Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Travel Time Data Collection Techniques: A Statistical Analysis

Measuring traffic congestion is a key element of Congestion Management Systems (CMS) and is utilized to document traffic information and make decisions regarding existing traffic congestion problems. With the improvement of Global Positioning Systems (GPS) technologies, amongst other Intelligent Transportation Systems (ITS) components, various data collection procedures can be performed more efficiently. This paper examines travel time data collection methods. Data collection approach is introduced and a non-parametric statistical analysis of three different data collection methods, namely GPS, DMI (Distance Measuring Instrument), and MAN (manual), is conducted. The statistical analysis shows that the GPS approach is more consistent in terms of accuracy than the other two methods. The results also indicate that for short travel distances and trips without any delay, the three methods are accepted as being equal with a 95% confidence level. However, segment with different characteristics need to be investigated further through more data collection and analysis.
Security of 5G-Mobile Backhaul Networks: A Survey

The rapid involution of the mobile generation with incipient data networking capabilities and utilization has exponentially increased the data traffic volumes. Such traffic drains various key issues in 5G mobile backhaul networks. Security of mobile backhaul is of utmost importance; however, there are a limited number of articles, which have explored such a requirement. This paper discusses the potential design issues and key challenges of the secure 5G mobile backhaul architecture. The comparisons of the existing state-of-the-art solutions for secure mobile backhaul, together with their major contributions have been explored. Furthermore, the paper discussed various key issues related to Quality of Service (QoS), routing and scheduling, resource management, capacity enhancement, latency, security-management, and handovers using mechanisms like Software Defined Networking and millimeter Wave technologies. Moreover, the trails of research challenges and future directions are additionally presented.
State of the Art Intrusion Detection System for Cloud Computing

The term Cloud computing is not new anymore in computing technology. This form of computing technology previously considered only as marketing term, but today Cloud computing not only provides innovative improvements in resource utilisation but it also creates a new opportunities in data protection mechanisms where the advancement of intrusion detection technologies are blooming rapidly. From the perspective of security, Cloud computing also introduces concerns about data protection and intrusion detection mechanism. This paper surveys, explores and informs researchers about the latest developed Cloud Intrusion Detection Systems by providing a comprehensive taxonomy and investigating possible solutions to detect intrusions in cloud computing systems. As a result, we provide a comprehensive review of Cloud Intrusion Detection System research, while highlighting the specific properties of Cloud Intrusion Detection System. We also present taxonomy on the key issues in Cloud Intrusion Detection System area and discuss the different approaches taken to solve the issues. We conclude the paper with a critical analysis of challenges that have not fully solved.
A versatile building block: the CMOS differential difference amplifier

An extension of the op-amp concept featuring two differential inputs is presented. In a closed-loop environment this circuit forces two floating voltages to the same value, and thus has many interesting applications in the analog circuit domain. A description of such a circuit, its nonidealities, and restrictions are given. A monolithic integration of this differential difference amplifier is implemented in a double-poly CMOS technology, its measured characteristics are described. Many applications of this circuit, including a voltage comparator with floating inputs, a voltage inverter without resistors, and an instrumentation amplifier with only two external gain determining resistors, are discussed.
Building Phrase Polarity Lexicons for Sentiment Analysis

Many approaches to sentiment analysis benefit from polarity lexicons. Most polarity lexicons include a list of polar (positive/negative) words, and sentiment analysis systems attempt to capture the occurrence of those words in text using polarity lexicons. Although there exist some polarity lexicons in many natural languages, most languages suffer from the lack of phrase polarity lexicons. Phrases play an important role in sentiment analysis because the polarity of a phrase cannot always be estimated based on the polarity of its parts. In this work, a hybrid approach is proposed for building phrase polarity lexicons which is experimented on Turkish as a low-resource language. The obtained classification accuracies in extracting and classifying phrases as positive, negative, or neutral, approve the effectiveness of the proposed methodology.
Removing Unclassified Hand Tremor Motion from Computer Mouse Input with Neural Networks

An artificial neural network based filter to remove unwanted tremor-induced motion in computer mouse input is presented and tested. A method to efficiently capture appropriate training data is shown to be important in the operation and training of the neural network filter. The architecture of the neural network as well as the numerous design choices are presented and explained. A simulation study proves the artificial neural network is successful at removing a simulated Parkinson’s tremor from computer mouse movements even with minimal training data. Resulting tremor-free motion estimated by the artificial neural network is shown to be similar to normal tremor free computer mouse movements.
Risk identification model of unconventional crisis based on immune system

