Reviewerly/exHarmony huggingface dataset:
ASQ-FastBM3D: An Adaptive Denoising Framework for Defending Adversarial Attacks in Machine Learning Enabled Systems

Machine learning has made significant progress in image recognition, natural language processing, and autonomous driving. However, the generation of adversarial examples has proved that the machine learning system is unreliable. By adding imperceptible perturbations to clean images can fool the well-trained machine learning systems. To solve this problem, we propose an adaptive image denoising framework Adaptive Scalar Quantization ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASQ-FastBM3D</i> ). The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASQ-FastBM3D</i> framework combines the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASQ</i> method with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FastBM3D</i> algorithm. The adaptive scalar quantization is the improvement of scalar quantization, which is used to eliminate most of the perturbations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FastBM3D</i> is proposed to improve the quality of the quantified image. The running time of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FastBM3D</i> is 50% less than that of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BM3D</i> . Compared with some traditional filter methods and some state-of-the-art neural network methods for recovering the adversarial examples, the accuracy rate of our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ASQ-FastBM3D</i> method is 99.73% and the F1 score is 98.01%, which is the highest.
Trainer: An Energy-Efficient Edge-Device Training Processor Supporting Dynamic Weight Pruning

Transfer learning, which transfers knowledge from source datasets to target datasets, is practical for adaptive deep neural network (DNN) applications. When considering user privacy and communication bandwidth issues, edge devices’ training is essential for transfer learning. Nevertheless, training requires repeating feedforward (FF), backpropagation (BP), and weight gradient (WG) millions of times, introducing prohibitive computation for edge devices. A promising method to reduce training computation is sparse DNN training (SDT), which dynamically prunes weights during training iterations and performs FF, BP, and WG only with unpruned weights. However, SDT suffers implicit redundancy and reuse imbalance for convolution layers. Besides, it turns bottlenecks into batch normalization (BN) layers. Therefore, it is challenging to achieve energy-efficient SDT computing. This article proposes a processor, Trainer, solving the above challenges with three features. First, a speculation mechanism removes implicit redundant operations, which have nonzeros’ input, weight, or output, but are ineffective for training. Second, a dynamic sparsity adaptive dataflow tackles the reuse imbalance, improving energy efficiency (EE) for dynamic sparse convolution in SDT. Third, a computational dependence decoupled BN unit eliminates BN’s repeated data access to reduce training energy and time. Trainer is fabricated in 28-nm CMOS technology and occupies 20.96 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of area. It achieves a peak EE of 173.28TFLOPS/W@FP16 (276.55TFLOPS/W@FP8) for a 90% activation sparsity and 90% weight sparsity. The sparsity to EE conversion ratio is 80.9, outperforming the previous work by 1.55 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> . When training a ResNet18 model with SDT, Trainer reduces energy by 2.23 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> and time by 1.76 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> than the state-of-the-art sparse training processor.
An Interpretive Perspective: Adversarial Trojaning Attack on Neural-Architecture-Search Enabled Edge AI Systems

In this article, we propose and analyze a group of adversarial backdoor attack methods on neural-architecture-search (NAS) enabled edge AI systems in industrial Internet of Things (IIoT) domain. NAS is a new popular way to generate scale-adaptive deep neural networks which can meet the respective requirements of cloud, edge, and terminal AI computing in IIoT domain. However, since most users in NAS-enabled edge side are not the generators of AI models, the deployed edge AI models may have some vulnerabilities such as backdoors. These might pose serious security issues in IIoT. We propose some effective policies to attack such edge AI systems and provide advice about how to defend them. The most significant attack through third-party pretrained NAS in IIoT may occur by backdoor attacks while the third party might introduce vulnerability in the training dataset. The article designs backdoor attack processes to NAS-enabled edge devices to identify NAS’s vulnerability to adversarial trojaning attacks and interpret the backdoor attacks. It shows that the existence of high impact nodes greatly weakens the robustness of the network. A malicious attacker can quickly paralyze the network by only selecting a few high impact nodes. Finally, it provides advice and possible solution on defending the adversarial backdoor attacks to NAS.
The impact of Artificial Intelligence on the marketing practices of Professional Services Firms

