Search results for: data retrieval

Authors: Tomy Prihananto, Damar Apri Sudarmadi

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Indonesia recognizes the right to privacy as a human right. Indonesia provides legal protection against data management activities because the protection of personal data is a part of human rights. This paper aims to describe the arrangement of data management and data management in Indonesia. This paper is a descriptive research with qualitative approach and collecting data from literature study. Results of this paper are comprehensive arrangement of data that have been set up as a technical requirement of data protection by encryption methods. Arrangements on encryption and protection of personal data are mutually reinforcing arrangements in the protection of personal data. Indonesia has two important and immediately enacted laws that provide protection for the privacy of information that is part of human rights.

Keywords: Indonesia, protection, personal data, privacy, human rights, encryption

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Authors: Amy Gooden

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When human genomic data is considered, this is often done through only one dimension of the law, or the interplay between the various dimensions is not considered, thus providing an incomplete picture of the legal framework. This research considers and analyzes the various dimensions in South African law applicable to genomic sequence data – including property rights, personality rights, and intellectual property rights. The effective use of personal genomic sequence data requires the acknowledgement and harmonization of the rights applicable to such data.

Keywords: artificial intelligence, data, law, genomics, rights

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Authors: X. Gumara Rigol, I. Martínez de Apellaniz Anzuola, A. Garcia Serrano, A. Franzi Cros, O. Vidal Calbet, A. Al Maruf

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Traditional federated architectures for data warehousing work well when corporations have existing regional data warehouses and there is a need to aggregate data at a global level. Schibsted Media Group has been maturing from a decentralised organisation into a more globalised one and needed to build both some of the regional data warehouses for some brands at the same time as the global one. In this paper, we present the architectural alternatives studied and why a custom federated approach was the notable recommendation to go further with the implementation. Although the data warehouses are logically federated, the implementation uses a single database system which presented many advantages like: cost reduction and improved data access to global users allowing consumers of the data to have a common data model for detailed analysis across different geographies and a flexible layer for local specific needs in the same place.

Keywords: data integration, data warehousing, federated architecture, Online Analytical Processing (OLAP)

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Authors: Sikander Singh Cheema, Jasmeen Kaur

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In this paper, the concept of data mining is summarized and its one of the important process i.e KDD is summarized. The data mining based on Genetic Algorithm is researched in and ways to achieve the data mining Genetic Algorithm are surveyed. This paper also conducts a formal review on the area of data mining tasks and genetic algorithm in various fields.

Keywords: data mining, KDD, genetic algorithm, descriptive mining, predictive mining

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Authors: Seung-Lock Seo

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This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults.

Keywords: data mining, process data, monitoring, safety, industrial processes

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Authors: Shen Jian, Hu Jie, Qi Jin, Liu Wei Jie, Chen Ji Yi, Peng Ying Hong

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In order to settle the problem of acquiring case in biological related to design problems, a biometrics instance acquisition method based on text mining is presented. Through the construction of corpus text vector space and knowledge mining, the feature selection, similarity measure and case retrieval method of text in the field of biology are studied. First, we establish a vector space model of the corpus in the biological field and complete the preprocessing steps. Then, the corpus is retrieved by using the vector space model combined with the functional keywords to obtain the biological domain examples related to the design problems. Finally, we verify the validity of this method by taking the example of text.

Keywords: text mining, vector space model, feature selection, biologically inspired design

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Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: biological ontology, linked data, semantic data integration, semantic web

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Lakshmi Prayaga, Chandra Prayaga. Aaron Wade, Gopi Shankar Mallu, Harsha Satya Pola

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Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN.

