Reviewerly/exHarmony huggingface dataset:
Efficient Velocity Estimation in Distributed RF Sensing

In this paper, we examine the problem of velocity estimation for a moving object using distributed measurements. The system employs a non-cooperative transmitter and multiple receivers to collect targets echoes. The problem is formulated by modelling the unknown transmitted waveform as a deterministic process. The exact maximum likelihood estimator (MLE) is developed which requires a multi-dimensional search procedure. To reduce the computational load, an efficient two-step estimator (TSE) is proposed. The TSE first finds the maximum likelihood estimates of pairwise differences of the Doppler frequencies observed by the receivers. Then, the target velocity can be estimated from the frequency differences in closed-form. We show that the maximum likelihood estimation of each frequency difference reduces to a cross-correlation process followed by peak finding, which can efficiently be implemented by the fast Fourier transform (FFT). As a result, the TSE is significantly more efficient than the MLE. Numerical results show the TSE achieves a similar estimation accuracy as that of the MLE except for very low signal-to-noise ratio (SNR) scenarios.
DLGR: A Rule-Based Approach to Graph Replacement for Deep Learning

In deep learning libraries like TensorFlow, compu-tations are manually batched as computation graphs. Graph replacement is then an optimization that replaces one subgraph of a computation graph with another whilst keeping the graphs before and after replacement functionally equivalent. Meanwhile, in practice, it remains a challenge how graph replacements can be performed efficiently: graph replacement is usually conducted by human engineers, and thus it incurs many human efforts since a variety of deep learning models do exist and a number of model-specific replacements can be performed; the functionality equivalence of graphs before and after replacement is also not easy to guarantee. To tackle with this challenge, we introduce in this paper DLGR, a rule-based approach to graph replacement for deep learning. The core idea of DLGR is to define a set of replacement rules, each of which specifies the source and the tar-get graph patterns and constraints on graph replacement. Given a computation graph, DLGR then performs an iterative process of matching and replacing subgraphs in the source graph, and generates a replaced, and usually optimized computation graph. We conduct experiments to evaluate the capabilities of DLGR. The results clearly show the strengths of DLGR: compared with two existing graph replacement techniques, it provides with more replacement rules and saves engineers' development efforts in reducing up to 68 % lines of code.
Sly Pete in Dynamic Semantics

In ‘Sly Pete’ or ‘standoff’ cases, reasonable speakers accept incompatible conditionals, and communicate them successfully to a trusting hearer. This paper uses the framework of dynamic semantics to offer a new model of the conversational dynamics at play in standoffs, and to articulate several puzzles posed by such cases. The paper resolves these puzzles by embracing a dynamic semantics for conditionals, according to which indicative conditionals require that their antecedents are possible in their local context, and update this body of information by eliminating the possibilities where the antecedent is true and the consequent is false. In this way, the dynamic analysis draws on insights from the material conditional and contextualist analyses, while explaining how standoffs are genuine disagreements.
Imbalanced learning for insurance using modified loss functions in tree-based models

Tree-based models have gained momentum in insurance claim loss modeling; however, the point mass at zero and the heavy tail of insurance loss distribution pose the challenge to apply conventional methods directly to claim loss modeling. With a simple illustrative dataset, we first demonstrate how the traditional tree-based algorithm's splitting function fails to cope with a large proportion of data with zero responses. To address the imbalance issue presented in such loss modeling, this paper aims to modify the traditional splitting function of Classification and Regression Tree (CART). In particular, we propose two novel modified loss functions, namely, the weighted sum of squared error and the sum of squared Canberra error. These modified loss functions impose a significant penalty on grouping observations of non-zero response with those of zero response at the splitting procedure, and thus significantly enhance their separation. Finally, we examine and compare the predictive performance of such modified tree-based models to the traditional model on synthetic datasets that imitate insurance loss. The results show that such modification leads to substantially different tree structures and improved prediction performance.
Evaluation of Proximal Policy Optimization with Extensions in Virtual Environments of Various Complexity

This paper presents an evaluation of the Proximal Policy Optimization deep reinforcement learning algorithm alone and with several extensions. The extensions used are imitation learning and intrinsic curiosity module. The evaluation takes place in a custom-made racing game, where the goal of an agent, a car, is to complete a racing circuit. The game is implemented in the Unity game engine and the machine learning algorithms are provided by the ML- Agents toolkit. The game provides three levels with racing circuits of different complexity. The results described focus on the hyperparameter search, training progress for all the combinations of the algorithm and the extensions and evaluation in the inference mode.
COVID-19 Outbreak: Detecting face mask types in real time