Artificial intelligence (AI) is rapidly evolving, influencing service industries and Professional Services Firms (PSFs) embracing AI to improve margins. In this paper, we reveal how AI impacts the characteristics and marketing practices of PSFs. AI has the potential to provide considerable efficiency and organizational benefits, yet simultaneously changes service attributes, threatens the competitive advantage of deep client relationships, and directly influences the marketing practices of PSFs, including pricing strategy. Based on an extensive literature review, we present a conceptual model illustrating the changes impacting the business model, marketing practices, and client relationships of PSFs.
Online Participatory Design of Heritage Projects

Participatory Design (PD) can help to produce sustainable and user-oriented outputs by involving end users and other stakeholders in early design stages. In heritage projects, PD is considered a democratic process for designing with communities to address local issues and to give them a central role in generating insights about the presentation and accessibility of heritage. As part of the cross-border collaborative project “Terra Mosana,” several PD workshops were planned in 2020 in different cities of the Meuse-Rhine Euregion, to involve and engage residents in design activities about the investigation and communication of the shared history of the region. The outbreak of COVID-19 made it challenging to conduct face-to-face activities. Therefore, we developed a scenario to move the workshops online, facilitating both synchronous and asynchronous activities. This chapter investigates how PD in heritage projects can be effectively organized and enacted in times of crisis when people cannot meet in person. Moving the workshops online provided us with an opportunity to learn about how different communication tools and infrastructures influenced individual and collective participation during the pandemic. We open up a discussion about the potential challenges and concerns that may be encountered by those designing participatory heritage projects in the future.
Educational Design Fictions: Imagining Learning Futures

Amidst social and technological changes, the changing nature of work, and in the face of global disruptions, we must consider how to forge education for an unknown future. We need tools for considering education futures in ways that allow for collective imaginings, explorations and conversations about the future and present shape of education. In design theory and practice, the approach of design futures provides a means for critically envisioning possible futures, not as predictions but as creative provocations—as imaginative near-futures that raise questions about direction, challenge understandings and provide a lens on the present. In this chapter we explore the potential for adopting the practice of design fiction writing in education, as “Educational Design Fictions”. We present a series of six Educational Design Fictions that showcase a range of narrative approaches, contexts and styles. We then—by way of a multivocal reflection—critically examine these stories for their potential in shaping discussion about education. We show that Educational Design Fictions prompt a shift in thinking from current trajectories, highlight a disjunction between possible worlds and the legacy of existing practices, and provide a new lens on the challenges and assumptions of the present. The Educational Design Fictions presented are not strict design fictions in terms of diegetic prototypes and are not exclusively in the future but rather succeed by evocatively defining possible worlds which lead conversation to different perspectives. In this chapter the “conversation” is through discussion between authors—who also reflect on the process of writing itself. We expect this chapter to be of interest to those who would see value in a generative method for informing and promoting discussion about the future direction of education.
RoLEX: The development of an extended Romanian lexical dataset and its evaluation at predicting concurrent lexical information

Abstract In this article, we introduce an extended, freely available resource for the Romanian language, named RoLEX . The dataset was developed mainly for speech processing applications, yet its applicability extends beyond this domain. RoLEX includes over 330,000 curated entries with information regarding lemma, morphosyntactic description, syllabification, lexical stress and phonemic transcription. The process of selecting the list of word entries and semi-automatically annotating the complete lexical information associated with each of the entries is thoroughly described. The dataset’s inherent knowledge is then evaluated in a task of concurrent prediction of syllabification, lexical stress marking and phonemic transcription. The evaluation looked into several dataset design factors, such as the minimum viable number of entries for correct prediction, the optimisation of the minimum number of required entries through expert selection and the augmentation of the input with morphosyntactic information, as well as the influence of each task in the overall accuracy. The best results were obtained when the orthographic form of the entries was augmented with the complete morphosyntactic tags. A word error rate of 3.08% and a character error rate of 1.08% were obtained this way. We show that using a carefully selected subset of entries for training can result in a similar performance to the performance obtained by a larger set of randomly selected entries (twice as many). In terms of prediction complexity, the lexical stress marking posed most problems and accounts for around 60% of the errors in the predicted sequence.
An Empirical Evaluation of the Usefulness of Word Embedding Techniques in Deep Learning-Based Vulnerability Prediction