Keywords: synthetic data generation, generative adversarial networks, conditional tabular GAN, Gaussian copula

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Authors: Amel Osman, Stewart McKenna

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Aim: Fever may present as the sole sign of a serious underlying infection in young infants. Febrile Infants aged less than 90 days are at an elevated susceptibility to invasive bacterial infections, thus presenting a challenge in ensuring the appropriate management of these cases. This study aims to ensure strict adherence to NICE guidelines for the management of fever in infants between 0 and 90 days presenting to Tallaght Hospital ED. A comprehensive audit, followed by a re-audit, was conducted to enhance the quality of care delivered to these patients. In accordance with NICE guidelines, all febrile infants should undergo blood tests. Additionally, LP should be performed in all neonates under 28 days, infants displaying signs of illness, and those with WCC below 5 or above 15. Method: A retrospective case review was performed, encompassing all patients aged between 0 to 90 days who presented with fever at Tallaght ED. Data retrieval was conducted from electronic records on two separate occasions, six months apart. The evaluation encompassed the assessment of body temperature as well as both partial and full septic workups. Results: Over the study period, 150 infants presented to the ED with fever in the initial audit, and 120 in the re-audit. In the first study, 81 patients warranted a full septic workup as per NICE, but only 48 received it. Conversely, 40 patients met criteria for a partial septic workup, with 12 undergoing blood tests. In the second study, 73 patients qualified for a full septic workup, of which 52 were completed. Additionally, 27 patients were indicated for a partial workup, with 20 undergoing blood tests. Conclusion: Managing febrile infants under three months of age presenting to Tallaght ED remains a persistent challenge, underscoring the need for continuous educational initiatives to guarantee that these patients receive the requisite assessments and treatments.

Keywords: infants, fever, septic workup, tallaght

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Authors: Feng Tao, Ma Jing, Guo Xian, Wang Jing

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Named Data Networking (NDN) replaces IP address of traditional network with data name, and adopts dynamic cache mechanism. In the existing mechanism, however, only one-to-one search can be achieved because every data has a unique name corresponding to it. There is a certain mapping relationship between data content and data name, so if the data name is intercepted by an adversary, the privacy of the data content and user’s interest can hardly be guaranteed. In order to solve this problem, this paper proposes a one-to-many fuzzy search scheme based on order-preserving encryption to reduce the query overhead by optimizing the caching strategy. In this scheme, we use hash value to ensure the user’s query safe from each node in the process of search, so does the privacy of the requiring data content.

Keywords: NDN, order-preserving encryption, fuzzy search, privacy

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Authors: Chellammal Surianarayanan

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Healthcare industry is generating large amounts of data driven by various needs such as record keeping, physician’s prescription, medical imaging, sensor data, Electronic Patient Record(EPR), laboratory, pharmacy, etc. Healthcare data is so big and complex that they cannot be managed by conventional hardware and software. The complexity of healthcare big data arises from large volume of data, the velocity with which the data is accumulated and different varieties such as structured, semi-structured and unstructured nature of data. Despite the complexity of big data, if the trends and patterns that exist within the big data are uncovered and analyzed, higher quality healthcare at lower cost can be provided. Hadoop is an open source software framework for distributed processing of large data sets across clusters of commodity hardware using a simple programming model. The core components of Hadoop include Hadoop Distributed File System which offers way to store large amount of data across multiple machines and MapReduce which offers way to process large data sets with a parallel, distributed algorithm on a cluster. Hadoop ecosystem also includes various other tools such as Hive (a SQL-like query language), Pig (a higher level query language for MapReduce), Hbase(a columnar data store), etc. In this paper an analysis has been done as how healthcare big data can be processed and analyzed using Hadoop ecosystem.

Keywords: big data analytics, Hadoop, healthcare data, towards quality healthcare

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Authors: Zakieh Piri, Shahla Damanabi, Peyman Rezaii Hachesoo

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Introduction: Healthcare organizations like other organizations suffer from a number of disorders such as Business Sponsor Disorder, Business Acceptance Disorder, Cultural/Political Disorder, Data Disorder, etc. As quality in healthcare care mostly depends on the quality of data, we aimed to identify data disorders and its symptoms in two teaching hospitals. Methods: Using a self-constructed questionnaire, we asked 20 questions in related to quality and usability of patient data stored in patient records. Research population consisted of 150 managers, physicians, nurses, medical record staff who were working at the time of study. We also asked their views about the symptoms and treatments for any data disorders they mentioned in the questionnaire. Using qualitative methods we analyzed the answers. Results: After classifying the answers, we found six main data disorders: incomplete data, missed data, late data, blurred data, manipulated data, illegible data. The majority of participants believed in their important roles in treatment of data disorders while others believed in health system problems. Discussion: As clinicians have important roles in producing of data, they can easily identify symptoms and disorders of patient data. Health information managers can also play important roles in early detection of data disorders by proactively monitoring and periodic check-ups of data.

