Reviewerly/exHarmony huggingface dataset:
An Overview of Cross-Language Information Retrieval

With the development of the Internet, various language resources are becoming more and more abundant. Users are no longer satisfied with obtaining information expressed only in their mother tongue, and there is a growing need to retrieve documents in other languages. It demands the advancement of technologies to eliminate the communication barriers among languages. Cross-language information retrieval (CLIR), as a sub-field of information retrieval, can meet the purpose of users to retrieve information in other languages which are different from the queries, thus greatly improving the ability of users to retrieve multi-language documents. This paper introduces the translation techniques such as query translation, document translation and machine translation used in cross-language information retrieval, and analyzes their advantages and disadvantages. Besides, we also discuss the main challenges in cross-language information retrieval. We also introduce some of the latest algorithms and techniques, such as word embedding and query expansion. Finally, we summarize the overview and make a further outlook about CLIR.
Access Control Based on Proxy Re-encryption Technology for Personal Health Record Systems

As an emerging electronic medical technology, PHR (Personal Health Record) systems play a key role in disease monitoring and improves medical efficiency. Because medical data carries patient privacy data, if it is falsified or illegally obtained, it will cause medical accidents and endanger the lives of patients. Therefore, strict medical data access control is required. Ciphertext-based attribute encryption (CP-ABE) supports access control of encrypted data, which is a promising technology for PHR systems. However, the current CP-ABE scheme consumes a large amount of computing resources during the encryption and decryption process. In addition, it is difficult to implement dynamically changing and a large variety of user private key updates. In this paper, a non-dual linear proxy re-encryption method is proposed, which reduces the computational overhead on both the encryption and decryption ends. At the same time, the concept of attribute group is used to realize the dynamic update of ciphertext.
Process of Inversion in Fuzzy Interpolation Model using Fuzzy Geometry

Fuzzy rule interpolation (FRI) predicts an accountable outcome of a possible course of action in sparse fuzzy rule base system (FRBS). However, in real life, we encounter some situations where the antecedent has to be predicted to obtain a desired consequent of FRBS. In this situation, inverse fuzzy rule interpolation (IFRI) or backward fuzzy rule interpolation (BFRI) is used to get the desired outcome. Here a geometry based inverse fuzzy rule base interpolation (GIFRI) is suggested. The mathematical detail of the proposed method is elaborated and its geometrical interpretation is given with the help of fuzzy geometry. It is to be noted that the proposed method ensures that the inverse of the inverse is the original one.
RES-TOOL To Overcome Requirements Elicitation Barriers In Pakistan Software Industry

Requirements Elicitation is the most integral part of the Requirement Engineering. It is observed that if requirements are gathered cautiously, the success rate is increased for a project. Requirements Elicitation requires cooperation with the client and all other stakeholders. With the help of different case studies, we observed that a lot of software fail due to Requirements Elicitation barriers. These barriers can be either of Cultural or Software context, and differences in perspectives of stakeholders or clients. This research investigates various barriers to Requirements Elicitation process in Pakistan Software Industry and proposes solutions to overcome them with the help of RES-TOOL (Requirements Elicitation System Tool) that enables us to calculate success rate based on requirements elicitation barriers occurrence.
MIDA: a Web Tool for MIssing DAta Imputation based on a Boosted and Incremental Learning Algorithm

One of the main issues in machine learning is related to the quality of data used to efficiently train statistical models for classification/regression tasks. Among these issues, the presence of missing values in data sets is particularly prone in affecting the accuracy performance of learning methods. As a consequence there is a strong emergence of software tools aimed at supporting machine learning users in "filling-in" their data sets before inputting them to training algorithms. This paper bridges this gap by introducing a web-based tool for MIssing DAta imputation (MIDA) based on a novel supervised learning method, namely Generalized Boosted Incremental Non Parametric Imputation algorithm (G-BINPI), able to address the missing values issue in scenarios where a "missing at random" assumption occurs. The proposed approach enables machine learning users to remotely imputing their data sets by means of an intuitive graphical user interface. As highlighted in the experimental section, the proposed approach yields better performance than conventional approaches for missing data imputation on different benchmark data sets.
Event-triggered particle filtering and Cramér–Rao lower bound computation

