Reviewerly/exHarmony huggingface dataset:
ARCT: An Efficient Aggregating Ring Confidential Transaction Protocol in Blockchain

Anonymous cryptocurrency is a branch of cryptocurrency designed to protect users' privacy. Ring Confidential Transaction (RingCT) protocol is widely used in anonymous cryptocurrencies (e.g. Monero) to hide transaction information. However, its expensive computing and storage costs slow down the performance of the system. In this work, we propose a high-efficient RingCT protocol which is called aggregation ring confidential transaction (ARCT). In our protocol, we build a compact aggregation proof for multiple accounts of the sender, which enables our protocol to linearly shorten the signature size compared to other protocols. In addition, in order to further protect the privacy information, we first implement hiding the amount of the sender's accounts. Our protocol can provide cryptocurrencies with better privacy protection and lower resource cost. Besides, it does not require a trust setting. For a typical n=2 input transaction with the ring size of 128, the signature size of our protocol is 93% less than it used in the original protocol. ARCT build on the techniques of inner product optimization algorithm and cryptographic accumulators. We show that our underlying algorithm satisfies the security under the random oracle model.
Image Decomposition Sensor Based on Conditional Adversarial Model

In this paper, we propose an Image Decomposition Sensor based on Conditional Adversarial Model (CAM). The presented device decomposes intrinsic image into reflectance layer and shadow layer and the Conditional Adversarial Model (CAM) trains the intrinsic image decomposition to obtain high quality reflectance image and shadow image. By enhancing the shadow image, we can get a larger range of contrast images. Besides, we adopt reduced lookup table based pipeline architecture to greatly reduce the overall computing time. The image decomposition device solves the problems such as limited contrast and color distortion and is used for application of image contrast enhancement. The sensor circuit provide excellent auxiliary effects for system that need to rely on high-speed image processing.
A Technique to Calculate National Happiness Index by Analyzing Roman Urdu Messages Posted on Social Media

National Happiness Index (NHI) is a national indicator of development that estimates the economic and social well-being of the nation's individuals. With the proliferation of the internet, people share a significant amount of data on social media websites. We can process the data with different sentiment analysis techniques to calculate the NHI. In the literature, different approaches have been used to calculate NHI, which include the lexicon-based approach and machine learning approach. All of these existing approaches are proposed to calculate NHI for the sentiments written in the English language. However, these methods fail for complex Roman Urdu tweets that contain more than two sub-opinions. There are three primary objectives of the research: (1) to investigate current sentiment analysis techniques are sufficient for the classification of complex Roman Urdu sentiments; (2) to propose rule-based classifier for the classification of Roman Urdu sentiments comprising multiple sub-opinions; (3) to calculate NHI using Roman Urdu sentiments. For this purpose, we proposed the discourse information extractor, the rule-based method (3-RBC), and the machine learning classifier. The experimental results show that 3-RBC is efficient for feature identification, and it is more statistically significant than the baseline classifiers. The 3-RBC has successfully increased the accuracy by 7% and precision by 8%, which provides evidence that the proposed technique significantly increased the calculation of NHI.
High-Dimensional Text Clustering by Dimensionality Reduction and Improved Density Peak

This study focuses on high-dimensional text data clustering, given the inability of K-means to process high-dimensional data and the need to specify the number of clusters and randomly select the initial centers. We propose a Stacked-Random Projection dimensionality reduction framework and an enhanced K-means algorithm DPC-K-means based on the improved density peaks algorithm. The improved density peaks algorithm determines the number of clusters and the initial clustering centers of K-means. Our proposed algorithm is validated using seven text datasets. Experimental results show that this algorithm is suitable for clustering of text data by correcting the defects of K-means.
A novel approach for automatic Bengali question answering system using semantic similarity analysis

Finding the semantically accurate answer is one of the key challenges in advanced searching. In contrast to keyword-based searching, the meaning of a question or query is important here and answers are ranked according to relevance. It is very natural that there is almost no common word between the question sentence and the answer sentence. In this paper, an approach is described to find out the semantically relevant answers in the Bengali dataset. In the first part of the algorithm, a set of statistical parameters like frequency, index, part-of-speech (POS) is matched between a question and the probable answers. In the second phase, entropy and similarity are calculated in different modules. Finally, a sense score is generated to rank the answers. The algorithm is tested on a repository containing a total of 275,000 sentences. This Bengali repository is a product of Technology Development for Indian Languages (TDIL) project sponsored by Govt. of India and provided by the Language Research Unit of Indian Statistical Institute, Kolkata. The shallow parser, developed by the LTRC group of IIIT Hyderabad is used for POS tagging. The actual answer is ranked as 1st in 82.3% cases. The actual answer is ranked within 1st to 5th in 90.0% cases. The accuracy of the system is coming as 97.32% and precision of the system is coming as 98.14% using confusion matrix. The challenges and pitfalls of the work are reported at last in this paper.
Spectral impact on PV in low-latitude sites: The case of southeastern Brazil

