Reviewerly/exHarmony huggingface dataset:
Part-of-Speech (POS) Tagging Using Deep Learning-Based Approaches on the Designed Khasi POS Corpus

Part-of-speech (POS) tagging is one of the research challenging fields in natural language processing (NLP). It requires good knowledge of a particular language with large amounts of data or corpora for feature engineering, which can lead to achieving a good performance of the tagger. Our main contribution in this research work is the designed Khasi POS corpus. Till date, there has been no form of any kind of Khasi corpus developed or formally developed. In the present designed Khasi POS corpus, each word is tagged manually using the designed tagset. Methods of deep learning have been used to experiment with our designed Khasi POS corpus. The POS tagger based on BiLSTM, combinations of BiLSTM with CRF, and character-based embedding with BiLSTM are presented. The main challenges of understanding and handling Natural Language toward Computational linguistics to encounter are anticipated. In the presently designed corpus, we have tried to solve the problems of ambiguities of words concerning their context usage, and also the orthography problems that arise in the designed POS corpus. The designed Khasi corpus size is around 96,100 tokens and consists of 6,616 distinct words. Initially, while running the first few sets of data of around 41,000 tokens in our experiment the taggers are found to yield considerably accurate results. When the Khasi corpus size has been increased to 96,100 tokens, we see an increase in accuracy rate and the analyses are more pertinent. As results, accuracy of 96.81% is achieved for the BiLSTM method, 96.98% for BiLSTM with CRF technique, and 95.86% for character-based with LSTM. Concerning substantial research from the NLP perspectives for Khasi, we also present some of the recently existing POS taggers and other NLP works on the Khasi language for comparative purposes.
Designing of a Hybrid Photovoltaic Structure for an Energy-Efficient Street Lightning System Using PVsyst Software

With the depletion of traditional fossil fuels, their disastrous impact on the environment and rising costs, renewable energy sources such as photovoltaic (PV) energy are rapidly emerging as sustainable and clean sources of power generation. The performance of photovoltaic systems is based on different factors such as the type of photovoltaic modules, irradiation potential and geographic location. In this research, PVsyst simulation software is used to design and simulate a hybrid photovoltaic system used to operate energy-efficient street lightning system. The simulation is performed to analyze the monthly/annual energy generated (kWh) by the hybrid system and specific power production (kWh/KWp). Additionally, various PV system losses are also investigated. The hybrid PV system has 4 parallel strings, and each string has 13 series-connected (mono crystalline 400 W Canadian Solar) PV modules. The energy storage system consists of 16 Narada (AcmeG 12 V 200) batteries with a nominal capacity of 1600 Ah. The simulation results show that the total annual energy production and specific energy production, were calculated to be 26.68 MWh/year and 1283 kWh/kWp/year, respectively. Simulation results also show the maximum energy injected into the utility grid in the month of June (1.814 MWh) and the minimum energy injected into the utility grid in the month of January (0.848 MWh). The battery cycle state of wear is 84.8%, and the static state of wear is 91.7%. Performance ratio (PR) analysis shows that the highest performance ratio of the hybrid system was 68.2% in December, the lowest performance ratio was 62.7% in May and the annual average performance ratio of a hybrid PV system is 65.57%. After identifying the major source of energy losses, the detailed losses for the whole year were computed and shown by the loss diagrams. To evaluate the cost effectiveness of the proposed system, a simple payback period calculation was performed.
Hand Gesture Recognition Methods and Applications: A Literature Survey

Automatic Hand Gesture Recognition has become more important in recent years. Due to an increasing number of the deaf and hearing impaired, the use of a variety of non-contact-based applications and devices has also been increased. With the development of modern technology, it also plays a key role in the human-computer interaction systems. Sign language is the main instrument of communication among the deaf, the hearing impaired and the non-verbal. However, there are barriers for these groups in their daily interaction with people who do not understand any sign language. The recent studies in algorithm analysis and computer vision have led to the development of innovative efficient and accurate gesture recognition methods. Since HGR is the basis for sign language analysis, this paper is devoted to conducting the literary survey of hand gesture detection and recognition methods and algorithms, existing sign languages and their applications. The results of this paper can be summarized as following: artificial neural networks, which use advanced methods and algorithms for hand gesture detection and recognition, is the most useful classifier for the recognition of Kazakh Sign Language. In this paper, about 70 references published from 2012 to 2021 were reviewed for identifying common methods for hand gesture recognition.
Benchmark Analysis of Deep Learning-based 3D Object Detectors on NVIDIA Jetson Platforms

