Reviewerly/exHarmony huggingface dataset:
QD-Compressor: a Quantization-based Delta Compression Framework for Deep Neural Networks

Deep neural networks (DNNs) have achieved remarkable success in many fields. Large-scale DNNs also bring storage challenges when storing snapshots for preventing clusters’ frequent failures, and bring massive internet traffic when dispatching or updating DNNs for resource-constrained devices (e.g., IoT devices, mobile phones). Several approaches are aiming to compress DNNs. The Recent work, Delta-DNN, notices high similarity existed in DNNs and thus calculates differences between them for improving the compression ratio.However, we observe that Delta-DNN, applying traditional global lossy quantization technique in calculating differences of two neighboring versions of the DNNs, can not fully exploit the data similarity between them for delta compression. This is because the parameters’ value ranges (and also the delta data in Delta-DNN) are varying among layers in DNNs, which inspires us to propose a local-sensitive quantization scheme: the quantizers are adaptive to parameters’ local value ranges in layers. Moreover, instead of quantizing differences of DNNs in Delta-DNN, our approach quantizes DNNs before calculating differences to make the differences more compressible. Besides, we also propose an error feedback mechanism to reduce DNNs’ accuracy loss caused by the lossy quantization.Therefore, we design a novel quantization-based delta compressor called QD-Compressor, which calculates the lossy differences between epochs of DNNs for saving storage cost of backing up DNNs’ snapshots and internet traffic of dispatching DNNs for resource-constrained devices. Experiments on several popular DNNs and datasets show that QD-Compressor obtains a compression ratio of 2.4× ~ 31.5× higher than the state-of-the-art approaches while well maintaining the model’s test accuracy.
Improving the Accuracy of Semantic Similarity Prediction of Arabic Questions Using Data Augmentation and Ensemble

Finding the semantic similarity of a pair of questions is an important application of Arabic natural language processing, e.g., for providing an answer to a new question similar to another question with a known answer. Modern pre-trained models are currently re-tasked for this application. We present here proposed solutions to improve the accuracy of such models. We investigate training the acclaimed pre-trained AraBERT model in two phases, using data augmentation, and using ensemble of predictors. Two-phase training and data augmentation are used to provide more data examples for the model to learn and generalize. The proposed solution improves the error rate from the best previous work of 4.08% to 3.12% on the benchmark Mawdoo3 dataset. The proposed model is first trained on a back-translation version of the dataset, then it is retrained in a second phase on the dataset with transitive, symmetric, and reflexive data augmentation methods. The prediction accuracy is further improved by constructing an ensemble of five such trained models and selecting the majority vote.
Chaotic Lorenz system: analysis of the main information properties, circuit realization and LED visualization using Arduino

In this paper, we study a three-dimensional Lorenz system that demonstrates chaotic behavior. We present the state equations and mathematical analysis of the system. We use a system-design platform, like LabView, to study and analyze main information properties, such as chaotic attractors, time series, bifurcation diagram, and dynamic behavior of the overall system. We implemented an Arduino Uno based design to display chaotic attractors of the Lorenz system. The Arduino is connected to a computer through the USB port, while graphs were uploaded using program software ArduinoIDE. Finally, the connection scheme and programming code are also presented.
Longer Stay Less Priority: Flow Length Approximation Used In Information-Agnostic Traffic Scheduling In Data Center Networks

Numerous scheduling approaches have been proposed to improve user experiences in a data center network (DCN) by reducing flow completion time (FCT). Mimicking the shortest job first (SJF) has been proved to be the prominent way to improve FCT. To do so, some approaches require flow size or completion time information in advance, which is not possible in scenarios like HTTP chunk transfer or database query response. Some information-agnostic schemes require involving end-hosts for counting the number of bytes sent. We present Longer Stay Less Priority (LSLP), an information-agnostic flow scheduling scheme, like Multi-Level Feedback Queue (MLFQ) scheduler in operating systems, that aims to mimic SJF using P4 switches in a DCN. LSLP considers all the flows as short flows initially and assigns them to the highest priority queue, and flows get demoted to the lower priority queues over time. LSLP estimates the active time of a flow by leveraging the state-of-the-art P4 switch’s programmable nature. LSLP estimates the active time of a group of new flows that arrive during a time interval and assigns their packets to the highest priority. At the beginning of the next time interval, arriving packets of old flows are placed one priority lower except for those already in the lowest priority queue. Therefore, short flows can be completed in the few higher priority queues while long flows are demoted to lower priority queues. We have evaluated LSLP via a series of tests and shown that its performance is comparable to the existing scheduling schemes.
Novel LSTM-GAN Based Music Generation

