Reviewerly/exHarmony huggingface dataset:
LEGO: A hybrid toolkit for efficient 2PC-based privacy-preserving machine learning

Recently, privacy-preserving machine learning (PPML) has received a lot of research attention, due to the increasing demand for multiple data owners in training machine learning models. Thus, many works have been proposed for privacy-preserving machine learning, in which many breakthroughs have been made. However, However, there is still much room for improvement in terms of efficiency and accuracy. In this paper, we design a toolkit called LEGO that efficiently combines garbled circuits, secret sharing, oblivious transfer, and homomorphic encryption to reduce time and improve accuracy. Our work focuses on the two-server model where the two servers train the models on the different owners’ private data. For fully connected layers and convolutional layers, we utilize the associated multiplication triplets to reduce the communication. For the offline phase, we design a novel protocol based on oblivious transfer and improved the previous protocol based on homomorphic encryption. We use the MPC-friendly activation function to improve the performance. Besides, we accelerate the local computation by adding GPU support. Our work is implemented in C++, and it can securely train machine learning models, including linear regression, logistic regression, neural networks, and convolutional neural networks. The experimental results show that our work is significantly faster than the state-of-the-art works.
Integrating Aster <scp>07XT</scp>, Landsat 8, and aeromagnetic data for the delineation of potential mineralization sites in North Cameroon

In mineral exploration, detailed hydrothermally altered minerals mapping is an important approach prior to the field campaign. The study area contains several gold‐bearing quartz veins associated with hydrothermally altered zones rich in sulphides, muscovite/sericite, iron oxides, and hydrous silica. In this study, Aster 07XT, Landsat 8, and aeromagnetic data were integrated to detect potential zones associated with gold mineralization in Northern Cameroon. For this purpose, the relative band absorption depth, band ratioing, and analytic signal techniques were applied to enhance, identify and map hydrothermal alteration minerals, that is, hydroxyl‐bearing, iron oxide/hydroxide. In addition, a semi‐automatic approach was applied to extract remotely sensed and aeromagnetic lineaments from PCI Geomatica and Centre for Exploration Targeting Grid Analysis respectively. Hence, several potential mining targets were delineated from the processing of both remote sensing and aeromagnetic data. The major directions of the lineaments affecting the area include NE–SW/ENE–WSW, E–W, and N–S. Detailed field campaigns, petrographic analyses, and spectral measurements of rock samples were carried out in Tchollire and environs to verify the results from remotely sensed data. The analytical observations indicated the presence of quartz, epidote, chlorite, magnetite, haematite, sericite, galena, sulphide, telluride minerals, etc. Moreover, the target hydrothermal alteration zones were proved to be spatially associated with known mining areas and gold occurrences in the study area. Consequently, new prospective mining areas have been designated. This study confirmed the reliability and applicability of the processing of satellite imagery in mineral exploration in Northern Cameroon. It is suggested that the dataset used in this study provides a suitable tool for mapping hydrothermal alteration minerals linked to gold deposits and can be applied in other provinces with similar geological settings.
An active learning method with entropy weighting subspace clustering for remote sensing image retrieval

The increasing volume of high-resolution satellite imagery made image retrieval a demanding research field in the remote sensing (RS) community. The RS data are often complex, with varied temporal and spatio-spectral properties, and are unlabeled, and the process of labeling is relatively expensive. Therefore, developing sophisticated models for retrieving relevant images from RS databases is necessary, especially when few labeled samples are available. Motivated by this fact, we propose a new cluster-guided active learning (CG-AL) framework for remote sensing image retrieval (RSIR). The CG-AL-RSIR utilizes an entropy weighting K-Means subspace (EWKMS) clustering algorithm for influencing an active learning (AL) framework using a support vector machine (SVM). The AL framework efficiently selects the most informative samples for labeling depending on a weighted entropy uncertainty sampling (WtEUS). A bag-of-visual-words representation of the scale-invariant feature transform (SIFT) image descriptors is extracted for the retrieval. The EWKMS clustering addresses the high dimensionality of the RSI dataset efficiently in obtaining quality clusters for labeling The CG-AL-RSIR resulted in an overall accuracy of up to 94.14% and 92.48% for the UCM and SIRI-WHU datasets, respectively, and an ANMRR value of 0.290 for UCM and 0.326 for SIRI-WHU datasets, utilizing less number of labeled samples.
EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized Views

