Reviewerly/exHarmony huggingface dataset:
The Skyline of Counterfactual Explanations for Machine Learning Decision Models

Counterfactual explanations are minimum changes of a given input to alter the original prediction by a machine learning model, usually from an undesirable prediction to a desirable one. Previous works frame this problem as a constrained cost minimization, where the cost is defined as L1/L2 distance (or variants) over multiple features to measure the change. In real-life applications, features of different types are hardly comparable and it is difficult to measure the changes of heterogeneous features by a single cost function. Moreover, existing approaches do not support interactive exploration of counterfactual explanations. To address above issues, we propose the skyline counterfactual explanations that define the skyline of counterfactual explanations as all non-dominated changes. We solve this problem as multi-objective optimization over actionable features. This approach does not require any cost function over heterogeneous features. With the skyline, the user can interactively and incrementally refine their goals on the features and magnitudes to be changed, especially when lacking prior knowledge to express their needs precisely. Intensive experiment results on three real-life datasets demonstrate that the skyline method provides a friendly way for finding interesting counterfactual explanations, and achieves superior results compared to the state-of-the-art methods.
A Video Surveillance based Security Model for Military Bases

Nowadays, security has become a major concern in various fields like parking areas, the bank sector, military environment, railway stations, airports, etc. Among these, security plays a very critical role in military border areas. These areas are usually tracked either manually or using technology but combining the technology with automation will be more efficient as this can prevent the errors and mistakes that could have been caused by human intervention. Military organizations which work with confidential data must function securely and should protect the data against unauthorized disclosure. There should be uttermost care taken to ensure that no stranger has entered the military base. In the current system, the military areas are monitored manually to prevent strangers from entering with the help of installed surveillance cameras. Hence to overcome any manual oversight, this paper presents an automated military surveillance system. The proposed system helps in identifying any spy hidden in the troop automatically instead of physical monitoring. This can be achieved using an application fetching the data from the surveillance cameras which in turn uses face detection and recognition algorithms to detect any stranger. It alerts the officials immediately to take further action if an intruder is detected. This implementation is shown in the results section where the persons (soldiers) are added to the database and any intruder can be detected even in the noisy environment.
The Choice Construct in the Soufflé Language

Datalog has become a popular implementation language for solving large-scale, real world problems, including bug finders, network analysis tools, and disassemblers. These applications express complex behaviour with hundreds of relations and rules that often require a non-deterministic choice for tuples in relations to express worklist algorithms.This work is an experience report that describes the implementation of a choice construct in the Datalog engine Soufflé. With the choice construct we can express worklist algorithms such as spanning trees in a few lines of code. We highlight the differences between rule-based choice as described in prior work, and relation-based choice introduced by this work. We show that a choice construct enables certain worklist algorithms to be computed up to 10k\(\times \) faster than having no choice construct.
Detection-Guided 3D Hand Tracking for Mobile AR Applications

Interaction using bare hands is experiencing a growing interest in mobile-based Augmented Reality (AR). Existing RGB-based works fail to provide a practical solution to identifying rich details of the hand. In this paper, we present a detection-guided method capable of recovery 3D hand posture with a color camera. The proposed method consists of key-point detectors and 3D pose optimizer. The detectors first locate the 2D hand bounding box and then apply a lightweight network on the hand region to provide a pixel-wise like-hood of hand joints. The optimizer lifts the 3D pose from the estimated 2D joints in a model-fitting manner. To ensure the result plausibly, we encode the hand shape into the objective function. The estimated 3D posture allows flexible hand-to-mobile interaction in AR applications. We extensively evaluate the proposed approach on several challenging public datasets. The experimental results indicate the efficiency and effectiveness of the proposed method.
Cross-scale global attention feature pyramid network for person search

Person search aims to locate the target person in real unconstrained scene images. It faces many challenges such as multi-scale and fine-grained. To address the challenges, a novel cross-scale global attention feature pyramid network (CSGAFPN) is proposed. Firstly, we design a novel multi-head global attention module (MHGAM), which adopts cosine similarity and sparse query location methods to effectively capture cross-scale long-distance dependence. Then, we design the CSGAFPN, which extends top-down feature pyramid network with bottom-up connections and embeds MHGAMs to the connections. CSGAFPN can capture cross-scale long-distance global correlation from multi-scale feature maps, selectively strengthen important features and restrain less important features. CSGAFPN is applied for both person detection and person re-identification (reID) subtasks of person search, it can well handle the multi-scale and fine-grained challenges, and significantly improve person search performance. Furthermore, the output multi-scale feature maps of CSGAFPN are processed by an adaptive feature aggregation with attention (AFAA) layer to further improve the performance. Numerous experiments with two public person search datasets, CUHK-SYSU and PRW, show our CSGAFPN based approach acquires better performance than other state-of-the-art (SOTA) person search approaches.
Building Digital Literacy Through Exploration and Curation of Emerging Technologies: A Networked Learning Collaborative

