Reviewerly/exHarmony huggingface dataset:
Designing Sensitive Personal Information Detection and Classification Model for Amharic Text

Sensitive information is a classified type of content that should not be disclosed to the public and that can harm the owner of the information if it is disclosed. To protect disclose of sensitive information first, it requires detecting the availability of sensitive information and its domain classification for further analysis. To the best of our knowledge, there is no work attempted for Amharic texts. Models developed for another language cannot be used for Amharic texts language because of morphology, grammar and semantics differences. To address these gaps, we have proposed a model for detecting and classifying personal sensitive information for Amharic texts. We have experimented with three deep learning algorithms: Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BI-LSTM) and Convolutional Neural Network (CNN) using 7.31K and 6.697K Amharic sentences for sensitivity detection and domain classification respectively. The accuracy of LSTM, BI-LSTM and CNN was 82%, 90% and 87% respectively for sensitivity classification and 88, 93, 89 respectively for domain classification.
Model-based sensor fusion and filtering for localization of a semi-autonomous robotic vehicle

This paper refines a physically-inspired model governing the dynamic motion of a vehicle. We present a method used to perform experimental parameter calibration, and then use this model to build an observer (an extended Kalman filter). Experimental results with a robotic vehicle fitted with a prototype kit focus on recovering the truthful real-world information in the context of systematic errors (a faulty wheel encoder sensor), randomly occurring errors (a faulty ultrasonic sensor) and simplifying model assumptions (e.g. usage of two identical motors). We show that our model-based approach is able to perform reasonably well even under these extreme circumstances.
Design and Implementation of an Automatic Single Axis Solar Tracking System to Enhance the Performance of a Solar Photovoltaic Panel

The power consumption rate is increasing daily, and people are greatly dependent on conventional energy sources. If it continues, the conventional energy sources will end very soon. So, it is the appropriate time to use renewable energy sources along with conventional energy sources. Solar energy is the cleanest and sustainable renewable energy source. By using a solar photovoltaic (PV) panel, solar power can be converted into electricity. The electricity production rate from a solar photovoltaic panel depends on some factors such as solar irradiance, solar cell materials, solar cell surface temperature, etc. When the solar cell captures more sunlight, the more power it produces. A fixed state solar panel can’t capture maximum sunlight during the sunlight hour because the sun’s position in the sky changes all day long. An automatic sunlight tracking system is required to ensure that the panel captures maximum solar irradiance. This research aims to design and implement a microcontroller-based automated single-axis solar tracking system to capture maximum sunlight and to extract maximum power from the solar PV panel in various sun positions. This system helps to face the solar panel towards the sunlight according to the sun’s movement in the sky. By using the tracking system, the panel can absorb a higher amount of solar power and increase the solar panel’s output power.
Map Learning via Adaptive Region-Based Sampling in Multi-robot Systems

We present a novel approach for informative path planning in multi-robot systems for mapping under time and communication constraints. The approach is based on modeling the mapping task as a regression problem using Gaussian processes (GP) and adaptively directing the robots towards the regions that are most informative for GP learning. The methodology is based on a multi-stage process where either a robot or a group of robots search for the best convex region where to sample new data, identify the most informative sampling locations in the region, and compute an optimized path through them. The process is iterated over time adapting to newly gathered evidence and is performed collaboratively. Techniques from Monte Carlo, sequential Bayesian inference, and orienteering optimization models are combined in an integrated strategy. Fully distributed and leader-follower architectures are designed to implement the multi-stage strategy and have been evaluated in simulation, showing up to 69% of improvement over a baseline strategy.
Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services

The number of mobile application services is showing an explosive growth trend, which makes it difficult for users to determine which ones are of interest. Especially, the new mobile application services are emerge continuously, most of them have not be rated when they need to be recommended to users. This is the typical problem of cold start in the field of collaborative filtering recommendation. This problem may makes it difficult for users to locate and acquire the services that they actually want, and the accuracy and novelty of service recommendations are also difficult to satisfy users. To solve this problem, a hybrid recommendation method for mobile application services based on content feature extraction is proposed in this paper. First, the proposed method in this paper extracts service content features through Natural Language Processing technologies such as word segmentation, part-of-speech tagging, and dependency parsing. It improves the accuracy of describing service attributes and the rationality of the method of calculating service similarity. Then, a language representation model called Bidirectional Encoder Representation from Transformers (BERT) is used to vectorize the content feature text, and an improved weighted word mover's distance algorithm based on Term Frequency-Inverse Document Frequency (TFIDF-WMD) is used to calculate the similarity of mobile application services. Finally, the recommendation process is completed by combining the item-based collaborative filtering recommendation algorithm. The experimental results show that by using the proposed hybrid recommendation method presented in this paper, the cold start problem is alleviated to a certain extent, and the accuracy of the recommendation result has been significantly improved.
Learning from Noisy Labeled Samples Using Prediction Norm for Image Classification

