Reviewerly/exHarmony huggingface dataset:
Efficient Metaheuristic Population-Based and Deterministic Algorithm for Resource Provisioning Using Ant Colony Optimization and Spanning Tree

Resource provisioning is the core function of cloud computing which is faced with serious challenges as demand grows. Several strategies of cloud computing resources optimization were considered by many researchers. Optimization algorithms used are still under reckoning and modification so as to enhance their potentials. As such, a dynamic scheme that can combine several algorithms' characteristics is required. Quite a number of optimization techniques have been reassessed based on metaheuristics and deterministic to map out with the challenges of resource provisioning in the Cloud. This research work proposes to involve the ant colony optimization (ACO) population-based mechanism by extending it to form a hybrid meta-heuristic through deterministic spanning tree (SPT) algorithm incorporation. Extensive experiment conducted in the cloudsim simulator provided an efficient result in terms of faster convergence, and makespan time minimization as compared to other population-based and deterministic algorithms as it significantly improves performance.
An Efficient Privacy Preserving Message Authentication Scheme for Internet-of-Things

As an essential element of the next generation Internet, Internet of Things (IoT) has been undergoing an extensive development in recent years. In addition to the enhancement of people's daily lives, IoT devices also generate/gather a massive amount of data that could be utilized by machine learning and big data analytics for different applications. Due to the machine-to-machine communication nature of IoT, data security and privacy are crucial issues that must be addressed to prevent different cyber attacks (e.g., impersonation and data pollution/poisoning attacks). Nevertheless, due to the constrained computation power and the diversity of IoT devices, it is a challenging problem to develop lightweight and versatile IoT security solutions. In this article, we propose an efficient, secure, and privacy-preserving message authentication scheme for IoT. Our scheme supports IoT devices with different cryptographic configurations and allows offline/online computation, making it more versatile and efficient than the previous solutions.
Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.
The Impact of a Self-Avatar, Hand Collocation, and Hand Proximity on Embodiment and Stroop Interference

Understanding the effects of hand proximity to objects and tasks is critical for hand-held and near-hand objects. Even though self-avatars have been shown to be beneficial for various tasks in virtual environments, little research has investigated the effect of avatar hand proximity on working memory. This paper presents a between-participants user study investigating the effects of self-avatars and physical hand proximity on a common working memory task, the Stroop interference task. Results show that participants felt embodied when a self-avatar was in the scene, and that the subjective level of embodiment decreased when a participant's hands were not collocated with the avatar's hands. Furthermore, a participant's physical hand placement was significantly related to Stroop interference: proximal hands produced a significant increase in accuracy compared to non-proximal hands. Surprisingly, Stroop interference was not mediated by the existence of a self-avatar or level of embodiment.
Research on Robot Path Planning Based on Dijkstra and Ant Colony Optimization

In this paper, the path planning problem in known environments was studied. According to Dijkstra algorithm and ant colony optimization (ACO), we designed a hybrid algorithm to search the path. Based on the environment model, constructed by using visual graph method, Dijkstra algorithm was used for initial path planning. Then the ACO was improved and used to optimizes the initial path to minimize the path of the robot. The simulation on MATLAB showed that the path planning algorithm based on Dijkstra-ACO has higher efficiency of path search and good effect of path planning. The algorithm is effective and feasible.
Random Number Generator Using Sensors for Drone

In recent years, drones have been widely used in many areas such as farming, movie making, surveillance and delivery. So, there is a need to protect these drones against security attacks including hijacking, spying and theft of stored data through the utilization of security mechanisms. Cryptographic keys are needed to operate these security mechanisms, and they must be generated by using random number generators which create unpredictable and non-regenerable random numbers. However, existing random number generators used in drones are not tailored for drones specifically as they use random sources generated on a desktop, not a drone. Recently, random number generators utilizing sensors in mobile phones and IoT (Internet of Things) devices have been studied, but are not appropriate for drones. In this paper, considering that drone sensors must be applied to flight and stationary modes, we proposed a drone specific random number generator called DroneRNG and implemented it. Then, we showed that our DroneRNG passed all of the NIST randomization tests and possesses better statistical properties and unpredictability than random number generators that are currently used in drones.
Attribute-Based Equality Test Over Encrypted Data Without Random Oracles

