Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of voluntary intellectual capital disclosure for disclosing firms

Purpose Mandatory disclosure of a firm’s intellectual capital (IC) is restricted by accounting regulations, leading companies to use voluntary disclosure to inform their stakeholders about their IC. However, voluntary IC disclosure (ICD) is costly and may lead to a leak of knowledge. Consequently, firms should only engage in voluntary ICD if it really reduces information asymmetries and leads to reduced cost of capital or a better reputation. The purpose of this paper is to review, integrate and critically discuss the results of studies examining various effects of voluntary ICD. Design/methodology/approach The authors use a structured literature review approach. Findings The results mainly support the expected positive effects of voluntary ICD on monetary value for disclosing firms, e.g. lower cost of capital, higher firm value or increased analysts’ following. Nevertheless, the studies mainly represent second stage IC research. Research limitations/implications Additional studies concerning effects of voluntary ICD outside capital markets are recommended. Future studies should be based on an improved study design concerning the theoretical underpinning and concept of value relevance, sufficient sample sizes and alternative sources of ICD. Practical implications Due to positive monetary effects, firms should engage in voluntary ICD. Originality/value The paper reviews and integrates the state-of-the-art of empirical research of effects of voluntary ICD. It contributes to and enlarges the debate concerning the value relevance of voluntary ICD with respect to the different stages of IC research.
On-line canceling of pathological tremor for computer interface

An adaptive filter designed to cancel pathological tremor in computer pen input is tested on-line. The adaptive filter, called the WFLC (weighted-frequency Fourier linear combiner) filter, estimates tremor frequency as well as amplitude and then cancels the tremor. The WFLC algorithm was implemented into pen driver software and tested with subjects with pathological tremor. A standard low-pass filter which is commonly used to attenuate tremor was also tested and compared with the WFLC filter. Tests were conducted through a series of quantitative target objectives. Best results were obtained when the WFLC filter was used.
Drive Less but Finish More: Food Delivery based on Multi-Level Workers in Spatial Crowdsourcing

In this paper, we study the problem of on-demand food delivery in a new setting where two groups of workers -- riders and taxi drivers (drivers for short) -- cooperate with each other for better service. The riders are responsible for the first and the last mile, and the drivers are in charge of the cross-community transportation. We show this problem is generally NP-hard by a reduction from the well-known 3-dimensional matching (3DM). To tackle with this problem, we first reduce it to the maximum independent set problem and use a simple greedy strategy to design an approximate algorithm which has a polynomial time. Considering the exponents in the polynomial are not very small, we then transform the 3DM into two rounds of 2-dimensional matching and propose a fast algorithm to solve it. Though 3DM problem is NP-hard, we find the cooperation between riders and drivers form a special tripartite graph, based on which we construct a flow network and employ the min-cost max-flow algorithm to efficiently compute the exact solution. We conduct extensive experiments to show the efficiency and the effectiveness of our proposed algorithms.
Parametric Optimization of Non Overlapped Clock Pulse Shift Register Design

– This paper discusses the area and power optimization of shift register. The shift register is design using edge triggered flip flops. The use of latch in shift register creates timing problems which can be avoided by using pulse clock base latches as a basic cell in shift register. This resolve the timing problem created in shift register by using several non overlapping clock in a duration of pulses. This also reduces the number of cells which in turn reduces area of circuit. The shift register cell CMOS layout is design using 50nm technology in MICROWIND layout simulator tool.
Collaborative Filtering Recommendation Algorithm Based on Comprehensive Interest Measure

To address the low accuracy of identifying nearest neighbors and bad recommendation performance in traditional collaborative filtering algorithms,the concept of comprehensive interest measure which is a full description of user interest is proposed. Based on this,a novel collaborative filtering algorithm is introduced. Experimental results suggest that this algorithm can efficiently improve the accuracy of computing nearest neighbors and provid better recommendation results than traditional collaborative filtering algorithm.
Climate information services for adaptation: what does it mean to know the context?

