Miracle-dz/UniFAR-FTUs huggingface dataset:
FRAMEWORK FOR AIDING SURVEYS BY NATURAL LANGUAGE PROCESSING

Writing survey and review papers is a tedious process requiring methodological and systematic collection and analysis of large quantities of scientific papers. Selection of relevant papers is a manual work that requires a lot of time. This paper proposes a framework based on natural language processing that aims to simplify the process of selection of relevant papers and identification of key properties. For a provided list of phrases, first it queries several digital libraries (i.e. Google Scholar, IEEE Xplore, Springer, ACM and Elsevier) and selects papers with at least one citation. Then it crawls all relevant information for the selected papers. Next, by searching for a list of predefined properties (i.e. keywords) and their synonyms obtained from Word Net, it processes the abstract. Note that to make the search more robust, stemming and lemmatization is applied. Finally, it aggregates the processed papers per year, property, citations, publisher, etc. and provides various charts and figures. This greatly simplifies the work of the researcher and allows to find relevant papers quickly that could be analyzed in more detail manually. Furthermore, it identifies publishers, conferences and journals that are relevant for a particular topic. The framework was evaluated with a use case from connected health and the results allowed identifying various trends very quickly.
RRAM-VAC: A Variability-Aware Controller for RRAM-based Memory Architectures

The growing need for connected, smart and energy efficient devices requires them to provide both ultra-low standby power and relatively high computing capabilities when awoken. In this context, emerging resistive memory technologies (RRAM) appear as a promising solution as they enable cheap fine grain technology co-integration with CMOS, fast switching and non-volatile storage. However, RRAM technologies suffer from fundamental flaws such as a strong device-to-device and cycle-to-cycle variability which is worsened by aging, forcing the designers to consider worst case design conditions. In this work, we propose, for the first time, a circuit that can take advantage of recently published Write Termination (WT) circuits from both the energy and performances point of view. The proposed RRAM Variability Aware Controller (RRAM-VAC) stores and then coalesces the write requests from the processor before triggering the actual write process. By doing so, it averages the RRAM variability and enables the system to run at the memory programming time distribution mean rather than the worst case tail. We explore the design space of the proposed solution for various RRAM variability specifications, benchmark the effect of the proposed memory controller with real application memory traces and show (for the considered RRAM technology specifications) 44 % to 50 % performances improvement and from 10% to 85% energy gains depending on the application memory access patterns.
Information Extraction for Graph Data Using Domain Speci c Ontology

Ontology provides a structured way of describing knowledge. Ontology behaves like a Knowledge Base (KB) with respect to any speci c domain. If we have a document repository with respect to any domain the same information can be converted as an ontology by using concepts and its relationships between them. RDF and RDFS have recently become very popular frameworks for representing data and meta-data in the form of the domain description. RDF/RDFS data can also be thought of as graph data. Here, our main focus is on keyword based querying on RDF/RDFS data. The studied and implemented approach adopts a reduced exploration mechanism, where we try to nd out closely related vertices using sub graph construction phase, then removing unwanted vertices/edges and joining by using suitable join vertices. We explore the type of (type/subclassof) relationship during the construction of output from the given query.
Taking Affect Analysis One Step Higher : The Idea of Contextual Appropriateness of Emotions and Its Perspectives

I present my research in Contextual Affect Analysis (CAA) for the need of future application in intelligent agents, such as conversational agents or artificial tutors. In agent-user discourse it is crucial that the artificial agent was able not only to detect user emotions, but also to verify towards whom they were directed and whether they were appropriate for the context of the conversation. I focus on verification of contextual appropriateness of emotions. I present the results of experiments and discuss implications and future directions in development of this method. Introduction Research on emotions in the fields of Artificial Intelligence and Natural Language Processing, like the ones described above has flourished rapidly through several years. Unfortunately, in much of such research contextuality of emotions is disregarded. Based only on behavioral approaches, methods for emotion recognition ignore the context of emotional expression. Therefore, although achieving good results in laboratory conditions, such methods are often inapplicable in real world tasks. For example, a system for recognition of emotions from facial expressions, assigning “sadness” when user is crying would be critically mistaken if the user was, e.g., cutting an onion in the kitchen. Similarly in language, not including con text in the processing could lead to various processing errors. For example, one can consider a system detecting happiness when user uses a word “happy”. The system would be critically mistaken if the user actually said: “I’m not happy at all! ” This shows that not considering at least grammatical context in the processing causes erroneous detection of opposite emotion. However, a deeper problem appears when the user said something like “I would be so happy if that bastard was hit by a car! ” Here a grammatical context does not suffice correct processing and deeper context is required in the processing. As the above examples show, recognizing emotions without recognizing their context is incomplete and cannot be sufficient for real-world applications. The outline of this paper is as follows. I firstly present the background for this research in which I describe the fields of Affect Analysis (AA) and Contextual Affect Analysis (CAA). Next I describe previously developed tools applied in the research. Further section describes the method for verifying whether the emotions expressed in conversations are appropriate to the context of the situation. Next section contains descriptions of the design of evaluation experiment and its results. In final section I present a discussion on further implication of context aware Affect Analysis. Lastly, I conclude the paper and propose some ideas to improve the described method.
A Review on the Existing Service Quality Measurement Models

