Reviewerly/exHarmony huggingface dataset:
Fall Detection Application for the Elderly in the Family Heroes System

Population aging is a considerable challenge for a global society. Indeed, it raises several concerns and gives more or fewer alarmist projections of social-economic problems, that it could engender. Technological solutions to address the issue of aging are now a necessity. Systems based on these solutions combine wearable and ambient sensors, processing software, and user interfaces. Their objective is to monitor dependent and frail elderly people in institutions and/or home. The healthcare monitoring for elderly people is a growing application axis thanks to the massive technology development. Family Heroes is a new concept of an automatic healthcare monitoring system based on the Internet of things. In this paper, we will present the global architecture of our system and the preliminary results of the fall detection using only the Kinect V2. The results show the system, in the first stage of validation, detect 70% of falls using a novel approach threshold-based on the variation of angels of the upper body during the movement cycle.
Constrained Real-Parameter Optimization Using the Firefly Algorithm and the Grey Wolf Optimizer

The main goal of this paper is to present the performance of two popular algorithms, the first is the Firefly Algorithm (FA) and the second one is the Grey Wolf Optimizer (GWO) algorithm for complex problems. In this case the problems that we are presenting are of the CEC 2017 Competition on Constrained Real-Parameter Optimization in order to realize a brief analysis, study and comparison between the FA and GWO algorithms respectively.
Synthesis of Fuzzy Terminal Controller for Chemical Reactor of Alcohol Production

The most investigated area in optimal control of discrete processes under uncertain conditions is optimal control of fuzzy systems, i.e. represented by different-type fuzzy equations. In this paper, the fuzzy terminal control problem described by a fuzzy relational equation (FRE) to take into account the fuzzy state and controls is discussed. Kernel of FRE-based method is Bellman-Zadeh approach. From this point of view, a fuzzy terminal control problem is transfigured to the multistage decision making scheme. The solution of the discussed problem is defined as intersection of fuzzy goal and fuzzy constraints. At the end, obtained fuzzy decisions were maximized.
The Effects of Information Technology and E-Learning Systems on Translation Pedagogy and Productivity of EFL Learners

This study addresses the increasing demands for reliable translation services due to the advent of the electronic text, the prolific production of texts in different disciplines, the increasing communication among individuals from different languages and cultures, and the unprecedented development of new technologies, software and applications (referred to as CAT tools). Human translation cannot address these increasing demands of customers and businesses. Hence, this study assumes that translation technologies and machine translation systems can be usefully integrated into translation classrooms for improving learners' performance and translation quality. An experimental study was carried out where 20 Level-6 students, who are attending one translation course in Prince Sattam Bin Abdulaziz University, volunteered to participate in an optional legal translation course. Participants were divided into 2 groups: an experimental group whose instruction was supported by CAT tools, and a control group who were taught using only conventional teaching methods. Results showed that there was a statistically significant difference among CAT users and nonusers in favor of the experimental group (p <; .05). It can be concluded then that translation systems and software can be usefully used for improving EFL students' translation skills. The integration of CAT tools into the learning environment not only support the practical and professional skills that well-qualify graduates for the translation industry, but it also creates an environment that enables them to acquire different skills in some related disciplines such as assessment of translation techniques, interaction between humans and the machine, and text analysis. The study suggested that translation instructors should adopt blended learning models where they integrate translation technology with conventional teaching methods. Educational institutions are also recommended to provide professional training for instructors on the use of language software and translation systems.
Towards Attribute-Based Encryption for RAMs from LWE: Sub-linear Decryption, and More

Attribute based encryption (ABE) is an advanced encryption system with a built-in mechanism to generate keys associated with functions which in turn provide restricted access to encrypted data. Most of the known candidates of attribute based encryption model the functions as circuits. This results in significant efficiency bottlenecks, especially in the setting where the function associated with the ABE key is represented by a random access machine (RAM) and a database, with the runtime of the RAM program being sublinear in the database size. In this work we study the notion of attribute based encryption for random access machines (RAMs), introduced in the work of Goldwasser, Kalai, Popa, Vaikuntanathan and Zeldovich (Crypto 2013). We present a construction of attribute based encryption for RAMs satisfying sublinear decryption complexity assuming learning with errors; this is the first construction based on standard assumptions. Previously, Goldwasser et al. achieved this result based on non-falsifiable knowledge assumptions. We also consider a dual notion of ABE for RAMs, where the database is in the ciphertext and we show how to achieve this dual notion, albeit with large attribute keys, also based on learning with errors.
Front vehicle detection based on multi-sensor fusion for autonomous vehicle

