Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel conversion scheme from a redundant binary number to two's complement binary number for parallel architectures

This paper introduces a novel direct conversion method to convert a redundant binary number to two's complement number. The method is based on the rules developed in this paper and is used to remove carry propagation during conversion. Therefore, the conversion process is truly parallel and the latency is fixed to 1 digit conversion time regardless of the word size. This converter receives one redundant binary number and converts it into one two's complement number. A redundant binary number is converted into two redundant binary numbers by using the rules. The new converted numbers are then added with a redundant binary number adder to produce a final sum. The last step is to convert the final sum into its correct two's complement number.
Electronic messaging and shared context in an ongoing work group

Using an in-depth multi-method field study of an ongoing workgroup, the author explored when and why electronic messaging (EM) was chosen by group members, and how that choice related to communication effectiveness. EM was effective when shared interpretive context was well-established, Where context was lacking, rich face-to-face communication was more effective. Additionally, communication was more effective when communicators chose the expected mode of communication in compliance with the group's communication procedures and habits. <<ETX>>
Pisces: A Scalable and Efficient Persistent Transactional Memory

Persistent transactional memory (PTM) programming model has recently been exploited to provide crashconsistent transactional interfaces to ease programming atop NVM. However, existing PTM designs either incur high reader-side overhead due to blocking or long delay in the writer side (efficiency), or place excessive constraints on persistent ordering (scalability). This paper presents Pisces, a read-friendly PTM that exploits snapshot isolation (SI) on NVM. The key design of Pisces is based on two observations: the redo logs of transactions can be reused as newer versions for the data, and an intuitive MVCC-based design has read deficiency. Based on the observations, we propose a dual-version concurrency control (DVCC) protocol that maintains up to two versions in NVMbacked storage hierarchy. Together with a three-stage commit protocol, Pisces ensures SI and allows more transactions to commit and persist simultaneously. Most importantly, it promises a desired feature: hiding NVM persistence overhead from reads and allowing nearly non-blocking reads. Experimental evaluation on an Intel 40-thread (20-core) machine with real NVM equipped shows that Pisces outperforms the state-of-the-art design (i.e., DUDETM) by up to 6.3× for micro-benchmarks and 4.6× for TPC-C new order transaction, and also scales much better. The persistency cost is from 19% to 50% for 40 threads.
Open morphology of Finnish

Omorfi is free and open source project containing various tools and data for handling Finnish texts in a linguistically motivated manner. The main components of this repository are: 1) a lexical database containing hundreds of thousands of words (c.f. lexical statistics), 2) a collection of scripts to convert lexical database into formats used by upstream NLP tools (c.f. lexical processing), 3) an autotools setup to build and install (or package, or deploy): the scripts, the database, and simple APIs / convenience processing tools, and 4) a collection of relatively simple APIs for a selection of languages and scripts to apply the NLP tools and access the database
Three Approaches to Align Business Direction and Information Systems and Processes in an Australian Organisation

Alignment of an organisation’s business direction and its information systems and processes is important to its business success. This study investigated three alignment approaches within the same organisation: One-Off Alignment; Continuous Alignment and Project Management. Using an interpretivist, qualitative approach, with the researcher embedded in the each of the case study engagements, it was determined that OneOff Alignment was an effective approach for business diversification while Continuous Alignment was effective for evaluating business driven IT initiatives. The Project Management discipline and the establishment of a projects office supported continuous alignment to business strategies. The study concluded that there was strong evidence for expected Defender and Prospector organisational characteristics and that different alignment approaches can be successful depending on the context. This was confirmed by the success of three different alignment approaches within the same organisation during the same time period.
Körperliche Aktivität und Prävention der Alzheimerdemenz

: There is accumulating evidence from animal and epidemiologic studies that physical exercise is neuroprotective in healthy animals and humans and can prevent cognitive decline in chronic neurodegenerative processes like Alzheimer's dementia. However, data from well-designed interventional, randomized non-pharmacologic trials is lacking in contrast to other areas of medicine like prevention of hypertension, diabetes or the antipsychotic-associated metabolic syndrome. The demonstration of a potential positive effect of physical exercise on preventing dementia using a controlled study design would represent a significant progress in the prevention of dementia and public health, especially as long as other treatments for dementia prevention are lacking.
Spatiotemporal traffic matrix prediction: A deep learning approach with wavelet multiscale analysis