In this article, an event-triggered particle filtering method is presented to estimate the states of stochastic nonlinear systems with the ultimate goal to reduce the information exchange in networked systems. In the event-triggered estimation, measurements are transferred to an estimator only if certain event conditions are satisfied. Using these event-triggered measurements leads to non-Gaussianity of the conditional posterior distribution in minimum mean square error estimators even in the presence of Gaussian process and measurement noises. Therefore, in this article, a particle filter–based method is employed to solve the non-Gaussianity issue in nonlinear systems due to event-triggered measurements. In the proposed scheme, when no information is sent to the estimator, particles weight update role is modified according to the event-triggering probability density function. To evaluate the performance of the proposed state estimation scheme, the conditional posterior Cramér–Rao lower bound is obtained using Monte Carlo simulations. The bound is also computed for nonlinear Gaussian systems with a Gaussian event-triggering mechanism as a special case. Finally, the efficiency of the proposed method is demonstrated for a networked interconnected four-tank system through simulation and a comparison study is also provided.
Hierarchical multi-label propagation using speaking face graphs for multimodal person discovery

TV archives are growing in size so fast that manually indexing becomes unfeasible. Automatic indexing techniques can be applied to overcome this issue, and this work proposes an unsupervised technique for multimodal person discovery. To achieve this goal, we propose a hierarchical label propagation technique based on quasi-flat zones theory, that learns from labeled and unlabeled data and propagates names through a multimodal graph representation. In this representation, we combine audio, video, and text processing techniques to model the data as a graph of speaking faces. In the proposed modeling, we extract names via optical character recognition and propagate them through the graph using audiovisual relationships between speaking faces. We also use a random walk label propagation and two graph clustering strategies to serve as baselines. The proposed label propagation techniques always outperform the clustering baselines on the quantitative assessments. Our approach also outperforms all literature methods tested on the same dataset except for one, which uses a different preprocessing step. The proposed hierarchical label propagation and the random walk baseline produce highly equivalent results according to the Kappa coefficient, but the hierarchical propagation is parameter-free and over 9 times faster than the random walk under the same configurations.
A Blockchain-Based Tracking System

A tracking system is used to locate the geographical coordinates of an object and provide visibility to the involved parties. Tracking systems have existed in various domains like manufacturing, warehousing, attendance, etc. These systems played an important role in the success of e-commerce platforms such as Amazon, Walmart, eBay, etc. The main problems with the existing tracking system are real-time traceability, inefficiency, and manual errors. As these systems store data at a centralized location, it can be tampered and manipulated leading to fraudulent transactions, thefts and counterfeiting. To overcome the inabilities of current systems, we propose a tracking system that uses blockchain technology at its core. The proposed system incorporates characteristics such as accountability, authorization, audit-ability, integrity, punctuality, and honesty, which make a robust tracking system. The design of the proposed system is based on QuarkChain, a highly flexible and scalable blockchain infrastructure based on a sharded blockchain protocol implemented using a two-layer architecture. The complete architecture, implementation methodology, system design are discussed in the paper.
Digital Learning: A Proficient Digital Learning Technology Beyond to Classroom and Traditional Learning

Education is a vital basis of any country towards the development and sustainability of continuous growth for a long term. The system of education should be ahead with new technologies to achieve more benefits at every stage of improvement or enlargement of a nation. Today’s era is fully digital era in every sectors; however, the education system is always trying to function according to latest trends, move parallel with new technological aspects throughout the earlier few years. Digital learning and ICT (information and communication technology) tools bond the teachers, academics, parents, experts and institutions. Digital learning also motivates to use latest technologies to deliver, communicate and share to each role players and vice versa. The key concern of the paper is to represent a clear glance of digital learning phenomena and to touch almost every aspects concern to digital learning in brief. This paper will try to deliver from general idea aspect to analysis aspect about digital learning.
Blockchain Technology

Blockchain, as the name suggests, is a linear chain of blocks. It is a digital ledger that holds information on transactions taking place over the web. So every block contains data in the form of coding that is organized in a chronological manner. In 2004, a concept called “reusable proofs of work” was introduced by Hal Finney. In 2009, a mysterious white paper titled “Bitcoin: A Peer to Peer Electronic Cash System,” by visionary Satoshi Nakamoto gave birth to the concept of blockchain. This is a survey of blockchain technology that first provides a short introduction of the blockchain, discussing its advantages and followed by possible limitations and their possibilities for the future.
Customer Support Chatbot Using Machine Learning

In customer support, chatbot by using machine learning customer can converse by a chatbot and acquire the query intent information. With the enhancement of globalization and industrialization, it becomes a problem for enterprises to interact with the customer and listen to their difficulties to a big extent. Chatbots make ease the pain that the industries nowadays facing. The aim of this chatbot is to support and reply to the client by giving him/her the relevant intent depending on the query request from the customers.
Homomorphic String Search with Constant Multiplicative Depth

