Miracle-dz/UniFAR-FTUs huggingface dataset:
Thyroid Disrupting Activities in Wastewater and Surface Waters: A Review of Their in Vitro Analyses

The developmental and reproductive effects of endocrine disruption on humans and wildlife rank among the most threatening of all environmental health concerns. Particularly vulnerable to chemical assaults are the hypothalamic-pituitary-gonadal (HPG) axis and the hypothalamic-pituitary-thyroid (HPT) axis of the endocrine system. While the effects of endocrine disrupting chemicals (EDCs) on the HPG axis have been the subject of intense research efforts, with comprehensive elucidation, a lot remains to be clarified on the effects of EDCs on thyroid functions. For instance, there are no clear-cut biomarkers of exposure and effects of thyroid disrupting chemicals (TDCs) in intact organisms. Consequently, a number of in vitro assays have been developed, and are particularly useful for the identification and mechanistic characterization of potential TDCs considering the increasing number of EDCs that are being released into the environment. However, with the in vitro assays, studies suggest that a plausible major mode of action of TDCs, thyroid hormone receptor (THR) agonist activity, is not environmentally relevant. Here, we reviewed in vitro detection of TDCs activities in wastewater and surface waters. Data strongly suggest that cell lines may be responsible for the less frequent detection of THR agonist activity in wastewater and surface water. It was concluded that the development of reporter gene assays with thyroid hormone function related cell lines, is required.
Honeywords: The New Approach for Password Security

: Abstract: The traditional systems only use username and password hashes for security. The research in the area of security shows that most of the passwords hashes have possibility of being compromised by hackers. Honeywords (false passwords) generation system is appropriate to protect hashed password files. In our system Honeywords are stored along with the original user password in the password file of the database. If the database is compromised by the hacker the hacker will not be able to recognize the real password. When the hacker tries to login into the system with one of the Honeywords, the system will trigger and redirect the hacker to the fake website and notify the user (via text message and mail).
Mamíferos del departamento de Risaralda, Colombia

Many researchers have documented mammalian diversity in the state of Risaralda; however, the information has not been consolidated nor updated due to changes in nomenclature. To construct the list presented here, a compilation of information from biological collections, scientific literature, and new records was made. The list contains 134 species of mammals from Risaralda, a number which is equivalent to 25 % of the mammal species of Colombia. Of these species, 12 species are endemic for Colombia and eight are in the categories of Critical, Endangered and Vulnerable. Bats are the richest group (59 spp.), and are followed by rodents (33 spp.), which are the group with the highest number of endemic species (8 spp.). Pereira, Santa Rosa de Cabal and Pueblo Rico are the richest municipalities, and are also those where most of the exploration and records are concentrated. In the municipalities Quinchia and Guatica no records with support in scientific collections were found. Possibly, species richness will increase if the sampling effort in other locations are increased. This list will hopefully be useful in guiding future studies as well as management and conservation plans for species and areas.
How and why did I get here? : a narrative inquiry into the paths to teaching of a group of EFL teachers in Hong Kong

The genesis for this exploratory narrative inquiry was high teacher attrition in a new vocational English programme in Hong Kong. The study sought to discover whether the paths to teaching of the remaining, successful teachers, as construed in their own accounts, might include commonalities acting as contributors to success. An understanding of such contributors could inform and enhance the teacher selection process. Seven non-native English speaking teachers of English participated in the study, in which they were interviewed and observed in their classrooms. Polkinghorne’s (1995) framework comprising ‘analysis of narratives’ (thematic analysis) and ‘narrative (storied) analysis’ was adopted to respond to the research aim and questions. Thematic analysis of narratives across participants revealed four pertinent themes: (1) perception of a good fit between person and profession, (2) centrality of the student in the teachers’ accounts (3) perception of teaching as an enjoyable and satisfying ‘virtuous cycle’ related to student achievement and (4) a high level of comfort with English as the medium of communication in the classroom. It was suggested that questions crafted to probe for these characteristics could enhance results in teacher recruitment interviews. Analysis of individual participant narratives using structural and literary techniques buttressed by observation data produced nuanced interpretations of the teachers set against the conceptual template of teacher motivation, career and identity derived from the literature and taking into account their cultural context. Portraits of these teachers add to the very limited literature to date on non-native English speaking teachers of English working in their home countries with students sharing their first language. The teachers’ stories and stories of teachers produced in this study illuminate the lives of these members of the teaching profession. Finally, it is suggested that interrogating success is an appealing approach to uncovering knowledge about teachers and addressing educational problems.
Model-Based Design of Complex Aeronautical Systems Through Digital Twin and Thread Concepts