Abstract Software security is a critical consideration for software development companies that want to provide their customers with high-quality and dependable software. The automated detection of software vulnerabilities is a critical aspect in software security. Vulnerability prediction is a mechanism that enables the detection and mitigation of software vulnerabilities early enough in the development cycle. Recently the scientific community has dedicated a lot of effort on the design of Deep learning models based on text mining techniques. Initially, Bag-of-Words was the most promising method but recently more complex models have been proposed focusing on the sequences of instructions in the source code. Recent research endeavors have started utilizing word embedding vectors, which are widely used in text classification tasks like semantic analysis, for representing the words (i.e., code instructions) in vector format. These vectors could be trained either jointly with the other layers of the neural network, or they can be pre-trained using popular algorithms like word2vec and fast-text. In this paper, we empirically examine whether the utilization of word embedding vectors that are pre-trained separately from the vulnerability predictor could lead to more accurate vulnerability prediction models. For the purposes of the present study, a popular vulnerability dataset maintained by NIST was utilized. The results of the analysis suggest that pre-training the embedding vectors separately from the neural network leads to better vulnerability predictors with respect to their effectiveness and performance.
Beyond Word Embeddings: Heterogeneous Prior Knowledge Driven Multi-Label Image Classification

Multi-Label Image Classification (MLIC) is a fundamental yet challenging task which aims to recognize multiple labels from given images. The key to solve MLIC lies in how to accurately model the correlation between labels. Recent studies often adopt Graph Convolutional Network (GCN) to model label dependencies with word embeddings as prior knowledge. However, classical word embeddings typically contain redundant information due to the imperfect distributional hypothesis it relies on, which may degrade model generalizability. To tackle this problem, we propose a novel deep learning framework termed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> isual- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> emantic based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> onvolutional <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork (VSGCN), which alleviates the negative impact of redundant information by utilizing heterogeneous sources of prior knowledge. Specifically, we construct both visual prototype and semantic prototype for each label as heterogeneous prior label representations, which are further mapped to multi-label classifiers via two Multi-Head GCNs separately. The Multi-Head GCN mechanism proposed in this paper aims to guide the information propagation between prototypes for each label, which constructs multiple correlation graphs to simultaneously model the label correlation in different subspaces. Notably, we alleviate the negative influence of needless information by decreasing the inconsistency of predictions that come from visual space and semantic space. Extensive experiments conducted on various multi-label image datasets demonstrate the superiority of our proposed method.
Sign Language Recognition Using Hand Mark Analysis for Vision-Based System (HMASL)

Sign language recognition (SLR) is an essential study area that allows us to provide a better communicating environment between humans and computers. Some prevailing and standard features extracted from sign language gestures include scale-invariant feature transform (SIFT), speeded-up robust feature (SURF), features from the accelerated segment (FAST), and oriented FAST and rotated Brief (ORB) are used. However, these element vectors contain a few highlights that are insignificant or excess, subsequently expanding the generally computational time just as acknowledgment error of a classification framework. To counter this issue, we have proposed another object detection calculation dependent on profound hand math. A novel approach called Hand mark analysis of sign language (HMASL) has been used in this concern. It combines the concept of feature extraction and hand geometry to reduce the computation and computes only and region in complex background. HMASL is compared to other classical feature extraction method and tested on several classifiers. The experimental results show that the HMASL eases the feature aspect to a meaningful amount as well as surges the recognition accuracy.
Contrastive Learning: An Alternative Surrogate for Offline Data-Driven Evolutionary Computation

Offline data-driven evolutionary algorithms (DDEAs), which learn problem models from historical data and then perform optimization, have attracted significant attention in the data-driven age. Most existing studies build surrogate models based on regression methods to predict the fitness of each solution, which depends heavily on the quality and quantity of offline data. Considering the evolution trait of evolutionary algorithms (EAs), the absolute fitness of each individual is not essential, instead, the relative strengths of individuals are adequate. This article explores an alternative way to realize DDEAs by establishing a contrastive learning model that performs binary classification to determine the pros and cons between individuals. The task of binary classification is relatively simpler than regression, and meanwhile the training data is inherently augmented to the square of the origin. The proposed contrastive learning model is implemented based on a siamese neural network to measure the differences between solutions. Further, we use the predicted pairwise relationship between individuals to construct a directed graph and propose a topological sort algorithm on the graph to obtain the ranking of the population. During the topological sort, a regression model based on local principle is used to resolve some conflicting issues. Integrating the above components, an offline DDEA named contrastive learning-based DDEA (CL-DDEA) is put forward. Experiments and comparisons with state-of-the-arts validate the powerfulness of CL-DDEA, especially on high-dimensional problems.
A Comparative Analysis of Different Despeckling Filters Using Breast Ultrasonographic Images

