Reviewerly/exHarmony huggingface dataset:
Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendation

An automated contextual suggestion algorithm is likely to recommend contextually appropriate and personalized ‘points-of-interest’ (POIs) to a user, if it can extract information from the user’s preference history (exploitation) and effectively blend it with the user’s current contextual information (exploration) to predict a POI’s ‘appropriateness’ in the current context. To balance this trade-off between exploitation and exploration, we propose an unsupervised, generic framework involving a factored relevance model (FRLM), constituting two distinct components, one pertaining to historical contexts, and the other corresponding to the current context. We further generalize the proposed FRLM by incorporating the semantic relationships between terms in POI descriptors using kernel density estimation (KDE) on embedded word vectors. Additionally, we show that trip-qualifiers, (e.g. ‘trip-type’, ‘accompanied-by’) are potentially useful information sources that could be used to improve the recommendation effectiveness. Using such information is not straightforward since users’ texts/reviews of visited POIs typically do not explicitly contain such annotations. We undertake a weakly supervised approach to predict the associations between the review-texts in a user profile and the likely trip contexts. Our experiments, conducted on the TREC Contextual Suggestion 2016 dataset, demonstrate that factorization, KDE-based generalizations, and trip-qualifier enriched contexts of the relevance model improve POI recommendation.
Indian Language Identification for Short Text

Language identification is used to categorize the language of a given document. Language identification categorizes the contents and can have a better search results for a multilingual document. In this work, we classify each line of text to a particular language and focused on short phrases of length 2–6 words for 15 Indian languages. It detects that a given document is in multilingual and identifies the appropriate Indian languages. The approach used is the combination of n-gram technique and a list of short distinctive words. The n-gram model applied is language independent whereas short word method uses less computation. The results show the effectiveness of our approach over the synthetic data.
An Evolutionary Game-Theoretic Trust Study of a Blockchain-Based Personal Health Data Sharing Framework

During the past few years, medical Internet-of-Things devices have experienced a massive growth and they are currently generating tremendous volumes of data every day, which are highly valuable as they can be of great importance to all stakeholders within the healthcare industry. In this context, many blockchain-based solutions have been proposed to mitigate the siloed burden in different healthcare systems which prevented achieving a patient-centric, transparent, and secure data sharing. However, when multiple independent entities try to share their data, trust is not necessarily guaranteed. The scope of this paper is to explore the impact of verification on the level of trust among the different entities of the health data-trading system by proposing an evolutionary game theoretic model. We also present the numerical analysis of the evolution of trust in terms of different game parameters adopted to evaluate their impact on eliminating malicious (i.e., untrustworthy) players from the system.
Benchmarking the Clustering Performances of Evolutionary Algorithms: A Case Study on Varying Data Size

Clustering is a widely used popular method for data analysis within many clustering algorithms for years. Today it is used in many predictions, collaborative filtering and automatic segmentation systems on different domains. Also, to be broadly used in practice, such clustering algorithms need to give both better performance and robustness when compared to the ones currently used. In recent years, evolutionary algorithms are used in many domains since they are robust and easy to implement. And many clustering problems can be easily solved with such algorithms if the problem is modeled as an optimization problem. In this paper, we present an optimization approach for clustering by using four well-known evolutionary algorithms which are Biogeography-Based Optimization (BBO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). the objective function has been specified to minimize the total distance from cluster centers to the data points. Euclidean distance is used for distance calculation. We have applied this objective function to the given algorithms both to find the most efficient clustering algorithm and to compare the clustering performances of algorithms against different data sizes. In order to benchmark the clustering performances of algorithms in the experiments, we have used a number of datasets with different data sizes such as some small scale, medium and big data. The clustering performances have been compared to K-means as it is a widely used clustering algorithm for years in literature. Rand Index, Adjusted Rand Index, Mirkin's Index and Hubert's Index have been considered as parameters for evaluating the clustering performances. As a result of the clustering experiments of algorithms over different datasets with varying data sizes according to the specified performance criteria, GA and GWO algorithms show better clustering performances among the others. The results of the study showed that although the algorithms have shown satisfactory clustering results on small and medium scale datasets, the clustering performances on Big data need to be improved.
How polarized is sub-Saharan Africa? A look at the regional distribution of consumption expenditure in the 2000s

