Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of barley malt, chickpea and peanut on quality of Barley based beverage

The present investigation had been done to optimize the effect of barley malt on production of barley based beverage. Malting of barley was carried out by steeping the cleaned and bold grains in tap water at 16Â°C for 2-3 days. The steeped grains were also germinated at 16Â°C for 2-3 days and the grains showing optimum growth were sorted out and kilning was done at 60Â°C for 1 day. Different levels of malted grain (i.e. 0, 1, 2, and 4 %) in barley extract were optimized. It was found that addition of 4 g malt to the extract was found to be effective in decreasing the viscosity and avoiding the formation of gruel like structure. There was non significant sensory change found on addition of roasted malt grain. Amylase activity of malt significantly increased on increasing time and no reducing sugars resulted at 90Â°C. Nutritive value of malted beverage was improved over control. Total soluble solids (TSS), viscosity, protein, fat, reducing sugar and total sugar of malted beverage was significantly increased as compared to control. Malted beverage was more organoleptically acceptable than control. Final beverage was made with 4 g malt, 25 g bengal gram and 15 g peanut per extract from 100 g barley with addition of sugar to 17Â°brix and homogenizing for proper mixing was autoclaved. Thus, malting could be an appropriate food-based strategy.
Performance improvement speed control of IPMSM drive based on nonlinear current control

Recently model predictive control (MPC) scheme emerges as an efficient current control technique for dynamic performance of motor drives. For excellent dynamic performance, maximum torque per ampere (MTPA) control technique is utilized to achieve maximum torque while using minimum current constrain in contrast to conventional qaxis current control. Model predictive current control (MPCC) scheme alongside MTPA control is employed to replace the traditional constant gain proportional-integral (PI) current control and a nonlinear hysteresis current (HC) control schemes. The PI and hysteresis current controller offers satisfactory performance at ideal conditions but, with variable speed and load conditions, these control schemes cause high current harmonics, high torque ripples, and reduces the speed tracking performance. Therefore, MPCC is proposed to increase the performance of motor drive and reduces the current harmonics and torque ripple at varying load conditions. The proposed design is modeled in Matlab (MathWorks, Inc., Natick, MA, USA), and the results are compared with the traditional speed control schemes to verify the effectiveness. The simulation result shows that the MPCC for IPMSM offers high dynamic performance with reduced steady-state error under variable load conditions compared to conventional control scheme. With MPCC, the overall performance of IPMSM is improved and show robustness.
Zero knowledge based data deduplication using in-line Block Matching protocol for secure cloud storage

: In the area of cloud computing, data deduplication enables the cloud server to store a single copy of data by eliminating redundant files to improve storage and network eﬀiciency. Proof-of-ownership (PoW) is a cryptographic function that verifies the user who really owns the data. Most of the existing schemes have tried to solve the deduplication problem by providing the same encryption key for identical data. However, these schemes suffer from dynamic changes in ownership management. In this paper, we propose an in-line block matching (IBM) protocol based on zero-knowledge proof for deduplication with dynamic ownership management, which eliminates the unauthorized access of sensitive data. In this proposed work, for a new file, the uploader randomly chooses a file encryption key and encrypts the file. The user also computes a unique proof for the uploaded file by dividing the file into number of blocks and stores this proof to the cloud server. The cloud server computes the group key for the ciphertext and re-encrypts it using this group key. The cloud server also does the proof verification against the subsequent uploader for an existing file. The cloud server is honest-but-curious, so the proposed scheme confirms that the cloud server does not know any information about file encryption key even though it plays a proxy role. The result shows that our proposed scheme protects the data from both cloud server and adversaries. Also, the computational cost is comparatively less than other
Spatiotemporal Analysis of Residents in Shanghai by Utilizing Chinese Microblog Weibo Data

Mobile applications are really important nowadays due to providing the accurate check-in data for research. The primary goal of the study is to look into the impact of several forms of entertainment activities on the density dispersal of occupants in Shanghai, China, as well as prototypical check-in data from a location-based social network using a combination of temporal, spatial, and visualization techniques and categories of visitors’ check-ins. This article explores Weibo for big data assessment and its reliability in a variety of categories rather than physically obtained information by examining the link between time, frequency, place, class, and place of check-in based on geographic attributes and related implications. The data for this study came from Weibo, a popular Chinese microblog. It was preprocessed to extract the most important and associated results elements, then converted to geographical information systems format, appraised, and finally displayed using graphs, tables, and heat maps. For data significance, a linear regression model was used, and, for spatial analysis, kernel density estimation was utilized. As per results of hours-to-day usage patterns, enjoyment activities and frequency distribution are produced. Our findings are based on the check-in behaviour of users at amusement locations, the density of check-ins, rush periods for visiting amusement locations, and gender differences. Our data provide light on different elements of human behaviour patterns, the importance of entertainment venues, and their impact in Shanghai. So it can be used in pattern recognition, endorsement structures, and additional multimedia content for these collections.
IoT Cloud-Based Framework for Face Spoofing Detection with Deep Multicolor Feature Learning Model

