Reviewerly/exHarmony huggingface dataset:
Vector Model Based Information Retrieval System With Word Embedding Transformation

Vector based information retrieval system has been one of the trending methods in Natural Language Processing. The embeddings vector generated from a document helps in identifying most relevant document related to the query. There is various approach were embedding vectors can be generated and some of them which have implemented are Word2vec, Glove2vec and Sentence BERT. For information retrieval system also used word embedding transformation like PCA and Factor Analysis to improvise the model's performance. Most of information retrieval system involves getting query from the user, preprocessing of the query and generating most relevant information to the query. Results obtained by post processing methods such as PCA and Factor Analysis shows a comparatively better results with an increase of 2–3% of Mean average precision.
Lightweight multi-scale convolutional neural network for real time stereo matching

In order to accurately estimate disparities in textureless and slim regions, spatial pyramid pooling and stacked 3D CNN, which can capture global context information, are widely used in state-of-the-art stereo matching algorithms. Unfortunately, the computational complexity and high memory consumption make these methods not friendly to real-time applications such as autonomous driving and augmented realities. In order to balance the real-time performance and accuracy, we design lightweight multi-scale convolutional neural network for real-time stereo matching. First, Lightweight multi-scale 2D and 3D CNN modules are proposed for feature extraction and initial disparity computation respectively. Both of above modules only run on a low resolution to further reduce the amount of calculation. Second, multi-scale RGB images guided network is utilized to refine the final disparity estimation. Experiments on several datasets show that the proposed algorithm can achieve competitive results with speed of 64fps on a NIVDIA 1080 GPU.
Dark Web Forensics: An Investigation of Tracking Dark Web Activity with Digital Forensics

The Dark Web has become home to malicious, illegal, and exploitative activities. Due to the nature of the Dark Web and the browsers used to reach it, uncovering these activities presents grave challenges to digital investigators or law enforcement agencies. In this work, we seek to use state of the art, closed-source Digital Forensics software to extract predefined Dark Web activities from a mobile device.
Handling heavy-tailed input of transformer inference on GPUs

Transformer-based models achieve superior accuracy in the field of natural language processing (NLP) and start to be widely deployed in production. As a popular deployment device, graphic processing units (GPUs) basically adopt the batch processing technique for inferring transformer-based models and achieving high hardware performance. However, as the input sequence lengths of NLP tasks are generally variable and in a heavy-tailed distribution, the batch processing will bring large amounts of redundant computation and hurt the practical efficiency.
English–Korean speech translation corpus (EnKoST‐C): Construction procedure and evaluation results

Abstract We present an English–Korean speech translation corpus, named EnKoST‐C. End‐to‐end model training for speech translation tasks often suffers from a lack of parallel data, such as speech data in the source language and equivalent text data in the target language. Most available public speech translation corpora were developed for European languages, and there is currently no public corpus for English–Korean end‐to‐end speech translation. Thus, we created an EnKoST‐C centered on TED Talks. In this process, we enhance the sentence alignment approach using the subtitle time information and bilingual sentence embedding information. As a result, we built a 559‐h English–Korean speech translation corpus. The proposed sentence alignment approach showed excellent performance of 0.96 f‐measure score. We also show the baseline performance of an English–Korean speech translation model trained with EnKoST‐C. The EnKoST‐C is freely available on a Korean government open data hub site.
CookDial: a dataset for task-oriented dialogs grounded in procedural documents

This work presents a new dialog dataset, CookDial, that facilitates research on task-oriented dialog systems with procedural knowledge understanding. The corpus contains 260 human-to-human task-oriented dialogs in which an agent, given a recipe document, guides the user to cook a dish. Dialogs in CookDial exhibit two unique features: (i) procedural alignment between the dialog flow and supporting document; (ii) complex agent decision-making that involves segmenting long sentences, paraphrasing hard instructions and resolving coreference in the dialog context. In addition, we identify three challenging (sub)tasks in the assumed task-oriented dialog system: (1) User Question Understanding, (2) Agent Action Frame Prediction, and (3) Agent Response Generation. For each of these tasks, we develop a neural baseline model, which we evaluate on the CookDial dataset. We publicly release the CookDial dataset, comprising rich annotations of both dialogs and recipe documents, to stimulate further research on domain-specific document-grounded dialog systems.
An energy efficient and runtime-aware framework for distributed stream computing systems