On account of the limitations of single sensor in obstacle detection, the paper investigates an obstacle detection method based on the fusion of 3D LiDAR and monocular visual. The spatial data fusion of the two sensors is realized according to their calibration results, and the time data fusion is realized by using double buffer technology. Considering the aspect ratio of vehicles, the image region of interest is determined based on the obstacle clustering of 3D LiDAR data. By using Haar-like features as effective characteristic of the front vehicle, integral figure is applied to extract Haar-like features of vehicle samples and non-vehicle samples. AdaBoost algorithm is used to choose weak classifiers to constitute strong classifiers, which combine into the cascade classifier. The cascade classifier has been trained to identify the vehicle target in the image region of interest. The relevant experimental results verify the effectiveness and real-time performance of the detection method.
Detecting organized eCommerce fraud using scalable categorical clustering

Online retail, eCommerce, frequently falls victim to fraud conducted by malicious customers (fraudsters) who obtain goods or services through deception. Fraud coordinated by groups of professional fraudsters that place several fraudulent orders to maximize their gain is referred to as organized fraud. Existing approaches to fraud detection typically analyze orders in isolation and they are not effective at identifying groups of fraudulent orders linked to organized fraud. These also wrongly identify many legitimate orders as fraud, which hinders their usage for automated fraud cancellation. We introduce a novel solution to detect organized fraud by analyzing orders in bulk. Our approach is based on clustering and aims to group together fraudulent orders placed by the same group of fraudsters. It selectively uses two existing techniques, agglomerative clustering and sampling to recursively group orders into small clusters in a reasonable amount of time. We assess our clustering technique on real-world orders placed on the Zalando website, the largest online apparel retailer in Europe1. Our clustering processes 100,000s of orders in a few hours and groups 35-45% of fraudulent orders together. We propose a simple technique built on top of our clustering that detects 26.2% of fraud while raising false alarms for only 0.1% of legitimate orders.
HIBoosting: A Recommender System Based on a Gradient Boosting Machine

Based on explicit data, collaborative filtering is one of the most valuable technologies of a recommender system.However, the further development of a recommender system has been restricted to some extent by the problems of cold start and data sparsity.To weaken the effect caused by data loss, some implicit feedback data are typically introduced into the recommendation such as social information and attribute data.In this paper, we propose a heterogeneous information boosting model for recommendations (HIBoosting).The model takes advantage of different network meta-paths to mine the potential information of user social networks, object metadata and interactive data in heterogeneous information networks.For different semantic information, we use a random-walk based on a meta-path to calculate the similarities of users or items, which are used to extract the low-dimensional embedded features of users or items by factorization, construct user profiles and item profiles.Finally, the embedded features are used for integrated learning by a gradient boosting decision tree (GBDT) to provide users with better recommendation services.The model not only integrates heterogeneous information in information networks, but also makes full use of mining latent relations of heterogeneous information networks.Experiments on the CiaoDVD and Lastfm datasets show that HIBoosting has made great progress in the accuracy of a recommender system.
Estimating Gaps in Martingales and Applications to Coin-Tossing: Constructions and Hardness

Consider the representative task of designing a distributed coin-tossing protocol for n processors such that the probability of heads is $$X_0\in [0,1]$$. This protocol should be robust to an adversary who can reset one processor to change the distribution of the final outcome. For $$X_0=1/2$$, in the information-theoretic setting, no adversary can deviate the probability of the outcome of the well-known Blum's "majority protocol" by more than $$\frac{1}{\sqrt{2\pi n}}$$, i.e., it is $$\frac{1}{\sqrt{2\pi n}}$$ insecure. In this paper, we study discrete-time martingales $$(X_0,X_1,\dotsc ,X_n)$$ such that $$X_i\in [0,1]$$, for all $$i\in \{0,\dotsc ,n\}$$, and $$X_n\in {\{0,1\}} $$. These martingales are commonplace in modeling stochastic processes like coin-tossing protocols in the information-theoretic setting mentioned above. In particular, for any $$X_0\in [0,1]$$, we construct martingales that yield $$\frac{1}{2}\sqrt{\frac{X_0(1-X_0)}{n}}$$ insecure coin-tossing protocols. For $$X_0=1/2$$, our protocol requires only 40% of the processors to achieve the same security as the majority protocol. The technical heart of our paper is a new inductive technique that uses geometric transformations to precisely account for the large gaps in these martingales. For any $$X_0\in [0,1]$$, we show that there exists a stopping time $$\tau $$ such that The inductive technique simultaneously constructs martingales that demonstrate the optimality of our bound, i.e., a martingale where the gap corresponding to any stopping time is small. In particular, we construct optimal martingales such that any stopping time $$\tau $$ has Our lower-bound holds for all $$X_0\in [0,1]$$; while the previous bound of Cleve and Impagliazzo (1993) exists only for positive constant $$X_0$$. Conceptually, our approach only employs elementary techniques to analyze these martingales and entirely circumvents the complex probabilistic tools inherent to the approaches of Cleve and Impagliazzo (1993) and Beimel, Haitner, Makriyannis, and Omri (2018). By appropriately restricting the set of possible stopping-times, we present representative applications to constructing distributed coin-tossing/dice-rolling protocols, discrete control processes, fail-stop attacking coin-tossing/dice-rolling protocols, and black-box separations.
Raindrops on the Windshield: Performance Assessment of Camera-based Object Detection