ABSTRACT Climate information services (CIS) can reduce climate vulnerability by enhancing information access, knowledge exchanges, and networks. Central to CIS is the need to understand the social and environmental context in which information is used. While researchers have identified many influential dimensions, there lacks rigorous analysis of all the dimensions salient to a CIS case study as well as a model to help CIS implementers design and evolve their CIS in its course. This research addresses these gaps by analysing a CIS we developed for coffee farmers in Jamaica that introduced new weather and climate information in workshops, text messages, and interviews. We identify nine dimensions related to the information providers, users, and their union, and we show how each influenced the design and evolution of our CIS. We further show their dynamic relations in an analytical model. We argue that the context is emergent, requiring flexible CIS, and that assessing the providers is as important as a focus on the users, which is often the emphasis in CIS scholarship. This study is a demonstration of how varied contextual dimensions affect the design, implementation, and use of a CIS, while also providing empirical detail about a coffee farming and climate context.
Performance analysis of IEEE 802.11 MAC protocols in wireless LANs : Emerging WLAN applications and technologies

IEEE 802.11 MAC protocol is the de facto standard for wireless local area networks (LANs), and has also been implemented in many network simulation packages for wireless multi-hop ad hoc networks. However, it is well known that, as the number of active stations increases, the performance of IEEE 802.11 MAC in terms of delay and throughput degrades dramatically, especially when each station's load approaches its saturation state. To explore the inherent problems in this protocol, it is important to characterize the probability distribution of the packet service time at the MAC layer. In this paper, by modeling the exponential backoff process as a Markov chain, we can use the signal transfer function of the generalized state transition diagram to derive an approximate probability distribution of the MAC layer service time. We then present the discrete probability distribution for MAC layer packet service time, which is shown to accurately match the simulation data from network simulations. Based on the probability model for the MAC layer service time, we can analyze a few performance metrics of the wireless LAN and give better explanation to the performance degradation in delay and throughput at various traffic loads. Furthermore, we demonstrate that the exponential distribution is a good approximation model for the MAC layer service time for the queueing analysis, and the presented queueing models can accurately match the simulation data obtained from ns-2 when the arrival process at MAC layer is Poissonian.
Evaluation of soft tissue thicknesses with the purpose of facial reconstruction in Brazilian

: the auxiliary technique of identification known as Facial Reconstruction makes possible to obtain face identification from the contours of the tissue around the skull, increasing the probabilities of recognition. the reliability of this technique depends on the evaluation of the thickness of the soft tissues that covers the skull. those measurements were evaluated on a sample of studied cadavers in são Paulo state, Brazil. the thickness has been manually measured using the needle puncture technique in 10 anatomical landmarks of the skull located in the midleline and in 11 bilateral points of 40 cadavers of both sexes, aged between 17 and 90 years, classified by skin color and nutritional state. Descriptive statistics calculations were made accordingly to t-tests, AnoVA and tukey tests. those calculations, when compared with other populations studies, showed different results, that lead to the need of using a specific table with values of the local population to implement the technique of facial reconstruction in skulls without an attributable identity.
KEEPING THE BEAT AS A MEASURE OF SPECIFIC LEARNING DIFFICULTIES USING MIDI CONTROLLER

The aim of our research is to work out a testing program to differentiate the typical and atypical sensory motor development. In this recent study we examined children with and without specific learning difficulties using midi-controller, a small percussion-like device as the tool for the testing. In a previous study we found age differences in children in the beat-keeping accuracy using the midicontroller, which indicates that this variable is crucial and indicative in the maturation of the brain and our test device is appropriate for the assessment (Kertesz, 2016). In our recent research the test results show significant differences between the beat keeping accuracy of children with and without specific learning difficulties, which means that the device can be used as an aid in the early identification of specific learning difficulties, and can be developed for a training device, too.
Digital Health Monitoring and Pervasive Healthcare Using Cloud-Connected Smart Wearable Devices

Mobile technology has made unprecedented progress in last 40 years, now combined with the unobtrusive power and efficiency of cloud computing. The next “…One more thing” in the technological arena from this intersection is “Digital Healthcare”. Featuring state-of-the-art yet inexpensive medical data acquisition devices comprising of Network-connected sensors, either in the form of wearable smart devices or embedded in local environments. These sensors make very accurate medical data acquisition for prolonged time intervals forming a database in the cloud, rich of information and raw data for accurate analysis of the physical and medical health of individuals. Acquired continuously, collected in cloud, effectively processed and analyzed, such a technology could yield disruptive results in the field of medical diagnosis and research. In this work, we review the cosplay of mobile cloud computing with the pervasive IOT technology to redefine healthcare and present a promising vision for its future.
On the Challenges of Podcast Search at Spotify

Online music streaming is enjoying ever-growing popularity in the last decades, enabled by the abundance of music content in digital format and online streaming services. In the recent years, podcasts, as a talk-focused media format, have witnessed a rapid growth among listeners. Podcasts come in many forms and sizes. They range from 20-minute daily meditation sessions, weekly recaps of global news, interviews with celebrities, and hosts bantering with each other for hours. More and more streaming services are now expanding their catalogs to support both music and podcasts on the same platform, such as Amazon Music, Pandora and Spotify. This setup requires an effective aggregated search system to assemble information from heterogeneous information sources and content types (e.g., artist profiles, playlists, podcast shows, etc.) into one result interface in order to support diverse information needs.
Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling

