Miracle-dz/UniFAR-FTUs huggingface dataset:
Adoption Factors of Mobile Services

This paper addresses simple but powerful framework for adoption factors of mobile services. The framework synthesizes, refines, and extends current approaches to understand adoption factors. The study started with a background research to identify factors determining the adoption of innovation and mobile services. Then, study is continued with a survey which had questions about two types of mobile services according to current adoption status of these services. For the first type of service, SMS had been selected as a widely used, already adopted service to seek for factors affecting consumer satisfaction. The second type of service was not currently widely adopted service in the market as opposed to already used SMS, which is called "Pocket Info&Enjoy" service in the paper. Pocket Info&Enjoy, which is information based service, had been described in the survey and asked questions to identify factors determining attitude to use this new service. Most of the findings were in line with literature, for an already adopted service, usefulness and attitude are direct factors influencing consumer satisfaction. On the other hand, usefulness and past experience are influencers and personalization and social factors are indirect determinants of consumer's attitude towards using new mobile services.
SALSA : A nalyzing Logs as StAte Machines

SALSA examines system logs to derive state-machine views of the sytem’s e x cution, along with control-flow, data-flow models and related statistics. Exploiting SALSA’s derived views and statistics, we can ffectively construct higher-level useful analyses. We demonstrate SALSA’s approach by analyzing system logs generated in Hadoop cluster, and then illustrate SALSA’s value by developing visualization and failure-diagnosis techniques, for three diffe rent Hadoop workloads, based on our derived statemachine views and statistics. Acknowledgements:This work is partially supported by the NSF CAREER Award CCR0238381, NSF Award CCF-0621508, and the Army Research Office grant number DAAD19-02-1-0389 ("Perpetuall y Available and Secure Information Systems") to the Center for C mputer and Communications Security at Carnegie Mellon University.
BULGARIAN ACADEMY OF SCIENCES

For cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach's effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.
Future Generation Computer Systems

Today, Social Media is a key information source for a wide range of domains as a means to gain a better understanding of information flows and user communities. This work introduces a methodology combining machine learning and graph mining approaches to address relevant aspects of quality of service and user intrinsic motivation from the user’s perspective. The focus of the present analysis is set on the social interactions among software developers via Stack Overflow. Over the last 10 years
TCAV: Relative concept importance testing with Linear Concept Activation Vectors

Despite neural network’s high performance, the lack of interpretability has been the main bottleneck for its safe usage in practice. In domains with high stakes (e.g., medical diagnosis), gaining insights into the network is critical for gaining trust and being adopted. One of the ways to improve interpretability of a NN is to explain the importance of a particular concept (e.g., gender) in prediction. This is useful for explaining reasoning behind the networks’ predictions, and for revealing any biases the network may have. This work aims to provide quantitative answers to \textit{the relative importance of concepts of interest} via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interests and test hypotheses using examples (e.g., a set of pictures that illustrate the concept). We show that CAV can be learned given a relatively small set of examples. Testing with CAV, for example, can answer whether a particular concept (e.g., gender) is more important in predicting a given class (e.g., doctor) than other set of concepts. Interpreting with CAV does not require any retraining or modification of the network. We show that many levels of meaningful concepts are learned (e.g., color, texture, objects, a person’s occupation), and we present CAV’s \textit{empirical deepdream} — where we maximize an activation using a set of example pictures. We show how various insights can be gained from the relative importance testing with CAV.
Energy Signature Analysis: Knowledge at Your Fingertips

Energy efficiency and energy consumption awareness are a growing priority for many countries. Among the large variety of methods proposed by energy scientists and professionals to evaluate building energy consumption, a widely adopted approach is the energy signature. Since the energy data easily scale towards very large datasets, the problem of characterizing energy efficiency through the energy signature from these huge data collections becomes challenging. This paper presents a distributed system, named ESA, for the collection, storage, and analysis of a large amount of energy-related data to keep continuously informed users on their energy consumption and building performance. ESA exploits a Big Data approach to perform a scalable and distributed computation of the building energy signature, which is exploited to forecast the expected power consumption for given contextual conditions in a specific time period. ESA characterizes monitored buildings through direct indicators designed to (i) evaluate the efficient use of the heating system by comparing latest observations with past energy demand in the same conditions, (ii) rank the overall building performance with respect to nearby and similarly characterized buildings. Experimental results on real energy consumption data demonstrate the effectiveness and the efficiency of the proposed distributed system to provide actionable knowledge at user fingertips for actors interacting with ESA.
Journal of Computer and System Sciences