Advances in deep learning have led to a resurgence of interest in video analytics. In an exploratory video analytics pipeline, a data scientist often starts by searching for a global trend and then iteratively refines the query until they identify the desired local trend. These queries tend to have overlapping computation and often differ in their predicates. However, these predicates are computationally expensive to evaluate since they contain user-defined functions (UDFs) that wrap around deep learning models.
Point-of-interest recommendation in location-based social networks based on collaborative filtering and spatial kernel weighting

The growing popularity of location-based social networks (LBSNs), as well as their financial benefits, has led to a variety of methods for point-of-interest (POI) recommendations. A review of various methodologies reveals the role of geography is rarely addressed individually in current procedures. Instead, it is treated as an overall probability distribution of distances between all check-in pairs. On the other hand, despite the increased accuracy, the incorporation of individualized probabilistic distributions complicates matters of recommender systems. To address this issue, the main objective of this study is to introduce a POI recommendation mechanism in LBSNs that could be simple to implement the geographical influence. Furthermore, the method could be easily integrated with collaborative filtering while also increasing the quality of recommendations. Hence, instead of establishing a unique probability function for all check-ins data using the power-law or the Gaussian distribution, a spatial kernel weighting technique inspired by the similarity-weighted average notion, called CFSKW, was introduced and implemented. CFSKW was found on the principle that POIs close to a person's favourite POIs are more likely to be visited. As expected, the proposed method outperformed state-of-the-arts when tested utilizing two Four square datasets, namely New York and Tokyo. Kernel bandwidth and the coefficient of integrating user preference with geographical influence were two critical factors to be tuned. Low bandwidth and a medium to high geographical coefficient, according to the findings, produce better results. In unproportionate density of POIs, like that of the Tokyo dataset, a proposed algorithm based on dynamic bandwidth showed promising results. For the New York dataset, Precision@5 and Recall@5 were obtained as 25.41% and 61.80% using the proposed method, respectively, indicating a 3% and 5.1% improvement over the reference techniques. Precision@5 and Recall@5 were 1.1% and 1.2% better for the Tokyo dataset, respectively, when using a dynamic bandwidth geographical similarity algorithm.
Entanglement-Assisted Joint Monostatic-Bistatic Radars

With the help of entanglement, we can build quantum sensors with sensitivity better than that of classical sensors. In this paper we propose an entanglement assisted (EA) joint monostatic-bistatic quantum radar scheme, which significantly outperforms corresponding conventional radars. The proposed joint monostatic-bistatic quantum radar is composed of two radars, one having both wideband entangled source and EA detector, and the second one with only an EA detector. The optical phase conjugation (OPC) is applied on the transmitter side, while classical coherent detection schemes are applied in both receivers. The joint monostatic-bistatic integrated EA transmitter is proposed suitable for implementation in LiNbO3 technology. The detection probability of the proposed EA joint target detection scheme outperforms significantly corresponding classical, coherent states-based quantum detection, and EA monostatic detection schemes. The proposed EA joint target detection scheme is evaluated by modelling the direct radar return and forward scattering channels as both lossy and noisy Bosonic channels, and assuming that the distribution of entanglement over idler channels is not perfect.
Flexible heterogeneous data fusion strategy for object positioning applications in edge computing environment

Object perception location data come from a wide range of sources with different structures and different presentation methods. It is worthwhile to study how to fuse the data from different sources of perception and location data to improve the accuracy of location data. We have proposed a data fusion method based on RFID virtual reference tags and video multi-feature matching and perform data fusion in an edge computing environment. Firstly, two types of data are processed separately. The positioning accuracy of the tags is improved by inserting virtual reference tags when using RFID data to obtain positioning information. When using video data to obtain positioning information, the valuable segments are extracted by keyframes, and the target detection is carried out by combining three-frame differencing and background differences. Then, the location information of the items to be positioned is obtained according to the multi-feature matching and coordinate transformation. The location information of the item to be located is obtained based on multi-feature matching and coordinate transformation. Finally, the Kalman filter is used to fuse the separately processed RFID positioning data with the video positioning data. The method combines the observed values with the predicted values calculated by the model for state updating, and the positioning data with higher accuracy is obtained after several iterations. Experimental results show that data fusion at the edge server better meets the real-time demand of video data. The fused localization data has higher accuracy than RFID or video data alone for localization.
A Mobile Terminal-Based College English Teaching Evaluation Method

