Reviewerly/exHarmony huggingface dataset:
Blockchain Based Direct Benefit Transfer System For Subsidy Delivery

Delivery of subsidies to deserving beneficiaries forms an essential part of government expenditure. In 2018-19 alone, the Government of India spent $60 Bn on welfare subsidies, majorly through the Public Distribution System(PDS). Of this amount, it is estimated that 40% was lost in the form of misuse, corruption and related inefficiencies in the system. Recognising this problem, the government began Direct Benefit Transfers in 2013 for a select few schemes, for instance, LPG subsidy. Using Aadhaar and biometric tokens for validation, the beneficiaries would receive the subsidy as direct cash transfers to their bank accounts. However, in reality, the DBT program has had the same efficiency as the PDS. According to the analysis of the DBT policy, the key drawbacks of this system are lack of auditability, inability to control the use of funds for intended purposes, and over-reliance on the banking infrastructure, which is underdeveloped in the rural areas. In order to plug loopholes in the DBT system, we propose a blockchain-based system. Blockchain consists of cryptographic hash secured distributed ledgers which maintain an immutable log of transactions between all participants of a blockchain network. They have the ability to execute Smart Contracts, which allow for automation of execution of real-world contracts given that certain specified conditions are met. Appropriating the Governments Aadhaar UID, we aim to develop a smart blockchain which automates the disbursement of subsidy which bypasses the need for banks in rural nodes while creating an auditable and transparent ecosystem to curb corruption and financial mismanagement.
Information Asymmetry and Insurance in Africa

In this study, we assess the relevance of decreasing information asymmetry on life and non-life insurance consumption, by using data from 48 African countries during the period 2004–2014. Reduced information asymmetry is proxied by information sharing offices, namely: public credit registries and private credit bureaus. The empirical evidence is based on the Generalized Method of Moments. The findings show that information sharing offices increase insurance consumption with a comparatively higher magnitude in life insurance penetration, relative to non-life insurance penetration. Practical and theoretical implications are discussed.
A Generalised Approach for Encoding and Reasoning with Qualitative Theories in Answer Set Programming

Abstract Qualitative reasoning involves expressing and deriving knowledge based on qualitative terms such as natural language expressions, rather than strict mathematical quantities. Well over 40 qualitative calculi have been proposed so far, mostly in the spatial and temporal domains, with several practical applications such as naval traffic monitoring, warehouse process optimisation and robot manipulation. Even if a number of specialised qualitative reasoning tools have been developed so far, an important barrier to the wider adoption of these tools is that only qualitative reasoning is supported natively, when real-world problems most often require a combination of qualitative and other forms of reasoning. In this work, we propose to overcome this barrier by using ASP as a unifying formalism to tackle problems that require qualitative reasoning in addition to non-qualitative reasoning. A family of ASP encodings is proposed which can handle any qualitative calculus with binary relations. These encodings are experimentally evaluated using a real-world dataset based on a case study of determining optimal coverage of telecommunication antennas, and compared with the performance of two well-known dedicated reasoners. Experimental results show that the proposed encodings outperform one of the two reasoners, but fall behind the other, an acceptable trade-off given the added benefits of handling any type of reasoning as well as the interpretability of logic programs.
An Intelligent Human Fall Detection System Using a Vision-Based Strategy

Elderly people is increasing dramatically during the current years, and it is expected that this population reaches 2.1 billion of individuals by 2050. In this regard, new care strategies are required. Assisted living technologies have proposed alternatives to support professional caregivers and families to take care of elderly people, such as in risk of falls. Currently, fall detection systems are able to alleviate the latter problem and reduce the time a person who suffered a fall receives assistance. Thus, this paper proposes a fall detection system based on image processing strategy to extract motion features through an optical flow method. For classification, we use these features as inputs to a convolutional neural network. We applied our approach in a dataset comprises video recordings of one subject performing different types of falls. In experimental results, our approach showed 92% accuracy on the dataset used.
Advances in Molecular Quantum Computing: from Technological Modeling to Circuit Design

