Reviewerly/exHarmony huggingface dataset:
The complexity of explaining neural networks through (group) invariants

Ever since the work of Minsky and Papert, it has been thought that neural networks derive their effectiveness by finding representations of the data that are invariant with respect to the task. In other words, the representations eliminate components of the data that vary in a way that is irrelevant. These invariants are naturally expressed with respect to group operations, and thus an understanding of these groups is key to explaining the effectiveness of the neural network. Moreover, a line of work in deep learning has shown that explicit knowledge of group invariants can lead to more effective training results. In this paper, we investigate the difficulty of discovering anything about these implicit invariants. Unfortunately, our main results are negative: we show that a variety of questions around investigating invariant representations are NP-hard, even in approximate settings. Moreover, these results do not depend on the kind of architecture used: in fact, our results follow as soon as the network architecture is powerful enough to be universal. The key idea behind our results is that if we can find the symmetries of a problem then we can solve it.
Securing Color Image Using Combined Elliptic Curve Crypto-System and Hill Cipher Encryption Along with Least Significant Bit - Steganography

Security of information is becoming main criteria while transferring information through communication networks. Nowadays images are used as information source; therefore its security from unauthorized or unintended access is important. Image encryption play an important role in protecting images from hacking and stealing while sending over an unsecured channel. The proposed method is a combination of encryption using Hill cipher algorithm along with Elliptic curve for enhanced security. Elliptic curve cryptographic method is used to make the system to asymmetric from the less secured symmetric key encryption technique; hill cipher algorithm. In our proposed method we use both grayscale images as well as color images regardless its size. Steganography of actual image with a cover image before encryption is an advantage. The cover image is selected as it mocks like an informative to unauthorized access. Least Significant Bit (LSB) steganography method is used here as it provides more security than any other methods.
Multirobot Simultaneous Path Planning and Task Assignment on Graphs with Stochastic Costs

Multi-robot task allocation problems where robots have to move to target destinations arises in a number of applications including search and rescue, goods or parts transfer in warehouses. The problem of task allocation, i.e., allocation of robots to target destinations is coupled with the problem of computing a path for the robots to the destination. We consider a situation, where there are static obstacles as well as dynamic obstacles in the environment. We assume that each robot is equipped with a local reactive collision detector and planner to avoid collision with dynamic obstacles. There could be multiple paths that may be available between a robot-destination pair [7]. The cost of travel (like energy consumed, time taken, etc.) is non-deterministic because the robot may have to slow down or stop to avoid moving obstacles.
Daily diffuse solar radiation estimation using adaptive neuro-fuzzy inference system technique

In this study, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed for the prediction of daily diffuse solar radiation. Eight factors including month of the year, sunshine duration, barometric pressure, relative humidity, mean temperature, wind speed, rainfall and daily global solar radiation are used as the inputs, while the daily diffuse solar radiation is the output. To compare the performance of the ANFIS, artificial neural network (ANN) and Iqbal models, two statistical benchmark indices, root-mean-squared error (RMSE) and coefficient of determination (R2), are adopted in this study. The results show that the proposed ANFIS model has potential in accurately predicting the daily diffuse solar radiation.
Information retrieval system in text-based skripsi document search file using vector space model method

Abstract Speed and simplicity in the process of finding documents and information have become a necessity in the campus library. The speed in finding a skripsi document that is currently still very long to find the data and more simple in displaying what you are looking for then we aim to search and find skripsi documents in a digital library at a private university in Jakarta more quickly and display what which is searched precisely. An approach is needed using the VSM method that encourages without complete keywords, and to see the effectiveness of research searches. The results of the VSM method can be applied for the development of prototype applications. The prototype used to facilitate the process of searching or finding the documents and information needed is fast and simple.
Sub-Graph Regularization on Kernel Regression for Robust Semi-Supervised Dimensionality Reduction

Dimensionality reduction has always been a major problem for handling huge dimensionality datasets. Due to the utilization of labeled data, supervised dimensionality reduction methods such as Linear Discriminant Analysis tend achieve better classification performance compared with unsupervised methods. However, supervised methods need sufficient labeled data in order to achieve satisfying results. Therefore, semi-supervised learning (SSL) methods can be a practical selection rather than utilizing labeled data. In this paper, we develop a novel SSL method by extending anchor graph regularization (AGR) for dimensionality reduction. In detail, the AGR is an accelerating semi-supervised learning method to propagate the class labels to unlabeled data. However, it cannot handle new incoming samples. We thereby improve AGR by adding kernel regression on the basic objective function of AGR. Therefore, the proposed method can not only estimate the class labels of unlabeled data but also achieve dimensionality reduction. Extensive simulations on several benchmark datasets are conducted, and the simulation results verify the effectiveness for the proposed work.
Hyperspectral X-ray Denoising: Model-Based and Data-Driven Solutions

