Reviewerly/exHarmony huggingface dataset:
Integrating Representation and Interaction for Context-Aware Document Ranking

Recent studies show that historical behaviors (such as queries and their clicks) contained in a search session can benefit the ranking performance of subsequent queries in the session. Existing neural context-aware ranking models usually rank documents based on either latent representations of user search behaviors or the word-level interactions between the candidate document and each historical behavior in the search session. However, these two kinds of models both have their own drawbacks. Representation-based models neglect fine-grained information on word-level interactions, whereas interaction-based models suffer from the length restriction of session sequence because of the large cost of word-level interactions. To complement the limitations of these two kinds of models, we propose a unified context-aware document ranking model that takes full advantage of both representation and interaction. Specifically, instead of matching a candidate document with every single historical query in a session, we encode the session history into a latent representation and use this representation to enhance the current query and the candidate document. We then just match the enhanced query and candidate document with several matching components to capture the fine-grained information of word-level interactions. Rich experiments on two public query logs prove the effectiveness and efficiency of our model for leveraging representation and interaction.
Design and performance evaluation of QoE/QoS-oriented scheme for reliable data transmission in Internet of Things environments

Internet of things (IoT) technology has become the focus of many researchers, and quality of service (QoS) is considered one of the most important challenges for IoT technology. Additionally, the expectations of end users in the IoT environment are of great importance, which means that quality of experience (QoE) evaluations are significant. Therefore, this paper proposed a scheme to guarantee QoS in IoT environments. For this scheme, the most efficient QoS methodology was selected based on the data and network types in the IoT environment. The proposed QoS scheme classified the data transmitted through IoT systems into two main statuses. In the first status, called “individual”, the data were exchanged through one type of large-scale network at a time. In the second status, “overlapped”, the data passed through an area that comprised a mixture of different networks. The proposed scheme handled the QoS issue for both statuses. A QoE scheme evaluating the IoT data using different layers was also proposed. The proposed QoS and QoE schemes were then combined to create a more generalized QoE/QoS-oriented scheme, which was used to adapt the QoS scheme to achieve the highest efficiency. The performance of the QoE/QoS-oriented scheme was measured in a simulation environment that was constructed using an NS-3 package. First, the performance of the proposed QoS scheme was measured by two groups of metrics. The first group was general and comprised throughput, delay, and energy consumption. The second group was particular to the proposed QoS scheme and comprised transformation between individual and overlapped statuses, usage percentages for each QoS model, and the degree of complexity of the overlapped areas. The first group of metrics showed that the proposed QoE/QoS scheme had a higher efficiency than that of the previous individual QoS models. The second group of metrics showed that the features of the proposed QoS scheme worked efficiently. Finally, delay, energy consumption, throughput, and jitter were improved after the QoS methodologies were adapted using the proposed QoE scheme.
Decentralized <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e343" altimg="si7.svg"><mml:mi>k</mml:mi></mml:math>-anonymization of trajectories via privacy-preserving tit-for-tat

Mobility data, and specifically trajectories, are used to monitor the mobility of the population and are crucial to improve public health, transportation, urban planning, economic planning, etc. However, trajectories are personally identifiable information and hence they should be anonymized before releasing them for secondary use. Anonymization cannot be limited to suppressing the metadata containing the subject’s identity, because the origin, the destination and even the intermediate points of a trajectory may allow re-identifying the subject who followed it. Proper anonymization requires masking detailed spatiotemporal information. The standard approach to build anonymized data sets is centralized: the subjects send their original movement data to a controller, who takes care of producing an anonymized mobility data set. This requires subjects to blindly trust the controller. In this paper, we empower subjects with the ability to anonymize their trajectories locally by adhering to a privacy model in order to achieve formal privacy guarantees. After reviewing the state of the art, we motivate our choice of k-anonymity as a privacy model. We then set out to decentralize k-anonymity in a rational setting: a subject k-anonymizes her completed trajectory by aggregating with k−1 similar trajectories obtained from other (unknown) subjects. The latter trajectories are gathered via an anonymous and privacy-preserving tit-for-tat data exchange protocol, which runs on a fully decentralized peer-to-peer network. Experiments show that, without relying on a (trusted) data controller and while ensuring privacy w.r.t. other peers, our approach yields k-anonymized mobility data sets that are still reasonably useful compared to the near-optimal data sets obtained in the centralized approach.
Deep Learning based Automatic Hindi Text Summarization