Molecules are serious candidates for building hardware for quantum computers. They can encode quantum information onto electron or nuclear spins and some of them show important features as the scalability of the number of qubits and a universal set of quantum gates. In this paper we present our advances in the development of a classical simulation infrastructure for molecular Quantum Computing: starting from the definition of simplified models taking into account the main physical features of each analyzed molecule, quantum gates are defined over these models, thus permitting to take into account the real behavior of each technology during the simulation. An interface with a hardware-agnostic description language has been also developed. The knowledge of the behavior of real systems permits to optimize the design of quantum circuits at both physical and compilation levels. Elementary quantum algorithms have been simulated on three different molecular technologies by changing the physical parameters of polarization and manipulation and quantum circuit design strategies. Results confirm the dependency of the fidelity of the results on both levels, thus proving that the choice of optimal operating points and circuit optimization techniques as virtual-Z gates are fundamental for ensuring the execution of quantum circuits with negligible errors.
Static Obstacles Avoidance in Autonomous Ground Vehicle using Fuzzy Logic Controller

Navigation technology is conceivably an important requirement for real time application of Autonomous vehicle. This paper focuses on the application of one of the soft computing technique i.e. a fuzzy logic based controller on the autonomous vehicle for the challenging job of obstacle avoidance. The proposed fuzzy logic controller is having two input and one output. The distances from left and right sensor have been considered as the input, whereas, the rotation angle is considered as the output. A total of 36 set of rule base have been used in designing the fuzzy logic controllers with Graphical User Interface (GUI), that provides the information in which the vehicle moves.
Hyper-Heuristic Framework for Sequential Semi-Supervised Classification Based on Core Clustering

Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization is responsible for selecting the best parameters of the core model to minimize the error. This integration aims to handle the concept drift. We designated this model as hyper-heuristic framework for semi-supervised classification or HH-F. Experimental results of the application of HH-F on real datasets prove the superiority of the proposed framework over the existing state-of-the art approaches used in the literature for sequential classification data with evolving nature.
Model-free-adaptive-based data-driven method for three-axis Gimbal control

This study uses a three-axis Gimbal model to inertially stabilize a platform that can be used to feed smooth images from a camera. In this article, three-axis Gimbal performance analysis is presented. An inertial measurement unit responds to movement and a three-phase brushless DC motor with 14 poles and 12 coils is used to rule out vibrations and movement from the surroundings. The controller combines sliding-mode control and model-free-adaptive control to design a novel control method based on data, which can decrease the computational time and difficulty of a nonlinear system. Simulations on MATLAB prove the efficiency of the given method. The simulation results validate that the designed controller has improved position control than the traditional proportional integral derivative, model-free-adaptive control, and model-free-learning-adaptive control.
Citation Classification Using Natural Language Processing and Machine Learning Models

In this paper, we address the problem of identifying the quality of citation as important or unimportant to the developments presented in the research papers. We gather features represented by four state-of-the-art machine learning techniques and combined them with newly engineered, natural language-based features. Using a known dataset of 465 citations, manually labeled by experts, our approach out-performed state-of-the-art by using fine-tuned Random Forest Classifier with 90.7% F1 score and 97.7% precision. We also employ Convolutional Neural Networks with AdamW optimizer with focal loss function - that converges quickly on small data to achieve considerably significant results.
Experimental Evaluation of Phishing Attack on High School Students

The effectiveness of phishing attacks is being analyzed by many researchers. At the same time, researches often deal with the random sample of people suffered a phishing attack and are limited with analysis of consequences of unrelated cases without conducting an actual phishing experiment. Experiments typically involve a small number of respondents. The novelty of present study is to analyze the educational institution’ susceptibility to phishing attack. Authors demonstrate a methodology of creating a group of targets homogeneous in age, place of study, level of knowledge and to conduct an experiment on a large group of respondents (3,661 people). The methodology of gathering and filtering of email addresses using open sources of information is explained. Emotionally neutral text of a phishing email to minimize the deceptive effect of the letter was formulated. The experiment showed the success rate of the attack on a large sample of students at 10.8%, and demonstrated the vulnerability of the educational institution’s infrastructure to the hidden preparation and conduct of the attack. Novelty of methodology includes use of a phishing letter that includes a questionnaire to gather statistics on responders’ awareness of phishing nature. It made possible to compare respondents’ beliefs with the real susceptibility to phishing based on sensitive data they provided in return to the phishing letter. We show how the data collected by phishing can be personalized and conclude that respondents need further training to detect phishing attacks. We also argue necessary organizational, infrastructural measures, recommendations of necessary mail server configuration changes.
A computational and analytical approach for cloud computing security with user data management