A face-based authentication system has become an important topic in various fields of IoT applications such as identity validation for social care, crime detection, ATM access, computer security, etc. However, these authentication systems are vulnerable to different attacks. Presentation attacks have become a clear threat for facial biometric-based authentication and security applications. To address this issue, we proposed a deep learning approach for face spoofing detection systems in IoT cloud-based environment. The deep learning approach extracted features from multicolor space to obtain more information from the input face image regarding luminance and chrominance data. These features are combined and selected by the Minimum Redundancy Maximum Relevance (mRMR) algorithm to provide an efficient and discriminate feature set. Finally, the extracted deep color-based features of the face image are used for face spoofing detection in a cloud environment. The proposed method achieves stable results with less training data compared to conventional deep learning methods. This advantage of the proposed approach reduces the time of processing in the training phase and optimizes resource management in storing training data on the cloud. The proposed system was tested and evaluated based on two challenging public access face spoofing databases, namely, Replay-Attack and ROSE-Youtu. The experimental results based on these databases showed that the proposed method achieved satisfactory results compared to the state-of-the-art methods based on an equal error rate (EER) of 0.2% and 3.8%, respectively, for the Replay-Attack and ROSE-Youtu databases.
Towards Supporting Complex Retrieval Tasks Through Graph-Based Information Retrieval and Visual Analytics

The retrieval result analysis approaches of existing retrieval solutions tend to be either too simple, provide too few features for exploring retrieval results or are very narrowly focused. We present an enhanced approach that attempts to address these issues and help the wider community to get more insight from their retrieved data. To this end, this paper presents an enhanced graph-based retrieval prototype built on the Collaboration Spotting platform. It combines information retrieval and visual analytics concepts to provide an advanced solution for data retrieval and exploration. It enables users to retrieve information, explore it from different perspectives using a graph representation and perform further searches based on their navigation and selection interactively. Compared to traditional retrieval solutions, a search action in CS can reveal more detailed aspects/techniques when visually analysing the search output. To gain initial feedback, we interviewed five domain experts in related fields. Findings reveal that the developed retrieval approach provides users with helpful ways of exploring search results and provides mechanisms of connecting features that are not explicitly linked otherwise. Furthermore, several research directions and improvements have been identified for future work, which should be addressed.
Detecting Offensiveness in Social Network Comments

Social media undoubtedly has a signiﬁcant inﬂuence on our lives. Although there are exists many advantages, there are also some disadvantages of social media on society, particularly youth. A very large number of social media users are subjected to diﬀerent types of abuse (such as harassment, racism, personal attacks) everyday. The main goal of MeOﬀendEs@IberLEF 2021 is to promote research on the analysis of oﬀensive language in social networks for Spanish. This paper describes our participation in the shared task of MeOﬀendEs@IberLEF 2021 [40]. We have explored diﬀerent deep learning models such as Long-Short Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) and also traditional Machine Learning models as Logistic Regression or Support Vector Machine (SVM), among others, to classify the comments (written in Spanish) into the four classes deﬁned in the OﬀendEs corpus. The results of our experiments show that BERT obtains the best results among all of our models.
Tractable Compliance Checking with Negation

PL is a low-complexity fragment of OWL2, expressly designed to automatically check the compliance of privacy policies with respect to the consent of data subjects and the regulations on personal data processing, such as the GDPR. Privacy policies, consent and regulations can all be expressed uniformly with PL classes, and compliance checking is reduced to subsumption checking. The latter is tractable and highly scalable to meet the performance requirements of some applications. Several use cases need negation in the queries; however, subsumption checking becomes coNP-hard, even if negation is restricted to atomic concepts. In this paper we show that an alternative encoding based on histories of positive and negative policies (where negation is applied to compound concepts) can be processed in polynomial time by a correct and complete algorithm.
HAHA at FakeDeS 2021: A Fake News Detection Method Based on TF-IDF and Ensemble Machine Learning

This paper describes our participation in the FakeDeS [5] Task at Iberlef 2021: Fake News Detection in Spanish. Base on this task, we propose the classic TF-IDF feature extraction technology and Stacking ensemble learning method base on weak classiﬁers. It not only analyzes the content of the news, but also combines eﬀective information such as publishers and topics to improve the performance of our model. We used ﬁve machine learning models, and achieved very competitive results on both the validation set and the test set, and got the second place in the ﬁnal evaluation phase.
N&&N at HAHA@IberLEF2021: Determining the Mechanism of Spanish Tweets using ColBERT