Network traffic analysis has always been a key technique for operating and managing a network. However, due to some (non) technical issues, it is not trivial to directly obtain network‐wide traffic data. Although a large number of traffic matrix (TM) prediction methods have been used to obtain future network‐wide traffic, they achieve somewhat limited accuracy due to neglecting spatiotemporal evolution features of TM series at different time scales. In order to improve the performance of TM prediction, we propose a novel end‐to‐end deep neural network based on wavelet multiscale analysis, called WSTNet. In this network, the original TM series is first decomposed into multilevel time‐frequency TM subseries at different time scales by using discrete wavelet decomposition, and then the convolutional neural network without pooling is used to extract the spatial patterns among traffic flows, and finally, the long short‐term memory neural network with a self‐attention mechanism by relating different positions of input sequences across entire time steps is employed to explore the temporal evolution features within TM series. To investigate the performance of our proposed model, extensive experiments are conducted on two real network traffic data sets from the Abilene and GÉANT backbone networks. The results show that WSTNet is significantly better than the other four state‐of‐the‐art deep learning methods.
UGV Direction Control by Human Arm Gesture Recognition via Deterministic Learning

In this paper, we present a novel method for controlling an unmanned ground vehicle (UGV) by using a new machine learning technique, called deterministic learning [1], to learn and recognize four specifically designed body languages, which represent four corresponding moving directions (i.e., left, right, up, and down) of the controlled UGV. The Microsoft Kinect sensor is employed to collect the human body skeleton data, from which four specifically-designed features are extracted for neural network training. The discrete-time deterministic learning algorithm is then utilized to train the radial basis function neural networks (RBFNNs). The dynamics of the human arm waving motion is guaranteed to be accurately identified, represented, and stored as a RBF NN model with converged constant NN weights. In the testing phase, a set of estimators are built based on the database established in the learning phase, so as to conduct real-time rapid recognition of new in-coming gesture commands. The smallest error principle is used to decode the human intention, the decoded result will then be sent to the UGV through TCP/IP to control the UGV’s moving directions. A full-integrated graphical user interface (GUI) has been developed based on Matlab to demonstrate effectiveness of the proposed approach and visualize the experimental results.
A Model for Processing Skyline Queries over a Database with Missing Data

Skyline queries provide a flexible query operator that returns data items (skylines) which are not being dominated by other data items in all dimensions (attributes) of the database. Most of the existing skyline techniques determine the skylines by assuming that the values of dimensions for every data item are available (complete). However, this assumption is not always true particularly for multidimensional database as some values may be missing. The incompleteness of data leads to the loss of the transitivity property of skyline technique and results into failure in test dominance as some data items are incomparable to each other. Furthermore, incompleteness of data influences negatively on the process of finding skylines, leading to high overhead, due to exhaustive pairwise comparisons between the data items. This paper proposed a model to process skyline queries for incomplete data with the aim of avoiding the issue of cyclic dominance in deriving skylines. The proposed model for identifying skylines for incomplete data consists of four components, namely: Data Clustering Builder, Group Constructor and Local Skylines Identifier, k-dom Skyline Generator, and Incomplete Skylines Identifier. Including these processes in the proposed model has optimized the process of identifying skylines in incomplete database by reducing the necessary number of pairwise comparison through eliminating the dominated data items as early as possible before applying the skyline technique.
An experimental study of the effectiveness of different techniques of questioning child witnesses

Two experiments were conducted to investigate the effects upon accuracy of recall of different techniques of obtaining evidence from 10- and 11-year-old children about a previously witnessed incident. The first experiment studied three techniques: (a) free report, (b) use of general questions, and (c) use of specific questions. It was expected that the free report method would produce the most accurate but least complete initial recall, but that this lack of completeness might have a detrimental effect upon the accuracy of later recall. The free report technique did produce highly accurate, although rather incomplete recall, but initial use of this technique did not have any detrimental effect upon later, more structured recall. The second experiment investigated the sole use of the free report technique over a 2-month period. It was expected that a high level of accuracy would be achieved at all recall sessions. The effect upon accuracy of recall of a delay of 2 weeks or 2 months prior to the first experimental recall session was also investigated in this experiment. A high level of accuracy was obtained at all recall sessions. However, a delay before the first recall session had an adverse effect upon the completeness of recall.
Object Tracking Based On Tracking-Learning-Detection