Camera systems capture images from the surroundings and process these data for detecting and classifying objects. One particular category responsible for faults and failures is adverse weather conditions. In this case, it is known that rain, for example, degrades a sensor’s performance due to absorption and scattering on falling raindrops. In addition, object detection with in-vehicle cameras is affected by raindrops on the windshield since the outside view is disturbed. To counteract, windshield wipers are installed to improve visual information for human drivers but also benefit image sensors.This paper investigates the image quality and the correctness of object detection within one wiping action under rainy weather conditions. The results suggest that the image quality and the object detection performance decrease as the raindrops accumulate on the windshield. Further, it is shown that there is a trade-off between performance and time. Therefore, adaptive weighting data fusion should be implemented so that decisions are made based on the frames in which the sensors’ data is the most reliable.
Dynamic Search Fireworks Algorithm with Adaptive Parameters

As a comparatively new algorithm of swarm intelligence, the dynamic search fireworks algorithm (dynFWA) imitates the explosion procedure of fireworks. With the goal of achieving global optimization and further boosting performance of dynFWA, adaptive parameters are added in this present study, called dynamic search fireworks algorithm with adaptive parameters (dynFWAAP). In this novel dynFWAAP, a self-adaptive method is used to tune the amplification coefficient Ca and the reduction coefficient Cr for fast convergence. To balance exploration and exploitation, the coefficient of amplitude α and the coefficient of sparks β are also adapted, and a new selection operator is proposed. Evaluated on twelve benchmark functions, it is evident from the experimental results that the dynFWAAP significantly outperformed the three variants of fireworks algorithms (FWA) based on solution accuracy and performed best in other four algorithms of swarm intelligence in terms of time cost and solution accuracy.
An Attribute Revocable CP-ABE Scheme

Ciphertext storage can effectively solve the security problems in cloud storage, among which the ciphertext policy attribute-based encryption (CP-ABE) is more suitable for ciphertext access control in cloud storage environment for it can achieve one-to-many ciphertext sharing. The existing attribute encryption scheme CP-ABE has problems with revocation such as coarse granularity, untimeliness, and low efficiency, which cannot meet the demands of cloud storage. This paper proposes an RCP-ABE scheme that supports real-time revocable fine-grained attributes for the existing attribute revocable scheme, the scheme of this paper adopts the version control technology to realize the instant revocation of the attributes. In the key update mechanism, the subset coverage technology is used to update the key, which reduces the workload of the authority. The experimental analysis shows that RCP-ABE is more efficient than other schemes.
Development of Omni-Wheeled Mobile Robot Based-on Inverse Kinematics and Odometry

This paper presents the development of an omni-wheeled mobile robot based on inverse kinematics and odometry for local and indoor navigation purposes, such as for automatic warehousing in industry or healthcare environment. The robot uses four-wheeled diagonal configuration to conform directional angles of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pmb{\alpha_{1}=45^{\mathrm{o}}, \alpha_{2}=135^{\mathrm{o}}, \alpha_{3}=225^{\mathrm{o}}}$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pmb{\alpha_{4}=315^{\mathrm{o}}}$</tex> Inverse kinematics is utilized to drive the robot to a point with specific trajectory and heading angle. Internal wheeled-encoders mounted in each DC-motors are used to read the angular speed and position. This research utilizes odometry technique to estimate the robot's position relative to the initial position. In order to develop a more precise odometry result, we combine the use of wheeled-encoders and an IMU. In order to maintain robot's position relative to the desired position, a PID control is applied to the algorithm. The result of the tests show that the developed omni-wheeled mobile robot is capable of performing locomotion to the desired position and to follow a controlled trajectory by maintaining a minimum error relative to the referenced trajectory.
Suggesting Gaze-based Selection for Surveillance Applications

The selection operation is a basic input operation when interacting with a computer. Traditional manual selection methods like mouse input are challenging when interacting with a dynamic scene containing moving objects as it occurs in surveillance applications. In this contribution, we give an overview on gaze-based selection as a fast and intuitive alternative method considering typical selection tasks in surveillance applications. In this context, we report the results of an evaluation on initialization of an object-tracking algorithm performed by eighteen expert video analysts. Besides its benefits, gaze-based selection is difficult if selection objects are small and close together. We provide first results of a pilot study evaluating a distance measure, which might have the potential of making gaze-based selection more robust. Finally, to leverage gaze-based selection becoming a common technique, low-cost eye-tracking devices have to be available which achieve the same accuracy as the high-end devices. As such cheap eye-trackers only recently became available, it is so far not evident, which accuracy we can expect. Hence, we report the results of an accuracy evaluation of the Tobii 4C conducted with twelve students performing a calibration-style task.
Lattice Trapdoors and IBE from Middle-Product LWE

