traditional machine learning models and combine them as ensemble models through logistic regression. They are trained to detect logical access (LA) and physical access (PA) attacks on the dataset released as part of the ASV Spoofing and Countermeasures Challenge 2019. We propose dataset partitions that ensure different attack types are present during training and validation to improve system robustness. Our ensemble model outperforms all our single models and the baselines from the challenge for both attack types. We investigate why some models on the PA dataset strongly outperform others and find that spoofed recordings in the dataset tend to have longer silences at the end than genuine ones. By removing them, the PA task becomes much more challenging, with the tandem detection cost function (t-DCF) of our best single model rising from 0.1672 to 0.5018 and equal error rate (EER) increasing from 5.98% to 19.8% on the development set.
Forensic Analysis of Electronic Mail Messages

E-mail has revolutionized business, academic, and personal communication. The advantages of e-mail include speedy delivery, ease of communication, cost effectiveness, geographical independence, and the portability of mailboxes. The last two are the biggest advantages over snail mail. However, with e-mail comes the threat of a genuine user being compromised through key loggers, social engineering, shoulder surfing, password guessing and other similar, though less technical, methods. This passive espionage can have a direct impact on the genuine user in terms of denial of information, loss of money, loss of time, mental harassment and an attack of personal privacy. To enable digital forensic analysis of e-mails, we propose behavioral biometric based authentication, which is analogous to a signature in paper documents. Keyword: E-mail, forensics, threats, digital analysis, security cyber crimes
Integration of Cloud in Mobile Forensics

With emerging technology and the connection of electronic devices to the internet, Internet of Things (IoT) has become part of human life. From the development of smartphones to smartwatches and smart-homes, electronic devices now have the capability of performing human activities or aiding humans in performing activities such as turning hall lights on or off with their voice. Although a large number of people use these devices for the greater good, a few individuals or group of people hide behind these devices to perform malicious activities. In order to apprehend and prosecute perpetuators who hide behind smart devices for evil gains, forensic examinations or investigations must be conducted. This review aims to identify digital forensic challenges in IoT. The inclusion criteria for this paper were international journals, articles, conference papers and case studies published from 2019 to 2022. Thematic analysis was used to analyze and synthesis the literature. Three themes emerged from the analysis; automated compromised smart-home tracer; data volatility and reconstruction; IoT forensic investigation framework. This integrative review combines evidence of digital forensic challenges in diverse IoT devices. Keyword IoT forensics, IoT challenges, Digital forensics, Smart-home forensics.
Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is significant adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed RDF processing engines. These engines employ various mechanisms to implement critical components of the query processing engines such as data storage, indexing, language support, and query execution. All these components govern how queries are executed and can have a substantial effect on the query runtime. For example, the storage of RDF data in various ways significantly affects the data storage space required and the query runtime performance. The type of indexing approach used in RDF engines is critical for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a crucial optimization component of the RDF storage solutions. Finally, query execution involving different join orders significantly affects the query response time. This paper provides a comprehensive review of centralized and distributed RDF engines in terms of storage, indexing, language support, and query execution.
The Complexity of Definability by Open First-Order Formulas

In this article we formally define and investigate the computational complexity of the Definability Problem for open first-order formulas (i.e., quantifier free first-order formulas) with equality. Given a logic $\mathbf{\mathcal{L}}$, the $\mathbf{\mathcal{L}}$-Definability Problem for finite structures takes as input a finite structure $\mathbf{A}$ and a target relation $T$ over the domain of $\mathbf{A}$, and determines whether there is a formula of $\mathbf{\mathcal{L}}$ whose interpretation in $\mathbf{A}$ coincides with $T$. We show that the complexity of this problem for open first-order formulas (open definability, for short) is coNP-complete. We also investigate the parametric complexity of the problem, and prove that if the size and the arity of the target relation $T$ are taken as parameters then open definability is $\mathrm{coW}[1]$-complete for every vocabulary $\tau$ with at least one, at least binary, relation.
Word Similarity Datasets for Thai: Construction and Evaluation

