Reviewerly/exHarmony huggingface dataset:
Learning to reweight examples in multi-label classification

This paper presents a new method for reweighting examples in the multi-label classification problem. Existing weighting functions in self-paced learning simply determine the weights of examples according to their loss values given the current multi-label model, but neglect the unique properties of multi-label examples. It is inaccurate to treat two distinct examples as equal even if their loss values are the same. Therefore, we upgrade the classical weight functions by considering instance complexities, which are described by the distances between instance features and their corresponding labels. The distance metric can be easily optimized during training. Experimental results on real-world datasets demonstrate the significance of investigating both the dynamic and static complexities of multi-label examples, as well as the advantages of the proposed example reweighting algorithm in multi-label classification problems.
Comparative Study of Machine Learning Algorithms for Portuguese Bank Data

Direct marketing involves telephone calls, personalized emails, and messages, newsletters, which catch the eye of the customer, and in turn, attract them towards the company. In this paper data of a Portuguese bank is considered. The bank makes a call to its potential customers regarding its term deposit schemes and is interested in customers that will invest in term deposits. With the advent of machine learning algorithms in prediction, comparative study of various algorithms such as Support Vector Machine (SVM), Gaussian Naïve Bayes, Random Forest, Light Gradient Boosting (GBM), and Extreme Gradient Boosting is performed for the said data set. Light GBM gives the best results among these in very less processing time.
Reversible Data Hiding in Encrypted Images Based on Block-Wise Multi-Predictor

Reversible data hiding of encrypted images (RDHEI) is a technology where after the original image is encrypted with a key, secret data can be embedded in the encrypted image without knowing the information of the original image. In recent years, RDHEI has attracted much attention due to its security and effectiveness, and many RDHEI methods have been proposed. However, the capacity of embedded data is still not high enough. In this paper, a high-capacity RDHEI algorithm based on block-wise multi-predictor, and improved Huffman coding is proposed. First, the original image is divided into non-overlapping blocks. To better exploit the spatial correlation of the original image, 16 prediction models are used to predict the current pixel value from the neighboring pixels. All the pixels in one block use the same prediction model. The improved Huffman encoding is used to compress the prediction error image and the optimal prediction model determined by the sum of the absolute prediction errors, which makes more room for data embedding. The experimental results show that our method is superior to some of the latest algorithms in terms of achieving a higher embedding capacity.
Forecasting the Accuracy of Forecasters from Properties of Forecasting Rationales

Geopolitical forecasting tournaments have stimulated the development of methods for improving probability judgments of real-world events. But these innovations have focused on easier-to quantify variables, like personnel selection, training, teaming, and crowd aggregation—and bypassed messier constructs, like qualitative properties of forecasters’ rationales. Here we adapt methods from natural language processing (NLP) and computational text analysis to identify distinctive reasoning strategies in the rationales of top forecasters, including: (a) cognitive styles, such as dialectical complexity, that gauge tolerance of clashing perspectives and efforts to blend them into coherent conclusions; (b) the use of comparison classes or base rates to inform forecasts; (c) metrics derived from the Linguistic Inquiry and Word Count (LIWC) program. Applying these tools to multiple forecasting tournaments and to forecasters of widely varying skill (from Mechanical Turkers to carefully culled “superforecasters”) revealed that: (a) top forecasters show higher dialectical complexity in their rationales, use more comparison classes, and offer more past-focused rationales; (b) experimental interventions, like training and teaming, that boost accuracy also influence NLP profiles of rationales, nudging them in a “superforecaster-like” direction.
Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.
Code-mixed sentiment analysis of Indonesian language and Javanese language using Lexicon based approach

Abstract Nowadays mixing one language with another language either in spoken or written communication has become a common practice for bilingual speakers in daily conversation as well as in social media. Lexicon based approach is one of the approaches in extracting the sentiment analysis. This study is aimed to compare two lexicon models which are SentiNetWord and VADER in extracting the polarity of the code-mixed sentences in Indonesian language and Javanese language. 3,963 tweets were gathered from two accounts that provide code-mixed tweets. Pre-processing such as removing duplicates, translating to English, filter special characters, transform lower case and filter stop words were conducted on the tweets. Positive and negative word score from lexicon model was then calculated using simple mathematic formula in order to classify the polarity. By comparing with the manual labelling, the result showed that SentiNetWord perform better than VADER in negative sentiments. However, both of the lexicon model did not perform well in neutral and positive sentiments. On overall performance, VADER showed better performance than SentiNetWord. This study showed that the reason for the misclassified was that most of Indonesian language and Javanese language consist of words that were considered as positive in both Lexicon model.
SiamDA: Dual attention Siamese network for real-time visual tracking