The risk identification of unconventional crisis is one of the core scientific problems of the emergency management. Due to the current situation of frequently facing a baseless simulation in academic study and practical application,risk identification has become a difficult issue that needs to be addressed as a matter of urgency. By integrating immunology theory,complex adaptive system theory and the computational experiment,this paper proposes a theory for the evolution from the antibody of risk recognizer into the antigen of unconventional crisis,and builds a standard system and a model for identification of the affinity of risk identification,the cleanup effect of antigen,the amount and energy of the risk recognizers and so on. It adopts a research idea of identifying sub-unconventional crisis and the syntagmatic relation among various events and its emergency to understand the unconventional crisis infinitely. By computational experiments and illustrative examples,the feasibility is uerified.
Social enterprises in quasi-markets: exploring the critical knowledge gaps

Purpose This paper aims to explore how social enterprises are treated in scholarly research on quasi-markets. In so doing, the paper aims to show that a number of critical knowledge gaps persist which require deeper engagement from researchers, practitioners and policymakers alike. Design/methodology/approach This study adopts a conceptual analysis of the existing literature concerning social enterprises and quasi-markets. Findings The paper finds that there are four main knowledge gaps in this area. First, there are moral dilemmas created by boundary shifts, arising from the development of quasi-markets. Second, the phenomenon of “tactical mimicry” (Day and Teasdale 2016) represents a key theoretical platform not yet fully explored. Third, the lack of clear, comparative assessments of social enterprises across quasi-markets, and other types of service providers is also apparent despite offering a significant methodological opportunity for scholars. Fourth, there is the issue of how social enterprises engage in, and resource the operational functions that will support their management of conflicting logics, especially rigorous impact measurement. Originality/value This paper uses a synthesis of key social enterprise and quasi-market studies to extend current debate in this area, which tends to be diffused and complex. By focussing on critical knowledge gaps, the paper contributes a meta-level appraisal of the key areas for future research, providing a focussed agenda for scholars to target their efforts in growing this important body of knowledge.
Sifting the Network: Performing Packet Triage with NFR

We describe a set of Network Flight Recorder scripts designed to detect network intrusions. After developing scripts that detect some known attacks, we focus on sifting scripts that attempt to remove "normal" traffic from a packet stream, leaving behind fewer packets requiring manual inspection. We conclude with a description of our experiences with the NFR product.
MDG-NoSQL : modelo de dados para bancos NoSQL baseados em grafos

Currently, Graph Databases are becoming popular along with other NoSQL initiatives. Thus, researchers and companies have been developing data models to manipulate information as graphs However, there is no standard notation involves several features and structures of the graph databases. This model introduces several notations and concepts for building a new graph data model, called Data Model for NoSQL Graph Databases (GDM-NoSQL). The GDM-NoSQL was veri ed through a database for investigate relationships between companies and people as well as information about the procurements that these companies participated in with the Federal Government. This database was designed to facilitate the process of searching for frauds and to be used on multiple contexts, i.e. several di erent national agencies. Finally, the designed model was implemented in both a relational and a graph database in order to validate the hypothesis that writing relationships is simpler and more e cient in graph models than relational databases.
Influence of Marketing Mix to Customer Loyalty in Sari Anggrek Store

This study aims to find the effect of 7P marketing mix on customer loyalty on Sari Anggrek Bookstore in Padang city. This type of research is Expost-Facto, the study population is the customer of Sari Anggrek bookstore. The purposive sampling technique of sampling is used. The sample amounted to 384 people. Primary data is collected through questionnaires using a Likert scale model that has been tested for validity and reliability. The data analysis technique used is Multiple Regression Analysis. The results of the research are: 1) there is a positive and significant effect of the product on customer loyalty. 2) there is a positive and significant price effect on customer loyalty. 3) there is a positive and significant influence of place / location on customer loyalty. 4) there is a positive and significant influence on promotion of customer loyalty. 5) there is a positive and significant influence of people / employees on customer loyalty. 6) there is a positive and significant effect of physical evidence on customer loyalty. 7) there is a positive and significant influence on the process of customer loyalty. The results of the analysis show that the value of R2 (R-Square) is 0.801, this means the magnitude of the influence of the product, price, place / location, promotion, person / employee, physical evidence and the process of customer loyalty Sari Anggrek bookstore is 80.1% and the remaining 19.9% is influenced by other variables not included in the research model.
MULTI-MODE DATA SECURITY FOR SECRET COMMUNICATION

Data Security in the current digitized era determines the ability of the system to protect, manage and distribute sensitive information. Privacy is the right of authorized users to ensure that confidential information maintained is being controlled and protected from unauthorized access or distribution. The major goals of data security are Confidentiality, Integrity, and Authentication. Text based passwords are the most common methods used for authentication. But these textual passwords are vulnerable to dictionary attacks, eves dropping and shoulder surfing. To address this problem, an approach for image based authentication has been proposed to authenticate a valid user with regards to enhancing the security of textual passwords which can be easily compromised. In this paper, algorithms for Image Cryptography, Image Steganography and Knowledge based Image CAPTCHA have been proposed to provide a one time communication. The image will be encrypted into two image shares at the server during password generation and the share returned to the client will be used as a token during authentication. At no time the entire password will be transmitted over the network or stored on any system during authentication. This hybrid multimode approach attempts to lessen the attacks towards password security thereby minimizing the risks of user impersonification and providing efficient means to transfer secret data using images. The proposed approach is well suited for private networks to transfer confidential messages without compromising the integrity of data. Keywords—authentication; visual cryptography; password; steganography; CAPTCHA -------------------------------------------------------------------***-------------------------------------------------------------------
Map operation method with multi touch interface in Focus+Glue+Context map

