Reviewerly/exHarmony huggingface dataset:
Real Time Vehicle Detection and Counting Using Deep Neural Network

The number of vehicles on the road is increasing rapidly. By using the number of vehicles as valuable data for detecting traffic congestion, it is required to detect vehicles on the road accurately and quickly, which is useful for traffic management. This paper presents a model to detect different vehicles and count them in a given video frame. With the help of YOLOv4 we can detect the different types of vehicles in the given video. Furthermore, it uses Deep SORT algorithm to help count the number of vehicles passing in the video effectively. Performance parameters like IOU and map are calculated for measuring the performance and validation of this work. After the implementation of algorithms, based on the results of YOLOv4 testing resulted in a detection accuracy rate with mAP of 84.50% where the combination of YOLOv4 with the Deep Sort algorithm can detect, track and count four types of vehicles.
Simulation of Car Driving by Voice Commands based on a Deep-Learning Model

Speech recognition is the field of research concerning the ability of machines to accept human voice as input and interpret it with the highest probability. This paper aims at simulating the driving of a robot by speech recognition of the dialect language "Darija" using the training of a deep-learning model, then the validation of the model will be done by simulation on the ROS environment, the simulation consists of driving a robot car that moves by voice commands in Darija. We develop here a deep-learning model based on TensorFlow to recognize this language. This model has been successfully tested in a car driving simulator (Robot Operating System, ROS) with the robot HUSKY with success rates up to 90% of recognition.
Analysis of the students' appreciation of teaching as a tool for managing educational processes

Educational quality requires the evaluation processes of the different agents involved. At the higher education level, teacher evaluation is carried out. One of the inputs is the appreciation of teaching by students. The latter is carried out each semester for each of the courses directed by each professor; According to the educational institution, if the professor obtains a grade lower than 4.0, he will enter a follow-up program to improve the aspects identified as negative in his teaching practice. The research presents an analysis of the results obtained in the appreciation of teaching by students in the periods 2020-01 to 2021-01. These results allowed establishing two improvement options to generate preventive actions to improve educational quality.
Online Appendix for: 'M&amp;amp;A and Cybersecurity Risk: Empirical Evidence'

This appendix contains supplementary material to the analyses reported in Lattanzio and Taillard (2022). In particular, all the tables reported in the main study are replicated below using the Lattanzio and Ma (2021) cybersecurity measure. All of our main findings are robust to using this alternate measure of cybersecurity risk.
The Gharuchah-Sofla Batholith (Northwestern Sanandaj-Sirjan Zone, Iran): Age, Petrology, Geochemistry and Tectonomagmatic Implications

The Cretaceous Gharuchah-Sofla batholith is a composite batholith consisting of a variety of mafic to felsic intrusive rocks in the northwestern Sanandaj-Sirjan zone, Iran, yet both its age and relationship between various rock types in the batholith are unknown. To address these issues, we conducted a detailed age, petrologic and geochemical study of the batholith, its country rocks, and mafic microgranular enclaves hosted therein. The main mass of the batholith, dated at ~110 Ma by in-situ zircon U-Pb method, is dominated by gabbroic, dioritic and quartz dioritic rocks with subordinate granodioritic rocks. Variably deformed granitoids occurring mostly along margins of the batholith have Late Ediacaran zircon U-Pb ages (~560 Ma). Both the main mass of the batholith and variably deformed granitoids are subalkaline with Na 2 O as the dominant alkali element over K 2 O; the former has I-type affinity whereas the latter S-type affinity. Both sets of rocks are characterized by continental crust-like trace element patterns showing negative anomalies in Nb, Ta, Sr, P, Eu and Ti of variable intensity. Gabbroic rocks of the main mass of the batholith exhibit unfractionated rare-earth element patterns (La/Yb = 1.9-3.0) and negative Zr-Hf anomalies in trace element patterns, in contrast with other rocks of the batholith and the variably deformed granitoids showing fractionated rare-earth element patterns (La/Yb = 11.0-16.8) and the lack of such anomalies. Mafic microgranular enclaves have major and trace element compositions similar to the gabbroic rocks. Contrasting Nd isotopic ranges are present between the main mass of the batholith and the mafic microgranular enclaves (ɛNd = +2.0 to +4.1) and variably deformed granitoids (ɛNd = -5.8 to -0.4). Based on the geochemical and isotopic data obtained, we suggest that (i) the variably deformed granitoids formed by melting of Cadomian basement rocks below the blocking temperature of U-Pb exchange between zircon and melt, (ii) the mafic microgranular enclaves represent frozen melts (i.e., calc-alkaline basalts) that formed the gabbroic rocks in the batholith, and (iii) significant fractional crystallization with slightly concomitant assimilation of Cadomian basement rocks was a key process forming intermediate and silicic rocks of the batholith. The ~110 Ma Gharuchah-Sofla batholith is likely associated with a continental margin arc related to subduction of the Neotethys, providing solid evidence that this segment of the arc was built directly on the Cadomian basement.
A Non-Contact Method for Real-Time Stacked-Sheet Counting Based on Broadband X-Ray Absorption Spectra and Artificial Neural Network

