Reviewerly/exHarmony huggingface dataset:
Deep Human Action Recognition System for Assistance of Alzheimer’s Patients

People with Alzheimer's disease find it difficult to perform their Activities of Daily Living (ADLs) due to limited abilities in cognitive functioning. They cannot remember the correct sequence of the performed activity steps, and sometimes they can spend a lot of time in an activity without finishing it. Therefore, to be successful with ADLs, they need human caregivers, but this reduces their autonomy. In response to these needs of the elderly, we have developed an assistance system for people with Alzheimer. The assistance system aims to ensure ADLs (like drinking beverage) performing correctly, based on action recognition from a video. Our assistance system provides patient assistance prompts as needed. The DemCare dataset was used to develop and validate the proposed assistance system. Obtained results are very promising and are comparable to the state-of-the-art approaches ones.
Robust Face Detection integrating Novel Skin Color Matching under Variant Illumination Conditions

Integration of skin color property in face detection algorithm is a recent trend to improve accuracy.The existing skin color matching techniques are illumination condition dependent, which directly impacts the face detection algorithm.In this study, a novel illumination condition invariant skin color matching method is proposed which is a composite of two rules to balance the high and low intensity facial images by individual rule.The proposed skin color matching method is incorporated into Haar Feature based Face Detection (HFFD) algorithm for face detection and is verified on a large set of images having variety of skin colors and also varying illumination intensities.Experimental results reveal the effectiveness and robustness of the proposed method outperforming other existing methods.
∞-Groupoid Generated by an Arbitrary Topological λ-Model

Abstract The lambda calculus is a universal programming language. It can represent the computable functions, and such offers a formal counterpart to the point of view of functions as rules. Terms represent functions and this allows for the application of a term/function to any other term/function, including itself. The calculus can be seen as a formal theory with certain pre-established axioms and inference rules, which can be interpreted by models. Dana Scott proposed the first non-trivial model of the extensional lambda calculus, known as $ D_{\infty }$, to represent the $\lambda $-terms as the typical functions of set theory, where it is not allowed to apply a function to itself. Here we propose a construction of an $\infty $-groupoid from any lambda model endowed with a topology. We apply this construction for the particular case $D_{\infty }$, and we see that the Scott topology does not provide enough information about the relationship between higher homotopies. This motivates a new line of research focused on the exploration of $\lambda $-models with the structure of a non-trivial $\infty $-groupoid to generalize the proofs of term conversion (e.g., $\beta $-equality, $\eta $-equality) to higher-proofs in $\lambda $-calculus.
Suspicious Activity Detection Using Transfer Learning Based ResNet Tracking from Surveillance Videos

Tracking objects in video surveillance is a challenging task for public security. The advent of the semantic approach has greatly raised the growth of anomaly detection. However, the current anomaly detection methods typically experience problems like inadequate use of movement patterns and inconsistency on various datasets. This research proposed a system to enhance the efficiency of anomaly detection in video surveillance. The proposed system consists of two parts that involves object tracking and suspicious activity detection. The overall framework detects and tracks the abnormal objects in video surveillance. The transfer learning-based ResNet tracking has been used for object tracking. Distance Metric Learning (DML) method has been used for detecting suspicious activities in video surveillance. The results are estimated to analyze the efficiency of the proposed method. The proposed network classifier is compared with the existing ResNet and VGG-16 network. The proposed method provides 99% accuracy that had high performance compared to other existing methods.
Principal component‐guided sparse regression

Abstract We propose a new method for supervised learning, the “ principal components lasso ” (“pcLasso”). It combines the lasso ( ℓ 1 ) penalty with a quadratic penalty that shrinks the coefficient vector toward the feature matrix's leading principal components (PCs). pcLasso can be especially powerful if the features are preassigned to groups. In that case, pcLasso shrinks each group‐wise component of the solution toward the leading PCs of that group. The pcLasso method also carries out selection of feature groups. We provide some theory and illustrate the method on some simulated and real data examples.
Artificial Intelligence and Ambient Intelligence

Artificial intelligence (AI) and its sister ambient intelligence (AmI) have in recent years become one of the main contributors to the progress of digital society and human civilization [...]
Differential Privacy Location Protection Scheme Based on Hilbert Curve

