Reviewerly/exHarmony huggingface dataset:
Impact of Saliency and Gaze Features on Visual Control

Predicting user intent from gaze presents a challenging question for developing real-time interactive systems like interactive search engine, implicit annotations of large datasets or intelligent robot behavior. Indeed, solutions to annotate easily large sets of images while reducing the burden of annotators is a key aspect for current machine learning techniques. We propose in this paper to design an estimator of the user interest for a given visual content based on eye-tracker feature analysis. We revise existing gaze-based interest estimator, and analyze the impact of the intrinsic saliency of the content displayed for interest estimation. We first explore low-level saliency prediction and propose a new gaze and saliency interest estimator. Experimental results show the advantage of our method for the annotation task in a weakly supervised context. In partic- ular, we extend previous evaluation criteria on new experimental protocol displaying four images by frame as a first step towards "Google Image search-like" interfaces. Our Gaze and Saliency Inter-est Estimator (GSIE) reaches an overall accuracy of 83% in average of user interest prediction. If we consider the accuracy reached in a limited time, the GSIE is 70% in average within about 500ms and 80% in average within 1000ms. This result confirms our GSIE as an efficient real-time visual control solution.
Toward large-scale histopathological image analysis via deep learning

The histopathological image has served as the gold standard of cancer diagnosis and played a vital role in diagnosing cancer in clinical settings for over a century. Traditionally, tissue samples were reviewed by pathologists under a microscope, which is time-consuming and prone to error. This is changing with the age of digitized glass slides. The current practice is to first digitize the glass slides into histopathological images with a whole-slide image (WSI) scanner and then employ specialized software (i.e., a virtual slide viewer) to view and inspect these images. WSI scanners can digitize glass slides into histopathological images in just a couple minutes, and the computer software can present histopathological images on a screen in real time. Nevertheless, due to the complexity and sheer volume of histopathological image data, effectively diagnosing cancer in gigapixel-sized histopathological images still requires a considerable amount of time spent by an experienced pathologist. In addition, the inter- and intraobserver errors introduced in this process make it more challenging. Computer-assisted diagnosis (CAD) is a potentially powerful tool to offer more reliable, efficient, and consistent cancer diagnosis from histopathological images. Currently, state-of-the-art deep learning-based algorithms can automatically analyze the properties (e.g., cells and cancer regions) of histopathological images. Nevertheless, these powerful algorithms are still at the prototype stage and are thus not widely used. In this chapter, we aim to bridge the gap by reviewing recent efforts toward CAD-based histopathological image analysis algorithms, introducing the unique challenges encountered when building effective cancer diagnostic tools and the essential knowledge needed to overcome some of them.
A Complementary Optimization Procedure for Final Cluster Analysis of Clustering Categorical Data

Clustering analysis has become an indispensable tool for obtaining and analyzing meaningful groups, irrespective of any numerical or categorical clustering problems. Algorithms such as fuzzy k-Modes, New fuzzy k-Modes, k-AMH, and the extended k-AMH algorithms such as Nk-AMH I, II, and III are usually employed to improve clustering of categorical problems. However, the performance of these algorithms is measured and evaluated according to the average accuracy scores taken from 100-run experiments, which require labeled data. Thus, the performance of the algorithms on unlabeled data cannot be measured explicitly. This paper extends complementary optimization procedures on the k-AMH model, known as Ck-AMH I, II, III, and IV, to obtain final and optimal clustering results. In experiments conducted, the complementary procedures produced optimal clustering results when tested on five categorical datasets: Soybean, Zoo, Hepatitis, Voting, and Breast. The optimal accuracy scores obtained were marginally lower than the maximum accuracy scores and, in some cases, were identical to the maximum accuracy scores obtained from the 100-run experiments. Consequently, using the complementary procedures, these clustering algorithms can be further developed as workbench clustering tools to cluster both unlabeled categorical and unlabeled numerical data.
Research and Application of Semi-automatic Construction of Structured Knowledge graph

At present, the construction of knowledge graph for poverty alleviation is relatively scarce. This paper builds a set of tools for semi-automatic generation of knowledge graph based on the data sources in the poverty alleviation field in Chongqing which. First, the LOAD statement is automatically generated and imported into the Neo4j graph database. Secondly, semantic mapping is performed between different data classes in the Chongqing poverty alleviation data source according to the rules defined by the ontology. Based on the results obtained by the semantic mapping, the MATCH statement imported graph database is automatically generated. Finally, query the relational data stored in the Neo4j graph database through the Echarts component. Users and developers can use and maintain knowledge graph conveniently through a feature-rich application interface, which is of great significance to the application and disclosure of basic data for poverty alleviation departments.
Optimization of Modular Neural Networks for Pattern Recognition with Parallel Genetic Algorithms

