Reviewerly/exHarmony huggingface dataset:
Clothing attribute recognition via a holistic relation network

Clothing attribute prediction is a fundamental image classification task in the field of computer vision. Motivated by the human recognition system, we investigate the task relationship and spatial importance where people usually utilize these useful clues to assist in recognizing clothing attributes. In this paper, we propose a novel Holistic Relation Network (HRNet) for clothing attribute recognition, considering the fusion of multiple relations, including spatial and spatial relation via a spatial relation module, spatial and task relation via a task attention module, and task and task relation via a graph context reasoning module. Specifically, we first use the backbone network to extract features from the input image, two types of attention models followed will further learn the features, then the graph context reasoning module will be used to further enhance the features, and finally, a classifier exploited to classify the clothing attributes with the learned representation information. Without using manual image feature filtering methods, this paper aims to achieve clothing attribute recognition by deeply exploring the relationships among different clothing attribute recognition tasks. In this paper, we use double-branches of the attention model to model the relevance of spatial context information and learn more discriminative feature representations from multitask features for clothing attribute prediction. Derived from the prior knowledge learned from the two above-mentioned attention models, we further propose a graph-relation model constructing relationships among different clothing attribute tasks by integrating the spatial association relationships among multitask. The proposed HRNet only uses image-level annotation but it owns a good ability for obtaining distinguishing feature representations. We obtain state-of-the-art performance, which is demonstrated by extensive experiments on three mainstream benchmarks, for example, woman clothing data set, man clothing data set, and shop-domain clothing data set.
GIS based integration of multi-spectral remote sensing and morphostructural analysis for delineating potential areas of hydrocarbon exploration in Western Vindhyan Basin, India

The Chambal Valley forms the western part of the Proterozoic Vindhyan Basin that has been less studied and poorly explored from the hydrocarbon perspective than its eastern counterpart—the Son Valley Vindhyans. In the present manuscript, attempts have been made to use remote sensing and GIS (geographic information system) based analysis to focus on the northern part of Chambal Valley to decipher its tectonics and structural configuration, its tectonic relation with the Son Valley Vindhyans, Bundelkhand Massif and Ganga Basin. This study also assumes significance as it attempts to delineate potential lead areas for hydrocarbon exploration in Chambal Valley. As part of the study, a detailed spectral analysis based on image data sets is carried out to identify areas of microseeps as proxies of potential hydrocarbon accumulations. Also, a neotectonics-based geomorphic analysis is carried out to delineate a fault network and geomorphic highs that act as aliases to subsurface faults and structural highs. The results are subsequently validated by available geophysical data and extensive fieldwork, and suggest four structural trends in the Chambal Valley as striking NE-SW, E-W, NW-SE and N–S, of which the NW-SE trend is the oldest, followed by the NE-SW, E-W and N–S trends. A GIS-based analysis of the E-W trends indicates that these are probably manifestations of the westward continuity of E-W-trending shear zones developed in Bundelkhand massif within the basement of the Chambal Valley. Further remote sensing studies of the shear zone network exposed in the Bundelkhand massif indicate the pattern to be replicated on a larger scale in the morphostructural fault layout in the adjacent Ganga Basin, implying these shear zones may be continuing in the basement of Ganga Basin and controlling structures developed later, and consequently the different petroleum system elements in the basin. A dip data based structural analysis of the Chambal Valley describes a regional synform attributed with an almost vertical NW-SE-trending axial trace. This axial trace may be in continuity with the NE-SW-trending synformal axis of the Son Valley with its trajectory sympathetic to the southern boundary of the Bundelkhand massif and the Central Indian Tectonic Zone (CITZ) – Aravalli Delhi Fold Belt (ADFB) geometry. Finally, based on the integration of the results of spectral and morphostructural analysis, a number of potential areas of hydrocarbon exploration are demarcated in the study area.
A 3D Reconstruction Method for Augmented Reality Sandbox Based on Depth Sensor

This paper builds an Augmented Reality Sandbox (AR Sandbox) system based on augmented reality technology, and performs a 3D reconstruction for the sandbox terrain using the depth sensor Microsoft Kinect in the AR Sandbox, as an entry point to pave the way for later development of related metaverse applications, such as the metaverse architecting and visual interactive modeling. The innovation of this paper is that for the AR Sandbox scene, a 3D reconstruction method based on depth sensor is proposed, which can automatically cut off the edge of the sandbox table in Kinect field of view, and accurately and completely reconstruct the sandbox terrain in Matlab.
Improvement of the COTA-Orthography system through language modeling