The purpose of this review is to assess the existing service quality measurement models. A review of the literature was conducted utilizing keywords such as “definitions of quality”, “meaning of quality”, “service quality and features of service quality”, “measurement models”, specifically, “Gronroo’s”, “SERVQUAL”, and “SERVPERF”. These studies are selected from well-known databases – such as “Emerald”, “ABI/Inform”, “ScienceDirect”, and “EBSCOhost”. As yet, no consensus has been reached among scholars on the definition, indicators and factors of the quality of the healthcare services. Moreover, most of the current models are of Western origin and incongruent with the cultural and economic contexts of developing countries. From the review it is clear that none of the models are currently perfect in diverse cultures and no reliable generic model has yet been developed for measuring the quality of service. Generic models have failed to capture the real dynamism of the pragmatic environment, and can therefore be of limited practical use. Existing service-quality models have widely been criticised for the number and composition of the dimensions. All these models comprise of pre-defined dimensions that are non-specific to all service organisations. This is therefore advising organisations to develop their own models for measuring the quality of their services.
Design Guidelines for Context-Aware Creativity Support Systems

Ubiquitous computing and context-awareness have become a major factor and opportunity for various fields of computer science. Especially with the increasing usage of mobile devices and the spatial coalescence of work and personal life, ubiquitous computing has brought many opportunities for context-aware software. Creativity support has already found its way to computer science and various creativity support systems have been developed and used. While research about group creativity and individual creativity addresses problems of ineffective teamwork, less research has been done considering the creative environment. In our literature review we identified several environmental conditions, which influence creativity. Furthermore we disclosed recent sensory technology to capture context information. From these findings we propose design guidelines for context-aware creativity support systems
Analyzing Application Performance Bottlenecks on Intel ’ s SCC

Intel’s Single-Chip Cloud Computer (SCC) is an experimental multi-core processor designed to scale to hundreds of cores. However, compared to traditional multicores, the SCC places a high burden on application programmers. To date, SCC-based systems have had no performance tool that can help programmers rapidly analyze an application’s performance bottlenecks. To remedy this problem, we ported Rice University’s HPCTOOLKIT performance tools to the SCC in order to analyze hot loops with hardware performance counters, pinpoint scaling bottlenecks in context, identify load imbalance with call path traces, and identify time-varying performance problems such as serialization. This paper describes our experiences using HPCTOOLKIT to analyze the performance of the triangularsolve-with-multiple-right-hand-sides (Trsm) implementation from the Elemental library and the SCC-version of the NAS LU benchmark. HPCTOOLKIT enabled us to understand Trsm’s performance in detail and provided insight that enabled us to improve the performance of the NAS LU benchmark by more than 30%.
Transforming recursive programs for parallel execution

We present a general approach to batching arbitrary computations for accelerators such as GPUs. We show orders-of-magnitude speedups using our method on the No U-Turn Sampler (NUTS), a workhorse algorithm in Bayesian statistics. The central challenge of batching NUTS and other Markov chain Monte Carlo algorithms is data-dependent control flow and recursion. We overcome this by mechanically transforming a single-example implementation into a form that explicitly tracks the current program point for each batch member, and only steps forward those in the same place. We present two different batching algorithms: a simpler, previously published one that inherits recursion from the host Python, and a more complex, novel one that implements recursion directly and can batch across it. We implement these batching methods as a general program transformation on Python source. Both the batching system and the NUTS implementation presented here are available as part of the popular TensorFlow Probability software package.
Picon: Control Flow Integrity on LLVM IR

Control flow integrity is a well explored field of software security for more than a decade. However, most of the proposed approaches are stalled in a proof of concept state – when the implementation is publicly available – or have been designed with minimal performance overhead as main objective, sacrificing security. Currently, none of the proposed approaches can be used to fully protect real-world programs compiled with most common compilers (e.g. GCC, Clang/LLVM). In this paper we describe a control flow integrity enforcement mechanism for LLVM IR, called Picon, whose main objective is security. Our approach is based on compile-time code instrumentation, making the program communicate with its external execution monitor. The program is terminated by the monitor as soon as a control flow integrity violation is detected. Our approach is implemented as an LLVM plugin and is working on LLVM’s Intermediate Representation.
The Minimum Wage , Teenage Employment and the Business Cycle * by

