Reviewerly/exHarmony huggingface dataset:
Human-Data Interaction Through Design

The increasing use of personal data and AI in everyday technologies has resulted in the amplification of complex and intertwined socio-technical challenges. These, often exemplified by data abuse, breaches, and exploitation, must be alleviated to support sustainable, resilient and human-centred data economies and positive global innovation. Here, we turn towards Human-Data Interaction, an interdisciplinary branch of research, inspired by HCI, that brings together diverse siloed perspectives to present three holistic response principles: data legibility, negotiability and agency. But, the emergent nature of this field calls for refinement of these theoretical tenets to help them translate into practical and tangible responses that are embedded in the technologies we create. We propose this workshop as a foundational step towards this agenda by opening these principles to the CHI community to encourage critique and dialogue about the strengths, weaknesses, value and opportunities of incorporating HDI into the design and evaluation of technology. The outcomes of this workshop, by engaging with HDI through Design, will form the basis for the next stages of research within HDI by contributing to foundational texts within academia and implementing HDI-infused systems within industry.
Cleanliness Automation: YOLOv3

Today as we all know that India is country that is very quickly developing in the field of Science and Technology. There various initiatives that are running on in the country like Digital India, Smart India to develop the smart cities in the country etc. So, there are already many programs that can control the traffic, but Waste management is an integral part of any Smart City. The garbage that is lying around the suburbs area of the city can cause a problem on the quality of life of residents in these areas. Our work focuses on developing a software that can detect the waste through the analysis of the video streams in real time. [1] The approach that we are going to use is Custom Object Detection by YOLOv3 which is going to detect the waste in real time and can also specify what kind of waste is showing on the video which is gathered from CCTV cameras. The model has been fine-tuned on the dataset collected for this purpose. The results that we get are pretty positive and it can help us to make a further more approach to help and manage waste in a very effective and efficient manner.
Bug Question Answering with Pretrained Encoders

Bug question answering is an effective way to acquire the required bug information and to help bug comprehension. Many existing approaches use keyword matching techniques to obtain more bug information directly without understanding the semantic information of bug data, which make the returned results irrelevant to the input queries. To alleviate this problem, we present a novel bug question answering approach named BERT-BugQA that takes advantage of the Bidirectional Encoder Representations from Transformers (BERT) which can fully consider the bidirectional context of bug information. In special, we design a common paradigm to construct the bug reading comprehension dataset for this approach. Empirical study demonstrates that BERT-BugQA is effective to automatically obtain the answers, and the F1-score values of Mozilla and Eclipse project are 0.84 and 0.83, respectively, which are better than the state-of-the-art Q&A approaches. Index Terms-Bug question answering, BERT, Bug natural language reading comprehension.
Understanding Responses to Phishing in Saudi Arabia via the Theory of Planned Behaviour

Saudi Arabia has seen an enormous growth in Internet usage over the past few years. With increasing adoption of this technology has come a rise in cybercrime, often enabled through use of social engineering. Phishing is a prime example, aiming to deceive users into revealing personal data. The paper describes efforts to understand individuals’ responses to phishing attacks through application of the Theory of Planned Behaviour (TPB). It reports a survey that considers three common social engineering persuading strategies, Authority, Social Proof and Scarcity. Results show correlations between these strategies and TPB. In particular, between attitude and intention to respond under the Authority strategy; subjective norms and intention to respond under the Social Proof strategy; and subjective norms and intention to respond under the Scarcity strategy.
Enhancing Data Protection in Cloud Computing using Key Derivation based on Cryptographic Technique

Cloud computing is one of the emerging information technologies, where millions of users store and access the information. As huge amount of data is stored in cloud data centers, data needs to be protected in all the aspects. Number of research works are proposed to address the data security and privacy issues. Since, huge amounts of data and applications are stored in the cloud, reliable and cost-effective data protection solutions are required. This paper has proposed a technique to enhance the data protection using key derivation based encryption technique. The proposed mechanism provides confidentiality, authentication and modification for the data stored in cloud. To provide data protection, a mechanism is designed to derive three separate secret keys from a single master key and each key is used for specific operation. The experimental results show that, the proposed method uses lower computational costs, while deriving the secret keys. The scheme also enhances data security by increasing the size of the keys and restricts hackers in cracking the secret keys.
Development of virtual reality-based learning media on electromagnetic wave radiation material