Fully convolutional Siamese network (SiamFC) has demonstrated high performance in the visual tracking field, but the learned CNN features are redundant and not discriminative to separate the object from the background. To address the above problem, this paper proposes a dual attention module that is integrated into the Siamese network to select the features both in the spatial and channel domains. Especially, a non-local attention module is followed by the last layer of the network, and this benefit to obtain the self-attention feature map of the target from the spatial dimension. On the other hand, a channel attention module is proposed to adjust the importance of different channels’ features according to the corresponding responses generated by each channel feature and the target. Additionally, the GOT10k dataset is employed to train our dual attention Siamese network (SiamDA) to improve the target representation ability, which enhances the discrimination of the model. Experimental results show that the proposed algorithm improves the accuracy by 7.6% and the success rate by 5.6% compared with the baseline tracker.
A Study on Vehicle Detection through Aerial Images: Various Challenges, Issues and Applications

Nowadays vehicle detection and counting at the border of countries, as well as states/cities, has become popular through aerial images because of security concerns. It will play a vital role to reduce the various crimes i.e. (children kidnapping, drug/alcohol smuggling, traffic misconduct, weapons smuggling, sexual misconduct and mission of country-related crime, etc.) at the border of the cities as well as countries. Vehicle detection and counting have various other applications like traffic management, parking allotment, tracking the rescue vehicle in hill areas, digital watermarking, vehicle tracking at the toll plaza and urban planning, etc. However, vehicle detection and counting task are very challenging and difficult because of the complex background, the small size of the vehicle, other similar visual appearance objects, distance, etc. Till now, traditional methodology introduced several robust algorithms which has limitations while extracting the features from aerial images. Recently, deep learning-based algorithms introduced and the outcomes of these algorithms are robust for such kind of applications in the area of computer vision. But accuracy of these algorithms is not optimized in aerial images because the deep learning algorithm required a huge amount of data to train the machine and the size of the object in aerial images is also too small. All these factors affecting the efficiency of the real-time device. This paper provides a brief description of traditional algorithms as well as machine learning and deep learning concepts to identifying the object through aerial images. The study has shown the comprehensive analysis of benchmark datasets and their parameters and corresponding challenges used by researchers and scientists in the area of object detection/tracking through aerial images.
Intrusion Detection on the In-Vehicle Network Using Machine Learning

Controller Area Network (CAN) is a protocol for the in-vehicle network that connects microcontrollers called Electronic Control Units (ECUs) and other components in a vehicle so that they may communicate among themselves and control the operations of the vehicle. The CAN protocol was initially not designed with security in mind, but as modern vehicles are increasingly becoming connected to the outside world through wired and wireless interfaces, the CAN bus has become susceptible to intrusions and attacks such as message injection, replay attacks, denial of service (DoS) attacks, and eavesdropping. This paper presents an intrusion detection method based on the Isolation Forest (iForest) algorithm that detects message insertion attacks using message timing information. The resulting intrusion detection system benefits from the linear time complexity and low memory requirement of the iForest algorithm, as well as the ability to train the classifier with only a small sample of normal CAN traffic. The usage of only timing information for intrusion detection makes it a vehicle-agnostic method that does not rely on the message content, which is often proprietary and confidential information. The intrusion detection system was trained with normal CAN traffic trace and tested with two spoof attack CAN datasets. The high values obtained for the Area Under Curve (AUC) measure in the two cases, 0.966 and 0.974, indicated the effectiveness of this approach for intrusion detection.
Design and Implementation of Tourism Route Recommendation System Based on LBS

