Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic programming for tuberculosis screening from raw X-ray images

Genetic programming has been successfully applied to several real-world problem domains. One such application area is image classification, wherein genetic programming has been used for a variety of problems such as breast cancer detection, face detection, and pedestrian detection, to name a few. We present the use of genetic programming for detecting active tuberculosis in raw X-ray images. Our results demonstrate that genetic programming evolves classifiers that achieve promising accuracy results compared to that of traditional image classification techniques. Our classifiers do not require pre-processing, segmentation, or feature extraction beforehand. Furthermore, our evolved classifiers process a raw X-ray image and return a classification orders of magnitude faster than the reported times for traditional techniques.
Single-Cell Tracking of A549 Lung Cancer Cells Exposed to a Marine Toxin Reveals Correlations in Pedigree Tree Profiles

Long-term video-based tracking of single A549 lung cancer cells exposed to three different concentrations of the marine toxin yessotoxin (YTX) reveals significant variation in cytotoxicity, and it confirms the potential genotoxic effects of this toxin. Tracking of single cells subject to various toxic exposure, constitutes a conceptually simple approach to elucidate lineage correlations and sub-populations which are masked in cell bulk analyses. The toxic exposure can here be considered as probing a cell population for properties and change which may include long-term adaptation to treatments. Ranking of pedigree trees according to a measure of “size,” provides definition of sub-populations. Following single cells through generations indicates that signaling cascades and experience of mother cells can pass to their descendants. Epigenetic factors and signaling downstream lineages may enhance differences between cells and partly explain observed heterogeneity in a population. Signaling downstream lineages can potentially link a variety of observations of cells making resulting data more suitable for computerized treatment. YTX exposure of A549 cells tends to cause two main visually distinguishable classes of cell death modalities (“apoptotic-like” and “necrotic-like”) with approximately equal frequency. This special property of YTX enables estimation of correlation between cell death modalities for sister cells indicating impact downstream lineages. Hence, cellular responses and adaptation to treatments might be better described in terms of effects on pedigree trees rather than considering cells as independent entities.
Distributed optimization in multi-agent robotics for industry 4.0 warehouses

Robotic automation is being increasingly proselytized in the industrial and manufacturing sectors to increase production efficiency. Typically, complex industrial tasks cannot be satisfied by individual robots, rather coordination and information sharing is required. Centralized robotic control and coordination is ill-advised in such settings, due to high failure probabilities, inefficient overheads and lack of scalability. In this paper, we model the interactions among robotic units using intelligent agent based interactions. The autonomous behavior of these agents requires task/resource allocation to be performed via distributed algorithms. We use the motivating example of warehouse inventory automation to optimally allocate and distribute delivery tasks among multiple robotic agents. The optimization is decomposed using primal and dual decomposition techniques to operate in minimal latency, minimal battery usage or maximal utilization scenarios. These techniques may be applied to multiple deployments involving coordination and task allocation between autonomous agents.
Binarization of Nonuniform Illumination Barcode

This paper proposes a novel binarization approach, appropriate for nonuniform illumination barcode. The proposed method firstly clusters pixels of the image into two categories, which is black and white. Next, the variations of these two parts are calculated and binarization is conducted in the part with less variation. Finally, the uneven illumination part is established and recovered in the part with greater variation through both gray-value information and spatial information. The experimental results show that the proposed approach is effective for the uneven illumination barcode.
Stochastic Constraint Optimization using Propagation on Ordered Binary Decision Diagrams

A number of problems in relational Artificial Intelligence can be viewed as Stochastic Constraint Optimization Problems (SCOPs). These are constraint optimization problems that involve objectives or constraints with a stochastic component. Building on the recently proposed language SC-ProbLog for modeling SCOPs, we propose a new method for solving these problems. Earlier methods used Probabilistic Logic Programming (PLP) techniques to create Ordered Binary Decision Diagrams (OBDDs), which were decomposed into smaller constraints in order to exploit existing constraint programming (CP) solvers. We argue that this approach has as drawback that a decomposed representation of an OBDD does not guarantee domain consistency during search, and hence limits the efficiency of the solver. For the specific case of monotonic distributions, we suggest an alternative method for using CP in SCOP, based on the development of a new propagator; we show that this propagator is linear in the size of the OBDD, and has the potential to be more efficient than the decomposition method, as it maintains domain consistency.
Semi-supervised learning assisted particle swarm optimization of computationally expensive problems