Automatic text summarization (ATS) has become very popular among researchers because it involves generating a smaller subset of text from the main text. This subset represents the text&#x2019;s entire and main idea. Natural Language Processing and Machine Learning are critical applications of ATS. Summarizations are classified into two types based on how they are generated: extractive and abstractive. In this project, we have implemented the Hindi text summarization, on which very little work has been done to date. It is a field where work can be proven helpful to the community if it is carried out. A machine learning model has been trained and tested through around 100,000 data entries so that it gives out very accurate summaries and, in turn, is very useful to society. The F-Score of the model is 58%, and the Rouge Score is 67.5%. Hence, we can consider that our model is decently accurate. Different libraries, like Pandas, NumPy, sklearn, etc., are used to develop the source code. The data model is trained using LSTM, word embedding, and seq2seq.
Self-Critical Sequence Training for Automatic Speech Recognition

Although automatic speech recognition (ASR) task has gained remarkable success by sequence-to-sequence models, there are two main mismatches between its training and testing that might lead to performance degradation: 1) The typically used cross-entropy criterion aims to maximize log-likelihood of the training data, while the performance is evaluated by word error rate (WER), not log-likelihood; 2) The teacher-forcing method leads to the dependence on ground truth during training, which means that model has never been exposed to its own prediction before testing. In this paper, we propose an optimization method called self-critical sequence training (SCST) to make the training procedure much closer to the testing phase. As a reinforcement learning (RL) based method, SCST utilizes a customized reward function to associate the training criterion and WER. Furthermore, it removes the reliance on teacher-forcing and harmonizes the model with respect to its inference procedure. We conducted experiments on both clean and noisy speech datasets, and the results show that the proposed SCST respectively achieves 8.7% and 7.8% relative improvements over the baseline in terms of WER.
THE BEI INDEX: AN APPROACH FOR MEASURING THE BUSINESS ENVIRONMENT IN AFRICA

Africa is becoming more and more attractive for trade and investment, while entrepreneurs are concerned about the quality of the business environment. This paper aims to analyze the Africa’s business environment and its dimensions in order to identify the most favorable destinations for starting a business. The methodology for this study includes research reviewing the existing literature and a quantitative approach based on a composite instrument. The results of the empirical study show that three regions have a favorable business environment for starting a business, while the most important dimensions of business are related to costs incurred, time and procedures required and trade aspects.
Relationship between chaotic vibrations and acoustic properties of percussion cymbals

Cymbals are a type of percussion instrument that have existed for a long time. However, the mechanism of sound generation has not yet been clarified, and there is no clear standard for the production process. Therefore, cymbals have been manufactured and processed using the experience of craftsmen to the present day. In this study, we evaluated and compared the vibration and acoustic characteristics of cymbals manufactured by different manufacturers and processing methods to clarify the sound generation mechanism of percussion cymbals. The cymbals were vibrated in the horizontal direction using a shaker, and various vibration evaluations using the Poincaré section and correlation dimensions, along with various psychoacoustic evaluations using loudness curves and sharpness, were used to evaluate and compare the cymbals. Consequently, it was clarified that the route to the chaotic vibration of cymbals was determined and that there was a correlation between the correlation dimension and sharpness.
Detecting genuine multipartite entanglement in three-qubit systems with eternal non-Markovianity

Abstract We devise a novel protocol to detect genuinely multipartite entangled states by harnessing quantum non-Markovian operations. We utilize a particular type of non-Markovianity known as the eternal non-Markovianity to construct a non-complete positive map to filter out the bi-separable states and detect genuine multipartite entanglement. We further propose a witness operator to detect genuinely multipartite entangled states experimentally based on this theory. Our study sheds light on a hitherto unexplored connection between entanglement theory and quantum non-Markovianity.
A study of Analytics and Exploration of Bitcoin Challenges and Blockchain

It has been a decade since the formation of the first cryptocurrency in the world. Bitcoin was The first ever crypto invented by Satoshi Nag moto in 2008. It is a digital currency or virtual currency which is not controlled by the central government and operates without a central bank or single administrator. Block chain technology is used in the cryptocurrency, the cryptocurrency block chain is the chain of the blocks, where each block contains a hash of the preceding block till the top block of the chain. A network gets formed by the blocks where block chain represents a public ledger of the transaction happened in the network. Bitcoin and other cryptocurrencies have seen a rise in the attention of different sectors in the last few years. It has been in the eyes of everyone for the perks. It offers to be comparable to a fiat currency structure of banking sector, stakeholders, government and individual investors. Although research and awareness about cryptocurrencies or digital currency are very less and it is on the initial stage, this paper provides an important look and guide about the different aspects of cryptocurrency. The authenticity of this paper is on the discussion at various intervals of law and regulation with the consumption of high energy, possibility of crash collisions and security threat on the network attacks. The relative observations on future of applications of bitcoin can be seen throughout the paper.
MIME

