Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysing the Structure, Dynamics and Contents of Social Networks

: Social media permits its users to have numerous accounts in diverse social networking podiums. Popular social networking sites have billions of users that possess similar interests. The aim of this paper is to determine the identity of users and map those identities with those having similar interests. The developed system can be utilized for business intelligence that would help in creating a database of all users that possess specific interests. In this study, a system is developed wherein a user’s core interest is recognized in well-known social networking sites (i
Concordance and Term Frequency in Analyzing API Calls for Malware Behavior Detection

Application Programming Interface (API) is used for the software to interact with an operating system to do certain task such as opening file, deleting file and many more. Programmers use this API to make it easier for their program to communicate with the operating system without having the knowledge of the hardware of the target system. Malware author is an attacker that may belong to an organization or work for themselves. Some malware author has the capabilities to write their own malware, uses the same kind of APIs that is used to create normal programs to create malware. There are many researches done in this field, however, most researchers used n-gram to detect the sequence of API calls and although it gave good results, it is time consuming to process through all the output. This is the reason why this paper proposed to use Concordance to search for the API call sequence of a malware because it uses KWIC (Key Word in Context), thus only displayed the output based on the queried keyword. After that, Term Frequency (TF) is used to search for the most commonly used APIs in the dataset. The results of the experiment show that concordance can be used to search for API call sequence as we manage to identify six malicious behaviors (Install Itself at Startup, Enumerate All Process, Privilege Escalation, Terminate Process, Process Hollowing and Ant debugging) using this method. And based on the TF score, the most commonly used API in the dataset is the Reg Close Key (TF: 1.388), which on its own is not a dangerous API, hence we can infer that most API is not malicious in nature, it is how they were implemented is making them dangerous.
The Impact of Task Abandonment in Crowdsourcing

Crowdsourcing has become a standard methodology to collect manually annotated data such as relevance judgments at scale. On crowdsourcing platforms like Amazon MTurk or FigureEight, crowd workers select tasks to work on based on different dimensions such as task reward and requester reputation. Requesters then receive the judgments of workers who self-selected into the tasks and completed them successfully. Several crowd workers, however, preview tasks, begin working on them, reaching varying stages of task completion without finally submitting their work. Such behavior results in unrewarded effort which remains invisible to requesters. In this paper, we conduct an investigation of the phenomenon of task abandonment, the act of workers previewing or beginning a task and deciding not to complete it. We follow a three-fold methodology which includes 1) investigating the prevalence and causes of task abandonment by means of a survey over different crowdsourcing platforms, 2) data-driven analysis of logs collected during a large-scale relevance judgment experiment, and 3) controlled experiments measuring the effect of different dimensions on abandonment. Our results show that task abandonment is a widely spread phenomenon. Apart from accounting for a considerable amount of wasted human effort, this bears important implications on the hourly wages of workers as they are not rewarded for tasks that they do not complete. We also show how task abandonment may have strong implications on the use of collected data (for example, on the evaluation of Information Retrieval systems).
MAGNETO HERBAL TREATMENT FOR CLEARING KIDNEY STONES.

Kidney Stone Disease (KSD) has become a global problem and number of those affected is increasing day by day. A 10 year study of kidney stone patients as herbal respondents is presented. Results of a four phase survey of Herbal respondents and the application of herbal therapy in management of the disease are presented. A five year analysis between 2000 to 2004 of 2783 patients with 1711 males and 1072 females with a 68.78 % of clearance using traditional medicinal plants using Thuja orientalis, Tribulus terrestris, Aerva lanata plus +Acupressure had been tabulated. 1843 patients were reported to suffer from Calcium oxalate stones. The correlation Co-efficient was 0.567 (p value < .0001) In the second phase of study between 2006 to 2010 a serial Magneto therapy, Acupressure, and Rajendra Kidney Formula treatment for 48 days were given with a success of 93.95% patients who had both calcium oxalate and other types of stones. In the third & fourth phases two sets of 140 and 100 patients were treated with a dietopathic way of life and Rajendra Kidney formula with 100% healing and cure of calcium oxalate, cystine, struvite and uric acid stones.
The contribution of skills to business performance