Learning of the convolutional neural network (CNN) from noisy labeled samples is a crucial problem, and many studies have been conducted. Although the state-of-the-art method called Joint training with Co-Regularization (JoCoR) has achieved high performance, it is a still challenging problem to accurately classify samples with asymmetric noise, i.e., wrong labels between similar classes (e.g., CAT ↔ DOG). In this paper, we newly found that there is a difference between the prediction norm for clean labeled samples and that for noisy labeled samples. In addition, we found that there is a positive correlation between the prediction norm difference and the classification accuracy for the CIFAR-10 dataset. Therefore, we hypothesize that the discriminative power would be improved if we increase the prediction norm difference. Based on this hypothesis, a novel method for learning CNN from noisy labeled samples is proposed. Specifically, we take JoCoR as the base architecture and weight the loss function of JoCoR to make the prediction norm difference large. Experimental results for the CIFAR-10 dataset suggest that our hypothesis is correct. The classification accuracy by the proposed method is higher than some state-of-the-art methods including JoCoR for samples with asymmetric noise as well as those with symmetric noise (i.e., wrong labels between other classes).
A Privacy-Preserved Online Personalized Federated Learning Framework for Indoor Localization

By taking advantage of Deep Learning (DL), Received signal strength (RSS) fingerprint-based indoor localization has attracted more attention. Training DL models require an immense amount of RSS samples, and crowdsourcing has been another way to collect data by users. Recently, some researchers apply Federated Learning (FL) in indoor localization instead of crowdsourcing for privacy protection. However, more practical issues are still not considered.For further guaranteeing privacy and deploying systems in real time, this paper proposes OPFL, a privacy-preserved online personalized federated learning framework for fingerprint-based indoor localization. In OPFL, users update model parameters and collect new labeled samples meanwhile. By adding artificial noise based on the Differential Privacy (DP) mechanism, it is harder for attackers to infer information. Besides, the deterioration of models results from DP is mitigating by applying the personalized algorithm. The experimental results show that under the noise perturbing, the performance of OPFL testing on the local test set is far better than the conventional federated averaging algorithm (FedAvg), and is even close to the centralized training.
Prioritizing vulnerability patches in large networks

We consider here the question of prioritizing the patching of security vulnerabilities to prevent network attacks. Patching all vulnerable machines at once in large modern organizations is not feasible due to the large scale of their networks and the inability to halt operation during maintenance. This article explores two aspects of security maintenance: a method for prioritizing vulnerability patches, and visualization of the priorities to aid in decision making. State-of-the-art methods rank vulnerabilities by analyzing the connectivity graph or the logical attack graph and present the results in a table form, a view of the organizational network with highlighted failure points, or even the complete attack graph, in either case flooding the human operator with a lot of hardly comprehensible information. We suggest a Network Topology Vulnerability Score (NTVS) which shows preferable results by ranking vulnerabilities in a planning graph — an interim data structure used by planners when analyzing logical attack graphs. We also suggest a new abstracted presentation of the network in order to ease the comprehension of NTVS scores. The principal results obtained on two real networks show that patching vulnerabilities prioritized by NTVS leads to a faster decrease in the number of available attack paths toward the critical assets. A user study with a panel of security experts shows that the proposed visualization is considerably better than current commercial tools, helping experts to both prioritize vulnerability patches, and explain their decisions to higher management and to operation teams.
VAMP: A Predictive Approach to Audio/Video Bitrate Adaptation Over Wireless Networks

Adaptive bitrate (ABR) algorithms are employed for delivering media content across wireless networks. Current ABR schemes only focus on video bitrate adaptation, considering that audio content encoding has negligible impact on streaming quality, due to its smaller size. However, many commercial platforms use high-quality audio which is significant in size. Improper audio bitrate selection thus leads to imbalanced audio/video buffer levels and adversely affects video bitrate selection. To tackle this issue, we propose VAMP, an approach that drives Video and Audio adaptation using Multi-step Predictions. Experimental results demonstrate VAMP’s vast superiority over SOTA solutions.
CSRDA: Cost-sensitive Regularized Dual Averaging for Handling Imbalanced and High-dimensional Streaming Data