String search finds occurrences of patterns in a larger text. This general problem occurs in various application scenarios, f.e. Internet search, text processing, DNA analysis, etc. Using somewhat homomorphic encryption with SIMD packing, we provide an efficient string search protocol that allows to perform a private search in outsourced data with minimal preprocessing. At the base of the string search protocol lies a randomized homomorphic equality circuit whose depth is independent of the pattern length. This circuit not only improves the performance but also increases the practicality of our protocol as it requires the same set of encryption parameters for a wide range of patterns of different lengths. This constant depth algorithm is about 12 times faster than the prior work. It takes about 5 minutes on an average laptop to find the positions of a string with at most 50 UTF-32 characters in a text with 1000 characters. In addition, we provide a method that compresses the search results, thus reducing the communication cost of the protocol. For example, the communication complexity for searching a string with 50 characters in a text of length 10000 is about 347 KB and 13.9 MB for a text with 1000000 characters.
Smart tourist guide application for the introduction of Malang city tourism potential using hybrid technology

Abstract Tourism in Malang City with Strategic City Branding (Beautiful Malang) is one sector that contributes significantly to efforts to increase regional income. The variety of types of tourism that exist, it needs to be supported by the existence of a good information dissemination system. The existence of this system makes it easy for women to get access to regional tourism information. In this study, two technologies were used, namely geographic information systems and augmented reality. The use of GIS technology aims to provide information that is geographically related to a tourism object. While Augmented reality is used to bring up 3D objects along with information relating to the tourism object. In its application, this application was successfully used with 90% of respondents expressed satisfaction with the performance of the application. The rest complained about the delay when scanning the tourism object markers. The use of this hybrid technology is expected to be able to provide tourism information with more attractive packaging as well as an educational media for tourism promotion. For further development, it is necessary to apply an efficient data parsing concept so that there is no delay when there is a data request.
Towards Emotion Independent Language Identification System

Language Identification (LID) is an integral part of multilingual speech systems. There are various conditions under which the performance of LID systems are sub-optimal, such as short duration, noise, channel variation, and so on. There has been effort to improve performance under these conditions, but the impact of speaker emotion variation on the performance of LID systems has not been studied. It is observed that the performance of LID systems degrade in the presence of emotional mismatch between train and test conditions. To that effect, we investigated adaptation approaches for improving the performance of LID systems by incorporating emotional utterances in form of adaptation dataset. Hence, we studied a prosody modification technique called Flexible Analysis Synthesis Tool (FAST) to vary the emotional characteristics of an utterance in order to improve the performance, but the results were inconsistent and not satisfactory. In this work, we propose a combination of Recurrent Convolutional Neural Network (RCNN) based architecture with multi stage training methodology, which outperformed state-ofart LID systems such as i-vectors, time delay neural network, long short term memory, and deep neural network x-vector.
Testing for adequacy of seasonal adjustment in the frequency domain

Peaks in the spectral density estimates of seasonally adjusted data are indicative of an inadequate adjustment. Spectral peaks are currently assessed in the X-13ARIMA-SEATS program via the visual significance (VS) approach; this paper provides a rigorous statistical foundation for VS by defining measures of uncertainty for spectral peak measures, allowing for formal hypothesis testing, using the framework of fixed-bandwidth fraction asymptotics for taper-based spectral density estimates. The simulation results show that the test has good size and power properties for a variety of peak features.
Using Smartphones as Constructivist Learning Tools

Although smartphones bring great advantages to different spheres of life, they could also bring negative influences. Numerous benefits of using mobile technology have been identified however, that does not exempt them from having many disadvantages or distractions. These smartphone distractions are at the center of this study whose aim is to examine perceptions of academic staff on the factors that are affecting the use of smartphones as a constructivist learning tool by students, rather than as a mere communication and distraction gadget. The existing research gap is on whether the distractions posed by smartphones in learning could be turned into a positive by using smartphones for constructivist learning. Students face many challenges that at times force them to not be able to physically attend classes. A good example of these challenges is Covid-19, where everyone was forced to stay at home in order to control the spread of it. These challenges or problems prove the necessity of technology such as smartphones, where students can be able to utilize them to pursue their learning while they are away from school. Literature was used to identify key constructivist learning tool characteristics, a framework for maximizing factors to use smartphones as a constructivist tool was created and this framework was empirically tested. A Likert-scale questionnaire-based survey of academic staff from a university in Durban, South Africa, was conducted to determine whether the factors identified in the framework, in their opinion, influence the successful use of smart phones as constructivist tools by students. The data were analyzed for reliability and validity of the data gathering instrument; descriptive and inferential statistical results, factor analysis, which included the principal component matrix, and regression was used. The results indicate that the participants, i.e. members of academic staff, agreed that the factors influencing students' use of smartphones as constructivist learning tools would be influenced by attitudes, facilitating conditions, perceived usefulness, performance expectation and perceived ease of use.
Preliminary investigations of the agreement between two wearable accelerometers for use in clinical studies

