Reviewerly/exHarmony huggingface dataset:
Designing a Secure Environment for IoT Networks Using Lightweight AES Algorithm

In recent years, the rapid development in the field of wireless technologies leaded to appear a new topic known as the Internet of things (IoT). The IoT applications can be found in various fields of our life such as smart home, health care, smart building, and etc. In all these applications the data collected from the real world are transmitted through the Internet, therefore these data have become a target of many attacks and hackers. So, a secure communication must be provided to protect the transmitted data from unauthorized access. This paper focuses on designing a secure IoT system to protect the sensing data. In this system, the security is provided by the use of Lightweight AES encryption algorithm to encrypt the data received from physical environment. The hardware used in this proposal is the Raspberry Pi 3 model B and two types of sensors. The LAES algorithm was embedded inside the Raspberry in order to protect the sensing data that comes from sensors connected to the Raspberry Pi before sending it through network. The analysis results show that the proposed IoT security system consumes less time in encryption/decryption and has high throughput when compared with others from related work. Its throughput is higher in about 19.24% than one system from the related studies.
Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis

Synthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic aperture radar (SAR) images despeckling. However, these CNN-based SAR images despeckling approaches usually require large computational resources, especially in the case of huge training data. In this paper, we proposed a SAR image despeckling method using a CNN platform with a new learnable spatial activation function, which required significantly fewer network parameters without incurring any degradation in performance over the state-of-the-art despeckling methods. Specifically, we redefined the rectified linear units (ReLU) function by adding a convolutional kernel to obtain the weight map of each pixel, making the activation function learnable. Meanwhile, we designed several experiments to demonstrate the advantages of our method. In total, 400 images from Google Earth comprising various scenes were selected as a training set in addition to 10 Google Earth images including athletic field, buildings, beach, and bridges as a test set, which achieved good despeckling effects in both visual and index results (peak signal to noise ratio (PSNR): 26.37 ± 2.68 and structural similarity index (SSIM): 0.83 ± 0.07 for different speckle noise levels). Extensive experiments were performed on synthetic and real SAR images to demonstrate the effectiveness of the proposed method, which proved to have a superior despeckling effect and higher ENL magnitudes than the existing methods. Our method was applied to coniferous forest, broad-leaved forest, and conifer broad-leaved mixed forest and proved to have a good despeckling effect (PSNR: 23.84 ± 1.09 and SSIM: 0.79 ± 0.02). Our method presents a robust framework inspired by the deep learning technology that realizes the speckle noise suppression for various remote sensing images.
Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models

Systems that can find correspondences between multiple modalities, such as between speech and images, have great potential to solve different recognition and data analysis tasks in an unsupervised manner. This work studies multimodal learning in the context of visually grounded speech (VGS) models, and focuses on their recently demonstrated capability to extract spatiotemporal alignments between spoken words and the corresponding visual objects without ever been explicitly trained for object localization or word recognition. As the main contributions, we formalize the alignment problem in terms of an audiovisual alignment tensor that is based on earlier VGS work, introduce systematic metrics for evaluating model performance in aligning visual objects and spoken words, and propose a new VGS model variant for the alignment task utilizing cross-modal attention layer. We test our model and a previously proposed model in the alignment task using SPEECH-COCO captions coupled with MSCOCO images. We compare the alignment performance using our proposed evaluation metrics to the semantic retrieval task commonly used to evaluate VGS models. We show that cross-modal attention layer not only helps the model to achieve higher semantic cross-modal retrieval performance, but also leads to substantial improvements in the alignment performance between image object and spoken words.
Lithological mapping using EO-1 Hyperion hyperspectral data and semisupervised self-learning method

