Reviewerly/exHarmony huggingface dataset:
Hardware Faults that Matter: Understanding and Estimating the Safety Impact of Hardware Faults on Object Detection DNNs

Object detection neural network models need to perform reliably in highly dynamic and safety-critical environments like automated driving or robotics. Therefore, it is paramount to verify the robustness of the detection under unexpected hardware faults like soft errors that can impact a system’s perception module. Standard metrics based on average precision produce model vulnerability estimates at the object level rather than at an image level. As we show in this paper, this does not provide an intuitive or representative indicator of the safety-related impact of silent data corruption caused by bit flips in the underlying memory but can lead to an over- or underestimation of typical fault-induced hazards. With an eye towards safety-related real-time applications, we propose a new metric IVMOD (Image-wise Vulnerability Metric for Object Detection) to quantify vulnerability based on an incorrect image-wise object detection due to false positive (FPs) or false negative (FNs) objects, combined with a severity analysis. The evaluation of several representative object detection models shows that even a single bit flip can lead to a severe silent data corruption event with potentially critical safety implications, with e.g., up to $$\gg $$ 100 FPs generated, or up to $$\sim $$ 90% of true positives (TPs) lost in an image. Furthermore, with a single stuck-at-1 fault, an entire sequence of images can be affected, causing temporally persistent ghost detections that can be mistaken for actual objects (covering up to $$\sim $$ 83% of the image). Furthermore, actual objects in the scene are continuously missed (up to $$\sim $$ 64% of TPs are lost). Our work establishes a detailed understanding of the safety-related vulnerability of such critical workloads against hardware faults.
A Transfer Representation Learning Approach for Breast Cancer Diagnosis from Mammograms using EfficientNet Models

Breast cancer is a deadly disease that affects the lives of millions of women throughout the world. Over time, the number of cases of breast cancer has increased. Preventing this disease is difficult and remains unidentified, but the survival percentage can be improved if diagnosed early. The progress of computer-assisted diagnosis (CAD) of breast cancer has seen a lot of improvements thanks to advances in deep learning. With the notable advancement of deep neural networks, diagnostic capabilities are nearing a human expert's. In this paper, we used EfficientNet to classify mammograms. This model is introduced with the new concept of model scaling called compound scaling. Compound scaling is the strategy which scales the model by adding more layers to extend the receptive field along with more channels to catch the detailed features of larger input. We also compare the performance of various variants of EfficientNet over CBIS-DDSM mammogram datasets. We used the optimum fine-tuning procedure to represent the importance of transfer learning (TL) during training.
Design and Field Test of a Mobile Augmented Reality Human–Machine Interface for Virtual Stops in Shared Automated Mobility On-Demand

Shared automated mobility on-demand (SAMOD) is considered as a promising mobility solution in the future. Users book trips on-demand via a smartphone, and service algorithms set up virtual stops (vStop) where users then need to walk to board the automated shuttle. Navigation and identification of the virtual pickup location, which has no references in the real world, can be challenging. Providing users with an intuitive information system in that situation is essential to achieve high user acceptance of new automated mobility services. Our novel vStop human–machine interface (HMI) prototype for mobile augmented reality (AR) supports users with information in reference to the street environment. This work firstly presented the results of an online interview study (N = 21) to conceptualize an HMI. Secondly, the HMI was prototyped by means of AR and evaluated (N = 45) regarding user experience (UX), workload, and acceptance. The results show that the AR prototype provided high rates of UX especially in terms of high pragmatic quality. Furthermore, cognitive workload when using the HMI was low, and acceptance ratings were high. The results show the positive perception of AR for navigation tasks in general and the highly assistive character of the vStop prototype in particular. In the future, SAMOD services can provide customers with vStop HMIs to foster user acceptance and smooth operation of their service.
The role of Artificial intelligence (AI) in Starting, automating and scaling businesses for Entrepreneurs.

Artificial intelligence is the ability to perform tasks usually associated with intelligent beings by a computer-controlled robot or a digital computer. Machine learning and deep learning are the applications of AI. Leads can be increased by more than 50% and call time can be reduced by 60 to 70% with a cost reduction of 40 to 60% by companies using Artificial intelligence for sales. Deeper insight can be acquired into targeted customers through Artificial intelligence. This increases conversion and reduces team’s workload. Artificial intelligence transforms the internal operations of the company. Recommendations are also based on computer vision. Artificial intelligence has made it easier to target a specific audience by identifying patterns in people’s search behaviors, providing them with the relevant information according to their needs. Employees workload can be decreased through Artificial intelligence by reducing task which requires repetitions in work. Artificial intelligence improves efficiency and accuracy, automate tedious activities, and uncover the trends which are hidden.Artificial intelligence helps managers in the hiring process by analyzing candidates’ potential on the basis of their interest and past work experience and selecting the right candidate for the right job. Through applying artificial intelligence, this system transforms into an auto-correcting and self-updating system which is a key to relationship management. It also helps in detecting threats, saving companies from cyber-attacks, maintaining organization system, making everything work efficiently. Artificial intelligence preserves infrastructure by recognizing cyber threats from data by monitoring patterns. After detecting a threat, it finds the source and help prevent such threats in future.
FormulaNet: A Benchmark Dataset for Mathematical Formula Detection

