Reviewerly/exHarmony huggingface dataset:
DP-A*: For Path Planing of UGV and Contactless Delivery

The unmanned logistics and distribution urgently require a large number of unmanned ground vehicles(UGVs) under the influence of the potential spread of the Coronavirus Disease 2019 (COVID-19). The path planning of UGV relies excessively on SLAM map, and has no self-optimization and learning ability for the space containing a large number of unknown obstacles. In this paper, a new dynamic parameter-A* (DP-A*) algorithm is proposed, which is based on the A* algorithm and enables the UGV to continuously optimize the path while performing the same task repeatedly. First, the original evaluation functions of the A* algorithm are modified by Q-Learning to memory the coordinates of unknown obstacle. Then, Q-table is adopted as an auxiliary guidance for recording the characteristics of environmental changes and generating heuristic factor to overcome the shortcoming of the A* algorithm. At last, the DP-A* algorithm can realize path planning in the instantaneous changing environment, record the actual situation of obstacles encountered, and gradually optimize the path in the task that needs multiple explorations. By several simulations with different characteristics, it is shown that our algorithm outperforms Q-learning, Sarsa and A* according to the evaluation criteria such as convergence speed, memory systems consume, Optimization ability of path planning and dynamic learning ability.
Universal noise-precision relations in variational quantum algorithms

Variational quantum algorithms (VQAs) are expected to become a practical application of near-term noisy quantum computers. Although the effect of the noise crucially determines whether a VQA works, the heuristic nature of VQAs makes it difficult to establish analytic theories. Analytic estimations of the impact of the noise are urgent for searching for quantum advantages, as numerical simulations of noisy quantum computers on classical computers are heavy and quite limited to small-scale problems. In this paper, we establish analytic estimations of the error in the cost function of VQAs due to the noise. The estimations are applicable to any typical VQAs under Gaussian noise, which is equivalent to a class of stochastic noise models. Notably, depolarizing noise is included in this model. As a result, we obtain estimations of the noise level to guarantee a required precision. Our formulas show how the Hessian of the cost function, the spectrum of the target operator, and the geometry of the ansatz affect the sensitivity to the noise. This insight implies trade-off relations between the trainability and the noise resilience of the cost function. We also obtain rough estimations which can be easily calculated without detailed information of the cost function. As a highlight of the applications of the formula, we propose a quantum error mitigation method which is different from the extrapolation and the probabilistic error cancellation.
SAFE: Security Door Lock System Using Haar-Cascade and LBPH Method

Door lock provides numerous benefits and has become indispensable in daily life as it acts as a security guard to prevent burglars and protect home belonging safely. The unlock methods of existing door lock system widely use keys and thumbprint involve touching the object may cause the spread of COVID-19. In this paper, a computer vision based security door lock system using Raspberry Pi (called SAFE) is proposed. Haar-Cascade classifier is employed as face detection classifier, while Local Binary Pattern Histogram (LBPH) is proposed as face recognition classifier. Recognition result is processed based on the usage of user to provide insights of SAFE. The accuracy of SAFE using pre-trained LBPH classifier achieves average of 86% based on the data obtained. The recognition speed outperforms existing work using principal component analysis and eigenfaces.
Deep Learning Aided Channel Estimation in OFDM Systems

Orthogonal frequency-division multiplexing has become broadly employed in modern communication technology with wireless systems. It subdivides a radio channel together into a significant number of clustered subchannels to provide more reliable data transmission at high rates of speed. Our project is to approximate the communication channel medium using deep learning in of DM systems. Deep learning has been shown to play a critical role in increasing system performance and lowering computing complexity in today’s wireless communication networks. The efficiency of the deep learning model is exploited to conduct channel estimation in the wireless medium. the proposed model is built on the network of Long short-term memory(LSTM) model associated with LS estimates. The profile of the channel estimated using least square (LS) and linear minimum mean square error (LMMSE) through pilot symbols is compared with the proposed model based on LSTM and evaluated using bit error rate (BER) and signal to noise ratio. This study focussed on using fewer pilots to estimate the channel and thereby increasing the spectral efficiency and data rate. The results provide evidence that a deep neural network guarantees a promising channel estimation in comparison with classic algorithms.
The AI Act Proposal: a New Right to Technical Interpretability?