In this paper we deal with the problem of hyperspectral X-Ray image denoising. In particular, we compare a classical model-based Wiener filter solution with a datadriven methodology based on a Convolutional Autoencoder. A challenging aspect is related to the specific kind of 2D signal we are processing: it presents mixed dimensions information since on the vertical axis there is the pixels position while, on the abscissa, there are the different wavelengths associated to the acquired X-Ray spectrum. The goal is to approximate the denoising function using a learning-from-data approach and to verify its capability to emulate the Wiener filter using a much less demanding approach in terms of signal and noise statistical knowledge. We show that, after training, the CNN is able to properly restore the 2D signal with results very close to the Wiener filter, honouring the proper signal shape.
Ontology-Mediated Queries over Probabilistic Data via Probabilistic Logic Programming

We study ontology-mediated querying over probabilistic data for the case when the ontology is formulated in EL(hdr), an expressive member of the EL family of description logics. We leverage techniques that have been developed (i) for classical ontology-mediated querying and (ii) for probabilistic logic programming and provide an implementation based on our findings. We include both theoretical considerations and an experimental evaluation of our approach.
The geological characteristics, geochemical signature and geophysical expression of porphyry copper-(gold) deposits in the circum-Pacific region

SummaryPorphyry deposits of the circum-Pacific region contain a large amount of the world’s copper, molybdenum and gold. These deposits are formed from calc-alkaline and potassic magmas characterized by varying oxidation states (fO2), which contribute to different ore mineral assemblages and contrasting relationships to hydrothermal alteration zones. Gold-rich porphyry copper deposits are typically characterized by hydrous and highly oxidized causal magmas. Variations in the styles of geology and mineralization contribute to differing geochemical expressions and geophysical signatures of the porphyry deposits. Exploration programs that integrate high-quality geological mapping and drill-core logging with a three-dimensional understanding of geochemical and geophysical data will facilitate the path to discovery.
Identification of Alzheimer’s Disease using Non-linguistic Audio Descriptors

Dementia is an overall term used to describe the reduced cognitive functioning in human beings, that is severe enough to impact their daily activities. Early diagnosis of dementia is imperative to provide timely treatment, either medication or therapy to alleviate the effects and sometimes slow the progression of dementia. In this work, we use speech processing and machine learning techniques to automatically classify speech into (a) healthy (HC) (b) with mild cognitive impairment (MCI) or (c) with Alzheimer's disease (AD). Only acoustic non-linguistic parameters are used for this purpose, making this a language independent approach. We evaluate our work using dementia and healthy speech from Pitt corpus of DementiaBank database. The performance of a three class Random Forest classifier is compared with our system comprising multiple two-class Random Forest classifiers cascaded to form a three class classifier, wherein a combination of approximate posterior probabilities is used to obtain a final class probability estimate. additional, patient speech is classified at segment level as well as at overall conversation level. Post processing on the patient speech classification at segment level provides a classification accuracy of 82% which is a significant absolute improvement of 8% over a simple three-class classifier performance.
Summarizing significant subgraphs by probabilistic logic programming

Although recent advances of significant subgraph mining enable us to find subgraphs that are statistically significantly associated with the class variable from graph databases, it is challenging to interpret the resulting subgraphs due to their massive number and their propositional representation . Here we represent graphs by probabilistic logic programming and solve the problem of summarizing significant subgraphs by structure learning of probabilistic logic programs. Learning probabilistic logical models leads to a much more interpretable, expressive and succinct representation of significant subgraphs. We empirically demonstrate that our approach can effectively summarize significant subgraphs with keeping high accuracy.
A security feature framework for programming languages to minimize application layer vulnerabilities

Abstract Pervasiveness of Internet‐based applications and computing devices has increased cybersecurity threats for wide range of users. Studies have shown that application security flaws have their roots in programming languages used for application development. Some vulnerabilities are due to programmer's negligence and others are due to the vulnerabilities present in the programming languages and their libraries. Developers may not be aware of the existing flaws in the programming languages and do not have time to take necessary measures as they develop applications. To cope with the challenge, this article proposes a security feature framework for programming languages to understand various exploitations and possible mitigations in programming languages. This security feature framework can be used to evaluate existing programming languages for potential vulnerabilities, level of security support, and the language features needed to mitigate these vulnerabilities. Moreover, language designers may use this framework as a guide to ensure that the language being designed has necessary and sufficient security feature set. The proposed security feature framework is then applied to several popular programming languages to evaluate the level of security feature coverage and gaps in these languages along with some recommendations on how to address these gaps.
Undergraduate Education of Remote Sensing Science and Technology in China: A Case of Study in Jiangsu Normal University