Task scheduling in distributed stream computing systems is an NP-complete problem. Current scheduling schemes usually have a pause or slow start process due to the fluctuation of input data stream, which affects the performance stability, especially the high throughput and low latency goals. In addition, idle compute nodes at runtime may result in large idle load energy consumption. To address these problems, we propose an energy efficient and runtime-aware framework (Er-Stream). This paper thoroughly discusses the framework from the following aspects: (1) The communication between real-time data streaming tasks is investigated; stream application, resource and energy consumption are modeled to formalize the scheduling problem. (2) After an initial topology is submitted to the cluster, task pairs with high communication cost are processed on the same compute node through a lightweight task partitioning strategy, minimizing the communication cost between nodes and avoiding frequent triggering of runtime scheduling. (3) At runtime, reliable task migration is performed based on node communication and resource usage, which in turn helps the dynamic adjustment of the node energy consumption. (4) Metrics including latency, throughput, resource load and energy consumption are evaluated in a real distributed stream computing environment. With a comprehensive evaluation of variable-rate input scenarios, the proposed Er-Stream system provides promising improvements on throughput, latency and energy consumption compared to the existing Storm’s scheduling strategies. • Modeling of a stream application, resource and energy consumption for stream computing environments. • Formalization of the scheduling problem and optimization of stream application at the initial and run-time stages. • Reliable migration based on operators to ensure data stream sustainability. • Reduction of system energy consumption based on the Karush–Kuhn–Tucker mathematical model. • Development and integration of the proposed Er-Stream into Apache Storm.
Weather Prediction from Imbalanced Data Stream using 1D-Convolutional Neural Network

Data stream classification is a complex task in the real world due to its varying characteristics. The most common challenges are concept drift and class imbalance. Concept drift shifts in the underlying function generating the data. The biggest obstacle in achieving an effective classifier is due to class imbalance. In general, batch and ensemble solutions are mainly used to train the classifier with chunks of imbalanced data streams. In the paper, we proposed a framework called Self Organizing Auto-Encoder based 1D-CNN for Weather data forecasting. The objective of this framework is to classify imbalanced non-stationary data streams. A proposed model's concept drift handling strength is assessed using Population Stability Index (PSI). The Weather dataset is used for the evaluation of the proposed model. The results shows improvement in prediction accuracy and the classifier is stable with good PSI.
Constellation Design for Deep Joint Source-Channel Coding

Deep learning-based joint source-channel coding (JSCC) has shown excellent performance in image and feature transmission. However, the output values of the JSCC encoder are continuous, which makes the constellation of modulation complex and dense. It is hard and expensive to design radio frequency chains for transmitting such full-resolution constellation points. In this paper, two methods of mapping the full-resolution constellation to finite constellation are proposed for real system implementation. The constellation mapping results of the proposed methods correspond to regular constellation and irregular constellation, respectively. We apply the methods to existing deep JSCC models and evaluate them on AWGN channels with different signal-to-noise ratios (SNRs). Experimental results show that the proposed methods outperform the traditional uniform quadrature amplitude modulation (QAM) constellation mapping method by only adding a few additional parameters.
Cyber-troops, digital attacks, and media freedom in Indonesia

This article examines media freedom in Indonesia in the age of social media and cyber-troops. Scholars and media advocates have extensively debated social media's effects on freedom of expression and media, with some arguing that it offers room for citizens’ voices to be heard. Countering this argument, this article explores how social media-based attackers have undermined media freedom in Indonesia. It does so through in-depth analysis of digital attacks conducted before and after the 2014 and 2019 presidential elections, during which politician-sponsored actors spread disinformation and waged cyberwar to achieve political goals. This study employs a descriptive qualitative method to contribute to the broader debate on media freedom and digital authoritarianism in the context of Indonesia's digital communication landscape. This paper shows how digital attacks and attackers are increasingly violating the media's autonomy and stymieing its service of the public interest. This paper expands on previous observations of media freedom, which have traditionally held that state intervention and media ownership concentration are political authorities’ main means of disciplining critical media and journalists.
Natural Language Processing based Refining Hindi to English Machine Translation

India is a country full of diversity in culture and languages. Hindi is one of the most spoken languages in India and translation of Hindi text into English text is the need of the hour. This paper proposes a seven-module system architecture that translates some Hindi sentences into English sentences correctly that Google translate fails to do so. There are about ten sentences to cross-check the results and it is found that the proposed architecture outperforms the Google Translator.
Efficient exact K-nearest neighbor graph construction for billion-scale datasets using GPUs with tensor cores

