Automated Recognition of Chemical Molecule Images Based on an Improved TNT Model

The automated recognition of optical chemical structures, with the help of machine learning, could speed up research and development efforts. However, historical sources often have some level of image corruption, which reduces the performance to near zero. To solve this downside, we need a dependable algorithmic program to help chemists to further expand their research. This paper reports the results of research conducted for the Bristol-Myers Squibb-Molecular Translation competition, which was held on Kaggle and which invited participants to convert old chemical images to their underlying chemical structures, annotated as InChI text; we define this work as molecular translation. We proposed a model based on a transformer, which can be utilized in molecular translation. To better capture the details of the chemical structure, the image features we want to extract need to be accurate at the pixel level. TNT is one of the existing transformer models that can meet this requirement. This model was originally used for image classification, and is essentially a transformer-encoder, which cannot be utilized for generation tasks. On the other hand, we believe that TNT cannot integrate the local information of images well, so we improve the core module of TNT—TNT block—and propose a novel module—Deep TNT block—by stacking the module to form an encoder structure, and then use the vanilla transformer-decoder as a decoder, forming a chemical formula generation model based on the encoder–decoder structure. Since molecular translation is an image-captioning task, we named it the Image Captioning Model based on Deep TNT (ICMDT). A comparison with different models shows that our model has benefits in each convergence speed and final description accuracy. We have designed a complete process in the model inference and fusion phase to further enhance the final results.

Machine-Learning Classifiers for Malware Detection Using Data Features

The spread of ransomware has risen exponentially over the past decade, causing huge financial damage to multiple organizations. Various anti-ransomware firms have suggested methods for preventing malware threats. The growing pace, scale and sophistication of malware provide the anti-malware industry with more challenges. Recent literature indicates that academics and anti-virus organizations have begun to use artificial learning as well as fundamental modeling techniques for the research and identification of malware. Orthodox signature-based anti-virus programs struggle to identify unfamiliar malware and track new forms of malware. In this study, a malware evaluation framework focused on machine learning was adopted that consists of several modules: dataset compiling in two separate classes (malicious and benign software), file disassembly, data processing, decision making, and updated malware identification. The data processing module uses grey images, functions for importing and Opcode n-gram to remove malware functionality. The decision making module detects malware and recognizes suspected malware. Different classifiers were considered in the research methodology for the detection and classification of malware. Its effectiveness was validated on the basis of the accuracy of the complete process.

Raising children in an technologically advanced world

“The greatest legacy one can pass on to one’s children and grandchildren is not money or other material things accumulated in one’s life, but rather a legacy of character and faith”. Evangelist Billy Graham.Dining together each night my father- in -law converses with us on how he was born and brought up by his parents long back. having his mother alive all hail and hearty, an old lady of 100 makes him recall his childhood each time he sees her. he flips back on how his father gave him valuable advices on lessons of life, and continues to walks on the principals that his father has imbibed on him as a young boy and he carries a strong impression of his fathers teachings, he expects that the coming generation should live a life as they then lived. childhood in itself is a sweet memory for each one of us, we all have our own bunch of memories to share. childhood is even the most correct time to mould a raw person into a fine personality. living in the 21st century world, things have seemed to be changing a lot. parenting, raising children has become a far more different aspect. since parenting is also an ethical and moral issue, perspective may vary according to the culture and civilization for different people belonging to different set ups. as health personals we too come across many cases that arise merely due to maladjustments or psychological impairments that many a times lead to serious psychiatric problems. the third important thing is that parenting as a complete process and according to the changing time parenting is not the same as it used to be in the past. in the previous years we have been undergoing tremendous technological advancement which in terms is a boon for us. our lifestyles have heavily changed, we no more live in an joined family, we all are working parents and our outlook for a settled life has changed the world around us. we all live in the a world that is modernly civilized. leaving our civilization far behind. raising kids in this advanced world is a challenge indeed. since technology has brought curses as well as boons to our lives. we need to balance them both. things may be difficult but not impossible.

Domain-Specific Languages for Workflows.A Systematic Literature Review

This paper aims to provide an overview of the complete process in the development of a Domain-Specific Language (DSL). It explains the construction steps such as preliminary research, language implementation, and evaluation. Moreover, it provides details for different key components which are commonly found in the DSLs such as the abstraction layer, DSL metamodel, and the applications. It also explains the general limitations related to the Domain-Specific Languages for Workflows.

Wideband MOEMS for the Calibration of Optical Readout Systems

The paper proposes a technology based on UV-LIGA process for microoptoelectromechanical systems (MOEMS) manufacturing. We used the original combination of materials and technological steps, in which any of the materials does not enter chemical reactions with each other, while all of them are weakly sensitive to the effects of oxygen plasma. This made it suitable for long-term etching in the oxygen plasma at low discharge power with the complete preservation of the original geometry, including small parts. The micromembranes were formed by thermal evaporation of Al. This simplified the technique compared to the classic UV-LIGA and guaranteed high quality and uniformity of the resulting structure. To demonstrate the complete process, a test MOEMS with electrostatic control was manufactured. On one chip, a set of micromembranes was created with different stiffness from 10 nm/V to 100 nm/V and various working ranges from 100 to 300 nm. All membranes have a flat frequency response without resonant peaks in the frequency range 0–200 kHz. The proposed technology potentially enables the manufacture of wide low-height membranes of complex geometry to create microoptic fiber sensors.

