Reviewerly/exHarmony huggingface dataset:
IRTEX: Image Retrieval with Textual Explanations

In a Content Based Image Retrieval (CBIR) system, images are retrieved based on the content of the images such as color, shapes and objects. However, often end users experience a semantic gap between the notion of similarity used by the ranking model versus the users’ perception about image similarity. Explainable AI (XAI) is an emerging research field that attempts to provide transparency of “black box” models, to make AI systems trustworthy and gain user trust. This work aims at building an I mage R etrieval system with T extual EX planations such as “The (global) results are similar to the query by X% due to shape, Y% due to color”. Local explanations are generated using various methods such as comparing images with respect to overlap between low level features such as color, shape and regions (MPEG-7 features). Additionally, high level features such as background-foreground segmentation, deep learned features and major key-points, objects identified (SIFT features) are used to enrich the explanations. We evaluate the quality of rankings on benchmark data-sets such as PASCAL VOC. The XAI facet of user satisfaction and usefulness of the system is evaluated in a lab based user study. Our results show that the semantic gap is better bridged using high level features. While low level features might be better suited for re-ranking of the retrieved images.
Toxic Comment Analysis for Online Learning

Due to recent circumstances of the pandemic, online platforms are becoming more and more essential for communication in many sectors. But because of this, a lot of negativity and toxic comments are surfacing, resulting in degradation and online abuse. Educational systems and Institutions heavily rely on such platforms for e-learning leading to unrestricted attacks of toxic and negative comments towards teachers and students. Due to this work, issues of constant bullying and online abuse will be reduced. The comments classified are according to the parameters from our self-prepared dataset combined with Kaggle's toxic comment dataset, named as toxic, severely toxic, obscene, threat, insult, and identity hate. Machine Learning algorithms such as Logistic Regression, Random Forest, and Multinomial Naive Bayes are used. For data evaluation, ROC and Hamming scores are used. The output will be shown as the rate of each category in percentile and in a graphical format. This work will help reduce the online bullying and harassment faced by teachers and students and help create a non-toxic learning environment. In this way, the main focus will be on studying and not getting de-motivated and discouraged by hateful comments and people commenting toxic comments will also get reduced.
A Fact-checking Assistant System for Textual Documents

This paper proposes a system for identifying which parts of textual documents the editors should do fact-checking. Using our system, we can reduce editors’ time and efforts by identifying descriptions that need fact-checking. To accomplish this purpose, we construct a machine-learning-based classifier of sentences, which classifies a part of documents into four classes: according to the necessity of fact-checking. We assume that there are typical descriptions which contain misinformation. Therefore, if we collect the documents and their revised documents, and labels whether their revisions are corrections or not, we can construct the classifier by learning the dataset. To construct this classifier, we build a dataset that includes a set of sentences which are revised more than once, from Wikipedia edit history. The labels indicate the degree of sentence corrections by editors. We develop a Web-based system for demonstrating our proposed approach. When we input texts, the system predicts which parts of the texts the editors should re-confirm the facts.
Spectral representations of digital signals using non-binary Galois fields.

The article shown that for digital signal processing varying in a limited range of amplitudes it is advisable to consider a set of signal levels through its mapping into certain Galois field, i.e., finite commutative body. In this case, the signal coding differs from binary, however, this creates quite definite advantages. In particular, a modified Walsh basis where the elements +1 and -1 are treated as elements of a non-binary Galois field can be used. The main difference of such use of the Walsh basis is that the elements of the Galois field corresponding to the spectral components belong to the same set as the original signal levels do. This provides a significant reduction in the amount of information when transmitting information about the signal, presented in the form of its spectrum. A specific example of using the Galois field for processing a time series of data that simulates a digital signal is presented
Scalable Monte Carlo Tree Search for CAV s Action Planning in Colliding Scenarios

Connected and autonomous vehiles (CAVs) require an effective cooperative action planning strategy in an emergency situation. Monte Carlo Tree Search (MCTS) is a promising planning technique for such problems with large state spaces. However, traditional MCTS-based techniques do not scale well with the number of vehicles. In this paper, we present a novel MCTS-based cooperative action planning algorithm for CAV s driving in a coalition formation. Our proposed algorithm improves the reliability and the scalability of M CTS. Explicit communication is used to ensure that mitigation action plans chosen by the CAVs are conflict-free when possible. We perform the evaluation of the proposed algorithm in a large scale multi-agent based traffic simulation system. Our simulated experiments show that our approach improves upon current state-of-the-art centralized and decentralized algorithms.
Improvement of Food Region Extraction based on Prior Knowledge and Comparative Evaluation using Actual Images