In this paper, we introduces a tourism route recommendation software based on the user's historical choices and his preference. The tourism information has been collected from web search based on Location Based Services(LBS). The software can offer tourism route recommendation based on both the location information from the web and the user's preference which determined the weight of tourism addresses. A refined weighted route calculation algorithm is proposed to help user finding a his/her most attractive tourism route. The software employs the map API to calculate the recommendation route and then show on the map. The whole system is designed and implemented on Android system to achieve better user experience. Leancloud database is employed by our system as a cloud storage database. Experiments show that, users can find their ideal route on the software's recommendation.
Effect of Annotation and Loss Function on Epiphyte Identification using Conditional Generative Adversarial Network

The deep neural networks are capable of processing large amounts of data for image processing applications like classification, segmentation and identification etc. Conditional- Generative Adversarial Network (C-GAN) is an example of Deep Learning (DL)-based image processing algorithm that is used widely for identifying targets in images. In a Deep Learning based image processing application the quality of the input image plays an important role. The quality and variations within the dataset helps the neural network filters to derive the best features for the classification and identification of the target in the image. Many deep learning approaches will have a preprocessing pipeline which improves the quality of the input training data. The objective of this study was to evaluate the effect of preprocessing methods to improve C-GAN's ability to detect epiphytes in images acquired by Unmanned Aerial Vehicles (UAVs). These methods include 1) trimming the training images to include mostly the target plant, 2) generating annotation images with a thresholding technique, and 3) incorporating binary cross entropy loss function. Results obtained from this study shows that the percent occupancy of input images and annotation method plays an important role in identifying the target plant.
A Novel Dynamic Attack on Classical Ciphers Using an Attention-Based LSTM Encoder-Decoder Model

Information security has become an intrinsic part of data communication. Cryptanalysis using deep learning–based methods to identify weaknesses in ciphers has not been thoroughly studied. Recently, long short-term memory (LSTM) networks have shown promising performance in sequential data processing by modeling the dependencies and data dynamics. Given an encrypted ciphertext sequence and corresponding plaintext, by taking advantage of sequential processing, LSTM can adaptively discover the decryption function regardless of the complexity level, which substantially outperforms traditional methods. However, a lengthy ciphertext sequence causes LSTM to lose important information along the sequence, leading to a decrease in network performance. To tackle these problems, we propose adding an attention mechanism to enhance the LSTM sequential processing power. This paper presents a novel, dynamic way to attack classical ciphers by using an attention-based LSTM encoder-decoder for different ciphertext sequence lengths. The proposed approach takes in a sequence of ciphertext and outputs a sequence of plaintext. The effectiveness and flexibility of the proposed model were evaluated on different classical ciphers. We got close to 100% accuracy in breaking all types of classical ciphers in character-level and word-level attacks. We empirically provide further insights into our results on two datasets with short and long ciphertext lengths. In addition, we provide a performance comparison of the proposed method against state-of-the-art methods. The proposed approach has the potential to attack modern ciphers. To the best of our knowledge, this is the first time an attention-based LSTM encoder-decoder has been applied to attack classical ciphers.
Epipolar Geometry Guided Highly Robust Structured Light 3D Imaging

Structured light (SL) based three-dimensional (3D) imaging technology has been widely employed in many fields of computer vision. However, currently available SL based depth sensors are sensitive to imaging noises, which severely limits the performance of subsequent advanced vision tasks. In this letter, we propose a robust and practical SL illumination pattern coding method based on epipolar geometry. The proposed pattern can effectively alleviate coding redundancy in traditional global random speckle SL patterns and make the stereo matching more robust to noise. Meanwhile, this coding strategy supplies sufficient non-local similar blocks, which inspires us to propose a uni-direction block-stacking 3D filtering algorithm to further improve the 3D imaging quality. To verify the proposed algorithms, we developed a prototype using the off-the-shelf projector and camera. Both simulation and real scene experimental results show that the proposed methods can achieve high-quality 3D imaging performance under different noisy conditions.
On the Feasibility of Automated Built-in Function Modeling for PHP Symbolic Execution

