Reviewerly/exHarmony huggingface dataset:
Detecting and Counting People's Faces in Images Using Convolutional Neural Networks

Computer Vision (CV) has so many applications such as but not limited to object recognition, which is a collection of computer vision tasks that involves identifying objects in images. One of CV applications is People counting, and it is useful for automatically counting the number of persons in a class, or a ceremony, or an event. People counting is based on face detection is a challenging task and still an open problem in computer vision. This research investigates two object detection models for detecting and counting people's faces. The first model is based on Faster-RCNN and the second one is based on SSD. These models are deep neural networks that are trained on object detection tasks. In this work, we train Faster-RCNN and SSD models on Wider-Face dataset, which is composed of faces in a variety of conditions relating to occlusion, illumination, expression, pose and scale. The evaluation result on the test part of the wider face dataset is 0.5 of accuracy for Faster-RCNN and SSD, also the Mean Relative Error for the Faster-RCNN is 0.3 and the SSD is 0.4. The Mean Absolute Error for the Faster-RCNN is 7.5 and the SSD is 8.6.
A General Hardware and Software Co-Design Framework for Energy-Efficient Edge AI

A huge number of edge applications including self-driving cars, mobile health, robotics, and augmented reality / virtual reality are enabled by deep neural networks (DNNs). Currently, much of this computation for these applications happens in the cloud, but there are several good reasons to perform the processing on local edge platforms such as smartphones: improved accessibility to different parts of the world, low latency, and data privacy. In this paper, we present a general hardware and software co-design framework for energy-efficient edge AI for both simple classification and structured output prediction tasks (e.g., 3D shapes from images). This framework relies on two key ideas. First, we design a space of DNNs of increasing complexity (coarse to fine) and perform input-specific adaptive inference by selecting a DNN of appropriate complexity depending on the hardness of input examples. Second, we execute the selected DNN on the target edge platform using a resource management policy to save energy. We also provide instantiations of our co-design framework for three qualitatively different problem settings: convolutional neural networks for image classification, graph convolutional networks for predicting 3D shapes from images, and generative adversarial networks on photo-realistic unconditional image generation. Our experiments on real-world benchmarks and mobile platforms show the effectiveness of our co-design framework in achieving significant gain in energy with little to no loss in accuracy of predictions.
SoK: Human, Organizational, and Technological Dimensions of Developers’ Challenges in Engineering Secure Software

Despite all attempts to improve software security, vulnerabilities are still propagated within software. A growing body of research is looking into why developers are unable to develop secure software from the beginning. However, despite this attention, research efforts on developer challenges lack a coherent framework. We present a systematization of existing knowledge on the factors that make secure software development challenging for developers. We evaluated 126 papers to develop a framework of challenges that includes 17 areas of challenges in three dimensions of Human, Organizational, and Technological. These areas appear to influence each other directly and indirectly. Our work highlights the interplay of these areas and their consequences for secure software development. We discussed lessons learned from the framework, shed light on its role in assisting practitioners, and proposed directions for future research.
Lightweight Classification of Normal Versus Leukemic Cells Using Feature Extraction

Current deep learning approaches, based on convolutional neural networks, produce models with excellent performance in lymphocyte classification tasks. However, they require long training times and specific hardware to work. To overcome these drawbacks, we present a lightweight image classification system capable of discriminating between cancerous and noncancerous lymphocytes of leukemia patients using image processing and deep learning techniques that require much less training time and can be run on a standard CPU. Feature vectors are built with statistical, textural, morphological, contour, and frequency features extracted from each lymphocyte image, and used to train a neural network that classifies the lymphocytes into malignant or healthy. The proposed method has lower computational cost than other deep learning methods in both learning time and neural network size. Our results contribute towards leukemia classification systems showing that high accuracy can be achieved by a neural network classifier trained with a rich set of features.
A Context Aware Embedding for the Detection of Hate Speech in Social Media Networks

Proliferation of social media platforms in recent past has resulted into upsurge in the number of users. Advent of these sites have paved way for the users to easily express share and communicate. In such a scenario, it is imperative to analyze the content and identify nasty content so as to avoid unpleasant situations. Machine learning techniques are extensively used for this purpose. In this paper, we propose a language model for the identification of hate speech in twitter data. Distil-BERT, a context aware embedding model along with Support Vector Machine (SVM) for the classification of hate speech has been used. SVM with a 10-fold cross validation and linear kernel has been found to provide better accuracy as compared to existing models. Results show that accuracy is improved with the use of context aware embedding model.
Performance Comparison of K-Nearest Neighbor, Random Forest, and Multiple Linear Regression to Predict Photovoltaic Panels’ Power Output