In this paper, we propose a method which can extract food regions from food images to improve extraction accuracy compared with a conventional method by decreasing misdetermination of the food regions as the background regions. The proposed method uses a convex hull based on the local extrema and their density to generate initial seeds for GrabCut, which can revise the food and background regions on the basis of color similarity and distribution. Our experiment demonstrated that the proposed method significantly increased the F-measure, which shows the comprehensive extraction accuracy, by 4.41% or more compared with the conventional method. The proposed method also increased the F-measure by 4.54% or more compared with SegNet based on deep convolutional neural network trained by 1017 food images available on the Internet. These results provided the fact that the proposed method was effective in the extraction accuracy compared with the existing methods which can be constructed by limited introduction cost.
A New Space-Variant Optimization Approach for Image Segmentation

In this paper, we introduce a new variational image segmentation model based on a non-convex regularization and an efficient parameter selection to better conserve the image structures and control the segmentation of the boundary with less intensity inhomogeneities. The proposed regularization associated to the automatic selection of the parameter \(\lambda \) allows us to segment an image with low intensities loss. In addition, we introduce fast and efficient optimization algorithms that can handle the non-smoothness of the objective function. In fact, the output segmented regions in the image, as well as edges construction are better conserved. Selected numerical experiments demonstrate that the segmented images can cope with some sate of art segmentation results.
Adaptive Control of Chaotic Signals: Investigated by Simulation Software and Real Electronic Circuits

Chaotic behavior is complicated, sensitive, and has the feature of great variety, which are the most potential signals to be applied in data encryption, secure communication, medical information protection, etc. As a consequence, in this paper, we try to propose three different ways to show our data generating results step by step, which means it can be proved effectively and used in practice: (1) Chaotic solutions simulated by MATLAB, (2) chaotic motion drawn via electronic circuits software Multisim, and (3) chaotic signal implemented on real electronic circuits with breadboard. In advance, following the same design principal, the adaptive chaotic signal is also designed and presented in the end of this article for further study, which provides a more flexible and variable chaotic signal to enhance the encryption effectiveness. The experimental results are extremely close to the two simulation results and can definitely be technically transferred to real encryption application.
Technical University Students’ Creativity Development in Competence-Based Foreign Language Classes

Technical university students should master general competencies which are as follows: critical thinking, project and teamwork, leadership, communication, cross-cultural interaction and self-development. In modern society graduates have to be highly-competitive, ready to further career development, capable of quick adaptation in constantly changing world. The article demonstrates the application of various creative learning technologies (participation at the Olympiads and forums; use of project technologies; debates; use of game techniques; listening and using various authentic sources) at foreign language classes based on a competence approach and it shows how learning English through creative technologies helps students better master the foreign language. The authors suggest the idea that creatively thinking teachers using the different creative learning activities at the lessons can contribute to formation of creatively thinking students capable to act at different language environment adequately and to become critically thinking specialists at their field, good professional team members and constantly developing personalities with high level of both language and soft skills. The results of the creative techniques usage were illustrated with the help of a pedagogical experiment showing substantiation of using creativity development technologies. The students at the experimental group, in which creative techniques were used, showed greater progress at language and soft skills mastering. The correlation between the development of competencies and the use of some creative techniques was summarized.
Aeolus: A Building Block for Proactive Transport in Datacenter Networks

