Reviewerly/exHarmony huggingface dataset:
CIPM: Common Identification Process Model for Database Forensics Field

Database Forensics (DBF) domain is a branch of digital forensics, concerned with the identification, collection, reconstruction, analysis, and documentation of database crimes. Different researchers have introduced several identification models to handle database crimes. Majority of proposed models are not specific and are redundant, which makes these models a problem because of the multidimensional nature and high diversity of database systems. Accordingly, using the metamodeling approach, the current study is aimed at proposing a unified identification model applicable to the database forensic field. The model integrates and harmonizes all exiting identification processes into a single abstract model, called Common Identification Process Model (CIPM). The model comprises six phases: 1) notifying an incident, 2) responding to the incident, 3) identification of the incident source, 4) verification of the incident, 5) isolation of the database server and 6) provision of an investigation environment. CIMP was found capable of helping the practitioners and newcomers to the forensics domain to control database crimes.
Efficient DLP-visor: An efficient hypervisor-based DLP

Many organization consider insider threat for data theft to be one of the most severe threats. An insider may also leak sensitive information without malicious intent (as a result of social engineering) Data leakage prevention (DLP) systems attempt to prevent intentional or accidental disclosure of sensitive information by monitoring the content or the context in which the information is transferred, for example, in a file system, an email server, instant messengers. We present a context-sensitive DLP system, called Efficient DLP-Visor. We implemented DLP-visor as a thin hypervisor capable of intercepting system calls in Windows operating systems equipped with Kernel Patch Protection. By intercepting system calls that govern the file system, inter-process communications, networking, system register and system clipboard, DLP-Visor guarantees that sensitive information can never leave a predefined set of directories. The performance overhead of Efficient DLP-Visor (7.2%) allows its deployment in real-world applications. Efficient DLP-visor logs were improved for better detection and logging of a DLP event. On idle time Efficient DLP-visor deletes most of the data log while maintaining the important data of leaks and attack.
VSR++: Improving Visual Semantic Reasoning for Fine-Grained Image-Text Matching

Image-text matching has made great progresses recently, but there still remains challenges in fine-grained matching. To deal with this problem, we propose an Improved Visual Semantic Reasoning model (VSR++), which jointly models 1) global alignment between images and texts and 2) local correspondence between regions and words in a unified framework. To exploit their complementary advantages, we also develop a suitable learning strategy to balance their relative importance. As a result, our model can distinguish image regions and text words in a fine-grained level, and thus achieves the current state-of-the-art performance on two benchmark datasets.
Palindromic trees for a sliding window and its applications

The palindromic tree (a.k.a. eertree) for a string S of length n is a tree-like data structure that represents the set of all distinct palindromic substrings of S, using O(n) space [Rubinchik and Shur, 2018]. It is known that, when S is over an alphabet of size σ and is given in an online manner, then the palindromic tree of S can be constructed in O(nlog⁡σ) time with O(n) space. In this paper, we consider the sliding window version of the problem: For a sliding window of length at most d, we present two versions of an algorithm which maintains the palindromic tree of size O(d) for every sliding window S[i..j] over S, where 1≤j−i+1≤d. The first version works in O(nlog⁡σ′) time with O(d) space where σ′≤d is the maximum number of distinct characters in the windows, and the second one works in O(n+dσ) time with (d+2)σ+O(d) space. We also show how our algorithms can be applied to efficient computation of minimal unique palindromic substrings (MUPS) and minimal absent palindromic words (MAPW) for a sliding window.
Deformation Recovery Control and Post-Impact Trajectory Replanning for Collision-Resilient Mobile Robots

The paper focuses on collision-inclusive motion planning for impact-resilient mobile robots. We propose a new deformation recovery and replanning strategy to handle collisions that may occur at run-time. Contrary to collision avoidance methods that generate trajectories only in conservative local space or require collision checking that has high computational cost, our method directly generates (local) trajectories with imposing only waypoint constraints. If a collision occurs, our method then estimates the post-impact state and computes from there an intermediate waypoint to recover from the collision. To achieve so, we develop two novel components: 1) a deformation recovery controller that optimizes the robot’s states during post-impact recovery phase, and 2) a post-impact trajectory replanner that adjusts the next waypoint with the information from the collision for the robot to pass through and generates a polynomial-based minimum effort trajectory. The proposed strategy is evaluated experimentally with an omnidirectional impact-resilient wheeled robot. The robot is designed in house, and it can perceive collisions with the aid of Hall effect sensors embodied between the robot’s main chassis and a surrounding deflection ring-like structure.
A New Step-Size Adaptation Rule for CMA-ES Based on the Population Midpoint Fitness

