Reviewerly/exHarmony huggingface dataset:
Ensembled Transfer Learning Models for Real Time Face Recognition

Face recognition application is applied in several fields including authentication through biometrics, security-enhancing applications, controlling automobiles, finding mental health, and person/people detection. Still face recognition remains a challenge due to variance in effects of the images such as illumination variation, poses, occlusions, varied facial expressions and etc. Although many techniques such as iris and fingerprint scanning yield prominent accuracy, face recognition is an admirable technique that is applied for many real-time recognitions and is a method of identification that is human-centric. Recognizing the human face plays a significant part in behavior analysis since most of the features can be extracted from facial features. In this paper, the face recognition using Convolution Neural Network(CNN) with its transfer learning architectures such as Visual Geometry Group, VGG16, InceptionResNet, DenseNet121, and MobileNet is demonstrated with its performance for the custom dataset built on 12 different human faces. Stacked the four models and compared the accuracy. HaarCascade classifier using Frontal face is used for experimenting. In this work, face detection and face recognition in video surveillance is explored. More significantly, the system executes the recognition process for the mentioned architectures and the results obtained prove VGG16 architecture provides better accuracy compared with other models.
Surveillance using Unmanned Aerial Vehicle for Triggered Activity Capturing

Unmanned aerial vehicles (UAVs) are being used extensively these days, for various applications such as package delivery, surveillance systems, shooting purposes, and whatnot. Artificial Intelligence in theory talks a lot about search and traversal problems in 2-dimensional space and various algorithms for it. The problem addressed by us is a traversal problem in 3-dimensional space where an unmanned aerial vehicle traverses a dynamic path based on a pre-programmed planner, halts in situations when it faces some hindrance, and reroutes its path to the destination or back to the base station depending upon the situation. In this paper, we propose a surveillance system, where a quick response vehicle acquires the destination address when a trigger is made through GPS by the person in trouble. The drone preplans its path before traversal, traverses the path, and then gets in the vicinity of the person or location from where the emergency signal was triggered. The camera module then captures the images, and the image processing module performs face detection and sends the images to the nearest safety station. In addition to that, if there is only one person detected by the code, the drone performs a 360 rotation to detect the presence of any other animal or bird around and sends the images detected by it so that some suitable action can be taken.
Secure Data Storage Resilient Against Compromised Users via an Access Structure

Consider a source and multiple users who observe the independent and identically distributed (i.i.d.) copies of correlated Gaussian random variables. The source wishes to compress and store its observation in a public database such that (i) authorized sets of users can reconstruct the source with some distortion level, and (ii) information leakage to non-authorized sets of colluding users is minimized. In other words, the recovery of the data is restricted to a predefined access structure of the users. One of the main results of this paper is a closed-form characterization of the fundamental trade-off between source coding rate and the information leakage rate when any authorized set of users has "better" side information than any set of unauthorized users.
Collaborative Handling of Two-Wheeled Deformable Robots

This paper presents techniques and methodologies for collaborative handling of two-wheeled deformable robots (TWDRs) by using image processing. These techniques include task planning, path planning using A* algorithm, QR-code localization and alignment, and cooperative formation. The task planning is proposed to study how to cooperatively move a heavy object step by step. The QR code is deployed for the robot to find and estimate the relative distance, and then localize the robots themselves and objects to be handled. The A* algorithm and path smooth method are adopted to plan the shortest path so as to finish desired handling tasks. A master-slave collaborative controller is presented to achieve collaborative handling. Experimental results are provide for illustration of the effectiveness and merits of the proposed method.
Aspect-Based Sentiment Analysis of Arabic Restaurants Customers' Reviews Using a Hybrid Approach

In this study, an Aspect-based Sentiment Analysis (ABSA) model was developed to classify restaurants' reviews in the Arabic language based on four predefined aspects: price, cleanliness, food quality, and service. A hybrid approach that combines machine learning with domain-specific dictionaries and sentiment word lists was proposed for ABSA. More than 3,000 reviews were collected from a restaurant reviews website. The reviews were annotated using a crowdsourcing method. The annotated reviews were pre-processed, then the dictionaries and sentiment word lists were extracted from the dataset. Moreover, a filter-based feature selection approach using the Chi2 method was applied to reduce the number of representative features. Four aspect models were built using Support Vector Machine (SVM) and another four models were built using Naïve Bayes (NB) classifiers, one model for each aspect. The models were evaluated using Accuracy, Precision, Recall, and F-Measure. The results were promising, as the price aspect model achieved the highest results by applying the SVM classifier with Accuracy 84.47%, Precision 84.3%, Recall 84.5%, and F-Measure 84.3%.
Ownership Protection of Specified Image Data Using Blockchain Technology