One unsolved sub-task of document analysis is mathematical formula detection (MFD). Research by ourselves and others has shown that existing MFD datasets with inline and display formula labels are small and have insufficient labeling quality. There is therefore an urgent need for datasets with better quality labeling for future research in the MFD field, as they have a high impact on the performance of the models trained on them. We present an advanced labeling pipeline and a new dataset called FormulaNet in this paper. At over 45k pages, we believe that FormulaNet is the largest MFD dataset with inline formula labels. Our experiments demonstrate substantially improved labeling quality for inline and display formulae detection over existing datasets. Additionally, we provide a math formula detection baseline for FormulaNet with an mAP of 0.754. Our dataset is intended to help address the MFD task and may enable the development of new applications, such as making mathematical formulae accessible in PDFs for visually impaired screen reader users.
Efficient Privacy-Preserving K-Means Clustering from Secret-Sharing-Based Secure Three-Party Computation

Privacy-preserving machine learning has become an important study at present due to privacy policies. However, the efficiency gap between the plain-text algorithm and its privacy-preserving version still exists. In this paper, we focus on designing a novel secret-sharing-based K-means clustering algorithm. Particularly, we present an efficient privacy-preserving K-means clustering algorithm based on replicated secret sharing with honest-majority in the semi-honest model. More concretely, the clustering task is outsourced to three semi-honest computing servers. Theoretically, the proposed privacy-preserving scheme can be proven with full data privacy. Furthermore, the experimental results demonstrate that our proposed privacy version reaches the same accuracy as the plain-text one. Compared to the existing privacy-preserving scheme, our proposed protocol can achieve about 16.5×-25.2× faster computation and 63.8×-68.0× lower communication. Consequently, the proposed privacy-preserving scheme is suitable for secret-sharing-based secure outsourced computation.
Few-Shot Multi-Modal Sentiment Analysis with Prompt-Based Vision-Aware Language Modeling

As a hot study topic in natural language processing, affec-tive computing and multimedia analysis, multi-modal senti-ment analysis (MSA) is widely explored on aspect-level and sentence-level tasks. However, the existing studies normally rely on a lot of annotated multi-modal data, which are difficult to collect due to the massive expenditure of manpower and re-sources, especially in some open-ended and fine-grained do-mains. Therefore, it is necessary to investigate the few-shot scenario for MSA. In this paper, we propose a prompt-based vision-aware language modeling (PVLM) approach to MSA, which only requires a few supervised data. Specifically, our PVLM can incorporate the visual information into pre-trained language model and leverage prompt tuning to bridge the gap between masked language prediction in pre-training and MSA tasks. Systematic experiments on three aspect-level and two sentence-level datasets of MSA demonstrate the effectiveness of our few-shot approach.
Generative Adversarial Networks and Data Clustering for Likable Drone Design

Novel applications for human-drone interaction demand new design approaches, such as social drones that need to be perceived as likable by users. However, given the complexity of the likability perception process, gathering such design information from the interaction context is intricate. This work leverages deep learning-based techniques to generate novel likable drone images. We collected a drone image database (N=360) applicable for design research and assessed the drone's likability ratings in a user study (N=379). We employed two clustering methodologies: 1. likability-based, which resulted in non-likable, neutral, and likable drone clusters; and 2. feature-based (VGG, PCA), which resulted in drone clusters characterized by visual similarity; both clustered using the K-means algorithm. A characterization process identified three drone features: colorfulness, animal-like representation, and emotional expressions through facial features, which affect drone likability, going beyond prior research. We used the likable drone cluster (N=122) for generating new images using StyleGAN2-ADA and addressed the dataset size limitation using specific configurations and transfer learning. Our results were mitigated due to the dataset size; thus, we illustrate the feasibility of our approach by generating new images using the original database. Our findings demonstrate the effectiveness of Generative Adversarial Networks (GANs) exploitation for drone design, and to the best of our knowledge, this work is the first to suggest GANs for such application.
Modulation Recognition of Communication Signals Based on Multimodal Feature Fusion