Recent approaches to behavioural user profiling employ Graph Neural Networks (GNNs) to turn users' interactions with a platform into actionable knowledge. The effectiveness of an approach is usually assessed with accuracy-based perspectives, where the capability to predict user features (such as gender or age) is evaluated. In this work, we perform a beyond-accuracy analysis of the state-of-the-art approaches to assess the presence of disparate impact and disparate mistreatment, meaning that users characterised by a given sensitive feature are unintentionally, but systematically, classified worse than their counterparts. Our analysis on two real-world datasets shows that different user profiling paradigms can impact fairness results. The source code and the preprocessed datasets are available at: https://github.com/erasmopurif/do_gnns_build_fair_models.
Performance of Non-Uniform Duty-Cycled ContikiMAC in Wireless Sensor Networks

Wireless Sensor Network (WSN) is a promising technology in Internet of Things (IoTs) because it can be implemented in many applications. However, a main drawback of WSN is it has limited energy because each sensor node is powered using batteries. Therefore, duty-cycle mechanisms are introduced to reduce power consumption of the sensor nodes by ensuring the sensor nodes in the sleep mode almost of the time in order to prolong the network lifetime. One of the de-facto standard of duty-cycle mechanism in WSN is ContikiMAC, which is the default duty-cycle mechanism in Contiki OS. ContikiMAC ensures nodes can participate in network communication yet keep it in sleep mode for roughly 99\% of the time. However, it is found that the ContikiMAC does not perform well in dynamic network conditions. In a bursty network, ContikiMAC provides a poor performance in term of packet delivery ratio, which is caused by congestion. One possible solution is ContikiMAC should increase its duty-cycle rate in order to support the bursty traffic. This solution creates a non-uniform duty-cycle rates among the sensor nodes in the network. This work aims to investigate the effect of non-uniform duty-cycle rates on the performance on ContikiMAC. Cooja simulator is selected as the simulation tool. Three different simulation scenarios are considered depending on the Clear Channel Assessment Rate (CCR) configurations: a low uniform CCR value (Low-CCR), a high uniform CCR value (High-CCR) and non-uniform CCR values (Non-uniform-CCR). The simulation results show that the Low-CCR scenario provides the worst performance of PDR. On the other hand, the High-CCR scenario provides the best performance of PDR. The Non-uniform-CCR provides PDR in between of Low-CCR and High-CCR.
Merrill's atlas of radiographic positions & radiologic procedures

VOLUME THREE 24. Mammography 25. Central Nervous System 26. Circulatory System and Cardiac Catheterization 27. Sectional Anatomy for Radiographers 28. Pediatric Imaging 29. Geriatrics 30. Mobile Radiography 31. Surgical Radiography 32. Computed Radiography 33. Computed Tomography 34. Computed Radiography 35. Digital Angiography and Digital Spot Imaging 36. Magnetic Resonance Imaging 37. Diagnostic Ultrasound 38. Nuclear Medicine 39. Bone Densitometry 40. Positron Emission Tomography 41. Radiation Oncology
MPEG Media Transport Protocol (MMTP)

The MPEG Media Transport Protocol (MMTP) is a transport protocol that is designed to support download, progressive download, and streaming applications simultaneously. MMTP provides a generic media streaming mode by optimizing the delivery of generic media data encapsulated according to the ISO-Base Media File Format (ISOBMFF). In the file delivery mode, MMTP supports the delivery of any type of file. MMTP may used in IP unicast or multicast delivery and supports both Forward Error Correction (FEC) and retransmissions for reliable delivery of content.
Tag-Based Interest-Matching Users Discovery Approach in Online Social Network

With the popularity of online social networks, such as Facebook, YouTube and Douban, have become a channel for fast information propagation and influence. The problem of discovering common interests shared by groups of users has become one of the central researches because it helps to connect users with common interests and can eventually influence as many users with common interests in the online social network as possible. Unfortunately, most of existing related works have only focused on the network topologies and so unable to identify the common interests of users who have no online connections. In an online social network, users tend to use descriptive tags to annotate the contents that they are interested in. User-generated tags are consistent with the content they are attached to. Thus, patterns of frequent co-occurrences of user tags can be used to characterize and capture topics of user interests. In this paper, we propose a novel common interests discovery approach based on user-generated tags to discover the set of interest-matching users whose interests are similar. The experiment results show that our approach can effectively discover user communities with common interests no matter if they have any online connections.
When Do Computers Reduce the Value of Worker Persistence?

