Reviewerly/exHarmony huggingface dataset:
WITHDRAWN: A novel approach for image forgery detection using improved crow search algorithm

In recent days, the image data plays a important role in every field. In this digital world generation of image data is becoming popular. A novel method has been proposed for a forgery detection technique in image using enhanced improved crow search algorithm. Preprocessing is done at the initial stage to convert RGB to LAB space conversion, Proceeded with feature selection and alalysis using Ada boost algorithm and cascade architecture. Next step is to extract the features like Haralick feature, Skewness and inverse difference movement. In Continuation with feature extraction, feature selection is performed using proposed algorithm followed by enhanced convolution neural network classification. Finally the comparison of the proposed classification algorithm and sensitivity analysis is done.
Research on Face Recognition Algorithm Based on Robust 2DPCA

As a new dimension reduction method, the two-dimensional principal component (2DPCA) can be well applied in face recognition, but it is susceptible to outliers.Therefore, this paper proposes a new 2DPCA algorithm based on angel-2DPCA.To reduce the reconstruction error and maximize the variance simultaneously, we choose F norm as the measure and propose the Fp-2DPCA algorithm.Considering that the image has two dimensions, we offer the Fp-2DPCA algorithm based on bilateral.Experiments show that, compared with other algorithms, the Fp-2DPCA algorithm has a better dimensionality reduction effect and better robustness to outliers.
Towards Predicting Software Defects with Clustering Techniques

The purpose of software defect prediction is to improve the quality of a software project by building a predictive model to decide whether a software module is or is not fault prone. In recent years, much research in using machine learning techniques in this topic has been performed. Our aim was to evaluate the performance of clustering techniques with feature selection schemes to address the problem of software defect prediction problem. We analysed the National Aeronautics and Space Administration (NASA) dataset benchmarks using three clustering algorithms: (1) Farthest First, (2) X-Means, and (3) selforganizing map (SOM). In order to evaluate different feature selection algorithms, this article presents a comparative analysis involving software defects prediction based on Bat, Cuckoo, Grey Wolf Optimizer (GWO), and particle swarm optimizer (PSO). The results obtained with the proposed clustering models enabled us to build an efficient predictive model with a satisfactory detection rate and acceptable number of features.
Recovering Private User Storages in Online Social Networks

Private user storages are used in Online Social Networks (OSN) to securely save private settings and keys on a server.This allows users to access these settings and keys from all their devices.Therefore, private user storages can synchronize these devices.When end-to-end encryption is applied in an OSN to ensure the privacy of users and the communication private user storages can be used to safely store all private and symmetric keys.A user can retrieve the encrypted storage from the server and decrypt it with its password.However, when the password is lost access to the private user storage is lost as well, as the symmetric key cannot be retrieved from the server in contrast to the recovery functions of some OSNs, where a new password can be created.We present a scheme that splits private user storages into different parts and applies two different secret sharing schemes to be able to recover the keys with the help of the other participants of the OSN.The scheme is more robust to missing shares than other secret sharing schemes.Therefore, even when a large fraction of the distributed shares are not accessible because shareholders are inactive or malicious high rates of successful reconstructions can be achieved.
A Consistent Estimator of Nontrivial Stationary Solutions of Dynamic Neural Fields

Dynamics of neural fields are tools used in neurosciences to understand the activities generated by large ensembles of neurons. They are also used in networks analysis and neuroinformatics in particular to model a continuum of neural networks. They are mathematical models that describe the average behavior of these congregations of neurons, which are often in large numbers, even in small cortexes of the brain. Therefore, change of average activity (potential, connectivity, firing rate, etc.) are described using systems of partial different equations. In their continuous or discrete forms, these systems have a rich array of properties, among which is the existence of nontrivial stationary solutions. In this paper, we propose an estimator for nontrivial solutions of dynamical neural fields with a single layer. The estimator is shown to be consistent and a computational algorithm is proposed to help carry out implementation. An illustrations of this consistency is given based on different inputs functions, different kernels, and different pulse emission rate functions.
A Semantic Memory Bank Assisted by an Embodied Conversational Agents for Mobile Devices

Alzheimer's disease is a type of dementia that causes memory loss and interferes with intellectual abilities seriously.It has no current cure and therapeutic efficiency of current medication is limited.However, there is evidence that non-pharmacological treatments could be useful to stimulate cognitive abilities.In the last few year, several studies have focused on describing and under-standing how Virtual Coaches (VC) could be key drivers for health promotion in home care settings.The use of VC gains an augmented attention in the considerations of medical innovations.In this paper, we propose an approach that exploits semantic technologies and Embodied Conversational Agents to help patients training cognitive abilities using mobile devices.In this work, semantic technologies are used to provide knowledge about the memory of a specific person, who exploits the structured data stored in a linked data repository and take advantage of the flexibility provided by ontologies to define search domains and expand the agent's capabilities.Our Memory Bank Embodied Conversational Agent (MBECA) is used to interact with the patient and ease the interaction with new devices.The framework is oriented to Alzheimer's patients, caregivers, and therapists.
Evaluation of U-net-based image segmentation model to digital mammography