Approximate nearest neighbor search plays a fundamental role in many areas, and the k-nearest neighbor graph (KNNG) becomes a promising solution, especially in high-dimensional space. The advantages of KNNG come at the expense of high construction time, which is in quadratic time complexity in the number of points. Many GPUs have adopted specialized hardware units for matrix multiplication, providing an even higher arithmetic throughput. This paper presents flyKNNG, a GPU KNNG construction algorithm for billion-scale datasets. It deploys the distance matrix calculation to matrix multiplication units and adopts on-the-fly top-k selection to avoid transferring the exa-scale distance matrix to/from device memory. flyKNNG co-designs the two key algorithms to optimize the overall performance: the distance matrix calculation algorithm considers the data communication costs and pruning strategy of top-k selection; the top-k selection algorithm is also specially designed for on-the-fly usage, which impacts the data reuse and instruction-level parallelism of the distance matrix calculation as little as possible. Moreover, our top-k selection algorithm is optimized for the special data layout adopted by most matrix multiplication units. Experiments show that flyKNNG achieves 4.67X speedup compared with CUML/FAISS, one of the state-of-the-art approaches.
The Novel Approach of QR Code detection with Smart Glasses

The Metaverse is evolving continuously, at the initial we can visualize this technology with the help of a Headset or Smart glasses. This paper aims the QR code detection with the help of Smart glass which will generate all the details of scanned QR codes in front of the eyes within seconds. This Research will not limit itself to QR code detection but also multiple Open CV projects like real-time human face recognition, Optical Character Recognition with a translator, etc., with this approach the whole operating system will be available in front of the eye of a particular user. This model has been achieved with the help of Raspberry Pi which worked as an operating system, 5 Megapixel Omnivision 5647 Camera Module for detection and scanning of QR code, 3.5inch RPi Display which will be embedded in the Smart glass in front of the human eye. This simultaneous integration will be able to recognise the QR code with the help of python script, for accessing the camera Open CV package will come to its role and generate the desired output on the screen of smart glasses.
Comparison of spectrogram, persistence spectrum and percentile spectrum based image representation performances in drone detection and classification using novel HMFFNet: Hybrid Model with Feature Fusion Network

Drone detection and classification are gaining great importance in military and civilian applications because of their availability and widespread usages. This study presents a new framework, named as Hybrid Model with Feature Fusion Network (HMFFNet), for drone detection and classification using Radio Frequency (RF) signals. It is composed of three stages. In the first stage, 1D RF signals are segmented to specific sizes and each segment is transformed into 2D signals in terms of spectrogram, persistence spectrum and percentile spectrum images to be employed in the well-known deep learning architectures through transfer learning approach. In the second stage, VGG19, one of these architectures, is utilized for feature extraction using the transformed images. Then the features are fused in various combinations to improve their discriminative property. Finally, these features are processed by a Support Vector Machine (SVM) classifier in the last stage of the proposed framework. To evaluate the performance of the HMFFNet, numerous experiments were conducted on the publicly available DroneRF dataset. The experimental results showed that the performance of the novel percentile spectrum based features are better compared to the widely-used and good-performing spectrogram based features in drone detection and classification. Also, fusion of features from three drone representing images further improve the performance of the proposed frameworks. For the quantitative evaluation, experiments yield to the accuracy rates of 100%, 99.55%, and 97.75% for 2-Class drone existence, 4-Class drone type detection and 10-Class drone mode detection, respectively, showing higher results than that of other state-of-the-art studies.
Load balancing scheduling algorithms for virtual computing laboratories in a Desktop-As-A-Service Cloud Computing Services

The present research work is interested in the study of the virtual computing laboratories in a Desktop-As-A-Service (DAAS) scheduling in a cloud computing environment. The issue emanates from a real-world study in which the Saudi Electronic University expresses a need to efficiently schedule the labs in predefined sessions for one of its colleges over all its branches. The problem under investigation consists, on the one hand, in load balancing the assignment of the labs to the sessions regarding the number of participants, and on the other hand, in determining the types and number of hosts required to ensure the best sessions hosts assignments. While the load balancing is evaluated by measuring the distance between the maximum and the minimum number of the virtual machines load, the maximum number of the virtual machines load, and the variance of number of the virtual machines load, the efficiency of the host selection and assignments are assessed in terms of the unbalance of the remaining capacity of each used host. We propose linearized mathematical models for the subproblems and the whole problem with the different objective functions. We firstly suggest a two-stage optimization approach in which the mathematical models via CPLEX are solved. Then, we deal with the combined method which does not divide the whole problem into its two sub-problems. Next, we run the combined model on CPLEX, and we propose two heuristics to have a feasible good solution in reasonable time. Subsequently, we compare the obtained results for diverse sizes of instances we generate according to the University features. The proposed heuristics are therefore proven to be a compelling solution to the powerful software CPLEX's failure to solve the problem for large-scale cases. Moreover, the obtained results demonstrate that they outperform those currently in use in the University.
Evolving the Ecosystem: Eclipse Arrowhead integrates Eclipse IoT