This paper analyses the effect of increases in the federal minimum wage on monthly black and white teenage employment based on recent disequilibrium research, which predicts regime changes between the demand and supply of labor that depend upon the phase of the business cycle. Both exploratory data analysis and econometric testing find substantial, significant negative employment effects in contractions and insignificant positive effects in expansions that apparently cancel out when the data are pooled. The statistical results support the hypothesis of changing regimes and suggest that minimum wage policy should depend upon the phase of the business cycle. JEL Classification: J42, J31, J38.
For a contingent approach of conflict resolution mechanisms: the case of innovation networks

Previous researches on interorganizational relations show contradictory findings on the link between conflict and members satisfaction. While recent literature sketches some insight to refine the influence of different type of conflicts, resolution mechanisms that overcome these conflicts remains less studied. Our research proposes that conflict resolution mechanisms moderate hub firm satisfaction, depending on the type of conflict. Study of 173 innovation networks hub's firms demonstrate contingent relationships between type of conflict and resolution mechanisms on hub firm satisfaction. The expected negative effect of task conflict on hub firm satisfaction is positively moderate by soft resolution mechanisms (discussion and soft pressure), and the degradation caused by the relational conflicts on the hub firm satisfaction is compensate by the hard conflict resolution mechanism (mediation and court). These results shed new light on how to mitigate conflicts in the context of innovation, prone to discordances. Theoretically, this research paves the way for a joint consideration of literature on conflict types and literature on resolution mechanisms that are typically treated separately.
Quality Enhancement of Pixel Level Image Fusion using DT-CWT and Neuro-Fuzzy

Image fusion is the process of combining two or more image to form single image by extracting the essential features of both the image. The aim of fusion is to produce an image which designates a scene better or even higher than whichever single image with respect to some significant properties providing an informative image. There are lots of techniques has been evolved for the image fusion while in this we proposes DT-CWT with neuro-fuzzy for extracting the useful information from two or more image of enhanced quality. The experimental analysis of the proposed work is done in MATLAB simulation toolbox using the parameters such as MSE, PSNR and IQI. The experimental result of proposed work generates an improved quality of image than the existing methodology DT-CWT. The result of MSE, PSNR is about 6-7% more than the existing methodology
Metal Enhanced Fluorescence Microarray Biochip using Glancing Angle Deposited Ag nanorods

Glancing angle deposition (GLAD) is a physical vapor deposition process in which the substrate is placed to have a large incidence angle (>75°, angle between incoming flux and substrate normal). The GLAD process can be considered a promising method to fabricate metallic nanostructure on a large area at a low cost. GLAD Ag nanorods were fabricated and their fluorescence enhancement performances were examined. A practical antibody chip was realized using a 500 nm thick vertical Ag nanorods substrate. To overcome the limitations of GLAD Ag nanorod MEF substrate, Ag nanorods on micro post array substrate were also investigated.
Symbolic versus quantitative descriptions of F 0 contours in German : Quantitative modeling can provide both

The problem of adequately describing F0 contours is far from being solved. Although symbolic representations such as the ToBI-system appear attractive and have been strongly promoted recently, they neither capture F0 contours in a way that permits their reproduction from the labels, a demand resulting from TTS, nor are the labels phonological in the strict sense. The current paper compares results yielded by automatic quantitative modeling with a GToBI-style annotation. On the accent level, a good correlation between tone labels and accent commands can be observed. On the phrase level, most level 3 and 4 break index boundaries are aligned with phrase commands whereas lower level boundaries are presumably marked with durational cues. The results of the study indicate that Fujisaki model parameters, while preserving the F0 contour information, can as well be used for deriving a ToBI-style representation.
Change in physiological and biochemical parameters under drought stress in salt-tolerant and salt-susceptible eggplant genotypes