Keywords: data disorders, quality, healthcare, treatment

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Authors: M. Ramesh Kanthan, S. Naga Nandini Sujatha

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The goal of haze removal algorithms is to enhance and recover details of scene from foggy image. In enhancement the proposed method focus into two main categories: (i) image enhancement based on Adaptive contrast Histogram equalization, and (ii) image edge strengthened Gradient model. Many circumstances accurate haze removal algorithms are needed. The de-fog feature works through a complex algorithm which first determines the fog destiny of the scene, then analyses the obscured image before applying contrast and sharpness adjustments to the video in real-time to produce image the fusion strategy is driven by the intrinsic properties of the original image and is highly dependent on the choice of the inputs and the weights. Then the output haze free image has reconstructed using fusion methodology. In order to increase the accuracy, interpolation method has used in the output reconstruction. A promising retrieval performance is achieved especially in particular examples.

Keywords: single image, fusion, dehazing, multi-scale fusion, per-pixel, weight map

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Authors: Byron Joseph A. Hallar, Annjeannette Alain D. Galang, Maria Visitacion N. Gumabay

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One of the unique challenges provided by the twenty-first century to Philippine higher education is the utilization of Big Data. The higher education system in the Philippines is generating burgeoning amounts of data that contains relevant data that can be used to generate the information and knowledge needed for accurate data-driven decision making. This study examines the status, relevance and future of Big Data and Analytics in Philippine higher education. The insights gained from the study may be relevant to other developing nations similarly situated as the Philippines.

Keywords: big data, data analytics, higher education, republic of the philippines, assessment

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Authors: G. Julius P. Roy, Prateek Saxena, Sanjeev Singh

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Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage.

Keywords: data management, analytics, energy data analytics, smart grid, smart utilities

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Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

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Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

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Authors: Behnam Tavakkol

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Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

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Authors: Agustyas Tjiptaningrum, Intanri Kurniati, Fadilah Fadilah, Linda Erlina, Tiwuk Susantiningsih

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Coronavirus disease-19 (COVID-19) is still one of the health problems. It can be a severe condition that is caused by a cytokine storm. In a cytokine storm, several proinflammatory cytokines are released massively. It destroys epithelial cells, and subsequently, it can cause death. The anti-inflammatory agent can be used to decrease the number of severe Covid-19 conditions. Melaleuca cajuputi is a plant that has antiviral, antibiotic, antioxidant, and anti-inflammatory activities. This study was carried out to analyze the prediction mechanism of the M. cajuputi extract from Lampung, Indonesia, as an anti-inflammatory agent for COVID-19. This study constructed a database of protein host target that was involved in the inflammation process of COVID-19 using data retrieval from GeneCards with the keyword “SARS-CoV2”, “inflammation,” “cytokine storm,” and “acute respiratory distress syndrome.” Subsequent protein-protein interaction was generated by using Cytoscape version 3.9.1. It can predict the significant target protein. Then the analysis of the Gene Ontology (GO) and KEGG pathways was conducted to generate the genes and components that play a role in COVID-19. The result of this study was 30 nodes representing significant proteins, namely NF-κβ, IL-6, IL-6R, IL-2RA, IL-2, IFN2, C3, TRAF6, IFNAR1, and DOX58. From the KEGG pathway, we obtained the result that NF-κβ has a role in the production of proinflammatory cytokines, which play a role in the COVID-19 cytokine storm. It is an important factor for macrophage transcription; therefore, it will induce inflammatory gene expression that encodes proinflammatory cytokines such as IL-6, TNF-α, and IL-1β. In conclusion, the blocking of NF-κβ is the prediction mechanism of the M. cajuputi extract as an anti-inflammation agent for COVID-19.