In many real-world optimization problems, it is very time-consuming to evaluate the performance of candidate solutions because the evaluations involve computationally intensive numerical simulations or costly physical experiments. Therefore, standard population based meta-heuristic search algorithms are not best suited for solving such expensive problems because they typically require a large number of performance evaluations. To address this issue, many surrogate-assisted meta-heuristic algorithms have been proposed and shown to be promising in achieving acceptable solutions with a small computation budget. While most research focuses on reducing the required number of expensive fitness evaluations, not much attention has been paid to take advantage of the large amount of unlabelled data, i.e., the solutions that have not been evaluated using the expensive fitness functions, generated during the optimization. This paper aims to make use of semi-supervised learning techniques that are able to enhance the training of surrogate models using the unlabelled data together with the labelled data in a surrogate-assisted particle swarm optimization algorithm. Empirical studies on five 30-dimensional benchmark problems show that the proposed algorithm is able to find high-quality solutions for computationally expensive problems on a limited computational budget.
Test Case Quality as Perceived in Sweden

In order to reach an acceptable level of confidence in the quality of a software product, testing of the software is paramount. To obtain "good" quality software it is essential to rely on "good" test cases. To define the criteria for what make up for a "good" test case is not a trivial task. Over the past 15 years, a short list of publications have presented criteria for "good" test cases but without ranking them based on their importance. This paper presents a non-exhaustive and non-authoritative tentative list of 15 criteria and a ranking of their relative importance. A number of the criteria come from previous publications but also from discussions with our industrial partners. The ranking is based on results collected via a questionnaire that was sent out to a limited number of randomly chosen respondents in the Swedish software industry. This means that the results are more indicative than conclusive.
Emotional and Cognitive Assessment of Use of Functional Animation

The animation transition in the interface can be divided into two parts: functional animation and delightful animation. In past studies, "function" and "emotion" are often viewed independently. This study is to, with functional transition effect as the research content, investigate whether the cognitive load generated by operation will affect the use of emotions. The author selects 4 experimental samples of animation transition that conform to this study, gets the emotive information in the way of combining the subject's actual operation and subjective questionnaire, and seek the association between cognitive load and emotion, and explore the emotional connection generated in the process of using different types of transitions. The experimental results show that the six faces in the cognitive load will respectively produce unequal connections with pleasure, arousal and dominance at the emotional level, and besides, there will exist emotional and cognitive differences due to different characteristics of animation transition.
Adaptive charting genetic programming for dynamic flexible job shop scheduling

Genetic programming has been considered as a powerful approach to automated design of production scheduling heuristics in recent years. Flexible and variable representations allow genetic programming to discover very competitive scheduling heuristics to cope with a wide range of dynamic production environments. However, evolving sophisticated heuristics to handle multiple scheduling decisions can greatly increase the search space and poses a great challenge for genetic programming. To tackle this challenge, a new genetic programming algorithm is proposed to incrementally construct the map of explored areas in the search space and adaptively guide the search towards potential heuristics. In the proposed algorithm, growing neural gas and principal component analysis are applied to efficiently generate and update the map of explored areas based on the phenotypic characteristics of evolved heuristics. Based on the obtained map, a surrogate assisted model will help genetic programming determine which heuristics to be explored in the next generation. When applied to evolve scheduling heuristics for dynamic flexible job shop scheduling problems, the proposed algorithm shows superior performance as compared to the standard genetic programming algorithm. The analyses also show that the proposed algorithm can balance its exploration and exploitation better than the existing surrogate-assisted algorithm.
SCAnDroid: Automated Side-Channel Analysis of Android APIs

Although the Android system has been continuously hardened against side-channel attacks, there are still plenty of APIs available that can be exploited. However, most side-channel analyses in the literature consider specifically chosen APIs (or resources) in the Android framework, after a manual analysis of APIs for possible information leaks has been performed. Such a manual analysis is a tedious, time consuming, and error-prone task, meaning that information leaks tend to be overlooked. To overcome this tedious task, we introduce SCANDROID, a framework that automatically profiles the Java-based Android API for possible information leaks. Events of interest, such as website launches, Google Maps queries, or application starts, are triggered automatically, and while these events are being triggered, the Java-based Android API is analyzed for possible information leaks that allow inferring these events later on. To assess the Android API for information leaks, SCANDROID relies on dynamic time warping. By applying SCANDROID on Android 8 (Android Oreo), we identified several Android APIs that allow inferring website launches, Google Maps queries, and application starts. The triggered events are by no means exhaustive but have been chosen to demonstrate the broad applicability of SCANDROID. Among the automatically identified information leaks are, for example, the java.io. File API, the android.os. storage.StorageManager API, and several methods within the android.net. Traffics tats API. Thereby, we identify the first side-channel leaks in the Android API on Android 8 (Android Oreo).
A cost-aware object management method for in-memory computing frameworks