Managing the lifecycle of the elements of a complex and safety-critical system, from conceptual design to support in operation, is still a relevant challenge in the industrial domain. Starting from research and engineering activities, up to production and delivery, the need of maintaining the trace of dependence, changes, and possible updates regarding the final product is a crucial aspect for success. Particularly, conceptual and preliminary design activities are affected by a high level of uncertainty and a certain degree of project instability, usually caused by the need to explore alternative solutions and variants. Different tasks are performed concerning the identification of system requirements, functional and physical behavior using model-based design (MBD), and model-based systems engineering, which, generally, are subjected to extensive trade studies aimed at identifying the best solution. In this context, this paper presents how model-based digital twin and threads concepts will likely change the way in which the MBD process is managed, overcoming the issues associated with federated IT infrastructures and with tools integration. Notably, lifecycle and nonlifecycle related interoperability aspects are described, with particular focus on the exploitation of standards for lifecycle collaboration and heterogeneous simulation. The design of an ice protection system for a regional aircraft is selected as case study, starting from the work performed by the authors within the EC-funded research project CRYSTAL.
Pluviothermal Regionalization of Poland in Light
of Present-Day Climate Change

This study presents three new regionalizations based on materials from 1971-2000 and from 1981-2010, and for an assumed scenario of a temperature increase of 1oC. Heat resources are expressed in these regionalizations as sums of effective temperatures ≥10oC, and water resources such as Sielianinov’s hydrothermal coefficient K from June to August. An increase was found in the area of the moderately warm region with a sum of temperatures ≥10oC from 61% in 1931-1960 to 62% in 1971-2000, 87% in the 30-year period 1981-2010, and 73% for the scenario with the assumed 1oC temperature increase. In the last case, a new thermal region appeared: a warm region with a sum of temperatures ≥10oC in the range of 2800-3200oC, covering almost the entire remaining area of the country. As warming progresses, in the absence of clear tendencies for atmospheric precipitation, the climate in Poland is becoming increasingly dry. Areas classified as dry with a hydrothermal coefficient of 1.0-1.3 increased from 13% of the area of the country in 1931-1960 to 20% in the 30-year period 1971-2000, 46% in the 30-year period 1981-2010, and 65% for the scenario with a 1oC temperature increase.
Effect of drying process of esterified plantain starch on sorption, thermodynamic and shelf-life characteristics

Abstract The plantain starch was modified with octenylsuccinic anhydride (OSA) and spray and convection dried. The degree of substitution (DS), the sorption and thermodynamic characteristics were evaluated. The drying process affected the DS (0.009 and 0.015 to spray and convection drying, respectively). The monolayer moisture content was 7.46–9.01 and 7.33–8.04 g H2O 100 g−1 (db) to convection and spray dried OSA-plantain starch, respectively. The isokinetic temperature was ∼255 K, less than harmonic temperature (308 K), indicating that the water adsorption was entropy driven. The free energy showed that adsorption process was spontaneous. The shelf life of convection and spray dried OSA-starch was 434–1566 days to PET/FOIL/PE and 64–616 days to polypropylene packing. Thermodynamic differences in the adsorption process of OSA-plantain starch influenced by the drying method were not found. Spray-drying process could be used for the OSA-plantain starch production without altered the thermodynamic and transport properties of the amphiphilic material. Graphical Abstract
ConnSpoiler: Disrupting C&C Communication of IoT-Based Botnet Through Fast Detection of Anomalous Domain Queries

The development of Internet of Things (IoT) dramatically facilitates the integration of computing systems with the physical world. However, as IoT devices are more easy to compromise than desktop computers, cybercriminals have founded IoT-based botnets to launch Distributed Denial of Service (DDoS) attacks with unprecedented traffic volume. To mitigate the damages associated with these attacks, the detection of IoT-based botnet has to preempt the command and control (C&C) communication to prevent the delivery of the attack codes. Motivated by the extensively implementation of domain generation algorithm in botnets, in this article, we propose ConnSpoiler, a lightweight system that detects IoT-based botnets by identifying the stream of algorithmically generated domains (AGDs) in a fast way. ConnSpoiler only needs negligible system resources to take effect and thus can execute well on the resource-restraint IoT devices. By outfitting a powerful statistical algorithm, i.e., threshold random walk, ConnSpoiler has a high probability (about 94%) of detecting infection before the compromised devices connect C&C servers, which can help to prevent the succeeding attacks. Moreover, ConnSpoiler only requires the benign domains to take effect and therefore does not need extra effort to label malicious samples for training phase. We evaluate ConnSpoiler based on real-world DNS traffics collected from two different large ISP networks and show that it accurately identifies devices that are compromised by unknown botnets.
EgoCart: A Benchmark Dataset for Large-Scale Indoor Image-Based Localization in Retail Stores