Symbolic execution has been widely applied in detecting vulnerabilities in web applications. Modeling language-specific built-in functions is essential for symbolic execution. Since built-in functions tend to be complicated and are typically implemented in low-level languages, a common strategy is to manually translate them into the SMT-LIB language for constraint solving. Such translation requires an excessive amount of human effort and deep understandings of the function behaviors. Incorrect translation can invalidate the final results. This problem aggravates in PHP applications because of their cross-language nature, i.e., , the built-in functions are written in C, but the rest code is in PHP.
A dual-stream framework guided by adaptive Gaussian maps for interactive image segmentation

Efficiently embedding user-annotation is a key issue for deep interactive image segmentation. In this paper, we propose a dual-stream framework guided by adaptive Gaussian maps for interactive image segmentation. The network architecture consists of two branches: a traditional fully convolutional neural network that produces coarse segmentation results, and an interactive shape stream that produces target boundary information by integrating user-annotations. The boundary information combined with user-intention can suppress the feature response outside the target, which boosts the performance of coarse segmentation. Additionally, we develop an adaptive Gaussian map with distinct variances to encode user-annotations, which promotes sensitivity to details by adaptively adjusting the affected region of the annotations. Specifically, when the distance between two interactions is smaller than a threshold, we shrink the Gaussian variance of these interactions to enhance the perception of details, thereby improving the segmentation performance of the details. Extensive experiments show that our algorithm effectively reduces the burden of user interaction under the restriction of clear target boundaries and excels at fine-tuning the details with the adaptive Gaussian maps.
Three Triangle Method for Face Recognition using Dlib and OpenCV

This study shows how useful and feasible in facial recognition a triangular feature of the face using the left and right Lateral Canthus, Nasal Bridge, and Apex of the Nose. These features are sometimes overlooked by traditional facial recognition programs. This paper explores a different approach using a triangle method for facial recognition utilizing DLIB and OpenCV to create the machine learning model. It has been found that the approach can be viable only with a certain distance of the camera to the face. With a 92% accuracy of the machine learning model, the research has concluded that the approach is feasible but has a constraint on the setup.
Automatic Form Filling with Form-BERT

Digital-forms are commonly used for collecting structured information from users. However, filling digital-forms that include a large number of fields is tedious and error-prone. Auto-filling form fields for the user is highly beneficial for improving user experience and potentially collecting more valuable information (in cases where not all fields are mandatory). Online E-commerce marketplaces quite often utilize such forms to collect listing attributes from sellers. In this work, we describe Form-BERT -- a Transformer-based model which is optimized for auto-filling listing attributes given the following inputs: free-text, list of known attribute names, and zero or more attribute values. Form-BERT can be further used iteratively to leverage filled out attributes as the form filling progresses.
Dynamic Embeddings for Interaction Prediction

In recommender systems (RSs), predicting the next item that a user interacts with is critical for user retention. While the last decade has seen an explosion of RSs aimed at identifying relevant items that match user preferences, there is still a range of aspects that could be considered to further improve their performance. For example, often RSs are centered around the user, who is modeled using her recent sequence of activities. Recent studies, however, have shown the effectiveness of modeling the mutual interactions between users and items using separate user and item embeddings.
Efficient parallel A* search on multi-GPU system

A* search is a best-first search algorithm that is widely used in pathfinding and graph traversal. To meet the ever-increasing demand of performance, various high-performance architectures (e.g., multi-core CPU and GPU) have been explored to accelerate the A* search. However, the current GPU based A* search approaches are merely designed based on single-GPU architecture. Nowadays, the amount of data grows at an exponential rate, making it inefficient or even infeasible for the current A* to process the data sets entirely on a single GPU. In this paper, we propose DA*, a parallel A* search algorithm based on the multi-GPU architecture. DA* enables the efficient acceleration of the A* algorithm using multiple GPUs with effective graph partitioning and data communication strategies. To make the most of the parallelism of multi-GPU architecture, in the state extension phase, we adopt the method of multiple priority queues for the open list, which allows multiple states being calculated in parallel. In addition, we use the parallel hashing of replacement and frontier search mechanism to address node duplication detection and memory bottlenecks respectively. The evaluation shows that DA* is effective and efficient in accelerating A* based computational tasks on the multi-GPU system. Compared to the state-of-the-art A* search algorithm based on a single GPU, our algorithm can achieve up to 3× performance speedup with four GPUs.