Recently with the emergence of Industry 4.0 and information technologies, Machine learning techniques within artificial intelligence (AI) have become very popular and widely used in many fields such as renewable energies (solar energy…). In fact, although Machine learning technologies have existed since the 1960s, it is only in the last years that they have been really exploited and have attracted many authors particularly with the advent of new algorithms that enable to benefit from the analysis and prediction capabilities of Machine learning as well as Data Lakes that are continuously produced in real time by sensors and Internet of Things (IoT) activators. Thus, one of the core objectives of this paper is to assess the performance of classical or traditional Machine learning methods namely Multiple linear regression and advanced ones such as Random forest and K-Nearest Neighbor for the short-term prediction done for one hour of the power generated by photovoltaic panels using multiple forecasters and identify the model that offers the best accuracy by using the most popular performance metrics: Root Mean Square Error, R-squared and Mean absolute error.
A hybrid method of phase shifting interferometry and optical tomographic techniques for determining the optical anisotropy of grafted nylon-6 fibres with PMMA polymer

A method combining the phase shifting interferometry PSI and optical tomographic techniques for detecting the optical properties of grafted nylon-6 fibres is introduced. The method is capable of detecting the optical anisotropy and morphological properties of the sample at same time for the same region of the tested sample. The effect of grafting of nylon-6 with poly(methyl methacrylate), PMMA, polymer on their optical and morphological properties was investigated. The two-beam Pluta polarising interference microscope attached to a fibre rotation device with the suggested method were used to produce both computed tomographic data for morphological characterisation of the grafted fibre and interferometric measurements of birefringence values along the fibre axis. A blank sample of nylon-6 fibre was used to confirm the impact of grafting process with the PMMA polymer on the optical and structural properties of the nylon-6 fibres. Two -beam interference patterns are included for illustration.
Reconstruction of financial time series data based on compressed sensing

Time series data are widely used in financial research; however, data frequency and completeness can greatly affect the research results. Although high-frequency financial time series data can be obtained, some scenarios, such as bank lending data, may lack high frequency. Currently, mainstream data interpolation methods should improve the data reconstruction accuracy. In this study, we improve the compressed sensing method to expand its field of application, specifically for reconstructing financial data. The results show that the data reconstruction based on compressed sensing can effectively improve the reconstruction accuracy.
Machine Learning Embedded Semiparametric Mixtures of Regressions with Covariate-Varying Mixing Proportions

A new class of semiparametric mixture regression models with covariate-varying mixing proportions is introduced by embedding machine learning methods into mixtures of regressions. The new method uses the neural network to estimate mixing proportions nonparametrically while using the maximum likelihood estimate to estimate all other component parameters. The new machine learning embedded semiparametric mixture regression models offer more flexible estimation compared to traditional parametric mixture regression models. More importantly, the new hybrid method could better estimate the effects of multivariate covariates nonparametrically than the traditional kernel regression methods, which suffer from the well known “curse of dimensionality”. The introduced hybrid idea can be easily extended to other semiparametric statistical models and other machine learning methods. Simulation studies and a real data application are used to demonstrate the effectiveness of the proposed new method and compare it with some other existing methods.
Softmax Pooling for Super Visual Semantic Embedding

The purpose of visual-semantic embedding is to respectively map image and text to a common embedding space and perform cross-modal semantic alignment learning. Image-text matching is also the main research content of visual semantic embedding. Existing researches have confirmed that in visual-semantic embedding, a simple pooling strategy can also achieve a good performance. However, the existing visual semantic pooling strategies (aggregators) generally have some problems, including adding additional training parameters, increasing training time, ignoring intra-modal semantic-related information, and so on. In this paper, we propose a Super Visual Semantic Embedding (SVSE) Model based on Softmax Pooling (SoftPool). We introduced the softmax pooling strategy into visual semantic embedding for the first time. SoftPool is not only simple to implement but also doesn't introduce new additional training parameters. It can adaptively calculate the weights between different feature values and preserve more intra-modal correlation information between different features. At the same time, we combine the enhanced semantic representation module and our softmax pooling strategy to construct the intra-modal semantic association, which is used to improve the performance of the visual semantic embedding in image-text matching. Undoubtedly, our proposed method possesses a higher engineering application value than other methods. Experiments are conducted on two widely used cross-modal image-text datasets, namely MS-COCO and Flickr-30K. Comparing with the best pooling strategy, our proposed softmax pooling strategy not only is better in training time but also outperforms by 0.48% (5K) on MS-COCO and 1.95% on Flickr-30K at R@1 (image retrieval). Moreover, comparing with the best visual semantic embedding model, our proposed SVSE outperforms by 2.83% (5K) on MS-COCO and 4.89% (1K) on Flickr-30K at R@1 (image retrieval), respectively. Our code is available at https://github.com/zengzhixian/SoftPool_SVSE.git.
Naive Bayes Classifier and Word2Vec for Sentiment Analysis on Bahasa Indonesia Cosmetic Product Reviews