We propose a method to protect ownership of specified areas on image data using blockchain. Different values are assigned corresponding to the object's importance in the region and the ownership of the area is managed and given to the required user. The detected areas are individually encrypted by XOR cipher, and a corresponding key image is created to decrypt to protect ownership of selected object regions detected by the object recognition algorithm. We use non-fungible tokens(NFTs) to secure key images by managing ownership of each object on image data. Key images to decrypt are registered as NFTs. Ownership NFTs are also created to access and retrieve the key image NFTs. Each key image NFT is generated by key holders and ownership NFTs are obtained by users requiring them. In addition, there is a judging function that clarifies if ownership and key NFTs match. Key NFTs appear on the screen only when they are matched.
A Semantic Text Expansion for Paraphrasing Identification in Arabic Microblog Posts

An enormous number of microblogs are being created and posted on the web each day. Many of these microblogs are repetitive in terms of content and similar in terms of topic. Being able to detect repetitive content can support various applications such as question answering and trendy topic detection. In this research, we aim to propose a model to detect paraphrasing among Arabic tweets, in addition to identifying tweets belonging to the same topic. The proposed model is based on Latent Dirichlet Allocation (LDA) topic modeling, as well as, semantic text expansion utilizing external resources i.e. BabelNet and Wikipedia. Tweets from multiple Arabic news agencies were collected, preprocessed, and divided into two groups. The first group was used to build the topic modeling and the other group of tweets was paired and classified based on the topic distributions. The results are promising in terms of precision on tweet pairs with a certain time overlap. The best-reported precision is 80.1% achieved using Wikipedia embedded content on the stemmed text mode with a large number of LDA topics.
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising Trees

The Chow-Liu algorithm has been a mainstay for the learning of tree-structured graphical models from i.i.d. sampled data vectors. Its theoretical properties have been well-studied and are well-understood. In this paper, we focus on the class of trees that are arguably even more fundamental, namely homogeneous trees in which each pair of nodes that forms an edge has the same correlation ρ. We ask whether we are able to further reduce the error probability of learning the structure of the homogeneous tree model when active learning is allowed. Our figure of merit is the error exponent, which quantifies the exponential rate of decay of the error probability with an increasing number of data samples. We design and analyze an algorithm Active Learning Algorithm for Trees with Homogeneous Edges (ACTIVE-LATHE), which surprisingly boosts (increases) the error exponent. Our analysis hinges on judiciously exploiting the minute but detectable statistical variation of the samples to allocate more data to parts of the graph in which we are less confident of being correct.
Information-theoretic de Finetti-style theorems

We review information-theoretic approaches to obtaining simple probabilistic representations for sequences of exchangeable random variables. Specifically, we examine information-theoretic proofs of finite versions of de Finetti’s celebrated representation theorem. Such results state, in a quantitative manner, that the joint distribution of the first k of n > k exchangeable random variables is close to a mixture of product distributions. Closeness is measured in terms of the relative entropy and explicit bounds are typically provided. First we review a recent information-theoretic proof a finite de Finetti theorem for binary random variables, and then we give a different, new proof for the case of arbitrary finite alphabets. This second proof is nicely motivated by the Gibbs conditioning principle in connection with statistical mechanics, and it follows along an appealing sequence of steps. The technical estimates required for these steps are obtained via the method of types.A full version of this paper is available online as [23].
Straggler-Resilient Differentially-Private Decentralized Learning

We consider straggler resiliency in decentralized learning using stochastic gradient descent under the notion of network differential privacy (DP). In particular, we extend the recently proposed framework of privacy amplification by decentralization by Cyffers and Bellet to include training latency—comprising both computation and communication latency. Analytical results on both the convergence speed and the DP level are derived for training over a logical ring for both a skipping scheme (which ignores the stragglers after a timeout) and a baseline scheme that waits for each node to finish before the training continues. Our results show a trade-off between training latency, accuracy, and privacy, parameterized by the timeout of the skipping scheme. Finally, results when training a logistic regression model on a real-world dataset are presented.
Formal limitations of sample-wise information-theoretic generalization bounds

Some of the tightest information-theoretic generalization bounds depend on the average information between the learned hypothesis and a single training example. However, these sample-wise bounds were derived only for expected generalization gap. We show that even for expected squared generalization gap no such sample-wise information-theoretic bounds exist. The same is true for PAC-Bayes and single-draw bounds. Remarkably, PAC-Bayes, single-draw and expected squared generalization gap bounds that depend on information in pairs of examples exist.
Composite Neyman-Pearson Hypothesis Testing with a Known Hypothesis

