Reviewerly/exHarmony huggingface dataset:
A Two-Phase Classifier Model for Predicting the Drug Satisfaction of the Patients Based on Their Sentiments

Since the corona virus has emerged, genuine clinical resources, such as a paucity of experts and healthcare workers, a lack of adequate equipment and medications, and so on, have reached their peak of inaccessibility. Several people have died as a consequence of the medical profession’s concern. Individuals began self-medicating due to a lack of supply, which exacerbated an already precarious health situation. A rise in new ideas for automation is being spurred by machine learning’s recent success in a varied variety of applications. In this paper, we have proposed a two-phase Decision Tree Classifier based on Artificial Neural networks (DTNN). The work is based on the satisfaction of the drugs among patients with the help of their comments as positive or negative polarity. The dataset of drugs used in this paper is Cymablta and Depopovera. The proposed results are compared with the existing methodology of Support Vector Machine Neural Network (SVMNN). The results are shown in graphical and tabular form which shows the efficiency of the proposed methodology.
Analysis and Simulation of Kali Linux Digital Forensic Tools

As the information capacity and utilization are expanding exponentially in today’s world, the hazard of information being abused is additionally increasing. This has come about within the information collected by machines or humans and put away in controllers, versatile gadgets, or computers being powerless to different cyberattacks. In today’s world, there is a part of computerized measurable tools and platforms that offer, assistance in conducting examinations by gathering prove through different methods. To work on this, a comprehensive study had done on different advanced measurable tools that are utilized by enterprises, government offices, and people to gain, extract and display the collected data in the Kali Linux platform. In this work, respective simulation and comparison of the forensic devices had done based on diverse criteria so that the clients can effectively select the apparatus agreeing to their needs and also based on the research done, its clear that many of these tools have contributed in Network and Image Forensics.
Implementation of high speed and lightweight symmetric key encryption algorithm-based authentication protocol for resource constrained devices

Encryption and decryption is accomplished by means of software or hardware, and has been in continuous usage since the 2000s for data encryption using any communication medium. A modern authentication algorithm is introduced based on the AES algorithmic program and SHA algorithms to encrypt information for encrypted communication with improved security features. SHA shall be applied at the same time as the AES mechanism for secrecy, reliability and honesty checks. The core concept of algorithmic application is to achieve a high degree of data protection by integrating SHA and AES algorithmic programs in software and hardware. Both transmitter and receiver sections are included in the proposed model to safely send and receive data. It has been developed using Xilinx ISE 14.2, so that the parameters of the proposed algorithmic program can be compared with various different FPGAs and then further comparison of parameters along with DES algorithm may lead to satisfactory results.
Metamorphic Testing of Logic Theorem Prover

The use of Artificial Intelligence methodologies including machine learning for object recognition and other tasks as well as reasoning has recently gained more attention. This is due to the fact of applications like autonomous driving but also apps for providing recommendations or schedules. In this paper, we focus on testing applications utilizing logic theorem proving for implementing their functionalities. Testing logic theorem prover is important in order to assure that the obtained results are correct and complete as specified. We show how metamorphic testing can be used in this context. In particular, the proposed method takes a logic sentence and modifies it without changing its logical status, i.e., satisfiability. The testing method can be applied to assure the correctness of reasoning via generating logic sentences of arbitrary sizes, but also for performance testing. We applied the presented testing method to 2 different theorem provers and report on obtained results.
On Transforming Cut- and Quantifier-Free Cyclic Proofs into Rewriting-Induction Proofs

An inductive definition set (IDS, for short) of first-order predicate logic can be transformed into a many-sorted term rewrite system (TRS, for short) such that a quantifier-free sequent is valid w.r.t. the IDS if and only if a term equation representing the sequent is an inductive theorem of the TRS. In this paper, to compare rewriting induction (RI, for short) with cyclic proof systems, under certain assumptions, we show that if a sequent has a cut- and quantifier-free cyclic proof, then there exists an RI proof for a term equation of the sequent. To this end, we propose a transformation of a cut- and quantifier-free cyclic proof of the sequent into an RI proof for the corresponding equation.
Informational Security (Infosec) (is)