Hyperspectral remote sensing data have been widely used in lithological identification and mapping. In existing studies, sufficient training samples are required. However, collecting sufficient labeled training samples for lithological classification in remote and inaccessible areas is generally time consuming, expensive, and even hard to implement, which causes the insufficient training sample problems in lithological classification using hyperspectral data. The semisupervised self-learning (SSL) method provides an alternative way of addressing this ill-posed problem by enlarging the training set from unlabeled samples using the limited labeled samples as a priori. This study evaluates and analyzes the SSL method in lithological mapping using EO-1 Hyperion hyperspectral image over a remote area. The performance of SSL with limited training samples was validated by comparing with multinomial logistic regression (MLR) and random forest (RF) classification using full training samples. The experimental results indicate that SSL method with limited training samples can produce results comparable with the MLR with all the training samples and better results than those of the RF with all the training samples. Therefore, the SSL method provides a useful way for hyperspectral lithological mapping with limited training samples over remote and inaccessible areas.
Representation Learning to Classify and Detect Adversarial Attacks Against Speaker and Speech Recognition Systems

Adversarial attacks have become a major threat for machine learning applications. There is a growing interest in studying these attacks in the audio domain, e.g, speech and speaker recognition; and find defenses against them. In this work, we focus on using representation learning to classify/detect attacks w.r.t. the attack algorithm, threat model or signal-to-adversarial-noise ratio. We found that common attacks in the literature can be classified with accuracies as high as 90%. Also, representations trained to classify attacks against speaker identification can be used also to classify attacks against speaker verification and speech recognition. We also tested an attack verification task, where we need to decide whether two speech utterances contain the same attack. We observed that our models did not generalize well to attack algorithms not included in the attack representation model training. Motivated by this, we evaluated an unknown attack detection task. We were able to detect unknown attacks with equal error rates of about 19%, which is promising.
Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing

Quantum annealing is a global optimization algorithm that uses the quantum tunneling effect to speed-up the search for an optimal solution. Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. Consequently, previous applications of quantum annealing to real-life use cases have focused on problems that are either native QUBO or close to native QUBO. By contrast, in this paper we propose to tackle inequality constraints and non-quadratic terms. We demonstrate how to handle them with a realistic use case-a bus charging scheduling problem. First, we reformulate the original integer programming problem into a QUBO with the penalty method and directly solve it on a D-Wave machine. In a second approach, we dualize the problem by performing the Hubbard-Stratonovich transformation. The dual problem is solved indirectly by combining quantum annealing and adaptive classical gradient-descent optimizer. Whereas the penalty method is severely limited by the connectivity of the realistic device, we show experimentally that the indirect approach is able to solve problems of a larger size, offering thus a better scaling. Hence, the implementation of the Hubbard-Stratonovich transformation carried out in this paper on a scheduling use case suggests that this approach could be investigated further and applied to a variety of real-life integer programming problems under multiple constraints to lower the cost of mapping to QUBO, a key step towards the near-term practical application of quantum computing.
Knowledge Distillation with Distribution Mismatch

Knowledge distillation (KD) is one of the most efficient methods to compress a large deep neural network (called teacher) to a smaller network (called student). Current state-of-the-art KD methods assume that the distributions of training data of teacher and student are identical to maintain the student’s accuracy close to the teacher’s accuracy. However, this strong assumption is not met in many real-world applications where the distribution mismatch happens between teacher’s training data and student’s training data. As a result, existing KD methods often fail in this case. To overcome this problem, we propose a novel method for KD process, which is still effective when the distribution mismatch happens. We first learn a distribution based on student’s training data, from which we can sample images well-classified by the teacher. By doing this, we can discover the data space where the teacher has good knowledge to transfer to the student. We then propose a new loss function to train the student network, which achieves better accuracy than the standard KD loss function. We conduct extensive experiments to demonstrate that our method works well for KD tasks with or without distribution mismatch. To the best of our knowledge, our method is the first method addressing the challenge of distribution mismatch when performing KD process.
How Should Artificial Intelligence Explain Itself? Understanding Preferences for Explanations Generated by XAI Algorithms

Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers. While the emerging field of Explainable Artificial Intelligence (XAI) has mainly focused on developing algorithms that generate such explanations, there has not yet been sufficient consideration of customers’ preferences for various types and formats of explanations. We discuss theoretically and study empirically people’s preferences for explanations of algorithmic decisions. We focus on three main attributes that describe automatically-generated explanations from existing XAI algorithms (format, complexity, and specificity), and capture differences across contexts (online targeted advertising vs. loan applications) as well as heterogeneity in users’ cognitive styles. Despite their popularity among academics, we find that counterfactual explanations are not popular among users, unless they follow a negative outcome (e.g., loan application was denied). We also find that users are willing to tolerate some complexity in explanations. Finally, our results suggest that preferences for specific (vs. more abstract) explanations are related to the level at which the decision is construed by the user, and to the deliberateness of the user’s cognitive style.
Augment BERT with average pooling layer for Chinese summary generation

The BERT pre-trained language model has achieved good results in various subtasks of natural language processing, but its performance in generating Chinese summaries is not ideal. The most intuitive reason is that the BERT model is based on character-level composition, while the Chinese language is mostly in the form of phrases. Directly fine-tuning the BERT model cannot achieve the expected effect. This paper proposes a novel summary generation model with BERT augmented by the pooling layer. In our model, we perform an average pooling operation on token embedding to improve the model’s ability to capture phrase-level semantic information. We use LCSTS and NLPCC2017 to verify our proposed method. Experimental data shows that the average pooling model’s introduction can effectively improve the generated summary quality. Furthermore, different data needs to be set with varying pooling kernel sizes to achieve the best results through comparative analysis. In addition, our proposed method has strong generalizability. It can be applied not only to the task of generating summaries, but also to other natural language processing tasks.
Refining activation downsampling with SoftPool

Convolutional Neural Networks (CNNs) use pooling to decrease the size of activation maps. This process is crucial to increase the receptive fields and to reduce computational requirements of subsequent convolutions. An important feature of the pooling operation is the minimization of information loss, with respect to the initial activation maps, without a significant impact on the computation and memory overhead. To meet these requirements, we propose SoftPool: a fast and efficient method for exponentially weighted activation downsampling. Through experiments across a range of architectures and pooling methods, we demonstrate that SoftPool can retain more information in the reduced activation maps. This refined downsampling leads to improvements in a CNN's classification accuracy. Experiments with pooling layer substitutions on ImageNet1K show an increase in accuracy over both original architectures and other pooling methods. We also test SoftPool on video datasets for action recognition. Again, through the direct replacement of pooling layers, we observe consistent performance improvements while computational loads and memory requirements remain limited.
Entangled symmetric states and copositive matrices

Entanglement in symmetric quantum states and the theory of copositive matrices are intimately related concepts. For the simplest symmetric states, i.e., the diagonal symmetric (DS) states, it has been shown that there exists a correspondence between exceptional (non-exceptional) copositive matrices and non-decomposable (decomposable) Entanglement Witnesses (EWs). Here we show that EWs of symmetric, but not DS, states can also be constructed from extended copositive matrices, providing new examples of bound entangled symmetric states, together with their corresponding EWs, in arbitrary odd dimensions.
Human optional stopping in a heteroscedastic world.

When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or "heteroscedasticity"). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Deterministic remote state preparation of arbitrary three-qubit state through noisy cluster-GHZ channel

We propose a novel scheme for remote state preparation of an arbitrary three-qubit state with unit success probability, utilizing a nine-qubit cluster-GHZ state without introducing auxiliary qubits. Furthermore, we proceed to investigate the effects of different quantum noises (e.g., amplitude-damping, phase-damping, bit-flip and phase-flip noises) on the systems. The fidelity results of three-qubit target state are presented, which are usually used to illustrate how close the output state is to the target state. To compare the different effects between the four common types of quantum noises, the fidelities under one specific identical target state are also calculated and discussed. It is found that the fidelity of the phase-flip noisy channel drops the fastest through the four types of noisy channels, while the fidelity is found to always maintain at 1 in bit-flip noisy channel.
Accelerated Device Placement Optimization with Contrastive Learning