We describe in this paper the use of Modular Neural Networks (MNN) for pattern recognition with parallel processing using a cluster of computers with a master-slave topology. In this paper, we are proposing the use of MNN for face recognition with large databases to validate the efficiency of the proposed approach. Also, a parallel genetic algorithm for architecture optimization was used to achieve an optimal design of the MNN. The main idea of this paper is the use of parallel genetic algorithms to find the best architecture with large databases of faces, because when the database to be considered is large, the main problem is the processing time to train the MNN. Network parameters are adjusted by a combination of the training pattern set and the corresponding errors between the desired output and the actual network response. To control a learning process, a criterion is needed to decide the time for terminating the process.
Congruence classes of large configurations in vector spaces over finite fields

In [1], Bennett, Hart, Iosevich, Pakianathan, and Rudnev found an exponent $s<d$ such that any set $E\subset \mathbb{F}_q^d$ with $|E|\gtrsim q^s$ determines $\gtrsim q^{\binom{k+1}{2}}$ congruence classes of $(k+1)$-point configurations for $k\leq d$. Because congruence classes can be identified with tuples of distances between distinct points when $k\leq d$, and because there are $\binom{k+1}{2}$ such pairs, this means any such $E$ determines a positive proportion of all congruence classes. In the $k>d$ case, fixing all pairs of distnaces leads to an overdetermined system, so $q^{\binom{k+1}{2}}$ is no longer the correct number of congruence classes. We determine the correct number, and prove that $|E|\gtrsim q^s$ still determines a positive proportion of all congruence classes, for the same $s$ as in the $k\leq d$ case.
Transforming learning through relevant STEM education for Nigerian students : (Work in Progress by the Social Innovation Enterprise; Carisma4U Educational Foundation)

It's no surprise to state that little has changed in how STEM is taught in Nigeria in the last three decades. As a result, it seems, Nigerian students have been left behind, falling further behind in science and math literacy.There have been futile national reform efforts to improve things. The issue with them has been a lot of inconsistency and lack of a cohesive effort as changes in teaching practices have been minimal.The study used student and teacher data from the programs conducted by the social innovation enterprise. The study presents the data from three programs over two years-Teacher training projects and STEM Bootcamps for children. The findings indicate that the programs do trigger learning and the connection to real world. Also, the study showed that though the exposure to that the teachers and students had to STEM pedagogy was the first time, they appreciated it and found it aided their understanding of the concepts they performed during the programs. This led to further discussion on extensions of the pilot programs and the implications for policy and research.
An Extensive Analysis of TrueCrypt Encryption Forensics

TrueCrypt is a popular and secure encryption software especially suitable for storage protection of personal privacy. In this paper, we present an extensive analysis of TrueCrypt's encryption forensics. At first, we explain the inner pivotal encryption principles including cryptographic algorithms selection, operation mode and key derivation process. In order to conduct data forensics in a practical scenario, we demonstrate a comprehensive data processing procedure that is beginning with encrypted volume identification and then the header extraction and finally password cracking with a targeted dictionary. We expect that our research is helpful for data investigation of entire disk, partitions and file containers generated by the TrueCrypt software.
Sub-Saharan Teacher STEM Professional Development with Digital Ink: Follow-Up (Sub-Saharan Pen-Based Computing Follow-Up)

This chapter reports on research activities originally reported at the 2015 WIPTTE conference, the last one held prior to the conference name change. The chapter, entitled “A Model and Research Agenda for Teacher and Student Collaboration Using Tablets in Digital Media Making: Results from Sub-Saharan Workshops” reported on a series of science, technology, engineering, and mathematics (STEM) projects funded by the Kenyan Ministry of Education, Microsoft Research, and the National Science Foundation. The research has continued since then, with additional support from the Namibian Ministry of Education, Arts and Culture, the US State Department’s Fulbright Program, and new, multiyear funding from NSF. The chapter describes the evolution of the project and five themes emerging from it, in addition to describing a new four-year effort related to the original paper. The original paper focused on activities in Kenya, Ghana, Uganda, and Namibia. This chapter centers on follow-up activities in Namibia.
A Secure String Class Compliant with PCI DSS

