Reviewerly/exHarmony huggingface dataset:
AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation

Rapid improvements in the performance of machine learning models have pushed them to the forefront of data-driven decision-making. Meanwhile, the increased integration of these models into various application domains has further highlighted the need for greater interpretability and transparency. To identify problems such as bias, overfitting, and incorrect correlations, data scientists require tools that explain the mechanisms with which these model decisions are made. In this paper we introduce AdViCE, a visual analytics tool that aims to guide users in black-box model debugging and validation. The solution rests on two main visual user interface innovations: (1) an interactive visualization design that enables the comparison of decisions on user-defined data subsets; (2) an algorithm and visual design to compute and visualize counterfactual explanations - explanations that depict model outcomes when data features are perturbed from their original values. We provide a demonstration of the tool through a use case that showcases the capabilities and potential limitations of the proposed approach.
Impact of Using Bidirectional Encoder Representations from Transformers (BERT) Models for Arabic Dialogue Acts Identification

In Human-Computer dialogue systems, the correct identification of the intent underlying a speaker's utterance is crucial to the success of a dialogue. Several researches have studied the Dialogue Act Classification (DAC) task to identify Dialogue Acts (DA) for different languages. Recently, the emergence of Bidirectional Encoder Representations from Transformers (BERT) models, enabled establishing state-of-the-art results for a variety of natural language processing tasks in different languages. Very few researches have been done in the Arabic Dialogue acts identification task. The BERT representation model has not been studied yet in Arabic Dialogue acts detection task. In this paper, we propose a model using BERT language representation to identify Arabic Dialogue Acts. We explore the impact of using different BERT models: AraBERT Original (v0.1, v1), AraBERT Base (v0.2, and v2) and AraBERT Large (v0.2, and v2), which are pretrained on different Arabic corpora (different in size, morphological segmentation, language model window, …). The comparison was performed on two available Arabic datasets. Using AraBERTv0.2-base model for dialogue representations outperformed all other pretrained models. Moreover, we compared the performance of AraBERTv0.2-base model to the state-of-the-art approaches applied on the two datasets. The comparison showed that this representation model outperformed the performance both state-of-the-art models.
Design of Digital Pulse-Position Modulation System

Abstract Pulse-Position Modulation (PPM) is a modulation method that only makes every pulse in the carrier pulse sequence change with time but without changing the shape and amplitude of the pulse signal. In this paper, a PPM system is designed. Firstly, an appropriate mathematical model is established to represent PPM transmission, and the shape of the pulse signal is designed. After that, we write the code and add a white Gaussian noise channel. Then the transmission process is simulated and visualized. At last, the error rate of the scheme is analyzed and discussed through MATLAB simulation then compared with other modulation methods. The goal of this paper is to study PPM by designing a PPM system fully. Besides, our method is compared with other modulation methods to understand the advantages and disadvantages of PPM. This may help other scholars to design and research the PPM system.
Reliability aware green workflow scheduling using ε-fuzzy dominance in cloud

Abstract The enormous energy consumed by cloud data centers (CDCs) increases the carbon footprints, operational cost and decreases the system reliability, so it becomes a great challenge for CDCs providers. Dynamic voltage and frequency scaling (DVFS) is an efficient approach for energy efficiency, which reduces the operating frequency, and supply voltage of the processor during the task’s execution. Recent research shows that scaling of the supply voltage and operating frequency has negative impact on the system’s reliability as it increases transient fault rate of the resources. Thus, the system’s reliability and the energy consumption are two prime concerns in a cloud computing environment that requires attention. Most workflow scheduling algorithms in literature do not consider energy and reliability simultaneously. In this paper, we proposed the ε-fuzzy dominance based reliable green workflow scheduling (FDRGS) algorithm, which optimizes the application’s reliability and energy consumption simultaneously using the ε-fuzzy dominance mechanism. The simulation results obtained using fast Fourier transform (FFT) and gaussian elimination (GE) task graphs manifest that our scheduling algorithm is more efficient in optimizing energy consumption and lifetime system’s reliability jointly than several widely used algorithms. The proposed algorithm will help scientists and engineers for further insight into future research in the area of cloud.
A survey on GAN acceleration using memory compression techniques