The debate about the concept of the so called right to explanation in AI is the subject of a wealth of literature. It has focused, in the legal scholarship, on art. 22 GDPR and, in the technical scholarship, on techniques that help explain the output of a certain model (XAI). The purpose of this work is to investigate if the new provisions introduced by the proposal for a Regulation laying down harmonised rules on artificial intelligence (AI Act), in combination with Convention 108 plus and GDPR, are enough to indicate the existence of a right to technical explainability in the EU legal framework and, if not, whether the EU should include it in its current legislation. This is a preliminary work submitted to the online event organised by the Information Society Law Center and it will be later developed into a full paper.
Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation

Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most of the existing methods require some specific data, architectures and training methods. In this paper, we first argue through an online study that arbitrarily removing all the biases in industrial recommendation may not consistently yield a desired performance improvement. For the situation that a randomized dataset is not available, we propose a novel self-sampling training and evaluation (SSTE) framework to achieve the accuracy-bias tradeoff in recommendation, i.e., eliminate the harmful biases and preserve the beneficial ones. Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data. A self-training module infers the beneficial biases and learns better tradeoff based on these subsets, and a self-evaluation module aims to use these subsets to construct more plausible references to reflect the optimized model. Finally, we conduct extensive offline experiments on two datasets to verify the effectiveness of our SSTE. Moreover, we deploy our SSTE in homepage recommendation of a famous financial management product called Tencent Licaitong, and find very promising results in an online A/B test.
Impact of Technology on Effectiveness of Knowledge Management: An Empirical Study

The importance of knowledge management in today"s world has been the topic of considerable discussion from the years ago.The development of knowledge management emphasizes its significance and need for organizational survival and the protection of competitive power.Organizations need to be effective at maintaining, expanding, managing, and utilizing the skills of their people if they want to stay on top.To achieve great success Knowledge should be the main focus.It is emphasized that KM knowledge Management and associated approach principles are crucial and essential for firms to thrive and sustain their competitive edge.In the innovative and technological era, Knowledge Management plays an important role and hence addressing it has become crucial for leaders and managers.This will help the mangers and top leaders in many ways.This paper will discuss the positive and negative impact of technology on the effectiveness of Knowledge Management.
Boosting the Robustness of Neural Networks with M-PGD

Neural networks have achieved state-of-the-art results in many fields. With further research, researchers have found that neural network models are vulnerable to adversarial examples which are carefully designed to fool the neural network models. Adversarial training is a process of creating adversarial samples during the training process and directly training a model on the adversarial samples, which can improve the robustness of the model to adversarial samples. In the adversarial training process, the stronger the attack ability of the adversarial samples, the more robust the adversarial training model. In this paper, we incorporate the momentum ideas into the projected gradient descent (PGD) attack algorithm and propose a novel momentum-PGD attack algorithm (M-PGD) that greatly improves the attack ability of the PGD attack algorithm. After that, we train a neural network model on the adversarial samples generated by the M-PGD attack algorithm, which could greatly improve the robustness of the adversarial training model. We compare our adversarial training model with the other five adversarial training models on the CIFAR-10 and CIFAR-100 datasets. Experiments show that our adversarial training model can be extremely more robust to adversarial samples than the other adversarial training models. We hope our adversarial training model will be used as a benchmark in the future to test the attack ability of attack models.
On Differential Privacy and Adaptive Data Analysis with Bounded Space

We study the space complexity of the two related fields of differential privacy and adaptive data analysis. Specifically, To obtain our results, we define and construct an encryption scheme with multiple keys that is built to withstand a limited amount of key leakage in a very particular way.
Using Content and Trace Feature Extractors for Exposing Fake Faces through Deep Learning

In this procedure, we make an effort to address the effect of these variables on the identification of a photo from a face sketch using a cascaded static and dynamic local feature extraction approach.to ensure that the feature vectors created are based on the appropriate patches.Along with that approach, we include the closest neighbour technique for matching sketches and photos, which concatenates the feature vectors obtained from local static extraction.Following the extraction of the image's characteristics, the most comparable images are narrowed down based on their closes neighbours.Eventually, feature vectors from the local dynamic extraction approach are used to rematch these images.The closest neighbour technique is used to match the feature vectors.The primary goals of this method are to increase the detection system's precision as well as identification rates and feature stability.
Breast Cancer Classification Approaches - A Comparative Analysis