A non-contact method based on broadband X-ray absorption spectra (XAS) and artificial neural network (ANN) was proposed. Five hundred sheets of standard A4 printing paper were taken as experimental samples. The broadband XAS detection equipment was used to scan the papers leading to 500 broadband XAS. ANN was built to count stacked papers, compared with Polynomial fitting Model(PFM) and linear regression (LR) to verify the difference in prediction accuracy of the three models. Mean square error (MSE), Mean absolute error (MAE), Max-error (MAXE) and Coefficient of determination ( R 2 ) were selected as evaluation indexes of above models. The experimental results showed that the proposed approach can count stacked-sheet accurately with the MAE was 1.7562 and the prediction time was less than 1 second. Therefore, this study using broadband XAS and ANN realized real-time stacked-sheet counting, and provided a new idea for counting stacked-sheet.
Backtracer: Improving Ray-Casting 3d Target Acquisition by Backtracking the Interaction History

Ray-casting is a fundamental 3D target acquisition technique in virtual environments. With ray-casting, users emit a ray to highlight a desired target before pressing a button to select it. However, due to hand tremor, tracker jitter, and the Heisenberg effect, the ray direction can be shifted during button pressing, causing wrong selections. To improve selection accuracy, instead of selecting an object at button press, we backtrack the interaction history to the time when the desired target is highlighted, and select that instead. Inspired by Kopper et al.'s predictive model of distal pointing, we hypothesized that the optimal backtrack time depends on object's angular size. We validated our hypothesis through an experiment and implemented BackTracer based on the results. We evaluated BackTracer against basic ray-casting, the state-of-the-art history-based ray-casting and the state-of-the-art progressive refinement ray-casting. Results indicated that BackTracer significantly improved accuracy and selection efficiency, making it a promising technique for 3D target acquisition.
Preventing Catastrophic Forgetting in Continual Learning of New Natural Language Tasks

Multi-Task Learning (MTL) is widely-accepted in Natural Language Processing as a standard technique for learning multiple related tasks in one model. Training an MTL model requires having the training data for all tasks available at the same time. As systems usually evolve over time, (e.g., to support new functionalities), adding a new task to an existing MTL model usually requires retraining the model from scratch on all the tasks and this can be time-consuming and computationally expensive. Moreover, in some scenarios, the data used to train the original training may be no longer available, for example, due to storage or privacy concerns. In this paper, we approach the problem of incrementally expanding MTL models' capability to solve new tasks over time by distilling the knowledge of an already trained model on n tasks into a new one for solving n+1 tasks. To avoid catastrophic forgetting, we propose to exploit unlabeled data from the same distributions of the old tasks. Our experiments on publicly available benchmarks show that such a technique dramatically benefits the distillation by preserving the already acquired knowledge (i.e., preventing up to 20% performance drops on old tasks) while obtaining good performance on the incrementally added tasks. Further, we also show that our approach is beneficial in practical settings by using data from a leading voice assistant.
Maximizing Auc to Learn Weighted Naive Bayes for Imbalanced Data Classification

Imbalanced data classification is a challenging problem frequently encountered in many real-world applications. Traditional classification algorithms are generally designed to maximize overall accuracy; therefore, their effectiveness tends to be impeded by imbalanced data. Similar to other traditional classifiers, naive Bayes (NB) sometimes fails at predicting minority instances owing to its sensitivity to class distribution. To cope with this challenge, we proposed RankOptAUC NB (RNB), a novel attribute weighting method for the NB. In the proposed method, learning a weighted NB classifier was formulated as a nonlinear optimization problem with the objective of maximizing the area under the ROC (AUC). The optimization formulation enabled the RNB method to select important variables by simply adding a regularization term to the objective function. We also provided theoretical evidence that, based on the AUC metric, the proposed method improved the performance of a weighted NB classifier. The results of numerical experiments conducted using 28 datasets from the UCI machine-learning repository proved that the proposed scheme successfully determined the optimal attribute weights for imbalanced data classification.
Deep Learning Approach to Cervical Cancer Classification