With the rapid development of deep learning, many models for music generation have emerged. There are, however, many problems for methods based on the general neural network model, such as slow calculation speed, complex calculation, and long-term dependence. This study proposes a combined model method for music generation, in which the long short-term memory (LSTM) neural network and generative adversarial network (GAN) are combined to form an LSTM-GAN model. In this paper, a new data preprocessing conversion rule is proposed to process the musical instrument digital interface (MIDI) message data obtained by the performance coding method. Finally, the effectiveness of the proposed model by the maximum mean discrepancy assessment is verified. Experimental results demonstrate that the proposed model can produce novel music automatically and have good performance.
Advancing Smart Home Awareness—A Conceptual Computational Modelling Framework for the Execution of Daily Activities of People with Alzheimer’s Disease

Utilizing context-aware tools in smart homes (SH) helps to incorporate higher quality interaction paradigms between the house and specific groups of users such as people with Alzheimer's disease (AD). One method of delivering these interaction paradigms acceptably and efficiently is through context processing the behavior of the residents within the SH. Predicting human behavior and uncertain events is crucial in the prevention of upcoming missteps and confusion when people with AD perform their daily activities. Modelling human behavior and mental states using cognitive architectures produces computational models capable of replicating real use case scenarios. In this way, SHs can reinforce the execution of daily activities effectively once they acquire adequate awareness about the missteps, interruptions, memory problems, and unpredictable events that can arise during the daily life of a person living with cognitive deterioration. This paper presents a conceptual computational framework for the modelling of daily living activities of people with AD and their progression through different stages of AD. Simulations and initial results demonstrate that it is feasible to effectively estimate and predict common errors and behaviors in the execution of daily activities under specific assessment tests.
Large Margin Graph Embedding-Based Discriminant Dimensionality Reduction

Discriminant graph embedding-based dimensionality reduction methods have attracted more and more attention over the past few decades. These methods construct an intrinsic graph and penalty graph to preserve the intrinsic geometry structures of intraclass samples and separate the interclass samples. However, the marginal samples cannot be accurately characterized only by penalty graphs since they treat every sample equally. In practice, these marginal samples often influence the classification performance, which needs to be specially tackled. In this study, the near neighbors’ hypothesis margin of marginal samples has been further maximized to separate the interclass samples and improve the discriminant ability by integrating intrinsic graph and penalty graph. A novel discriminant dimensionality reduction named LMGE-DDR has been proposed. Several experiments on public datasets have been conducted to verify the effectiveness of the proposed LMGE-DDR such as ORL, Yale, UMIST, FERET, CMIU-PIE09, and AR. LMGE-DDR performs better than other compared methods, and the corresponding standard deviation of LMGE-DDR is smaller than others. This demonstrates that the evaluation method verifies the effectiveness of the introduced method.
Ransomware scenario oriented financial quantification model for the financial sector

More and more companies are becoming victims of cyber-attacks and most of these attacks are the result of a ransomware infection. Currently, there are few organizations that perform a quantitative analysis of cyber risks, allowing them to calculate their impact in monetary terms. In this paper, we propose a model with a quantitative approach, so that organizations can express in financial terms the potential impact of a ransomware attack. The proposed model was implemented in a process of a Peruvian company in the financial sector, with which an optimization of 32.2% in cybersecurity investment was achieved.
Phase-matching quantum key distribution with light source monitoring

The transmission loss of photons during quantum key distribution (QKD) process leads to the linear key rate bound for practical QKD systems without quantum repeaters. Phase matching quantum key distribution (PM-QKD) protocol, an novel QKD protocol, can overcome the constraint with a measurement-device-independent structure, while it still requires the light source to be ideal. This assumption is not guaranteed in practice, leading to practical secure issues. In this paper, we propose a modified PM-QKD protocol with a light source monitoring, named PM-QKD-LSM protocol, which can guarantee the security of the system under the non-ideal source condition. The results show that our proposed protocol performs almost the same as the ideal PM-QKD protocol even considering the imperfect factors in practical systems. PM-QKD-LSM protocol has a better performance with source fluctuation, and it is robust in symmetric or asymmetric cases.
Initial Analysis of the Impact of Emotional Speech on the Performance of Speaker Recognition on New Serbian Emotional Database