Modulation recognition is the indispensable part of signal interception analysis, which has always been the research hotspot in the field of radio communication. With the increasing complexity of the electromagnetic spectrum environment, interference in signal propagation becomes more and more serious. This paper proposes a modulation recognition scheme based on multimodal feature fusion, which attempts to improve the performance of modulation recognition under different channels. Firstly, different time- and frequency-domain features are extracted as the network input in the signal preprocessing stage. The residual shrinkage building unit with channel-wise thresholds (RSBU-CW) was used to construct deep convolutional neural networks to extract spatial features, which interact with time features extracted by LSTM in pairs to increase the diversity of the features. Finally, the PNN model was adapted to make the features extracted from the network cross-fused to enhance the complementarity between features. The simulation results indicated that the proposed scheme has better recognition performance than the existing feature fusion schemes, and it can also achieve good recognition performance in multipath fading channels. The test results of the public dataset, RadioML2018.01A, showed that recognition accuracy exceeds 95% when the signal-to-noise ratio (SNR) reaches 8dB.
A novel technique for detecting sudden concept drift in healthcare data using multi-linear artificial intelligence techniques

A financial market is a platform to produce data streams continuously and around 1. 145 Trillion MB of data per day. Estimation and the analysis of unknown or dynamic behaviors of these systems is one the challenging tasks. Analysis of these systems is very much essential to strengthen the environmental parameters to stabilize society activities. This can elevate the living style of society to the next level. In this connection, the proposed paper is trying to accommodate the financial data stream using the sliding window approach and random forest algorithm to provide a solution to handle concept drift in the financial market to stabilize the behavior of the system through drift estimation. The proposed approach provides promising results in terms of accuracy in detecting concept drift over the state of existing drift detection methods like one class drifts detection (OCDD), Adaptive Windowing ADWIN), and the Page-Hinckley test.
Machine Learning with Differentially Private Labels: Mechanisms and Frameworks

Label differential privacy is a relaxation of differential privacy for machine learning scenarios where the labels are the only sensitive information that needs to be protected in the training data. For example, imagine a survey from a participant in a university class about their vaccination status. Some attributes of the students are publicly available but their vaccination status is sensitive information and must remain private. Now if we want to train a model that predicts whether a student has received vaccination using only their public information, we can use label-DP. Recent works on label-DP use different ways of adding noise to the labels in order to obtain label-DP models. In this work, we present novel techniques for training models with label-DP guarantees by leveraging unsupervised learning and semi-supervised learning, enabling us to inject less noise while obtaining the same privacy, therefore achieving a better utility-privacy trade-off. We first introduce a framework that starts with an unsupervised classifier f0 and dataset D with noisy label set Y , reduces the noise in Y using f0 , and then trains a new model f using the less noisy dataset. Our noise reduction strategy uses the model f0 to remove the noisy labels that are incorrect with high probability. Then we use semi-supervised learning to train a model using the remaining labels. We instantiate this framework with multiple ways of obtaining the noisy labels and also the base classifier. As an alternative way to reduce the noise, we explore the effect of using unsupervised learning: we only add noise to a majority voting step for associating the learned clusters with a cluster label (as opposed to adding noise to individual labels); the reduced sensitivity enables us to add less noise. Our experiments show that these techniques can significantly outperform the prior works on label-DP.
Pika: Secure Computation using Function Secret Sharing over Rings

Machine learning algorithms crucially depend on non-linear mathematical functions such as division (for normalization), exponentiation (for softmax and sigmoid), tanh (as an activation function), logarithm (for crossentropy loss), and square root (for back-propagation of normalization layers). However, when machine learning is performed over secure computation, these protocols incur a large communication overhead and high round complexity. In this work, we propose new multi-party computation (MPC) protocols for such functions. Our protocols achieve constant round complexity (3 for semi-honest, 4 for malicious), an order of magnitude lower communication (54 − 121× lower than prior art), and high concrete efficiency (2−1163× faster runtime). We rely on recent advances in function secret sharing (FSS) to construct these protocols. Our contributions can be summarized as follows: (1) A constant round protocol to securely evaluate nonlinear functions such as division, exponentiation, logarithm, and tanh (in comparison to prior art which uses round complexity proportional to the rounds of iterative methods/required precision) with high accuracy. This construction largely follows prior work in look-up style secure computation. (2) Our main contribution is the extension of the above protocol to be secure in the presence of malicious adversaries in the honest majority setting. We provide a malicious sketching protocol for FSS schemes that works over rings and in order to prove its security, we extend (and prove) a corresponding form of SchwartzZippel lemma over rings. This is the first such extension of the lemma and it can be of independent interest in other domains of secure computation. (3) We implement our protocol and showcase order of magnitude improvements in runtime and communication. Given the low round complexity and substantially lower communication, our protocols achieve even better performance over network constrained environments such as WAN. Finally, we showcase how such functions can lead to scalability in machine learning. Note that techniques presented are applicable beyond the application of machine learning as the protocols effectively present an efficient 1-out-of-N oblivious transfer or an efficient private information retrieval protocol.
Context-Aware Sentiment Analysis using Tweet Expansion Method