Recent years have seen a paradigm shift towards multi-task learning. This calls for memory and energy-efficient solutions for inference in a multi-task scenario. We propose an algorithm-hardware co-design approach called MIME. MIME reuses the weight parameters of a trained parent task and learns task-specific threshold parameters for inference on multiple child tasks. We find that MIME results in highly memory-efficient DRAM storage of neural-network parameters for multiple tasks compared to conventional multi-task inference. In addition, MIME results in input-dependent dynamic neuronal pruning, thereby enabling energy-efficient inference with higher throughput on a systolic-array hardware. Our experiments with benchmark datasets (child tasks)- CIFAR10, CIFAR100, and Fashion-MNIST, show that MIME achieves ~3.48x memory-efficiency and ~2.4-3.1x energy-savings compared to conventional multi-task inference in Pipelined task mode.
Clustering Student Learners based on performance using K-Means Algorithm

Information Processing Speed is to how a learner can quickly process the incoming information. Many researchers consider Information Processing Speed as a central aspect of IQ. Many children with attention problems are unable to cope up with the lesson presented by the teacher. A student with such attention problems quickly give up trying if they can’t able to process the initial information quickly. Processing speed is one of four psychometric constructs that are considered important in helping us understanding an individual child’s learning abilities. Each individual has its own processing speed so that teaching should also be done according to the processing speed cognitive of an individual. In this work a new method is developed to determine the processing speed of each student and classifies them using K-means clustering algorithm into pre-defined number of clusters.
The Role of Prosody in Disambiguating English Indirect Requests

Ambiguity pervades language. The sentence "My office is really hot" could be interpreted as a complaint about the temperature or as an indirect request to turn on the air conditioning. How do comprehenders determine a speaker's intended interpretation? One possibility is that speakers and comprehenders exploit prosody to overcome the pragmatic ambiguity inherent in indirect requests. In a pre-registered behavioral experiment, we find that human listeners can successfully determine whether a given utterance was intended as a request at a rate above chance (55%), above and beyond the prior probability of a given sentence being interpreted as a request. Moreover, we find that a classifier equipped with seven acoustic features can detect the original intent of an utterance with 65% accuracy. Finally, consistent with past work, the duration, pitch, and pitch slope of an utterance emerge both as significant correlates of a speaker's original intent and as predictors of comprehenders' pragmatic interpretation. These results suggest that human and machine comprehenders alike can use prosody to enrich the meaning of ambiguous utterances, such as indirect requests.
What happens in my code reviews? An investigation on automatically classifying review changes

Abstract Code reviewing is a widespread practice used by software engineers to maintain high code quality. To date, the knowledge on the effect of code review on source code is still limited. Some studies have addressed this problem by classifying the types of changes that take place during the review process ( a.k.a. review changes ), as this strategy can, for example, pinpoint the immediate effect of reviews on code. Nevertheless, this classification (1) is not scalable, as it was conducted manually, and (2) was not assessed in terms of how meaningful the provided information is for practitioners. This paper aims at addressing these limitations: First, we investigate to what extent a machine learning-based technique can automatically classify review changes. Then, we evaluate the relevance of information on review change types and its potential usefulness, by conducting (1) semi-structured interviews with 12 developers and (2) a qualitative study with 17 developers, who are asked to assess reports on the review changes of their project. Key results of the study show that not only it is possible to automatically classify code review changes, but this information is also perceived by practitioners as valuable to improve the code review process. Data and materials: 10.5281/zenodo.5592254
A categorical data clustering framework on graph representation

Clustering categorical data is an important task of machine learning, since the type of data widely exists in real world. However, the lack of an inherent order on the domains of categorical features prevents most of classical clustering algorithms from being directly applied for the type of data. Therefore, it is very key issue to learn an appropriate representation of categorical data for the clustering task. In order to address this issue, we develop a categorical data clustering framework based on graph representation. In this framework, a graph-based representation method for categorical data is proposed, which learns the representation of categorical values from their similar graph to provide similar representations for similar categorical values. We compared the proposed framework with other representation methods for categorical data clustering on benchmark data sets. The experiment results illustrate the proposed framework is very effective, compared to other methods.
UIPS: A Novel Image Resolution and Clarity Enhancement Scheme for Underwater Image Processing Scheme