Cloud computing is emerging trend in today's research world. The main benefits of cloud computing are the adaptability, domain compatibility and better service use. So, the applicability of cloud computing in different domain is the main strength with the ease of resource sharing. As the increasing demand of cloud computing make a broader span in the direction of user-data security. In this paper empirical meta-analysis has been provided in the direction of cloud computing security. It also includes complete discussion based on the results achieved in the same. This paper provides the analytical and empirical view of cloud computing security. The major aspect covered in this paper are data security, data management and efficient resource sharing mechanism. It also covers the computational aspect their applicability and the analytical view for the study and analysis. It explores the wider view and the area of applicability in different domain with the ease of data management and secure data sharing mechanism.
GCI: A GPU Based Transfer Learning Approach for Detecting Cheats of Computer Game

Cheating in massive multiple online games (MMOGs) adversely affect the game’s popularity and reputation among its users. Therefore, game developers invest large amount of efforts to detect and prevent cheats that provide an unfair advantage to cheaters over other naive users during game play. Particularly, MMOG clients share data with the server during game play. Game developers leverage this data to detect cheating. However, detecting cheats is challenging mainly due to the limited client-side information, along with unknown and complex cheating techniques. In this article, we aim to leverage machine learning-based models to predict cheats over encrypted game traffic during game play. Concretely, network game traffic during game play from each player can be used to determine whether a cheat is employed. A major challenge in developing such a prediction model is the availability of sufficient training data, which is sparingly available in practice. Game traffic obtained from a few known players can be easily labeled. However, if such players are not a good representation of the population (i.e., other players), then a supervised model trained on labeled game traffic from these set of players may not generalize well for the population. Here, we propose a Graphics Processing Unit (GPU) based scalable transfer learning approach to overcome the constraints of limited labeled data. Our empirical evaluation on a popular MMOG demonstrates significant improvement in cheat prediction compared to other competing methods.
Differentially Private Range Counting in Planar Graphs for Spatial Sensing

This paper considers the problem of privately reporting counts of events recorded by devices in different regions of the plane.Unlike previous range query methods, our approach is not limited to rectangular ranges.We devise novel hierarchical data structures to answer queries over arbitrary planar graphs.This construction relies on balanced planar separators to represent shortest paths using O(log n) number of canonical paths, where n is the number of nodes in the graph.Pre-computed sums along these canonical paths allow efficient computations of 1D counting range queries along any shortest path.We make use of differential forms together with the 1D mechanism to answer 2D queries in which a range is a union of faces in the planar graph.The methods are designed such that the range queries could be answered with differential privacy guarantee on any single event, with only a poly-logarithmic error.They also allow private range queries to be performed in a distributed setup.Theoretical and experimental results confirm that the methods are efficient and accurate on real data and incur less error than competing existing methods.
TrustServing: A Quality Inspection Sampling Approach for Remote DNN Services

Deep neural networks (DNNs) are being applied to various areas such as computer vision, autonomous vehicles, and healthcare, etc. However, DNNs are notorious for their high computational complexity and cannot be executed efficiently on resource constrained Internet of Things (IoT) devices. Various solutions have been proposed to handle the high computational complexity of DNNs. Offloading computing tasks of DNNs from IoT devices to cloud/edge servers is one of the most popular and promising solutions. While such remote DNN services provided by servers largely reduce computing tasks on IoT devices, it is challenging for IoT devices to inspect whether the quality of the service meets their service level objectives (SLO) or not. In this paper, we address this problem and propose a novel approach named QIS (quality inspection sampling) that can efficiently inspect the quality of the remote DNN services for IoT devices. To realize QIS, we design a new ID-generation method to generate data (IDs) that can identify the serving DNN models on edge servers. QIS inserts the IDs into the input data stream and implements sampling inspection on SLO violations. The experiment results show that the QIS approach can reliably inspect, with a nearly 100% success rate, the service qualtiy of remote DNN services when the SLA level is 99.9% or lower at the cost of only up to 0.5% overhead.
Towards Automated Generation of Semantic Annotation for Activity Recognition Problems

Ground truth is essential for activity recognition problems. It is used to apply methods of supervised learning, to provide context information for knowledge-based methods, and to quantify the recognition performance. Semantic annotation extends simple symbolic labelling by assigning semantic meaning to the label and enables reasoning about the semantic structure of the observed activity. The development of semantic annotation for activity recognition is a time consuming task, which involves a lot of effort and expertise. To reduce the time needed to develop semantic annotation, we propose an approach that automatically generates semantic models based on manually assigned symbolic labels. We provide a detailed description of the automated process for annotation generation and we discuss how it replaces the manual process. To validate our approach we compare automatically generated semantic annotation for the CMU grand challenge dataset with manual semantic annotation for the same dataset. The results show that automatically generated models are comparable to manually developed models but it takes much less time and no expertise in model development is required.
Hungarian layer: A novel interpretable neural layer for paraphrase identification