For in-memory computing frameworks such as Apache Spark [5, 6], objects (i.e., the intermediated data) can be accommodated in the main memory for speeding up the execution process. In this paper, we propose a cost-aware object management method for in-memory computing frameworks. When the main memory space of any worker node is not enough to accommodate the new computed or the retrieved object, we first pick appreciate objects which are already accommodated in the main memory as candidates for eviction and then evict objects with the minimal sum of the creation cost and the maximum sum of the occupied main memory space. According to the experimental results, we can achieve the goal under the 80/20 and 50/50 principles.
Review-level aspect-based sentiment analysis using an ontology

The rapid growth of the World Wide Web has led to an explosion of information that is available on this platform. This has resulted in an increased interest in sentiment analysis, where the goal is to determine the opinion regarding a topic. Aspect-based sentiment analysis aims to capture the sentiment within a segment of text for mentioned aspects, rather than for the text as a whole. The task we consider is aspect-based sentiment analysis at the review-level for restaurant reviews. We focus on ontology-enhanced methods that complement a standard machine learning algorithm. For this task we use two different algorithms, a review-based and a sentence aggregation algorithm. By using an ontology as a knowledge base, the classification performance of our models improves significantly. Furthermore, the review-based algorithm gives more accurate predictions than the sentence aggregation algorithm.
Potential Role of MicroRNAs in the Regulation of Antiviral Responses to Influenza Infection

Influenza is a major health burden worldwide and is caused by influenza viruses that are enveloped and negative stranded RNA viruses. Little progress has been achieved in targeted intervention, either at a population level or at an individual level (to treat the cause), due to the toxicity of drugs and ineffective vaccines against influenza viruses. MicroRNAs (miRNAs) are small non-coding RNAs that play critical roles in gene expression, cell differentiation, and tissue development and have been shown to silence viral replication in a sequence-specific manner. Investigation of these small endogenous nucleotides may lead to new therapeutics against influenza virus infection. Here, we describe our current understanding of the role of miRNAs in host defense response against influenza virus, as well as their potential and limitation as new therapeutic approaches.
Surface Modification of Hollow Glass Microspheres

Hollow Glass Microspheres are high-strength, low-density additives made from water resistant and chemically-stable soda-lime-borosilicate glass. These hollow glass microspheres offer a variety of advantages over conventional irregularly-shaped mineral fillers or glass fiber. Their spherical shape helps reduce resin content in a variety of applications. They also create a ball bearing effect that can result in higher filler loading and improved flow. In this research, amine terminated hollow glass microspheres were prepared by adopting three different routes. The results were investigated using FT-IR and SEM to establish the formation of amine groups and observe the morphological structure of the modified HGMs. The results obtained were used to select a suitable less toxic and environmental friendly modification method based on the chemicals used.
Image classification by combining local and mid-level features

It is meaningful to study high performance image classification algorithms for massive image management and effective organization. Image feature representations directly affect the performance of classification algorithm, however, in the current popular sparse coding framework for image classification, the relationship between local features is neglected, and there is still considerable room left for further improvement. In this paper, mid-level features are extracted efficiently in the superpixel region, which embeds the structural information of local features. Then we combine the local features and mid-level features to achieve better discriminative ability under the sparse coding framework. Compared to using only local features, the new methods improve the classification performance about 3% on several popular benchmarks.
The Challenges of Creating, Maintaining and Exploring Graphs of Financial Entities

1 OVERVIEW & MOTIVATION The integration of a wide range of structured and unstructured information sources into a uniformly integrated knowledge base is an important task in the nancial sector. As an example, modern risk analysis methods can bene t greatly from an integrated knowledge base, building in particular a dedicated, domain-speci c knowledge graph. Knowledge graphs can be used to gain a holistic view of the current economic situation so that systemic risks can be identi ed early enough to react appropriately. The use of this graphical structure thus allows the investigation of many nancial scenarios, such as the impact of corporate bankruptcy on other market participants within the network. In this particular scenario, the links between the individual market participants can be used to determine which companies are a ected by a bankruptcy and to what extent. We took these considerations as a motivation to start the development of a system capable of constructing and maintaining a knowledge graph of nancial entities and their relationships. The envisioned system generates this particular graph by extracting and combining information from both structured data sources such as Wikidata and DBpedia, as well as from unstructured data sources such as newspaper articles and nancial lings. In addition, the system should incorporate proprietary data sources, such as nancial transactions (structured) and credit reports (unstructured). The ultimate goal is to create a system that recognizes nancial entities in structured and unstructured sources, links them with the information of a knowledge base, and then extracts the relations expressed in the text between the identi ed entities. The constructed knowledge base can be used to construct the desired knowledge graph. Our system design consists of several components, each of which addresses a speci c subproblem. To this end, Figure 1 gives a general overview of our system and its subcomponents.
Design and Implementation of Automatic Question Answering System in Information Retrieval