Abstract The aim of this study to develop virtual reality-based learning media on electromagnetic wave radiation material. The research method used the research and development (R &amp; D) with the ADDIE models (Analysis, Design, Development, Implement, Evaluation). Data collection techniques used through observation and questionnaires. The development of virtual reality - based learning media was done in School of St. Yoseph Bekasi. This development research was conducted on science subject teachers. This virtual reality-based learning media has been validated by experts, that is content experts and media experts by providing validation instruments. Virtual reality-based learning media can be used to simulate a learning process that does not allow direct involvement because it will be dangerous for learning participants in this covid19 pandemic era. So, the virtual reality-based learning media just tried to the teachers and experts. The conclusion of this research was the development of virtual reality-based learning media on electromagnetic wave radiation material can be used as a learning resource for students.
Describing Image with Attention based GRU

Generating descriptions for images are popular research topic in current world. Based on encoder-decoder model, CNN works as an encoder to encode the images and then passes it to decoder RNN as input to generate the image description in natural language sentences. LSTM is widely used as RNN decoder. Attention mechanism has also played an important role in this field by enhancing the object detection. Inspired by this recent advancement in this field of computer vision, we used GRU in place of LSTM as a decoder for our image captioning model. We incorporated attention mechanism with GRU decoder to enhance the precision of generated captions. GRU have lesser tensor operations in comparison to LSTM, hence it will be faster in training.
Bengali Stop Word Detection Using Different Machine Learning Algorithms

Bangla language is an enriched language and many words are used frequently for the sentence formation purpose holding negligible information value. These words are stop words, widely used in the field of text mining, information retrieval, text summarizer, indexing, search and retrieval systems, Bangla Language Processing (BLP), etc. For the better performance of the system, stop words should be eliminated properly, as it can decrease the calculation speed, take lots of space, and deteriorate the system performance. But there is no standard stop word detection approach without using a pre-defined stop word list. But the main disadvantage is, it cannot detect stop word which is used as a content word in the text sometimes as the word is not analyzed contextwise. So, a new method is proposed to detect stop word from sentences without using a pre-defined stop word list by creating a dictionary for the conversion of words to numeric values, where each 61 available Bengali character is assigned with a distinct numerical value, also words are checked according to the stored index position and highest accuracy 89% is achieved by applying random forest classifiers. The proposed approach is the first attempt for this task in the Bengali language.
Polygons characterizing the joint statistical properties of the input and output sequences of the binary shift register

Binary non-autonomous shift registers, which are widely used as intermediate nodes in cascade pseudo-random sequence generators, are considered. The joint statistical properties of the symbols of the input and output sequences of such registers are studied depending on the used output function. A geometric approach is proposed for description of such properties, within the framework of which the output function can be characterized by its polygon in the unit square [0, 1] × [0, 1]. The properties of such polygons and their relationship with the properties of the used output function are investigated.
Concept Embedding through Canonical Forms: A Case Study on Zero-Shot ASL Recognition

In the recognition problem, a canonical form that expresses the spatio-temporal relation of concepts for a given class can potentially increase accuracy. Concepts are defined as attributes that can be recognized using a soft matching paradigm. We consider the specific case study of American Sign Language (ASL) to show that canonical forms of classes can be used to recognize unseen gestures. There are several advantages of a canonical form of gestures including translation between gestures, gesture-based searching, and automated transcription of gestures into any spoken language. We applied our technique to two independently collected datasets: a) IMPACT Lab dataset: 23 ASL gestures each executed three times from 130 first time ASL learners as training data and b) ASLTEXT dataset: 190 gestures each executed six times on an average. Our technique was able to recognize 19 arbitrarily chosen previously unseen gestures in the IMPACT dataset from seven individuals who are not a part of 130 and 34 unseen gestures from the ASLTEXT dataset without any retraining. Our normalized accuracy on the ASLTEXT dataset is 66% which is 13.6 % higher than the state-of-art technique. Comparison with deep learning techniques revealed that incorporation of concept level knowledge can potentially alleviate under-fitting problems.
Research on omnidirectional movement and environment detection function of inspection robot