The lack of a single standard orthography causes multiple forms of writing. This orthographic inconsistency is a frequent issue for Natural Language Processing (NLP). In this paper, we present a contextual method based on the orthography convention CODA-TUN [34] to improve the semi-automatic normalization tool, COTA Orthography [7], [25]. Our method targets words having multiple possible corrections which are semi-treated by this system. Therefore, we trained and improved a trigram language model based on a large corpus. We introduced, also, a generative algorithm to retrieve candidates for sentence having the target words. The selection of the correct correction is based on the trigram model. The evaluation results show that the selection accuracy reaches 79.38%.
Towards a Serverless Java Runtime

Java virtual machine (JVM) has the well-known slow startup and warmup issues. This is because the JVM needs to dynamically create many runtime data before reaching peak performance, including class metadata, method profile data, and just-in-time (JIT) compiled native code, for each run of even the same application. Many techniques are then proposed to reuse and share these runtime data across different runs. For example, Class Data Sharing (CDS) and Ahead-of-time (AOT) compilation aim to save and share class metadata and compiled native code, respectively. Unfortunately, these techniques are developed independently and cannot leverage the ability of each other well. This paper presents an approach that systematically reuses JVM runtime data to accelerate application startup and warmup. We first propose and implement JWarmup, a technique that can record and reuse JIT compilation data (e.g., compiled methods and their profile data). Then, we feed JIT compilation data to the AOT compiler to perform profile-guided optimization (PGO). We also integrate existing CDS and AOT techniques to further optimize application startup. Evaluation on real-world applications shows that our approach can bring a 41.35% improvement to the application startup. Moreover, our approach can trigger JIT compilation in advance and reduce CPU load at peak time.
The quantum version of prediction for binary classification problem by ensemble methods

In this work, we consider the performance of using a quantum algorithm to predict the result of a binary classification problem when a machine learning model is an ensemble of any simple classifiers. This approach is faster than classical prediction and uses quantum and classical computing, but it is based on a probabilistic algorithm. Let <i>N</i> be the number of classifiers from an ensemble model and <i>O</i>(<i>T</i>) be the running time of prediction of one classifier. In classical case, the final result is obtained by "averaging" outcomes of all ensemble model's classifiers. The running time in classical case is <i>O</i> (<i>N</i> · <i>T</i>). We propose an algorithm that works in <br/> <i>O</i> (√<i>N</i> · <i>T</i> ).
Effectiveness of Wavelet Synchrosqueezed Transform for Improved Epoch Estimation from Telephonic Speech Signals Using Zero Frequency Filtering

Epochs are impulse like discontinuities in speech that represent the instants of glottal closure during phonation. The accurate estimation of epochs is essential for epoch synchronous speech processing. There are many algorithms proposed in the literature for the accurate estimation of epochs from speech signals. However majority of those algorithms show degradation during the epoch extraction from telephonic speech signals. The restricted bandwidth (300 Hz-3.4 kHz) of the telephone channel is the reason for degradation in epoch estimation performance. Therefore, the objective of the present work is to propose a wavelet synchrosqueezed transform (WSST) based signal preprocessing stage to improve the performance of zero frequency filtering (ZFF) which is a well known epoch estimation method. Improved time-frequency resolution of WSST reinforce the strength of impulses around the fundamental components to estimate accurate epoch locations. A multiscale product of WSST modified speech and the original speech is computed further enhance the impulse like discontinuities. Effectiveness of the proposed method is confirmed by the epoch estimation performance analysis of phonetically balanced CMU-Arctic database with electroglottogram recordings. The telephone quality in the database is simulated using G.191 software tools.
Pose-Invariant Facial Expression Recognition

Pose-invariant facial expression recognition is quite challenging due to variations in facial appearance and self-occlusion caused by head rotations. In this paper, we propose an adversarial multi-view subspace learning method for pose-robust facial expression recognition. Specifically, a deep neural network is trained from face images of a certain pose to learn facial representations. Then, an adversarial strategy is adopted to force statistical similarity among the learned representations from facial images with different poses. Simultaneously, an expression classifier is trained on the learned pose-robust facial representations. Through adversarial learning, the proposed method leverages inherent dependencies among multiple pose facial images to construct pose-robust image representations and a classifier during training. The ensemble method is adopted to combine the predictions of multiple deep neural networks and the common expression classifier, so pose estimation is not required. Experimental results on four benchmark databases demonstrate the superiority of the proposed method to state-of-the-art works.
Assessment of Rehabilitation Exercises from Depth Sensor Data