We propose a composite hypothesis test in the Neyman-Pearson setting where the null distribution is known and the alternative distribution belongs to a certain family of distributions. The proposed test interpolates between Hoeffding’s test and the likelihood ratio test and achieves the optimal error exponent tradeoff for every distribution in the family. In addition, the proposed test is shown to attain the type-I error probability prefactor of ${n^{\frac{{\bar d - 1}}{2}}}$, where $\bar d$ is the dimension of the family of distributions projected onto a relative entropy ball centered at the null distribution. This can be significantly smaller than the prefactor ${n^{\frac{{a - 2}}{2}}}$ achieved by the Hoeffding’s test where d is the dimension of the probability simplex. In addition, the proposed test achieves the optimal type-II error probability prefactor for every distribution in the family.
Breaking Blockchain’s Communication Barrier with Coded Computation

Although blockchain, the supporting technology of various cryptocurrencies, has offered a potentially effective framework for numerous decentralized trust management systems, its performance is still sub-optimal in real-world networks. With limited bandwidth, the communication complexity for nodes to process a block scales with the growing network size and hence becomes the limiting factor of blockchain’s performance.In this paper, we suggest a re-design of existing blockchain systems, which addresses the issue of the communication burden. First, by employing techniques from Coded Computation, our scheme guarantees correct verification of transactions while reducing the communication complexity dramatically such that it grows logarithmically with network size. Second, by adopting techniques from Information Dispersal and State Machine Replication, our design is provably resilient to Byzantine faults under standard cryptographic assumptions. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Robust Private Information Retrieval with Optimal Server Computation

Private information retrieval (PIR) schemes allow a user to retrieve entries of a database without revealing the index of the desired item. The focus of this paper lies on constructions of PIR schemes with optimal computational complexity for the servers, which play a crucial part in fast retrieval. This paper first proposes a generic construction of t-private PIR schemes using circulant permutation matrices (CPMs), which can protect the user’s perfect privacy from any collusion of up to t servers. Then this paper takes Byzantine and unresponsive servers into account in the t-private PIR schemes using CPMs and proposes a generic construction of t-private robust PIR schemes using CPMs. The proposed constructions of PIR schemes enjoy the advantages of optimal computational complexity for the servers, competitive user computational complexity, acceptable communication complexity, low memory space for storing all possible queries for the user, and low encoding complexity upon encoding the database.
Using data compression and randomisation to build an unconditionally secure short key cipher

We consider the problem of constructing an unconditionally secure cipher for the case when the key length is less than the length of the encrypted message. (Unconditional security means that a computationally unbounded adversary cannot obtain information about the encrypted message without the key.) In this article, we propose data compression and randomisation techniques combined with entropically-secure encryption for the case when message statistics are known. The resulting cipher can be used for encryption in such a way that the key length does not depend on the entropy or the length of the encrypted message; instead, it is determined by the required security level.
A Machine Learning Framework for Privacy-Aware Distributed Functional Compression over AWGN Channels

In many diverse fields, distributed IoT devices perform collaborative inference by communicating with an edge router. Often sensory data contains sensitive attributes that should not be revealed to the router. To address this, we develop, to the best of our knowledge, the first privacy-aware machine learning framework for distributed functional compression over AWGN channels. The key feature of our approach to privacy is that we focus only on sensitive attributes of data rather than paying a high cost to protect everything. Employing a mutual information based privacy constraint, we first propose a novel approximate upper bound to protect sensitive attributes in the compressed representations of the sensory data. Next, in conjunction with the upper bound, we propose an adversarial lower bound to enhance the protection further. Thirdly, we propose novel decompositions to these bounds such distributed edge devices can ensure overall privacy by independently privatizing their components. This allows us to propose an enhanced privacy-aware algorithm that protects sensitive information during training and inference. Our experiments show that the privacy-utility trade-off from our proposed methods is significantly better than existing mechanisms.
An Alphabet of Leakage Measures

We introduce a family of information leakage measures called maximal α, β-leakage, parameterized by real numbers α and β. The measure is formalized via an operational definition involving an adversary guessing an unknown function of the data given the released data. We obtain a simple, computable expression for the measure and show that it satisfies several basic properties such as monotonicity in β for a fixed α, non-negativity, data processing inequalities, and additivity over independent releases. Finally, we highlight the relevance of this family by showing that it bridges several known leakage measures, including maximal α-leakage (β = 1), maximal leakage (α = ∞, β = 1), local differential privacy (α = ∞, β = ∞), and local Rényi differential privacy (α = β).
Asymmetric Local Information Privacy and the Watchdog Mechanism