Distributional semantics in the form of word embeddings are an essential ingredient to many modern natural language processing systems. The quantification of semantic similarity between words can be used to evaluate the ability of a system to perform semantic interpretation. To this end, a number of word similarity datasets have been created for the English language over the last decades. For Thai language few such resources are available. In this work, we create three Thai word similarity datasets by translating and re-rating the popular WordSim-353, SimLex-999 and SemEval-2017-Task-2 datasets. The three datasets contain 1852 word pairs in total and have different characteristics in terms of difficulty, domain coverage, and notion of similarity (relatedness vs.~similarity). These features help to gain a broader picture of the properties of an evaluated word embedding model. We include baseline evaluations with existing Thai embedding models, and identify the high ratio of out-of-vocabulary words as one of the biggest challenges. All datasets, evaluation results, and a tool for easy evaluation of new Thai embedding models are available to the NLP community online.
Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks

Learning good representations without supervision is still an open issue in machine learning, and is particularly challenging for speech signals, which are often characterized by long sequences with a complex hierarchical structure. Some recent works, however, have shown that it is possible to derive useful speech representations by employing a self-supervised encoder-discriminator approach. This paper proposes an improved self-supervised method, where a single neural encoder is followed by multiple workers that jointly solve different self-supervised tasks. The needed consensus across different tasks naturally imposes meaningful constraints to the encoder, contributing to discover general representations and to minimize the risk of learning superficial ones. Experiments show that the proposed approach can learn transferable, robust, and problem-agnostic features that carry on relevant information from the speech signal, such as speaker identity, phonemes, and even higher-level features such as emotional cues. In addition, a number of design choices make the encoder easily exportable, facilitating its direct usage or adaptation to different problems.
Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images

Single image dehazing is an ill-posed problem that has recently drawn important attention. Despite the significant increase in interest shown for dehazing over the past few years, the validation of the dehazing methods remains largely unsatisfactory, due to the lack of pairs of real hazy and corresponding haze-free reference images. To address this limitation, we introduce Dense-Haze - a novel dehazing dataset. Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes. The hazy scenes have been recorded by introducing real haze, generated by professional haze machines. The hazy and haze-free corresponding scenes contain the same visual content captured under the same illumination parameters. Dense-Haze dataset aims to push significantly the state-of-the-art in single-image dehazing by promoting robust methods for real and various hazy scenes. We also provide a comprehensive qualitative and quantitative evaluation of state-of-the-art single image dehazing techniques based on the Dense-Haze dataset. Not surprisingly, our study reveals that the existing dehazing techniques perform poorly for dense homogeneous hazy scenes and that there is still much room for improvement.
Optimal non-signalling violations via tensor norms

In this paper we characterize the set of bipartite non-signalling probability distributions in terms of tensor norms. Using this characterization we give optimal upper and lower bounds on Bell inequality violations when non-signalling distributions are considered. Interestingly, our upper bounds show that non-signalling Bell inequality violations cannot be significantly larger than quantum Bell inequality violations.
Extended Regularized Dual Averaging Methods for Stochastic Optimization

We introduce a new algorithm, extended regularized dual averaging (XRDA), for solving regularized stochastic optimization problems, which generalizes the regularized dual averaging (RDA) method. The main novelty of the method is that it allows a flexible control of the backward step size. For instance, the backward step size used in RDA grows without bound, while for XRDA the backward step size can be kept bounded. We demonstrate experimentally that additional control over the backward step size can significantly improve the convergence rate of the algorithm while preserving desired properties of the iterates, such as sparsity. Theoretically, we show that the XRDA method achieves the same convergence rate as RDA for general convex objectives.
A Polymatroid Approach to Generalized Weights of Rank Metric Codes

We consider the notion of a $(q,m)$-polymatroid, due to Shiromoto, and the more general notion of $(q,m)$-demi-polymatroid, and show how generalized weights can be defined for them. Further, we establish a duality for these weights analogous to Wei duality for generalized Hamming weights of linear codes. The corresponding results of Ravagnani for Delsarte rank metric codes, and Martinez-Penas and Matsumoto for relative generalized rank weights are derived as a consequence.
PERBANDINGAN KUALITAS LAYANAN KINERJA PERANGKAT JARINGAN TP-LINK WIRELESS N ROUTER DAN GL- INET WIRELESS ROUTER BERBASIS FIRMWARE OPENWRT