The primary goal of this system is to enhance the quality and efficiency of the underwater optical image, which is influenced by forward and reverse diffraction, absorption coefficient of light and marine snow, among other factors. Additionally, the suggested system is expanded to improve its perception through the identification of underwater mineral items. This approach is intended to facilitate analysis and reduce the time required for classifying and identifying mining objects through the use of a random forest classifier. This methodology is primarily implemented by integrating mine classification approaches to object identification using a random forest classifier. Additionally, to enhance the amount of picture data collected in order to perform accurate underwater object detection utilizing depth-based analysis. Automatic-Detection-and-Classification (ADC) of Underwater Structures for Mines Hunt Systems has been proposed in conjunction with the Random Forest Classifier in order to achieve the highest accuracy in image quality improvements. The term image ‘Super-Resolution’ provides a hint to image quality enhancement and robust feature assesment of digital images, in which the logic is applied to the underwater streams to enhance the quality of it in clear manner. This system utilizes the underwater image to enhance the quality and this technology enables real-time integration and efficient analysis of items resembling mines. The overall integration of ADC and RF scheme is called as Underwater Image Processing Scheme (UIPS), in whichit provides an efficient image quality in outcomes and the resulting section of this paper provides a proper proof of that in clear manner.
FiNCAT-2: An enhanced Financial Numeral Claim Analysis Tool

In recent times, investors tend to use digital financial content available online while making investment-related decisions. Numerals presented in such content may be claims of executives for alluring them. Numerals that represent claims i.e. ‘in-claim’ numerals may not necessarily be real facts. Investors should not get swayed by the information presented using ‘in-claim’ numerals. We developed an open-source tool, FiNCAT-2 that distinguishes ‘in-claim’ numerals from those which are not (i.e. ‘out-of-claim’ numerals). It consists of a logistic regression model trained using contextual BERT embeddings (768-dimensions) of the target numeral. It can be accessed from GitHub, Google Colab and HuggingFace Spaces.
Generalization of solar power yield modeling using knowledge transfer

Recently, the energy issue is an important factor all over the world due to the fact of sustainability perspective. This paper has proposed a new Transfer Learning (TL) based PV energy yield model, which generalizes the knowledge transfer process from the source domain to the target domain differ on time and location. The solar power yield forecasting highly depends on meteorological data, which has high variability influenced by both time and target place/location. Therefore, a solar energy forecasting model from a source may not perform well to a target domain due to differences in the data distribution as evident by the experiment result presented in this paper. The extended historical data-driven solar power yield models may perform better to a certain extent with the complex nonlinear meteorological data relationships. But due to rapid climate change issue, the knowledge learned from historical data may not apply to the near future. Furthermore, it is impractical to assume the availability of a large dataset and ignore the probability of negative knowledge transfer tendency of traditional regression models. To address this gap, this paper has proposed Transfer Support Vector Regression (Tr-SVR) that uses a novel weighting model based on patterns of a hybrid dataset, both source and target data for training. The proposed Tr-SVR only transfer the knowledge fits the target domain to reduce negative transfer and dependency on long historical data. The experimental results show that when the distribution of training and test data differs, the traditional machine learning method represented by ANN may face serious generalization challenges. In this scenario, the proposed Tr-SVR model has clear advantages in terms of error metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Maximum error, Mean Absolute Percentage Error (MAPE), and Sum of Squares of the Error (SSE).
RASCv2: Enabling Remote Access to Side-Channels for Mission Critical and IoT Systems

The Internet of Things (IoT) and smart devices are currently being deployed in systems such as autonomous vehicles and medical monitoring devices. The introduction of IoT devices into these systems enables network connectivity for data transfer, cloud support, and more, but can also lead to malware injection. Since many IoT devices operate in remote environments, it is also difficult to protect them from physical tampering. Conventional protection approaches rely on software. However, these can be circumvented by the moving target nature of malware or through hardware attacks. Alternatively, insertion of the internal monitoring circuits into IoT chips requires a design trade-off, balancing the requirements of the monitoring circuit and the main circuit. A very promising approach to detecting anomalous behavior in the IoT and other embedded systems is side-channel analysis. To date, however, this can be performed only before deployment due to the cost and size of side-channel setups (e.g., and oscilloscopes, probes) or by internal performance counters. Here, we introduce an external monitoring printed circuit board (PCB) named RASC to provide r emote a ccess to s ide- c hannels. RASC reduces the complete side-channel analysis system into two small PCBs (2 \( \times \) 2 cm), providing the ability to monitor power and electromagnetic (EM) traces of the target device. Additionally, RASC can transmit data and/or alerts of anomalous activities detected to a remote host through Bluetooth. To demonstrate RASCs capabilities, we extract keys from encryption modules such as AES implemented on Arduino and FPGA boards. To illustrate RASC’s defensive capabilities, we also use it to perform malware detection. RASC’s success in power analysis is comparable to an oscilloscope/probe setup but is lightweight and two orders of magnitude cheaper.