As datacenter network bandwidth keeps growing, proactive transport becomes attractive, where bandwidth is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">proactively</i> allocated as “credits” to senders who then can send “scheduled packets” at a right rate to ensure high link utilization, low latency, and zero packet loss. Consequently, proactive solutions such as ExpressPass, NDP, Homa, etc., have been proposed recently. While promising, a fundamental challenge is that proactive transport requires at least one-RTT for credits to be computed and delivered. In this paper, we show such one-RTT “pre-credit” phase could carry a substantial amount of flows at high link-speeds, but none of existing proactive solutions treats it appropriately. We present Aeolus, a solution focusing on “pre-credit” packet transmission as a building block for proactive transports. Aeolus contains unconventional design principles such as scheduled-packet-first (SPF) that de-prioritizes the first-RTT packets, instead of prioritizing them as prior work. It further exploits the preserved, deterministic nature of proactive transport as a means to recover lost first-RTT packets efficiently. Aeolus is compatible with all existing proactive solutions and readily implementable with commodity switches. We have integrated Aeolus into ExpressPass, NDP and Homa, and shown, via both implementation and simulations, that the Aeolus-enhanced solutions deliver significant performance or deployability advantages. For example, it improves the average FCT of ExpressPass by 56%, cuts the tail FCT of Homa by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\times $ </tex-math></inline-formula> , while achieving similar performance as NDP without switch modifications.
Low-Resource Neural Machine Translation Based on Improved Reptile Meta-learning Method

Multilingual transfer learning has been proved an effective method to solve the problem of low-resource neural machine translation (NMT). However, the global optimal parameters obtained through transfer learning can not effectively adapt to new tasks, which means the problem of local optimum will be caused when training the new task model. Although this problem can be alleviated by optimization-based meta-learning methods, but meta-parameters are determined by the second-order gradient term corresponding to the model parameters of a specific task, which consumes a lot of computing resources. Therefore, we proposed improved reptile meta-learning method. First, a multilingual unified word embedding method is proposed to represent multilingual knowledge. Secondly, the direction of meta-gradient is guided by calculating cumulative gradients on multiple specific tasks. In addition, the midpoint is taken as the meta-parameter in the space of the initial meta-parameter and the final task-specific model parameter to ensure that the meta-model has better multi-feature generalization ability. We conducted experiments in the CCMT2019 Mongolian-Chinese (Mo-Zh), Uyghur-Chinese (Uy-Zh) and Tibetan-Chinese (Ti-Zh), and the results show that our method has significantly improved the translation quality compared with the traditional methods.
Application and prospect of blockchain and smart contract technology in pharmaceutical supply chain

The advent of blockchain technology has transformed traditional business processes from centralized to decentralized. By eliminating the unnecessary intervention of middlemen, it can reduce the overall cost of patient medication by turning the drug supply chain entirely into a point-to-point decentralized business. This paper presents a reliable and encouraging P2P drug trading block chain technology and four related smart contracts. These contracts include consumer contracts and the supply, bidding and trading of drugs, which have been deployed on the Ethereum blockchain for the decentralized trading of drugs. We will use the Approach of Real cost descending (RCD) to achieve incentive transactions for suppliers and patients. This method provides P2P transactions and ensures the safety and transparency of drug data, as well as the anonymity of users in the transaction process. Finally, according to the requirements of Good Supplying Practice(GSP), the effectiveness of the proposed model is evaluated and analyzed.
Modular Adder Designs Based On Thermometer Coding And One-Hot Coding

Modular adders are used in various applications of computer systems. Modular addition is commonly used in residue number system processors. It is used mainly in the residue arithmetic unit and also in both the forward and reverse converters. Residue number system is highly efficient when compared with positional number system, because it provides high speed computation as well as less area requirement. In order to improve the computation speed, efficient modular adders are required. Modular adders based on thermometer code residue and one hot code residue are used for this purpose. This results in less latency and area. This approach reduces the area and delay of modular adders since there is no carry bit propagation during modular addition operation. It also simplifies the structure of modular adders compared to conventional binary based modular adders. All the proposed modular adders are described in verilog HDL and verified using Xilinx ISE.
Feature Selection Approach for Oil Palm Fruit Grading Expert System

The developing need to supply evaluated quality palm oil items inside a brief timeframe has given high need to Automated Grading of Agricultural Products. There have been numerous endeavors by scientists around the globe to create arranging machines equipped for reviewing natural products by size, color yet in addition fit for perceiving extra highlights and different deformities utilizing various systems. Since color of fruit fluctuates from one locale to another as a result of geological areas, extra component can been added to help the choice cycle of evaluating utilizing fuzzy logic. We present a productive technique for choosing significant information factors when fabricating a fuzzy model from information. Earlier techniques for feature selection required producing various models while looking for the ideal blend of factors; our strategy requires creating just one model that utilizes all conceivable information factors. To decide the significant factors, premises in the fuzzy rules of this underlying model are efficiently eliminated to look for the best worked on model without really creating any new models. This expert system will without a doubt eliminate the vulnerability in decision making and lower the mistakes presented utilizing human reviewing. The proposed technique additionally improves the viability when contrasted with the traditional algorithms and strategies.
A Novel Security System for IoT Applications