This paper proposes a novel watchdog privatization scheme by generalizing local information privacy (LIP) to enhance data utility. To protect the sensitive features S correlated with some useful data X, LIP restricts the lift, the ratio of the posterior belief to the prior on S after and before accessing X. For each x, both maximum and minimum lift over sensitive features quantify the privacy risk of publishing this symbol and should be restricted for the privacy-preserving purpose. Previous works enforce the same bound for both max-lift and min-lift. However, empirical observations show that the min-lift is usually much smaller than the max-lift. In this work, we generalize the LIP definition to consider the unequal values of max and min lift, i.e., considering different bounds for max-lift and min-lift. This new definition is applied to the watchdog privacy mechanism. We demonstrate that the utility is enhanced under a given privacy constraint on local differential privacy. At the same time, the resulting max-lift is lower and, therefore, tightly restricts other privacy leakages, e.g., mutual information, maximal leakage, and α-leakage.
Integrity on Low-cost INS/GNSS/Odometer Tightly Coupled Integration using Extended and Unscented Kalman Filter

For navigation, perception, control, and all-around safe operation, autonomous vehicles ought to know its accurate location and orientation in any kind of weather and traffic. Because autonomous vehicles operate under a wide range of conditions, several sensors are utilized to assure the necessary performance. The Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) are utilized as sensor fusion algorithms to fuse low-cost INS/GNSS/Odometer. Nevertheless, there are a number of sources of error exist. It must be limited by the acceptable amount of risk for the application. Therefore, in this paper, we focus on Horizontal Protection Level (HPL) and choose the kSigma algorithm to compute it. By using tightly coupled integration, the comparison between EKF and UKF in the sense of HPL is analyzed. First, we analyzed the difference RMS position error between EKF and UKF. Then, the result both algorithms in Horizontal Position Error (HPE) subject to HPL are investigated. Overall, the UKF solution has a better result in accuracy and integrity than EKF. It indicated with lower percentage of epoch UKF solution that satisfied HPE < HPL condition than EKF and lower RMS position error.
Sharp asymptotics on the compression of two-layer neural networks

In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M <N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimize the population L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> loss between the outputs of the target and of the compressed network, under the assumption of Gaussian inputs. By using tools from high-dimensional probability, we show that this non-convex problem can be simplified when the target network is sufficiently over-parameterized, and provide the error rate of this approximation as a function of the input dimension and N. In this mean-field limit, the simplified objective, as well as the optimal weights of the compressed network, does not depend on the realization of the target network, but only on expected scaling factors. Furthermore, for networks with ReLU activation, we conjecture that the optimum of the simplified optimization problem is achieved by taking weights on the Equiangular Tight Frame (ETF), while the scaling of the weights and the orientation of the ETF depend on the parameters of the target network. Numerical evidence is provided to support this conjecture.
Simulation of Reinforcement Learning Algorithm for Motion Control of an Autonomous Humanoid

Autonomy is an issue in robotics systems. Currently the robotics research community is focusing on autonomy in the decision. The pre-programmed humanoid robots perform the operation in a known scenario. The presence of an obscure environment, a lack of awareness of the environment, humanoid robot fails to perform the random task. In such cases, the preprogrammed robot needs to be reprogrammed to enable it to perform in the changing environment. There is very limited success achieved in the autonomy of the decision-making process. This research work considered the problem of an autonomy while the deicion making in th real time interaction. The reinforcement algorithm helps to do the task in such type of unstructured and unknown environment. Reinforcement learning problems are categorized into partial Markov Decision Processes (MDP). The goal of RL agent is to minimize its immediate and expected costs. When the system interacts with the Markov Decision Process, RL agent passes through an intermediate sequence of states that depends on another by transition probabilities. The agents action takes, and the agents experience a sequence of immediate costs incurred. Reinforcement Learning and teaching approach like Queue Learning (Q-Learning) is implemented for humanoid robot for navigation and exploration. The Q-learning expresses the expected costs to go of a state action pair defined, which is meant to express the expected costs arising after having taken action in the state following policy. Based on the optimal policy of the reinforcement algorithm, a reinforcement controller was implemented. The transition probabilities of the controller depend on the randomness of the controller. The random values of the controller decide the action. Simulations were carried out for the different positions of the proposed model, and an interesting result were was observed while the transition from the sitting position to the goal position.