Informational security is the ninth element of national security. Information, as it is now, is a topic that gained recognition as a decision tool in the last century. Information is the decision accelerator. Informational security needs to be governed as a wholesome topic for decision information including security of vital information that is critical to national governance. On the other side, the people should have access to the right information about governance. Maintaining secrecy or keeping information opaque, unless meant for the well-being of people, is not otherwise a matter of well-being. Information transparency is required to be to the desired extent within all limits. It is a hard task. A government in whatever type it may be is an agent of the people. It cannot hide information from them. It is also important that the correct information is delivered to them in feedback. A government faces many limitations in doing so. Notwithstanding, governing information is necessary to optimise informational security governance along with other elements. It also includes eliminating distortions. There are many governmental and non-governmental agencies involved in information acquisition, collation, process, delivery and dissemination. Government is accountable to the people for them for dissemination of correct information and holding back those that cannot be disclosed to avoid dilution and compromise of action decisions. Information security impacts the people through various routs and gradients starting with the old-fashioned grapevine that causes distortions and deliberate or unknown variations on passage. This causes many paradoxes in information available to people. Information comes from data. The information further refined becomes intelligence where one knows what is going to happen into time yet to come. There is also information formatted by time as past in history, legend and myth that could be professionally deciphered and cleaned to get to the truth as a function of time. The problem with the governance of informational security is in appreciating information arrived at in correct perspective as it could be distorted seriously. Lacuna in information management can impact national security decisions and strategies. Information security about governing of decision information to the best advantage of the nation as the accountability of the government is to the nation first. Thereafter, it is to the people. In this process, the governments may endure that the information they govern is actually of the right kind and well balanced between citizen’s rights and national security objectives.
Information and Knowledge

We describe the basic concepts of computing, including information, and computation as a process involving rules that can be executed by machines. We define knowledge as information that, embedded in the right network of semantic relationships, can be used either to understand a situation or to act to achieve a goal. We discuss key issues related to the development and application of knowledge. In particular, we attempt to analyze the links between scientific and technical knowledge and to provide a methodological framework comparing informatics to other fields of knowledge.
Topic Evolution Model for Interactive Information Search

In the modern context, as the data generated is exponential, finding meaningful patterns from large datasets is an urgent need. A ‘Topic Evolution Model’ could generate the evolutions related to a topic of user interest and assist in the exploration of patterns. In a generic setting, the proposed ‘topic evolution model’ assist researchers and domain experts for the relevant information extraction on scientific field progress and innovations in technological field or domain from large archives. The evolution patterns uncover the emerging, decay/fading, peculiar, and long-lasting research topics and subtopics. The performance evaluation on coherence metrics asserts that the proposed model significantly minimizes the domain expert user efforts in topic analysis, as evolving patterns easily reveal underlying statistical and machine learning details. The perplexity metrics highlights the capability of the topic model toward the cognitive view of the user, i.e., change of ideas and knowledge through a period of time reducing the citation bias.
Measuring the Interference Hierarchy of Single- and Two-Particle Correlations with Coherent Light

In the previous section, we have generalized Sorkin’s idea of interference orders to many-particle correlations and introduced a family of many-particle Sorkin parameters for mutually coherent sources. We established that M-particle interference is limited to order 2M and that according to Born’s rule all higher-order interference is prohibited. The corresponding M-particle Sorkin parameter, proportional to the $$(2M+1)$$ th-order term, can thus be used to detect any indications of higher-order interference in these many-particle correlations due to a deviation from quantum mechanics (Born’s rule). Here, we turn to experimentally verify the developed theory for single- and two-particle correlations for mutually coherent sources. We determine the interference orders in these correlations by measuring and evaluating first- and second-order correlation function of up to 32 different configurations in a slit-based experiment with coherent light. In the following, we first show that coherent states exhibit analog properties as M-particle Fock states in terms of the interference hierarchy in the intensity and photon correlation regime.
Panoptic Segmentation in Industrial Environments Using Synthetic and Real Data

Being able to understand the relations between the user and the surrounding environment is instrumental to assist users in a worksite. For instance, understanding which objects a user is interacting with from images and video collected through a wearable device can be useful to inform the worker on the usage of specific objects in order to improve productivity and prevent accidents. Despite modern vision systems can rely on advanced algorithms for object detection, semantic and panoptic segmentation, these methods still require large quantities of domain-specific labeled data, which can be difficult to obtain in industrial scenarios. Motivated by this observation, we propose a pipeline which allows to generate synthetic images from 3D models of real environments and real objects. The generated images are automatically labeled and hence effortless to obtain. Exploiting the proposed pipeline, we generate a dataset comprising synthetic images automatically labeled for panoptic segmentation. This set is complemented by a small number of manually labeled real images for fine-tuning. Experiments show that the use of synthetic images allows to drastically reduce the number of real images needed to obtain reasonable panoptic segmentation performance.
Modelling and Visualisation of Traffic Accidents in Botswana Using Data Mining