Map operation method with multi touch interface in Focus+Glue+Context map Atsushi HOTTA†, Daisuke YAMAMOTO††, and Naohisa TAKAHASHI†† † Department of Computer Sience, Nagoya Institute of Technology Gokiso,Syouwa,Nagoya,Aichi 466–8555 Japan †† Department of Computer Sience and Engineering Graduate School of Engineering, Nagoya Institute of Technology Gokiso,Syouwa,Nagoya,Aichi 466–8555 Japan E-mail: †hotta@moss.elcom. nitech.ac. jp, ††{yamamoto.daisuke,naohisa}@nitech.ac. jp
Design for a Brain

During the past 20 years, research in the behavioral sciences has become increasingly sophisticated. New findings concerning the brain and behavior have forced upon us the unpleasant necessity of reexamining our models. Concepts once useful, such as "the unconscious," "regression," and "motivation," no longer serve to adequately encompass the data. Ashby is a representative of this new order of sophistication in research, as he proceeds to investigate the problem of the brain's orderliness. He accepts, as fact, that the nervous system behaves adaptively and assumes that it is, in its essentials, mechanistic. These two data are approached as not irreconcilable. From this point, he proceeds to examine dynamic systems from the standpoint of rigorous scientific —dominantly mathematical—logic until he has evolved a theoretical counterpart of brain functioning. He then builds and experiments with a prototype electronic model, which he names the "Homeostat," and tests various aspects of his theories of
Infectious Diseases of the Dog and Cat

Infectious Diseases of the Dog and Cat, by Craig E. Greene. Published by Elsevier, 2006, 3rd edition, hardback, 1424 pages, Price £73.99, ISBN‐10: 1416036008, ISBN‐13: 978‐1416036005
EARLY VS. ADVANCED OSTEOARTHRITIS FROM A RADIOLOGICAL ASPECT

bolesti, te hemoglobinopatija, kao što je anemija srpastih stanica, talasemija, neuroartropatija koje vode Charcotovoj artropatiji ili siringomijeliji, tabesu i dijabetesu. Unatoč svim terapijskim dostignućima nema efikasnog lijeka za OA, što se odražava u nedostatku potpunog shvaćanja patofiziologije i prirodnog procesa. Upalne promjene u OA vjerojatno su sekundarne; prvo nastanu promjene u hrskavici i subhondralnoj kosti, zato se i smatra da to nije pasivna degenerativna bolest, nego aktivan proces koji pokreće mehanički čimbenik. Primarni i sekundarni OA imaju istu patohistološku osnovu. Bilateralna i simetrična afekcija nađe se uvijek u primarnom OA, posebice kada su zahvaćene šake. U OA prve promjene se nađu na artikulacijskoj hrskavici sinovijalnih zglobova, a promjene nastanu i na sinovijskoj tekućini, kao i na subhondralnoj kosti, na kapsuli zgloba i ostalom zglobnom staničju.
Beyond Antimicrobials: A Review of Adjunctive Therapies for Pressure Ulcers in Older Adults

Abstract: Pressure ulcers are a major source of morbidity and mortality for older adults, especially frail elders who may be malnourished and have significant comorbid conditions. Successful treatment of pressure ulcers requires coordinated efforts in wound care, pressure relief, and optimization of medical conditions that impact healing, such as diabetes mellitus and congestive heart failure. The role of newer therapies such as growth factors, artificial skin equivalents, and vacuum-assisted closure is growing. Infectious disease physicians should develop a working knowledge of these treatment modalities and the potential advantages and disadvantages of each.
BASE DEFICIT IN THE ELDERLY

