Reviewerly/exHarmony huggingface dataset:
Accelerometer-Based Hand Gesture Control Robot Using Arduino and 3-Axis Accelerometer

The past decades have seen the rapid development of robotics in many fields. Gestures are an important part of human communication in conveying emotions and feelings. So, the diminutive technology of gesture-controlled robot should be lightened in today’s world. This paper will review the design and implementation of an accelerometer-based hand gesture control robot using Arduino and 3-axis accelerometer. The system can be broadly divided into two parts. The transmitting part consists of an Arduino board, RF transmitting module and ADXL335 accelerometer sensor. The second part is the receiver part that contains an Actual robot with Arduino board, L233D motor Driver Shield, RF module, BO motors, batteries and tires.
MultiCode: A Unified Code Analysis Framework based on Multi-type and Multi-granularity Semantic Learning

Code analysis is one of the common way to ensure software reliability. With the development of machine learning technology, more and more learning-based code analysis methods are proposed. However, most existing methods are aimed at specific code analysis tasks, which leads to the extra effort to implement different models for different tasks in industrial applications. In this paper, we propose MultiCode, a novel unified code analysis framework, which learns code semantic information of different types and granularities to cover the semantic information required by different tasks, so that it can be effectively adapted to multiple tasks with higher accuracy. To prove the effectiveness of MultiCode, we demonstrate and evaluate it on two common tasks: vulnerability detection and code clone detection. Experimental results show that MultiCode achieves F1-scores of 94.6%, 92.5% and 97.1% on SARD-BE, SARD-RME and OJClone datasets, which is significantly higher than the advanced existing methods.
Investigating project sustainability: technology as a development object in a community-based project in Naryn, Kyrgyzstan

The imperative of project sustainability has become explicit policy within development. This is especially true for technology transfer: ‘development objects’ are to be used by prospective beneficiaries long after the project’s closure. We argue that the link between project sustainability, technology and ‘success’ requires deeper scrutiny. We investigate a community-based project in Naryn, Kyrgyzstan, which included the transfer of smartphones, weather stations and camera traps. Upon the project’s closure, we compare the stakeholders’ viewpoints regarding the future use of the equipment, showing how technological objects attract new actors into the project’s network, change its course and enhance its impact. We use actor-network theory to explain how development objects shape development processes by generating own networks and transforming social relations of power. We propose a dynamic view of sustainability as: (i) continuation of delivery of project’s goods and services, (ii) durability of the achieved changes and (iii) feasibility of independent growth..
Geothermobarometry of Tekieh intrusive complex (south of Qorveh, Kurdistan Province): Constraint on using mineral chemistry of amphibole

Geothermobarometry of Tekieh intrusive complex (south of Qorveh, Kurdistan Province): Constraint on using mineral chemistry of amphibole
Design and Development of Marathi Word Stemmer

Stemming is a basic morphological analysis tool or a process used to remove variants of a particular word and to identify its root or stem. In this, morphological analysis is carried out to identify various forms of a word to obtain its stem. Stemming is used as a preprocessing operational tool for the development of various natural language text applications, such as part-of-speech tagging, sentiment analysis, text segmentation, text classification, text summarization, information extraction, information retrieval applications, and named entity recognition. Stemming is generally used to achieve better performance and evaluation of information retrieval systems. It is achieved by increasing the standard in the format of the text. There may be various forms of the same root word or base that are used in the system database to increase storage requirements by stemming. Index files are reduced to the words present in the text with their linguistic variants in the documents. It is useful for memory management and system performance and for reducing memory requirements. Our proposed system is designed specifically for Marathi language text because for some other languages in Deonagri scripts like Hindi or Konkani stemmers are available and have good results and performance. But for Marathi, very little work has been done on stemmers. The proposed idea is to develop a stemmer for the Marathi language by using a supervised machine learning approach and our own developed handwritten grammar rule set and word dictionary using which the suffixes of the different variants are successfully removed for generating their respective stem words. Finally, the system is evaluated for its performance. It is measured at 61.34%.
An Energy-Efficient Bit-Split-and-Combination Systolic Accelerator for NAS-Based Multi-Precision Convolution Neural Networks