Detecting suspicious lesions in medical imaging is the important first step in computer-aided detection (CAD) systems. However, detecting abnormalities in breast tissue is difficult due to the lesion's varying size, shape, margin, and contrast with the background tissue. We focused on mass segmentation, a method that provides notable morphological features by outlining contours of masses. Accurate segmentation is crucial for correct diagnosis. Recent advancements in deep learning have improved object detection and segmentation, and these techniques are also being applied to medical imaging studies. We focused on U-net, which is a recently developed mass segmentation algorithm based on a fully convolutional network. The U-net architecture consists of (1) a contracting path to increase the resolution of the output and (2) a symmetric expanding path to better locate the region of interest. The performance of a U-net model was tested with 63 digital mammograms from INbreast, a publicly available database. We trained the model with images resized to 40x40 pixels and conducted 10-fold cross-validation to prevent overfitting. The model's performance with respect to breast density and the lesion's BI-RADS rating was also investigated. Dice coefficients (DC) were used as a performance measure to compare the predicted segmentation of the model with the ground truth. Logistic regression and an analysis of variance were performed to determine the significance of the DCs with regards to breast density and lesion behavior and to calculate the 95% confidence interval. The average DC was 0.80. The difference between DCs for BI-RADS 2 and 4c and for BI-RADS 2 and 5 were significant, suggesting that the model has more difficulty in segmenting benign lesions.
Two-Agent Approximate Agreement from an Epistemic Logic Perspective

We investigate the two agents approximate agreement problem in a dynamic network in which topology may change unpredictably, and where consensus is not solvable.It is known that the number of rounds necessary and sufficient to guarantee that the two agents output values 1/k 3 away from each other is k.We distil ideas from previous papers to provide a self-contained, elementary introduction, that explains this result from the epistemic logic perspective.
Grammatical Error Analysis in Essay Writing

&lt;p class="5AbstrakIsi"&gt;Students cannot avoid making errors which might enrich learners’ knowledge in using a foreign language. Error as such a systematic deviation which occurs when the student has not learned something of the language and gets it wrong constantly, because he has not learned the correct form. However, there are some factors that cause the errors such as carelessness, first language interference, and translation. When using English in their essays writing, students might make errors in various aspects of grammar, such as tenses, aspects, word classes, word order, inflections, and punctuation. Therefore, the accuracy of using them in the writing of English sentences must be considered, especially by students who are developing writing skills in English as a foreign language. By writing English sentences according to the grammatical structure, the meaning of it will be accurate, easily understood and acceptable. This article aims to identify types of grammatical errors made by the students of English education department at STKIP PGRI Ponorogo in writing essay and find out the factors causing the students’ grammatical errors in writing essay.&lt;/p&gt;
Virtual Reality Technology for Developing Intercultural Leadership Competence: A Research Proposal

Increased globalization requires employees to develop not just technical competencies in their specific disciplines but also intercultural leadership competence, which includes a range of cognitive, affective, and behavioural skills leading to effective and appropriate communication with culturally different others. To date, Virtual Reality (VR) and Augmented Reality (AR) have demonstrated potential in their effectiveness for achieving technical learning outcomes by providing an immersive, safe learning environment that is both engaging and personalized. Yet, while learning environments such as VR and AR have created significant interest with much promise to revolutionize learning environments, the adoption of these technologies in non-technical areas is limited. Most approaches to teaching non-technical skills such as intercultural knowledge and competence do not leverage these recent educational advancements but still depend heavily on older technologies based on one-way passive learning environments, including videos and web-based platforms. Therefore, interdisciplinary comparative research in leadership development is needed to examine the effectiveness of existing and emerging learning environments (e.g., AR, VR) on cognitive (knowledge), affective (attitudes) and behavioral (skills) aspects of intercultural competence. In addition, we have identified important methodological gaps in the realm of research in leadership development field that require the field to move beyond self-report approaches by utilizing biometric data. We propose a research project to assess leadership development in an innovative research design that triangulates data from traditional quantitative pre-posttesting with qualitative analysis of learner reflections as well as with data from non-invasive biometric devices.
Information-theoretic regularization for learning global features by sequential VAE