In the paper we compared three ML methods (kNN, SVM and MLP) to build the optimal models for speaker recognition for two datasets with different recording conditions. We studied the impact of different speech features on classification performance, with the main focus given to MFCCs. All models were built using neutral speech, but their performance on emotional test data is also analyzed. The achieved accuracy on speech in neutral style was ~99% for SEAC dataset and ~97% for VCTK. We observed a significant decrease in the results on emotional data. An improvement occured when other features from Interspeech 2009 feature set were added to MFCC in the model creation.
Sentiment Analysis of Tweets During COVID-19 Pandemic Using BLSTM

social media is the new way for people to express and share their thoughts and it plays a huge role in spreading the anxiety widely during the pandemics and Twitter is one of the social media channels with high number of users and daily tweets. Clarifying and understanding what people thinks and their shared opinions during such hard times can reduce the burden on the health systems and entities and redirects the concerned entities by highlighting the areas to spread the awareness in. The aim of this study is to analyze and assess the sentiment of the Tweets shared during the COVID-19 pandemic and testing the Bidirectional Long Term Short Memory (BLTSM) of Recurrent Neural Network (RNN) in predicting the sentiment class which are Negative, Positive and Neutral. The results show slight difference between the positive and negative tweets which needs an attention to spread the awareness and hence, reduce the negativity. Furthermore, the BLSTM predicted the sentiment classes (Negative, Positive and Neutral) and obtained 86.15&#x0025; accuracy rate. The high accuracy concludes that BLSTM can adapt and predict the sentiment of a text.
Applying Bayesian Networks to help Physicians Diagnose Respiratory Diseases in the context of COVID-19 Pandemic

The differential diagnosis of respiratory diseases is usually a challenge for medical specialists in the first line of care, increased under the current COVID-19 pandemic. A Clinical Decision Support System-CDSS - is being developed using Bayesian Networks &#x2013; BNs &#x2013; to help physicians diagnose respiratory diseases, including those related to COVID-19. Network structure has been elicited from expert physicians, and network parameters (diseases prevalence, symptoms, findings, and lab results conditional probabilities) were extracted from relevant bibliography or currently standard global information sources. The CDSS is being tested using case studies taken from real situations, provided and validated by physicians. The resulting system demonstrates the suitability and flexibility of BNs for diagnosis support and healthcare training.
Auction-based Federated Learning using Software-defined Networking for resource efficiency

The training of global models using federated learning (FL) strategies is complicated by variations in local model quality arising from variation in data distribution across individual clients. A wide range of training strategies could be created by varying the size and distribution of the training data and the number of training iterations to be performed. All these variables affect both model quality and resource consumption. To facilitate the selection of good training strategies, we propose an auction-based FL method that can identify a training strategy that is optimal in terms of resource management efficiency subject to a given model quality requirement. An auction method is used to dynamically select resource-efficient FL clients and local models to minimize resource usage. This is enabled by using Software-defined Networking (SDN) to support the dynamic management of FL clients. We show that resource-optimal FL strategies can be implemented in the cloud/edge services market; dynamic quality-based model selection can reduce resource costs by up to 17% from the FL server's perspective. Moreover, the client utility function presented herein helps FL clients adopt practical trading strategies to cooperate efficiently with FL servers.
Chinese Named Entity Recognition based on BERT-Transformer-BiLSTM-CRF Model

Among many named entity recognition modes in natural languages, most of the processing in the text preprocessing stage only pays attention to the vector representation of single words and characters, and seldom pays attention to the semantic relationship in the text. In the language text information, there are many pronouns and polysemous words, which makes the problem of polysemous words appear in the text preprocessing stage. Based on this problem, this paper adopts a Chinese named entity recognition method based on the BERT-Transformer-BiLSTM-CRF model. First, use the pre-trained BERT model in a large-scale corpus to dynamically generate a sequence of word vectors according to its input context, then use the Transformer encoder to model the contextual long-distance semantic features of the text, and use the BiLSTM model to perform sentence context features Extract, and finally input the feature vector sequence into CRF (Conditional Random Field) to get the final prediction result. Tested on the public MSRA Chinese corpus. Experimental results on the corpus show that the model has improved accuracy, recall and F1 value than most models.
Sports Parameter Acquisition Based on Internet of Things and Wavelet Analysis