This study was carried out to determine the changes in chlorophyll and ion contents, lipid peroxidation level, and antioxidative enzyme activities of 4 eggplant genotypes (Mardin Kiziltepe (MK), Burdur Merkez (BM), Artvin Hopa (AH), and Kemer (K)) with different salt tolerance levels under drought stress. Experiments were conducted in a greenhouse with controlled climatic conditions. When the plants reached the 3-4 leaves stage, 3 different treatments were applied to create the drought levels. The first condition in the study was obtained through control application treatment (D0), which was depletion of 40% of the available water holding capacity. The second application (D1) was depletion of 90% of the available water holding capacity. The third application (D2) was no applied irrigation water. Plants were analyzed with regards to chlorophyll and ion contents, lipid peroxidation levels, and antioxidative enzyme activities. Significant differences were observed among interactions of genotypes and levels of drought stress in chlorophyll and ion (K+, Ca2+, Zn2+, Mn2+, and Fe2+) contents, lipid peroxidation levels, and activities of antioxidant enzymes (superoxide dismutase-SOD, catalase-CAT, glutathione reductase-GR, and ascorbate peroxidase-APX). The chlorophyll and ion contents of eggplant genotypes under drought stress decreased. However, different levels of lipid peroxidation and antioxidative enzyme activities were observed. The situation changed depending on genotypes. Lipid peroxidation levels increased under drought stress, especially in salt-sensitive genotypes (AH and K). On the other hand, the activities of antioxidative enzymes in the salt tolerant MK and BM genotypes significantly increased. With regard to the investigated traits, salt-tolerant genotypes were also found to be quite tolerant against drought stress.
WHY DO PEOPLE DO WHAT THEY DO ? – INFLUENCING THE HUMAN FACTORS IN ACCIDENTS AT WORK

This paper describes a project initiated by BP, and later adopted by other hazardous industries, which developed and implemented human factors analytical methods to help incident investigators analyse behaviour, and put in place more effective recommendations / behavioural measures to prevent a similar accident happening again. The project was initiated to address shortcomings with existing root-cause analysis methodologies, which were good at establishing “what” happened, but not so successful in establishing “why” people did what they did, and how to prevent a recurrence. Two methods to analyse human behaviour were developed; one for intentional violations, and another for unintentional human errors. These were trialled; peer-reviewed by industry, regulatory & academic experts; then modified and used to train 200 investigators with varied levels of experience in four companies. In one of these companies, their existing root-cause analysis model was linked to a model of safety culture, allowing a series of incidents to be trended to identify underlying weaknesses in the safety culture. Case studies are presented to illustrate the benefits obtained, including a violation which led to an environmental release, and a human error near-miss concerning spillage of hydrocarbons. In addition, some of the difficulties and challenges involved in implementing such a project will be described. It is anticipated that the methods and experiences described will be of interest to other process industry organisations, who wish to deepen their understanding of how to apply human factors analytical techniques to improve performance.
A Comparison between Letters and Phones as Input to Sequence-to-Sequence Models for Speech Synthesis

Neural sequence-to-sequence (S2S) models for text-to-speech synthesis (TTS) may take letter or phone input sequences. Since for many languages phones have a more direct relationship to the acoustic signal, they lead to improved quality. But generating phone transcriptions from text requires an expensive dictionary and an error-prone grapheme-to-phoneme (G2P) model, and the relative improvement over using letters has yet to be quantiﬁed. In approaching this question, we pre-sume that letter-input S2S models must implicitly learn an in-ternal counterpart to G2P conversion and therefore inevitably make errors. Such a model may thus be viewed as phone-input S2S with inaccurate phone input. To quantify this inaccuracy, we compare in this paper a letter-input S2S system to several phone-input systems trained on data with a varying level of error in the phonetic transcription. Our ﬁndings show our letter-input system is equivalent in quality to the phone-input system in which 25% of word tokens in the training data have incorrect phonetic transcriptions. Furthermore, we ﬁnd that for phone-input systems up to 15% of word tokens in the training data can have incorrect phonetic transcriptions without any signiﬁ-cant difference in performance to a 0% error rate system. This suggests it is acceptable to use G2P to predict pronunciations for out-of-vocabulary words (OOVs) provided they are less than around 15% of the training data, removing the need to manually add OOVs to the dictionary for every new training set.
TRIZ Developers Summit 2019 June 13-15 , 2019 . Minsk , Belarus TOWARDS ONTOLOGY OF TRIZ

Today, the development and transformation of TRIZ continues in the following directions: 1. Emergence of new TRIZ tools and refinement of the existing TRIZ concepts. 2) Integration of TRIZ with other knowledge domains (e.g. Design Thinking, Product Development, Lean Production, Product Management, Business System Engineering). In the course of such integration, the necessity arises to match the concepts and models of TRIZ against concepts that belong to the other domains. 3) Development of systems of TRIZ Education and evaluation of knowledge and skills of TRIZ. Due to their limited contents, dictionaries and glossaries of TRIZ terms available do not help much with the development of TRIZ along abovementioned directions. Can an ontology of TRIZ eliminate the deficiency of the dictionaries and glossaries to enhance further development of TRIZ through establishing relations between TRIZ concepts and models? The paper presents an approach targeted at developing, applying and maintaining the ontology of TRIZ.