Q & A is an indispensable part in the teaching process. Developing a dialogue-based automated question answering system can answer students' questions accurately and assist the effective teaching of the course. Information retrieval has become one of the core courses in information technology education in major universities in China. However, there are still some problems in the current information retrieval course. This study takes the course of information retrieval as an example to design and implement a QA system based on Lucene. This system mainly includes four core function modules of data set construction, keyword extraction, problem retrieval and answer extraction. The workflow of the system is as follows: First, the system segments the textual content entered by the user into words and extracts the keywords. Then it would retrieve the keywords in the FAQ. Later, the system would extract the matching question and corresponding answer from the retrieved questions, and finally return the answer to the user. The development of QA system is based on the B/S architecture, which is finally presented to the user in the form of web pages. At the same time, the responsive design allows the system to render well in multiple terminals. Finally, the function test and performance test of the system are also carried out. The result shows that after the user enters the question, the system can return the relevant answer, and the accuracy rate is 0.84, which indicates that the automatic question answering system can basically meet the needs of users in both function and performance.
Using Latent Dirichlet Allocation for Topic Modeling and Document Clustering of Dumaguete City Twitter Dataset

Online communication channel, such as social media is predominantly becoming common nowadays as it allows people to fearlessly and instantly share opinions and exchange information at one's convenience. One popular social media site and microblogging service, Twitter, has made it easy for people to express or share their experiences, adventures, and opinions on places they visited. These short messages, called tweets contain useful information that can be analyzed to generate topics of what people are talking about and their sentiments on that particular topic. To process these huge amounts of twitter dataset requires substantial effort of information filtering just to successfully drill down relevant topics and determine sentiments of those topic clusters. This paper discusses the process and the methods of generating topics and topic clusters on Twitter dataset about Dumaguete City and generates a probable sentiment analysis of each topic clusters. Latent Dirichlet Allocation (LDA) model was used to generate topics out of 99,942 tweets and clusters the tweets by calculating the probability on which topic cluster the tweet belongs. A supervised machine learning algorithm, Support Vector Machine (SVM) was used to generalize the sentiment of each cluster into positive, negative, or neutral.
Search-Based Optimization for the Testing Resource Allocation Problem: Research Trends and Opportunities

This paper explores the usage of search-based techniques for the Testing Resource Allocation Problem (TRAP). We focus on the analysis of the literature, surveying the research proposals where search-based techniques are exploited for different formulations of the TRAP. Three dimensions are considered: the model formulation, solution, and validation. The analysis allows to derive several observations, and finally outline some new research directions towards better (namely, closer to real-world settings) modelling and solutions, highlighting the most promising areas of investigation.
Vitamin D in Neurological Diseases: A Rationale for a Pathogenic Impact

It is widely known that vitamin D receptors have been found in neurons and glial cells, and their highest expression is in the hippocampus, hypothalamus, thalamus and subcortical grey nuclei, and substantia nigra. Vitamin D helps the regulation of neurotrophin, neural differentiation, and maturation, through the control operation of growing factors synthesis (i.e., neural growth factor [NGF] and glial cell line-derived growth factor (GDNF), the trafficking of the septohippocampal pathway, and the control of the synthesis process of different neuromodulators (such as acetylcholine [Ach], dopamine [DA], and gamma-aminobutyric [GABA]). Based on these assumptions, we have written this review to summarize the potential role of vitamin D in neurological pathologies. This work could be titanic and the results might have been very fuzzy and even incoherent had we not conjectured to taper our first intentions and devoted our interests towards three mainstreams, demyelinating pathologies, vascular syndromes, and neurodegeneration. As a result of the lack of useful therapeutic options, apart from the disease-modifying strategies, the role of different risk factors should be investigated in neurology, as their correction may lead to the improvement of the cerebral conditions. We have explored the relationships between the gene-environmental influence and long-term vitamin D deficiency, as a risk factor for the development of different types of neurological disorders, along with the role and the rationale of therapeutic trials with vitamin D implementation.