Background: Base deficit has been used as a marker of significant injury and to predict resource utilization and mortality. The significance of base deficit in trauma patients 55 years and older has not been specifically evaluated. The purpose of this study was to determine the utility of base deficit in assessing older trauma patients versus a younger cohort. Methods: Data were obtained from the trauma registry on trauma patients admitted to a Level I trauma center. Arterial blood gases were obtained within 1 hour of arrival, by protocol, in 2,631 patients, and of these, 274 patients were 55 years or older. Data are presented as means ± SEM. Statistical analysis was done by paired t test, analysis of variance, and X 2 analysis. Significance was attributed to a p value < 0.05. Results: Patients older than 55 years were significantly more likely to have sustained blunt trauma (86 vs. 69%; p < 0.001). Despite similar Injury Severity Scores and base deficit values, older patients had markedly greater mortality and intensive care unit lengths of stay. A base deficit of ≤ -6 had positive predictive values for Injury Severity Scores ≥ 16 for 76% of patients younger than 55 years and 78% of patients 55 years and older. The negative predictive value of a normal base deficit for Injury Severity Scores < 16 was 60% for the younger cohort and only 40% for patients 55 years and older (p < 0.001; X 2 ). Conclusions: A base deficit of ≤ -6 is a marker of severe injury and significant mortality in all trauma patients, but it is particularly ominous in patients 55 years and older. Patients older than 55 years may have significant injuries and mortality risk without manifesting a base deficit out of the normal range.
Speckle filtering of SAR images: a comparative study between complex-wavelet-based and standard filters

We present a comparative study between a complex Wavelet Coefficient Shrinkage (WCS) filter and several standard speckle filters that are widely used in the radar imaging community. The WCS filter is based on the use of Symmetric Daubechies wavelets which share the same properties as the real Daubechies wavelets but with an additional symmetry property. The filtering operation is an elliptical soft- thresholding procedure with respect to the principal axes of the 2D complex wavelet coefficient distributions. Both qualitative and quantitative results (signal to mean square error ratio, equivalent number of looks, edgemap figure of merit) are reported. Tests have been performed using simulated speckle noise as well as real radar images. It is found that the WCS filter performs equally well as the standard filters for low-level noise and slightly outperforms them for higher-level noise.
Cost-sensitive linear regression with costly features

Linear regression models, some of the most widely used and well-studied models for regression, often assume that all features can be evaluated without difficulty, while in many real-world regression scenarios this assumption simply does not hold. For example, in a medical domain features used in linear regression may correspond to the results of various diagnostic tests which have a cost and incur discomfort to the patient, or in a financial setting a regression model often requires evaluating information-gathering features that incur time and monetary costs. These and related problems can be formalized more precisely with the notion of feature costs. Assuming that prediction error and feature costs can be measured in commensurable units, we can reformulate our linear regression problem w.r. t. a parsimonious objective criterion: clearly there is no benefit in reducing error by using more features if the financial advantage gained by obtaining more accurate predictions does not outweigh the financial cost.
Sixty years of diabetes management in primary care

The incidence of diabetes has increased exponentially over the last 60 years, meaning that the management of diabetes solely by specialist healthcare professionals is no longer feasible. Since the 1970s, primary and community healthcare professionals have increasingly treated patients with diabetes. Advances in diabetes equipment and new treatments have further enabled patients to be treated more conveniently in the community and this has enhanced their quality of life. There has also been an evolution in health service strategies for diabetes – notably growing acknowledgement of the benefits of intensive glycaemic treatment for patients with type 2, as well as type 1 diabetes, and the now well-recognised importance of effective shared care programmes between primary and secondary healthcare professionals. Thus, the organisation and delivery of care for patients with diabetes has improved dramatically since 1952. Br J Diabetes Vasc Dis 2012;12:315-320.
Hierarchical multi-label classification for tree and DAG hierarchies

The core of supervised classification consists in assigning to an object or phenomenon one of a previously specified set of categories or classes. There are more complex problems where, instead of a single label, a set of labels are assigned to each instance, this is called multi-label classification. When the labels in a multi-label classification problem are ordered in a predefined structure, typically a tree or a Direct Acyclic Graph (DAG); the task is called Hierarchical Multi-label Classification (HMC). There are HMC methods that create a global model which take advantage of the relations (predefined structure) of the labels. However these methods tend to create too complex models unusable for large scale data. Other methods divide the problem in a set of subproblems, which usually does not benefit from the predefined structure. This thesis addresses the problem of hierarchical classification for tree and DAG structures considering large datasets with a considerable number of labels. A local classifier per parent node is trained for each non-leaf node in the hierarchy. Our method exploits the correlation of the labels with its ancestors in the hierarchy and evaluates each possible path from the root to a leaf node, taking into account the level of the predicted labels to give a score to each path and finally return the one with the best score. In some cases there are instances whose labels do not reach a leaf node, for this cases we developed an extension of the base method for Non Mandatory Leaf Node Prediction (NMLNP); in which a pruning phase is performed before selecting the best path. We noticed that many evaluation measures scored the short paths that only predict the most general cases better than longer more specific paths, that is why we also propose a new evaluation measure that avoids the bias toward conservative predictions in the case of NMLNP. We tested our methods with 18 datasets with tree and DAG structured hierarchies against a number of state-of-the-art methods. The evaluation shows the advantages of these methods, in terms of predictive performance, execution time and scalability compared with other methods for hierarchical