Computer programs often work with a variety of sensitive data and class String is widely used in object-oriented programming languages for this purpose. However, saving sensitive data to a String object is not safe as it is not encrypted and may still be in the operating memory even after it is no longer needed. Due to non-deterministic behaviour of mechanism responsible for removing unused items from the memory, we cannot say with certainty when String with sensitive data will actually be removed. If an attacker gets either part of or even the entire memory image, then they can easily read these sensitive data. This paper discusses the options in object oriented languages that provide programmers with a way of storing the data in memory in an encrypted form. We present a pseudo code for a secure String class that is compliant with Data retention and Cryptography requirements of the PCI DSS standard.
A Simple Proposal for Sentiment Analysis on Movies Reviews with Hidden Markov Models

Sentiment analysis of texts is the field of study which analyses and studies opinions, sentiments, value judgments, affections and emotions in texts like blogs, news and treating of products, organisations, events and topics. If information on subjective content is required, such as the emotion aroused by an event, computer techniques must be applied to analyse the pattern of public opinion. A common technique for analysing texts is the "Bag of Words", which provides good results assuming that the words are independent of one another. In this work we propose the use of Hidden Markov Chains to determine the polarity of the opinions expressed on movie reviews. We propose a method for simulating hidden states through clustering techniques; we then carry out a sensitivity analysis of the model in which we apply variations to model parameters such as the number of hidden states or the number of words used. The results show that our proposal gives a 3% improvement over the basic model using F-score for real databases of public opinion. Sentiment analysis of texts is the field of study which analyses and studies opinions, sentiments, value judgments, affections and emotions in texts like blogs, news and treating of products, organisations, events and topics. If information on subjective content is required, such as the emotion aroused by an event, computer techniques must be applied to analyse the pattern of public opinion. A common technique for analysing texts is the "Bag of Words", which provides good results assuming that the words are independent of one another. In this work we propose the use of Hidden Markov Chains to determine the polarity of the opinions expressed on movie reviews. We propose a method for simulating hidden states through clustering techniques; we then carry out a sensitivity analysis of the model in which we apply variations to model parameters such as the number of hidden states or the number of words used. The results show that our proposal gives a 3% improvement over the basic model using F-score for real databases of public opinion.
Sailing Skweezee: An Exploration of Squeeze Interaction in VR

Sailing Skweezee is a game that explores squeeze interaction in virtual reality. Squeezing a soft controller stuffed with conductive wool steers a virtual boat through a course on water. The game is played with an Oculus Rift headset and is developed in Unity. An open source Skweezee library for Unity implements the squeeze interaction. Sailing Skweezee is found to deliver an immersive experience, aligning subtle physical manipulations of soft material with a floating experience of sailing on a virtual sea.
Semantics of Extended Argumentation Frameworks Defined by Renovation Sets

Dung’s theory of abstract argumentation frameworks plays an increasingly important role in artificial intelligence. Extended argumentation frameworks extend Dung’s AF by considering attacks on attacks. In this paper we introduce a new EAF semantics that deals with infinite deductive defence. This EAF semantics is underpinned by a new notion called renovation sets. Based on this, the concepts of conflict-freeness and acceptability are re-defined.
Network Security Situation Prediction Based on Improved WGAN

The current network attacks on the network have become very complex. As the highest level of network security situational awareness, situation prediction provides effective information for network administrators to develop security protection strategies. The generative adversarial network (GAN) is a popular generation model, which is difficult to train, collapse mode and gradient instability in this network. A Wasserstein distance as a loss function of GAN is proposed. And a difference term is added on the loss function. The improved Wasserstein-GAN (IWGAN) is to improve the classification precision of the situation value. Compared with other forecasting methods, the results show that the method has obvious advantages.
DynGraph: Visual Question Answering via Dynamic Scene Graphs

Due to the rise of deep learning, reasoning across various domains, such as vision, language, robotics, and control, has seen major progress in recent years. A popular benchmark for evaluating models for visual reasoning is Visual Question Answering (VQA), which aims at answering questions about a given input image by joining the two modalities: (1) the text representing the question, as well as, (2) the visual information extracted from the input image. In this work, we propose a structured approach for VQA that is based on dynamic graphs learned automatically from the input. Unlike the common approach for VQA that relies on an attention mechanism applied on a cell-structured global embedding of the image, our model leverages the rich structure in the image depicted in the object instances and their interaction. In our model, nodes in the graph correspond to object instances present in the image while the edges represent relations among them. Our model automatically constructs the scene graph and attends to the relations among the nodes to answer the given question. Hence, our model can be trained end-to-end and it does not require additional training labels in the form of predefined graphs or relations. We demonstrate the effectiveness of our approach on the challenging open-ended Visual Genome [14] benchmark for VQA.
A Formal Modeling Approach for QOS in MQTT Protocol