Healthcare providers continue to address the challenges caused by aging populations, chronic disease management, hospitalization costs, as well as significant legal risks. Characterisation of physical activity and sedentary behaviour within ambulatory conditions is becoming increasingly popular in view of growing evidence for the health implications of these tendencies. Wearable technology can potentially offer a solution to these problems by using state-of-the-art sensors and wearable systems as well as secure and effective networks of communication between patients and clinicians. The primary aim of this study is to investigate levels of agreement between two wearable sensors for use in clinical studies. Establishing validity of these wearable devices is of particular interest as they may be utilised in future clinical studies to monitor sleep and activity patterns over prolonged time periods. Initial visualization of data from physical activity and periods of inactivity show high similarity between devices for ambulatory conditions and standardized activities. However, future steps concerning alignment of timestamps needs to be utilized in order to coordinate the devices' outputs.
Speaker-Dependent BiLSTM-Based Phrasing

Phrase boundary detection is an important part of text-to-speech systems since it ensures more natural speech synthesis outputs. However, the problem of phrasing is ambiguous, especially per speaker and per style. This is the reason why this paper focuses on speaker-dependent phrasing for the purposes of speech synthesis, using a neural network model with a speaker code. We also describe results of a listening test focused on incorrectly detected breaks because it turned out that some mistakes could be actually fine, not wrong.
Graph Convolutional Networks for Student Answers Assessment

Graph Convolutional Networks have achieved impressive results in multiple NLP tasks such as text classification. However, this approach has not been explored yet for the student answer assessment task. In this work, we propose to use Graph Convolutional Networks to automatically assess freely generated student answers within the context of dialogue-based intelligent tutoring systems. We convert this task to a node classification task. First, we build a DTGrade graph where each node represents the concatenation of the student answer and its corresponding reference answer whereas the edges represent the relatedness between nodes. Second, the DTGrade graph is fed to two layers of Graph Convolutional Networks. Finally, the output of the second layer is fed to a softmax layer. The empirical results showed that our model reached the state-of-the-art results by obtaining an accuracy of 73%.
Formally Verified Timing Computation for Non-deterministic Horizontal Turns During Aircraft Collision Avoidance Maneuvers

We develop a library of proofs to support rigorous mathematical reasoning about horizontal aircraft turning maneuvers, and apply it to formally verify a timing computation for use during mixed horizontal and vertical aircraft collision avoidance maneuvers. We consider turns that follow non-deterministic circular turn-to-bearing horizontal motion, formalizing path-length and timing properties. These kinematics are the building blocks for Dubins trajectories, and can be used to formalize a variety of techniques, including those that contain non-determinism. The timing computation establishes, for intersecting trajectories, the exact bounds of time intervals when the horizontal position of the aircraft might coincide, and during which they must be at different altitudes to avoid collision.
Verifiable and Scalable Mission-Plan Synthesis for Autonomous Agents

The problem of synthesizing mission plans for multiple autonomous agents, including path planning and task scheduling, is often complex. Employing model checking alone to solve the problem might not be feasible, especially when the number of agents grows or requirements include real-time constraints. In this paper, we propose a novel approach called MCRL that integrates model checking and reinforcement learning to overcome this limitation. Our approach employs timed automata and timed computation tree logic to describe the autonomous agents’ behavior and requirements, and trains the model by a reinforcement learning algorithm, namely Q-learning, to populate a table used to restrict the state space of the model. Our method provides means to synthesize mission plans for multi-agent systems whose complexity exceeds the scalability boundaries of exhaustive model checking, but also to analyze and verify synthesized mission plans to ensure given requirements. We evaluate the proposed method on various scenarios involving autonomous agents, as well as present comparisons with two similar approaches, TAMAA and UPPAAL STRATEGO. The evaluation shows that MCRL performs better for a number of agents greater than three.