Road traffic accidents (RTAs) are the major cause of deaths worldwide, which imposes a substantial economic burden on the society. RTAs are triggered by several factors that can manifest individually or in association resulting in discernible patterns. This paper employs data mining approach, namely association rule mining algorithm, to discover latent relationships between various factors triggering RTAs in Botswana. The findings show that most traffic accidents in Botswana occur in Kweneng, Central and South East districts. Of these, 69% are minor, 22% serious and 9% fatal accidents. Furthermore, these accidents occur often on Saturdays and Fridays, with about 20% and 15% casualties involved, respectively. The drivers are mostly young males who are recurrently under the influence of alcohol or drugs. Furthermore, 99% of RTAs drivers hold valid driver’s licences and 61% often collide with pedestrians crossing the road in daylight. The primary critical factors accounting to RTAs are alcohol/drugs, driver age, driver licence validity, weather, road curvature and road surface type, which relate to the driver attribute class and the accident attribute class congruently. The findings reveal that there are several strong relationships among the factors, such as the driver licence validity, driver alcohol/drugs, victim, weather, accident severity and road condition. These factors, show positive correlations in their co-occurrences in unique traffic accidents events in Botswana. Findings of the study are projected to provide useful information on understanding the main critical factors triggering road accidents in Botswana, in addition to supporting management to identify and implement accident prevention mechanisms.
Human Action Recognition in Still Images Using SIFT Key Points

Pavan, M. Jyothi, K.Recognition of the human action in still images is one of the challenging problems due to the unavailability of movement information of the human body parts. In this work, we addressed this problem by generating scale-invariant feature transform (SIFT) key interest points over human body and used the statistical information of SIFT key points for representing human actions. We applied standard classifiers on the model and compared the efficiency using K-fold and hold-out validation technique. For the experiment video frames from KTH action dataset are used as static image. Our method is simple and recognizes human action with 85.8% accuracy.
Towards the Target: Self-regularized Progressive Learning for Unsupervised Domain Adaptation on Semantic Segmentation

Unsupervised domain adaptation for semantic segmentation aims to transfer the knowledge learned from a labeled synthetic source domain to an unlabeled real-world target domain. The main challenge lies in the difference between the two domains, i.e., the so-called “domain gap”. Although the two domains are supposed to share the same set of class labels, the semantics encoded by the source labels are not always consistent with those of the target data. Some recent efforts have been taken to explore the domain-specific semantics by conducting a within-domain adaptation using the predicted pseudo labels of the target data. The quality of the pseudo labels is therefore essential to the within-domain adaptation. In this paper, we propose a unified framework to progressively facilitate the adaptation towards the target domain. First, we propose to conduct the cross-domain adaptation through a novel source label relaxation. The relaxed labels offer a good trade-off between the source supervision and the target semantics. Next, we propose a dual-level self-regularization to regularize the pseudo-label learning and also to tackle the class-imbalanced issue in the within-domain adaptation stage. The experiment results on two benchmarks, i.e., GTA5 $$\rightarrow $$ Cityscapes and SYNTHIA $$\rightarrow $$ Cityscapes, show considerable improvement over the strong baseline and demonstrate the superiority of our framework over other methods.
Can the Price of BTC Bitcoin Be Forecast Successfully with NARX Neural Networks?