At the moment, the existing teaching evaluation system cannot be used for wireless data input and reception, limiting the system's application scope and making it less practical. As a result, a new system for evaluating college English teaching is being developed using a mobile terminal. The English teaching quality model is constructed using the Internet of Things mobile platform, and the network teaching structure is analyzed using the model parameters to monitor and control the teaching progress. A classification and evaluation system will be established in conjunction with the campus network and intelligent sensor networking technology. The development environment and overall design of the mobile terminal are used to create an evaluation system for English teaching in an Internet of Things environment. Create the critical architecture based on the B/S model. In the section on system software design, we mine data from English teaching resources, evaluate students’ progress toward English proficiency, and use convolutional neural networks to evaluate the quality of English teaching. The teaching results are output according to the distribution of convolutional layers, allowing us to analyze the level of English instruction in colleges and universities. Comparative experiments demonstrate that this research system has a higher information query efficiency, a lower convergent error of 4 ms, and a higher evaluation accuracy for oral English, English writing, and English reading.
Bind the gap: compiling real software to hardware FFT accelerators

Specialized hardware accelerators continue to be a source of performance improvement. However, such specialization comes at a programming price. The fundamental issue is that of a mismatch between the diversity of user code and the functionality of fixed hardware, limiting its wider uptake. Here we focus on a particular set of accelerators: those for Fast Fourier Transforms. We present FACC (Fourier ACcelerator Compiler), a novel approach to automatically map legacy code to Fourier Transform accelerators. It automatically generates drop-in replacement adapters using Input-Output (IO)-based program synthesis that bridge the gap between user code and accelerators. We apply FACC to unmodified GitHub C programs of varying complexity and compare against two existing approaches. We target FACC to a high-performance library, FFTW, and two hardware accelerators, the NXP PowerQuad and the Analog Devices FFTA, and demonstrate mean speedups of 9x, 17x and 27x respectively
A Path-Planning Approach Based on Potential and Dynamic Q-Learning for Mobile Robots in Unknown Environment

The path-planning approach plays an important role in determining how long the mobile robots can travel. To solve the path-planning problem of mobile robots in an unknown environment, a potential and dynamic Q-learning (PDQL) approach is proposed, which combines Q-learning with the artificial potential field and dynamic reward function to generate a feasible path. The proposed algorithm has a significant improvement in computing time and convergence speed compared to its classical counterpart. Experiments undertaken on simulated maps confirm that the PDQL when used for the path-planning problem of mobile robots in an unknown environment outperforms the state-of-the-art algorithms with respect to two metrics: path length and turning angle. The simulation results show the effectiveness and practicality of the proposal for mobile robot path planning.
Energy and bandwidth efficiency optimization of quantum-enabled optical communication channels

Abstract We present a systematic study of quantum receivers and modulation methods enabling resource efficient quantum-enhanced optical communication. We introduce quantum-inspired modulation schemes that theoretically yield a better resource efficiency than legacy protocols. Experimentally, we demonstrate below the shot-noise limit symbol error rates for M ≤ 16 legacy and quantum-inspired communication alphabets using software-configurable optical communication time-resolving quantum receiver testbed. Further, we experimentally verify that our quantum-inspired modulation schemes boost the accuracy of practical quantum measurements and significantly optimize the combined use of energy and bandwidth for communication alphabets that are longer than M = 4 symbols.
Supporting a Sustainable and Engaging Online Transition for Co-Design through Gamification

Co-design approach is increasingly popular in many organizations that address global change and social sustainability challenges, thanks to its unique and diverse methods of engaging relevant people in design processes and decision-making. However, the social distancing led by the COVID-19 pandemic seriously problematized the traditional in-person co-design activities. A sustainable online transition is unprecedentedly pressing. By acknowledging the limitations of online co-design, i.e., lack of means for participant engagement, we argue that gamification holds great promise for online co-design. This paper presents an empirical study to investigate this potential qualitatively. Based on the data collected from three gamified online co-design implementations, we examine the benefits of gamification and how future activities should be designed and implemented from the participants’ perspectives. Based on the participants’ perceptions, we propose several recommendations for designing impactful gamification. The finding suggests that gamification can facilitate online co-design activities in an enjoyable, relaxing, structuring, and creative manner, since they are perceived and recognized by the participants. Moreover, the successful implementation of online co-design implies that great sustainability benefits can be achieved through online transition, i.e., reducing paper consumption and time spent on meetings and unproductive discussions, supporting extensive diversity and density in representation. Online can enable this by overcoming not only the geographic and time limitations but also relevant social issues.
An Efficient Mechanism for Deep Web Data Extraction Based on Tree-Structured Web Pattern Matching