Sensitive data would be encrypted before uploading to the cloud due to the privacy issue. However, how to compare the encrypted data efficiently becomes a problem. Public Key Encryption with Equality Test (PKEET) provides an efficient way to check whether two ciphertexts (of possibly different users) contain the same message without decryption. As an enhanced variant, Attribute-based Encryption with Equality Test (ABEET) provides a flexible mechanism of authorization on the equality test. Most of the existing ABEET schemes are only proved to be secure in the random oracle model. Their security, however, would not be guaranteed if random oracles are replaced with real-life hash functions. In this work, we propose a construction of CP-ABEET scheme and prove its security based on some reasonable assumptions in the standard model. We then show how to modify the scheme to outsource complex computations in decryption and equality test to a third-party server in order to support thin clients.
A Dynamic AI System for Extending the Capabilities of Blind People

We introduce an advanced computer vision-based AI system that offers people with vision impairments (VI) dynamic, in-situ access to information about the location, identity and gaze-direction of other people nearby. Our AI system utilizes the camera technology of a head-worn HoloLens device, which captures a near 180° field-of-view surrounding the person who is wearing it. Captured images are then processed by multiple state-of-the-art perception algorithms whose outputs are integrated into a real-time tracking model of all people that the system detected. Users can receive information about those people acoustically (via spatialized audio) using a wrist-worn input controller. Having such dynamic access to information, through AI system interactions, enables people with VI to: develop their communication skills; more easily focus on others; and be more confident in their social interactions. Thus, our work explores how AI systems can serve as a useful resource for humans, helping expand their agency to develop new or extend existing skills.
Overall Model Normalization towards Adequate Prediction and Presentation of QoE in Overall Telecommunication Systems

A method for QoE parameters prediction in an overall telecommunication system, consisting users and telecommunication network, based on QoS indicators' values prediction, is overviewed. Four normalization techniques are discussed. An indicators' Scale Normalization is proposed. Numerical illustrationis presented.
FPGA implementation of a chaotic oscillator with odd/even symmetry and its application

We propose a mathematical system capable of exhibiting chaos with a chaotic attractor which is odd symmetrical in the x − y phase plane but even symmetrical in the x − z and y − z phase planes respectively. A hardware implementation of the system is done on a digital FPGA platform for verification. The system is also attractive in the sense that (i) its dynamics are single-parameter controlled and (ii) it inherently generates two chaotic clock signals. As an application, an FPGA design methodology using this oscillator for speech encryption is demonstrated. The security of the proposed encryption scheme is evaluated and results confirm its robustness. Due to the efficient hardware resource utilization, the encrypted system delivers a throughput of 1.3Gbit/sec using a Xilinx Kintex 7.
MSL: A pattern language for engineering self-adaptive systems

In architecture-based self-adaptation of decentralized systems, design patterns have been introduced to ease the design of complex adaptation solutions that usually require the interaction of different MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge) control loops, each dealing with an adaptation concern of the managed system. Such MAPE patterns have been proposed by means of a graphical notation, but without a well-defined way to document them and to express the semantics of components interactions. In this paper, we propose an approach to overcome these limitations. We present a domain-specific language, called MSL for MAPE Specification Language, to define and instantiate MAPE patterns and to give semantics to some semantic variation points of the equivalent graphical notation for MAPE pattern. We also provide a formal semantics of the language by means of self-adaptive Abstract State Machines, an extension of the Abstract State Machines (ASMs) formalism to model self-adaptation. Such semantics definition comes with an automatic transformation of MSL models into formal executable models, and opens to the possibility of performing rigorous analysis (validation w.r.t. the adaptation requirements and verification of adaptation properties) of MSL models. Moreover, we present our current results toward a (long-term) realization of an MSL-centric framework, where MSL is the notation of a modeling front-end, on top of richer and more specific modeling, analysis, and implementation back-end frameworks. As proof of concept of our approach, we show the application of MSL and its formal support to a running case study in the field of home automation, by modeling an adaptive control of a virtual smart home developed with the OpenHAB runtime platform.
A Rolling Hash Algorithm and the Implementation to LZ4 Data Compression

LZ77 is a dictionary compression algorithm by replacing the repeating sequence with the addresses of the previous referenced data in the stream.To find out these repetition, the LZ77 encoder maintains a hashing table, which have to frequently calculate hash values during the encoding process.In this paper, we present a class of rolling hash functions, that can calculate multiple hash values via a carry-less multiplication instruction.Then the proposed hash function is implemented in LZ4, which is a derivative of LZ77.The simulation shows that the encoding throughput of LZ4 has 15.7% improvement in average, and the compression ratio is ±1% in most cases.
Socially-Aware Reactive Obstacle Avoidance Strategy Based on Limit Cycle