Location-based services (LBS) applications provide convenience for people’s life and work, but the collection of location information may expose users’ privacy. Since these collected data contain much private information about users, a privacy protection scheme for location information is an impending need. In this paper, a protection scheme DPL-Hc is proposed. Firstly, the users’ location on the map is mapped into one-dimensional space by using Hilbert curve mapping technology. Then, the Laplace noise is added to the location information of one-dimensional space for perturbation, which considers more than 70% of the nonlocation information of users; meanwhile, the disturbance effect is achieved by adding noise. Finally, the disturbed location is submitted to the service provider as the users’ real location to protect the users’ location privacy. Theoretical analysis and simulation results show that the proposed scheme can protect the users’ location privacy without the trusted third party effectively. It has advantages in data availability, the degree of privacy protection, and the generation time of anonymous data sets, basically achieving the balance between privacy protection and service quality.
MQuadE: a Unified Model for Knowledge Fact Embedding

The task of knowledge graph embedding (KGE) tries to find appropriate representations for entities and relations and appropriate mathematical computations between the representations to approximate the symbolic and logical relationships between entities. One major challenge for KGE is that the relations in real-world knowledge bases exhibit complex behaviors: they can be injective (1-1) or non-injective (1-N, N-1, or N-N), symmetry or skew-symmetry; one relation may be the inversion of another relation; one relation may be the composition of other two relations (where the composition can be either Abelian or non-Abelian). To our knowledge, there has not been any theoretical guarantee that these complex behaviors can be modeled by existing KGE methods.
Task-Space Decomposed Motion Planning Framework for Multi-Robot Loco-Manipulation

This paper introduces a novel task-space decomposed motion planning framework for multi-robot simultaneous locomotion and manipulation. When several manipulators hold an object, closed-chain kinematic constraints are formed, and it will make the motion planning problems challenging by inducing lower-dimensional singularities. Unfortunately, the constrained manifold will be even more complicated when the manipulators are equipped with mobile bases. We address the problem by introducing a dual-resolution motion planning framework which utilizes a convex task region decomposition method, with each resolution tuned to efficient computation for their respective roles. Concretely, this dual-resolution approach enables a global planner to explore the low-dimensional decomposed task-space regions toward the goal, then a local planner computes a path in high-dimensional constrained configuration space. We demonstrate the proposed method in several simulations, where the robot team transports the object toward the goal in the obstacle-rich environments.
Enhanced State Estimation Based on Particle Filter and Sensor Data With Non-Gaussian and Multimodal Noise

This paper presents a novel approach to state estimation based on particle filter dealing with measurement data effected by non-Gaussian, multimodal noise. The implementation focusses on autonomous underwater vehicles (AUVs) utilizing data of a magnetic compass and a mechanical scanning sonar for spatial navigation. Nowadays, particle filter approaches often require complicated feature extraction methods culminating in semantic interpretation of the data. This is not suitable for low-cost and low-weight AUVs, because these steps require high computational power. Therefore, efficient CPUs and higher power delivery are required. To test the novel approach, the algorithm is simulated in different scenarios with different parameters. Additionally, the filter is applied to real environment data. Finally, the performance is tested and evaluated by several methods. We demonstrate the computational efficiency and superiority of our method over other approaches through simulations.
Cost optimization of distributed data centers via computing workload distribution for next generation network systems

Recently, the research on sixth generation (6G) network systems has started as the deployments of fifth generation (5G) network systems go with a swing. One research aspect is the operation cost of 6G data centers because the energy consumption of the data centers has become a major portion of data centers' operation cost as the request on 6G services gradually increases. Optimizing the energy consumption for lowering operating cost of data centers gains a lot of attention by the data center operators and becomes a hot issue of the current and future data center's research. Most of the efforts in related studies have been made to minimize the operation cost from cooling, servers, and electricity aspects. However, most of the existing research studies address above operation cost issue in a separate manner which leads to inferior optimization results. Effective joint solutions are eagerly sought to optimize the operation cost of data centers. In this paper, a scheme of operation cost optimization is proposed for distributed data centers. Under the guide of the proposed scheme, electricity cost is optimized by taking into account of the multiple input single output (MISO) server power model, electricity price, and cooling efficiency. The workload distribution affecting the electricity cost as a major factor is studied and the optimal value of allocated workload is found to reduce the electricity cost. First, the whole system model is introduced. Next, based on the system model, the problem of cost optimization via workload dispatching is constructed without compromising service delay guarantee in service level agreement (SLA). Moreover, in order to conveniently solve the optimization problem, an algorithm is presented. Evaluation results demonstrate that the proposed scheme outperforms the existing scheme in terms of the total electricity cost reduction.
A decision support model to manage overspecification in system development projects