The OR-SAT problem asks, given Boolean formulae φ 1 ,. ,φ m each of size at most n , whether at least one of the φ i ’s is satisﬁable. We show that there is no reduction from OR-SAT to any set A where the length of the output is bounded by a polynomial in n , unless NP ⊆ coNP / poly, and the Polynomial-Time Hierarchy collapses. This result settles an open problem proposed by Bodlaender et al. (2008) [6] and Harnik and Naor (2006) [20] and has a number of implications. (i) A number of parametric NP problems, including Satisﬁability, Clique, Dominating Set and Integer Programming, are not instance compressible or polynomially kernelizable unless NP ⊆ coNP / poly. (ii) Satisﬁability does not have PCPs of size polynomial in the number of variables unless NP ⊆ coNP / poly. (iii) An approach of Harnik and Naor to constructing collision-resistant hash functions from one-way functions is unlikely to be viable in its present form. (iv) (Buhrman–Hitchcock) There are no subexponential-size hard sets for NP unless NP is in co-NP/poly. We also study probabilistic variants of compression, and show various results about and connections between these variants. To this end, we introduce a new strong derandomization hypothesis, the Oracle Derandomization Hypothesis, and discuss how it relates to traditional derandomization assumptions.
Offline and Online Algorithms for SSD Management

Flash-based solid-state drives (SSDs) are a key component in most computer systems, thanks to their ability to support parallel I/O at sub-millisecond latency and consistently high throughput. At the same time, due to the limitations of the flash media, they perform writes out-of-place, often incurring a high internal overhead which is referred to as write amplification. Minimizing this overhead has been the focus of numerous studies by the systems research community for more than two decades. The abundance of system-level optimizations for reducing SSD write amplification, which is typically based on experimental evaluation, stands in stark contrast to the lack of theoretical algorithmic results in this problem domain. To bridge this gap, we explore the problem of reducing write amplification from an algorithmic perspective, considering it in both offline and online settings. In the offline setting, we present a near-optimal algorithm. In the online setting, we first consider algorithms that have no prior knowledge about the input and show that in this case, the greedy algorithm is optimal. Then, we design an online algorithm that uses predictions about the input. We show that when predictions are relatively accurate, our algorithm significantly improves over the greedy algorithm. We complement our theoretical findings with an empirical evaluation of our algorithms, comparing them with the state-of-the-art scheme. The results confirm that our algorithms exhibit an improved performance for a wide range of input traces.
Amenities and the Attractiveness of New Zealand Cities

We analyse which factors attract people and firms (and hence jobs) to different settlements across New Zealand. Using theoretically consistent measures derived within the urban economics literature, we compile quality of life and quality of business indicators for 130 ‘cities’ (settlements) from 1976 to 2013, using census rent and wage data. Our analyses both include and exclude the three largest cities (Auckland, Wellington, Christchurch). Places that are attractive to live in tend to be sunny, dry and near water (i.e. the sea or a lake). Since the mid-1990s, attractive places have also had relatively high shares of the workforce engaged in education and (to a lesser extent) health. Attractive places have high employment shares in the food, accommodation, arts and recreation service sectors; however (unlike for education and health) we find no evidence that quality of life is related to changes in employment share for these sectors. The quality of business is highest in larger cities, and this relationship is especially strong when the country’s three largest cities are included in the analysis.
Using Multilevel Circulant Matrix Approximate to Speed Up Kernel Logistic Regression

Kernel logistic regression (KLR) is a classical nonlinear classifier in statistical machine learning. Newton method with quadratic convergence rate can solve KLR problem more effectively than the gradient method. However, an obvious limitation of Newton method for training large-scale problems is the O(n) time complexity and O(n) space complexity, where n is the number of training instances. In this paper, we employ the multilevel circulant matrix (MCM) approximate kernel matrix to save in storage space and accelerate the solution of the KLR. Combined with the characteristics of MCM and our ingenious design, we propose an MCM approximate Newton iterative method. We first simplify the Newton direction according to the semi-positivity of the kernel matrix and then perform a two-step approximation of the Newton direction by using MCM. Our method reduces the time complexity of each iteration to O(n logn) by using the multidimensional fast Fourier transform (mFFT). In addition, the space complexity can be reduced to O(n) due to the built-in periodicity of MCM. Experimental results on some large-scale binary and multi-classification problems show that our method makes KLR scalable for large-scale problems, with less memory consumption, and converges to test accuracy without sacrifice in a shorter time.
Saline and sodic irrigation effects on performance of wheat cultivars and soil health