In this paper, we present the CMA-ES algorithm with a step-size adaptation rule which is inspired by the 1/5-th success rule. The method, called PPMF (Previous Population Midpoint Fitness), adjusts the step-size multiplier σ using the comparison of fitness values of the current population of points and the midpoint of the previous population. In the paper we compare the performance of CMA-ES coupled with PPMF and with two other step-size adaptation rules: Cumulative Step-size Adaptation (CSA) and Median Success Rule (MSR). For the comparison we apply a version of the IPOP-CMA-ES strategy and we test its performance using three benchmark suites: CEC 2013, CEC 2017 and CEC 2021. The results evidence that the efficiency of PPMF is comparable with CSA and superior to MSR.
Comparative Analysis of Exemplar-Based Approaches for Students’ Learning Style Diagnosis Purposes

A lot of computational models recently are undergoing rapid development. However, there is a conceptual and analytical gap in understanding the driving forces behind them. This paper focuses on the integration between computer science and social science (namely, education) for strengthening the visibility, recognition, and understanding the problems of simulation and modelling in social (educational) decision processes. The objective of the paper covers topics and streams on social-behavioural modelling and computational intelligence applications in education. To obtain the benefits of real, factual data for modeling student learning styles, this paper investigates exemplar-based approaches and possibilities to combine them with case-based reasoning methods for automatically predicting student learning styles in virtual learning environments. A comparative analysis of approaches combining exemplar-based modelling and case-based reasoning leads to the choice of the Bayesian Case model for diagnosing a student’s learning style based on the data about the student’s behavioral activities performed in an e-learning environment.
Research on FPGA Based Convolutional Neural Network Acceleration Method

In recent years, with the continuous breakthrough in the field of algorithms, the computational complexity of current target detection algorithms is getting higher and higher. In the forward inference stage, many practical applications often have low latency and strict power consumption restrictions. How to realize a low-power, low-cost and high-performance target detection platform has gradually attracted attention. Given the current mobile scene's requirements for high performance and low power consumption, hardware acceleration architecture suitable for different CNNs is designed by combining the working principle of CNN and the computing characteristics of FPGA. CNN's basic operation unit is realized through high-level synthesis technology, including convolution operation unit, pool operation unit, activation function unit, etc. Optimization strategies such as pipeline, dynamic fixed-point quantization, and ping-pong caching are adopted to reduce the use of on-chip and off-chip memory access and storage resources. Finally, two convolutional neural networks with different structures, the LeNet-5 classification network and, the YOLOv2 detection network, are selected for functional verification and performance analysis. The experimental results show that the convolutional neural network FPGA accelerator designed in this paper can provide better performance with fewer resources and power consumption and can efficiently use the hardware resources on the FPGA.
Dog Breed Classification Using Deep Learning

In this paper, we have proposed two models to classify dogs according to their breeds. Since the classification of dogs is becoming very difficult and moreover, these classifications are taken on the deep learning concept and training a fully defined data set helps in training both models which predicts the different accuracy levels at both ends. Since every now and then predictions are taken for every model. During the study, we came across many types of functionality levels that were not taken in previous studies too. Also, our approach also works on the main concept of transfer learning which deals with data augmentation technique with its properties to increase the size of data set, after which accuracy levels are matched or it is compared with both the models so that a comparison can be made for both the models and the classification is also done with a profound approach.
StreamSoNG: A Soft Streaming Classification Approach

Examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models. They model existing and newly discovered structures via summary information that we call footprints. Incoming data is normally assigned a crisp label (into one of the structures) and that structure's footprint is incrementally updated. There is no reason that these assignments need to be crisp. In this paper, we propose a new streaming classification algorithm that uses Neural Gas prototypes as footprints and produces a possibilistic label vector (of typicalities) for each incoming vector. These typicalities are generated by a modified possibilistic k-nearest neighbor algorithm. The approach is tested on synthetic and real image datasets. We compare our approach to three other streaming classifiers based on the Adaptive Random Forest, Very Fast Decision Rules, and the DenStream algorithm with excellent results.
Out Domain Data Augmentation on Punjabi Children Speech Recognition using Tacotron