IoT contains a large amount of data as well as privacy sensitive information. This information can be easily identified by the various cyber-attackers. Attackers may steel the data or they can introduce viruses and other malicious software to damage the network. So to overcome these issues, we need to establish an efficient security mechanism for data protection in IoT systems. Advanced Encryption standard is one of the popular security protocols for IoT systems. The main disadvantage of this standard is power consumption for large sized IoT networks. In this paper we proposed an novel mechanism for secure communication in large sized IoT systems. In our design, we use a Hash based Message Authentication Code (HMAC) and a privacy-preserving communication protocol for chaos-based encryption. We observe that proposed mechanism consumes relatively less power over AES with less storage resources.
Performance Analysis of Light Illuminations and Image Quality Variations and its Effects on Face Recognition

Nowadays face recognition is considered as an active and important area of research in the field of biometric technology as it is a non-contact method. Face recognition technology is employed in wide area of applications such as access management, authentication and also used in defence surveillance systems. The challenges like quality of image, different light illuminations, pose of a person and distance of person from camera may affect accuracy of face recognition. To overcome some of these challenges we proposed and evaluated our approach with two preprocessing techniques for image enhancement namely Histogram equalization (HE) and Contrast limited adaptive histogram equalization (CLAHE). The results are validated with variations in light intensity and person to camera distances in indoor as well as outdoor environments. The performance is measured in terms of Peak signal-to-noise ratio, mean square error and entropy and is evaluated for distance variation from 1 meter to 100 meter and lux variation from almost 0 lux to 32000 lux.
Knowledge Adaptation for Cross-Domain Opinion Mining

Automatic opinion mining of web 2.0 texts is critical for understanding people's viewpoints and assisting them in making informed decisions. Trained machines perform well in the same domain to predict the sentiment polarity but performance decreases drastically when the same machine is applied directly to other domains. Creating a labeled data for every field is an expensive and inefficient procedure. We introduce a framework to determine the domain-independent words in both domains by employing feature extraction techniques to bridge the gap across the domains. To train a classifier and analyze the sentiment polarity of the target domain, we employed these features. The experimental results are compared with different existing state-of-art approaches and evaluate the execution and effectiveness of the suggested framework.
Stochastic Simulation Techniques for Inference and Sensitivity Analysis of Bayesian Attack Graphs

A vulnerability scan combined with information about a computer network can be used to create an attack graph, a model of how the elements of a network could be used in an attack to reach specific states or goals in the network. These graphs can be understood probabilistically by turning them into Bayesian attack graphs (BAGs), making it possible to quantitatively analyse the security of large networks. In the event of an attack, probabilities on the graph change depending on the evidence discovered (e.g., by an intrusion detection system or knowledge of a host’s activity). Since such scenarios are difficult to solve through direct computation, we discuss three stochastic simulation techniques for updating the probabilities dynamically based on the evidence and compare their speed and accuracy. From our experiments we conclude that likelihood weighting is most efficient for most uses. We also consider sensitivity analysis of BAGs, to identify the most critical nodes for protection of the network and solve the uncertainty problem for the assignment of priors to nodes. Since sensitivity analysis can easily become computationally expensive, we present and demonstrate an efficient sensitivity analysis approach that exploits a quantitative relation with stochastic inference.
Cherry Picking - Agile Software Development Teams Applying Design Thinking Tools

Abstract Design Thinking (DT) is an established approach to conceptualize software products before starting the product development work. Research suggests that software development can benefit from a continuous integration of DT throughout Agile development processes. However, practitioners and researchers lack an in-depth understanding of which tools from the ever-growing DT toolbox are suited to support software development teams and their processes and how these tools can be applied to the teams’ daily work. As initial steps towards closing this knowledge gap, we present our experiences from testing five different DT tools from a previously developed toolbox with four Agile software development teams. Each team chose three tools to apply to their product, problem, and context during a workshop. We present summarised findings regarding the use cases, benefits, and challenges of these tools as experienced by the participants. Overall, the teams welcomed the DT tools and were able to independently apply them to achieve the desired effects, e.g., to highlight user needs, find product issues, and discover team challenges.