The spectral impact on various PV materials –amorphous silicon (a-Si), CdTe, CIGS, single crystalline silicon (sc-Si) and multi crystalline (mc-Si)– in two nearby cities of Southeastern Brazil is presented. For every PV technology studied, the values of the spectral mismatch factor on instantaneous, monthly and annual basis were computed by means of spectra recorded over a 12-month experimental campaign carried out in São Paulo and São José dos Campos. A blue-biased however seasonal spectrum prevails in both cities, which leads to annual spectral gains of up to around 6% and 2% exhibited by a-Si and CdTe, respectively. On the other hand, CIGS, sc-Si and mc-Si show negligible annual gains that lie between around −1% and 0%. These results are well aligned with previous findings obtained using both experimental and modelled spectra in other low-latitude sites with tropical climate. Consequently, spectral effects cannot be ignored in such sites, especially when modelling the outdoor behavior of larger bandgap PV devices. Last, a quasi-linear relationship exists between the monthly average photon energy and the monthly spectral mismatch factor for all the PV materials under scrutiny. This conclusion is in good agreement with previous works carried out in mid-latitude sites.
Handwriting Text-line Detection and Recognition in Answer Sheet Composition with Few Labeled Data

Automatic location and recognition are the new trends in today's education industry. However, the dataset based on the scene of English text-line written by school students. Works always focus on text detector' improvement other than a faster detection training period. This article introduces a data synthesis method for synthesizing a dataset that can be used to locate composition text-lines from scanned answer sheet images with few labeled data. The synthetical handwritten text-line dataset includes 5k composite images and more than 2.5k coordinate annotations. This method can also make CTPN text-line detecting network trained from scratch. Besides, Handwriting Recognition (HWR) is a key to revising large-batch English composition on answer sheet. However, handwriting feature is very different from the scene text feature, which challenges the traditional recognition. Hence, this article introduces MLC-CRNN, a refined handwritten text-line recognizer based on CRNN, which can improve recognition accuracy. The proposed method focuses on depth of network and MLC module respectively, and shows can both contribute to handwritten text-line recognition.
Integration of Home Assistance with a Gesture Controlled Robotic Arm

Technology today has escalated to a level that artificially intelligent robots are now programmed and implemented to replace human activities. The advent of these robots is made to serve as humanitarian assistance. Nowadays robots are designed to perform and imitate housekeeping and human maneuvers in various kinds of work. These robots are pre-programmed and can be operated remotely without any human intervention. This paper shall discuss in detail about such a robot, with the improved performance of its specially designed robotic arm. This robotic arm is tooled with leap motion to intensify and reflect the very touch of its user. Putting the traditional controllers aside, the Leap Motion is a new technology that is being widely used to make robotic arm control more realistic and easier. With this device, we can track the human hand to minute movements. This technology uses the position of the human hand to calculate angles of the joints and help maneuver the robotic arm with greater accuracy. This level of accuracy shall not only increase the acceptance of robots in housekeeping but also leverage its use to fend for young children and the elderly.
Constacyclic Codes of Length 3<i>p</i> <sup> <i>s</i> </sup> Over F<i> <sub>p</sub> </i> <sup> <i>m</i> </sup> + <i>u</i>F<i> <sub>p</sub> </i> <sup> <i>m</i> </sup> and Their Application in Various Distance Distributions

Let p = 3 be any prime.The structures of all λ-constacyclic codes of length 3p s over the finite commutative chain ring F p m + uF p m (u 2 = 0) are established in the term of their generator polynomials.As an application, Hamming and homogeneous distance of a class of such codes and RT distances of all are given.Among such λ-constacyclic codes, the unique maximum-distance-separable (briefly, MDS) code with respect to the RT distance is obtained.Moreover, when λ is not a cube in F p m , the necessary and sufficient condition for the λ-constacyclic code of length 3p s over F p m + uF p m (u 2 = 0) be an MDS constacyclic code with respect to Hamming distance is provided.
3PP-R: Enabling Natural Movement in 3rd Person Virtual Reality

We propose 3PP-R, a novel Virtual Reality display and interaction technique that allows natural movement in 3rd-person perspective (3PP), including body rotation without losing sight of the avatar. A virtual display such as a World-in-Miniature model orbits around the user when the user turns, but does not rotate except for the user's avatar. From the user's perspective, the display appears fixed in the field of vision, while the world rotates around the avatar. 3PP-R combines the strengths of 3PP and 1st-person perspective (1PP): Similar to 1PP, it allows interacting with rich natural movements, while also reaping the benefits of 3PP, i.e., superior spatial awareness and animating the avatar without nauseating viewpoint movement, e.g., for joystick-controlled locomotion. We test 3PP-R in a maze navigation study, which indicates considerably less cybersickness in 3PP-R than in 1PP. We also demonstrate 3PP-R in dynamic game interaction including running, jumping, swinging on bars, and martial arts.
Cost- and Robustness-Based Query Optimization for Linked Data Fragments