In this study, a sports parameter acquisition model based on the internet of things and wavelet analysis is studied to improve the accuracy and timeliness of human sports parameter acquisition. A motion parameter acquisition model including a sensing layer, transmission layer, and application layer is designed. The acceleration sensor and temperature sensor in the information acquisition node in the sensing layer are used to collect the motion parameter data, which are uploaded to the application layer by the network in the transmission layer. The received data are denoised by the wavelet analysis method through the data processing unit in this layer and then sent to the ZigBee coordinator for coordination. The results show that the model can achieve the effective acquisition of different sports parameters of different moving objects and analyze the actual movement of moving objects according to the acquisition results. In the acquisition process, the signal burr can be effectively removed, the signal noise can be reduced, the high signal-to-noise ratio signal can be output, and the accuracy of acquisition is improved. It has high timeliness, stable performance, and strong practical application, which can provide an effective guarantee for users to monitor sports parameter data in real time.
Emotions Classification using Bidirectional Encoder Representations from Transformers

Recently, people used social media to convey their emotions in the text. Text classification is required for group a text containing emotion or not and identify the type of emotion. Most of the research in emotion classification use machine learning methods. So this research offers the use of deep learning for the classification of emotions in text. One of the algorithms that is widely used for deep learning is BERT (Bidirectional Encoder Representations from Transformers). For the experiment, classification is divided into two phases, namely the first phase groups emotion or not emotion tweets, and the second phase groups emotion tweets into five types of emotions, namely happy, angry, sad, scared and surprised. The experiment uses three data scenarios, namely the commuter line, transjakarta and combination data. The BERT method was compared with the BiLSTM and LSTM methods for emotion classification. The result of this study is BERT generates the highest accuracy compared to other methods for the three data scenarios with values of 87.24%, 86.27%, and 86.99% in the first phase. Then, the next accuracy value followed by obtained BiLSTM, and the last is LSTM. In the second phase, BERT also obtained the highest accuracy values for the three data scenarios with values of 94.26%, 93.71 %, and 93.98%. Then, the evaluation using Precision, Recall and F1-Measure results that BERT provides the best overall method. In this study, BERT has good performance for emotion classification.
The World of Ch. Aitmatov in Interpretation and Perception

Research relevance: In the works of Ch. Aitmatov, the cardinal problems of mankind are mainly considered, through which various sections and layers of life are realistically described in their contradiction and dialectical relationship. Research objectives: to reveal the world of Ch. Aitmatov through the interpretation and perception of the works by the readers, to experience, to sympathize with the heroes of the work, and thus, as it were, he makes them responsible for everything that happens on Earth. Research methods and materials: the article is based on a literary review and analysis of the works of the great writer Ch. Aitmatov. Research results: The world of Aitmatov is a world where kindness, love, compassion, respect, friendship and honesty live, and all this is the essence of the Universe itself. The Universe is immense, the Universe is limitless, but it fits into a small human heart, he carries it with him, he lives in it, and he cannot but obey its laws. Conclusions: Modern challenges urge people to be accountable to the Universe and to humanity.
Word Embedding for Social Bot Detection Systems

In recent years, the growth of online social network (OSN) has been very phenomenal with great social and economic impact. However, some accounts are created for malicious activities whose objective is to influence elections, sully reputations, spread fake information or attack legitimate users. Practitioners as well as researchers are attracted by this problem and attempt to propose some solutions to prevent any OSN malicious activity. Fake account can be managed by bots, but all bots are not malicious systematically. In this paper we study the impact of using word embedding with different Machine Learning (ML) techniques on the of social bot detection performance. The experiments are based only on comment features from Cresci-17 dataset. For this purpose, we used the most ML algorithms such as Logistic Regression (LR), Decision Tree (DT), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB),Adaboost, XGBoost and MultiLayer Perceptron (MLP) with different word embedding methods such as BOW, TF-IDF, Doc2Vec, Bert, Word2Vec, and FastText. The results showed that RF and DT algorithms performed the highest precision of 99.96% with Bert, and Doc2Vec gave performed a precision score of100%.
Improving Quality of HTTP Adaptive Streaming with Server and Network-Assisted DASH

Having a high-level overview of network resources, traffic pattern and return feedback to clients help them to adapt to the appropriate video quality, and ultimately achieve better Quality of Experience (QoE). Network-assisted approach's offer flexibility in traffic engineering and resource management while it has an overview of the network condition and client distribution. Leveraging flexibility of Software Defined Networking (SDN) combined with Server and Network-Assisted DASH (SAND) features allows enhance the quality of video streaming. This study provides a network-assisted approach for improving QoE in Over-the-Top (OTT) applications with exchanging network and client information. We show that in cooperation between the SDN controller and DASH Aware Network Elements (DANEs), network resources are efficiently used. Consequently, message exchange between network elements and manage the video quality requested by the client through a central controller along with prefetching specific segments at the edge, and directing clients to the appropriate edge can improve the video quality.