The aim of this study is to explore the literature on the relationship between skills and organisational performance in order to: provide a consistent, motivating and evidence based message to employers about the ‘bottom line’ business benefits of investing in skills and training; develop a framework of human capital, human resource management (HRM) and organisational performance measures/indicators for use by employers; develop methodologies for interrogating the relationship between the measures with the goal of generating evidence of causal chains (where possible) and relationships, which can inform both executive-level business decisions and government policy interventions; develop a framework of measures and methodologies which would be capable of underpinning future research, and which would take account of, and make reference to, existing measures and methodologies used by both business and government departments and agencies to ensure the widest alignment; and create a framework that would be able to facilitate the gathering of cumulative and comparative data sets that would allow more robust conclusions to be drawn in the future. Based on the findings of the literature review, an explanatory framework is developed which helps present and make sense of the research. This model has two key dimensions to human capability: the first dimension has the development of capability at one end and its deployment at the other; and the second dimension has the individual at one end and the organisation at the other. These two dimensions create four quadrants of activity: access - the effective resourcing of roles in the organisation; ability - the skills of the workforce; attitude - the engagement, motivation and morale of the workforce; and application - the opportunities available to ensure skills and motivation are effectively applied. The report then details all the measures which have been used in the literature to successfully identify a link to performance and identifies a set of core and desirable measures of human resource (HR) practice against each of the quadrants in the model which meet the following criteria: the measures should be meaningful to employers and align as far as possible with measures already in use; alignment and compatibility with existing national and international measures to ensure that data generated can be used in comparison or in conjunction with other data that is tracked and reported; the measures should generate data which is as valid, reliable and unambiguous as possible; the measures should be able to track organisational inputs, outputs and performance over time; and the input measures should be able to reflect activities that are under the control of employers or by policy makers.
An NFV-Based Service Framework for IoT Applications in Edge Computing Environments

Emerging Internet of Things (IoT) applications share the same characteristics of involving multiple processing components (i.e., function modules) and requiring a massive amount of data to be processed with low latency. To meet these needs, edge/fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. Thanks to the development of Network Functions Virtualization (NFV), with which edge computing platform can virtualize function modules and deploy them on any edge devices to provide flexible services on the edge networks. However, such platform would need to deal with complicated function module calling relationship (i.e., call graph) of applications and user mobility, and both are not thoroughly considered by existing works of NFV and edge computing. In this paper, based on our previous idea of virtual local-hub (VLH), we propose a complete design of edge computing framework, which applies NFV technology on edge computing environment for IoT applications. To handle the complicated call graphs of IoT applications with better resource utilization, the VLH framework adapts the technologies of container-based virtualization and microservice architecture, which enables remote function module sharing on the edge computing environment. The framework includes the heuristic algorithm for function module allocation with the objective of minimizing total bandwidth consumption. We also present a design of protocols for system operations and mobility handling in the framework. Then we implement the framework on commodity hardware as a testbed. Via simulations under a large-scale environment with practical settings and experiments on the testbed under real-world scenarios, we demonstrate and verify the effectiveness and practicability of the proposed VLH framework for IoT application service provision.
Risk, colon cancer & physical activity: a qualitative exploration in older adults

Rationale and Objectives There is convincing evidence that physical activity (PA) reduces risk of colon cancer (CC) and may improve survival after cancer, although few older adults achieve recommended PA guidelines. Numerous barriers to participation exist, though few studies focus on socio-cultural influences. This study explores barriers specific to individuals at elevated risk of CC following screening colonoscopy, as well as how health professionals or a ‘diagnosis’ may provide additional motivation to change. Methods Interviews were conducted with colonic polyp patients and CC survivors over 60 years old, selectively sampled from a feasibility study for a randomised controlled PA intervention. Narrative accounts enabled discussion of influences on health behaviour throughout participants’ lifetimes, the impact of their recent ‘diagnosis’, and attitudes towards PA. Interviews and focus groups were conducted with health professionals to triangulate data collection. All interviews were transcribed verbatim and a constructivist grounded theory approach to data analysis was followed. Findings Despite not meeting current PA guidelines, participants perceived a lifetime of ‘natural’ PA. CC survivors were more inclined to initiate PA engagement to improve their health; conversely, elevated risk individuals were often not aware of their change in health status, leading them to conclude that no lifestyle change was necessary. Professionals confirmed that no PA guidance is currently offered to screening patients, but believed that there may be scope to implement health promotion advice. Barriers towards this however, are complex and numerous. Conclusions The ‘meaning of PA’ is situated and understandings may differ. Despite reporting perceptions of high PA, this study sample did not seem to understand what constitutes sufficient PA to elicit a positive health response. Risk status awareness and the benefits of PA is lacking in elevated risk individuals. For the screening setting to be utilised, questions around ‘why’, ‘when’ ‘who’ and ‘how’ health promotion should be delivered, need to be addressed.
Design of an Isolated DC/DC Topology With High Efficiency of Over 97% for EV Fast Chargers