Client-side SPARQL query processing enables evaluating queries over RDF datasets published on the Web without producing high loads on the data providers’ servers. Triple Pattern Fragment (TPF) servers provide means to publish highly available RDF data on the Web and clients to evaluate SPARQL queries over them have been proposed. For clients to devise efficient query plans that minimize both the number of requests submitted to the server as well as the overall execution time, it is key to accurately estimate join cardinalities to appropriately place physical join operators. However, collecting accurate and fine-grained statistics from remote sources is a challenging task, and clients typically rely on the metadata provided by the TPF server. Addressing this shortcoming, we propose CROP, a cost- and robust-based query optimizer to devise efficient plans combining both cost and robustness of query plans. The idea of robustness is determining the impact of join cardinality estimation errors on the cost of a query plan and to avoid plans where this impact is very high. In our experimental study, we show that our concept of robustness complements the cost model and improves the efficiency of query plans. Additionally, we show that our approach outperforms existing TPF clients in terms of overall runtime and number of requests.
ML for UX? - An Inventory and Predictions on the Use of Machine Learning Techniques for UX Research

Machine learning (ML) techniques have successfully been applied to many complex domains. Yet, applying it to UX research (UXR) received little academic attention so far. To better understand how UX practitioners envision the synergies between empathy-focused UX work and data-driven ML techniques, we surveyed 49 practitioners experienced in UX, ML, or both and conducted 13 semi-structured interviews with UX experts. We derived an inventory of ML's impact on current UXR activities and practitioners' predictions about its potentials. We learned that ML methods may help to automate mundane tasks, complement decisions with data-driven insights, and enrich UXR with insights from users' emotional worlds. Challenges may arise from a potential obligation to utilize data and a more restrictive access to user data. We embed our insights into recent academic work on ML for UXR and discuss automated UX evaluation as a promising use case for future research.
Unsupervised Concept Drift Detection Using a Student–Teacher Approach

Concept drift detection is a crucial task in data stream evolving environments. Most of the state of the art approaches designed to tackle this problem monitor the loss of predictive models. Accordingly, an alarm is launched when the loss increases significantly, which triggers some adaptation mechanism (e.g. retrain the model). However, this modus operandi falls short in many real-world scenarios, where the true labels are not readily available to compute the loss. These often take up to several weeks to be available. In this context, there is increasing attention to approaches that perform concept drift detection in an unsupervised manner, i.e., without access to the true labels. We propose a novel approach to unsupervised concept drift detection, which is based on a student-teacher learning paradigm. Essentially, we create an auxiliary model (student) to mimic the behaviour of the main model (teacher). At run-time, our approach is to use the teacher for predicting new instances and monitoring the mimicking loss of the student for concept drift detection. In a set of controlled experiments, we discovered that the proposed approach detects concept drift effectively. Relative to the gold standard, in which the labels are immediately available after prediction, our approach is more conservative: it signals less false alarms, but it requires more time to detect changes. We also show the competitiveness of our approach relative to other unsupervised methods.
Software for Interactive and Collaborative Creation in the Classroom and Beyond: An Overview of the Soundcool Software

Abstract This article presents a free framework for collaborative creation of interactive and experimental computer music called Soundcool. It is designed to fill a gap between rigid ready-to-use applications and flexible programming languages. The system offers easy-to-use elements for generating and processing sound, much like ready-made applications, but it enables flexible configuration and control, more like programming languages. The system runs on personal computers with an option for control via smartphones, tablets, and other devices using the Open Sound Control (OSC) protocol. Originally developed to support a new music curriculum, Soundcool is being used at different educational institutions in Spain, Portugal, Italy, and Romania through EU-funded Erasmus+ projects. In this article, we present our system and showcase three different scenarios as examples of how our system meets its objectives as an easy-to-use, versatile, and creative tool.
Spectrum Resource Trading and Radio Management Data Sharing Based on Blockchain