Optimized convolutional neural network (CNN) models and energy-efficient hardware design are of great importance in edge-computing applications. The neural architecture search (NAS) methods are employed for CNN model optimization with multi-precision networks. To satisfy the computation requirements, multi-precision convolution accelerators are highly desired. The existing high-precision-split (HPS) designs reduce the additional logics for reconfiguration while resulting in low throughput for low precisions. The low-precision-combination (LPC) designs improve the low-precision throughput with large hardware cost. In this work, a bit-split-and-combination (BSC) systolic accelerator is proposed to overcome the bottlenecks. Firstly, BSC-based multiply-accumulate (MAC) unit is designed to support multi-precision computation operations. Secondly, multi-precision systolic dataflow is developed with improved data-reuse and transmission efficiency. The proposed work is designed by Chisel and synthesized in 28-nm process. The BSC MAC unit achieves maximum 2.40× and 1.64× energy efficiency than HPS and LPC units, respectively. Compared with published accelerator designs Gemmini, Bit-fusion and Bit-serial, the proposed accelerator achieves up to 2.94 × area efficiency and 6.38 × energy-saving performance on the multi-precision VGG-16, ResNet-18 and LeNet-5 benchmarks.
A Comparison on Instance Segmentation Models

Object detection is an important process in computer vision projects/tasks. It is a technique which tries to predict the location of an object by drawing a bounding box around it. This however does not give us any idea about the actual shape of the object. For this we must employ the next stage of computer vision task which is known as Instance Segmentation. This task can be used to find the shape of an object along with its bounding box. In this survey paper, we discuss some of the models that can achieve the task of instance segmentation and a dataset has been discussed. The goal of the paper is to give the reader an idea about the field of instance segmentation.
Accumulative Quantization for Approximate Nearest Neighbor Search

To further improve the approximate nearest neighbor (ANN) search performance, an accumulative quantization (AQ) is proposed and applied to effective ANN search. It approximates a vector with the accumulation of several centroids, each of which is selected from a different codebook. To provide accurate approximation for an input vector, an iterative optimization is designed when training codebooks for improving their approximation power. Besides, another optimization is introduced into offline vector quantization procedure for the purpose of minimizing overall quantization errors. A hypersphere-based filtration mechanism is designed when performing AQ-based exhaustive ANN search to reduce the number of candidates put into sorting, thus yielding better search time efficiency. For a query vector, a self-centered hypersphere is constructed, so that those vectors not lying in the hypersphere are filtered out. Experimental results on public datasets demonstrate that hypersphere-based filtration can improve ANN search time efficiency with no weakening of search accuracy; besides, the proposed AQ is superior to the state of the art on ANN search accuracy.
Payment fraud detection using machine learning techniques

Credit card usage for online transactions has expanded tremendously as a result of the development and quick expansion of E-Commerce, resulting in an explosion in credit card fraud. There are many tools available in online to generate fake credit card numbers. Most fraudsters use an online credit card to generate fake cards to use for gaming platforms and E-Commerce platforms. We can use machine learning techniques like random forest, logistic regression for improve fraud detection in credit cards. The primary goal of this project is to improve current fraud detection processes by better predictingfake accounts.
Data Duplication Removal Technology Using AWS Services

Cloud computing comes all through core interest advancement of network computing, virtualization just as web advancements. With an expansion in the use of cloud storage, powerful strategies should be utilized to diminish equipment costs, meet the data transmission necessities, and build stockpiling proficiency. This can be accomplished by utilizing Data Deduplication. Using this, less information will be on the server thus require less equipment and users would have the option to put more data in the additional space available. At present utilization of cloud storage is expanding and to conquer expanding information issues, Data deduplication methods are utilized. Information Deduplication methods can't be applied straightforwardly with security instruments. In this paper, we are eliminating copy information to save storage space and speed up the organization. Here we applied MD5 hashing to generate a hash value (when uploaded to cloud storage) and then compare those with values (when the same file uploaded with a different name) to find out the duplicate data in the cloud environment. When deduplication is accomplished framework plan for secure information change in the organization. Security is accomplished through encryption and decoding of the information. This paper inspects secure deduplication strategy. After removal of duplicate data pointers will give reference to the original file.
User Evaluation Sentiment Analysis Model Based on Machine Learning