Worker persistence – the ability to perform tasks consistently for long periods of time – is important in many occupations, particularly in routine occupations that value consistency of worker performance. However, computers are very persistent, potentially reducing the value of human persistence in occupations that are computerized. Using a well-defined measure of individual persistence across a nationally-representative 16-year sample of 4,235 individuals, we investigate the extent to which occupations value persistence in the presence of computers. In contrast to a broad replacement effect documented in other studies, we hypothesize and find evidence to support a distributional effect of computers and persistence. We find that, in routine jobs, the wage premium for human persistence diminishes with the degree of workplace computerization. Yet, in non-routine jobs, the premium does not diminish with computerization. Our findings add empirically-grounded nuance to the nature of workplace computerization, showing that persistent computers make persistent workers less valuable in routine occupations. These findings have important theoretical, policy and managerial implications for the future of work and workers.
Speech Recognition with Segmental Conditional Random Fields

The goal of this workshop group is to advance the state-of-the-art in core speech recognition by developing new kinds of informative segment level features for use in a Segmental Conditional Random Field (SCRF). In the recently proposed SCRF approach [Zweig & Nguyen 2009], we generalize Conditional Random Fields to operate at the segment level, rather than at the traditional frame level. Key to our approach, each segment is labeled directly with a word. To simplify somewhat, denote a specific segmentation by q and specific word sequence by w. We compute the conditional probability of the word sequence using a set of features fi which are combined in a log-linear model with weights i:
El estudio de los índices de vegetación como base para conocer las relaciones entre la vegetación y el clima

The vegetation indexes are a result of remote sensing instrument to determine the status of different plant elements that are located above ground. This tool is young, but increasingly developed and studied. In this paper I plan to try to relate the results we offer the vegetation indexes in Spain for the past five years with the real climate results, we provide the observatories in several areas of the study area, in order to know the reliability of these indices on climate issues from the state of vegetation and not merely ecological. Establish a correct correlation, would be of vital importance to study these rates as important today in the field of physical geography and, above all, of the Climatology and Biogeography, such as climate intervariability and especially the climate change.
Survey of Recent Cyber Security Attacks on Robotic Systems and Their Mitigation Approaches

With the rapid expansion of digital media and the advancement of the artificial intelligence, robotics has drawn the attention of cyber security research community. Robotics systems use many Internet of Things (IoT) devices, web interface, internal and external wireless sensor networks and cellular networks for better communication and smart services. Individuals, industries and governments organisations are facing financial loses, losing time and sensitive data due these cyber attacks. The use these different devices and networks in robotics systems are creating new vulnerabilities and potential risk for cyber attacks. This chapter discusses about the possible cyber attacks and economics losses due to these attacks in robotics systems. In this chapter, we analyse the increasing uses of public and private robots, which has created possibility of having more cyber-crimes. Finally, contemporary and important mitigation approaches for these cyber attacks in robotic systems have been discussed in this chapter.
Data analysis in microarray experiment

Genechip, representing the integration of multiple scientific field progress, is a newly developing high technology used to solve biology puzzle with automated and high-flux features. It has a successful application in large-scale research in genes function. After a relative long developing period, the method how to efficiently analyse mass data generated by chip experiment has become a hotspot in the chip research community. In principal, the method can be divided into two categories, the unsupervised method and the supervised method. Besides data analysis itself, two procedures performing before and after the analysis need to be paid attention to: the former is data normalization and reduction, the later is statistical test and biologic verification. Many commonly used statis- tical approaches and their merits and demerits will be introduced.
SSVEP Extraction Applying Wavelet Transform and Decision Tree With Bays Classification

Background: SSVEP signals are usable in BCI systems (Brain-Computer interface) in order to make the paralysis movement more comfortable via his Wheelchair. Methods: In this study, we extracted The SSVEP from EEG signals, next we attained the features from it then we ranked them to obtain the best features among all feature and at the end we applied the selected features to classify them. We want to show the degree of accuracy we applied in this work. Results: In this study Bayes (applied for classifying of selected features) got the highest level of accuracy (83.32%) with t-test method, until the SVM took the next place of having the highest accuracy to itself with t-test method (79.62%). In the next place according to the feature selection method, decision tree took the next place with Bayes classification (79.13%) and then with SVM classification (78.70%). Conclusion: Bays obtained the better results to itself rather than SVM with t-test.