Cancer of the breast is a difficult disease to treat since it weakens the patient's immune system.Particular interest has lately been shown in the identification of particular immune signals for a variety of malignancies in this regard.In recent years, several methods for predicting cancer based on proteomic datasets and peptides have been published.The cells turns into cancerous cells because of various reasons and get spread very quickly while detrimental to normal cells.In this regard, identifying specific immunity signs for a range of cancers has recently gained a lot of interest.Accurately categorizing and compartmentalizing the breast cancer subtype is a vital job.Computerized systems built on artificial intelligence can substantially save time and reduce inaccuracy.Several strategies for predicting cancer utilizing proteomic datasets and peptides have been reported in the literature in recent years.It is critical to classify and categorize breast cancer treatments correctly.It's possible to save time while simultaneously minimizing the likelihood of mistakes using machine learning and artificial intelligence approaches.Using the Wisconsin Breast Cancer Diagnostic dataset, this study evaluates the performance of various classification methods, including SVC, ETC, KNN, LR, and RF (random forest).Breast cancer can be detected and diagnosed using a variety of measurements of data (which are discussed in detail in the article) (WBCD).The goal is to determine how well each algorithm performs in terms of precision, recall, and accuracy.The variation of each classification threshold has been tested on various algorithms and SVM turned out to be very promising.
Handling catastrophic forgetting using cross-domain order in incremental deep learning

In the present era of big data applications, incremental learning has emerged as the most admired area of research where ever-ending tasks from different application domains arrive temporarily, and the learning models majorly focus on newly arriving tasks while forgetting the historically acquired knowledge of old tasks. In this context, when a deep neural network (DNN) performs a sequence of learning tasks, it usually forgets the previously learned tasks. This phenomenon is known as catastrophic forgetting (CF), and the existing state-of-the-art deep learning approaches are unable to handle this issue. Following this, many DNN-based state-of-the-art solutions have been proposed to alleviate the problem that ensures a more natural way of human brain imitation. During training, deep learning algorithms are subjected to a random sequence of diversified datasets (also referred to as cross-domain datasets) that typically belong to multiple domains and have a variety of feature representation, distribution, density, etc. In such cases, CF may become a serious issue because the model tends to forget the previously learned features that affect the overall performance. Because there has been very little research in this area, a more robust incremental deep learning algorithm is proposed in our study to learn desired tasks incrementally. To overcome this situation, proper sequencing of cross-domain datasets is important so that related datasets are kept together to enhance the learnability of the model. Our research proposes a cross-domain order approach with hard attention to the task (HAT) methodology that uses an attention-gated mechanism to efficiently learn the task-specific features. Intense experiments have been performed, and the performance of the proposed approach is compared with the changing cross-domain orders with the baseline models over the following datasets: MNIST, P-MNIST, CIFAR-10, CIFAR-100, and SVHN, etc. The results of the experiments show that learning through cross-domain orders results in better incremental learning.
Multi-vendor robustness analysis of a commercial artificial intelligence system for breast cancer detection

Population-based screening programs for the early detection of breast cancer have significantly reduced mortality in women, but they are resource intensive in terms of time, cost, and workload and still have limitations mainly due to the use of 2D imaging techniques, which may cause overlapping of tissues, and interobserver variability. Artificial intelligence (AI) systems may be a valuable tool to assist radiologist when reading and classifying mammograms based on the malignancy of the detected lesions. However, there are several factors that can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of our work is to analyze the robustness of the diagnostic ability of an AI system designed for breast cancer detection.Mammograms from a population-based screening program were scored with the AI system. The sensitivity and specificity by means of the area under the receiver operating characteristic (ROC) curve were obtained as a function of the mammography unit manufacturer, demographic characteristics, and several factors that may affect the image quality (age, breast thickness and density, compression applied, beam quality, and delivered dose).The area under the curve (AUC) from the scoring ROC curve was 0.92 (95% confidence interval = 0.89 - 0.95). It showed no dependence with any of the parameters considered, as the differences in the AUC for different interval values were not statistically significant.The results suggest that the AI system analyzed in our work has a robust diagnostic capability, and that its accuracy is independent of the studied parameters.
Image processing-based detection method of learning behavior status of online calssroom students

Due to the outbreak of the new crown epidemic, online teaching is booming, but compared with traditional offline teaching, there are many problems, such as the difficulty of detecting the voice status of students. Therefore, the research on students’ online status detection system is of great significance. In this paper, based on image processing, the detection method of online classroom students’ learning behavior status is studied, and the learning status of students is detected from the perspective of face detection and face recognition fatigue detection. In this study, the students’ learning status is detected by the facial expressions in the video during the students’ learning process. When the students have negative emotions and become tired, the system can detect and record them in time and issue a warning. Therefore, this research can well solve the problems existing in online teaching, and to a certain extent, the teaching quality has been greatly improved.
Taking Advantage of a Very Simple Property to Efficiently Infer NFAs