Abstract The resurgence of economic growth over the last two decades in sub-Saharan Africa has recently come under scrutiny by scholars, the main criticism being the lack of inclusiveness. While studies on inequality in sub-Saharan Africa are becoming numerous, less attention has been devoted so far to the growing polarization the region is undergoing. Polarization, as distinct from inequality, refers to the tendency of shifting away from the centre of a distribution to its tails, creating a hollowed-out middle. This paper, using a set of sub-Saharan African national household surveys, provides a first estimate of the regional expenditures’ polarization. This latter steadily increased throughout the 2000s and its growth was mainly driven by increasing polarization between countries, meaning sub-Saharan Africa tended to polarize spatially, with the Southern cone countries and some Western African countries performing above the average, and the rest of the region lagging behind.
Learning behavioral models by recurrent neural networks with discrete latent representations with application to a flexible industrial conveyor

Recurrent neural networks (RNN) are being extensively exploited in industry to address complex predictive tasks by leveraging on the increased availability of data from processes. However, the rationale behind model response is encoded in an implicit way, which is difficult to be explained by practitioners. If revealed, such mechanisms could provide deeper insights into RNN execution, enhancing conventional performance evaluations. We propose a new approach based on the introduction of a model-based clustering layer, constraining the network to operate on a discrete latent state representation. By processing context-input conditioned transitions between clusters, a Moore Machine characterizing the RNN computations is extracted. The proposed approach is demonstrated on both synthetic experiments from an open benchmark problem and via the application to a pilot industrial plant, by the behavior cloning of the flexible conveyor of a Remanufacturing process. The finite-state RNN attains the prediction accuracy of RNN with continuous state, providing in addition a more interpretable structure.
An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments

The methods of task assignment and path planning have been reported by many researchers, but they are mainly focused on environments with prior information. In unknown dynamic environments, in which the real-time acquisition of the location information of obstacles is required, an integrated multi-robot dynamic task assignment and cooperative search method is proposed by combining an improved self-organizing map (SOM) neural network and the adaptive dynamic window approach (DWA). To avoid the robot oscillation and hovering issue that occurs with the SOM-based algorithm, an SOM neural network with a locking mechanism is developed to better realize task assignment. Then, in order to solve the obstacle avoidance problem and the speed jump problem, the weights of the winner of the SOM are updated by using an adaptive DWA. In addition, the proposed method can search dynamic multi-target in unknown dynamic environment, it can reassign tasks and re-plan searching paths in real time when the location of the targets and obstacle changes. The simulation results and comparative testing demonstrate the effectiveness and efficiency of the proposed method.
Combining trunk movement and facial expression enhances the perceived intensity and believability of an avatar's pain expression

The use of computer-generated characters (avatars) is increasingly being used to study emotion and social cognition in humans, as it offers a highly controllable, yet potentially interactive experimental set-up. However, avatars often fall short at conveying credible emotions. This study explored the interaction between body motion and facial expression on the perceived intensity and believability of an avatar's pain expression. Adults were shown videos of an agematched avatar displaying facial expressions of pain while the body was static or while the trunk was oscillating at varied amplitudes, which represented human dynamic equilibrium (idle motion) or trunk rocking expressing a sustained pain (pain behavior). Pupil size was recorded during the task as an objective marker of emotional reaction. Results showed that the avatar's pain was perceived to be more intense and more believable in the presence of both idle motion and trunk rocking than in the static condition. Pupil dilated more when facial pain expression was combined with trunk rocking than in the static and idle conditions. This work demonstrated the critical role of idle motion when creating dynamical pain-expressing avatars, as well as the potentiating effect of body motion when combined with facial expression on the perception of avatar's pain.
Gabor Filter and ICA-Based Facial Expression Recognition Using Two-Layered Hidden Markov Model

This paper introduces the framework based on the extraction of features using Gabor filters and modified hidden Markov model for classification. The three regions of interest (nose, mouth and eyes) are extracted using Gabor filters and dimensions are reduced by independent component analysis. Then these reduced dimensions are input to our two-layered HMM for training and testing. Seven facial expressions are recognized using publicly available JAFFE dataset. Experimental data shows the efficient and robust nature of our framework and shows its uniqueness on comparing it with other existing available methods.
Energy Analysis of Lightweight AEAD Circuits