While the COVID-19 outbreak poses several major hazards to the world, it also serves as a reminder that we must take care to prevent the virus from spreading. Wearing a mask is one of the most effective non-pharmaceutical strategies for preventing the spread of infectious diseases. Therefore, to aid in the prevention of a public epidemic, an automatic real-time mask recognition and categorization solution is urgently required. The efficiency of facemasks has been called into question, owing to poor mask selection. N95 masks must be worn in jobs where there is a high danger of contracting the virus. Surgical, DIY, and N95 masks all have varied degrees of effectiveness. To safeguard public safety, we created a deep learning model that can classify different types of masks as well as determine if there is no mask in real-time video or images. Our framework consists of three main phases: Face detection using object detection API, application of face mask classifier and classification of masks into four different categories. In this study, we show that SSD MobileNet V2 outperforms both SSD MobileNet V1 and SSD Resnet50 V1 in terms of sensitivity and precision.
Experimental virtual exoskeletal environment for neurorehabilitation support

This paper deals with the creation of a experimental virtual exoskeletal environment for neurorehabilitation support. In this work, the design of the hierarchical structure of the system was analyzed, as well as the possibility of wireless communication between the individual elements that were physically constructed. A mobile application was also designed and created for setting up and controlling the system using connected sensors for sensing the user and his surroundings but also using connected effectors to display outputs in response to measured values. This experimental platform is used for testing and development of rehabilitation system support based on virtual reality without the physical presence of the patient.
Report on the 6th workshop on search-oriented conversational AI (SCAI 2021)

The 6th edition of the Search-Oriented Conversational AI workshop (SCAI 2021) was organised as a discussion platform on conversational AI for intelligent information access. The workshop was designed to be multidisciplinary, bringing together researchers and practitioners across the fields of natural language processing (NLP), information retrieval (IR), machine learning (ML) and human-computer interaction (HCI). The workshop included four sessions featuring invited talks, a separate poster session, and a session discussing the results of a shared task on conversational question answering (SCAI-QReCC). Date : 8 October, 2021. Website : https://scai.info.
Hardware Development of a Humanoid Robot Head: “Gabot”

The development aims to mimic the anthropomorphic specifications of a human head by using available modern equipment such as the 3D printing machine. The proponents developed mechanisms to meet the Anthropomorphic data of a human head, angle of actuation, and the angular velocity of the mouth, eyes, and neck. The proponents also tested motor torque and stress on the parts to ensure the robustness of the machine, which yields 520 N-mm and 27.74 MPa on the neck tilting, 188 N-mm and 24.74 Mpa on neck swinging, 114 N-mm, and 12.09 Mpa on the neck panning, 73 and 7.052 MPa on the Eye Tilting. The maximum angular velocity of each part is 266.33 deg/sec on neck tilting, 262.33 deg/sec on neck swinging, 314 deg/sec on neck panning, 795.66 deg/sec on eye tilting, and 785.66 deg/sec on eye panning. The Proponents used the MPU-6050 accelerometer to test for the result of this study to achieve the required data. The effectiveness of the machine is as follows; eyes, 92.43% for panning, and 93.60% for tilting, neck, 89.20% for panning, 75.66% for tilting, 75.52% for swinging, for the mouth, 81.94%.
Digital Signature with RSA Public Key Cryptography for Data Integrity in SOSE-Based E-Government Systems

Purpose: SOSE is a novel software paradigm suitable for development of flexible, loose-coupled and end-to-end E-government system. However, the use of this innovation for E-government system is dwindled by security challenge. Apparently, the TLS (transport layer security) solution traditionally applied to protect SOSE-based E-government systems is inadequate since it can only secure point-to-point channels of communicating. Whereas, an end-to-end security protection is necessary to adequately protect SOSE-based applications. Design/Methodology/Approach: Consequently, this paper proposed use of Digital Signature by RSA (Rivest-Shamir-Adleman) public key cryptographic algorithm at the message level to achieve data integrity in SOSE-based E-government system. Findings/Result: The SOAP message content shows adequate formulation of digital signature with appropriate indicators of RSA public key to ensure data integrity. The SOAP message shows that SignedInfo, Signature Value and KeyInfo were formulated correctly. The SignedInfo contains Canonicalization Method, Signature Method and Reference subelements. Also, SignatureValue element has encrypted digest and the KeyInfo element has security token reference. Originality/Value: This security solution integrates many technologies including OASIS’s web service security standard and W3C’s (World Wide Web Consortium) XML digital signature into SOAP envelope to ensure data integrity for E-government system at the message level. Paper Type: Applied Research.
Handwriting Image Generation by Adversarial Training