Grammatical inference consists in learning a formal grammar as a finite state machine or as a set of rewrite rules. In this paper, we are concerned with inferring Nondeterministic Finite Automata (NFA) that must accept some words, and reject some other words from a given sample. This problem can naturally be modeled in SAT. The standard model being enormous, some models based on prefixes, suffixes, and hybrids were designed to generate smaller SAT instances. There is a very simple and obvious property that says: if there is an NFA of size k for a given sample, there is also an NFA of size k+1. We first strengthen this property by adding some characteristics to the NFA of size k+1. Hence, we can use this property to tighten the bounds of the size of the minimal NFA for a given sample. We then propose simplified and refined models for NFA of size k+1 that are smaller than the initial models for NFA of size k. We also propose a reduction algorithm to build an NFA of size k from a specific NFA of size k+1. Finally, we validate our proposition with some experimentation that shows the efficiency of our approach.
Analyzing Deceptive Opinion Spam Patterns: the Topic Modeling Approach

Deceptive Opinion Spam commonly takes the form of fake reviews (negative or positive) posted by a malicious web user to hurt or inflate a company's image. As these reviews have been deliberately written to deceive the reader, human reviewers are faring little better than a chance in detecting these deceptive statements. Thus, there is a dire need to address this issue as extracting text patterns from the fraudulent texts with meaningful substructures still remains a challenge. In our research, to obtain a deeper understanding of how lies are expressed in texts, we consider the task as a topic modeling problem, in which we constructed a model to learn the patterns that constitute a fake review, and then explore the outputs of this model to identify those patterns. Topic models may be useful in this task due to their ability to group multiple documents into smaller sets of key topics. As the linguistic cues of the lies are still unknown, a key advantage of this approach is that the algorithm encourages the mixtures composed of only few topics, which makes the representation more interpretable and provides additional opportunities to reveal the patterns and structures within the systems of documents. Our methodology proved to be useful for this study, revealing the lexical cues generally applied by human reviewers to generate deceptive language.
Trusted Collaboration for MEC-Enabled VR Video Streaming: A Multi-Agent Reinforcement Learning Approach

Collaboration among mobile edge computing (MEC) has been envisioned as a promising paradigm to meet the requirements of wireless virtual reality (VR) applications. However, trust risks create tremendous challenges in MEC collaboration due to the distributed, complex, and unreliable nature of resource providers. In this paper, we present a trusted collaboration framework for VR video streaming to manage the video buffer in VR devices (VDs) under a more realistic distributed environment. In the framework, the rendering tasks can be processed collaboratively among edge servers (ESs) by exploring their behaviors (e.g., selfish behavior, malicious behavior, and cooperative behavior). Considering the collaborator may not be fully trustworthy, we present a novel trust evaluation method by combining direct and indirect values, aiming to ensure reliable collaborator selection. Then, we formulate an optimization problem to maintain an effective buffer state in VR devices (VDs) through jointly optimizing collaborator selection, spectrum allocation, and rendering resource allocation. Due to the fluctuating wireless fading channel and the dynamic video rate, the optimization problem is intractable by adopting traditional methods. Then, we adopt the multi-agent deep deterministic policy gradient (MADDPG) to tackle this dynamic and distributed problem. Simulation results indicate that the proposed approach can achieve a good performance.
Role of IT in Effectiveness of Knowledge Management Practices in Business: An Empirical Study

Vital source for corporate competitiveness is knowledge and there are huge number of information technology solutions providing support to knowledge management. One of the significant problem to be discussed is role of information technology in success or failure to implement Knowledge management. To reflect knowledge of employees the term “Knowledge is Power” is used in organizations and considered as asset in companies. Knowledge management is a major driving force for organizational changes and creation of wealth and company utilizing employee’s knowledge and recognizing it as an intellect capital having competitive advantage. Effective leveraging of knowledge needs usage of information technology. Though, knowledge management in companies is more than just application of information technology, it needs it as an enabler promoting creation, storing, distributing, and retrieving knowledge for reuse. Knowledge management activities cannot be achieved with implementation of information technology in this century where there is need of rapid access to significant knowledge is enormously rising. The sample size of the study was 191 respondents. The data analysis was performed with the help of t-test and mean.