In order to obtain a correlation between the online evaluation of three online sales products provided by Amazon and their corresponding star rating, we use the tf-idf algorithm for text mining on three online product evaluations, extract each product based on the AFINN sentiment dictionary of sentiment words with tf-idf values included in consumer reviews, and add their intersections into a thesaurus. We also extract the brand information implied in the information and observe a certain difference in the consumer evaluation of each brand. Based on our established thesaurus, we conduct a sentiment analysis of consumer text reviews, assign corresponding sentiment scores, and obtain the corresponding review score variables. Then, we find a significant correlation between the consumer sentiment score and its corresponding star rating. Based on the star rating, verified purchase, and helpful votes variables in the data, we use machine learning models to learn the sample characteristics of each star rating. The final three-product decision tree model has an accuracy of 78.33%. The accuracy rate of the 5-star evaluation is 99.41%, which can better distinguish the extreme evaluations of consumers to a certain extent. An evaluation model based on product brand, consumer star rating, and market share of product brand is established to predict the potential success or failure of the product to a certain extent.
More or Less (MoL): Defending against Multiple Perturbation Attacks on Deep Neural Networks through Model Ensemble and Compression

Deep neural networks (DNNs) have been adopted in many application domains due to their superior performance. However, their susceptibility under test-time adversarial perturbations and out-of-distribution shifts has attracted extensive research efforts. The adversarial training provides an effective defense method withstanding evolving attacking methods. However, DNNs obtained by adversarial training are usually robust to only a single type of adversarial perturbation that they are trained with. To tackle this problem, improvements have been made to incorporate multiple perturbation types into adversarial training process, but with limited flexibility in terms of perturbation types. This work investigates the design problem of deep learning (DL) systems robust to multiple perturbation attacks. To maximize flexibility, we adopt the model ensemble approach, where an ensemble of expert models dealing with various perturbation types are integrated through a trainable aggregator module. Expert models are obtained in parallel through adversarial training, targeting at respective perturbation types. Then, the aggregator module is (adversarially) trained together with fine-tuning of expert models, addressing the obfuscated gradients issue in adversarial robustness. Furthermore, in order to practically implement the robust ensemble model onto edge devices, the model compression approach is leveraged to reduce the ensemble model size. By exploring the most suitable model compression scheme, we significantly reduce the overall model size without compromising robustness. Proposed More or Less (MoL) defense outperforms state-of-the-art defenses against multiple perturbations.
Learning to Weight Filter Groups for Robust Classification

In many real-world tasks, a canonical “big data” problem is created by combining data from several individual groups or domains. Because test data will likely come from a new group of data, we want to utilize the grouped structure of our training data to enforce generalization between groups of data, not just individual samples. This can be viewed as a multiple-domain generalization problem. Specifically, the goal is to encourage generalization between previously seen labeled source data from multiple domains and unlabeled target domain data. To address this challenge, we introduce Domain-Specific Filter Group (DSFG), where each training domain has a unique filter group and each test data point is predicted by a weighted sum over the outputs of different domain filters. A separate neural network learns to estimate the appropriate filter group weights through a meta-learning strategy. Empirically, experiments on three benchmark datasets demonstrate improved performance compared to current state-of-the-art approaches.
Assessment of the digital footprint of Russian Agricultural Universities and analysis of its impact on the entry of Russian universities into the world rankings

The article considers the formation of ratings of Russian universities on the example of agricultural universities based on their digital footprint, which is classified into active, passive and semi-active. This tool shows the ways to improve the effectiveness of the implementation of the decree of the President of the Russian Federation of 2012 on the introduction of five Russian universities in the top 100 rankings of the world's leading universities. The list of methods used included methods of sitometry, which allowed us to obtain information representing a passive digital footprint. On its basis, the methodology for calculating the resulting assessments of universities with the corresponding ratings has been developed. The paper compares the obtained ratings of agricultural universities based on their passive digital footprint with the ratings of the same universities obtained on the basis of a comprehensive assessment of the effectiveness of the use of scientific and educational resources by agricultural universities, as well as information about electronic labor exchanges and trading platforms that reflect current trends in the provision of these types of services, and together represent an active digital footprint on the Internet. Analysis of the results of the comparison showed that most universities do not worry about their image, paying attention only to the content on the sites that the regulatory authorities require of them, and leaving a mostly semi-active digital footprint on the Internet. At the same time, a small group of universities that pay attention to this factor has also been identified. For example, in the top ten leaders of both rankings were only four universities: Kuban State University, RGAU-MSHA, Novosibirsk State University and Saratov State University. It is shown that in order to implement the decree of the President of the country on the introduction of five Russian universities in the top 100 ratings, it is necessary that the Ministry of Education and Science include the information included in the active and passive digital footprint considered in the work in the requirements for the content of university websites.
Static analyzer for Go

This paper describes a static analysis tool for soft-ware defects detection in source code written in Go language. We developed a fast lightweight AST-based analyzer (GOA) to support detection of syntactic-level issues (linter) and a powerful interprocedural summary-based analyzer (SVENG) with its own intermediate representation.
Effective underwater image restoration based on color channel compensation

