Reviewerly/exHarmony huggingface dataset:
Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition

Heterogeneous face recognition (HFR) is still a challenging problem in computer vision community due to large appearance difference between near infrared (NIR) and visible light (VIS) modalities. Recently, breakthroughs have been made for traditional face recognition by applying deep learning on a huge amount of labeled VIS face samples. However, the same deep learning approach cannot be simply applied to HFR task due to large domain difference as well as insufficient pairwise images in different modalities during training. In general, the pooling layer of deep network can play the role of feature reduction, but also lead to the loss of useful face information, resulting in a decrease in the performance of HFR problem. It is important to eliminate modal-related information and retain more facial identity information. In this paper, we propose a novel method called Discriminant Deep Feature Learning Based on Joint Supervision Loss and Multi-layer Feature Fusion (DDFLJM) for HFR task. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new loss function called Scatter Loss (SL), which embeds both inter- and intra-class information for effectively training the deep model. To make full use of the various layers of the deep network, a Dimension Reduction Block (DRB) is designed to effectively extract the auxiliary features on multiple mid-level layers. An orthogonality constraint is introduced to the DRB block to reduce spectrum variations of two different modalities. The proposed SL is applied to multiple layers of network for joint supervision training, which enables multiple layers of the network to obtain discriminative identity features. Moreover, a Modified Gate Two-stream Neural Network (MGTNN) is adopted to fuse multiple-layer features. Extensive experiments are carried out on two challenging NIR-VIS HFR datasets CASIA NIR-VIS 2.0 and Oulu-CASIA NIR-VIS, demonstrating the superiority of the proposed method.
Towards End-to-end Speech-to-text Translation with Two-pass Decoding

Speech-to-text translation (ST) refers to transforming the audio in source language to the text in target language. Mainstream solutions for such tasks are to cascade automatic speech recognition with machine translation, for which the transcriptions of the source language are needed in training. End-to-end approaches for ST tasks have been investigated because of not only technical interests such as to achieve globally optimized solution, but the need for ST tasks for the many source languages worldwide which do not have written form. In this paper, we propose a new end-to-end ST framework with two decoders to handle the relatively deeper relationships between the source language audio and target language text. The first-pass decoder generates some useful latent representations, and the second-pass decoder then integrates the output of both the encoder and the first-pass decoder to generate the text translation in target language. Only paired source language audio and target language text are used in training. Preliminary experiments on several language pairs showed improved performance, and offered some initial analysis.
Design an Empirical Framework for Sentiment Analysis from Bangla Text using Machine Learning

Natural Language Processing (NLP) lends a helping hand for programming the computers to inspect a huge amount of data. Sentiment analysis is an application of NLP which deals with data to examine the sentiment or opinion that can be either positive or negative. Using Bangla text, sentiment analysis has become a challenge as there were only few works on it. As a decision maker, sentiment extrication not only capturing consumer attitudes but also helps in social behavior observance, politics and policy making. This paper quantifies total positivity and negativity against a document or sentence using Random Forest Classifier to classify sentiments. We contemplate the use of unigram, POS tagging, negation handling and classifier.
SENSEI: Harnessing Community Wisdom for Local Environmental Monitoring in Finland

The way people participate in decision making has radically changed over the last few decades. Technology has facilitated the sharing of knowledge, ideas and opinions across social structures and, has allowed grass-root initiatives to flourish. Participatory civic technology has helped local communities to embrace civic action on matters of shared concern. In this case study, we describe SENSEI, a year-long participatory sensing movement. Local community organisations, decision makers, families, individuals and researchers worked together to co-create civic technologies to help them address environmental issues of shared interest, such as invasive plant species, abandoned items in the forests and nice places. Over 240 local participants have taken part to the different stages of this year long process which included ten community events and workshops. As a result, over hundred concrete ideas about issues of common interest were generated, nearly thirty civic tech prototypes were designed and developed, along hundreds of environmental observations. In this paper, we describe the process or orchestration of this initiative and present key reflections from it.
Interactive learning media innovation: utilization of augmented reality and pop-up book to improve user’s learning autonomy

This study discusses the design of innovative learning media based on Augmented Reality (AR) and its development for students of Higher Education. Innovative Learning Media is now an important part of improving the quality of learning. Utilization of mobile device technologies such as android tablets and smartphones with camera features to run AR technology is the main point. Several students and expert of learning media were involved in this research and development. Questionnaires are used as measuring instruments. The results of this study are AR-based interactive learning media to improve user's learning autonomy and recommendations for further development.
Controlled partial image encryption based on LSIC and chaotic map