In modern technologies humor determination has value for the evaluation of chatbots and personal assistants, the purpose of humor detection is to detect whether a tweet, a sentence or even a word have humor or not. We have participated in HAHA@IberLEF2021 competition for detecting humor in Spanish tweets based on the general linguistic structure of humor task1 and determining the mechanism of these tweets for task 3. So, we used the dataset available on this competition, about 24,000 tweets. Our approach was using ColBERT to generate embed-dings for each tweet and then use these embeddings as inputs of hidden layers in a neural network, to helping measure if the tweet is humor or not for task 1. Also, we used sequential Bert for detecting the mechanism of humor for task 3. Our results in evaluation phase for competition given 0.7693 for task 1 (position 12), and 0.0404 for task 3 (position 9).
GuillemGSubies at IDPT2021: Identifying Irony in Portuguese with BERT

This paper describes a system created for the IDPT 2021 shared task, framed within the IberLEF 2021 workshop. We present an approach mainly based on ﬁne-tuned BERT models using a Grid-Search and Data Augmentation with MLM substitution. Our models far out-perform the baselines and achieve results close to to the state-of-the-art.
MultiAzterTest@VaxxStance-IberLEF 2021: Identifying Stances with Language Models and Linguistic Features

Detecting stances is a Natural Language Processing task that has focused mainly on analysing debates and controversial topics. In this case, the VaxxStance@IberLEF 2021 shared task has focused on the Antivaxxers movement in Basque and Spanish tweets. In this paper, we present the participation of the MultiAzterTest team and test two approaches: a language model based approach and a linguistic and stylistic feature based approach. We also introduce the “one stance per tuiter@lari ” heuristic to integrate contextual information. The best re-sults are obtained with language models, but the linguistic and stylistic feature based approach oﬀers more interpretability.
Conversational AI from an Information Retrieval Perspective: Remaining Challenges and a Case for User Simulation

Conversational AI is an emerging field of computer science that engages multiple research communities, from information retrieval to natural language processing to dialogue systems. Within this vast space, we focus on conversational information access, a problem that is uniquely suited to be addressed by the information retrieval community. We argue that despite the significant research activity in this area, progress is mostly limited to component-level improvements. There remains a disconnect between current efforts and truly conversational information access systems. Apart from the inherently challenging nature of the problem, the lack of progress, in large part, can be attributed to the shortage of appropriate evaluation methodology and resources. This paper highlights challenges that render both offline and online evaluation methodologies unsuitable for this problem, and discusses the use of user simulation as a viable solution.
ConvEx-DS: A Dataset for Conversational Explanations in Recommender Systems

Conversational explanations are a novel and promising means to support users’ understanding of the items proposed by a recommender system (RS). Providing details about items and the reasons why they are recommended in a conversational, language-based style allows users to question recommendations in a flexible, user-controlled manner, which may increase the perceived transparency of the system. However, little is known about the impact and implications of providing such explanations, using for example a conversational agent (CA). In particular, there is a lack of datasets that facilitate the implementation of dialog systems with explanatory purposes in RS. In this paper we validate the suitability of an intent model for explanations in the domain of hotels, collecting and annotating 1806 questions asked by study participants, and addressing the perceived helpfulness of the responses generated by an explainable RS using such intent model. Thus, we release an English dataset (ConvEx-DS), containing intent annotations of users’ questions, which can be used to train intent classifiers, and to implement a dialog system with explanatory purpose in the domain of hotels.
Developing ContemporaryWeb-Based Interaction Logging Infrastructure: The Design and Challenges of LogUI

Studies involving user interfaces typically involve the capturing and recording (logging) of key user interactions between the user and the system being examined. However, anecdotal evidence suggests that researchers often implement their own logging infrastructure—sometimes in a piecemeal fashion—which can lead to numerous implementation mistakes (due to misunderstanding or ignoring differences between web browsers , for example). While efforts have been made to develop interaction logging solutions for experimentation and commercial use, many solutions either use obsolete technology, are prohibitively expensive, are complex to use (and require extensive programming knowledge), or have no source code available. To address these issues, we have developed LogUI , an easy-to-use yet powerful interaction logging framework that can capture virtually any user interaction within a web-based environment. LogUI has been successfully used in several user studies since its launch. This paper provides an in-depth discussion into how we have designed LogUI , and provides narrative on the key challenges that we are looking to address moving forward.
What if the whole is greater than the sum of the parts? Modelling Complex (Multiword) Expressions (invited paper)