Keywords: antiinflammation, COVID-19, cytokine storm, NF-κβ, M. cajuputi

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Authors: Nirmala Gopal, Sheetal Bhoola, Audecious Mugwagwa

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This paper discusses the continued infringement and exploitation of data by non-state actors for destructive purposes, emphasizing radical terrorist organizations. It will discuss how terrorist organizations access and use data to foster their nefarious agendas. It further examines how cybersecurity, designed as a tool to curb data exploitation, is ineffective in raising global citizens' concerns about how their data can be kept safe and used for its acquired purpose. The study interrogates several policies and data protection instruments, such as the Data Protection Act, Cyber Security Policies, Protection of Personal Information(PPI) and General Data Protection Regulations (GDPR), to understand data use and storage in democratic states. The study outcomes point to the fact that international cybersecurity and cybercrime legislation, policies, and conventions have not curbed violations of data access and use by radical terrorist groups. The study recommends ways to enhance cybersecurity and reduce cyber risks using democratic principles.

Keywords: cybersecurity, data exploitation, terrorist organizations, data democracy

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Authors: Eugenia Jilinguirian

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In the healthcare industry, data mining is essential since it transforms the field by collecting useful data from large datasets. Data mining is the process of applying advanced analytical methods to large patient records and medical histories in order to identify patterns, correlations, and trends. Healthcare professionals can improve diagnosis accuracy, uncover hidden linkages, and predict disease outcomes by carefully examining these statistics. Additionally, data mining supports personalized medicine by personalizing treatment according to the unique attributes of each patient. This proactive strategy helps allocate resources more efficiently, enhances patient care, and streamlines operations. However, to effectively apply data mining, however, and ensure the use of private healthcare information, issues like data privacy and security must be carefully considered. Data mining continues to be vital for searching for more effective, efficient, and individualized healthcare solutions as technology evolves.

Keywords: data mining, healthcare, big data, individualised healthcare, healthcare solutions, database

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Authors: Yunus Doğan, Ahmet Durap

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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: clustering algorithms, coastal engineering, data mining, data summarization, statistical methods

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Authors: Nikitha Francis, Anjana Hoode, Vinitha George, Jayashree S. Bhat

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The interaction between cognition and language, referred as cognitive-communication, is very intricate, involving several mental processes such as perception, memory, attention, lexical retrieval, decision making, motor planning, self-monitoring and knowledge. Cognitive-communication disorders are difficulties in communicative competencies that result from underlying cognitive impairments of attention, memory, organization, information processing, problem solving, and executive functions. Traumatic brain injury (TBI) is an acquired, non - progressive condition, resulting in distinct deficits of cognitive communication abilities such as naming, word-finding, self-monitoring, auditory recognition, attention, perception and memory. Cognitive-communication intervention in TBI is individualized, in order to enhance the person’s ability to process and interpret information for better functioning in their family and community life. The present case report illustrates the cognitive-communicative behaviors and the intervention outcomes of an adult with TBI, who was brought to the Department of Audiology and Speech Language Pathology, with cognitive and communicative disturbances, consequent to road traffic accident. On a detailed assessment, she showed naming deficits along with perseverations and had severe difficulty in recalling the details of the accident, her house address, places she had visited earlier, names of people known to her, as well as the activities she did each day, leading to severe breakdowns in her communicative abilities. She had difficulty in initiating, maintaining and following a conversation. She also lacked orientation to time and place. On administration of the Manipal Manual of Cognitive Linguistic Abilities (MMCLA), she exhibited poor performance on tasks related to visual and auditory perception, short term memory, working memory and executive functions. She attended 20 sessions of cognitive-communication intervention which followed a domain-general, adaptive training paradigm, with tasks relevant to everyday cognitive-communication skills. Compensatory strategies such as maintaining a dairy with reminders of her daily routine, names of people, date, time and place was also recommended. MMCLA was re-administered and her performance in the tasks showed significant improvements. Occurrence of perseverations and word retrieval difficulties reduced. She developed interests to initiate her day-to-day activities at home independently, as well as involve herself in conversations with her family members. Though she lacked awareness about her deficits, she actively involved herself in all the therapy activities. Rehabilitation of moderate to severe head injury patients can be done effectively through a holistic cognitive retraining with a focus on different cognitive-linguistic domains. Selection of goals and activities should have relevance to the functional needs of each individual with TBI, as highlighted in the present case report.