In China, Remote Sensing Science and Technology (RSST) is a young undergraduate program offered by the colleges and universities since 2001. As of 2018, there are as many as 45 colleges and universities authorized to offer the programs and to train the undergraduate students in the field of remote sensing and related applications. To ensure the smooth implementation of the undergraduate education, the Ministry of Education of the People's Republic of China has issued a series of policies and measures, including the program objective, requirements, and main curriculum structure. In Jiangsu Normal University, following the relevant policies and regulations, the students in the RSST program are trained and classified as three modules: innovative, compound and international talents, to meet the kinds of interests of the students and the different needs of employers.
Realtime Object Detection via Deep Learning-based Pipelines

Ever wonder how the Tesla Autopilot system works (or why it fails)? In this tutorial we will look under the hood of self-driving cars and of other applications of computer vision and review state-of-the-art tech pipelines for object detection such as two-stage approaches (e.g., Faster R-CNN) or single-stage approaches (e.g., YOLO/SSD). This is accomplished via a series of Jupyter Notebooks that use Python, OpenCV, Keras, and Tensorflow. No prior knowledge of computer vision is assumed (although it will be help!). To this end we begin this tutorial with a review of computer vision and traditional approaches to object detection such as Histogram of oriented gradients (HOG).
Understanding How and When Human Factors Are Used in the Software Process: A Text-Mining Based Literature Review

Human Factors (HF) is the study of the interaction between users and technology with the aim of improving the user’s experience of a product and avoid unwanted issues in the usage of the system. HF is largely applied in several fields such as industrial processes, education, training, and design. In software development, HF plays a crucial role in the efficient and effective development of a software product and the success of the final product. This paper aims at indicating the state of the art of the literature on HF in software, in general and in the software development process in particular. To do so, a preliminary literature review using text mining has been performed. This work gathered papers using the terms “human factors” and “software” from four of the most used scientific digital databases (ACM DL, Scopus, Science Direct and IEEE Xplore). A total of 2192 papers were selected and automatically gathered into three clusters by using the X-means algorithm, which automatically recommended that number of clusters. The results show that there are three main areas where HF have been researched within software development: (1) the field of product evaluation (user experience) (2) the field of software development process, especially in the project management processes (3) the field of education. The results are an initial indication of the evolution of research in this area and where and how HF is applied in software engineering.
Different Approaches in Sarcasm Detection: A Survey

Sarcasm is an unwelcome impact or a linguistic circumstance to express histrionic and bitterly opinions. In sarcasm single word in a sentence can flip the polarity of positive or negative statement totally. Therefore sarcasm occurs when there is an imbalance between text and context. This paper surveys different approaches and datasets for sarcasm detection. Different approaches surveyed are statistical approach, rule based approach, classification approach and deep learning approach. It also gives insight to different methodologies used in past for sarcasm detection. After surveying we found deep learning is generating a good result as compare to other approaches.
Economic reasoning with demand and supply graphs

We investigate an interaction between mathematical logic and economics by exploring a mathematical model of economic reasoning. To this purpose, we focus on economic reasoning with linear demand and supply graphs. Linear graphs have the following advantages: Linear graphs are easily constructed and reasoning using such graphs can be effectively managed; linear graphs are qualitatively determinate with respect to our problems. We investigate the types of problems regarding general demand and supply functions that can be reduced to linear graphs and discuss abductive reasoning with demand and supply graphs. Then, we propose a model of economic reasoning by formalizing reasoning with linear graphs in the framework of natural deduction developed through proof theory of mathematical logic.
Power Analysis Resilient SRAM Design Implemented with a 1% Area Overhead Impedance Randomization Unit for Security Applications

Power analysis attacks are an effective tool to extract sensitive information using side-channel analysis, forming a serious threat to IoT systems-on-a-chip (SoCs). Embedded memories implemented with conventional 6T SRAM macrocells often dominate the area and power of these SoCs. In this paper, for the first time, we use silicon measurements to prove that conventional SRAM arrays leak valuable information and that their data can be extracted using power analysis attacks. In order to provide a power analysis resilient embedded memory and adhere to the area constraints of modern SoCs, we implement a low-cost impedance randomization unit, which is integrated into the periphery of a conventional 6T SRAM macro. Preliminary silicon measurements of a 55 nm test-chip implementing the proposed memory array demonstrate a significant information leakage reduction at a low-cost 1% area overhead and no speed and power penalties compared to a conventional SRAM design.
Feature-Specific Named Entity Recognition in Software Development Social Content