Abstract Since its invention, generative adversarial networks (GANs) have shown outstanding results in many applications. GANs are powerful, yet resource-hungry deep learning models. The main difference between GANs and ordinary deep learning models is the nature of their output and training instability. For example, GANs output can be a whole image versus other models detecting objects or classifying images. Thus, the architecture and numeric precision of the network affect the quality and speed of the solution. Hence, accelerating GANs is pivotal. Data transfer is considered the main source of energy consumption, that is why memory compression is a very efficient technique to accelerate and optimize GANs. Two main types of memory compression exist: lossless and lossy ones. Lossless compression techniques are general among all models; thus, we will focus in this paper on lossy techniques. Lossy compression techniques are further classified into (a) pruning, (b) knowledge distillation, (c) low-rank factorization, (d) lowering numeric precision, and (e) encoding. In this paper, we survey lossy compression techniques for CNN-based GANs. Our findings showed the superiority of knowledge distillation over pruning alone and the gaps in the research field that needs to be explored like encoding and different combination of compression techniques.
INCREMENTAL DEEP NEURAL NETWORK INTRUSION DETECTION IN FOG BASED IOT ENVIRONMENT: AN OPTIMIZATION ASSISTED FRAMEWORK

IoT has gained more attention on sharing data and directing respective tasks.IoT also has the potential to enhance the lifestyle of people.Fog computing has been evolved to process information to make its service more efficient.However, the evolution of various technologies also raises difficulties in network security.This paper intends to introduce a new intrusion detection model in fog-based IoT considering the phases like Preprocessing, Feature Extraction, and Intrusion Detection.Initially, data normalization is carried out in the raw data (considered as pre-processing).Then, the pre-processed data is subjected to the feature extraction phases, where the proposed entropy-based features, gain information, and gain ratio features are extracted.Subsequently, the extracted features are subjected to the attack detection phase, where the Incremental Deep Neural Network (DNN) will detect the presence of an intruder in the network based on the data attributed.The training of DNN is made optimal via tuning the weights.This optimization is carried out by a Modified Electric Fish Optimization Algorithm (M-EFO).At last, the supremacy of the proposed developed model is examined via evaluation over extant techniques.
The teaching hand in remote accounting education: bringing mirror neurons into the debate

This study sheds light on the challenges of communicating knowledge in remote accounting education that has traditionally been demonstrated on a blackboard or whiteboard. In a synchronous online education setting, the study investigated whether students’ learning experience is facilitated by observing the instructor’s hand while examples are demonstrated. Students of two courses in fundamental accounting observed four demonstrations where the conditions instructor’s hand visible/not visible were alternated using a document camera (hand visible) and a writing pad (hand not visible). The study also explored the reasons for students’ preferences for demonstrations with or without visible hands. In terms of theory, the study contributes to previous research on cognitive load theory and social agency theory. Providing helpful directions for instructional design, the findings indicate that observing the instructor’s hand facilitates students’ learning experience to a considerably higher degree than not observing the instructor’s hand. Preferences for demonstrations with visible hands primarily relate to embodied cognition effects that are generated when observing and imitating human movement due to the mirror neuron system. Other explanations relate to social cue advantages – observing the instructor’s hand induces a feeling of connectedness, which in turn enhances cognitive engagement.
Multi-class inverted stippling

We introduce inverted stippling , a method to mimic an inversion technique used by artists when performing stippling. To this end, we extend Linde-Buzo-Gray (LBG) stippling to multi-class LBG (MLBG) stippling with multiple layers. MLBG stippling couples the layers stochastically to optimize for per-layer and overall blue-noise properties. We propose a stipple-based filling method to generate solid color backgrounds for inverting areas. Our experiments demonstrate the effectiveness of MLBG in terms of reducing overlapping and intensity accuracy. In addition, we showcase MLBG with color stippling and dynamic multi-class blue-noise sampling, which is possible due to its support for temporal coherence.
HDR-cGAN

The prime goal of digital imaging techniques is to reproduce the realistic appearance of a scene. Low Dynamic Range (LDR) cameras are incapable of representing the wide dynamic range of the real-world scene. The captured images turn out to be either too dark (underexposed) or too bright (overexposed). Specifically, saturation in overexposed regions makes the task of reconstructing a High Dynamic Range (HDR) image from single LDR image challenging. In this paper, we propose a deep learning based approach to recover details in the saturated areas while reconstructing the HDR image. We formulate this problem as an image-to-image (I2I) translation task. To this end, we present a novel conditional GAN (cGAN) based framework trained in an end-to-end fashion over the HDR-REAL and HDR-SYNTH datasets. Our framework uses an overexposed mask obtained from a pre-trained segmentation model to facilitate the hallucination task of adding details in the saturated regions. We demonstrate the effectiveness of the proposed method by performing an extensive quantitative and qualitative comparison with several state-of-the-art single-image HDR reconstruction techniques.
Diversity-Aware Anonymization for Structured Health Data

Patients' health data are captured by local hospital facilities, which has the potential for data analysis. However, due to privacy and legal concerns, local hospital facilities are unable to share the data with others which makes it difficult to apply data analysis and machine learning techniques over the health data. Analysis of such data across hospitals can provide valuable information to health professionals. Anonymization methods offer privacy-preserving solutions for sharing data for analysis purposes. In this paper, we propose a novel method for anonymizing and sharing data that addresses the record-linkage and attribute-linkage attack models. Our proposed method achieves anonymity by formulating and solving this problem as a constrained optimization problem which is based on the k-anonymity, l-diversity, and t-closeness privacy models. The proposed method has been evaluated with respect to the utility and privacy of data after anonymization in comparison to the original data.
Quantum error correction with the color-Gottesman-Kitaev-Preskill code