With the rising demandArchana, E. for IoT devices, communication protocols like MQTT, CoAP, and many more, haveRajeev, Akshay become an integral part of the system to ensureKuruvila, Aby safe and reliable data transfer. Using lightweight communication protocols such as the Message QueuingNarayankutty, Revathi Telemetry Transport (MQTT) protocol makes it much easier to establish communication between distributed devicesKannimoola, Jinesh M. as it easily recovers from connectivity loss, component failures, and loss of packets. The pivotal contribution of this paper is the method of approach to formally model, analyze, and verify the Quality of Service (QoS) levels of the MQTT protocol. A complete analysis of the Quality of Service levels is performed to confirm that it behaves correctly as specified when used in communication between different components. Formal modeling has been done using PROMELA language and the model verification is done using a system verification tool called SPIN Model Checker.
Object-Oriented Data Flow Diagram Similarity Measurement Using Greedy Algorithm

Measuring software similarity base on software design artifacts has been one of the research topics in software engineering. Software similarity measurement is used in software reuse, clone detection, and learning assessment. Previous research focus on universal modeling language design artifacts. These design artifacts concern on the semantic and structure of a set of objects within a system and their interaction with themselves and objects outside the system. There are also design artifacts that model how data or information transformed into different forms, sizes, and time. This study focus on developing diagram similarity measurement base on one of those artifacts, i.e., object-oriented data flow diagrams. The measurement uses the greedy approach to calculate the similarity score of two diagrams using four elements derived from elements of object oriented data flow diagram, i.e. external entities, processes, data stores, and classes. The initial experimentation result shows that the proposed diagram similarity measurement is feasible.
A Method of Calculating the Semantic Similarity Between English and Chinese Concepts

In the big data era, data and information processing is a common concern of diverse fields. To achieve the two keys “efficiency” and “intelligence” to the processing process, it’s necessary to search, define and build the potential links among heterogeneous data. Focusing on this issue, this paper proposes a knowledge-driven method to calculate the semantic similarity between (bilingual English-Chinese) words. This method is built on the knowledge base “HowNet”, which defines and maintains the “atom taxonomy tree” and the “semantic dictionary” - a network of knowledge system describing the relationships between word concepts and attributes of the concepts. Compared to other knowledge bases, HowNet pays more attention to the connections between words based on concepts. Besides, this method is more complete in the analysis of concepts and more convenient in calculation methods. The non-relational database MongoDB is employed to improve the efficiency and fully use the rich knowledge maintained in HowNet. Considering both the structure of HowNet and characteristics of MongoDB, a certain number of equations are defined to calculate the semantic similarity.
Building a Trustworthy Ethical Approach to Cloud Computing

Cloud computing has gained wide acceptance by business enterprises all over the globe. With the advancement in the popularity of cloud computing, research is being directed to study the effect of various aspects affecting it. One of the most important issues that impact cloud computing is the human perception of it. The inclination of users, across the globe, towards cloud computing is affected by the amount of trust they repose in the services and the perceived level of ethics being observed in maintaining these services. This paper studies the impact of ethics and trust on Cloud computing and its subsequent effect on e-commerce. A model is proposed to incorporate trust and ethics for an improved e-commerce environment (ETCM), Ethics Trust Cloud model. Further a survey is conducted to deduce the benefits of cloud computing on e-commerce where the respondent provides opinion about the prominent factors which contribute towards building of trust in the service provider and the possible relationship between observance of ethical practices and trust-building. The data is collected by a specific questionnaire, which is designed to understand the user’s perception about these concepts and their possible influence on cloud computing environment. Findings of the survey and subsequent analysis reveal a positive correlation and regression between trust and ethics.
Mobile Deployment of NoiseNet: Noise Characteristics Assessment in Real-World Images

Processing of real-world images permanently faces noisy conditions during data acquisition. Distortions in images introduced by noise can decrease efficiency of subsequent treatment significantly. To suppress the noise effectively or to get the rationale of denoising expedience, noise characteristics should be assessed in an accurate way. If not, inaccurate estimation of noise properties can affect denoising performance like oversmoothing or insufficient noise attenuation. This paper considers the use of deep convolutional neural network NoiseNet and other tools to estimate characteristics of signal-dependent noise in real-world images acquired by mobile devices. Noise characteristics' estimation is conducted in different conditions of photo capturing and using mobile devices from different manufacturers. It is shown that, in most cases, the noise in images is spatially correlated. Additionally, it is noticed that image content greatly influences noise variance estimation accuracy, especially in cases of heterogeneous or highly-textured images. Ready-to-use trained model of NoiseNet and the demo Android application with full dataset are available at https://github.com/radiuss/NoiseNet.