The World Wide Web comprises of huge web databases where the data are searched using web query interface. Generally, the World Wide Web maintains a set of databases to store several data records. The distinct data records are extracted by the web query interface as per the user requests. The information maintained in the web database is hidden and retrieves deep web content even in dynamic script pages. In recent days, a web page offers a huge amount of structured data and is in need of various web-related latest applications. The challenge lies in extracting complicated structured data from deep web pages. Deep web contents are generally accessed by the web queries, but extracting the structured data from the web database is a complex problem. Moreover, making use of such retrieved information in combined structures needs significant efforts. No further techniques are established to address the complexity in data extraction of deep web data from various web pages. Despite the fact that several ways for deep web data extraction are offered, very few research address template-related issues at the page level. For effective web data extraction with a large number of online pages, a unique representation of page generation using tree-based pattern matches (TBPM) is proposed. The performance of the proposed technique TBPM is compared to that of existing techniques in terms of relativity, precision, recall, and time consumption. The performance metrics such as high relativity is about 17-26% are achieved when compared to FiVaTech approach.
Designing, testing and adapting navigation techniques for the immersive web

One of the most essential interactions in Virtual Reality (VR) is the user's ability to move around and explore the virtual environment. The design of the navigation technique plays a crucial role in the user experience since it determines key usability aspects. VR devices allow for an immersive exploration of 3D worlds, but navigation in VR is challenging for many users, due to potential usability issues related to specific VR controllers, user skills, and motion sickness. Although hundreds of interaction techniques have been proposed for this task, VR navigation still poses a high entry barrier for many users. In this paper we argue that adapting the navigation technique to its context of use can lead to substantial improvements in navigation usability and accessibility. The context of use includes the type of scene, the available physical space, as well as the profile of the user. We present a test platform to facilitate the design and fine-tuning of interaction techniques for 3D navigation. We focus on mainstream VR devices (headsets and controllers) and support the most common navigation metaphors (walking, flying, teleportation). The key idea is to let developers specify, at runtime, the exact mapping between user actions and locomotion changes, for any of the supported metaphors. Such mappings are described by a collection of parameters (e.g. maximum speed) whose values can be adjusted interactively through a GUI, or be provided by user-defined code which can be edited at runtime. Feedback obtained from developers suggests that this approach can be used to quickly adapt the navigation techniques to various people including persons with no previous 3D navigation skills, elderly people, and people with disabilities, as well as to the type, size and semantics of the virtual environment.
Lightweight integrity auditing of edge data for distributed edge computing scenarios

With the increase of smart devices, edge computing is becoming a new computing paradigm that coexist with centralized cloud computing to process data distributed at the edge of the network. Based on this new paradigm, app vendors can store data replicas on geographically distributed edge servers to serve surrounding users to reduce access delay. However, since edge servers have limited storage space and computing ability, these edge data are vulnerable to various corruption. Therefore, how to efficiently audit the integrity of edge data has become an urgent problem to be solved. To tackle the Edge Data Integrity (EDI) problem, we propose a lightweight auditing scheme, namely EDI-SA. Firstly, inspired by the shuffle algorithm and the bucket sorting algorithm, we propose an improved sampling algorithm , which is a new lightweight challenge block sampling method. Secondly, based on algebraic signature, EDI-SA can achieve efficient aggregation verification and the signature computation cost is independent of the number of data blocks, which greatly reduces the computation overhead of app vendors and edge servers. Thirdly, EDI-SA supports batch auditing and provable dynamic update, app vendors can also uniquely locate the corrupted edge data. Finally, security and performance analyses demonstrate the security and effectiveness of EDI-SA. • A lightweight integrity auditing scheme for distributed edge data is proposed. • Achieve efficient aggregation verification via algebraic signature technology. • Support batch audit, provable dynamic update and the localization of corrupted data. • Computation costs of edge servers and app vendors during auditing phase are constant. • Security and efficiency are proved by numerical analyses and simulation experiments.
Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on.SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools.The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification.The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format.Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments.Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.