Assessing the rehabilitation exercises are essential in the recovery and treatment of various musculoskeletal conditions following surgery. According to reports, over 90% of all rehabilitative exercise sessions are conducted in a home environment. As the number of patients grows, this method becomes prohibitively expensive. Providing technology support for home-based rehabilitation is an excellent approach to address this. The patient remains at home and does the exercises in front of the camera, with the footage or data being sent to the physician for comments on the exercises. In this paper, we propose two machine learning-based models to assess the quality of exercises where the data is captured by such kinect 3D sensors. The proposed models consist of a long short-term memory(LSTM) network which uses the time series skeletal data to predict the quality of the exercises. The first model uses the predefined features proposed by the physicians. For the second model, we extract features using graph convolutional network(GCN) on the skeletal data where each node represents a body part or joint in the body and the edges represent the connection between the body parts. We conclude that LSTM is more accurate at predicting the results when GCN features are used.
Why Human Firewall Fails in the Battle with Sophisticated Spear Phishing Campaigns

The aim of this chapter is to familiarize readers with the results of long-lasting research that was conducted in several organizations in the Czech Republic during the last four years. The integral part of this research was an experiment examining the cyber security resilience of these organizations and their employees to sophisticated cyber-attacks, and test a hypothesis regarding human firewall as a main countermeasure to social engineering techniques, especially targeted phishing used by a skilled attacker. The results of this experiment revealed that although the security awareness and training significantly decrease the click rate after the first run, it is rather impossible to reach a score below a few percentage units in consequent years. The results and recommendations stated in this chapter can help the organizations to set up their security awareness, training and testing, as well as security metrics.
Improving the Quality of Training Future Engineering Personnel on the Basis of the Partnership “University-Industrial Enterprise”

The article describes the experience of the department in organizing practical training at the base enterprise. The functions of the base enterprise and its role in the training of future specialists are determined. Training methods that activate the cognitive activity of students during practice are considered. The results of the questionnaire after internship showed that students are satisfied with the chosen profession, the level of competency formation has increased.
How Does the Effect of Absorptive Capacity on Innovation Capacity Change According to Countries’ Technology Manufacturing Value-Added Levels?

The aim of this study is to analyze the effects of countries' absorptive capacity on their innovation capacity according to their tendency to produce medium and high technology. In this context, the number of patent applications is used to represent innovation capacity. Ethics and corruption, foreign direct investment, tertiary education enrollment rate, technological literacy, university-industry research collaboration, and scientific and technical publications are used to represent social and technological absorption capacity. The variables used in the data set are taken from the Competitive Industrial Index, Global Innovation Index, and Global Competition Index. The study uses a special type of simultaneous equation models which is error correction two-stage least squares model for a panel data for years 2013–2017 and 60 countries. According to the analysis' result, absorptive capacity has a significant impact on innovation capacity for both country groups. In addition, while in countries with a high value added on medium- and high-technology production technological readiness has an impact on innovation capacity, ethics and corruption are more effective in the group of countries with a low rate of value added on medium- and high-technology manufacturing.
A Novel Prediction Error Histogram Shifting-based Reversible Data Hiding Scheme for Medical Image Transmission

Reversible data hiding (RDH) is an actively emerging area in the domain of Information Security having wide applications in clinical data transmission along with medical images. In our research, we came up with a new RDH scheme to keep clinical data in the medical image to ensure secure data transmission. Histogram shifting-based RDH schemes are widely explored for RDH in images. The conventional histogram shifting-based RDH schemes have two major concerns: low embedding rate and overflow or underflow. In this approach, we discuss a prediction error histogram shifting-based approach with an improved overflow handling technique. The pixels in the images are divided into two different categories: black and white. The classification of the pixels has been carried out based on the checkerboard pattern. As we know that as per the checkerboard pattern, a black pixel will have four 4-neighbourhood pixels (left, right, top and bottom). To predict the black pixel value in the middle we used the average of three pixels out of 4-neighbourhood which are very close to the central pixel value. By considering the predicted pixel value and the actual pixel value, we have computed the prediction error. The histogram of prediction error is generated based on the prediction error corresponds to all the black pixels in the image. The prediction error histogram is considered for further data hiding through the histogram shifting approach. The overflow/underflow is a critical issue in the histogram shifting-based RDH scheme, so we have came up with an improved overflow/underflow handling technique in this approach. We have validated the results after carrying out the proposed scheme on medical and natural images.
Emotion Recognition and Sentiment Classification using BERT with Data Augmentation and Emotion Lexicon Enrichment