The large source of information space produced by the plethora of social media platforms in general and microblogging in particular has spawned a slew of new applications and prompted the rise and expansion of sentiment analysis research. We propose a sentiment analysis technique that identifies the main parts to describe tweet intent and also enriches them with relevant words, phrases, or even inferred variables. We followed a state-of-the-art hybrid deep learning model to combine Convolutional Neural Network (CNN) and the Long Short-Term Memory network (LSTM) to classify tweet data based on their polarity. To preserve the latent relationships between tweet terms and their expanded representation, sentence encoding and contextualized word embeddings are utilized. To investigate the performance of tweet embeddings on the sentiment analysis task, we tested several context-free models (Word2Vec, Sentence2Vec, Glove, and FastText), a dynamic embedding model (BERT), deep contextualized word representations (ELMo), and an entity-based model (Wikipedia). The proposed method and results prove that text enrichment improves the accuracy of sentiment polarity classification with a notable percentage.
The elementary forms of digital communication

Although a tremendous amount of modern interaction is electronic, our understanding of everyday digital communications—including what they look like and how their properties vary by medium and relationship type—is still growing. In this paper, we examine digital exchange in two of its simplest forms: email and SMS. Specifically, our data consist of 2,004 messages provided by a diverse sample of college students, supplemented by in-depth interviews with their authors. These data were collected in 2010—a time when both mediums were widespread but devoid of most of their modern complexity. Based on these data, we make two contributions: First, we develop an empirically grounded typology of the basic properties of text-based digital communication; second, we document the distribution of these properties across five common relationship types. Respectively, these findings provide a starting point to understanding the substance of digital exchange in all its many forms and an empirical benchmark for comparison.
Exploration and Exploitation Methods Using (1+1)-Evolutionary Algorithms for Better Ranking Models

Abstract Exploration and Exploitation methods in Evolutionary Algorithms (EA) play a fundamental role for obtaining an optimal solution. Exploration technique target is for search large portion of the search space, while Exploitation procedure is used for converge the current solution to the nearest local or global optimal solution. Example of Exploration procedure in EA is mutation process and Exploitation procedure can be accomplished using crossover process or better initialization at the beginning. This paper uses four probability distributions random generator for mutation process in (1+1)-Evolutionary Algorithms ((1+1)-EAs) for examining their mutation behaviors. In addition, this paper uses ranking models from Dependent Click Model and Linear Regression algorithms as initial solution in (1+1)-EAs for better Exploitation. From the experimental results, the exploration process using Gaussian Random Number (GRN) generator with exploitation procedure using Linear Regression (LR) model and Dependent Click (DC) model initialization in (1+1)-EAs outperformed other methods for evolving steps on training dataset. On the other hand, Levy Random Number generator with LR outperformed other methods used for results on unseen test datatset, while GRN is the strongest competitor compared to Levy for these Predictive rank results. Furthermore, state-of-the-art method in the previous studies Combining Simulated Annealing with (1+1)-Evolutionary Strategy outperformed the predictive ranking and evolving results on testing and training data for both with or without initialization by other Linear ranking compared to novel (1+1)-Evolutionary Gradient Strategy method and other (1+1)-Evolutionary Strategy. This paper demonstrated the results and findings obtained using MQ2008 dataset and providing their package codes.
A Victim-Based Framework for Telecom Fraud Analysis: A Bayesian Network Model

The increasingly rampant telecom network fraud crime will cause serious harm to people's property safety. The way to reduce telecom fraud has shifted from passive combat to active prevention. This paper proposes a victim analysis and prediction method based on Bayesian network (BN), which models victims from age, gender, occupation, marriage, knowledge level, etc. We describe the fraud process in terms of whether to report to the police, property loss, and realizing the reasoning of the whole process of telecom fraud. This paper uses expert experience to obtain a Bayesian network structure. 533 real telecom fraud cases are used to learn Bayesian network parameters. The model is capable of quantifying uncertainty and dealing with nonlinear complex relationships among multiple factors, analyzing the factors most sensitive to property damage. According to the characteristics of victims, we conduct situational reasoning in the Bayesian network to evaluate property damage and alarm situations in different scenarios and provide decision support for police and community prevention and control. The experimental results show that male staff in government agencies are the most vulnerable to shopping fraud and women in schools are the most vulnerable to phishing and virus fraud and have the greatest property loss after being deceived; victim characteristics have very limited influence on whether to report to the police.