The present investigation was conducted to observe the effects of saline and alkali irrigation water on performance of six wheat cultivars and soil health. Five levels of saline (EC 0, EC 3, EC 6, EC 9 and 12 dS m -1 ) and four levels of alkali water (RSC 0, RSC 3, RSC 6.5 and RSC 10 me L -1 ) were used for irrigating six wheat cultivars (KRL 210, PBW 621, HD 2967, PBW 590, PBW 658 and Berbet ) during two years of research study. The results revealed that increasing salinity and alkalinity of irrigation water significantly increased soil pH , EC, SAR, ESP and soluble Na + , Cl and HCO3 and decreased organic carbon , available nutrients (N, P, K and S) , DTPA-Zn and soluble ions (K + , Ca+Mg 2+ , SO4 2) concentration in both years. Soil physical properties such as bulk density and dispersion ratio increased with increasing salinity and alkalinity in irrigation water whereas MWD, SHC, IR and WHC decreased under these treatments. Biological soil properties (MBC, MBN, DHA and CWEC) were significantly reduced to almost 50% at highest level of salinity (EC= 12 dS m -1 ) and alkalinity (RSC = 10 me L -1 ) of irrigation water in 2013-14 and 2014-15. In 84 days of incubation study cumulative respiration, MBC, NH4 + and NO3 N was low under saline and alkaline irrigated soil samples which improved on addition of carbon @ 1%. Plant and yield parameters were adversely impacted due to elevated salinity and alkalinity levels of irrigation water in both years. Pooled over two years, among different cultivars the maximum grain yield was produced by PBW 658 (4.23 t ha -1 ) at highest level of saline water (EC = 12 dS m -1 ) and by PBW 621 (5.19 t ha -1 ) for highest level of alkaline water (RSC = 10 me L -1 ) whereas the minimum yield in both cases was recorded for PBW 590 (3.82 and 3.79 t ha -1 ), respectively. Among all cultivars performance of HD 2967 was better at EC 9 dSm -1 and RSC 6.5 meL -1 , therefore can be grown without much significant loss in grain yield at respective levels but at highest level of salinity and alkalinity of irrigation water PBW 658 and PBW 621 should be preferred to get higher yield.
Show and Write: Entity-aware Article Generation with Image Information

Many vision-language applications contain long articles of text paired with images ( e.g ., news or Wikipedia articles). Prior work learning to encode and/or generate these articles has primarily focused on understanding the article itself and some related metadata like the title or date it was written. However, the images and their captions or alt-text often contain crucial information such as named entities that are difficult to be correctly recognized and predicted by language models. To address this shortcoming, this paper introduces an ENtity-aware article Generation method with Image iNformation, Engin , to incorporate an article’s image information into language models. Engin represents articles that can be conditioned on metadata used by prior work and information such as captions and named entities extracted from images. Our key contribution is a novel Entity-aware mechanism to help our model better recognize and predict the entity names in articles. We perform experiments on three public datasets, GoodNews, VisualNews, and WikiText. Quantitative results show that our approach improves generated article perplexity by 4-5 points over the base models. Qualitative results demonstrate the text generated by Engin is more consistent with embedded article images. We also perform article quality annotation experiments on the generated articles to validate that our model produces higher-quality articles. Finally, we investigate the effect Engin has on methods that automatically detect machine-generated articles.
Machine to machine communication enabled internet of things: a review

Internet of things (IoT) will be the main part in upcoming generation devices that would not simply sense and report, also will have the controlling capability. It may be a connected vehicle, connected devices, robot, a building automation system, a door lock or a thermostat, these connected machines or devices will provide greater impact on our daily lives. Control data and the operating instructions could be protected to ensure control and autonomy for our safety and security, this could be a critical task. Privacy and security are important consideration in designing the system. With the intense growth of devices or devices with facilities such as computing and communication are carried out using a profound technology known as machine to machine (M2M) communication, which is specially designed for cross‐platform integration. In many industries, smart homes, smart cities, smart agriculture, government, connected devices, security, healthcare, education, public safety, and supply chain management. Internet of things (IoT) and machine to machine communication have to be implemented in near future. Also, this paper gives an in depth view about the different M2M techniques with interconnected IoT for truly connected, smart, and sustainable world.
Interpreting natural language database updates