Partial image encryption plays a vital role in the field of medical image encryption, because small amount of encryption leads to high security with low computation complexity. This paper presents controlled partial image encryption using LSIC and chaotic map. It consists of two methods. In method-1 image is divided into 16x16 blocks. Entropy of each block is compared with threshold entropy from entropy selection table. If entropy of each block is greater than predefined threshold entropy then that block is XORed with 16x16 random block generated by converting each row of LSIC into 16x16 block. In method-2 entropy of block greater than predefined threshold entropy then pixels within that block is shuffled using chaotic map. Based on different threshold entropy values different partial encrypted images are obtained. From the experimental results shows that proposed methods can controlled amount of encryption as per the requirements.
A multi-objective search based approach to identify reusable software components

Component-based-software-development (CBSD) is one of the most recent trends in the software development industry and its success majorly depends on the quality of the software components. Good quality software components are those components, which are internally strongly cohesive and are independent of others. Use of such components helps in faster development of a software and reduces the maintenance efforts in future. Such components can be identified from different software repositories and can be reused whenever needed. Hence, proper identification of such reusable components is a promising area of research, and the same has been targeted in this paper. This paper model Reusable Software Component Identification (RSCI) problem as a search-based multi-objective problem in order to identify optimized reusable software components from the object-oriented (OO) source code of a software system. For OO software paradigm, we consider the component as an individual class or group of connected classes which can be reused with least modifications. To identify this, three types of relationship are proposed in this paper, namely (1) Frequent Usage Pattern (FUP) based cohesion (2) semantic relatedness based cohesion and (3) co-change based coupling. The proposed approach optimizes by simultaneously maximizing both types of cohesion and minimizing the coupling of a given component. The FUP based cohesion maximization is based on the newly proposed cohesion metric called PatternCohesion which is computed using FUP information extracted from a given software element (class/ interface in an object-oriented language). The FUP of a class comprises of those member variables of all classes of the software, which are directly or indirectly accessed by member functions defined in the class under consideration. The PatternCohesion metric helps to measure the functional relatedness among different pairs of software elements (classes). The semantic relatedness based cohesion maximization is based on TF-IDF based Cosine Similarity measurement using tokens extracted from three main parts of the source code of a given software element namely class/ interface declaration statement, data types of different member variables and member function signatures. The co-change based coupling is computed from the change-history of the underlying software system using well-known data mining metric called Support. Finally, reusable components are identified as different cohesive groups (consisting of one or more connected classes) using NSGA-III Algorithm. The proposed approach is empirically evaluated over six open source software systems belonging to different domains. The need of considering all three types of relations is established by comparing their performance with relations taken individually and two at a time. The obtained results indicate a higher quality for different software components measured in terms of reusability characteristics.
Challenges for meaningful interpersonal communication in a digital era

As digital tools and social networks became the main mode of interaction for many people, interpersonal communication has changed. Computer-mediated communication (CMC) has become more important than face-to-face communication in many contexts. Younger generations prefer CMC. Personal interaction normally consists of verbal and non-verbal communication. Computer-mediated communication lacks traditional non-verbal cues, which may cause misunderstandings, influencing meaningful interpersonal communication. Because of a lack of face-to-face communication, people often present an idealised version of themselves, thus becoming less inhibited involving more inappropriate self-disclosure on, for instance, social networks. The research question for this literature review was whether communicating with others mainly through digital means without adequate non-verbal cues would influence meaningful interaction between people. This study used the cues-filtered-out approach and the social presence theory with a literature review to get some clarity on the above question. The premise of the author was that because of the lack of non-verbal cues, CMC messages could influence the understanding of emotions and attitudes, thus compromising meaningful communication and personal understanding of the other. The outcome was that in today’s world people have to use CMC, but for meaningful interpersonal communication, they should try to combine it with face-to-face interaction.
A robust active contour model driven by fuzzy c-means energy for fast image segmentation