Good quality products are developed to satisfy the needs of their customers and gain their satisfaction. This could be attained by delivering products embedding only features that really meet customer's needs and reduce cost. Unfortunately, many products are plagued with overspecification that lead to complicating the design and marketing of products with characteristics or functions neither really necessary nor desired by customers or the market and are unnecessarily expensive. This paper presents a model to identify and manage overspecification in system development projects. It mainly focuses on large-scale systems development, specified by the customer, rather than the design of consumer goods that follow market research. First, an optimisation problem with project constraints to mitigate the impacts of overspecification is formalised and subsequently, a practical model to manage overspecification in projects is presented and demonstrated on a case study.
Using Clustering Analysis and Association Rule Technology in Cross-Marketing

In this paper, according to the perspective of customers and products, by using clustering analysis and association rule technology, this paper proposes a cross-marketing model based on an improved sequential pattern mining algorithm, where an improved algorithm AP (Apriori all PrefixSpan) is applied. The algorithm can reduce the time cost of constructing a projection database and the influence of the increase of support on the algorithm efficiency. The improved idea is that when the first partition is used to generate the projection database, the number of itemsets in the projection database is sorted from small to large, and when the second partition is used, the sequence patterns are generated directly from the mined sequence patterns, so as to reduce the construction of the database. The experimental results show that this method can quickly mine the effective information in complex data sets, improve the accuracy and efficiency of data mining, and occupy less memory consumption, which has good theoretical value and application value.
BitConduite: Exploratory Visual Analysis of Entity Activity on the Bitcoin Network

We present BitConduite, a visual analytics approach for explorative analysis of financial activity within the Bitcoin network, offering a view on transactions aggregated by entities, i.e., by individuals, companies, or other groups actively using Bitcoin. BitConduite makes Bitcoin data accessible to nontechnical experts through a guided workflow around entities analyzed according to several activity metrics. Analyses can be conducted at different scales, from large groups of entities down to single entities. BitConduite also enables analysts to cluster entities to identify groups of similar activities as well as to explore characteristics and temporal patterns of transactions. To assess the value of our approach, we collected feedback from domain experts.
Booking.com Multi-Destination Trips Dataset

We introduce a novel dataset of real multi-destination trips booked through Booking.com's online travel platform. The dataset consists of 1.5 million reservations representing 359,000 unique journeys made across 39,000 destinations. As such, the data is particularly well suited to model sequential recommendation and retrieval problems in a high cardinality target space. To preserve user privacy and protect business-sensitive statistics, the data is fully anonymized, sampled and limited to five user origin markets. Even so, the dataset is representative of the general travel purchase behavior and therefore presents a uniquely valuable resource for Machine Learning and information retrieval researchers. This work provides an overview of the dataset. It reports several benchmark results for relevant recommendation problems obtained as part of the recently held Booking.com data challenge during the WSDM WebTour workshop.
Visual analytics of set data for knowledge discovery and member selection support

Visual analytics (VA) is a visually assisted exploratory analysis approach in which knowledge discovery is executed interactively between the user and system in a human-centered manner. The purpose of this study is to develop a method for the VA of set data aimed at supporting knowledge discovery and member selection. A typical target application is a visual support system for team analysis and member selection, by which users can analyze past teams and examine candidate lineups for new teams. Because there are several difficulties, such as the combinatorial explosion problem, developing a VA system of set data is challenging. In this study, we first define the requirements that the target system should satisfy and clarify the accompanying challenges. Then we propose a method for the VA of set data, which satisfies the requirements. The key idea is to model the generation process of sets and their outputs using a manifold network model. The proposed method visualizes the relevant factors as a set of topographic maps on which various information is visualized. Furthermore, using the topographic maps as a bidirectional interface, users can indicate their targets of interest in the system on these maps. We demonstrate the proposed method by applying it to basketball teams, and compare with a benchmark system for outcome prediction and lineup reconstruction tasks. Because the method can be adapted to individual application cases by extending the network structure, it can be a general method by which practical systems can be built.
Code Specialization through Dynamic Feature Observation