This paper presents an DC/DC topology achieving high efficiency of over 97-% for electric vehicle (EV) fast chargers requiring a very wide output voltage range. The proposed topology consists of an unregulated resonant converter and a non-isolated buck converter. The unregulated resonant converter is responsible for the galvanic isolation for overall system, and the buck converter carries out the charging of the battery inside EV by constant-current or constant-voltage mode control. This structure provides a number of advantages such as easy realization of a very wide voltage range, no circulating-current, zero-voltage-switching operation on all the switches of the unregulated resonant converter running at high frequency and high voltage, and the achievement of high efficiency at every time over the entire battery charging profile. The availability of small passive components is a benefit because it enables the design of high power-density. In this paper, the feasibility of the proposed topology in EV fast charger applications has been verified by a prototype converter developed with the specification of 120-kHz or 50-kHz switching frequencies, maximum power rating of 20-kW, maximum charging current of 30-A, and battery voltage range of 50-650-Vdc.
Event Detection on Microposts: A Comparison of Four Approaches

Microblogging services such as Twitter are important, up-to-date, and live sources of information on a multitude of topics and events. An increasing number of systems use such services to detect and analyze events in real-time as they unfold. In this context, we recently proposed ArmaTweet—a system developed in collaboration among armasuisse and the Universities of Oxford and Fribourg to support semantic event detection on Twitter streams. Our experiments have shown that ArmaTweet is successful at detecting many complex events that cannot be detected by simple keyword-based search methods alone. Building up on this work, we explore in this paper several approaches for event detection on microposts. In particular, we describe and compare four different approaches based on keyword search (Plain-Seed-Query), information retrieval (Temporal Query Expansion), Word2Vec word embeddings (Embedding), and semantic retrieval (ArmaTweet). We provide an extensive empirical evaluation of these techniques using a benchmark dataset of about 200 million tweets on six event categories that we collected. While the performance of individual systems varies depending on the event category, our results show that ArmaTweet outperforms the other approaches on five out of six categories, and that a combined approach offers highest recall without adversely affecting precision of event detection.
ANALYTICAL APPROACHES IN UNVEILING THE COMPATIBILITY OF COLLAGENASE WITH ADDITIVES IN VIEW OF ITS COLLOIDAL DELIVERY

The core objective of present work was to investigate the chemical interactions of selected additives with therapeutic enzyme Collagenase (COLG) and determination of its compatibility for futuristic colloidal formulation. Initially, solubility of COLG with additives was determined and emulsification efficiency of surfactants was examined. FTIR spectra of COLG with its binary blends were compared. Non-denaturing SDS-PAGE and Circular Dichroism (CD) used to investigate primary and secondary structure of enzyme in binary mixture. Finally, the control and accelerated studies was performed by spectroscopy.
Creación de un corpus de artículos de prensa y generación automática de resúmenes

Automatic summary generation is a current and very attractive field in the area of natural language processing. For this reason, in this work, it has been decided to analyse and compare different techniques for automatic abstract generation both based on neural net-work and based on classical algorithms. For the evaluation of these techniques, syntactic metrics used in most studies of this field and semantic metrics proposed in this work have been used. Through this study, differences between the two evaluations discussed above can be seen. Moreover, improvements introduces by the semantic measure proposal are also presented. For the use of summary approaches based on neural networks and for the evaluation of all systems, a corpus of documents and summaries is required. For this reason, through a crawling process, a corpus of news that come from different digital press websites has been prepared in both Spanish and Catalan languages. Finally, the work contains an extensive analysis of the experimental results obtained with the corpus. In it, the existing differences between the news contained in both corpus can be observed. At the same time, a comparison of the different measures has been included. In addition, the generated corpus will be compared with other corpus that take part of the state of the art.
Author identification of short texts using dependency treebanks without vocabulary