In this paper, we propose a robust region-based active contour model driven by fuzzy c-means energy that draws upon the clustering intensity information for fast image segmentation. The main idea of fuzzy c-means energy is to quickly compute the two types of cluster center functions for all points in image domain by fuzzy c-means algorithm locally with a proper preprocessing procedure before the curve starts to evolve. The time-consuming local fitting functions in traditional models are substituted with these two functions. Furthermore, a sign function and a Gaussian filtering function are utilized to replace the penalty term and the length term in most models, respectively. Experiments on several synthetic and real images have proved that the proposed model can segment images with intensity inhomogeneity efficiently and precisely. Moreover, the proposed model has a good robustness on initial contour, parameters and different kinds of noise.
BDNN: Binary convolution neural networks for fast object detection

Object detection methods based on neural networks have made considerable progress. However, methods like Faster RCNN and SSD that adopt large neural networks as the base models. It’s still a challenge to deploy such large detection networks in mobile or embedded devices. In this paper, we propose a low bit-width weight optimization approach to train Binary Neural Network for object detection using binary weights in training and testing. We introduce a greedy layer-wise method to train the detection network. This method boosts the performance instead of training the entire network at the same time. Our binary detection neural network (BDNN) can reduce the computational requirements and storage with competitive performance. For example, the binary network based on Faster RCNN with VGG16 can save 95% compression. In our experiments, BDNN achieves comparable performance with mAP 63.3% and outperforms SPPNet by 4.4% on PASCAL VOC 2007.
DER-TEE: Secure Distributed Energy Resource Operations Through Trusted Execution Environments

The high penetration of renewable energy means the grid is increasingly dependent on consumer-owned devices operation, providing a growing nexus between the Internet of Things (IoT) and the smart grid. However, these devices are much more vulnerable as they are connected, through interconnections to utility, manufacturers, third-party operators, and other consumer IoT devices. Therefore, novel security mechanisms are needed to protect these devices, especially ensuring the integrity of critical measurements and control messages. Fortunately, the growing prevalence of hardware-enforced trusted execution environments (TEEs) provides an opportunity to utilize their secure storage and cryptographic functions to provide enhanced security to various IoT platforms. This paper will demonstrate a TEE-based architecture for smart inverters that utilizes hardware and software-based isolation to prevent tampering of inverter telemetry data. Furthermore, it provides an implementation of the proposed architecture on an ARM TrustZone-enabled platform using open portable TEE (OP-TEE) on a Raspberry-Pi. The developed implementation is evaluated under a set of cybersecurity metrics.
Elemental geochemistry to complement stable isotope data of fossil travertine: Importance of digestion method and statistics

Elemental data from travertines are a treasure of depositional and diagenetic information. However, correct interpretation requires proper data acquisition and treatment. This study shows how results from elemental analyses complement sedimentological and other geochemical, i.e. isotopic, data and thereby contribute to our understanding of fossil travertines. Multivariate statistical element analyses, after multiple travertine digestion methods, demonstrate the link between elements, their mineralogical phases, and ultimately their origin. This study reveals that carbonate-phase related elements in travertines (Sr, S, Ba, Mg and Na) originate dominantly from the fluid source rocks. In combination with the δ18O and δ13C signatures, they are thus key geochemical variables for comparison of different travertine geobodies. Geochemical data analysis (elemental concentrations and isotope signatures), as illustrated here for the Turkish Ballık travertines, supports interpretation with regard to fluid source rock, distance from the vent and relative intensity of processes like evaporation and degassing. For fossil travertines, geochemical data can thus provide crucial insights for understanding the hydrologic system. In particular when information is restricted to borehole data, like in subsurface reservoirs, their application could be decisive.
Classification of high dimensional biomedical data based on feature selection using redundant removal

High dimensional biomedical data contain tens of thousands of features, accurate and effective identification of the core features in these data can be used to assist diagnose related diseases. However, there are often a large number of irrelevant or redundant features in biomedical data, which seriously affect subsequent classification accuracy and machine learning efficiency. To solve this problem, a novel filter feature selection algorithm based on redundant removal (FSBRR) is proposed to classify high dimensional biomedical data in this paper. First of all, two redundant criteria are determined by vertical relevance (the relationship between feature and class attribute) and horizontal relevance (the relationship between feature and feature). Secondly, to quantify redundant criteria, an approximate redundancy feature framework based on mutual information (MI) is defined to remove redundant and irrelevant features. To evaluate the effectiveness of our proposed algorithm, controlled trials based on typical feature selection algorithm are conducted using three different classifiers, and the experimental results indicate that the FSBRR algorithm can effectively reduce the feature dimension and improve the classification accuracy. In addition, an experiment of small sample dataset is designed and conducted in the section of discussion and analysis to clarify the specific implementation process of FSBRR algorithm more clearly.
DreamGigs