Cosmetic products are products that are widely sold on e-commerce. A product, including a cosmetic product can generate mixed sentiments in the form of customer reviews. Therefore, customer reviews are one of the most important to be paid attention to. This is because from the customer reviews, it can be known the level of customer satisfaction about the product that has been purchased. Sentiment analysis is a solution that can be used to measure customer satisfaction. Sentiment analysis is a text-based research field that is suitable to discuss the problem of customer satisfaction about the product. The analysis used is based on several aspects of cosmetic products, namely aroma, packaging, price, and product. In this study, the problem was solved by analyzing sentiment using the Naive Bayes and Word2Vec methods. Based on experiment results, Naïve Bayes and Word2Vec could be used to classify the sentiment. The best model of this research produces an accuracy of 68.17 % with an accuracy of 56.36 % for product aspects, 70.96 % for price aspects, 68.79 % for packaging aspects, and 76.57 % for aroma aspects. This result is obtained when the translated data is used and validated using 2-fold cross validation. The parameters for Word2Vec are window = 7 and size = 300.
Random walk autonomous groups of particles for particle swarm optimization

Autonomous groups of particles swarm optimization (AGPSO), inspired by individual diversity in biological swarms such as insects or birds, is a modified particle swarm optimization (PSO) variant. The AGPSO method is simple to understand and easy to implement on a computer. It has achieved an impressive performance on high-dimensional optimization tasks. However, AGPSO also struggles with premature convergence, low solution accuracy and easily falls into local optimum solutions. To overcome these drawbacks, random-walk autonomous group particle swarm optimization (RW-AGPSO) is proposed. In the RW-AGPSO algorithm, Levy flights and dynamically changing weight strategies are introduced to balance exploration and exploitation. The search accuracy and optimization performance of the RW-AGPSO algorithm are verified on 23 well-known benchmark test functions. The experimental results reveal that, for almost all low- and high-dimensional unimodal and multimodal functions, the RW-AGPSO technique has superior optimization performance when compared with three AGPSO variants, four PSO approaches and other recently proposed algorithms. In addition, the performance of the RW-AGPSO has also been tested on the CEC’14 test suite and three real-world engineering problems. The results show that the RW-AGPSO is effective for solving high complexity problems.
PSACS: Highly-Parallel Shuffle Accelerator on Computational Storage

Shuffle is an indispensable process in distributed online analytical processing systems to enable task-level parallelism exploitation via multiple nodes. As a data-intensive data reorganization process, shuffle implemented on general-purpose CPUs not only incurs data traffic back and forth between the computing and storage resources, but also pollutes the cache hierarchy with almost zero data reuse. As a result, shuffle can easily become the bottleneck of distributed analysis pipelines.Our PSACS approach attacks these bottlenecks with the rising computational storage paradigm. Shuffle is offloaded to the storage-side PSACS accelerator to avoid polluting computing node memory hierarchy and enjoy the latency, bandwidth and energy benefits of near-data computing. Further, the microarchitecture of PSACS exploits data-, subtask-, and task-level parallelism for high performance and a customized scratchpad for fast on-chip random access.PSACS achieves 4.6x&#x2014;5.7x shuffle throughput at kernel-level and up to 1.3x overall shuffle throughput with only a twentieth of CPU utilization comparing to software baselines. These mount up to 23% end-to-end OLAP query speedup on average.
The shift from static college textbooks to customizable content: A case study at zyBooks

College textbook publishing is transforming from a model of static textbooks to a modern model of customizable textbooks. Customization may involve reconfiguring content, combining textbooks, authoring one's own content, adding notes to content, and more. As such, publishing is moving away from a model of selling static textbooks, and toward a model of providing a library of content from which instructors can build a course. This Full Paper provides data for one digital-only publisher, zyBooks, on the prevalence and trends around reconfiguring and combining Computer Science and Engineering textbooks, instructor-authored sections, and instructor-added notes. The data show that for over 4,000 classes in 2020, over 85% of classes reconfigured their books, over 30% of classes combined two or more books with hundreds combining three or more, about 30% of books had instructor notes added, and about 65% of zyLabs-enabled zyBooks included instructor-created labs. The trend away from static textbooks and toward customizable content has substantial implications on how content is authored, requiring more modularity of content sections to support reconfiguration, and requiring more consistency across subjects to enable combining content. The trend also has substantial implications on book marketing, pricing, renewals, and more.
Path planning of ROS autonomous robot based on 2D lidar-based SLAM