Radio spectrum resource is a scarce resource. How to use the limited spectrum resource to provide users with the maximum communication opportunities is worth further study. In fact, the traditional “command and control” mode of spectrum management is very inefficient in the utilization of spectrum, resulting in a great waste of frequency resources. At the same time, in radio management, it is necessary to set up databases of different services to store different monitoring information. Although the system plays a certain control ability, the radio monitoring records form an island of information, and the information of each service does not circulate, which is not conducive to integrated management. In this paper, based on the current radio spectrum resources in dynamic spectrum sharing method of resource sharing, the spectrum value of trust transfer problem, and the information management system each other information, collaborative management difficulties and other issues, put forward a kind of spectrum sharing method based on blockchain and by business combining intelligent contract database, realize data sharing solution.
Predicting <scp>just‐in‐time</scp> software defects to reduce <scp>post‐release</scp> quality costs in the maritime industry

Abstract Background The importance of software in maritime transportation is rapidly increasing as the industry seeks to develop and utilize innovative future ships, which can be realized using software technology. Due to the safety‐critical nature of ships, software quality assurance (SQA) has become an essential prerequisite for such development. Objective Based on the unique characteristics of the maritime domain, the purpose of this study was to achieve effective SQA resource allocation to reduce post‐release quality costs. Method Software defect prediction (SDP) is employed to predict defects in newly developed software based on models trained with past software defects and to update information using machine learning. This study demonstrated that just‐in‐time SDP is applicable to maritime domain practice and can reduce post‐release quality costs via combination with an estimation model, qCOPLIMO. Results Using real‐world datasets collected from the maritime industry, performance and cost‐benefit analyses of SDP were performed. A successful model was obtained that meets the performance criterion of 0.75 in within‐project defect prediction (WPDP) but not cross‐project defect prediction (CPDP). In addition, the cost‐benefit analysis results showed that 20% effort enables the detection of 56% of defects on average and that the post‐release quality cost can be reduced by 37.3% in the maritime domain. Conclusion SDP can be successfully applied to the maritime domain. Further, it is desirable to utilize WPDP instead of CPDP once minimum high‐quality commits are available that can be identified as defective or not. Finally, SDP can help reduce review effort and post‐release quality costs.
A Global Path Planner for Safe Navigation of Autonomous Vehicles in Uncertain Environments

Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under sensor uncertainty, with the understanding that there is no perfect sensing solution for all driving environments. In this paper, we propose a global safe path planner that analyzes sensor uncertainty and determines optimal paths. The path planner has two components: sensor analytics and path finder. The sensor analytics component combines the uncertainties of all sensors to evaluate the positioning and navigation performance of an AV at given locations and times. The path finder component then utilizes the acquired sensor performance and creates a weight based on safety for each road segment. The operation and quality of the proposed path finder are demonstrated through simulations. The simulation results reveal that the proposed safe path planner generates paths that significantly improve the navigation safety in complex dynamic environments when compared to the paths generated by conventional approaches.
Memory Cities: Visualizing Heap Memory Evolution Using the Software City Metaphor

Tool support is essential to help developers in understanding the memory behavior of complex software systems. Anomalies such as memory leaks can dramatically impact application performance and can even lead to crashes. Unfortunately, most memory analysis tools lack advanced visualizations (especially of the memory evolution over time) that could facilitate developers in analyzing suspicious memory behavior.In this paper, we present Memory Cities, a technique to visualize an application's heap memory evolution over time using the software city metaphor. While this metaphor is typically used to visualize static artifacts of a software system such as class hierarchies, we use it to visualize the dynamic memory behavior of an application. In our approach, heap objects can be grouped by multiple properties such as their types or their allocation sites. The resulting object groups are visualized as buildings arranged in districts, where the size of a building corresponds to the number of heap objects or bytes it represents. Continuously updating the city over time creates the immersive feeling of an evolving city. This can be used to detect and analyze memory leaks, i.e., to search for suspicious growth behavior. Memory cities further utilize various visual attributes to ease this task. For example, they highlight strongly growing buildings using color, while making less suspicious buildings semi-transparent.We implemented memory cities as a standalone application developed in Unity, with a JSON-based interface to ensure easy data import from external tools. We show how memory cites can use data provided by AntTracks, a trace-based memory monitoring tool, and present case studies on different applications to demonstrate the tool's applicability and feasibility.
A Facial Expression Recongnition Method Based on Dlib, RI-LBP and ResNet

Abstract A method of facial expression recognition using a composite feature is proposed. The method combines the expanded Dlib facial feature detector, the rotation-invariant local binary pattern (RI-LBP) and the 50-layer ResNet neural network model (ResNet_50). First, the expanded Dlib was used to locate 83 feature points on the face, obtainting the Dlib feature after preprocessing and dimentionality reduction (PCA). Then, the rotation-invariant LBP feature was extracted from 8 important regions after tilt correction. Furthermore, a 50-layer ResNet neural network was used to extract the low level features from the images. Finally, the three features were combined and extreme learning machine (ELM) was used to classify the composite facial features. The experimental results on Jaffe and CK+ datasets showed that the proposed method performs better compared with other methods.