3D object detection could be highly beneficial for autonomous driving of mobility platforms such as robots and drones. Since the function provides a one-shot inference that extracts 3D position with depth information and heading direction of neighboring objects, robots can generate a reliable path to navigate without a collision. In order to enable the smooth functioning of 3D object detection, there have been several approaches to build detectors using deep learning for fast and accurate inference. In this paper, we investigate 3D object detection frameworks and analyze their performance on Jetson boards released by NVIDIA. Since mobility platforms often require real-time control to avoid dynamic obstacles, onboard processing with a built-in computer is an emerging trend. Jetson series solve such requirement with a lightweight size of the board and a suitable computational performance for autonomous navigation. Recently, many robotic applications using the Jetson series have been studied owing to their clear benefits. To examine the Jetson series for a computationally expensive task like point cloud processing, we test the performance of the boards (i.e., AGX and nano) using deep learning-based 3D object detectors. We present benchmark results in terms of three metrics including accuracy, frame per second (FPS), and resource usages.
Assessing the impact of minor modifications on the interior structure of GRU: GRU1 and GRU2

Abstract In this study, two GRU variants named GRU1 and GRU2 are proposed by employing simple changes to the internal structure of the standard GRU, which is one of the popular RNN variants. Comparative experiments are conducted on four problems: language modeling, question answering, addition task, and sentiment analysis. Moreover, in the addition task, curriculum learning and anti‐curriculum learning strategies, which extend the training data having examples from easy to hard or from hard to easy, are comparatively evaluated. Accordingly, the GRU1 and GRU2 variants outperformed the standard GRU. In addition, the curriculum learning approach, in which the training data is expanded from easy to difficult, improves the performance considerably.
Seamless equal accuracy ratio for inclusive CTC speech recognition

Concerns have been raised regarding performance disparity in automatic speech recognition (ASR) systems as they provide unequal transcription accuracy for different user groups defined by different attributes that include gender, dialect, and race. In this paper, we propose "equal accuracy ratio", a novel inclusiveness measure for ASR systems that can be seamlessly integrated into the standard connectionist temporal classification (CTC) training pipeline of an end-to-end neural speech recognizer to increase the recognizer's inclusiveness. We also create a novel multi-dialect benchmark dataset to study the inclusiveness of ASR, by combining data from existing corpora in seven dialects of English (African American, General American, Latino English, British English, Indian English, Afrikaaner English, and Xhosa English). Experiments on this multi-dialect corpus show that using the equal accuracy ratio as a regularization term along with CTC loss, succeeds in lowering the accuracy gap between user groups and reduces the recognition error rate compared with a non-regularized baseline. Experiments on additional speech corpora that have different user groups also confirm our findings.
FreeLunch: Compression-based GPU Memory Management for Convolutional Neural Networks

Recently, there is a trend to develop deeper and wider Convolutional Neural Networks (CNNs) to improve task accuracy. Due to this reason, the GPU memory quickly becomes the performance bottleneck since its capacity cannot keep up with the increase of the memory requirement of CNN models. Existing solutions exploit techniques such as swapping and recomputation to accommodate the shortage of memory. However, they suffer from performance degradations due to either the limited CPU-GPU bandwidth or the significant recomputation cost. This paper proposes a compression-based technique called FreeLunch that actively compresses the intermediate data to reduce the memory footprint of training large CNN models. Based on our evaluation, FreeLunch has up to 35% less memory consumption and up to 70% better throughput than swapping and recomputation.
QR-SCAN: Traversable Region Scan for Quadruped Robot Exploration using Lightweight Precomputed Trajectory

Exploring an unknown environment with unmanned robots has attracted many interests in the robotics field to replace human resources. Especially, quadruped robots, which can operate in challenging terrains and have high payload capacity, have been widely researched. However, quadruped robots are facing the contact constraints of the legs, namely traversability. In this paper, to explore unknown environments with quadruped robots safely, we propose a novel local exploration planner that utilizes the precomputed trajectories to check traversability and collision at the same time. By strictly checking the collision and traversability in two steps, the admissible path is guaranteed in the receding-horizon manner. In addition, the trajectory considers the kinodynamics and is lightweight thanks to its precomputation. The performance of the proposed method is verified by exploring 3D simulation environments in comparison with one of the state-of-the-art methods.
Formal Methods Boost Experimental Performance for Explainable AI