Although the problems of querying databases in natural language are well understood, the performance of database updates via natural language introduces additional difficulties. This thesis examines the problems encountered in interpreting natural language updates, and describes an implemented system that performs simple updates. The difficulties associated with natural language updates result from the fact that the user will naturally phrase requests with respect to his conception of the domain, which may be a considerable simplification of the actual underlying database structure. Updates that are meaningful and unambiguous from the user's standpoint may not translate into reasonable changes to the underlying database. The PIQUE system (Program for Interpretation of Queries and Updates in English) operates by maintaining a simple model of the user and interpreting update requests with respect to that model. For a given request, a limited set of "candidate updates"— alternative ways of fulfilling the request—are considered, and ranked according to a set of domain-independent heuristics that reflect general properties of "reasonable" updates. The leading candidate may be performed, or the highest ranking alternatives presented to the user for selection. The resultant action may also include a warning to the user about unanticipated side effects, or an explanation for the failure to fulfill a request This thesis describes the PIQUE system in detail, presents examples of its operation, and discusses the effectiveness of the system with respect to coverage, accuracy, efficiency, and portability. The range of behaviors required for natural language update systems in general is discussed, and implications of updates on the design of data models are briefly considered. This research, within the Knowledge-Based Management Systems (KBMS) project, has been supported by ARPA contract N39-82-C-250. The views and conclusions in this document are those of the author and should not be interpreted as representative of the official policies, either expressed or implied, of the U.S. Government This is a revised version of a thesis submitted to the Department of Computer Science and the Committee on Graduate Studies of Stanford University in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Author's current address: Teknowledge, Inc. 1850 Embarcadero Rd Palo Alto, CA 94303 Phone:(415)424-0500
Customizable SCF Acquisition in Italian

Lexica of predicate-argument structures constitute a useful tool for several tasks in NLP. This paper describes a web-service system for automatic acquisition of verb subcategorization frames (SCFs) from parsed data in Italian. The system acquires SCFs in an unsupervised manner. We created two gold standards for the evaluation of the system, the first by mixing together information from two lexica (one manually created and the second automatically acquired) and manual exploration of corpus data and the other annotating data extracted from a specialized corpus (environmental domain). Data filtering is accomplished by means of the maximum likelihood estimate (MLE). The evaluation phase has allowed us to identify the best empirical MLE threshold for the creation of a lexicon (P=0.653, R=0.557, F1=0.601). In addition to this, we assigned to the extracted entries of the lexicon a confidence score based on the relative frequency and evaluated the extractor on domain specific data. The confidence score will allow the final user to easily select the entries of the lexicon in terms of their reliability: one of the most interesting feature of this work is the possibility the final users have to customize the results of the SCF extractor, obtaining different SCF lexica in terms of size and accuracy.
Ieee Standard Test Access Port and Boundary-scan Architecture Ieee-sa Standards Board

No part of this publication may be reproduced in any form, in an electronic retrieval system or otherwise, without the prior written permission of the publisher. Abstract: Circuitry that may be built into an integrated circuit to assist in the test, maintenance, and support of assembled printed circuit boards is defined. The circuitry includes a standard interface through which instructions and test data are communicated. A set of test features is defined, including a boundary-scan register, such that the component is able to respond to a minimum set of instructions designed to assist with testing of assembled printed circuit boards. Also, a language is defined that allows rigorous description of the component-specific aspects of such testability features. IEEE Standards documents are developed within the IEEE Societies and the Standards Coordinating Committees of the IEEE Standards Association (IEEE-SA) Standards Board. The IEEE develops its standards through a consensus development process, approved by the American National Standards Institute, which brings together volunteers representing varied viewpoints and interests to achieve the final product. Volunteers are not necessarily members of the Institute and serve without compensation. While the IEEE administers the process and establishes rules to promote fairness in the consensus development process, the IEEE does not independently evaluate, test, or verify the accuracy of any of the information contained in its standards. Use of an IEEE Standard is wholly voluntary. The IEEE disclaims liability for any personal injury, property or other damage , of any nature whatsoever, whether special, indirect, consequential, or compensatory, directly or indirectly resulting from the publication, use of, or reliance upon this, or any other IEEE Standard document. The IEEE does not warrant or represent the accuracy or content of the material contained herein, and expressly disclaims any express or implied warranty, including any implied warranty of merchantability or fitness for a specific purpose, or that the use of the material contained herein is free from patent infringement. IEEE Standards documents are supplied " AS IS. " The existence of an IEEE Standard does not imply that there are no other ways to produce, test, measure, purchase, market, or provide other goods and services related to the scope of the IEEE Standard. Furthermore, the viewpoint expressed at the time a standard is approved and issued is subject to change brought about through developments in the state of the art and comments received from users of the standard. Every IEEE …