The letter proposes a combination of ideas to support navigation for a mobile robot across dynamic environments, cluttered with obstacles and populated by human beings. The combination of the classical potential field methods and limit cycle based approach with an innovative shape for the limit cycles, generates paths which are reasonably short, smooth and comfortable to follow (which can be very important for assistive robots), and it respects the safety and psychological comfort of the bystanders by staying clear of their private space.
mHealth Apps for Older Adults: A Method for Development and User Experience Design Evaluation

This study details a method for mHealth app development and user experience design (UX) evaluation, which generates a comprehensive list of stakeholder-users, acknowledges UX barriers, advocates multiple methods, and argues that developers should address the UX needs of each stakeholder-user in a complex health-care system. A case study of a research project on an mHealth app for women who are considering prevention of or treatment for osteoporosis assists to elaborate and define the method. To find any measure of success, a fully functional app for older users should be integrated into the entire health-care system.
Speaker-Aware Speech-Transformer

Recently, end-to-end (E2E) models become a competitive alternative to the conventional hybrid automatic speech recognition (ASR) systems. However, they still suffer from speaker mismatch in training and testing condition. In this paper, we use Speech-Transformer (ST) as the study platform to investigate speaker aware training of E2E models. We propose a model called Speaker-Aware Speech-Transformer (SAST), which is a standard ST equipped with a speaker attention module (SAM). The SAM has a static speaker knowledge block (SKB) that is made of i-vectors. At each time step, the encoder output attends to the i-vectors in the block, and generates a weighted combined speaker embedding vector, which helps the model to normalize the speaker variations. The SAST model trained in this way becomes independent of specific training speakers and thus generalizes better to unseen testing speakers. We investigate different factors of SAM. Experimental results on the AISHELL-1 task show that SAST achieves a relative 6.5% CER reduction (CERR) over the speaker-independent (SI) baseline. Moreover, we demonstrate that SAST still works quite well even if the i-vectors in SKB all come from a different data source other than the acoustic training set.
Knowledge graphs completion via probabilistic reasoning

Constructing large-scale knowledge base has encountered a bottleneck because of the limitation of natural language processing. Many approaches have been put forward to infer new facts based on existing knowledge. Graph feature models mine rule-like patterns from a knowledge base and use them to predict missing edges. These models take account of the graph structure information and they can explain the existence of a fact reasonably. Existing models only describe local interaction between entities, but how to model co-relationships among facts globally is a tough problem. In this paper, we develop an efficient model which uses association rules to make inferences. First, we use a rule mining model to detect simple association rules and use them to produce large amounts of evidence. Second, based on all the produced evidence and the connections among them, we construct a factor graph which represents the inference space. Then, we develop an EM inference model, wherein the E-step we use Belief Propagation to calculate the marginal distribution of candidate edges and, in the M-step we propose a Generalized Iterative Proportional Fitting algorithm to re-learn the confidence of soft rules. Experiments show that our approach outperforms state-of-the-art approaches in knowledge base completion (KBC) tasks.
Batch Steganography via Generative Network

Batch steganography is a technique that hides information into multiple covers. To achieve a better performance on the security of data hiding, we propose a novel strategy of batch steganography using a generative network. In this method, the approaches of cover selection, payload allocation, and distortion evaluation are considered in the round. We define a quality metric to evaluate the distortion between the cover image and the stego. When training the generation function, we define an objective function containing two parts: the entropy loss and the steganalytic loss. While the entropy loss is used to represent the gap between the payload inside stego images and the entire embedding capacity, the steganalytic loss is used to assess the data embedding impact using the proposed quality metric. With back-propagation, we minimize the objective function to obtain an optimal solution. Accordingly, different payloads can be allocated to different images, and the ± 1 modification probability for pixels in each cover can be calculated. Finally, we embed information into the selected images by STC. Experimental results show that the proposed method achieves a better undetectability against modern steganalytic tools.
Ensemble Bayesian Decision Making with Redundant Deep Perceptual Control Policies