Eclipse Arrowhead is an open and flexible framework for industrial IoT scenarios based on service-oriented principles. It allows building highly configurable Systems of Systems by connecting systems running on devices in a local cloud. Part of Arrowhead’s flexibility is its ability to integrate other systems and tools through core services. In a previous study, we identified projects from Eclipse IoT, one of the largest open source communities for Internet of Things (IoT), building production-ready systems and tools for IoT. Based on this study, we integrated suitable projects into the Eclipse Arrowhead Framework, validating our initial findings and providing integrations complementing Arrowhead’s core functionality. The paper summarizes the integrations and gives an outlook on the next steps.
Simple-action-guided dictionary learning for complex action recognition

Complex action recognition is an important yet challenging problem because of uncontrolled scenes, such as partial occlusions, viewpoint changes and dynamically changing backgrounds. Meanwhile obtaining a robust and performance model requires sufficient labeled training data, and it is hard to obtain. Due to the fact that each complex action is composed of a sequence of simple actions, they can share the common dictionary. To utilize such decomposition form, we build a simple-action-guided dictionary learning model (SAG-DLM) for complex action recognition. Especially, a common dictionary is learned by simple actions to model action-shared features, and we will transfer the common dictionary to help complex action learning. Further the difference complex action dictionary is studied to better obtain sparse representation. Finally, the complex actions are reconstructed by the common dictionary and the difference complex action dictionary. We validate the proposed SAG-DLM on two complex datasets: Olympic Sports dataset and UCF50 dataset. Extensive experiments prove that the effectiveness of the proposed SAG-DLM, and the learned common dictionary can provide promising improvement.
Security of E-healthcare data using DWT-SVD based Hybrid watermarking

Due to advancement in technology nowadays, it is easy to manipulate and produce illegal multimedia data. As a consequent, different strategy of information security has been created by the researchers and specialists every day. Digital watermarking is one of the various techniques used these days to secure the data in efficient manner. In this data has been embedded using a suitable algorithm and watermark. Again at the receiving end watermark has been extracted for the reconstruction of original data. Watermarking can be done using spatial domain or transform domain. In this paper watermarking has been done using transform domain techniques on digital images. The proposed method includes DWT-SVD hybrid watermarking technique in addition to watermark generation. This method has been performed on medical images for securing the data. The original image is firstly processed by using DWT followed by SVD. The results have been tested by applying various attacks for the robustness of algorithm. The quality of an image can be evaluated by different parameters as PSNR, SNR and SSIM. The proposed method achieved a good value of PSNR and SSIM.
Deep Learning-Based Text Emotion Analysis for Legal Anomie

Text emotion analysis is an effective way for analyzing the emotion of the subjects’ anomie behaviors. This paper proposes a text emotion analysis framework (called BCDF) based on word embedding and splicing. Bi-direction Convolutional Word Embedding Classification Framework (BCDF) can express the word vector in the text and embed the part of speech tagging information as a feature of sentence representation. In addition, an emotional parallel learning mechanism is proposed, which uses the temporal information of the parallel structure calculated by Bi-LSTM to update the storage information through the gating mechanism. The convolutional layer can better extract certain components of sentences (such as adjectives, adverbs, nouns, etc.), which play a more significant role in the expression of emotion. To take advantage of convolution, a Convolutional Long Short-Term Memory (ConvLSTM) network is designed to further improve the classification results. Experimental results show that compared with traditional LSTM model, the proposed text emotion analysis model has increased 3.3 and 10.9% F1 score on psychological and news text datasets, respectively. The proposed CBDM model based on Bi-LSTM and ConvLSTM has great value in practical applications of anomie behavior analysis.
Keyword spotting in continuous speech using convolutional neural network

Keyword spotting is a process of finding some specific words or phrases in recorded speeches by computers. Deep neural network algorithms, as a powerful engine, can handle this problem if they are trained over an appropriate dataset. To this end, the football keyword dataset (FKD), as a new keyword spotting dataset in Persian, is collected with crowdsourcing. This dataset contains nearly 31 000 samples in 18 classes. The proposed continuous speech synthesis method allows the FKD dataset to be extended for practical use with applications that process continuous speeches. Besides, we proposed a lightweight architecture called EfficientNet-A0 (absolute zero) by applying the compound scaling method on EfficientNet-B0 for keyword spotting task. Finally, the proposed architecture is evaluated with various models. In our experimental results, the proposed EfficientNet-A0 and ResNet models outperform other models on the FKD dataset.