Paraphrase identification serves as an important topic in natural language processing while sequence alignment and matching underlie the principle of this task. Traditional alignment methods take advantage of attention mechanism. Attention mechanism, i.e. weighting technique, could pick out the most similar/dissimilar parts, but is weak in modeling the aligned unmatched parts, which are the crucial evidence to identify paraphrases. In this paper, we empower neural architecture with Hungarian algorithm to extract the aligned unmatched parts. Specifically, first, our model applies BiLSTM/BERT to encode the input sentences into hidden representations. Then, Hungarian layer leverages the hidden representations to extract the aligned unmatched parts. Last, we apply cosine similarity to metric the aligned unmatched parts for a final discrimination. Extensive experiments show that our model outperforms other baselines, substantially and significantly.
A Novel Method of Modular Multiplication Based on Karatsuba-like Multiplication

In this paper, we propose a novel method of modular multiplication which embeds the modular reduction in the evaluation and interpolation parts of the Karatsuba-like multiplication. Before, the modular reduction can only be performed independently between multiplication. However, applying our method, the interpolation of the previous multiplication, modular reduction and evaluation of the next multiplication are merged as a whole step, which leads to the simplification of computations and improvement of parallelism. This method can be applied to the modular multiplication with simple moduli like NIST primes, and for general moduli, we can apply this method by using Montgomery modular multiplication instead.
Side-channel countermeasures utilizing dynamic logic reconfiguration: Protecting AES/Rijndael and Serpent encryption in hardware

Dynamic logic reconfiguration is a concept that allows for efficient on-the-fly modifications of combinational circuit behavior in both ASIC and FPGA devices. The reconfiguration of Boolean functions is achieved by modification of their generators (e.g., shift register-based look-up tables) and it can be controlled from within the chip, without the necessity of any external intervention. This hardware polymorphism can be utilized for the implementation of side-channel attack countermeasures, as demonstrated by Sasdrich et al. for the lightweight cipher PRESENT. In this work, we adapt these countermeasures to two of the AES finalists, namely Rijndael and Serpent. Just like PRESENT, both Rijndael and Serpent are block ciphers based on a substitution–permutation network. We describe the countermeasures and adjustments necessary to protect these ciphers using the resources available in modern Xilinx FPGAs. We describe our implementations and evaluate the side-channel leakage and effectiveness of different countermeasures combinations using a methodology based on Welch’s t-test. Furthermore, we attempt to break the protected AES/Rijndael implementation using second-order DPA/CPA attacks. We did not detect any significant first-order leakage from the fully protected versions of our implementations. Using one million power traces, we detect second-order leakage from Serpent encryption, while AES encryption second-order leakage is barely detectable. We show that the countermeasures proposed by Sasdrich et al.are, with some modifications, successfully applicable to AES and Serpent.
De-anonymization of Social Networks: the Power of Collectiveness

The interaction among users in different social networks raises deep concern on user privacy, as it may facilitate the assailants to identify user identities by matching the anonymized networks with a correlated sanitized one. Prior arts regarding such de-anonymization problem can be primarily divided into a seeded case or a seedless one, depending on whether or not there are a subset of pre-identified nodes. The seedless case is much more complicated since the adjacency matrix representation of one-hop user relations delivers limited structural information. To address this issue, we, for the first time, integrate the multi-hop neighborhood relationships, which exhibit more structural commonness between the anonymized and the sanitized networks, into seedless de-anonymization process. Our aim is to sufficiently leverage these multi-hop neighbors of all nodes and minimize the total disagreements of these multi-hop adjacency matrices, which we call collective adjacency disagreements (CADs), between two networks of different sizes. Theoretically, we demonstrate that CAD enlarges the difference between wrongly matched node pairs and correctly matched pairs, whereby two networks can be correctly matched with high probability even when the network density is below logn. Algorithmically, we adopt the conditional gradient descending method on a collective-form objective, which can efficiently find the minimal CADs for networks with broad degree distributions. Experiments on both synthetic and realworld networks return desirable de-anonymization accuracies thanks to the rich structural information manifested by such collectiveness, since most nodes can be correctly matched with their correspondences, especially in sparse networks where merely utilizing adjacency relations might fail to work.