Software development social networking sites, such as Stack Overflow or CSDN, contain rich information on software functions. The SFF(Software Function Feature)-specific named entities could be mined from this information. And furthermore, the usage patterns of entities and issues-solutions could also be mined based on the recognition of SFF-specific named entities. However, the existing workes on named entity recognition mainly focuses on the medicine filed and news filed. In the field of software engineering, there is little research on software named entity recognition. In fact, the contents of Software Development Social Networking sites are always freeform texts, which is composed of source code, link, abbreviation and Chinese or English words. It brings some difficulties to the SFF-specific named entity recognition. So in this paper, we gives an approach for SFF-specific named entity recognition with the BI-LSTM(Bidirectional Long Short-Term Memory) model and word embedding technique. A preliminary empirical evaluation demonstrates that our approach is effective to recognize SFFspecific named entities in software development social networking sites contents.
Transportation of Deformable Payload through Static and Dynamic Obstacles using Loosely Coupled Nonholonomic Robots

The following topics are dealt with: mobile robots; multi-robot systems; path planning; collision avoidance; multi-agent systems; decentralised control; motion control; learning (artificial intelligence); optimisation; control engineering computing.
Strategizing SCM-M Interface Using DeLone and McLean Model of IS Success and Fuzzy Cognitive Maps

The article presents a unique approach to explore and evaluate the critical factors of supply chain management-marketing (SCM-M) interfaces in e-commerce. This article systematically identifies the critical factors of SCM-M interface in e-commerce using DeLone and McLean (D&amp;M) Model of IS Success. The critical factors identified are used to form the dynamic scenarios based on Fuzzy Cognitive Maps (FCM) for strategizing the e-commerce success. To understand and evaluate the proposed research methodology, some scenarios were developed based on the critical factors of SCM-M interface in e-commerce. The analysis of these scenarios aid in identifying the improvement areas as well as strategizing and improving those areas of SCM-M interface in e-commerce for enhanced customer satisfaction and leveraging organizational success.
Learning and Reasoning on Graph for Recommendation

Recommendation methods construct predictive models to estimate the likelihood of a user-item interaction. Previous models largely follow a general supervised learning paradigm --- treating each interaction as a separate data instance and performing prediction based on the ''information isolated island''. Such methods, however, overlook the relations among data instances, which may result in suboptimal performance especially for sparse scenarios. Moreover, the models built on a separate data instance only can hardly exhibit the reasons behind a recommendation, making the recommendation process opaque to understand. In this tutorial, we revisit the recommendation problem from the perspective of graph learning. Common data sources for recommendation can be organized into graphs, such as user-item interactions (bipartite graphs), social networks, item knowledge graphs (heterogeneous graphs), among others. Such a graph-based organization connects the isolated data instances, bringing benefits to exploiting high-order connectivities that encode meaningful patterns for collaborative filtering, content-based filtering, social influence modeling and knowledge-aware reasoning. Together with the recent success of graph neural networks (GNNs), graph-based models have exhibited the potential to be the technologies for next-generation recommendation systems. This tutorial provides a review on graph-based learning methods for recommendation, with special focus on recent developments of GNNs and knowledge graph-enhanced recommendation. By introducing this emerging and promising topic in this tutorial, we expect the audience to get deep understanding and accurate insight on the spaces, stimulate more ideas and discussions, and promote developments of technologies.
Analyzing Integrated Learning Scenarios for Outdoor Settings

Research suggests that an interdisciplinary approach, where students can gather and evaluate evidence, and make sense of information they receive, can enhance students’ learning and better model various processes and phenomena in the real world [1]. However, it might create challenges for teachers to design integrated learning scenarios in authentic outdoor settings. The general aim of the paper is to analyze the content of integrated learning scenarios supported with technologies (mobile devices, online applications, sensors and educational robotics) in outdoor settings created by teachers from 6 K-12 schools to understand the characteristics of outdoor learning scenarios, the type of knowledge and level of contextualization these scenarios anticipate. The content analysis of the integrated learning scenarios demonstrates that the teachers tend to design learning scenarios, which hardly embrace learning contexts and enable to support higher order knowledge building types.