Background: Cervical cancer is caused in the vast majority of cases by the human papillomavirus through sexual contact and requires a specific analysis with specialised equipment and health professionals to be detected. However, its incidence is up to ten times higher in areas without such resources. To overcome these shortcomings and perform mass screening, classifiers based on convolutional neural networks are being developed and, to speed up sample collection, liquid cytology samples have been used in recent years. Even so, these systems are not infallible and require verification by a healthcare professional to make a final diagnosis, which is why explainable deep learning techniques are taking advantage in this area.Objective: In this work, we perform an exhaustive analysis of the classifier systems developed in recent years and develop our own classifier through the evaluation of multiple combinations and the detailed adjustment of the system's hyperparameters.Methods: The classifiers developed (for two and four classes following the Bethesda System nomenclature) are evaluated in depth and compared with the works published in the last five years in terms of both efficiency and computational cost. Moreover, a complementary report generation system is presented which, in addition to the classifier results, provides the healthcare professional a detailed report containing the classification confidence of each of the categories and the visual annotation on the original image of the area (or areas) where the classifier has been focused to obtain its results.Results: The results determine an accuracy close to 98\% for the four-class classifier (with no false positives for the negative class) and 100\% for the two-class classifier. The final report include a detailed confidence list and a heat map containing the regions of interest used by the system to classify the given image.Conclusions: Compared to the previous works, this classifier obtains better accuracy results with lower computational cost. Moreover, the use of Explainable Artificial Intelligence techniques is an useful tool for the healthcare professional, as it determines the confidence in the results and the areas of the image used for classification, which can be used for an incremental learning process (allowing the professional to point out the areas he/she thinks are most important and cross-checking them against those detected by the system).
A Two-Stage Out-Of-Box Method for Detecting Side-Channel Attacks in Cloud Computing

In this paper, we proposed a two-stage out-of-box method for detecting side-channel attacks in cloud computing. The method detects side-channel attacks from outside of the virtual machine, utilizing hardware support of performance events and hypervisor's ability to introspect the virtual machine, which is robust and stealthy to attackers. By utilizing information of hardware performance counters to train a classification model, we can quickly locate the suspicious attacking virtual machine with 96.7% of precision and 95% of recall rate. By adjusting the sampling duration and interval, we can get a F1-score of 98.9%. By utilizing virtual machine introspection method to extract syscall information of suspicious virtual machine, we can precisely locate the suspicious process. Experiments demonstrate our method's effectiveness in detecting cache side-channel attacks.
Evaluating Knowledge Graph Accuracy Powered by Optimized Human-machine Collaboration

Estimating the accuracy of an automatically constructed knowledge graph (KG) becomes a challenging task as the KG often contains a large number of entities and triples. Generally, two major components information extraction (IE) and entity linking (EL) are involved in KG construction. However, the existing approaches just focus on evaluating the triple accuracy that indicates the IE quality, completely ignoring the entity accuracy. Motivated by the fact that the major advance of machines is the strong computing power while humans are skilled in correctness verification, we propose an efficient interactive method to reduce the overall cost for evaluating the KG quality, which produces accuracy estimates with a statistical guarantee for both triples and entities. Instead of annotating triples and entities separately, we design a general annotation cost that blends triples and entities generated from the identical source text. During human verification, the machine can pre-compute and infer triples to be annotated in the next round by speculating human feedback. The human-machine collaborative mechanism is optimized by formulating an order selection problem of triples which is NP-hard. Thus, a Monte Carlo Tree Search is proposed to guide the annotation process by finding an approximate solution. Extensive experiments demonstrate that our method takes less annotation cost while yielding higher accuracy estimation quality compared to the state-of-the-art approaches.
TaxoTrans

Taxonomies describe the definitions of entities, entities' attributes and the relations among the entities, and thus play an important role in building a knowledge graph. In this paper, we tackle the task of taxonomy entity translation, which is to translate the names of taxonomy entities in a source language to a target language. The translations then can be utilized to build a knowledge graph in the target language. Despite its importance, taxonomy entity translation remains a hard problem for AI models due to two major challenges. One challenge is understanding the semantic context in very short entity names. Another challenge is having deep understanding for the domain where the knowledge graph is built.
BE3R: BERT based Early-Exit Using Expert Routing

Pre-trained language models like BERT have reported state-of-the-art performance on several Natural Language Processing (NLP) tasks, but high computational demands hinder its widespread adoption for large scale NLP tasks. In this work, we propose a novel routing based early exit model called BE3R (BERT based Early-Exit using Expert Routing), where we learn to dynamically exit in the earlier layers without needing to traverse through the entire model. Unlike the exiting early-exit methods, our approach can be extended to a batch inference setting. We consider the specific application of search relevance filtering in Amazon India marketplace services (a large e-commerce website). Our experimental results show that BE3R improves the batch inference throughput by 46.5% over the BERT-Base model and 35.89% over the DistilBERT-Base model on large dataset with 50 Million samples without any trade-off on the performance metric. We conduct thorough experimentation using various architectural choices, loss functions and perform qualitative analysis. We perform experiments on public GLUE Benchmark and demonstrate comparable performance to corresponding baseline models with 23% average throughput improvement across tasks in batch inference setting.
Active Model Adaptation Under Unknown Shift