In this paper; we present a novel tracking framework (TLD) approach for long-term tracking of unknown objects in a video stream. The object is defined by its location and extent in a single frame. A novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates detector's errors and updates it to avoid these errors in the future. A novel learning method (P-N learning) which estimates the errors by a pair of "experts": (i) P-expert estimates missed detections, and (ii) N-expert estimates false alarms. The learning process is modeled as a discrete dynamical system and the conditions under which the learning guarantees improvement are found.
Context Networks for Annotating Personal Media

Specifying the search space is an important step in designing multimedia annotation systems. With the large amount of data available from sensors and web services, context-aware approaches for pruning search spaces are becoming increasingly common. In these approaches, the search space is limited to the contextual information obtained from a fixed set of sources. For example, a system for tagging faces in photos might rely on a static list of candidates obtained from the photo owner’s Facebook profile. These contextual sources can get extremely large and can therefore, explode the search space, which in turn leads to decreased accuracy in the multimedia classification. We present our novel Context Discovery Algorithm, a technique to progressively discover the most relevant search space from a dynamic set of context sources. This allows us to reap the benefits of context, while keeping the size of the search space within bounds. As a concrete application for our approach, we present a simple photo management application, accomplishing the task of tagging faces in its users’ personal photos. We empirically study the role of CueNet in assisting the face tagging application to tag photos taken at academic conferences. Our results show that the availability of event context, and its dynamic discovery, can produce search spaces with almost all correct solutions. We also show that the sizes of these search spaces were substantially smaller than the ones produced by approaches using a fixed set of sources.
Development of a new automated land cover change detection system from remotely sensed imagery based on artificial neural networks

The research is designed to develop and implement the algorithms for an automated spatial change information extraction system from remotely sensed imagery based on artificial neural networks. First, the authors investigate the suitability of the application of neural networks in automated change detection using TM imagery and its related network design problems unique to change detection. They then develop a neural networks-based change detection system using backpropagation training algorithm. This trained network is then able to efficiently detect land cover changes and provide complete information about the nature of change. Based on their experiments, it has been proven that this technique is successful and has immense implications on land cover change detection and quantification at all levels of applications ranging from local to global in scale.
Implementation of the Tree Structure in the XML and Relational Database

A tree structure is a powerful tool for storing and manipulating all kind of data, without differences is it used for natural language analysis, compiling computer languages or storing and analyzing scientific or business data. Algorithms for searching and browsing tree structures are well known, and for free are available numbers of tools for manipulating tree structures implemented in different computer languages. Even is impossible to avoid these algorithms and tools, primary focus in this paper is XML and XML tools. Storing and retrieving, as well as validation of XML tree structures in relational database can be a challenge. This document describes these challenges and currently available solutions.
An Insight to Steganography

Steganography is a useful tool that allows us to covert transmission of information using certain means or a communications channel. Combining the channel with the encryption methods of substitution is similar to the age old method of cryptography, steganography enables the user to transmit information masked inside a file in plain view. The hidden data is both difficult to detect and when combined with known encryption algorithms, equally difficult to decipher. Steganography is the art of hiding information and an effort to conceal the existence of the embedded information. It serves as a better way of securing message than cryptography which only conceals the content of the message not the existence of the message. Original message is being hidden within a carrier such that the changes so occurred in the carrier are not observable. In this paper we will discuss how digital images can be used as a carrier to hide messages. This paper also analyses the performance of some of the steganography tools. Steganography is a useful tool that allows covert transmission of information over the communications channel. Combining secret image with the carrier image gives the hidden image. The hidden image is difficult to detect without retrieval. This paper will take an in-depth look at this technology by introducing the reader to various concepts of Steganography, a brief history of Steganography and a look at some of the Steganographic technique.
Design and Analysis Performance of Fixed Wing VTOL UAV

Mainly there is two type of UAV, multirotor and fixed wing and each of this type have their own advantages. Combination of both type UAV can enhance the capability of UAV. The problem to this idea are the effect of modification on thrust performance as the wing loading increase. This study presents a conceptual design of fixed wing vertical take-off and landing (VTOL) UAV based on ready-to-fly (RTF) fixed wing UAV. This mean the UAV has ability not only to take-off, landing or hover as multirotor aircraft but also able to cruise similar to the fixed wing UAV. This paper discussed on the design modification of fixed wing UAV and thrust performance for take-off, hover and landing during multirotor mode. Mass balance analysis is performed to ensure that addition and placement of motors and other equipment are in such way the center of gravity does not change from the original UAV to retain its original stability. For the thrust performance, thrust required for multirotor mode is compared with the thrust available produce by the motors obtained by doing the thrust test. Based on the analysis, the capability of UAV to fulfil the mission requirement is determined.
A robotics testbed : the design & implementation with applications.