The Gottesman-Kitaev-Preskill (GKP) code is an important type of bosonic quantum error-correcting code. Since the GKP code only protects against small shift errors in $\hat{p}$ and $\hat{q}$ quadratures, it is necessary to concatenate the GKP code with a stabilizer code for the larger error correction. In this paper, we consider the concatenation of the single-mode GKP code with the two-dimension (2D) color code (color-GKP code) on the square-octagon lattice. We use the Steane type scheme with a maximum-likelihood estimation (ME-Steane scheme) for GKP error correction and show its advantage for the concatenation. In our main work, the minimum-weight perfect matching (MWPM) algorithm is applied to decode the color-GKP code. Complemented with the continuous-variable information from the GKP code, the threshold of 2D color code is improved. If only data GKP qubits are noisy, the threshold reaches $\sigma\approx 0.59$ $(\bar{p}\approx13.3\%)$ compared with $\bar{p}=10.2\%$ of the normal 2D color code. If measurements are also noisy, we introduce the generalized Restriction Decoder on the three-dimension space-time graph for decoding. The threshold reaches $\sigma\approx 0.46$ when measurements in the GKP error correction are noiseless, and $\sigma\approx 0.24$ when all measurements are noisy. Lastly, the good performance of the generalized Restriction Decoder is also shown on the normal 2D color code giving the threshold at $3.1\%$ under the phenomenological error model.
Design and formal verification of a copland-based attestation protocol

We present the design and formal analysis of a remote attestation protocol and accompanying security architecture that generate evidence of trustworthy execution for legacy software. For formal guarantees of measurement ordering and cryptographic evidence strength, we leverage the Copland language and Copland Virtual Machine execution semantics. For isolation of attestation mechanisms we design a layered attestation architecture that leverages the seL4 microkernel. The formal properties of the protocol and architecture together serve to discharge assumptions made by an existing higher-level model-finding tool to characterize all ways an active adversary can corrupt a target and go undetected. As a proof of concept, we instantiate this analysis framework with a specific Copland protocol and security architecture to measure a legacy flight planning application. By leveraging components that are amenable to formal analysis, we demonstrate a principled way to design an attestation protocol and argue for its end-to-end correctness.
Detection of Hidden Facial Surface Masking in Stored and Real Time Captured Images: A Deep Learning Perspective in Covid Time

A novel coronavirus has spread over the world and has become an outbreak. This, according to a WHO report, is an infectious disease that aims to spread. As a consequence, taking precautions is the only method to avoid catching this virus. The most important preventive measure against COVID-19 is to wear a mask. In this paper, a framework is designed for face mask detection using a deep learning approach. This paper aims to predict a person having a mask or unmask and also presents a proposed dataset named RTFMD (Real-Time Face Mask Dataset) to accomplish this objective. We have also taken the RFMD dataset from the internet to analyze the performance of system. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is applied at the time of pre-processing to enhance the visual quality of images. Subsequently, Inceptionv3 model used to train the face mask images and SSD face detector model has been used for face detection. Therefore, this paper proposed a model CLAHE-SSD_IV3 to classify the mask or without mask images. The system is also tested at VGG16, VGG19, Xception, MobilenetV2 models at different hyperparameters values and analyze them. Furthermore, compared the result of the proposed dataset RTFMD with the RFMD dataset. Additionally, proposed approach is compared with the existing approach on Face Mask dataset and RTFMD dataset. The outcomes have obtained 98% test accuracy on this proposed dataset RTFMD while 97% accuracy on the RFMD dataset in real-time.
A Comparative Analysis of Tree-Based Models for Day-Ahead Solar Irradiance Forecasting

Recently, solar photovoltaic (PV) techniques have been attracting lots of attention for sustainable development, and solar irradiance forecasting is crucial to estimate PV output. However, accurate solar irradiance forecasting is challenging because solar irradiance exhibits complex patterns due to various weather factors. Decision tree (DT)-based methods can effectively train complex internal and external factors so that they have been widely used in energy forecasting. In this paper, we developed several solar irradiation forecasting models using tree-based methods such as DT, bagging, random forest, gradient boosting machine, extreme gradient boosting, and Cubist. We then compared their prediction performance in terms of mean square error, root-mean-square-error (RMSE), and normalized RMSE. Experiment results for two regions on Jeju Island showed that Cubist could derive better prediction performance of day-ahead hourly solar irradiation than other tree-based methods.
SEMANTIC ANALYSIS ON THE USE OF ENGLISH LANGUAGE SLOGAN

