Reviewerly/exHarmony huggingface dataset:
Extended pre-processing pipeline for text classification: On the role of meta-feature representations, sparsification and selective sampling

Text Classification pipelines are a sequence of tasks needed to be performed to classify documents into a set of predefined categories. The pre-processing phase (before training) of these pipelines involve different ways of transforming and manipulating the documents for the next (learning) phase. In this paper, we introduce three new steps into the pre-processing phase of text classification pipelines to improve effectiveness while reducing the associated costs. The distance-based Meta-Features (MFs) generation step aims at reducing the dimensionality of the original term-document matrix while producing a potentially more informative space that explicitly exploits discriminative labeled information. The second step is a sparsification one aimed at making the MF representation less dense to reduce training costs and noise. The third step is a selective sampling (SS) aimed at removing lines (documents) of the matrix obtained in the previous step, by carefully selecting the "best" documents for the learning phase. Our experiments show that the proposed extended pre-processing pipeline can achieve significant gains in effectiveness when compared to the original TF-IDF (up to 52%) and embedding-based representations (up to 46%), at a much lower cost (up to 9.7x faster in some datasets). Other main contributions of our work include a thorough and rigorous evaluation of the trade-offs between cost and effectiveness associated with the introduction of these new steps into the pipeline as well as a comprehensive comparative experimental evaluation of many alternatives in terms of representations, approaches, etc.
Assessment of Ordinary Kriging and Inverse Distance Weighting Methods for Modeling Chromium and Cadmium Soil Pollution in E-Waste Sites in Douala, Cameroon

Several studies have demonstrated that chromium (Cr) and cadmium (Cd) have adverse impacts on the environment and human health. These elements are present in electronic waste (e-waste) recycling sites. Several interpolation methods have been used to evaluate geographical impacts on humans and the environment.The aim of the present paper is to compare the accuracy of inverse distance weighting (IDW) and ordinary kriging (OK) in topsoil analysis of e-waste recycling sites in Douala, Cameroon.Selecting the proper spatial interpolation method is crucial for carrying out surface analysis. Ordinary kriging and IDW are interpolation methods used for spatial analysis and surface mapping. Two sets of samples were used and compared. The performances of interpolation methods were evaluated and compared using cross-validation.The results showed that the OK method performed better than IDW prediction for the spatial distribution of Cr, but the two interpolation methods had the same result for Cd (in the first set of samples). Results from Kolmogorov-Smirnov and Shapiro-Wilk tests showed that the data were normally distributed in the study area. The p value (0.302 and 0.773) was greater than 0.05 for Cr and for Cd (0.267 and 0.712). In the second set of samples, the OK method results (for Cd and Cr) were greatly diminished and the concentrations dropped, looking more like an average on the maps. However, the IDW interpolation gave a better representation of the concentration of Cd and Cr on the maps of the study area. For the second set of samples, OK and IDW for Cd and Cr had more similar results, especially in terms of root mean square error (RMSE).Many parameters were better identified from the RMSE statistic obtained from cross-validation after exhaustive testing. Inverse distance weighting appeared more adequate in limited urban areas.The authors declare no competing financial interests.
Strong photon coupling to the quadrupole moment of an electron in a solid-state qubit

The implementation of circuit quantum electrodynamics allows coupling distant qubits by microwave photons hosted in on-chip superconducting resonators. Typically, the qubit-photon interaction is realized by coupling the photons to the electric dipole moment of the qubit. A recent proposal suggests storing the quantum information in the electric quadrupole moment of an electron in a triple quantum dot. The qubit is expected to have improved coherence since it is insensitive to dipolar noise produced by distant voltage fluctuators. Here we experimentally realize a quadrupole qubit in a linear array of three quantum dots in a GaAs/AlGaAs heterostructure. A high impedance microwave resonator coupled to the middle dot interacts with the qubit quadrupole moment. We demonstrate strong quadrupole qubit--photon coupling and observe improved coherence properties when operating the qubit in the parameter space where the dipole coupling vanishes.
ExplainExplore: Visual Exploration of Machine Learning Explanations

Machine learning models often exhibit complex behavior that is difficult to understand. Recent research in explainable AI has produced promising techniques to explain the inner workings of such models using feature contribution vectors. These vectors are helpful in a wide variety of applications. However, there are many parameters involved in this process and determining which settings are best is difficult due to the subjective nature of evaluating interpretability. To this end, we introduce EXPLAINEXPLORE: an interactive explanation system to explore explanations that fit the subjective preference of data scientists. We leverage the domain knowledge of the data scientist to find optimal parameter settings and instance perturbations, and enable the discussion of the model and its explanation with domain experts. We present a use case on a real-world dataset to demonstrate the effectiveness of our approach for the exploration and tuning of machine learning explanations.
Philosophy of education in a new key