Abstract The performance of Automatic Speech Recognition (ASR) is directly proportional to the quality of the corpus used and the training data quantity. Data scarcity and more children’s speech variability degrades the performance of ASR systems. As Punjabi is a tonal language and low resource language, less data is available for Punjabi children’s speech. It leads to poor ASR performance for Punjabi children speech recognition. To overcome limited data conditions, in this paper, two corpora of different domains are evaluated for testing the feasibility of ASR performance. We have implemented Tacotron as an artificial speech synthesis system for Punjabi Language. The speech audios synthesized by Tacotron are merged with available speech corpus and tested on Punjabi children ASR using Mel Frequency Cepstral Coefficients (MFCC) + pitch feature extraction, and Deep Neural Network (DNN) acoustic modeling. It is noticed that the merged data corpus has shown reduced Word Error Rate (WER) of the ASR system with a Relative Improvement (RI) of 9-12%.
Design of an Architecture Contributing to the Protection and Privacy of the Data Associated with the Electronic Health Record

The Electronic Health Record (EHR) has brought numerous challenges since its inception that have prevented a unified implementation from being carried out in Colombia. Within these challenges, we find a lack of security, auditability, and interoperability. Moreover, there is no general vision of the patient’s history throughout its life since different systems store the information separately. This lack of unified history leads to multiple risks for patients’ lives and the leakage of private data because each system has different mechanisms to safeguard and protect the information, and in several cases, these mechanisms do not exist. Many researchers tried to build multiple information systems attempting to solve this problem. However, these systems do not have a formal and rigorous architectural design to analyze and obtain health needs through architectural drivers to construct robust systems to solve these problems. This article describes the process of designing a software architecture that provides security to the information that makes up the Electronic Health Record in Colombia (EHR). Once we obtained the architectural drivers, we proposed Blockchain mainly due to its immutable distributed ledger, consensus algorithms, and smart contracts that securely transport this sensitive information. With this design decision, we carried out the construction of structures and necessary architectural documentation. We also develop a Proof of Concept (POC) using Hyperledger Fabric according to the literature analysis review in order to build a primary health network, in addition to a Smart Contract (Chaincode) using the Go programming language to perform a performance evaluation and do a safety analysis that demonstrates that the proposed design is reliable. The proposed design allows us to conclude that it is possible to build a secure architecture that protects patient health data privacy, facilitating the EHR’s construction in Colombia.
An Improved Capsule Network Based on Capsule Filter Routing

Capsule network (CapsNet) is a novel type of network that can retain spatial information, because each capsule can integrate more information than scalar-output features. However, the CapsNet learns all the features in the input image due to the lack of pooling operation, and there is no connection between different layers in the multi-layer network structure. In this paper, we propose an improved capsule network (CapsNet) based on Capsule Filter Routing (CFR) to address this problem, namely CFR-CapsNet. Firstly, we propose a new routing method called CFR for filtering capsules based on capsule activation values, which can speed up the operation of the model, and then introduce a self-attention mechanism to improve the performance of the primary capsule in the capsule space. Furthermore, in the multi-layer network structure, we transmit the information of the classified capsule with the largest activation value in the previous capsule layer to the primary capsules of the next layer, which improves the relevance of the overall structure. We conduct experiments on Fashion-MNIST, SVHN, and CIFAR-10/100, and the experimental results show that our method can improve the performance of the CapsNet more effectively than other state-of-the-arts.
An ellipsoidal-polytopic based approach for aggressive navigation using nonlinear model predictive control

This work addresses the development of a nonlinear model predictive controller (NMPC) for controlling autonomous motion systems navigating within cluttered environments with unknown obstacles. More specifically, this work concerns the aggressive maneuverability of motion systems, allowing them to navigate at high speed while reacting dynamically to obstacles. Additionally, these applications require the ability to generate and control large linear and angular accelerations. In this regard, a new ellipsoidal-polytopic approach for obstacle avoidance is incorporated by the NMPC. As a result, the new control method considers the orientation of the motion system in the collision avoidance, allowing aggressive maneuvers to improve the motion system performance for the obstacle deviation. The new controller is corroborated with a quadrotor UAV, which accomplishes a point-to-point motion avoiding obstacles positioned between the starting point and the destination point.
Systematic review of the status of veterinary epidemiological research in two species regarding the FAIR guiding principles