Successful machine learning typically relies on fixed data distribution. However, due to unforeseen situations in the open world, distribution shift often occurs in applications. For instance, in the image recognition task, an unpredictable distributional shift may occur due to changes in background or lighting. Furthermore, to alleviate the harm of distribution shift, the resource budget is not infinite and often constrained. To cope with such a novel problem Resource Constrained Adaptation under Unknown Shift, in this paper we study active model adaptation both theoretically and empirically. First, we present a generalization analysis of active model adaptation for distribution shift. In theory, we show that active model adaptation could improve the generalization error from O(1/N) to O(1/N), with only a few queried samples. Second, based on the theoretical analysis, we present a systemic solution Auto, consisting of three sub-steps, that is, distribution tracking, sample selection and model adaptation. Specifically, we design a shifted distribution detection module to locate the distributional shifted samples. To fit the labeling budget, we employ a core-set algorithm to enhance the informativeness of the selected samples. Finally, we update the model through the newly queried labeled data. We conduct empirical studies of nine existing active strategies on diverse real world data sets and the results show that Auto could remarkably outperform all the baselines.
Large-Scale Information Extraction under Privacy-Aware Constraints

In this digital age, people spend a significant portion of their lives online and this has led to an explosion of personal data of users due to their activities. Typically, this data is private and nobody else, except the user, is allowed to look at it. To provide better experience and assist users in their activities, it is critical to mine certain information from this data. This poses interesting and complex challenge from scalable information extraction point of view: building information extraction models where there is little data to learn from due to privacy constraints but need highly accurate models to run on a large amount of diverse data across different users. Anonymization of data is typically used to convert private data into publicly accessible data. But this may not always be feasible and may require complex differential privacy guarantees to be safe from any potential negative consequences. Further, the anonymization process needs to ensure that it retains sufficient information for modeling purposes post anonymization. Other techniques involve building extraction models using a small amount of seen (eyes-on) data with no privacy restrictions (hence, can be labeled) and a large amount of unseen (eyes-off) data which only a machine or a program can access. In this tutorial, we use emails as the canonical example of private data to explain in detail the challenges and solutions for scalable information extraction (IE) under privacy-aware constraints.
Discovering Invariant and Changing Mechanisms from Data

While invariance of causal mechanisms has inspired recent work in both robust machine learning and causal inference, causal mechanisms may also vary over domains due to, for example, population-specific differences, the context of data collection, or intervention. To discover invariant and changing mechanisms from data, we propose extending the algorithmic model for causation to mechanism changes and instantiating it using Minimum Description Length. In essence, for a continuous variable Y in multiple contexts C, we identify variables X as causal if the regression functions g : X → Y have succinct descriptions in all contexts. In empirical evaluations we show that our method, VARIO, finds invariant variable sets, reveals mechanism changes, and discovers causal networks, such as on real-world data that gives insight into the signaling pathways in human immune cells.
Design Domain Specific Neural Network via Symbolic Testing

Deep sequence networks such as multi-head self-attention networks provide a promising way to extract effective representations from raw sequence data in an end-to-end fashion and have shown great success in various domains such as natural language processing, computer vision, $etc$. However, in domains such as financial risk management and anti-fraud where expert-derived features are heavily relied on, deep sequence models struggle to dominate the game.In this paper, we introduce a simple framework called symbolic testing to verify the learnability of certain expert-derived features over sequence data. A systematic investigation over simulated data reveals the fact that the self-attention architecture fails to learn some standard symbolic expressions like the count distinct operation. To overcome this deficiency, we propose a novel architecture named SHORING, which contains two components:event network andsequence network. Theevent network efficiently learns arbitrary high-orderevent-level conditional embeddings via a reparameterization trick while thesequence network integrates domain-specific aggregations into the sequence-level representation, thereby providing richer inductive biases compare to standard sequence architectures like self-attention. We conduct comprehensive experiments and ablation studies on synthetic datasets that mimic sequence data commonly seen in anti-fraud domain and three real-world datasets. The results show that SHORING learns commonly used symbolic features well, and experimentally outperforms the state-of-the-art methods by a significant margin over real-world online transaction datasets. The symbolic testing framework and SHORING have been applied in anti-fraud model development at Alipay and improved performance of models for real-time fraud-detection.