Michael Peters, Sonja Arndt & Marek TesarThis is a collective writing experiment of PESA members, including its Executive Committee, asking questions of the Philosophy of Education in a New Key. Co...
Privacy-Preserving Efficient Verifiable Deep Packet Inspection for Cloud-Assisted Middlebox

With the increasing traffic volume, enterprises choose to outsource their middlebox services, such as deep packet inspection, to the cloud to acquire rich computational and communication resources. However, since the traffic is redirected to the public cloud, information leakages, such as packet payload and inspection rules, arouse privacy concerns of both middlebox owner and packet senders. To address the concerns, we propose an efficient verifiable deep packet inspection (EV-DPI) scheme with strong privacy guarantees. Specifically, a two-layer architecture is designed and deployed over two non-collusion cloud servers. The first layer fast filters out most of legitimate packets and the second layer supports exact rule matching. During the inspection, the privacy of packet payload and the confidentiality of inspection rules are well preserved. To improve the efficiency, only fast symmetric crypto-systems, such as hash functions, are used. Moreover, the proposed scheme allows the network administrator to verify the execution results, which offers a strong control of outsourced services. To validate the performance of the proposed EV-DPI scheme, we conduct extensive experiments on the Amazon Cloud. Large-scale dataset (millions of packets) is tested to obtain the key performance metrics. The experimental results demonstrate that EV-DPI not only preserves the packet privacy, but also achieves high packet inspection efficiency.
Delayed Authentication: Preventing Replay and Relay Attacks in Private Contact Tracing

Currently several projects aim at designing and implementing protocols for privacy preserving automated contact tracing to help fight the current pandemic. Those proposal are quite similar, and in their most basic form basically propose an app for mobile phones which broadcasts frequently changing pseudorandom identifiers via (low energy) Bluetooth, and at the same time, the app stores IDs broadcast by phones in its proximity. Only if a user is tested positive, they upload either the beacons they did broadcast (which is the case in decentralized proposals as DP-3T, east and west coast PACT or Covid watch) or received (as in Popp-PT or ROBERT) during the last two weeks or so. Vaudenay [eprint 2020/399] observes that this basic scheme (he considers the DP-3T proposal) succumbs to relay and even replay attacks, and proposes more complex interactive schemes which prevent those attacks without giving up too many privacy aspects. Unfortunately interaction is problematic for this application for efficiency and security reasons. The countermeasures that have been suggested so far are either not practical or give up on key privacy aspects. We propose a simple non-interactive variant of the basic protocol that Towards this end we introduce the concept of "delayed authentication", which basically is a message authentication code where verification can be done in two steps, where the first doesn't require the key, and the second doesn't require the message.
Relating the spatial variability of chemical weathering and erosion to geological and topographical zones

The relationship between the spatial variability of soil elements, weathering and erosion rates can be very complex. Topography, abruptly-changing elevation gradients and slopes of varying geological composition exert an important influence on weathering trajectories and erosion. This complex interconnectivity is only rarely addressed in weathering studies. The main objectives of this investigation are, therefore, to relate weathering and erosion to various and geomorphic units in a dry-alpine and threshold landscape having steep slopes (Kan catchment, Tehran Province, Iran). A number of common weathering indices were tested using a genetic algorithm. The best indices are the (K + Na)/Ti ratio, the WIP (weathering index according to Parker), and the PI (product index according to Ruxton). However, the recently suggested 4Si-M+-R2 and M-F-Wmin systems discriminate weathering trends more accurately. Our results show that these soils have a low to moderate weathering stage. Weathering trajectories point to the active formation of kaolinite and oxyhydroxides. River sediments are slightly more weathered and contain the most-weathered topsoils and suggest erosion in the catchment is predominantly due to topsoil removal. Chemical weathering is influenced by the dominant geomorphic units (unconsolidated deposits of the Quaternary and solid bedrock) and landform features (combination of altitude and slope). Aspect does not appear to have a significant impact on weathering. Denudation (~erosion) rates were determined using meteoric 10Be since the fluvial sediments did not contain enough quartz for in situ 10Be analyses. This procedure has higher uncertainties due to difficulties in estimating the depositional flux of meteoric 10Be. Due to tectonic uplift, the erosion rates are very high. To maintain a soil layer, soil production rates must be also high. Although annual precipitation is relatively low, slope and soil dynamics seem particularly high, which leads to a fast turnover of the soil material and maintains the weathering intensity at a rather low to intermediate level.
Probabilistic SVM classifier ensemble selection based on GMDH-type neural network