This exploratory study examines instructor discussion, instructional development, and study of student building of digital literacy as a result of the use and/or curation of Fabric of Digital Life (https://fabricofdigitallife.com/) collections on emerging technologies. Over three phases (January 2019 through May 2020), instructors and students across multiple institutions developed an understanding of digital literacy, designing and using a set of instructional materials for student exploration and/or curation of collections in this repository as a means to build student digital literacy. Strong human/inter-personal relationships evolved from instructors’ joint inquiry, problem solving, development and deployment of instructional resources.Critical to this work is the collaborative engagement in place to foster and support instructional development, pedagogical deployments, and associated research. This collaborative engagement, spearheaded by the Digital Life Institute at Ontario Tech University in Canada with research affiliates at the University of Minnesota and Texas Tech University in the United States, evolved as a networked learning collaborative to promote connections between people, sites, and contexts; to build a network in support of joint problem solving and knowledge creation; and to build a strong community for collective intention surrounding digital literacy understanding and student digital literacy development.
High dimensional quantum network coding based on prediction mechanism over the butterfly network

Abstract The high-dimensional quantum system greatly improve the quantum channel capacity and information storage space, and achieve high-dimensional quantum information transmission, which enhance the speed of quantum computing and quantum information processing. In this paper, a high-dimensional quantum teleportation protocol without information loss is proposed. We consider pre-sharing a high-dimensional non-maximum entangled state as a quantum channel between sender and receiver. By adding auxiliary particle and performing high-dimensional local operations, it is possible to achieve high-dimensional quantum teleportation without information loss. Simultaneously, we apply the protocol to butterfly network, and propose a novel high-dimensional quantum network coding based on prediction mechanism. In our scheme, we use Z -{|0⟩, |1⟩} basis to predict the transmission of high dimensional states over the butterfly network. When the prediction is successful, the deterministic transmission of high-dimensional quantum states can be realized over the butterfly network. Our scheme greatly saves the usage of quantum and classical channels, which improves the utilization efficiency of both channels.
A Safe Reinforcement Learning Architecture for Antenna Tilt Optimisation

Safe interaction with the environment is one of the most challenging aspects of Reinforcement Learning (RL) when applied to real-world problems. This is particularly important when unsafe actions have a high or irreversible negative impact on the environment. In the context of network management operations, Remote Electrical Tilt (RET) optimisation is a safety-critical application in which exploratory modifications of antenna tilt angles of base stations can cause significant performance degradation in the network. In this paper, we propose a modular Safe Reinforcement Learning (SRL) architecture which is then used to address the RET optimisation in cellular networks. In this approach, a safety shield continuously benchmarks the performance of RL agents against safe baselines, and determines safe antenna tilt updates to be performed on the network. Our results demonstrate improved performance of the SRL agent over the baseline while ensuring the safety of the performed actions.
Quantum-enhanced SU(1,1) interferometry via a Fock state and an arbitrary state

In this paper, we derive a general expression of the quantum Fisher information of an SU(1,1) interferometer with an arbitrary state and a Fock state as inputs by the phase-averaging method. Our results show that the same quantum Fisher information can be obtained regardless of the specific form of the arbitrary state. Then, we analytically prove that the parity measurement can saturate the quantum Cramer–Rao bound when the estimated phase sits at the optimal working point. For practical reasons, we investigate the phase sensitivity when the arbitrary state is a coherent or thermal state. We further show that a Fock state can indeed enhance the phase sensitivity within a constraint on the total mean photon number inside the interferometer.
High Voltage Insulators Condition Analysis using Convolutional Neural Network

High voltage insulators are nonconductive materials that are in use to isolate the conductor from the earthed transmission tower. However, they are regularly subjected to operational environmental contamination and detrimental electrical or mechanical stress. Undesirable operational conditions can cause insulations’ failure and ultimately the power transmission line outage. Therefore, monitoring and condition analysis of the insulators are essential tasks for the utility operators as well as transmission companies. With the widespread applications of deep learning techniques in engineering, in this study, we propose Convolutional Neural Network (CNN) as the backbone data analysis method and aerial images as the primary dataset. We conducted a model selection through an exhaustive search within a limited hyperparameter space based on our computational resources. We show that the constructed CNN classifier achieved a remarkable accuracy of 83.3% in classifying a clean insulator surface from the one covered with water droplets.
Real-time Social Distancing Detection System with Auto-calibration using Pose Information