In this study the objectives to be achieved are: (1) To identify what are the types of English slogans. (2) To identify how the semantic meanings of English slogans. The type of research used is qualitative. While the methods and instruments used to collect and analyze and answer all existing problems are qualitative methods and checklist observations. The process of data analysis in this research is carried out in a process starting from data collection and done intensively until the end of the study. Data that researchers get from observations from several sources such as the internet and journals, namely: (1) in general there are 4 types of slogans such as product / advertising slogans, health slogans, environmental slogans and educational slogans. The types of product / advertising slogans are 6 slogans, health slogans 2, environmental slogans 5 and education slogans totaling 8. (2) semantic meanings of 4 types of slogans from internet and journal sources there are 5 namely conceptual meaning, associative meaning, connotative meaning, affective meaning and stylist. Of the 20 slogans education slogans and products that contain more conceptual meaning. The environmental slogans contain more conceptual and affective meanings. Slogans that contain conceptual meaning in the form of a situation context and based on words that have been conceptualized in the dictionary.
Learning Universal Authorship Representations

Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods.Recently, authorship representations learned using neural networks have been found to outperform alternatives, particularly in large-scale settings involving hundreds of thousands of authors.But do such representations learned in a particular domain transfer to other domains?Or are these representations inherently entangled with domain-specific features?To study these questions, we conduct the first large-scale study of cross-domain transfer for authorship verification considering zero-shot transfers involving three disparate domains: Amazon reviews, fanfiction short stories, and Reddit comments.We find that although a surprising degree of transfer is possible between certain domains, it is not so successful between others.We examine properties of these domains that influence generalization and propose simple but effective methods to improve transfer.
Hate Speech Detection on Twitter in Indonesia with Feature Expansion Using GloVe

Twitter is one of the popular social media to channel opinions in the form of criticism and suggestions. Criticism could be a form of hate speech if the criticism implies attacking something (an individual, race, or group). With the limit of 280 characters in a tweet, there is often a vocabulary mismatch due to abbreviations which can be solved with word embedding. This study utilizes feature expansion to reduce vocabulary mismatches in hate speech on Twitter containing Indonesian language by using Global Vectors (GloVe). Feature selection related to the best model is carried out using the Logistic Regression (LR), Random Forest (RF), and Artificial Neural Network (ANN) algorithms. The results show that the Random Forest model with 5.000 features and a combination of TF-IDF and Tweet corpus built with GloVe produce the best accuracy rate between the other models with an average of 88,59% accuracy score, which is 1,25% higher than the predetermined Baseline. The number of features used is proven to improve the performance of the system.
Tissue Type Recognition in Whole Slide Histological Images

Automatic layers recognition of the wall of the stomach and colon on whole slide images is an extremely urgent task in digital pathology as it can be used for automatic determining the depth of invasion of the digestive tract tumors. In this paper we propose a new CNN-based method of automatic tissue type recognition on whole slide histological images. We also describe an effective pipeline of training that uses 2 different training datasets. The proposed method of automatic tissue type recognition achieved 0.929 accuracy and 0.903 balanced accuracy on CRC-VAL-HE-7K dataset for 9-types classification and 0.98 accuracy and 0.926 balanced accuracy on the test subset of whole slide images from PATH-DT- MSU dataset for 5-types classification. The developed method makes it possible to classify the areas corresponding to the gastric own mucous glands in the lamina propria and also to distinguish the tubular structures of a highly differentiated gastric adenocarcinoma with normal glands.
Distinguishability of keystroke dynamic template

When keystroke dynamics are used for authentication, users tend to get different levels of security due to differences in the quality of their templates. This paper addresses this issue by proposing a metric to quantify the quality of keystroke dynamic templates. That is, in behavioral biometric verification, the user's templates are generally constructed using multiple enrolled samples to capture intra-user variation. This variation is then used to normalize the distance between a set of enrolled samples and a test sample. Then a normalized distance is compared against a predefined threshold value to derive a verification decision. As a result, the coverage area for accepted samples in the original space of vector representation is discrete. Therefore, users with the higher intra-user variation suffer higher false acceptance rates (FAR). This paper proposes a metric that can be used to reflect the verification performance of individual keystroke dynamic templates in terms of FAR. Specifically, the metric is derived from statistical information of user-specific feature variations, and it has a non-decreasing property when a new feature is added to a template. The experiments are performed based on two public keystroke dynamic datasets comprising of two main types of keystroke dynamics: constrained-text and free-text, namely the CMU keystroke dynamics dataset and the Web-Based Benchmark for keystroke dynamics dataset. Experimental results based on multiple classifiers demonstrate that the proposed metric can be a good indicator of the template's false acceptance rate. Thus, it can be used to enhance the security of the user authentication system based on keystroke dynamics.