Technology allows us to scale the number of jobs we search for and apply to, train for work, and earn money online. However, these technologies do not benefit all job seekers equally and must be designed to better support the needs of underserved job seekers. Research suggests that underserved job seekers prefer employment technologies that can support them in articulating their skills and experiences and in identifying pathways to achieve their career goals. Therefore, we present the design, implementation, and evaluation of DreamGigs, a tool that identifies the skills job seekers need to reach their dream jobs and presents volunteer and employment opportunities for them to acquire those skills. Our evaluation results show that DreamGigs aids in the process of personal empowerment. We contribute design implications for mitigating aspects of powerlessness that low-resource job seekers experience and discuss ways to promote action-taking in these job seekers.
Benchmarking Privacy Preserving Scientific Operations

In this work, we examine the efficiency of protocols for secure evaluation of basic mathematical functions ($$\mathtt {sqrt}, \mathtt {sin}, \mathtt {arcsin}$$, amongst others), essential to various application domains. e.g., Artificial Intelligence. Furthermore, we have incorporated our code in state-of-the-art Multiparty Computation (MPC) software, so we can focus on the algorithms to be used as opposed to the underlying MPC system. We make use of practical approaches that, although, some of them, theoretically can be regarded as less efficient, can, nonetheless, be implemented in such software libraries without further adaptation. We focus on basic scientific operations, and introduce a series of data-oblivious protocols based on fixed point representation techniques. Our protocols do not reveal intermediate values and do not need special adaptations from the underlying MPC protocols. We include extensive computational experimentation under various settings and MPC protocols.
Context-Informed Scheduling and Analysis

Mobile self-reports are a popular technique to collect participant labelled data in the wild. While literature has focused on increasing participant compliance to self-report questionnaires, relatively little work has assessed response accuracy. In this paper, we investigate how participant context can affect response accuracy and help identify strategies to improve the accuracy of mobile self-report data. In a 3-week study we collect over 2,500 questionnaires containing both verifiable and non-verifiable questions. We find that response accuracy is higher for questionnaires that arrive when the phone is not in ongoing or very recent use. Furthermore, our results show that long completion times are an indicator of a lower accuracy. Using contextual mechanisms readily available on smartphones, we are able to explain up to 13% of the variance in participant accuracy. We offer actionable recommendations to assist researchers in their future deployments of mobile self-report studies.
The "Comadre" Project

Participation in out-of-school learning programs has been shown to generate significant academic, social/emotional, and institutional benefits for young learners, and today's wealthy families are disproportionately reaping these benefits. This paper presents the results of an asset-based/human-centered design research process and pilot aimed at connecting low-income families in a Southern California city with local low-cost out-of-school learning opportunities. Based on background research including qualitative interviewing, home visits, technology inventories and use walkthroughs with 40 low-income, majority Latinx families, we created and piloted a free subscription SMS service that automatically pushes bilingual SMS messages with curated information on local low-cost enrichment learning opportunities to low-income families. We framed our human-centered design process through an intersectional, "asset-based approach," which recognizes that marginalized communities have already developed robust, culturally-specific social practices to enable them to navigate the world, seeks to amplify them, and refrains from imposing a top-down or pre-conceived "idea" of intervention.
Secure Model Fusion for Distributed Learning Using Partial Homomorphic Encryption

Distributed learning has emerged as a useful tool for analyzing data stored in multiple geographic locations, especially when the distributed data sets are large and hard to move around, or the data owner is reluctant to put data into the Cloud due to privacy concerns. In distributed learning, only the locally computed models are uploaded to the fusion server, which however may still cause privacy issues since the fusion server could implement various inference attacks from its observations. To address this problem, we propose a secure distributed learning system that aims to utilize the additive property of partial homomorphic encryption to prevent direct exposure of the computed models to the fusion server. Furthermore, we propose two optimization mechanisms for applying partial homomorphic encryption to model parameters in order to improve the overall efficiency. Through experimental analysis, we demonstrate the effectiveness of our proposed mechanisms in practical distributed learning systems. Furthermore, we analyze the relationship between the computational time in the training process and several important system parameters, which can serve as a useful guide for selecting proper parameters for balancing the trade-off among model accuracy, model security and system overhead.