In this demo, we present a real-time social distancing system with auto-calibration using human pose information. Our system first calculates geometric parameters of a camera in 3D space, i.e. position and rotation, then, measures absolute distances between pedestrians. Detection results are visualized as 3D circles in input images and a bird’s eye view. All processing steps are completely automatic, therefore, our system can be applied for uncalibrated surveillance cameras, which are already installed in town. A demo video is available in the supplemental material.
Fake Video Detection using Modified XceptionNet

Deepfakes are a significant threat to society from the development of deep learning technology, which is able to manipulate digital images. Manipulated digital content potentially become disinformation. This study proposes the task of detecting fake video using the XceptioNet architecture through transfer learning. The proposed algorithm train and test with publicly available datasets FaceForensics ++. The dataset is pre-processed to filter the face only using MTCNN. This research proposes an improvement of XceptionNet architecture with fine tuning and transfer learning. The purpose of this study is to determine the effect of architectural changes and the number of frames per video (FPV) towards the accuracy. Research shows the addition of 3 fully-connected layers in front of the softmax layer with the fine tuning lead to superior accuracy. The highest testing accuracy achieved on Celeb-DF dataset at 83,75%.
Anomaly Detection for In situ Marine Plankton Images

Machine learning and deep learning algorithms have achieved great success in plankton image recognition, but most of them are proposed to deal with closed-set tasks, where the distribution of the test data is the same as the training one. In reality, however, we face the challenges of open-set tasks, which are also recognized as the anomaly detection problems. In these tasks, there often exist abnormal classes, which are not in the training set, and the final goal of anomaly detection is to detect the anomalies correctly so that the misclassification of them can be reduced. However, little attention has been paid to anomaly detection in marine related fields. In this paper, to help marine plankton observers to detect anomalies conveniently and efficiently, we propose an anomaly detection pipeline including both the training and the testing phases. The training phase includes two parts, the pre-training and the post-training. In the pre-training phase, we propose a new loss function to better detect the abnormal classes and classify the normal classes simultaneously, which incorporates the expected cross-entropy loss, the expected Kullback-Leibler divergence, and the Anchor loss. We conduct several experiments to show the efficacy of the proposed method and compare its performance with other competitors based on a newly released dataset of in situ marine plankton images. Numerical results show that the proposed method outperforms its competitors in terms of classification accuracy and other commonly used criteria.
BiCMTS: Bidirectional Coupled Multivariate Learning of Irregular Time Series with Missing Values

Multivariate time series (MTS) such as multiple medical measures in intensive care units (ICU) are irregularly acquired and hold missing values. Conducting learning tasks on such irregular MTS with missing values, e.g., predicting the mortality of ICU patients, poses significant challenge to existing MTS forecasting models and recurrent neural networks (RNNs), which capture the temporal dependencies within a time series. This work proposes a bidirectional coupled MTS learning (BiCMTS) method to represent both forward and backward value couplings within a time series by RNNs and between MTS by self-attention networks; the learned bidirectional intra- and inter-time series coupling representations are fused to estimate missing values. We test BiCMTS on both data imputation and mortality prediction for ICU patients, showing a great potential of leveraging the deep and hidden relations captured in RNNs by the BiCMTS-learned intra- and inter-time series value couplings in MTS.
Deep Learning Traffic Classification in Resource-Constrained Community Networks

Community networks are infrastructures that are run by the citizens for the citizens. These networks are often run with limited resources compared to traditional Internet Service Providers. For such networks, careful traffic classification can play an important role in improving quality of service. Deep learning techniques have been shown to be effective for this classification task, especially since classical approaches struggle to deal with encrypted traffic. However, deep learning models often tend to be computationally expensive, which limits their suitability for low-resource community networks. This paper explores the computational efficiency and accuracy of Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) deep learning models for packet-based classification of traffic in a community network. We find that LSTM models attain higher out-of-sample accuracy than traditional support vector machines classifiers and the simpler multi-layer perceptron neural networks, given the same computational resource constraints. The improvement in accuracy offered by the LSTM has a tradeoff of slower prediction speed, which weakens their relative suitability for use in real-time applications. However, we observe that by reducing the size of the input supplied to the LSTMs, we can improve their prediction speed whilst maintaining higher accuracy than other simpler models.
Development of technical and statistical algorithm using Business Intelligence tools for energy yield assessment of large rooftop photovoltaic system ensembles