Modern software (both programs and libraries) provides large amounts of functionality, vastly exceeding what is needed for a single given task. This additional functionality results in an increased attack surface: first, an attacker can use bugs in the unnecessary functionality to compromise the software, and second, defenses such as control-flow integrity (CFI) rely on conservative analyses that gradually lose precision with growing code size.
Convolution Neural Network based Approach for Breast Cancer Type Classification

Breast cancer is ranked high amongst the main causes of death that badly effected women worldwide. Cancer diagnosis is commonly carried out from Hematoxylin and Eison (H&E) stained breast histopathology images. However, possible varying conditions in slide preparation and stain color variations in H&E images could induce the chances of misdiagnoses. Therefore, in this paper, we have proposed a new deep learning based method for breast Hematoxylin and Eison (H&E) stained breast histopathology images. In pipeline of the designed approach color normalization method and sample extraction strategy are explained which are used for pre-processing of H&E histology images. Further deep feature computation with deep convolution neural network (CNN) & classification with various deep classifiers is also explained. The performance of designed approach is tested on publically available challenging breast histological BreakHis dataset. Results proved that the presented system has good potential for cancer type classification and outperforms the existing methods.
Exploring superposition state in multi-scale quantum harmonic oscillator algorithm

Research on the merging of quantum mechanics and intelligence algorithms has received extensive attention in recent years. When applying quantum mechanics to intelligent algorithms, the first noteworthy problem is the representation of solutions in the form of a probability superposition. In the recently proposed multi-scale quantum harmonic oscillator algorithm (MQHOA), the representation of the solutions is not a superposition of the probability distributions, but a single distribution. This incorrect representation of the superposition state reduces the diversity of the solutions and limits the performance of the algorithm. To alleviate this problem, in this paper, a multiple-population-based mechanism that uses a quantum superposition is presented for MQHOA, which increases the search capacity of the algorithm by enhancing the diversity of the solutions. With the proposed method, the eigenstate is represented by the different size and distribution subpopulations of the particles. The optimization process is the process of continually changing the size and the distribution of the subpopulations to make the superposition state collapse to the ground state. The multiple-population-based superposition state greatly increases the diversity of the optimization system and enhances the movement efficiency of the particles. The effectiveness of the method based on the performance of the algorithm was thoroughly investigated by comparing the method with the original MQHOA and several well-known intelligent algorithms on unimodal and multimodal benchmark functions and on a real-world charge management problem for plug-in hybrid electric vehicles. The experimental results reveal the effectiveness of the multiple-population-based superposition state used by the proposed method.
Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand.
Qualitative and Quantitative Analysis of Callgraph Algorithms for Python

As one of the most popular programming languages, PYTHON has become a relevant target language for static analysis tools. The primary data structure for performing an inter-procedural static analysis is call-graph (CG), which links call sites to potential call targets in a program. There exists multiple algorithms for constructing callgraphs, tailored to specific languages. However, comparatively few implementations target PYTHON. Moreover, there is still lack of empirical evidence as to how these few algorithms perform in terms of precision and recall. This paper thus presents EVAL_CG, an extensible framework for comparative analysis of Python call-graphs. We conducted two experiments which run the CG algorithms on different Python programming constructs and real-world applications. In both experiments, we evaluate three CG generation frameworks namely, Code2flow, Pyan, and Wala. We record precision, recall, and running time, and identify sources of unsoundness of each framework. Our evaluation shows that none of the current CG construction frameworks produce a sound CG. Moreover, the static CGs contain many spurious edges. Code2flow is also comparatively slow. Hence, further research is needed to support CG generation for Python programs.