Keywords: cognitive-communication, executive functions, memory, traumatic brain injury

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Authors: Anny Retnowati, Elisabeth Sundari

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This research aims to elaborate the meaning of personal data protection principles on patient access to health data in medical records in Indonesia and its implications. The method uses normative legal research by examining health law in Indonesia regarding the patient's right to access their health data in medical records. The data will be analysed qualitatively using the interpretation method to elaborate on the limitation of the meaning of personal data protection principles on patients' access to their data in medical records. The results show that patients only have the right to obtain copies of their health data in medical records. There is no right to inspect directly at any time. Indonesian health law limits the principle of patients' right to broad access to their health data in medical records. This restriction has implications for the reduction of personal data protection as part of human rights. This research contribute to show that a limitaion of personal data protection may abuse the human rights.

Keywords: access, health data, medical records, personal data, protection

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Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

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The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research.

Keywords: data management, digitization, industry 4.0, knowledge engineering, metamodel

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Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: Saeed Ullah Jan, Shaukat Ullah

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Purpose of the study: The main theme of this study is to explore the information literacy skills of the law practitioners in Khyber Pakhtunkhwa-Pakistan under the heading "Information Literacy Skills of Legal Practitioners in Khyber Pakhtunkhwa-Pakistan: An Empirical Study." Research Method and Procedure: To conduct this quantitative study, the simple random sample approach is used. An adapted questionnaire is distributed among 254 lawyers of Dera Ismail Khan through personal visits and electronic means. The data collected is analyzed through SPSS (Statistical Package for Social Sciences) software. Delimitations of the study: The study is delimited to the southern district of Khyber Pakhtunkhwa: Dera Ismael Khan. Key Findings: Most of the lawyers of District Dera Ismail Khan of Khyber Pakhtunkhwa can recognize and understand the needed information. A large number of lawyers are capable of presenting information in both written and electronic forms. They are not comfortable with different legal databases and using various searching and keyword techniques. They have less knowledge of Boolean operators for locating online information. Conclusion and Recommendations: Efforts should be made to arrange refresher courses and training workshops on the utilization of different legal databases and different search techniques for retrieval of information sources. This practice will enhance the information literacy skills of lawyers, which will ultimately result in a better legal system in Pakistan. Practical implication(s): The findings of the study will motivate the policymakers and authorities of legal forums to restructure the information literacy programs to fulfill the lawyers' information needs. Contribution to the knowledge: No significant work has been done on the lawyers' information literacy skills in Khyber Pakhtunkhwa-Pakistan. It will bring a clear picture of the information literacy skills of law practitioners and address the problems faced by them during the seeking process.

Keywords: information literacy-Pakistan, infromation literacy-lawyers, information literacy-lawyers-KP, law practitioners-Pakistan

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Authors: Yashas Bedre Raghavendra, Pim Vullers