We consider the task of localizing shopping carts in a retail store from egocentric images. Addressing this task allows to infer information on the behavior of the customers to understand how they move in the store and what they pay more attention to. To study the problem, we propose a large dataset of images collected in a real retail store. The dataset comprises 19, 531 RGB images along with depth maps, ground truth camera poses, as well as class labels specifying the areas of the store in which each image has been acquired. We release the dataset to the public to encourage research in large-scale image-based indoor localization and to address the scarcity of large datasets to tackle the problem. We hence perform a benchmark of several image-based localization techniques exploiting images and depth information on the proposed dataset. In our study, both localization performances and space/time requirements are compared. The results show that, while state-of-the-art approaches allow to achieve good results, there is space for improvement.
A SVM Multi-Class Image Classification Method Based on DE and KNN in Smart City Management

When directly operated in an image, good results are always difficult to achieve via conventional methods because they have poor high-dimensional performance. Support vector machine (SVM) is a type of machine learning method with solid foundation that is developed based on traditional statistics. It is also a theory for statistical estimation and predictive learning of objects. This paper optimizes the structure of SVM classification tree with differential evolution (DE) and designs the corresponding DE algorithm to effectively solve the problem of image classification of complex background cases in smart city management systems. In the training process of SVM classification tree, it obtains an optimal two-class classification scheme in every node by means of DE, initially separates the classes that are easy to be separated and then the less easy ones, and finally adaptively generates the best classification tree. The simulation experiment proves that the proposed algorithm is effective when applied to smart city management systems.
Multi-GPU sliding tile puzzle solving with GA*, Groute and Abstract Zobrist Hashing

The A* algorithm has been a key search algorithm and part of AI literature for a long time. It has applications in computational biology, natural language processing, pathfinding, puzzle solving, and more. The complexity of some of these applications has brought about many improvements and variations of the original algorithm, and recent research has shown that using GPUs to accelerate A* search can achieve substantial speed-ups over conventional CPU-based implementations. Compute nodes with multiple GPUs have become commonplace, and techniques and methods for programming such systems is a popular area of research. We bring to life the first implementation of the A* search algorithm that distributes the search space across multiple GPU devices, and identify challenges that must be addressed in order to achieve appreciable performance. Our implementation is based on the GA* algorithm of Zhou and Zeng, and builds on the asynchronous multi-GPU programming framework Groute by Ben-Nun, Sutton, Pai, and Pingali. We use Abstract Zobrist Hashing for distributing the search space across GPUs. The implementation is benchmarked by solving sliding tile puzzles, a commonly used benchmark for search algorithms. Our multi-GPU version of A* allows for solving more difficult search problems than a single GPU can handle by utilizing the increased memory capacity of multiple GPUs. However, it does not in general yield improved performance in terms of wall-clock runtime when increasing the number of GPUs used. We propose how to eliminate much of the overhead introduced when scaling horizontally, and show that if this is done successfully, each additional GPU added may yield a search rate increase of at least 50% of the performance of a lone GPU.
Building Large-Vocabulary ASR Systems for Languages Without Any Audio Training Data

When building automatic speech recognition (ASR) systems, typically some amount of audio and text data in the target language is needed. While text data can be obtained relatively easily across many languages, transcribed audio data is challenging to obtain. This presents a barrier to making voice technologies available in more languages of the world. In this paper, we present a way to build an ASR system system for a language even in the absence of any audio training data in that language at all. We do this by simply re-using an existing acoustic model from a phonologically similar language, without any kind of modification or adaptation towards the target language. The basic insight is that, if two languages are sufficiently similar in terms of their phonological system, an acoustic model should hold up relatively well when used for another language. We describe how we tailor our pronunciation models to enable such re-use, and show experimental results across a number of languages from various language families. We also provide a theoretical analysis of situations in which this approach is likely to work. Our results show that it is possible to achieve less than 20% word error rate (WER) using this method.
EVALUATION OF HTML TAG SUSCEPTIBILITY TO STATISTICAL FINGERPRINTING FOR USE IN CENSORSHIP EVASION