Multiword Expressions (MWEs) such as idioms (make ends meet), light verb constructions (give a sigh), verb particle constructions (shake up) and noun compounds (loan shark), are an integral part of the mental lexicon of native speakers often used to express complex ideas in a simple and conventionalised way accepted by a given linguistic community. As they may display a wealth of idiosyncrasies, from lexical, syntactic and semantic to statistical, they have represented a real challenge for natural language processing. However, their accurate integration has the potential for improving the precision, naturalness and fluency of downstream tasks like text simplification. In this paper I discuss some advances in the identification and modelling of MWEs, concentrating on techniques for identifying their degree of idiomaticity and approximating their meaning. One of the challenges is that their interpretation often needs more knowledge than can be gathered from their individual components and their combinations to differentiate combinations whose meaning can be (partly) inferred from their parts (as apple juice: juice made of apples) from those that cannot (as dark horse: an unknown candidate who unexpectedly succeeds). In particular, the paper presents results obtained with the use of contextualised word representation models, which have been successfully used for capturing different word usages, and therefore could provide an attractive alternative for representing idiomaticity in language.
The effects of the electrochemical properties of pyrite by polypyrrole (PPy)

ABSTRACT With the shortage of energy and the environmental problems becoming more and more serious, it has become one of the most urgent problems to find a new type of high specific energy. FeS2 has become one of the most promising cathode materials for high energy lithium batteries because of its advantages. In this paper, the polypyrrole (PPy) polymerization method was used, through preparation of Fe0.9Ni0. 1S2/PPy composite materials with FeCl3 precursor solution, which had smooth surface and regular structure, and which can be seen between the polymerization of pyrrole around the large particles Fe0.9Ni0. 1S2, and the electrochemical performance is greatly improved. At the rate of 0.02 of discharge, the maximum discharge capacity of the battery was 1100 mAh/g, which was 20% higher than that of the primary powder. The discharge capacity at the rate of 1 was 400 mAh /g and remained at 300 mAh/g after the 20 cycles.
User Knowledge and Search Goals in Information Retrieval: A Benchmark and Study on the Evolution of Users' Knowledge Gain

This abstract presents an Information Retrieval framework that personalises results based on the user’s knowledge and search goals. The framework utilises the content of the pages visited by the user to represent his/her knowledge, and a set of questions/statements the user wishes to answer to represent his/her search goals. In the absence of related datasets and benchmarks, we propose a methodology to evaluate the framework.
A Binary Adaptive Clone Shuffled Frog Leaping Algorithm for Three-Dimensional Low-Energy Target Coverage Optimization in Environmental Monitoring Wireless Sensor Networks

In recent years, more and more researchers have paid attention to the three-dimensional target coverage of environmental monitoring wireless sensor networks (EMWSNs) under real environmental conditions. However, the target coverage method studied in the traditional two-dimensional plane is full of loopholes when applied in the real three-dimensional physical world. Most coverage algorithms usually only optimize for a single problem of target coverage or network energy consumption and cannot reduce network energy consumption while improving coverage. This paper proposes a novel binary adaptive clone shuffled leapfrog algorithm (BACSFLA) suitable for EMWSNs. BACSFLA has an excellent performance in the coverage of three-dimensional nodes, which can significantly reduce the network energy consumption of ENWSNs in the coverage process, and greatly improve the coverage of nodes. Through simulation experiments, BACSFLA was compared with simulated annealing (SA) and genetic algorithm (GA) in the same conditional parameters. The coverage rate of BACSFLA in EMWSNs is 3.9 % higher than that of GA and 5.4 % higher than that of SA. The network energy consumption of BACSFLA is 36.0 % lower than GA and 35.9 % lower than SA. Moreover, BACSFLA can significantly reduce the calculation time and get better results in a shorter time.
A Feasibility Study on Monitoring Pile-Soil Bonding Condition Using Piezoceramic Transducer and Horizontal Impact

The in situ evaluation of pile-soil bonding condition plays an important role for pile safety assessment in its life cycle. However, so far, there is still no fully mature tool to analyze such couplings, since the pile-soil coupling exhibits complex and time-varying relationships. This paper innovatively proposes a health monitoring approach to evaluate the bonding status of the soil and pile contact area. An impact method based on a piezoelectric ceramic sensor is proposed to monitor the bond of pile and soil. A horizontal impact was introduced near the top of the pile, and the induced stress waves were detected by the piezoceramic smart aggregate (SA) sensor embedded in the pile. Different crack damage sizes were made between the soil and the pile to investigate the change of the bonding. An energy index was developed to quantitatively evaluate the quality of the bonding as a pile-soil bonding index. The proposed approach inspired a potential way to directly judge if there is crack damage between the pile and soil and to evaluate pile safety.