Indoor robots are more and more widely used, and their path planning directly affects traverse efficiency and quality, which has always been a hot research topic. Excessive requirements for sensor accuracy and accuracy may reduce the adaptability of path planning or cause unacceptable costs. Based on the analysis of the path planning requirements of the indoor robot, this paper proposes a ROS path planning method based on 2D Lidar-based SLAM. The algorithm is simple and effective. In the simulation test, the robot has a high coverage of the entire area and strong environmental adaptability.
Periocular Recognition Based on Features from Thermal and Visible-Light Images

Periocular recognition can be used for authentication even when the mouth is hidden by a mask and is thus potentially useful in a wide range of situations. A method is proposed here that uses not only visible-light images but also thermal images for periocular recognition with the expectation that the method will be robust against changes in illumination and facial expression. The NVIE database was used for experimental evaluation. It consists of visible-light and thermal facial images with various expressions captured in different lighting environments. After a score for each subject image was calculated from the features, score-level fusion was performed using the XGBoost algorithm. Experimental results show that our proposed method is more accurate than a method using only visible-light images.
A Novel classification framework for the Thirukkural for building an efficient search system

Thirukkural, a Tamil classic literature, which was written in 300 BCE is a didactic literature. Though Thirukkural comprises 1330 couplets which are organized into three sections and 133 chapters, in order to retrieve meaningful Thirukkural for a given query in search systems, a better organization of the Thirukkural is needed. This paper lays such a foundation by classifying the Thirukkural into ten new categories called superclasses that is helpful for building a better Information Retrieval (IR) system. The classifier is trained using Multinomial Naïve Bayes algorithm. Each superclass is further classified into two subcategories based on the didactic information. The proposed classification framework is evaluated using precision, recall and F-score metrics and achieved an overall F-score of 82.33% and a comparison analysis has been done with the Support Vector Machine, Logistic Regression and Random Forest algorithms. An IR system is built on top of the proposed system and the performance comparison has been done with the Google search and a locally built keyword search. The proposed classification framework has achieved a mean average precision score of 89%, whereas the Google search and keyword search have yielded 59% and 68% respectively.
FCLQC: fast and concurrent lossless quality scores compressor

Advances in sequencing technology have drastically reduced sequencing costs. As a result, the amount of sequencing data increases explosively. Since FASTQ files (standard sequencing data formats) are huge, there is a need for efficient compression of FASTQ files, especially quality scores. Several quality scores compression algorithms are recently proposed, mainly focused on lossy compression to boost the compression rate further. However, for clinical applications and archiving purposes, lossy compression cannot replace lossless compression. One of the main challenges for lossless compression is time complexity, where it takes thousands of seconds to compress a 1 GB file. Also, there are desired features for compression algorithms, such as random access. Therefore, there is a need for a fast lossless compressor with a reasonable compression rate and random access functionality.This paper proposes a Fast and Concurrent Lossless Quality scores Compressor (FCLQC) that supports random access and achieves a lower running time based on concurrent programming. Experimental results reveal that FCLQC is significantly faster than the baseline compressors on compression and decompression at the expense of compression ratio. Compared to LCQS (baseline quality score compression algorithm), FCLQC shows at least 31x compression speed improvement in all settings, where a performance degradation in compression ratio is up to 13.58% (8.26% on average). Compared to general-purpose compressors (such as 7-zip), FCLQC shows 3x faster compression speed while having better compression ratios, at least 2.08% (4.69% on average). Moreover, the speed of random access decompression also outperforms the others. The concurrency of FCLQC is implemented using Rust; the performance gain increases near-linearly with the number of threads.The superiority of compression and decompression speed makes FCLQC a practical lossless quality score compressor candidate for speed-sensitive applications of DNA sequencing data. FCLQC is available at https://github.com/Minhyeok01/FCLQC and is freely available for non-commercial usage.
Analyzing Probabilistic Adversarial Samples to Attack Cloud Vision Image Classifier Service

The deep learning model has been widely applicable in all areas, from the agricultural to the medical domain. Integrating machine learning models with different cloud services reduced the cost of developing applications. In recent years the performance of deep models surpassed human performance. Despite that, recently, in 2013, it has been proved that deep models are easily deceived simply by adding a small amount of noise into the input samples. Cloud service providers offer various services, from image classification to object identification. Machine learning engineers use these services to build automated predictive models. This work focuses on generating adversarial samples to improve the robustness of the model designed using cloud vision services. The differential evolution method uses only the probability of labels to generate the adversarial inputs. The effectiveness of the generated samples evaluated in a cloud-based environment. The proposed work proves the attack success rate to 87.39% for image classification problems on CIFAR-10 and 67.89% on ImageNet successfully fool Giotto Vision service.