Support vector machine (SVM) provides a good classification and regression ability, especially, for small sample learning. However, in practice, the learning ability of implemented SVM is occasionally far from the expected level. Group method of data handling neural network (GMDH-NN) has been applied in various fields for pattern recognition and data mining. It makes it possible to automatically find interrelations in data, to select an optimal structure of network or model and to improve the accuracy of existing algorithms. In this work we propose to take the advantages of GMDH-NN for further increasing the classification performance of SVM. One weakness of the symmetric regularity criterion of GMDH-NN is that if one of the input attributes has a relatively big range, then it may overcome the other attributes. Thus, we first define a standardized symmetric regularity criterion (SSRC) to evaluate and select the candidate models, and optimize a classifier ensemble selection approach. Secondly, we define a novel structure of initial model of GMDH-NN which is from the posterior probability outputs of SVMs. These probabilistic outputs are generated from the improved Platt’s probabilistic outputs. Thirdly, in real classification tasks, different classifiers usually have different classification advantages. So we use probabilistic SVM as base learner and integrate the probabilistic SVMs with GMDH-NN, and then propose a special classifier ensemble selection approach for probabilistic SVM classifiers based on GMDH-NN called GMDH-PSVM. Moreover, we use the Borda sorting and Random weighted Borda sorting to discuss the results of our experiments. Experiments on standard UCI datasets demonstrate the effectiveness of our method.
Evaluating the Benefits of the Immersive Space to Think

Sensemaking with large multimedia dataset is a cognitively intensive task that requires analysts to understand the underlying stories that the dataset tells. Often, analysts use tools in order to offload cognition as well as convey their new understanding of the dataset; however, existing tools are limited by their underlying technologies. We have proposed a novel virtual reality tool to support sensemaking called the Immersive Space to Think (IST). IST can aid the process of analyzing multimedia data, but it remains unproven whether IST improves sensemaking performance over a traditional desktop setting. In a study performed over six weeks, one participant used both IST and traditional methods of sensemaking with a dataset of 100 text documents of transcribed survey responses from World War 2 soldiers to perform historical analysis. The participant was asked guided questions that produced three essays with their understanding of the data. After conducting a blind evaluation of the participant’s interpretation of the data, a team of three experts in historical analysis concluded that the essays written with IST displayed a better understanding of the dataset. Furthermore, the participant gave positive feedback on IST, and also suggested possible improvements.
Comparing Augmented Reality-Assisted Assembly Functions—A Case Study on Dougong Structure

The Dougong structure is an ancient architectural innovation of the East. Its construction method is complex and challenging to understand from drawings. Scale models were developed to preserve this culturally-unique architectural technique by learning through their assembly process. In this work, augmented reality (AR)-based systems that support the manual assembly of the Dougong models with instant interactions were developed. The first objective was to design new AR-assisted functions that overcome existing limitations of paper-based assembly instructions. The second one was to clarify whether or not and how AR can improve the operational efficiency or quality of the manual assembly process through experiments. The experimental data were analyzed with both qualitative and quantitative measures to evaluate the assembly efficiency, accuracy, and workload of these functions. The results revealed essential requirements for improving the functional design of the systems. They also showed the potential of AR as an effective human interfacing technology for assisting the manual assembly of complex objects.
You never fake alone. Creative AI in action

Creative AI (notably GANs and VAEs) can generate convincing fakes of video footage, pictures, graphics, etc. In order to conceptualize the societal role of creative AI a new conceptual toolbox is needed. The paper provides metaphors and concepts for understanding the functioning of creative AI. It shows how the role of creative AI in relation to FAT ideals can be enriched by a dynamic and constructivist understanding of creative AI. The paper proposes to use Greimas' actantial model as a heuristic in the operationalization of this type of understanding of creative AI.
A Single-Stream Segmentation and Depth Prediction CNN for Autonomous Driving

Convolutional neural networks (CNN) have been used successfully in solving many challenging visual perception tasks facing mobile robots and self-driving cars. To facilitate deploying such models on embedded hardware onboard mobile robots that have limited resources, multitask learning approaches have become common. In typically used multitask learning, a shared encoder network extracts features from inputs whereas multiple task-specific decoders transform these features into their target output. However, properly combining different tasks’ losses into the final network loss such that each task is making progress learning is a major challenge in these approaches. In this article, we present an innovative approach to extend a typical single-task network with the capability of performing two tasks without multiple decoders, i.e., a single-stream two-task network. The two output tasks are semantic segmentation and monocular depth prediction which are essential tasks in visual perception for autonomous driving. The method is centered on solving semantic segmentation with a regression loss function rather than a classification one. With our approach, we seize multitask learning benefits of reduced overhead and enhanced generalization while alleviating the need to balance different loss functions. Experimental evaluations with baseline single tasks and a multitask network are presented.
Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots

In this work, we present a complete hybrid navigation system for a two-wheel differential drive mobile robot that includes static-environment- global-path planning and dynamic environment obstacle-avoidance tasks. By the given map, we propose a multi-agent A-heuristic algorithm for finding the optimal obstacle-free path. The result is less time-consuming and involves fewer changes in path length when dealing with multiple agents than the ordinary A-heuristic algorithm. The obtained path was smoothed based on curvature-continuous piecewise cubic Bézier curve (C2 PCBC) before being used as a trajectory by the robot. In the second task of the robot, we supposed any unforeseen obstacles were recognized and their moving frames were estimated by the sensors when the robot tracked on the trajectory. In order to adapt to the dynamic environment with the presence of constant velocity obstacles, a weighted-sum model (WSM) was employed. The 2D LiDAR data, the robot’s frame and the detected moving obstacle’s frame were collected and fed to the WSM during the movement of the robot. Through this information, the WSM chose a temporary target and a C2 PCBC-based subtrajectory was generated that led the robot to avoid the presented obstacle. Experimentally, the proposed model responded well in existing feasible solution cases with fine-tuned model parameters. We further provide the re-path algorithm that helped the robot track on the initial trajectory. The experimental results show the real-time performance of the system applied in our robot.
Multi-level semantic representation enhancement network for relationship extraction

Discovering the relationship between two entities in a sentence is one of the most important tasks in natural language processing. Due to the superior feature learning capacity, deep learning-based relationship extraction methods have become prevailing. However, most of them rely on the features provided by tools such as dependency parser, part-of-speech tagger, or named entity recognizers. Moreover, although models such as convolutional neural networks or recurrent neural networks have their merits in text feature extraction, the semantic representation of entities’ relationships are still far from optimal. To address this issue, this paper proposes a novel end-to-end multi-level semantic representation enhancement network (MLSREN). MLSREN can enhance the semantic representation of entities from word, phrase, and context level. Furthermore, to extract deeper semantic information of phrases with fewer network parameters, a LightText-CNN is proposed for local feature extraction. Extensive experiments are conducted on the SemEval-2010 task 8 dataset, the results demonstrate that our model significantly improves state-of-the-art performance. To better understand performance achieved by MLSREN, we also analyze the contribution of each network component as well as the problems in the feature selection of the current relationship extraction models.
Quantum and classical machine learning for the classification of non-small-cell lung cancer patients

The ability to accurately classify disease subtypes is of vital importance, especially in oncology where this capability could have a life saving impact. Here we report a classification between two subtypes of non-small cell lung cancer, namely Adenocarcinoma versus Squamous cell carcinoma. The data consists of approximately 20,000 gene expression values for each of 104 patients. The data was curated from Kuner (Geo lung cancer data set—gse10245, 2009, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10245 ) and Golumbic (Geo lung cancer data set—gse18842, 2010, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18842 ). We used a combination of classical and quantum machine learning models to successfully classify these patients. We utilized feature selection methods based on univariate statistics in addition to XGBoost (Guestrin and Chen in Xgboost: a scalable tree boosting system, 2007, https://dl.acm.org/citation.cfm?id=1273596 ). A novel and proprietary data representation method developed by one of the authors called QCrush was also used as it was designed to incorporate a maximal amount of information under the size constraints of the D-Wave quantum annealing computer. The machine learning was performed by a quantum Boltzmann machine. This paper will report our results, the various classical methods, and the quantum machine learning approach we utilized.
DT-CNN: An Energy-Efficient Dilated and Transposed Convolutional Neural Network Processor for Region of Interest Based Image Segmentation

An energy-efficient convolutional neural network (CNN) processor is proposed for real-time image segmentation on mobile devices. The proposed processor utilizes Region of Interest (ROI) based image segmentation to speed up the process and reduce the overall external memory access. Although the ROI based image segmentation degrades the segmentation accuracy, the proposed dilation rate adjustment algorithm, which regulates the receptive field depending on the ROI resolution during dilated convolution, compensates for the accuracy degradation up to 0.2310 mean Intersection over Union (mIoU). In addition, the processor accelerates the dilated and transposed convolution by skipping the redundant zero computations with the proposed delay cells. As a result, the throughput of dilated and transposed convolution is increased up to ×159 and ×3.84 . The delay cells can also support the variable dilation rates in dilated convolution caused by the dilation rate adjustment algorithm. Moreover, the processor selects the operating frequency based on the ROI resolution to save power consumption up to 81.2%. The processor is simulated in 65 nm CMOS technology, and the 6.8 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> processor consumes the 206 mW power consumption with the 4.66 ms of processing time and 3.22 TOPS/W energy-efficiency at the target image segmentation dataset.