With the ever-increasing size and complexity of deep neural network models, it is difficult to fit and train a complete copy of the model on a single computational device with limited capability. Therefore, large neural networks are usually trained on a mixture of devices, including multiple CPUs and GPUs, of which the computational speed and efficiency are drastically affected by how these models are partitioned and placed on the devices. In this paper, we propose Mars, a novel design to find efficient placements for large models. Mars leverages a self-supervised graph neural network pre-training framework to generate node representations for operations, which is able to capture the topological properties of the computational graph. Then, a sequence-to-sequence neural network is applied to split large models into small segments so that Mars can predict the placements sequentially. Novel optimizations have been applied in the placer design to achieve the best possible performance in terms of the time needed to complete training the agent for placing models with very large sizes. We deployed and evaluated Mars on benchmarks involving Inception-V3, GNMT, and BERT models. Extensive experimental results show that Mars can achieve up to 27.2% and 2.7% speedup of per-step training time than the state-of-the-art for GNMT and BERT models, respectively. We also show that with self-supervised graph neural network pre-training, our design achieves the fastest speed in discovering the optimal placement for Inception-V3.
Contributions of the Brazilian Act for the Protection of Personal Data for treating Digital Legacy

In this article, we analyze how the Brazilian Act for the Protection of Personal Data --- Lei Geral de Proteção de Dados Pessoais (LGPD) in Portuguese --- can contribute to handling some situations involving post-mortem digital legacy. For that purpose, we investigate some aspects of this act that can concur with the development of software and internet applications that cope with users’ digital assets. We analyzed the role of LGPD in the Brazilian legal system. The research was carried out based on a literature review and on the analysis of the relevant legislation and some bills proposed to regulate the matter. In line with the national constitutional order, the results point to the possibility of applying the principles and foundations of the data protection act as an axiological matrix for the treatment of the existing digital legacy.
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency

In the evasion attacks against deep neural networks (DNN), the attacker generates adversarial instances that are visually indistinguishable from benign samples and sends them to the target DNN to trigger misclassifications. In this paper, we propose a novel multi-view adversarial image detector, namely Argos, based on a novel observation. That is, there exist two “souls” in an adversarial instance, i.e., the visually unchanged content, which corresponds to the true label, and the added invisible perturbation, which corresponds to the misclassified label. Such inconsistencies could be further amplified through an autoregressive generative approach that generates images with seed pixels selected from the original image, a selected label, and pixel distributions learned from the training data. The generated images (i.e., the “views”) will deviate significantly from the original one if the label is adversarial, demonstrating inconsistencies that Argos expects to detect. To this end, Argos first amplifies the discrepancies between the visual content of an image and its misclassified label induced by the attack using a set of regeneration mechanisms and then identifies an image as adversarial if the reproduced views deviate to a preset degree. Our experimental results show that Argos significantly outperforms two representative adversarial detectors in both detection accuracy and robustness against six well-known adversarial attacks. Code is available at: https://github.com/sohaib730/Argos-Adversarial_Detection
Personally Identifiable Information (PII) Detection in the Unstructured Large Text Corpus using Natural Language Processing and Unsupervised Learning Technique

Personally Identifiable Information (PII) has gained much attention with the rapid development of technologies and the exploitation of information relating to an individual.The corporates and other organizations store a large amount of information that is primarily disseminated in the form of emails that include personnel information of the user, employee, and customers.The security aspects of PII storage have been ignored, raising serious security concerns onindividual privacy.A significant concern arises about comprehending the responsibilities regarding the uses of PII.However, in real-time scenarios, email data is regarded as unstructured text data, detecting PII from such an unstructured large text corpus is quite challenging.This paper presents an intelligent clustering approach for automatically detecting personally identifiable information (PII) from a large text corpus.The focus of the proposed study is to design a model that receives text content and detects possible PII attributes.Therefore, this paper presents a clustering-based PII Model (C-PPIM) based on NLP and unsupervised learning to address detection of PII in the unstructured large text corpus.NLP is used to perform topic modeling, and Byte mLSTM, a different approach of sequence model, is implemented to address clustering problems in PII detection.The performance analysis of the proposed model is carried out existing hierarchical clustering concerning silhouette and cohesion score.The outcome indicatedthe effectiveness of the proposed system that highlights significant PII attributes, with significant scope in real-time implementation.In contrast, existing techniques are too expensive to function and fit in realtime environments.