Abstract Sequential variational autoencoders (VAEs) with a global latent variable z have been studied for disentangling the global features of data, which is useful for several downstream tasks. To further assist the sequential VAEs in obtaining meaningful z , existing approaches introduce a regularization term that maximizes the mutual information (MI) between the observation and z . However, by analyzing the sequential VAEs from the information-theoretic perspective, we claim that simply maximizing the MI encourages the latent variable to have redundant information, thereby preventing the disentanglement of global features. Based on this analysis, we derive a novel regularization method that makes z informative while encouraging disentanglement. Specifically, the proposed method removes redundant information by minimizing the MI between z and the local features by using adversarial training. In the experiments, we trained two sequential VAEs, state-space and autoregressive model variants, using speech and image datasets. The results indicate that the proposed method improves the performance of downstream classification and data generation tasks, thereby supporting our information-theoretic perspective for the learning of global features.
A Survey on Recent Deep Learning Architectures

In artificial intelligence, the area is going rapidly towards tackling and solving problems that are intellectually challenging for human beings, its almost straightforward for machines. A list of formal and analytical rules creates the problem. The computer gains experience automatically by executing the same problem again and again by repeating the ideas by defining the relationship between the concepts. There are many architectures to enhance the system to perform accurately and efficiently. The architecture helps to classify and extract the multiple unique features using many stages from the source data. This innovative CNN architecture reduces the complex problem by breaking into simple concepts, and then it is fed into hidden layers of the architecture. Further, it concentrates on loss function, structural reformulation, optimization, weight sharing, parameter regularization and generalization. Thus, the computer learns more complexity about the concepts on its own, and it works more accurately and efficiently.
Semi-automatic decision-making process in histopathological specimens from Barrett’s carcinoma patients using hyperspectral imaging (HSI)

Abstract Discrimination of malignant and non-malignant cells of histopathologic specimens is a key step in cancer diagnostics. Hyperspectral Imaging (HSI) allows the acquisition of spectra in the visual and near-infrared range (500-1000nm). HSI can support the identification and classification of cancer cells using machine learning algorithms. In this work, we tested four classification methods on histopathological slides of esophageal adenocarcinoma. The best results were achieved with a Multi-Layer Perceptron. Sensitivity and F1-Score values of 90% were obtained.
An enhancement of mammogram images for breast cancer classification using artificial neural networks

&lt;p&gt;&lt;span id="docs-internal-guid-12eaaa5f-7fff-c428-95bf-97a7381b2976"&gt;&lt;span&gt;Breast cancer is the most driving reason for death in women in both developed and developing nations. For the plan of effective classification of a system, the selection of features method must be used to decrease irregularity part in mammogram images. The proposed approach is used to crop the region of interests (ROIs) manually. Based on that number of features are extracted. In this proposed method a novel hybrid optimum feature selection (HOFS) method is used to find out the significant features to reach maximum accuracy for this classification. A number of selected features is applied to train the neural network. In this proposed method accessible informational index from the mini–mammographic image analysis society (MIAS) database was used. The classification of this mammogram database involved a neural networks classifier which attained an accuracy of 99.7% with a sensitivity of 99.5%, and specificity of 100% as the area under the curve (AUC) is 0.9975 and matthew’s correlation coefficient (MCC) represents a binary class value which reached the value of 0.9931. It can be useful in a computer-aided diagnosis system (CAD) framework to help the radiologist in analyzing breast cancer. Results achieved with the proposed method are better compared to recent work.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
Diagnostic software proposal for bone scan follow-up using false color based on the gammagrams analysis from gray tone histograms

In this work we introduce a technique to speed up the interpretation of bone scans with the aim of determining the presence or absence of metastatic disease. We use gray tone histograms, resembling the use of band-pass filters, in order to ensure a reliable interpretation of the bone scan, therefore providing an accurate diagnosis. We draw particular attention to three cases. The first case corresponds to shifted histograms. If the histogram is shifted toward the origin, the bone scan is free of metastasis. If it is shifted to the right and slightly broadened, this indicates the presence of a bone scan anomaly other than metastasis. On the other hand, if the histogram is broadened and shifted to the left, this suggests the presence of metastatic disease. The second case corresponds to a histogram with noticeable fluctuations, indicating the presence of metastasis. Such fluctuations could become local maxima peaks, indicating the advance of the metastasis. The third case corresponds to the false color results, displayed in terms of the gray tones, observed in the histogram. Such false color is assigned from the construction of a 7-color palette and is selected in terms of the gray tones range. This eases the ad hoc false color assignation for visualization purposes. The final diagnosis is carried out in terms of the color, geometry, extension, and location of the region of interest in the images. Our proposed technique has the potential to be used in high-demand oncology centers due to its simplicity and diagnostic efficiency, confirmed and tested by specialists in the Centro Medico Siglo XXI (XXI Century Medical Center), CDMX-México.
Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection

The Discriminative Correlation Filter (DCF) has been universally recognized in visual object tracking, thanks to its excellent accuracy and high speed. Nevertheless, these DCF-based trackers perform poorly in long-term tracking. The reasons include the following aspects—first, they have low adaptability to significant appearance changes in long-term tracking and are prone to tracking failure; second, these trackers lack a practical re-detection module to find the target again after tracking failure. In our work, we propose a new long-term tracking strategy to solve these issues. First, we make the best of the static and dynamic information of the target by introducing the motion features to our long-term tracker and obtain a more robust tracker. Second, we introduce a low-rank sparse dictionary learning method for re-detection. This re-detection module can exploit a correlation among these training samples and alleviate the impact of occlusion and noise. Third, we propose a new reliability evaluation method to model an adaptive update, which can switch expediently between the tracking module and the re-detection module. Massive experiments demonstrate that our proposed approach has an obvious improvement in precision and success rate over these state-of-the-art trackers.
Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction

Although traffic conflict techniques have proven to be effective means for road safety analysis, they still suffer from incomplete conceptualization, observer subjectivity, and high data collection cost. To address these problems, video analysis has been increasingly applied to gain a better understanding of the behaviors of road users based on detailed motion data. However, the motion patterns underlying these data are rarely extracted to study the safety of their interactions. This article presents a vision-based method of traffic conflict detection through learning motion patterns from trajectories, for which an original algorithm was established through clustering and subsequent modeling. Using the extracted path and velocity information, we clustered trajectories hierarchically by applying an improved fuzzy K-means algorithm with a modified Hausdorff distance. Each obtained cluster was taken as a labeled set to determine the structure and train the parameters of a hidden Markov model (HMM) that encoded the spatiotemporal characteristics of the trajectories as motion patterns. Based on the targeted trajectory predictions by the learned HMMs following the conflict development, a probabilistic model was developed to estimate the collision likelihood between vehicles to identify traffic conflicts. The experimental results obtained using actual traffic videos demonstrated the applicability of the algorithms for learning motion patterns and the feasibility of the approach for traffic conflict detection. The predicted trajectories were sufficiently accurate to calculate the collision probability, which was qualified as an indicator for measuring the conflict severity. These findings will have important implications for effective improvements in active road safety.
3D Hand Pose Estimation via Graph-Based Reasoning

Hand pose estimation from a single depth image has recently received significant attention owing to its importance in many applications requiring human-computer interaction. The rapid progress of convolutional neural networks (CNNs) and technological advances in low-cost depth cameras have greatly improved the performance of the hand pose estimation method. Nevertheless, regressing joint coordinates is still a challenging task due to joint flexibility and self-occlusion. Previous hand pose estimation methods have limitations in relying on a deep and complex network structure without fully utilizing hand joint connections. A hand is an articulated object and consists of six parts that represent the palm and five fingers. The kinematic constraints can be obtained by modeling the dependency between adjacent joints. This paper proposes a novel CNN-based approach incorporating hand joint connections to features through both a global relation inference for the entire hand and local relation inference for each finger. Modeling the relations between the hand joints can alleviate critical problems for occlusion and self-similarity. We also present a hierarchical structure with six branches that independently estimate the position of the palm and five fingers by adding hand connections of each joint using graph reasoning based on graph convolutional networks. Experimental results on public hand pose datasets show that the proposed method outperforms previous state-of-the-art methods. Specifically, our method achieves the best accuracy compared to state-of-the-art methods on public datasets. In addition, the proposed method can be utilized for real-time applications with an execution speed of 103 fps in a single GPU environment.
UniBO @ KIPoS: Fine-tuning the Italian “BERTology" for PoS-tagging Spoken Data

The use of contextualised word embeddings allowed for a relevant performance increase for almost all Natural Language Processing (NLP) applications. Recently some new models especially developed for Italian became available to scholars. This work aims at applying simple fine-tuning methods for producing high-performance solutions at the EVALITA KIPOS PoS-tagging task (Bosco et al. 2020).
Disputing Dijkstra, and birthdays in base 2

The Communications Web site, http://cacm.acm.org, features more than a dozen bloggers in the BLOG@CACM community. In each issue of Communications , we'll publish selected posts or excerpts. twitter Follow us on Twitter at http://twitter.com/blogCACM http://cacm.acm.org/blogs/blog-cacm Mark Guzdial takes issue with Dijkstra's metaphors, while Joel C. Adams considers how birthdays might differ if based on binary numbers.