The development of the internet network and network equipment is currently very rapid, to meet the needs of internet usage, network devices are used as supporting tools in service access and network deployment. According to research conducted in a performance comparison analysis between the default firmware and the openwrt firmware [1], the performance of the network device is not only determined by the hardware specifications contained in the network device, but is also determined by the firmware in the network device. according to a literature review from various sources that the openwrt firmware can be used as a firmware solution to improve the performance of network devices that have limited service features and as firmware updates for network devices that do not get development support from vendors.The research will be conducted by analyzing the comparison of the performance of the Gl iNet Wireless Router network device which was created for the openwrt firmware and already using the openwrt firmware from the manufacturer, with the Tp Link Wireless Router network device, which is a network device that will implement the openwrt firmware. The design is carried out using the PPDIOO Life-Cycle Approach network development method, and a comparative analysis of network quality using QoS (Quality of Service) parameters.The final result of this research is how to implement and how the openwrt firmware runs on network devices that are not actually using the original firmware based on the results of network parameters jitter, delay, packet loss, throughput and signal strength testing in internet service access.&#x0D; Keywords : OpenWrt, Quality of Service, PPDIOO, Life-Cycle Approach.
Vector Field Visualization of Single-Qubit State Tomography

As the variety of commercially available quantum computers continues to increase so does the need for tools that can characterize, verify and validate these computers. This work explores using quantum state tomography for characterizing the performance of individual qubits and develops a vector field visualization for presentation of the results. The proposed protocol is demonstrated in simulation and on quantum computing hardware developed by IBM. The results identify qubit performance features that are not reflected in the standard models of this hardware, indicating opportunities to improve the accuracy of these models. The proposed qubit evaluation protocol is provided as free open-source software to streamline the task of replicating the process on other quantum computing devices.
Machine Learning Techniques, Features, Datasets, and Algorithm Performance Parameters for Sentiment Analysis: A Systematic Review

The purpose of this paper is to review various studies on current machine learning techniques used in sentiment analysis with the primary focus on finding the most suitable combinations of the techniques, datasets, data features, and algorithm performance parameters used in most applications. To accomplish this, we performed a systematic review of 24 articles published between 2013 and 2020 covering machine learning techniques for sentiment analysis. The review shows that Support Vector Machine as well as Naïve Bayes techniques are the most popular machine learning techniques; word stem and n-grams are the most extensively applied features, and the Twitter dataset is the most predominant. This review further revealed that machine learning algorithms' performance depends on many factors, including the dataset, extracted features, and size of data used. Accuracy is the most commonly used algorithm performance metric. These findings offer important information for researchers and businesses to use when selecting suitable techniques, features, and datasets for sentiment analysis for various business applications such as brand reputation monitoring.
A new Local Radon Descriptor for Content-Based Image Search

Content-based image retrieval (CBIR) is an essential part of computer vision research, especially in medical expert systems. Having a discriminative image descriptor with the least number of parameters for tuning is desirable in CBIR systems. In this paper, we introduce a new simple descriptor based on the histogram of local Radon projections. We also propose a very fast convolution-based local Radon estimator to overcome the slow process of Radon projections. We performed our experiments using pathology images (KimiaPath24) and lung CT patches and test our proposed solution for medical image processing. We achieved superior results compared with other histogram-based descriptors such as LBP and HoG as well as some pre-trained CNNs.
A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification

Despite the growing popularity of metric learning approaches, very little work has attempted to perform a fair comparison of these techniques for speaker verification. We try to fill this gap and compare several metric learning loss functions in a systematic manner on the VoxCeleb dataset. The first family of loss functions is derived from the cross entropy loss (usually used for supervised classification) and includes the congenerous cosine loss, the additive angular margin loss, and the center loss. The second family of loss functions focuses on the similarity between training samples and includes the contrastive loss and the triplet loss. We show that the additive angular margin loss function outperforms all other loss functions in the study, while learning more robust representations. Based on a combination of SincNet trainable features and the x-vector architecture, the network used in this paper brings us a step closer to a really-end-to-end speaker verification system, when combined with the additive angular margin loss, while still being competitive with the x-vector baseline. In the spirit of reproducible research, we also release open source Python code for reproducing our results, and share pretrained PyTorch models on torch.hub that can be used either directly or after fine-tuning.