Characterization and classification of numerical data patterns using Annotated Paraconsistent Logic and the effect of contradiction

This work describes the development of a computational mathematical model that uses Annotated Paraconsistent Logic - APL and a concept derived from it, the effect of contradiction, to identify patterns in numerical data for pattern classification purposes. The APL admits paraconsistent and paracomplete logical principles, which allow the manipulation of inconsistent and contradictory data, and its use allowed the identification and quantization of the attribute related to the contradiction. To validate the model, series of Raman spectroscopies obtained after exposure of proteins, lipids and nucleic acids, collected from cutaneous tissue cell samples previously examined for the detection of cancerous lesions, identified as basal carcinoma, melanoma and normal, were used. Initially, the attributes related to contradiction, derivative and median obtained from spectroscopies were identified and quantified. A machine learning process with approximately 31.6% of each type of samples detects a sequence of spectroscopies capable of characterizing and classifying the type of lesion through the chosen attributes. Approximately 68.4% of the samples are used for classification tests. The proposed model identified a segment of spectroscopies where the classification of test samples had a hit rate of 76.92%. As a differential and innovation of this work, the use of APL principles in a complete process of training, learning and classification of patterns for numerical data sets stands out, with flexibility to choose the attributes used for the characterization of patterns, and a quantity of samples of about one third of the total required for characterization.

For Learning Analytics to Be Sustainable under GDPR—Consequences and Way Forward

Personalized learning is one of the main focuses in 21st-century education, and Learning Analytics (LA) has been recognized as a supportive tool for enhancing personalization. Meanwhile, the General Data Protection Regulations (GDPR), which concern the protection of personal data, came into effect in 2018. However, contemporary research lacks the essential knowledge of how and in which ways the presence of GDPR influence LA research and practices. Hence, this study intends to examine the requirements for sustaining LA under the light of GDPR. According to the study outcomes, the legal obligations for LA could be simplified to data anonymization with consequences of limitations to personalized interventions, one of the powers of LA. Explicit consent from the data subjects (students) prior to any data processing is mandatory under GDPR. The consent agreements must include the purpose, types of data, and how, when and where the data is processed. Moreover, transparency of the complete process of storing, retrieving, and analysing data as well as how the results are used should be explicitly documented in LA applications. The need for academic institutions to have specific regulations for supporting LA is emphasized. Regulations for sharing data with third parties is left as a further extension of this study.

SENSE: A Flow-Down Semantics-Based Requirements Engineering Framework

The processes involved in requirements engineering are some of the most, if not the most, important steps in systems development. The need for well-defined requirements remains a critical issue for the development of any system. Describing the structure and behavior of a system could be proven vague, leading to uncertainties, restrictions, or improper functioning of the system that would be hard to fix later. In this context, this article proposes SENSE, a framework based on standardized expressions of natural language with well-defined semantics, called boilerplates, that support a flow-down procedure for requirement management. This framework integrates sets of boilerplates and proposes the most appropriate of them, depending, among other considerations, on the type of requirement and the developing system, while providing validity and completeness verification checks using the minimum consistent set of formalities and languages. SENSE is a consistent and easily understood framework that allows engineers to use formal languages and semantics rather than the traditional natural languages and machine learning techniques, optimizing the requirement development. The main aim of SENSE is to provide a complete process of the production and standardization of the requirements by using semantics, ontologies, and appropriate NLP techniques. Furthermore, SENSE performs the necessary verifications by using SPARQL (SPIN) queries to support requirement management.

Enforcement On Land Tax Arrears in Malaysia: A Framework Based on National Land Code 1965

Tax enforcement is needed in the forms of negative incentives such as sanctions, penalties and property forfeiture to deter non-compliance on the property tax payment conduct by the taxpayers. To implement such legal actions and proceedings effectively, it is crucial for tax authority to have a set of complete process flow of the enforcement as part of the enforcement framework to ensure the law is enforced with utmost fair manner. Thus, this article aims to establish a comprehensive enforcement workflow of land tax arrears in Malaysia based on the legislations provided in the National Land Code 1965. This is done by carrying out a content analysis of the legislation in force in National Land Codes 1965, which regulates the enforcement actions by the Land Administrators and State Authorities on the land tax arrears. The authors have performed the validation of the workflow by conducting semi structured interviews with land matter experts from at the federal level and Land Administrator (state level). This article will fill the gap in discussions on complete process flow of enforcement against land tax arrears outlined by National Land Code 1965.

Agarose Gel Electrophoresis for quality control of DNA aptamers in biosensor assays v1

This protocol describes the procedure for electrophoresis in agarose gel to check the quality, purity, and size of DNA aptamers. The general objective is that this is the first and fundamental step to start aptamer loading on LIG electrodes.The complete process requires approximately 2 hours and 15 minutes to complete.