IN “Towards Explainability in Machine Learning: The Formal Methods Way,”1 we illustrated last year how Explainable AI can profit by formal methods in terms of its explainability. In fact, Explainable AI is a new branch of AI, directed to a finer granular understanding of how the fancy heuristics and experimental fine tuning of hyperparameters influence the outcomes of advanced AI tools and algorithms. We discussed the concept of “explanation,” and showed how the stronger meaning of explanation in terms of formal models leads to a precise characterization of the phenomenon under consideration. We illustrated how, following the Algebraic Decision Diagram (ADD)-based aggregation technique originally established in Gossen and Steffen's work2 we can produce precise information about and exact, deterministic prediction of the outcome from a random forest consisting of 100 trees.
Bit cyclic shift method to reinforce digital chaotic maps and its application in pseudorandom number generator

When chaotic systems are realized on finite precision devices, dynamical degradation occurs, which causes many flaws in chaos-based applications. In this paper, a new bit cyclic shift method is introduced to reinforce digital chaotic maps and counteract the problem of dynamical degradation. The state value of digital chaotic map is modified by circularly shifting its bits according to a designed shift strategy. This method is applied to the Logistic map to generate many new improved chaotic maps by changing the shift strategy. Theoretical analysis and numerical experiments demonstrate their chaotic parameter ranges and chaotic behaviors. Furthermore, the simulation results under low computing precision indicate that our method has the ability to counteract the dynamical degradation. This method has high flexibility and can apply to any chaotic system. Finally, a new pseudorandom number generator (PRNG) is designed to show the good performance of the improved digital chaotic maps.
Simplify: A Python library for optimizing pruned neural networks

Neural network pruning allows for impressive theoretical reduction of models sizes and complexity. However it usually offers little practical benefits as it is most often limited to just zeroing out weights, without actually removing the pruned parameters. This precludes from the actual advantages provided by sparsification methods. We propose Simplify, a PyTorch compatible library for achieving effective model simplification. Simplified models benefit of both a smaller memory footprint and a lower inference time, making their deployment to embedded or mobile devices much more efficient.
The spread of agriculture in Iberia through Approximate Bayesian Computation and Neolithic projectile tools

In the present article we use geometric microliths (a specific type of arrowhead) and Approximate Bayesian Computation (ABC) in order to evaluate possible origin points and expansion routes for the Neolithic in the Iberian Peninsula. In order to do so, we divide the Iberian Peninsula in four areas (Ebro river, Catalan shores, Xúquer river and Guadalquivir river) and we sample the geometric microliths existing in the sites with the oldest radiocarbon dates for each zone. On this data, we perform a partial Mantel test with three matrices: geographic distance matrix, cultural distance matrix and chronological distance matrix. After this is done, we simulate a series of partial Mantel tests where we alter the chronological matrix by using an expansion model with randomised origin points, and using the distribution of the observed partial Mantel test's results as a summary statistic within an Approximate Bayesian Computation-Sequential Monte-Carlo (ABC-SMC) algorithm framework. Our results point clearly to a Neolithic expansion route following the Northern Mediterranean, whilst the Southern Mediterranean route could also find support and should be further discussed. The most probable origin points focus on the Xúquer river area.
Neural network pruning: An effective way to reduce the initial network for deep learning based human activity recognition

Neural network pruning methods can decrease the parameter counts of trained neural networks along with improving the computational performance of inference without compromising the accuracy. This work has demonstrated the effectiveness of the neural network pruning methodology, which was applied to deep learning-based human activity recognition. A long short-term memory (LSTM) architecture was used to recognize daily human activities from smartphone-based accelerometer data. An android-based application has been developed to collect the accelerometer data from smartphones. Data has been preprocessed before training the LSTM network. The accuracy of the proposed LSTM model was 97.2% while recognizing the human activities. Neural network pruning was applied to the model once the model was trained. After 70% of weight pruning on the trained/initial network, the Pruned network accuracy was similar (97.2%) as the original one. However, the accuracy of the pruned network was better than the initial model, while the network has been pruned by 25% (accuracy 97.26%), 50% (accuracy 97.29%), and 60% (accuracy 97.22%). The initial model started to slightly decline the accuracy (from 97.2% to 96.72%), while the model was pruned by 80%. In terms of neural/unit pruning, slightly declination in the accuracy (97.2% to 97.09%) occurred after 50% of pruning.
Differentially private facial obfuscation via generative adversarial networks