The emergence of social networking sites has paved the way for researchers to collect and analyze massive data volumes. Twitter, being one of the leading micro-blogging sites worldwide, provides an excellent opportunity for its users to express their states of mind via short text messages known as tweets. Much research focusing on identifying emotions and sentiments conveyed through tweets has been done. We propose a BERT model fine-tuned to the emotion recognition and sentiment classification tasks and show that it performs better than previous models on standard datasets. We also explore the effectiveness of data augmentation and data enrichment for these tasks.
Human action recognition by multiple spatial clues network

Human action can be recognized in still images since the whole image represents an action with some spatial clues, such as human poses, action-specific parts, and global surroundings. To represent the spatial clues, the recent methods require labor-intensive annotations to locate the human body and objects, which are computationally intensive. To eliminate strong supervision, a Multiple Spatial Clues Network (MSCNet) is proposed to represent the spatial clues with only image-level action label. Neither accurately manual annotated bounding boxes nor extra labeled datasets are required as additional supervision. First, the proposed MSCNet exploits spatial-attention module to generate spatial attention regions, and detects the spatial clues with minimal supervision. Then, spatial clues exploitation is proposed to utilize the learned spatial clues with three modules: the context module, body + context module and body + semantics module. Experiments on three benchmark datasets demonstrate the effectiveness of the proposed MSCNet.
A general iterative clustering algorithm

The quality of a cluster analysis of unlabeled units depends on the quality of the between units dissimilarity measures. Data dependent dissimilarity is more objective than data independent geometric measures such as Euclidean distance. As suggested by Breiman, many data driven approaches are based on decision tree ensembles, such as a random forest (RF), that produce a proximity matrix that can easily be transformed into a dissimilarity matrix. A RF can be obtained using labels that distinguish units with real data from units with synthetic data. The resulting dissimilarity matrix is input to a clustering program and units are assigned labels corresponding to cluster membership. We introduce a General Iterative Cluster (GIC) algorithm that improves the proximity matrix and clusters of the base RF. The cluster labels are used to grow a new RF yielding an updated proximity matrix which is entered into the clustering program. The process is repeated until convergence. The same procedure can be used with many base procedures such as the Extremely Randomized Tree ensemble. We evaluate the performance of the GIC algorithm using benchmark and simulated data sets. The properties measured by the Silhouette Score are substantially superior to the base clustering algorithm. The GIC package has been released in R: https://cran.r-project.org/web/packages/GIC/index.html.
An Attack Simulation and Evidence Chains Generation Model for Critical Information Infrastructures

Recently, the rapid growth of technology and the increased teleworking due to the COVID-19 outbreak have motivated cyber attackers to advance their skills and develop new sophisticated methods, e.g., Advanced Persistent Threat (APT) attacks, to leverage their cybercriminal capabilities. They compromise interconnected Critical Information Infrastructures (CIIs) (e.g., Supervisory Control and Data Acquisition (SCADA) systems) by exploiting a series of vulnerabilities and launching multiple attacks. In this context, industry players need to increase their knowledge on the security of the CIs they operate and further explore the technical aspects of cyber-attacks, e.g., attack’s course, vulnerabilities exploitability, attacker’s behavior, and location. Several research papers address vulnerability chain discovery techniques. Nevertheless, most of them do not focus on developing attack graphs based on incident analysis. This paper proposes an attack simulation and evidence chains generation model which computes all possible attack paths associated with specific, confirmed security events. The model considers various attack patterns through simulation experiments to estimate how an attacker has moved inside an organization to perform an intrusion. It analyzes artifacts, e.g., Indicators of Compomise (IoCs), and any other incident-related information from various sources, e.g., log files, which are evidence of cyber-attacks on a system or network.
Fusion estimation for stochastic uncertain systems with time-correlated rician fading channels

In this article, a fusion estimation scheme is proposed for stochastic uncertain systems with time-correlated fading channels (TFCs). A batch of random variables obeying Gaussian distributions is employed to describe the parameter uncertainties. The sensor communicates with the local filter through a TFC where the evolution of the channel coefficient is characterized by a certain dynamic process with one-step correlated noises. For further analyzing the effects of TFCs, a class of additional variables is first introduced by augmenting the dynamics of channel coefficients and the concerned system. Then, a new group of modified local filters is developed and the unbiasedness of local filters is examined by means of inductive method. Furthermore, the filter gains which minimize the local filtering error covariances are designed for the modified local filters in the simultaneous presence of stochastic uncertainties and TFCs. Subsequently, the cross-covariances among local estimates are computed iteratively and, based on the obtained cross-covariances as well as the unbiased local estimates and their corresponding filtering error covariances, a fusion estimate is obtained by using weighted least square fusion method. Finally, the effectiveness of the proposed fusion estimation scheme is verified by two examples.