EVALUATION OF HTML TAG SUSCEPTIBILITY TO STATISTICAL FINGERPRINTING FOR USE IN CENSORSHIP EVASION Kelly Scott French, M.Sc. The University of Texas at Arlington, 2016 Supervising Professor: Matthew Wright The ability to speak freely has always been a source of conflict between rulers and the people over which they exert power. This conflict usually takes the form of State-sponsored censorship with occasional instances of commercial efforts typically to silence criticism or squelch dissent, and people’s efforts to evade such censorship. This is even more so evident in the current environment with its ever-growing number of communication technologies and platforms available to individuals around the world. If the face of efforts to control communication before it is posted or to prevent the discovery of information that exists outside of the control of the authorities, users attempt to slip their messages past the censor’s gaze by using keyword replacement. These methods are effective but only as long as those synonyms are not identified. Once the new usage is discovered it is a simple matter to add the new term to the list of black-listed words. While various methods can be used to create mappings between blocked words and their replacements, the difficulty is doing so in a way that makes it clear to a human reader how to perform the mapping in reverse while maintaining readability but without attracting undue attention from systems enforcing the
Quality performance in human services : leadership, values, and vision

The cornerstone of truly effective and responsive human services is quality -- quality that stems from dynamic leadership and a focus on personal outcomes. This forward-thinking book shows how it's done by presenting a leadership model that is person centered, performance driven, and, best of all, achievable. The authors -- leaders in disability services and business management -- offer practical tips for boosting quality levels in any workplace. Convenient summary checklists and ready-to-use exercises help service providers, administrators, and staff training specialists work through contemporary challenges in the fields of social work, special education, and mental health. Promoting effective leadership by example, this essential guide provides a new framework for leading and managing human services organizations.
FACULTY OF INFORMATION TECHNOLOGY AND ELECTRICAL ENGINEERING DEGREE PROGRAMME IN WIRELESS COMMUNICATIONS ENGINEERING

High degree of flexibility, customization and the rapid deployment methods are needed in future communication systems required by different vertical sectors. These requirements will be beyond the traditional mobile network operators’ offerings. The novel concept called micro-operator enables a versatile set of stakeholders to operate local 5G networks within spatially confined environment with a guaranteed quality and reliability to complement mobile network operators’ offerings. To enable the case specific requirements of different stakeholders, micro-operator architecture should be tailored to cater such requirements, so that the service is optimized. The novel micro-operator architecture proposed in this thesis using 5G access and core network functions, serves the communication needs of an Industry 4.0 environment having three use cases namely augmented reality, massive wireless sensor networks and mobile robots. Conceptual design of the proposed architecture is realized using simulation results for latency measurements, relating it with the results of a mobile network operator-based deployment. Latency analysis is carried out with respect to the core network distance and the processing delay of core network functions. Results demonstrate the advantages of the micro-operator deployment compared with mobile network operator deployment to cater specialized user requirements, thereby concluding that the micro-operator deployment is more beneficial.
Identifying Influential Nodes in Complex Networks Based on Local Neighbor Contribution

The identification of influential nodes in complex networks has been widely used to suppress rumor dissemination and control the spread of epidemics and diseases. However, achieving high accuracy and comprehensiveness in node influence ranking is time-consuming, and there are issues in using different measures on the same subject. The identification of influential nodes is very important for the maintenance of the entire network because they determine the stability and integrity of the entire network, which has strong practical application value in real life. Accordingly, a method based on local neighbor contribution (LNC) is proposed. LNC combines the influence of the nodes themselves with the contribution of the nearest and the next nearest neighbor nodes, thus further quantifying node influence in complex networks. LNC is applicable to networks of various scales, and its time complexity is considerably low. We evaluate the performance of LNC through extensive simulation experiments on seven real-world networks and two synthetic networks. We employ the SIR model to examine the spreading efficiency of each node and compare LNC with degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, PageRank, Hyperlink-Induced Topic Search(HITS), ProfitLeader, Gravity and Weighted Formal Concept Analysis(WFCA). It is demonstrated that LNC ranks nodes effectively and outperforms several state-of-the-art algorithms.
Scalable unified data analytics

The volume of data that is being generated each day is rising rapidly. There is a need to analyze this data efficiently and produce results quickly. Data science offers a formal methodology for processing and analyzing data. The goal of processing and analyzing data is to produce actionable insights from this data. The insight gathered from the data enables individuals and businesses to make informed and important decisions. Data science involves a workflow with multiple stages, such as data collection, data wrangling, statistical analysis, and machine learning. The first part of this thesis evaluates data analytics systems that support the data science work-flow by introducing a data science benchmark, Sanzu. Further, we believe that data analysts and scientists would want to use a single system that can perform both data analysis tasks and SQL querying, without requiring data movement between different systems. Ideally, this system would have adequate performance, scalability, built-in data analysis functionality, and usability. Currently, relational databases and big data frameworks are used for SQL querying and complex data analysis respec-