Speckle noise is inherently present in breast ultrasonographic images, and it degrades the overall visual quality of these images which leads to complex interpretation and misdiagnosis of breast abnormalities. Hence to diminish its effect, different despeckling filters are employed. The breast ultrasonographic images are filtered using different despeckling filters, which are classified into two categories as linear despeckling filters and nonlinear despeckling filters. The performance of these despeckling filters is determined by computing PSNR, MSE, and SNR. Result of performance assessment indicates that Lee Sigma filter yields highest value of PSNR, SNR, and lowest value of MSE in linear category while SRAD filter yields highest value of PSNR, SNR and lowest value of MSE in nonlinear category and hence more suitable for despeckling of breast ultrasonographic images.
Augmentation Techniques on Mammogram Images for CNN Based Breast Cancer Classification

Deep learning is now the fastest expanding area of several medical image classification and identification. Convolutional neural networks (CNN) are the primary method used for classification across many deep neural networks (DNN). In breast cancer, classification using mammogram image has several challenges such as small dataset size and class imbalance issues. Small dataset issue is a major challenge while performing classification of medical images. Large set of training data is required to build a reliably performing machine learning model for classification. Practically, it is very difficult to generate a bench marked, pathologically tested, large set of medical images. To overcome this problem by proposed and implemented the image data augmentation is a method that can be used. We choose 115 breast mammography photographs with masses from the INbreast database for this study. The amount of breast mammography images was increased to 7732 image data by data augmentation. We utilize the preprocessing process to the breast mammography images, and then apply the CNN ideal is used to classify the images as benign or malignant. In this comparison, the quantitative analysis of classification performance between two processes such as before augmentation technique achieved 94.56% and after augmentation technique achieved better classification accuracy of 98.91%, respectively.
Social distance monitoring using YoloV4 on aerial drone images

Monitoring social distancing in public spaces plays a crucial role in controlling and slowing the spread of the coronavirus during the COVID-19 pandemic. Using camera-equipped drone, the system presented in this paper detect unsafe social distance between people by applying deep learning algorithms namely the YoloV4 CNN algorithm to detect persons in images, in combination with trans-formation equations to calculate the real world position of each person, and finally calculate the distance between each pair in order to determine whether it is safe. We show also the results of training and testing a model using YoloV4 algorithm, and test the system for social distance calculation.
Performance of an Asymmetric on–Off Keying Modulation for Space Communications Using Single-Photon Superconducting Nanowire Detectors

Communications from deep space need to overcome two major issues: the strong attenuation due to the large distances and the limited power available at the transmitter. A class of sensors which looks promising for deep space communications is provided by the superconducting nanowire single-photon detectors that are characterized by very small jitter (few tens of ps), fast rise (hundreds of ps), and comparatively slow decay (few ns). Although it could seem that the bottleneck is represented by the long reset time, in this article, we present a scheme whose effective rate is limited by the jitter and that is able to transmit, with a superconducting nanowire single-photon detector with a ratio between decay time and jitter equal to 170 (a typical value), almost six times faster than the rate that the decay time would allow.
Selected Concepts of Quantum State Tomography

Quantum state tomography (QST) refers to any method that allows one to reconstruct the accurate representation of a quantum system based on data obtainable from an experiment. In this paper, we concentrate on theoretical methods of quantum tomography, but some significant experimental results are also presented. Due to a considerable body of literature and intensive ongoing research activity in the field of QST, this overview is restricted to presenting selected ideas, methods, and results. First, we discuss tomography of pure states by distinguishing two aspects—complex vector reconstruction and wavefunction measurement. Then, we move on to the Wigner function reconstruction. Finally, the core section of the article is devoted to the stroboscopic tomography, which provides the optimal criteria for state recovery by including the dynamics in the scheme. Throughout the paper, we pay particular attention to photonic tomography, since multiple protocols in quantum optics require well-defined states of light.