The selection criteria for NIST's Lightweight Crypto Standardization (LWC) have been slowly shifting towards the lightweight efficiency of designs, given that a large number of candidates already establish their security claims on conservative, well-studied paradigms. The research community has accumulated a decent level of experience on authenticated encryption primitives, thanks mostly to the recently completed CAESAR competition, with the advent of the NIST LWC, the de facto focus is now on evaluating efficiency of the designs with respect to hardware metrics like area, throughput, power and energy. In this paper, we focus on a less investigated metric under the umbrella term lightweight, i.e. energy consumption. Quantitatively speaking, energy is the sum total electrical work done by a voltage source and thus is a critical metric of lightweight efficiency. Among the thirty-two second round candidates, we give a detailed evaluation of the ten that only make use of a lightweight or semi-lightweight block cipher at their core. We use this pool of candidates to investigate a list of generic implementation choices that have considerable effect on both the size and the energy consumption of modes of operation circuit, which function as an authenticated encryption primitive. In the second part of the paper, we shift our focus to threshold implementations that offer protection against first order power analysis attacks. There has been no study focusing on energy efficiency of such protected implementations and as such the optimizations involved in such circuits are not well established. We explore the simplest possible protected circuit: the one in which only the state path of the underlying block cipher is shared, and we explore how design choices like number of shares, implementation of the masked s-box and the circuit structure of the AEAD scheme affect the energy consumption.
Producing An Instagram Dataset For Persian Language Sentiment Analysis Using Crowdsourcing Method

with the rapid growth of using the internet and social media, people can easily share their opinions on these platforms. due to this fact, user's comments are considering as a rich source for natural language processing tasks such as sentiment analysis. This paper represents our work on producing Insta-text, which is an Instagram comments Persian language sentiment analysis dataset. In this study, about 111,000 Instagram comments have been scrapped and about 9,000 of them have been labeled using the crowdsourcing method. Word2vec model also has been used to validate the dataset.
FPGA Based Digital Quantum Computer Verification

Quantum computers are analog and probabilistic computers and, in fact, they play the role of coprocessors in relation to classical computers. The components of quantum coprocessors are called qubits. Their behavior should be described by a wave function. In particular, qubits can be discrete objects - finite states machines. Such qubits will be called digital. Quantum coprocessors and quantum computers built on their basis will also be called digital. External influences on digital qubits are fed through digital quantum gates. So a digital qubit is a unique chain of digital quantum gates. Digital quantum coprocessor can be implemented in FPGA as several chains of digital quantum.The article explores FPGA-based digital quantum coprocessor. It consists of 32 qubits. The structures of digital quantum gates, digital qubits, and a digital quantum coprocessor capable of performing the quantum Fourier transform are presented. Digital qubits were implemented for the case of real amplitudes of the wave functions.The results of the quantum Fourier transform of a 32-qubit digital quantum coprocessor implemented on one FPGA are presented.The execution time of one quantum Fourier transform does not depend on the number of qubits in the digital quantum coprocessor and is commensurate with the time of electron spin change (near 10 ns). But the time complexity of the quantum Fourier transform is probabilistic polynomial in time. The measured ratio of correct results for a 32-qubit coprocessor is approximately 1700 ppm.
Complete hyperentangled Bell state analysis assisted by hyperentanglement

We present a simple scheme for the complete analysis of hyperentangled Bell states in polarization and spatial-mode degrees of freedom (DOFs) assisted by time-bin–frequency hyperentanglement. In previous schemes of distinguishing the 16 hyperentangled Bell states, many non-linear interaction systems are widely used to construct a quantum gate, such as cross-Kerr non-linearity, quantum-dot spin in an optical microcavity and nitrogen-vacancy centers in a resonator. In our scheme, we utilize the time-bin and frequency DOFs of a photon system as the auxiliary DOFs to analyze polarization–spatial-mode hyperentangled Bell states, and the distinguishing principle is quite different from the former schemes that use the non-linear interactions. As the manipulation of time-bin and frequency DOFs of photons is efficient and feasible with current technology, this scheme will be useful for high-capacity quantum information processing based on hyperentanglement.
Real-Time Outlier Detection and Bayesian Classification using Incremental Computations for Efficient and Scalable Stream Analytics for IoT for Manufacturing