Class-imbalance is one of the most challenging problems in online learning due to its impact on the prediction capability of data stream mining models. Most existing approaches for online learning lack an effective mechanism to handle high-dimensional streaming data with skewed class distributions, resulting in insufficient model interpretation and deterioration of online performance. In this paper, we develop a cost-sensitive regularized dual averaging (CSRDA) method to tackle this problem. Our proposed method substantially extends the influential regularized dual averaging (RDA) method by formulating a new convex optimization function. Specifically, two <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R$</tex> 1 -norm regularized cost-sensitive objective functions are directly optimized, respectively. We then theoretically analyze CSRDA's regret bounds and the bounds of primal variables. Thus, CSRDA benefits from achieving a theoretical convergence of balanced cost and sparsity for severe imbalanced and high-dimensional streaming data mining. To validate our method, we conduct extensive experiments on six benchmark streaming datasets with varied imbalance ratios. The experimental results demonstrate that, compared to other baseline methods, CSRDA not only improves classification performance, but also successfully captures sparse features more effectively, hence has better interpretability.
JStrack: Enriching Malicious JavaScript Detection Based on AST Graph Analysis and Attention Mechanism

Malicious JavaScript is one of the most common tools for attackers to exploit the vulnerability of web applications. It can carry potential risks such as spreading malware, phishing, or collecting sensitive information. Though there are numerous types of malicious JavaScript that are difficult to detect, generalizing the malicious script's signature can help catch more complex JavaScripts that use obfuscation techniques. This paper aims at detecting malicious JavaScripts based on structure and attribute analysis of abstract syntax trees (ASTs) that capture the generalized semantic meaning of the source code. We apply a graph convolutional neural network (GCN) to process the AST features and get a graph representation via neural message passing with neighborhood aggregation. The attention layer enriches our method to track pertinent parts of scripts that may contain the signature of malicious intent. We comprehensively evaluate the performance of our proposed approach on a real-world dataset to detect malicious websites. The proposed method demonstrates promising performance in terms of detection accuracy and robustness against obfuscated samples.
Distribution-free robust linear regression

We study random design linear regression with no assumptions on the distribution of the covariates and with a heavy-tailed response variable. In this distribution-free regression setting, we show that boundedness of the conditional second moment of the response given the covariates is a necessary and sufficient condition for achieving nontrivial guarantees. As a starting point, we prove an optimal version of the classical in-expectation bound for the truncated least squares estimator due to Gy\"{o}rfi, Kohler, Krzy\.{z}ak, and Walk. However, we show that this procedure fails with constant probability for some distributions despite its optimal in-expectation performance. Then, combining the ideas of truncated least squares, median-of-means procedures, and aggregation theory, we construct a non-linear estimator achieving excess risk of order $d/n$ with an optimal sub-exponential tail. While existing approaches to linear regression for heavy-tailed distributions focus on proper estimators that return linear functions, we highlight that the improperness of our procedure is necessary for attaining nontrivial guarantees in the distribution-free setting.
Implementation and Performance Analysis of Hybrid Cryptographic Schemes applied in Cloud Computing Environment

Cloud computing provides customers with infrastructure, platform, and software as services through the internet in a cost-effective way based on user demand. Sensitive or confidential information might be stored on the cloud servers and infrastructures that are neither controlled nor maintained by the customers, which might be a potential data breach. Therefore, security is the basic concern that limits the advantages of cloud computing. Cryptography plays a vital role to achieve that level of security required on the cloud. Hybrid cryptography takes the advantage of integrating more than one cryptographic algorithms to enhance the overall security and performance. In our research, we have proposed a comparative study for two-tier versus three-tier hybrid cryptographic models applied to secure data on the cloud. Practical implementation was made to the studied hybrid models using python simulation. Finally, a performance analysis was conducted in terms of encryption time, decryption time, average throughput, and efficiency using relatively larger data files. According to the experimental results, we observed the superiority of the Advanced Encryption Standard (AES) hybrid model among the other algorithms in terms of performance. Also, we found that the two-tier hybrid cryptographic model is more efficient than the three-tier model, but the latter provides more security. We intend to present a scientific contribution in the field of cloud computing data security.
Quantum Advantage on Proof of Work

Proof-of-Work (PoW) is a fundamental underlying technology behind most major blockchain cryptocurrencies. It has been previously pointed out that quantum devices provide a computational advantage in performing PoW in the context of Bitcoin. Here we make the case that this quantum advantage extends not only to all existing PoW mechanisms, but to any possible PoW as well. This has strong consequences regarding both quantum-based attacks on the integrity of the entirety of the blockchain, as well as more legitimate uses of quantum computation for the purpose of mining Bitcoin and other cryptocurrencies. For the first case, we estimate when these quantum attacks will become feasible, for various cryptocurrencies, and discuss the impact of such attacks. For the latter, we derive a precise formula to calculate the economic incentive for switching to quantum-based cryptocurrency miners. Using this formula, we analyze several test scenarios, and conclude that investing in quantum hardware for cryptocurrency mining has the potential to pay off immensely.
Toward Real Hyperspectral Image Stripe Removal via Direction Constraint Hierarchical Feature Cascade Networks