How to classify short texts effectively remains an important question in computational stylometry. This study presents the results of an experiment involving authorship attribution of ancient Greek texts. These texts were chosen to explore the effectiveness of digital methods as a supplement to the author’s work on text classification based on traditional stylometry. Here it is crucial to avoid confounding effects of shared topic, etc. Therefore, this study attempts to identify authorship using only morpho-syntactic data without regard to specific vocabulary items. The data are taken from the dependency annotations published in the Ancient Greek and Latin Dependency Treebank. The independent variables for classification are combinations generated from the dependency label and the morphology of each word in the corpus and its dependency parent. To avoid the effects of the combinatorial explosion, only the most frequent combinations are retained as input features. The authorship classification (with thirteen classes) is done with standard algorithms—logistic regression and support vector classification. During classification, the corpus is partitioned into increasingly smaller ‘texts’. To explore and control for the possible confounding effects of, e.g. different genre and annotator, three corpora were tested: a mixed corpus of several genres of both prose and verse, a corpus of prose including oratory, history, and essay, and a corpus restricted to narrative history. Results are surprisingly good as compared to those previously published. Accuracy for fifty-word inputs is 84.2–89.6%. Thus, this approach may prove an important addition to the prevailing methods for small text classification.
Coronary Artery Disease and Carotid Artery Disease Progress Despite Normal Lipid Levels by Standard Statin Therapy in Asymptomatic Patients without Known Coronary Artery Disease: Implications for Statin Therapy

Many mega-trials of statin therapy demonstrate that a 28% drop in LDL-cholesterol (LDL-C) in 5-year statin trial is associated with a 25% to 35% drop in CAD risk, leaving a substantial residual risk. We studied whether standard statin therapy would prevent progression of coronary artery disease (CAD) and carotid artery disease in asymptomatic patients without known CAD. We retrospectively selected 45 patients who continued statin therapy for one year and underwent multidetector computed tomography and carotid ultrasound measurement one year apart. Follow-up period was 1.37±0.49 (1.0 to 2.5) years. Total cholesterol changed from 213.2±46.2mg/dl to 196.0±34.3mg/dl (p=0.045). Triglyceride changed from 125.5±72.3mg/ dl to 107.1±58.2mg/dl (p=0.184). HDL-cholesterol changed from 60.8±15.8mg/dl to 60.0±12.3mg/dl (p=0.789). LDL-cholesterol changed from 101.7±25.2mg/dl to 102.7±23.0mg/dl (p=0.844). However coronary calcium score significantly increased (230.1±414.5 to 298.3±479.2, p=0.009) and the number of coronary plaques significantly increased (2.5±1.7 to 3.0±1.7, p=0.001). Also carotid IMT significantly increased (1.42±0.81 to 1.78±0.92, p=0.049) and the number of carotid plaques significantly increased (0.5±1.1 to 1.1±1.5, p=0.001). In conclusion, our results showed that CAD and carotid artery disease progressed significantly despite normal lipid values using standard statin therapy in asymptomatic patients without known CAD. This suggests that more intense lipid lowering will be needed to prevent progression of CAD and carotid artery disease.
Hypofund: an online platform for personalised mortgage advice

This is a project report on the research, implementation and empirical test of an online system for mortgage advice that have been completed in the context of our bachelor project. The project objective was to build a platform for online mortgage advice, which could substitute a real-life, human adviser. The use of such a platform costs far less than hiring a human adviser, but a major problem to solve is that a website usually cannot give personal treatment to the user. In the research phase, we looked at how to apply the fields of affective and adaptive interfaces to our problem domain, as well as how A/B testing is commonly done for software products and how the results are statistically analyzed. With the knowledge we gained from the research, we came to an agreement of what to build with the client. The result of this agreement is the specification of the ’hybrid workflow’ interface (tailored to the philosophy of the client’s vision), a personalisation feature and an empirical test. We implemented said specification and conducted a proof-of-concept A/B test with 47 users. After statistical analysis, we concluded that the results of these tests were not significant: there was no significant difference between the users that got a variant of the platform with the personalisation feature and the users that got the variant without our feature. In the discussion segments, we discuss the results and make further recommendations.
Part defect recognition based on 2D and 3D feature combination

Surface defect recognition is used to test product’s quality. The current way of recognition is traditional 2D imagebased method. But 2D image lacks 3D information which results in false inspection and missed inspection, which has become a bottleneck of current classification model. Because of the recent rapid development of 3D measurement technology, we can apply 3D data information in surface defect detection to improve the recognition ability of defects. We propose a new convolutional network model to identify surface defects, and realize the feature depth fusion of 3D point cloud and 2D image in the model. In this work, we introduce an attention network to extract features from a 3D point cloud to generate a 2D attention mask. The high quality feature map is produced by combining the 2D attention mask with a 2D image. We further merge the attention network and the classification network into a single network. The attention network is used to analyze which part of the image should be more concerned by the classification network. Therefore, mutual learning of 2D data and 3D data is realized in the training process, which reduces the dependence on the number of samples and enhances the generalization performance of the model. Experiments on the defect dataset verify that our method can improve the classification effect of the model.