As the manufacturing industry progresses towards the Internet of Things (IoT) and Cyber-Physical Systems (CPS), current methods of historical data analytics face difficulties in addressing the new challenges which follow Industry 4.0. Industry 4.0 and IoT technologies facilitate the acquisition of ubiquitous data from machine tools and processes. However, these technologies also lead to the generation of a large number of data that are complex to be analyzed. Due to the streaming nature of the IoT systems, however, stream analytics could be used to extract features as the data are generated and published, which can prevent the need to store the data and perform advanced analytics that require high performance computing. This manuscript aims at demonstrating how traditional historical methods can be modified to be used as stream analytics tools for IoT data streams. Since data analytics is a wide domain, this paper has only focused on the two light-weighted methods that have been popular in the industry: Statistical Process Control Chart (SPCC), and Bayesian classification. This paper has defined, tested, and evaluated the accuracy and latency of the novel variation of these methods. It is concluded that by modifying the traditional methods and defining incremental solutions, methods such as Real-Time Dynamic Statistical Process Control Chart (RTDSPCC) and Incremental Gaussian Naïve Bayes (IGNB) can be formed that are highly beneficial for IoT applications as they are highly scalable, require minimal storage, and can update the models in real-time.
Review and Classification of Trending Background Subtraction-Based Object Detection Techniques

Object detection in the surveillance video is in favor of both researchers and industries due to its wide benefits and broad usage in numerous applications. Human activity monitoring and controlling the flow of people in airports are among critical applications that need rigid control and surveillance. This paper presents state-of-the-art algorithms of background subtraction-based object detection techniques. The techniques are W4 Method, Mixture of Gaussian, Three Frame Differencing, Differencing and Summing Technique, KDE, CodeBook, Fuzzy Logic Based, SACON, BNN, SOBS, Vibe, RaBS and PBAS. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) categorization/classifications of the methods namely recursive/parametric approaches and non-recursive/non-parametric approaches (2) stating most of the prominent methods used in this field in a hierarchical way (3) explaining pros and cons of each methods and their performance (4) presenting the gaps exist at the end of each method that can open the understanding and doors to new researches and/or improvements.
The Design of Applying Gamification in an Immersive Virtual Reality Virtual Laboratory for Powder-Bed Binder Jetting 3DP Training

The integration of Virtual Reality (VR) and gamification techniques can be used to produce a fun virtual laboratory, including virtual spaces and educational content. This study developed a prototype for a virtual laboratory for powder-bed binder jetting three-dimensional printing (3DP) training in universities. The 3DP virtual laboratory is expected to address problems encountered in teaching, training, and practicing with powder-bed binder jetting 3DP. The 3DP Training Virtual Laboratory was developed by using immersive VR technology to simulate two-handed operations. The user evaluation of the first version prototype revealed that the students lacked learning interest and motivation when using the prototype. Thus, gamification technology was integrated into the 3DP virtual laboratory prototype in the mid-implementation development phase. After refining and systematically developing the model to meet the modified requirements, user evaluations on the game elements showed positive feedback. This study concluded that elements of gamification design should be considered at the beginning of the educational or training system development in order to enhance students’ motivation or engagement.
Feature Selection in Big Data: Trends and Challenges

Big data is a term used to represent data that is big in volume, speed, and variety. With inflammation, these characteristics are also inflated to 42 V’s. We have focused our survey for feature selection in big data, as feature selection is one of the most used methods for dimensionality reduction techniques. Feature selection is used for elimination of irrelevant and redundant features from dataset to improve the classification performance. This paper includes big data characteristics, different feature selection method, and current research challenges of feature selection. We observed that swarm intelligence techniques are the most popular methods among researchers for feature selection in big data. Further, we conclude that gray wolf optimization and particle swarm optimization are the most preferred algorithms by researchers.
Interactive Natural Language Grounding via Referring Expression Comprehension and Scene Graph Parsing

Natural language provides an intuitive and effective interaction interface between human beings and robots. Currently, multiple approaches are presented to address natural language visual grounding for human-robot interaction. However, most of the existing approaches handle the ambiguity of natural language queries and achieve target objects grounding via dialogue systems, which make the interactions cumbersome and time-consuming. In contrast, we address interactive natural language grounding without auxiliary information. Specifically, we first propose a referring expression comprehension network to ground natural referring expressions. The referring expression comprehension network excavates the visual semantics via a visual semantic-aware network, and exploits the rich linguistic contexts in expressions by a language attention network. Furthermore, we combine the referring expression comprehension network with scene graph parsing to achieve unrestricted and complicated natural language grounding. Finally, we validate the performance of the referring expression comprehension network on three public datasets, and we also evaluate the effectiveness of the interactive natural language grounding architecture by conducting extensive natural language query groundings in different household scenarios.