In recent years, the big data industry is developing rapidly; the computer room of the data center has become an important part of all companies. In order to ensure a safe and stable operating environment for the equipment, it is necessary to inspect the computer room. The traditional manual inspection method has the disadvantages of low efficiency, poor quality, high cost and power consumption. In this paper, a kind of inspection robot is proposed, which can replace human to complete inspection task, so as to reduce cost and improve the reliability of inspection work. The robot chassis has omnidirectional movement feature in structure. In functionally, the robot adopts temperature humidity sensors DHT11, gas pressure sensor BMP180 and smoke sensor MQ-2 to inspection computer room environment. Furthermore, kinematics analysis and MATLAB simulation were carried out on the omnidirectional mobile chassis of the robot; the environmental detection function of the robot was tested by experiment. The results show that the robot can realize omnidirectional mobile and environment detection function.
Dolueg: A Measurement Network’s Face

isualization is a major part of any scientific work, serving to communicate results in an way.Whether figures, plots, or graphs, they all play a central role in publications, teaching, and public outreach.Furthermore, visualization is one of the integral parts besides automatic tests of the data that help to ensure the quality of meteorological data from one or more weather stations of measurement networks.This last aspect, in particular, requires the automatic generation of figures and their dynamic presentation.Our homepage, named "Dolueg" for "here, look" in German dialect/Swiss-German, allows researchers and the public to view relevant and up-to-date data in an easy way that includes further information such as measurement height, device type, and other relevant metadata.Dolueg has proven its value for the detection of malfunctioning devices, showcasing interesting meteorological phenomena, and above all ensuring that the flow of data from our stations into the database can be easily checked and fixed, if need be.Because Dolueg is free, easy to use, and adjustable, it is especially suitable for measurement network operators, that is, when time series are constantly collected and are in need of visual quality control in addition to any automatic checks.
Standardizing digital forensic examination procedures: A look at Windows 10 in cases involving images depicting child sexual abuse

Abstract As a topic area, the need for the standardization of operational practices in digital forensics has seen much discussion. There are clear benefits for digital forensics if its procedures can be harmonized including increasing the reliability of the work produced by its practitioners, consistency of practice, and the potential for greater quality control, however, attaining standardization is a difficult task, and further work in this field is required. This work discusses the “standardization challenge” assessing both the need for standardization and the feasibility of achieving it. It is suggested that those actively contributing in this area should consider the development of models for defining operational practice which are offence‐specific , device‐specific and operating system‐specific . To support this proposal, an example standard is offered and discussed which suggests and documents the minimum expected requirements for the examination of Windows 10 devices in cases involving images depicting child sexual abuse. This article is categorized under: Digital and Multimedia Science &gt; Cybercrime Investigation
Incorporating domain ontology information into clustering in heterogeneous networks

Abstract Clustering of structure‐rich heterogeneous information networks composed of multiple types of objects and relationships, which has become a challenge in data mining. Most of the existing clustering heterogeneous network methods focus on the internal information of the dataset while ignoring the domain knowledge outside the dataset. However, in real‐world scenarios, domain knowledge can often offer valuable information for clustering. In this study, we propose a three‐layer model OntoHeteClus, which is able to cluster multitype objects in star‐structured heterogeneous networks by considering both the dataset itself and the background information quantified via the ontology. OntoHeteClus first evaluates the similarity between central objects according to formalized domain ontology information, based on which central objects are subsequently clustered. Finally, attribute objects are clustered according to the central object clustering result. A numerical example is presented to illustrate the modeling concept and working principle of the proposed method, and experiments on a real‐world dataset demonstrate the effectiveness of the proposed algorithms. This article is categorized under: Technologies &gt; Structure Discovery and Clustering Algorithmic Development &gt; Structure Discovery
Hierarchically Aggregated Residual Transformation for Single Image Super Resolution