This work presents a novel ensemble of Bayesian Neural Networks for control of safety-critical systems. Decision making for safety-critical systems is challenging due to performance requirements with significant consequences in the event of failure. In practice, failure of such systems can be avoided by introducing redundancies of control. Neural Networks are generally not used for safety-critical systems as they can behave in unexpected ways in response to novel inputs. In addition, there may not be any indication as to when they will fail. BNNs have been recognized for their ability to produce not only viable outputs but also provide a measure of uncertainty in these outputs. This work combines the knowledge of prediction uncertainty obtained from BNNs and ensemble control for a redundant control methodology. Our technique is applied to an agile autonomous driving task. Multiple BNNs are trained to control a vehicle in an end-to-end fashion on different sensor inputs provided by the system. We show that an individual network is successful in maneuvering around the track but crashes in the presence of unforeseen input noise. Our proposed ensemble of BNNs shows successful task performance even in the event of multiple sensor failures.
Emerging Ontology Formulation of Optimized Internet of Things (IOT) Services with Cloud Computing

Tiwari, Ashish Sharma, R. M. Garg, RituThe eagle expresses of cloud computing plays a pivotal role in the development of technology. The technology momentum is gaining cloud system environment across the emerging computational intelligence as artificial intelligence in IOTs. It works on the concept of everywhere, everything, and every time service on demand for end users. Developers with innovative ideas for greenhorn Web services do not want the massive capital outlays in hardware to deploy their service or the human expense to regulate it. Cloud computing playing a pivotal role in providing the optimized algorithms for the issues in cloud computing which are the global challenges. The problem aims to solve in such a way that it will provide an optimized solution. Among all these research areas, the works are developing at a rapid speed. The rapid vision, models, and challenges for efficient optimization of resource are in cloud computing world. The key role is of allocating these efficient resources and making the algorithms for its time and cost optimization keeping in consideration of its quality of services and characteristics. These both affecting the performance of these techniques is a major drawback due to low accuracy and large computational complexity of the algorithms. Therefore, the concept of fuzzy system is playing a pivotal role in designing such strategies in which it makes use of the concept fuzzy sets, fuzzy logic and reasoning, fuzzy controllers, rough sets, etc. As per the scenario, the approach of the research is based on technology acceptance model (TAM) and the rough set theory (RST). RST is a great method for making a large difference in qualitative analysis situations. It is a technique to find the knowledge discovery and handle the problems such as inductive reasoning, automatic classification, pattern recognition, learning algorithms, and data reduction. The rough set theory is the new method in cloud service selection so that the best services are provided for cloud users and efficient service improvement for cloud providers. There square measure numerous routes by that its measured, the variety of them square measure as per the following: Feature alternative, security services, algorithmic, mathematical, etc. The simulation of the work is finished at intervals the merchandise utilized for the formation of philosophy framework. The simulation that deals with the IoT services provided by the IoT service supplier to the user is the best utilization with the parameters and ontology technique.
Mapping Himalayan leucogranites using a hybrid method of metric learning and support vector machine

Rare metals play a considerable role in the development of new materials and energy, making them key mineral resources for global competition. Widely distributed along the Himalayan orogen, the Himalayan leucogranite belt is expected to be an important rare metal metallogenic belt in China. Thus, mapping the spatial distribution of Himalayan leucogranites is critical for prospecting rare metal deposits. The distribution characteristics of geochemical elements are important indicators for lithological identification. The differences in mineral composition and major oxide content between leucogranites and the surrounding rocks facilitate lithological mapping. However, significant uncertainty could arise owing to limited geochemical data due to particularly adverse working conditions and to difficulty in handling similar geochemical data. In this study, a metric learning-based approach is used for mapping leucogranites based on regional geochemical exploration datasets. Defined as a measure of similarity between two samples, metric learning reveals a “better distance” by converting original data into a more suitable Mahalanobis metric space with maximum separation of the target and the background. In this approach, a local weighted metric learning method is first used to assign weights to the training samples in the neighborhood, with respect to their reconstruction contributions in learning the local metric. Then, a discriminative local ensemble learning method is employed to integrate all learned metrics and to convert the original geochemical data into a metric space. This enables more effective separation of highly similar target leucogranites from the surrounding rocks with the help of a support vector machine. The distribution of leucogranites mapped by such a hybrid method showed high consistency with the geological map, indicating that this approach is reasonable for providing the indicated signature of leucogranites mapping in the study area. These results further provide an alternative way for identifying favorable intrusions based on geochemical exploration data.