The generative adversarial network (GAN) has become a popular research direction in the field of artificial intelligence, which has been applied to the image and visual computing, speech and language processing, information security, chess games, etc. In this paper, using GAN to generate handwriting numerals, we propose a new framework to generate handwriting images through the confrontation process. To be specific, we train the generation model G and the discrimination model D iteratively. The generation model G aims to estimate the empirical data distribution. In contrast, the discrimination model D judges whether the input is a real data instance or a fake sample generated by G. The training process is an adversarial game. The generator learns to realistic synthetic data to fool the discriminator. In contrast, the discriminator tries to distinguish the fake data from the real data. In this way, G will gradually approach the empirical data distribution, thus deceiving the discriminator. As a result, the generator can generate plausible handwriting images. The experiment proved the potential of the framework by qualitatively and quantitatively evaluating the generated samples.
Maya: Using Formal Control to Obfuscate Power Side Channels

The security of computers is at risk because of information leaking through their power consumption. Attackers can use advanced signal measurement and analysis to recover sensitive data from this side channel. To address this problem, this article presents Maya, a simple and effective defense against power side channels. The idea is to use formal control to re-shape the power dissipated by a computer in an application-transparent manner—preventing attackers from learning any information about the applications that are running. With formal control, a controller can reliably keep power consumption close to a desired target function even when runtime conditions change unpredictably. By selecting the target function intelligently, the controller can make power to follow any desired shape, appearing to carry activity information which, in reality, is unrelated to the application. Maya can be implemented in privileged software, firmware and hardware. We implement Maya on three machines using only privileged threads against machine learning based attacks, and show its effectiveness and ease of deployment. Maya has already thwarted a newly developed remote power attack.
Machine Learning for Healthcare

The advancements in various technologies have changed the way various industries approach their work and also the way they took corrective steps for betterment in their routine work. The healthcare industry is also one of the adopters of innovative technologies. In recent times, artificial intelligence and its counterpart machine leaning becomes a vital part of healthcare industry which may handle new medical procedures and effective maintenance of patient data. Such advanced technology like machine leaning has a huge trend in the industry and is widely used in many applications. Pattern discovery from versatile medical data sources, prediction of diseases, drug reaction and applications, etc. are the major applications of machine learning in the healthcare domain. Such unprecedented opportunities in the healthcare domain are available because of the explosive growth of health-related data. The chapter will highlight the various applications and techniques of machine leaning in the field of healthcare. It will bridge the gap between available data and effective implementation.
Realization of Combinational Circuits Using A Proposed Reversible Gate

Because of the tremendous importance of very low power consumption, reversible logic synthesis came out as a significant method in circuit design. It is one of several design strategies available for creating digital logic circuits. Here we have presented a reversible gate which, the ‘HAS’ gate, that can function as a Universal circuit. The suggested gate can carry out fundamental operations such as AND, OR, NOT, NAND, NOR, XOR, and so on. It also can be used to perform the complex functionalities of adder, subtractor, decoder, multiplexer, magnitude comparator etc. The proposed design efficiently implements all the basic functionalities of all combinational circuits with ease.
Numerical Methods for Solving High-Order Mathematical Problems using Quantum Linear System Algorithm on IBM QISKit Platform

Researchers are currently working on computational solutions based on quantum systems to accelerate the speed of complex mathematical models. This work presented how to formulate complex computational problems as a quantum system of linear equations and find solutions using Quantum Linear System Algorithm (QLSA), also called Quantum Harrow-Hassidim-Lloyd (HHL) algorithm. This paper showed experimental evaluation of multiple problem statements (curve-fitting functions, interpolating polynomials) as a quantum system of linear equations that involve computation of Vandermonde matrices as co-efficient matrices on IBM Quantum Information Software Kit for Quantum Computation (QISKit) platform. Along with a few examples demonstrating its evaluation on diagonal, Hermitian, and Non-Hermitian matrices as co-efficient matrices. The fidelity is used as a measure of performance for comparing the accuracy of quantum results with respect to existing classical solutions on IBM QISKit and drawing conclusions from the experimental results. Experimental evaluation shows that the fidelity depends on the sparsity of the input matrices and therefore the results vary depending on those matrices.
OPN-DTSP: Optimized Pointer Networks for Approximate Solution of Dynamic Traveling Salesman Problem