Visual patterns usually appear at different scales/sizes in natural images. Multi-scale feature representation is of great importance for the single-image super-resolution (SISR) task to reconstruct image objects at different scales. However, such characteristic has been rarely considered by CNN-based SISR methods. In this work, we propose a novel building block, i.e. hierarchically aggregated residual transformation (HART), to achieve multi-scale feature representation in each layer of the network. Within each HART block, we connect multiple convolutions in a hierarchical residual-like manner, which efficiently provides a wide range of effective receptive fields at a more granular level to detect both local and global image features. To theoretically understand the proposed HART block, we recast SISR as an optimal control problem and show that HART effectively approximates the classical 4th-order Runge-Kutta method, which has the merit of small local truncation error for solving numerical ordinary differential equation. By cascading the proposed HART blocks, we establish our high-performing HARTnet. Through extensive experiments on various benchmark datasets under different degradation models, we demonstrate that HARTnet compares favourably against existing state-of-the-art methods (including those in the NTIRE 2019 SR Challenge leaderboard) in terms of both quantitative metrics and visual quality. Moreover, the same HARTnet architecture achieves promising performance on such other image restoration tasks as image denoising and low-light image enhancement.
Identity-Preserved Face Beauty Transformation with Conditional Generative Adversarial Networks

Identity-preserved face beauty transformation aims to change the beauty scale of a face image while preserving the identity of the original face. In our framework of conditional Generative Adversarial Networks (cGANs), the synthesized face produced by the generator would have the same beauty scale indicated by the input condition. Unlike the discrete class labels used in most cGANs, the condition of target beauty scale in our framework is given by a continuous real-valued beauty score in the range [1 to 5], which makes the work challenging. To tackle the problem, we have implemented a triple structure, in which the conditional discriminator is divided into a normal discriminator and a separate face beauty predictor. We have also developed another new structure called Conditioned Instance Normalization to replace the original concatenation used in cGANs, which makes the combination of the input image and condition more effective. Furthermore, Self-Consistency Loss is introduced as a new parameter to improve the stability of training and quality of the generated image. In the end, the objectives of beauty transformation and identity preservation are evaluated by the pretrained face beauty predictor and state-of-the-art face recognition network. The result is encouraging and it also shows that certain facial features could be synthesized by the generator according to the target beauty scale, while preserving the original identity.
Riding the Bus in Los Angeles: Creating Cultural Micro-Exposures via Technology

The HCI community has largely failed to serve the millions of people in rural communities in the developed world. In part, I believe this is because our plural values do not match the more traditional, conservative values often found in rural communities. However, these rural communities, and in particular the marginalized populations within them, could greatly benefit from our work. I believe that one way to set the stage for deeper engagement with rural communities is by creating cultural micro-exposures—small brushes with the everyday realities of a culture or lived experience that is different from one’s own—using technology.
Detection of Cyberhectoring on Instagram

Cyberhectoring is a growing problem affecting more than half of the population. Cyberhectoring is affecting mostly among teenagers. This problem has to be tackled which is been done by many researchers. The main goal of this is to understand and automatically detect the incidents of cyberhectoring. This paper focuses on collecting data sets of Instagram i.e. images and their associated comments. A detailed analysis of the labelled data, including a study of relationships between cyberbullying and a host of features such as profanity, temporal commenting behavior, linguistic content and image content is made. The collected data is then processed and classified using classification algorithms and is further classified into bullying and non bullying content. Using the labelled data, we further design and evaluate the performance of classifiers to automatically detect incidents if cyberhectoring.
Signatures of Heavy Metal concentration over Laterites identified through Regional Stream Sediment Geochemical Survey in parts of Katni, Panna and Satna Districts of Madhya Pradesh, India

As a part of National Geochemical Mapping (NGCM) programme of GSI, regional geochemical mapping was carried out for an area of 705 sq km in parts of Katni, Panna and Satna districts of Madhya Pradesh covering toposheet number 64A/05 bounded by latitudes N23°45′00″ to N24°00′00″ and longitudes E80°15′00″ to E80°30′00″. Chemical analyses of 182 composite stream/slope sediments samples were carried out and baseline geochemical maps of oxides and elements were prepared. Data analyses has led to significant conclusions on environment hazard due to metal contamination and possible mineral potential due to REE enrichment. Study of the spatial distribution of individual variables showed Cr and Co have values higher than the permissible level in dry soils in some parts of the area which requires investigation by concerned agencies. Some samples over the laterites show higher than threshold values of REEs which are much higher than their crustal abundance. Factor analyses on the geochemical data with 46 oxides and elements has led to the identification of five factors accounting for 84% of the total variance of the data set and describing five physico-chemical processes controlling the spatial geochemical variation. The analyses points to the enrichment of the REEs in the lateritisation process. The laterites covering 171 sq km of the area need to be considered for investigation of REE potential.