In this work we introduce an effective underwater restoration technique built on the Dark Channel Prior (DCP). However, different than the previous solutions, we built on the statistical observation that in underwater images the vanishing of a color channel tends to move to the the opponent color away from the origin. As a result, we first perform a color compensation step, which consists in considering that the spatial average of colors in a natural scene is neutral gray. After employing this color-correction step, we estimate the transmission map from the weakest color value over a neighborhood and the backscattered light value by searching for the brightest location of the image. Finally, in order to compute the restored image, we simply invert the optical model equation using these two estimated values. The experimental results prove that our approach is competitive, yielding comparable and even better results than compared with the recent specialized underwater techniques.
Underwater Color Correction via Deep Reinforcement Learning

Due to light is scattered and absorbed while it traveling in water, underwater captured images often suffer from color cast and low visibility. To address the above problems, we propose a deep reinforcement learning (DRL) based method for underwater color correction, which explicitly indicates the step-by-step nature of the color correction process. We cast a single underwater image processing process as a Markov Decision Process with several existing simple traditional image processing operations and white balance methods as actions. In addition, the deep Q-learning network is used to establish our agent for underwater color correction by learning the optimal sequence of the actions. Through extensive experiments, we show that our method produces decent underwater color correction results and makes underwater image processing interpretable and extensible.
Fusing clinical and image data for detecting the severity of breast cancer by a novel hierarchical approach

In this paper, we developed an innovative approach combining clustering and classification routines to detect breast cancer severity (stage) and recognise whether or not it metastasises. We use Fuzzy C-mean to cluster data and a proper classification routine to recognise the severity of cancer for each cluster. In other words, we use the divide-and-conquer rule to overcome the nonlinearity of relations between features. Moreover, to have a more accurate classification in the test or real data, we impose the fuzzy membership of each data to a cluster along with other features as the set of input into the classification method. Another advantage of our research study is to use both clinical and image features and to extract new features using principal component analysis (PCA) for the classification phase. Whereas a patient might belong to more than one cluster, the results of all corresponding classification methods for the respective patient are appropriately combined to end up with the stage of the cancerous patient. Ultimately, to investigate the efficiency of the proposed hybrid approach, we use seven real data sets with both clinical and image data.
A Sequence-to-Sequence Model for Large-scale Chinese Abbreviation Database Construction

Abbreviations often used in our daily communication play an important role in natural language processing. Most of the existing studies regard the Chinese abbreviation prediction as a sequence labeling problem. However, sequence labeling models usually ignore label dependencies in the process of abbreviation prediction, and the label prediction of each character should be conditioned on its previous labels. In this paper, we propose to formalize the Chinese abbreviation prediction task as a sequence generation problem, and a novel sequence-to-sequence model is designed. To boost the performance of our deep model, we further propose a multi-level pre-trained model that incorporates character, word, and concept-level embeddings. To evaluate our methods, a new dataset for Chinese abbreviation prediction is automatically built, which contains 81,351 pairs of full forms and abbreviations. Finally, we conduct extensive experiments on a public dataset and the built dataset, and the experimental results on both datasets show that our model outperforms the state-of-the-art methods. More importantly, we build a large-scale database for a specific domain, i.e., life services in Meituan Inc., with high accuracy of about 82.7%, which contains 4,134,142 pairs of full forms and abbreviations. The online A/B testing on Meituan APP and Dianping APP suggests that Click-Through Rate increases by 0.59% and 0.86% respectively when the built database is used in the searching system. We have released our API on http://kw.fudan.edu.cn/ddemos/abbr/ with over 87k API calls in 9 months.
Research on the Exploration and Reflection of Foreign Language Teaching Based on “Artificial Intelligence + Education” in the Big Data Era

In the Big Data era, Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education filed today, innovate teaching and learning practices, and thus accelerate the progress to digital education. Traditional foreign language teaching focuses more on traditional teaching methods, for instance, Grammar Translation, Direct Method in the classroom; however, with the emergence of artificial intelligence in education, innovative teaching and learning such as mobile learning occur, which allows teaching and learning take place outside the classroom. This paper aims to investigate the application of AI into foreign language teaching, which takes College English Teaching as an example. The development of AI in education, mobile learning, big data, blended learning, flipped classrooms, and smart learning platforms is introduced as literature review, and then the design and application of AI into FLT, to be specific, College English Teaching is outlined to illustrate the implementation of AI in education.