In hyperspectral imaging (HSI), stripe noise is one of the most common noise types that adversely affects its application. Convolutional neural networks (CNNs) have contributed to state-of-the-art performance in HSI destriping given their powerful feature extraction and learning capabilities. However, it is difficult to obtain paired training samples for real data. Most CNN destriping methods construct a paired training dataset with simulated stripe noise for network training. However, when the stripe noise of real data is complex, destriping performance of the model is constrained. To solve this problem, this study proposes a real HSI stripe removal method using a toward real HSI stripe removal via direction constraint hierarchical feature cascade network (TRS-DCHC). TRS-DCHC uses the stripe noise extract subnetwork to extract stripe patterns from real stripe-containing HSI data and incorporates clean images to form paired training samples. The destriping subnetwork advantageously utilizes a wavelet transform to explicitly decompose stripe and stripe-free components. It also adopts multi-scale feature dense connections and feature fusion to enrich feature information and deeply mine the discriminate features of stripe and stripe-free components. Our experiments on both simulated and real data of various loads showed that TRS-DCHC features better performance in both simulated and real data compared with state-of-the-art method.
Partial sums on the ultra-wide word RAM

We consider the classic partial sums problem on the ultra-wide word RAM model of computation. This model extends the classic w-bit word RAM model with special ultrawords of length w2 bits that support standard arithmetic and boolean operation and scattered memory access operations that can access w (non-contiguous) locations in memory. The ultra-wide word RAM model captures (and idealizes) modern vector processor architectures. Our main result is a new in-place data structure for the partial sum problem that only stores a constant number of ultrawords in addition to the input and supports operations in doubly logarithmic time. This matches the best known time bounds for the problem (among polynomial space data structures) while improving the space from superlinear to a constant number of ultrawords. Our results are based on a simple and elegant in-place word RAM data structure, known as the Fenwick tree. Our main technical contribution is a new efficient parallel ultra-wide word RAM implementation of the Fenwick tree, which is likely of independent interest.
Neural Network With Hierarchical Attention Mechanism for Contextual Topic Dialogue Generation

The encoder-decoder model has achieved remarkable results in natural language generation.However, in the dialogue generation work, we often ignore the influence of the dialogue context information and topic information in the generation, resulting in the generated replies not close to the context or lack of topic information leads to general responses.In this work, we study the generation of multi-turn dialogues based on a large corpus and take advantage of the context information and topic information of the conversation in the process of dialogue generation to generate more coherent context-sensitive responses.We improve upon existing models and attention mechanisms and propose a new hierarchical model to better solve the problem of dialogue context (the HAT model).This method enables the model to obtain more contextual information when processing and improves the ability of the model in terms of contextual relevance to produce high-quality responses.In addition, to address the absence of topics in the responses, we pre-train the LDA(Latent Dirichlet Allocation) topic model to extract topic words of the dialogue content and retain as much topic information of dialogue as possible.Our model is extensively tested in several corpora, and the experiments illustrate that our model is superior to most hierarchical and non-hierarchical models with respect to multiple evaluation metrics.
Structured abbreviation expansion in context

Ad hoc abbreviations are commonly found in informal communication channels that favor shorter messages.We consider the task of reversing these abbreviations in context to recover normalized, expanded versions of abbreviated messages.The problem is related to, but distinct from, spelling correction, in that ad hoc abbreviations are intentional and may involve substantial differences from the original words.Ad hoc abbreviations are productively generated on-the-fly, so they cannot be resolved solely by dictionary lookup.We generate a large, open-source data set of ad hoc abbreviations.This data is used to study abbreviation strategies and to develop two strong baselines for abbreviation expansion.
Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN)

Symbolic reasoning is a key component for enabling practical use of data-driven planners in autonomous driving. In that context, deterministic finite state automata (DFA) are often used to formalize the underlying high-level decision-making process. Manual design of an effective DFA can be tedious. In combination with deep learning pipelines, DFA can serve as an effective representation to learn and process complex behavioral patterns. The goal of this work is to leverage that potential. We propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">automaton generative network</i> (AGN), a differentiable representation of DFAs. The resulting neural network module can be used standalone or as an embedded component within a larger architecture. In evaluations on deep learning based autonomous vehicle planning tasks, we demonstrate that incorporating AGN improves the explainability, sample efficiency, and generalizability of the model.