From smartphones owned by the majority of teenage and adult populations to omnipresent closed-circuit television systems, the ubiquity of image-capturing devices in our everyday lives ensures that digital images of individuals are taken in the hundreds of millions on a daily basis. Many of these images capture individuals' faces which, through facial recognition techniques, identify the individuals and thus represent a major privacy concern. Many countries and companies require facial obfuscation to conform to privacy laws or policies. Since images should be useful and look realistic, a trade-off arises between privacy and utility. The task is therefore to find a method of obfuscation that offers a formal privacy guarantee while preserving visual quality and maintaining facial attributes deemed acceptable for release (e.g., the pose of the head, gender, etc.). We address this task by proposing facial identity obfuscation through the application of differential privacy to image encodings in a generative adversarial network. We provide details on the design of the model architecture and training process that allow for the generation of photo-realistic obfuscated images. Through the use of principal component analysis, we control the application of noise to the model encodings in order to achieve a favourable trade-off between privacy and utility. We demonstrate the effectiveness of our approach through an experimental comparison against other methods of obfuscation which also offer a formal guarantee of privacy.
Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches.
The Construction of Machine Translation Model and Its Application in English Grammar Error Detection

In order to solve the problems of low accuracy, recall rate, and F1 value of traditional English grammar error detection methods, a new machine translation model is constructed and applied to English grammar error detection. In the encoder-decoder framework, the machine translation model is constructed through the steps of word vector generation, encoder language model construction, decoder language model construction, word alignment, output module, and so on. On this basis, the machine translation model is trained to detect English grammatical errors through dependency analysis and alternative word generation. Experimental results show that the accuracy, recall rate, and F1 value of the proposed method are higher than those of the experimental comparison method for detecting English grammatical errors such as articles, prepositions, nouns, verbs, and subject-verb agreement, indicating that the proposed method is of high practical value.
Plateaued functions on finite abelian groups and partial geometric difference sets

Abstract As a generalization of plateaued functions on finite fields and bent functions (perfect nonlinear functions) on finite abelian groups, plateaued functions on finite abelian groups were introduced in [B. Xu, Plateaued functions, partial geometric difference sets, and partial geometric designs, J. Combin. Des. 27 (2019), 756–783]. In this paper, we continue the research in the paper mentioned above. We will obtain various characterizations of plateaued functions; these characterizations establish close connections between plateaued functions and some combinatorial objects: partial geometric difference sets and related partial geometric difference families. Then we introduce the complementary matrix and Cayley matrix for a subset of a finite group and use them to characterize partial geometric difference sets. As applications, we will show how to construct directed strongly regular graphs from partial geometric difference sets and establish a natural relation between partial geometric difference sets and partial geometric designs. The tensor product of a group algebra and a cyclotomic field is an important tool for our discussions.
Dicos: Discovering Insecure Code Snippets from Stack Overflow Posts by Leveraging User Discussions

Online Q&A fora such as Stack Overflow assist developers to solve their faced coding problems. Despite the advantages, Stack Overflow has the potential to provide insecure code snippets that, if reused, can compromise the security of the entire software.
Information Security Management Metamodel (ISMM) Validation and Verification through Frequency-based Selection Technique

Information protection considers as a vital planned topic in organizational management. Information Security Management (ISM) is a regular policy and procedure of efficiently managing an organization's information security threats and hazards. However, the successful of security information management still largely depends on the participation of domain users and stakeholders in the security phases and securing the information system. Problem in a management of ISM commonly arise due to many domain users and stakeholders still lacking in term of knowledge understanding about ISM issues that would allow them to play an essential role in the implementation and operation of ISM. Therefore, in this paper the authors developed a metamodel to overcome this issue called ISM Metamodel (ISMM). Thus, this paper aims to validate the ISMM using a Frequency-based Selection (FBS) technique. Results showed that four missing important concepts and excluded two less critical concepts after the FBS validation against the initial ISMM v1.0.