In order to study the movements of autonomous mobile robots, a tool is needed to quantify those movements. A testbed is an apparatus that provides a designated space for multiple mobile robots while tracking their position in real-time. That tracking information can also be communicated to the robots themselves to serve as closed-loop feedback. With this tool, many techniques can be developed and validated through various control experiments. A design and implementation of a testbed is presented here. The testbed is analyzed for its performance and several applications are presented to demonstrate its usefulness.
The Risk of Racial Bias in Hate Speech Detection

We investigate how annotators’ insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. We first uncover unexpected correlations between surface markers of African American English (AAE) and ratings of toxicity in several widely-used hate speech datasets. Then, we show that models trained on these corpora acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others. Finally, we propose *dialect* and *race priming* as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet’s dialect they are significantly less likely to label the tweet as offensive.
Improvement of UWB - multiband OFDM BER performance using Barker signaling

Orthogonal Frequency Division Multiplexing (OFDM) technology is a key technique for achieving the high data rate and spectral efficiency requirements for wireless communication systems of the near future. UWB Multiband OFDM has an acceptable BER performance. In a multipath noisy channel the bit error is very high. In this Paper we present the Barker code as a coding technique and solution to increase the BER performance using the correlation detection. The system performance is increased up to 11 dB for autocorrelation detection. The drawbacks of the system are the additional required bandwidth, congestion of the channel and the unexpected error position in the Barker sequence. In order to minimize bandwidth, Time-Frequency-Time using Direct and reversed Fourier transforms is used based on acoustic electronic processors.
Soft Labels for Ordinal Regression

Ordinal regression attempts to solve classification problems in which categories are not independent, but rather follow a natural order. It is crucial to classify each class correctly while learning adequate interclass ordinal relationships. We present a simple and effective method that constrains these relationships among categories by seamlessly incorporating metric penalties into ground truth label representations. This encoding allows deep neural networks to automatically learn intraclass and interclass relationships without any explicit modification of the network architecture. Our method converts data labels into soft probability distributions that pair well with common categorical loss functions such as cross-entropy. We show that this approach is effective by using off-the-shelf classification and segmentation networks in four wildly different scenarios: image quality ranking, age estimation, horizon line regression, and monocular depth estimation. We demonstrate that our general-purpose method is very competitive with respect to specialized approaches, and adapts well to a variety of different network architectures and metrics.
Personal correlates of problematic types of social media and mobile phone use in emerging adults

We investigated the occurrence of selected types of problematic social media and mobile phone use in emerging adults, specifically social media and mobile phone overuse, phubbing, creeping, and catfishing. Contemporaneous relations with age, gender, and Big Five personality traits were examined. The participants comprised 459 Slovenian emerging adults, aged 18 to 29 years (68% female). The results suggest that problematic behaviors associated with social media and mobile phone use, with the exception of catfishing, are relatively common among young people. The examined behaviors were negatively related to age, and overuse of mobile phones, social media, and creeping were more prevalent in females than males. The Big Five personality traits, most notably high neuroticism and low conscientiousness, uniquely predicted problematic social media and mobile phone use, after accounting for age, gender, and time spent on social media. Mobile phone overuse and phubbing were also associated with high extraversion and low openness, while low agreeableness was related to creeping and catfishing.
Web Usage Mining to Discover Visitor Group with Common Behavior Using DBSCAN Clustering Algorithm

Web Usage Mining is application of data mining techniques to discover interesting usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Analyzing data through web usage mining can help effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The aim of this paper is to emphasize on finding visitor group with common behavior from web log file of website. Web usage mining includes three phases namely preprocessing, pattern discovery and pattern analysis. Web Log file is considered as input here. This paper gives detailed description of how data from Web Log file are used for finding visitor's having common behavior using DBSCAN clustering algorithm.