Abstract Background The FAIR (Findable, Accessible, Interoperable, Reusable) principles were proposed in 2016 to set a path towards reusability of research datasets. In this systematic review, we assessed the FAIRness of datasets associated with peer-reviewed articles in veterinary epidemiology research published since 2017, specifically looking at salmonids and dairy cattle. We considered the differences in practices between molecular epidemiology, the branch of epidemiology using genetic sequences of pathogens and hosts to describe disease patterns, and non-molecular epidemiology. Results A total of 152 articles were included in the assessment. Consistent with previous assessments conducted in other disciplines, our results showed that most datasets used in non-molecular epidemiological studies were not available (i.e., neither findable nor accessible). Data availability was much higher for molecular epidemiology papers, in line with a strong repository base available to scientists in this discipline. The available data objects generally scored favourably for Findable, Accessible and Reusable indicators, but Interoperability was more problematic. Conclusions None of the datasets assessed in this study met all the requirements set by the FAIR principles. Interoperability, in particular, requires specific skills in data management which may not yet be broadly available in the epidemiology community. In the discussion, we present recommendations on how veterinary research could move towards greater reusability according to FAIR principles. Overall, although many initiatives to improve data access have been started in the research community, their impact on the availability of datasets underlying published articles remains unclear to date.
Conceptual text region network: Cognition-inspired accurate scene text detection

Segmentation-based methods are widely used for scene text detection due to their superiority in describing arbitrary-shaped text instances. However, two major problems still exist: (1) current label generation techniques are mostly empirical and lack theoretical support, discouraging elaborate label design; and (2) as a result, most methods rely heavily on text kernel segmentation which is unstable and requires deliberate tuning. To address these challenges, we propose a human cognition-inspired framework, termed, Conceptual Text Region Network (CTRNet). The framework utilizes Conceptual Text Regions (CTRs), which is a class of cognition-based tools inheriting good mathematical properties, allowing for sophisticated label design. Another component of CTRNet is an inference pipeline that, with the help of CTRs, completely omits the need for text kernel segmentation. Compared with previous segmentation-based methods, our approach is not only more interpretable but also more accurate. Experiment results show that CTRNet achieves state-of-the-art performance on benchmark CTW1500, Total-Text, MSRA-TD500, and ICDAR 2015 datasets, yielding performance gains of up to 2.0%. Notably, to the best of our knowledge, CTRNet is among the first detection models to achieve F-measures higher than 85.0% on all four of the benchmarks, demonstrating remarkable consistency and stability.
Joint learning for biomedical NER and entity normalization

Named entity recognition (NER) and normalization (EN) form an indispensable first step to many biomedical natural language processing applications. In biomedical information science, recognizing entities (e.g., genes, diseases, or drugs) and normalizing them to concepts in standard terminologies or thesauri (e.g., Entrez, ICD-10, or RxNorm) is crucial for identifying more informative relations among them that drive disease etiology, progression, and treatment. In this effort we pursue two high level strategies to improve biomedical ER and EN. The first is to decouple standard entity encoding tags (e.g., "B-Drug" for the beginning of a drug) into type tags (e.g., "Drug") and positional tags (e.g., "B"). A second strategy is to use additional counterfactual training examples to handle the issue of models learning spurious correlations between surrounding context and normalized concepts in training data. We conduct elaborate experiments using the MedMentions dataset, the largest dataset of its kind for ER and EN in biomedicine. We find that our first strategy performs better in entity normalization when compared with the standard coding scheme. The second data augmentation strategy uniformly improves performance in span detection, typing, and normalization. The gains from counterfactual examples are more prominent when evaluating in zero-shot settings, for concepts that have never been encountered during training.
‘Open’ relationships: reflections on the role of the journal in the contemporary scholarly publishing landscape

What are academic journals for? In a digital era, we access single articles; personal subscribers are few, and those who skim or read whole issues of scholarly periodicals are likely even fewer. So...
PathBIX—a web server for network-based pathway annotation with adaptive null models

Pathway annotation is a vital tool for interpreting and giving meaning to experimental data in life sciences. Numerous tools exist for this task, where the most recent generation of pathway enrichment analysis tools, network-based methods, utilize biological networks to gain a richer source of information as a basis of the analysis than merely the gene content. Network-based methods use the network crosstalk between the query gene set and the genes in known pathways, and compare this to a null model of random expectation.We developed PathBIX, a novel web application for network-based pathway analysis, based on the recently published ANUBIX algorithm which has been shown to be more accurate than previous network-based methods. The PathBIX website performs pathway annotation for 21 species, and utilizes prefetched and preprocessed network data from FunCoup 5.0 networks and pathway data from three databases: KEGG, Reactome, and WikiPathways.https://pathbix.sbc.su.se/.erik.sonnhammer@scilifelab.se.Supplementary data are available at Bioinformatics Advances online.