The cryptocurrency market data is highly noisy and nonlinear and conditioning the effectiveness and efficiency of the models that can be used to forecast their price, making this task a challenge, as shows the technical literature. Therefore, the present study proposes the following alternative: to train, test, and validate a NARX Open Loop Neural Network using the preprocessed historical data (Phase 1); then, to use a step by step technique with a NARX Closed Loop neural network to forecast in time (Phase 2). According to the results obtained, the forecasting of the prices of BTC Bitcoin is promising using data from the years 2017–2021, a period of high price variation. Finally, the subjacent idea in this study is that if appropriately modeled, BTC Bitcoin prices can be predicted reasonably accurately, although the cryptocurrency market is too hard to model.
From the history of the Tambov Labor Faculty: an attempt at a “didactic revolution”

Based on the materials of the Tambov Labor Faculty, an attempt at a decisive restructuring of the Soviet sphere of education in the second half of the 1920s is studied by replacing the traditional class-lesson system on a fixed schedule with a free one (Dalton plan), aimed at developing skills for self-building knowledge in the course of “laboratory work”. Based mainly on archival materials introduced into scientific circulation for the first time, the stages of the “struggle” for Dalton are traced, during which it was relatively quickly revealed that it was impossible to introduce a “pure” free system, which exceeded the material organizational and methodological capabilities of the Tambov Labor Faculty, which practiced semi-free (combining classroom and laboratory schemes) system. The gradual increase by the Center requirements for the quality of training in labor faculty and universities showed the impossibility of solving these problems within the framework of Dalton, which led to a return to a modified orthodox didactics.
“Secret” Left by Turing—Privacy Computing

AI has been applied in an increasing number of fields and scenarios, such as automatic driving, medical treatment, media, finance, industrial robots, and Internet services. On the one hand, it has improved efficiency and cut costs; on the other hand, the autonomy of AI systems makes algorithm decision-making gradually replace human decision-making, which sometimes leaves the existing problems undone or even harder to be dealt with, and completely new problems may also emerge in society. All these old and new problems not only cause extensive discussion in society but also become important factors restricting the implementation of AI technology. In the face of AI technology with as great risks as its potential, people urgently need a broad and universal ethical discussion to find paths and comb the norms to guarantee the benign development of AI.
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Distributionally Robust Optimization for Service Network Design Considering Different Trustable Moments of Historical Data

Service network design (SND) problem is designing the service selection and flow distribution to satisfy demands under limited assets. A growing body of research has shown that ignoring uncertainty can increase total operational costs when fluctuations arise and must be managed in the SND. In this case, stochastic programming (SP) and robust optimization (RO) are commonly utilized. Nevertheless, SP requires precise distribution data that may not be accessible in some situations, while RO typically delivers an over-conservative solution under the worst case. In order to avoid the disadvantages, we focus on distributionally robust optimization (DRO) for the uncertain SND. DRO formulation does not need accurate distribution information of stochastic variables, which is useful for insufficient historical data or inaccurate distribution prediction situations. DRO utilizes restricted historical information to construct an ambiguity set for possible distributions rather than specifying an uncertain set artificially as RO does, whichÂ optimizes the formulation under the worst distribution and reduces the conservative degree. In this paper, we build three DRO formulations based on different ambiguity sets with varying trustable historical knowledge and reformulate them to be solvable as a mixed integer linear program, a second-order cone program, and a semi-definite program, respectively. A delayed constraint generation framework is designed to obtain optimal solutions for the formulations. We contrastively design several groups of experiments to evaluate the performance of distributionally robust SNDs and the difference among different methodologies, based on which we give some network design insights to managers.
Graphical Interpretation and Multi-dimensional Data Visualization on Heart Disease Dataset

Heart disease is the most leading cause of death in most developed countries among young adults and aged peoples. Sometimes, even after spending enormous amount of money for treatment, the patient cannot be saved due to their medical condition. Doctors and hospitals in the verge of finding new techniques and methodologies in treating patients following applicable procedures. With the emerging latest medical devices, in recent days, the diagnosis is mostly data-oriented. The historical readings of patient are recorded, and suitable diagnoses are administered to increase the chance survivability. Such a well-defined heart disease-based readings are provided in the UCI heart disease datasets. In this article, we are motivated to perform exploratory data analysis to uncover interesting patterns in the datasets. The patterns are illustrated as a graphs and charts throughout this article.
Double-Layer Stacked Denoising Autoencoders for Regression

A Stacked Denoising Autoencoder (SDAE) is a deep neural network (NN) model trained and designed in one-by-one stacked layers to reconstruct the non-noisy version of the original input data. It is an architecture used with great success in statistical pattern recognition problems. The objective of this contribution is to determine if an MSDAE can benefit from the greater capabilities of representation of features obtained when two layers are introduced in the stacking process instead of a single one. To do this, the design and performance of these machines in regression problems are presented and analyzed both in terms of error and calculation cost. The experimental results underline interesting performance capabilities for specific purposes.