The aim of this paper is to develop a methodology for the energy yield assessment of a large ensemble of residential rooftop photovoltaic (PV) generators. The exploration and strategic analysis of data were applied for the first time, through Business Intelligence (BI) concepts combined with a technical-statistical algorithm for 1250 identical distributed rooftop PV systems installed in Santa Catarina, South of Brazil. Tilted solar irradiation maps were divided into four Ranges (statistical-quartiles) and, for the base year of 2019, the case-study results showed, with 95% reliability, an average annual daily energy yield of 3.35 kWh/kWp/day (Range 1); 3.74 kWh/kWp/day (Range 2); 3.82 kWh/kWp/day (Range 3); and 3.90 kWh/kWp/day (Range 4) for the four different quartiles into which the Santa Catarina state surface area was divided. The annual average PV generation of each PV rooftop analyzed was 3,353 kWh/year, which corresponds to an avoided CO2 emission of 268.25 kgCO2/year. For the combined 1250 PV rooftops ensemble, the average avoided CO2 emission was 335.30 tCO2/year. The implementation of BI concepts combined with the technical-statistical algorithm, provided relevant information about the performance of the PV systems, supporting fast decision-making strategies. The methodology applied can be used to predict the monthly/annual generation of future PV ensembles to be installed in any location for distributed generation asset allocation planning.
Task-specific dependency-based word embedding methods

While most traditional word embedding methods target generic tasks, two task-specific dependency-based word embedding methods are proposed for better performance in text classification tasks in this work. First, we exploit the dependency parsing tree structure to capture the structural information of a sentence, and develop a method called dependency-based word embedding (DWE). It finds keywords and neighbor words of a target word as contexts via dependency parsing. Next, we leverage the word-class co-occurrence statistics to model the class distributional information and incorporate it into the embedding learning process. This leads to the class-enhanced dependency-based word embedding (CEDWE) method. Task-specific corpora and the matrix-factorization-based framework are used to train DWE and CEDWE. Seven text classification datasets are used to evaluate the performance of DWE and CEDWE, and experimental results show that they outperform several state-of-the-art word embedding methods.
Design of Nonlinear Components Over a Mordell Elliptic Curve on Galois Fields

Elliptic curve cryptography ensures more safety and reliability than other public key cryptosystems of the same key size. In recent years, the use of elliptic curves in public-key cryptography has increased due to their complexity and reliability. Different kinds of substitution boxes are proposed to address the substitution process in the cryptosystems, including dynamical, static, and elliptic curve-based methods. Conventionally, elliptic curve-based S-boxes are based on prime field but in this manuscript; we propose a new technique of generating S-boxes based on mordell elliptic curves over the Galois field . This technique affords a higher number of possibilities to generate S-boxes, which helps to increase the security of the cryptosystem. The robustness of the proposed S-boxes against the well-known algebraic and statistical attacks is analyzed to classify its potential to generate confusion and achieve up to the mark results compared to the various schemes. The majority logic criterion results determine that the proposed S-boxes have up to the mark cryptographic strength.
Evaluating Resilience of Domains in PKI

Domain Validation of PKI, allows to verify ownership over domains and poses the basis for cryptography. A number of recent attacks led to efforts to enhance the security of domain validation by improving the resilience of the vantage points used by the certificate authorities.
Consensus Agreement for Secure Transactions in Self Help Groups

Microfinance institutions play a prominent role in promoting entrepreneurs and small businesses for their livelihood. Self help groups are one such institution that provides small-scale financial assistance to women in rural India. Two of the emerging protocols that promise a lot of scope for microfi-nance institutions are consensus algorithms and smart contracts. This paper proposes how the consensus based agreements can improve the transparency, auditability and verifiability of the transactions in a self help group “AmritaSREE”. The current system of this self help group utilises physical ledgers to record the group transactions and loans. This can cause errors and lack of transparency in the ledger. In order to improve the functioning of this SHG, a mobile application is designed to digitize the current physical ledger-based system. In this paper, we discuss the structure and functioning of this self help group and a human centered design approach for a mobile application to assist the functioning of the SHG. We also present the design and rationale for consensus based agreements and a comparative study with smart contracts.