The Traveling Salesman Problem (TSP), a classic problem in combinatorial optimization, is a well-known NP-hard problem with a wide range of real-world applications. Dynamic TSP is a further upgrade of TSP. Its dynamic change information leads to the greater complexity of the problem. Over the years, numerous excellent algorithms have been proposed by researchers to solve this problem, from the early exact algorithms to approximate algorithms, heuristics, and more recently, machine learning algorithms. However, these algorithms either only work with static TSP or have an unacceptable time consumption. To this end, we propose an optimized pointer network for approximate solution of dynamic TSP, which guarantees a high-quality approximate solution with very low time consumption. We introduce an attention mechanism in our model to fuse the dynamically changing edge information and the statically invariant node coordinate information and use reinforcement learning to enhance the decision-making of the model. Finally, the superior performance for dynamic TSP with low time-cost is verified on comparison experiments.
Data-Weighted Discrete-Time Adaptive ILC

In Chap. 2 , the DAILC methods can achieve an almost perfect tracking performance over a finite time interval even though both the initial states and the target trajectories vary iteratively. However, all of them have to impose linear growth conditions on the nonlinearities to provide global stability.
Boosting Unsupervised Domain Adaptation with Soft Pseudo-Label and Curriculum Learning

By leveraging data from a fully labeled source domain, unsupervised domain adaptation (UDA) improves classification performance on an unlabeled target domain through explicit discrepancy minimization of data distribution or adversarial learning. As an enhancement, category alignment is involved during adaptation to reinforce target feature discrimination by utilizing model prediction. However, there remain unexplored problems about pseudo-label inaccuracy incurred by wrong category predictions on target domain, and distribution deviation caused by overfitting on source domain. In this paper, we propose a model-agnostic two-stage learning framework, which greatly reduces flawed model predictions using soft pseudo-label strategy and avoids overfitting on source domain with a curriculum learning strategy. Theoretically, it successfully decreases the combined risk in the upper bound of expected error on the target domain. In the first stage, we train a model with distribution alignment-based UDA method to obtain soft semantic label on target domain with rather high confidence. To avoid overfitting on source domain, in the second stage, we propose a curriculum learning strategy to adaptively control the weighting between losses from the two domains so that the focus of the training stage is gradually shifted from source distribution to target distribution with prediction confidence boosted on the target domain. Extensive experiments on two well-known benchmark datasets validate the universal effectiveness of our proposed framework on promoting the performance of the top-ranked UDA algorithms and demonstrate its consistent superior performance.
Mobile Application Voting System: A Means to Achieve a Seamless Election Process in Developing Countries

Voting is a concept used to describe the part of the election process. It is a means by which the citizens choose who to lead them for a designated period. There is various type of manual and electronic voting processes currently in use. Manual voting processes have become a tool by which government bodies in Nigeria and other African countries at considerable take advantage of to push unworthy people into power. The Nigeria voting system is a typical example of this misfortune, where voters are subjected to long queues before they can perform their legal duty as a citizen. This existing system is faced with numerous challenges such as hooliganism where glorified thugs snatch ballot boxes and disrupt the peace and tranquillity of the voting process. Therefore, a loyal citizen who is bound to vote is unable to perform their legal duty, leading to the manipulation of results and other voting crises. This research proposed a mobile voting platform to deal with the challenges as mentioned earlier associated with a manual voting system that is ineffective and inconvenient for citizens. The proposed system will improve how the election is being conducted in Nigeria and other countries that are faced with similar challenges in the voting process. The scheme aims to allow eligible voters with registered voters card (PVC) in Nigeria and diaspora to cast their votes in their respective places of residence as long as the mobile application is accessible on their mobile devices which will be available on various versions such as Android, iOS, Windows operating system. Each voter’s details will be secure through the use of various cryptographic techniques and verified with the use of one-time password during the voting process. This process will make the election process flawless, efficient, convenient, secured and timely in the area of result compilation and final verdict. Also, the system will eliminate violence and result in manipulation.
Research of Siamese Network based Object Tracking Algorithm using Variational Bayes feature point matching

In recent years, the tracking algorithm of Siamese network has shown great advantages. Under the premise of ensuring real-time and accuracy, this paper proposes an object tracking algorithm based on Variational Bayes feature point matching for Siamese network. The algorithm framework is divided into three branches, the Siamese network extracts the feature point branch, the mask generates the object contour point branch and the classification branch. In this paper, the extracted feature points are combined with the object contour points for Variational Bayesian point set matching, and the template image is updated according to the matching results, so as to solve the problem of tracking object loss caused by object contour change and half occlusion. The effectiveness of the proposed algorithm is verified by evaluating and comparing the public datasets VOT and OTB.