Middle-product learning with errors (MP-LWE) was recently introduced by Rosca, Sakzad, Steinfeld and Stehlé (CRYPTO 2017) as a way to combine the efficiency of Ring-LWE with the more robust security guarantees of plain LWE. While Ring-LWE is at the heart of efficient lattice-based cryptosystems, it involves the choice of an underlying ring which is essentially arbitrary. In other words, the effect of this choice on the security of Ring-LWE is poorly understood. On the other hand, Rosca et al. showed that a new LWE variant, called MP-LWE, is as secure as Polynomial-LWE (another variant of Ring-LWE) over any of a broad class of number fields. They also demonstrated the usefulness of MP-LWE by constructing an MP-LWE based public-key encryption scheme whose efficiency is comparable to Ring-LWE based public-key encryption. In this work, we take this line of research further by showing how to construct Identity-Based Encryption (IBE) schemes that are secure under a variant of the MP-LWE assumption. Our IBE schemes match the efficiency of Ring-LWE based IBE, including a scheme in the random oracle model with keys and ciphertexts of size $$\tilde{O}(n)$$ (for n-bit identities). We construct our IBE scheme following the lattice trapdoors paradigm of [Gentry, Peikert, and Vaikuntanathan, STOC’08]; our main technical contributions are introducing a new leftover hash lemma and instantiating a new variant of lattice trapdoors compatible with MP-LWE. This work demonstrates that the efficiency/security tradeoff gains of MP-LWE can be extended beyond public-key encryption to more complex lattice-based primitives.
Actor-Critic Reinforcement Learning for Linear Longitudinal Output Control of a Road Vehicle

Reinforcement Learning provides a way to learn optimal controllers but struggles when the state is only partially observed. This is the case with the actuator dynamics in vehicle longitudinal control that translate a controller output into acceleration. We propose a structure for approximating the value function that exploits this property. It allows the critic to learn an observer in the form of a finite impulse response filter and reduces the variance in temporal difference learning. We show that our approach learns faster and with higher precision than an agent that ignores the unobserved states. This is still the case even when using a realistic simulation model with disturbances for the longitudinal dynamics of a road vehicle.
Automatic Tool for Searching for Differential Characteristics in ARX Ciphers and Applications

Motivated by the algorithm of differential probability calculation of Lipmaa and Moriai, we revisit the differential properties of modular addition. We propose an efficient approach to generate the input-output difference tuples with non-zero probabilities. A novel concept of combinational DDT and the corresponding construction algorithm are introduced to make it possible to obtain all valid output differences for fixed input differences. According to the upper bound of differential probability of modular addition, combining the optimization strategies with branch and bound search algorithm, we can reduce the search space of the first round and prune the invalid difference branches of the middle rounds. Applying this tool, the provable optimal differential trails covering more rounds for SPECK32/48/64 with tight probabilities can be found, and the differentials with larger probabilities are also obtained. In addition, the optimal differential trails cover more rounds than exisiting results for SPARX variants are obtained. A 12-round differential with a probability of $$2^{-54.83}$$ for SPARX-64, and a 11-round differential trail with a probability of $$2^{-53}$$ for SPARX-128 are found. For CHAM-64/128 and CHAM-128/*, the 39/63-round differential characteristics we find cover 3/18 rounds more than the known results respectively.
A hierarchical Gamma Mixture Model-based method for estimating the number of clusters in complex data

This paper proposes a new method for estimating the true number of clusters and initial cluster centers in a dataset with many clusters. The observation points are assigned to the data space to observe the clusters through the distributions of the distances between the observation points and the objects in the dataset. A Gamma Mixture Model (GMM) is built from a distance distribution to partition the dataset into subsets, and a GMM tree is obtained by recursively partitioning the dataset. From the leaves of the GMM tree, a set of initial cluster centers are identified and the true number of clusters is estimated. This method is implemented in the new GMM-Tree algorithm. Two GMM forest algorithms are further proposed to ensemble multiple GMM trees to handle high dimensional data with many clusters. The GMM-P-Forest algorithm builds GMM trees in parallel, whereas the GMM-S-Forest algorithm uses a sequential process to build a GMM forest. Experiments were conducted on 32 synthetic datasets and 15 real datasets to evaluate the performance of the new algorithms. The results have shown that the proposed algorithms outperformed the existing popular methods: Silhouette, Elbow and Gap Statistic, and the recent method I-nice in estimating the true number of clusters from high dimensional complex data.