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This study presents the design and implementation of an abstract cryptographic coprocessor, leveraging AMBA(Advanced Microcontroller Bus Architecture) protocols - APB (Advanced Peripheral Bus) and AHB (Advanced High-performance Bus), to enable seamless integration with the main CPU(Central processing unit) and enhance the coprocessor’s algorithm flexibility. The primary objective is to create a versatile coprocessor that can execute various cryptographic algorithms, including ECC(Elliptic-curve cryptography), RSA(Rivest–Shamir–Adleman), and AES (Advanced Encryption Standard) while providing a robust and secure solution for modern secure embedded systems. To achieve this goal, the coprocessor is equipped with a tightly coupled memory (TCM) for rapid data access during cryptographic operations. The TCM is placed within the coprocessor, ensuring quick retrieval of critical data and optimizing overall performance. Additionally, the program memory is positioned outside the coprocessor, allowing for easy updates and reconfiguration, which enhances adaptability to future algorithm implementations. Direct links are employed instead of DMA(Direct memory access) for data transfer, ensuring faster communication and reducing complexity. The AMBA-based communication architecture facilitates seamless interaction between the coprocessor and the main CPU, streamlining data flow and ensuring efficient utilization of system resources. The abstract nature of the coprocessor allows for easy integration of new cryptographic algorithms in the future. As the security landscape continues to evolve, the coprocessor can adapt and incorporate emerging algorithms, making it a future-proof solution for cryptographic processing. Furthermore, this study explores the addition of custom instructions into RISC-V ISE (Instruction Set Extension) to enhance cryptographic operations. By incorporating custom instructions specifically tailored for cryptographic algorithms, the coprocessor achieves higher efficiency and reduced cycles per instruction (CPI) compared to traditional instruction sets. The adoption of RISC-V 128-bit architecture significantly reduces the total number of instructions required for complex cryptographic tasks, leading to faster execution times and improved overall performance. Comparisons are made with 32-bit and 64-bit architectures, highlighting the advantages of the 128-bit architecture in terms of reduced instruction count and CPI. In conclusion, the abstract cryptographic coprocessor presented in this study offers significant advantages in terms of algorithm flexibility, security, and integration with the main CPU. By leveraging AMBA protocols and employing direct links for data transfer, the coprocessor achieves high-performance cryptographic operations without compromising system efficiency. With its TCM and external program memory, the coprocessor is capable of securely executing a wide range of cryptographic algorithms. This versatility and adaptability, coupled with the benefits of custom instructions and the 128-bit architecture, make it an invaluable asset for secure embedded systems, meeting the demands of modern cryptographic applications.

Keywords: abstract cryptographic coprocessor, AMBA protocols, ECC, RSA, AES, tightly coupled memory, secure embedded systems, RISC-V ISE, custom instructions, instruction count, cycles per instruction

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Authors: Shakhawan K. Mawlood, Catriona Pickard, Benjamin Pickard

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In summer 2015 the remains of 800 children are among 1,967 bodies were exhumed by archaeologists at St Peter's Burial Ground in Blackburn, Lancashire. One hundred samples from these 19th century ancient bones were selected for DNA analysis. These comprised samples biased for those which prior osteological evidence indicated a potential for microbial infection by Mycobacterium tuberculosis (causing tuberculosis, TB) or Treponema pallidum (causing Syphilis) species, as well a random selection of other bones for which visual inspection suggested good preservation (and, therefore, likely DNA retrieval).They were subject to polymerase chain reaction (PCR) assays aimed at detecting traces of DNA from infecting mycobacteria, with the purpose both of confirming the palaeopathological diagnosis of tuberculosis and determining in individual cases whether disease and death was due to M. tuberculosis or other reasons. Our secondary goal was to determine sex determination and age prediction. The results demonstrated that extraction of vast majority ancient bones DNA samples succeeded.

Keywords: ancient bone, DNA, tuberculosis, age prediction

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Authors: Jeroen S. de Bruin, Karin Schindler, Christian Schuh

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With increasingly more mobile health applications appearing due to the popularity of smartphones, the possibility arises that these data can be used to improve the medical diagnostic process, as well as the overall quality of healthcare, while at the same time lowering costs. However, as of yet there have been no reports of a successful combination of patient-generated data from smartphones with data from clinical routine. In this paper, we describe how these two types of data can be combined in a secure way without modification to hospital information systems, and how they can together be used in a medical expert system for automatic nutritional classification and triage.

Keywords: mobile health, data integration, expert systems, disease-related malnutrition

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