Reviewerly/exHarmony huggingface dataset:
Learning Effect of Implicit Learning in Joining-in-type Robot-assisted Language Learning System

The introduction of robots into language learning systems has been highly useful, especially in motivating learners to engage in the learning process and in letting human learners converse in more realistic conversational situations. This paper describes a novel robot-assisted language learning system that induces the human learner into a triad conversation with two robots through which he or she improves practical communication skills in various conversational situations. The system applies implicit learning as the main learning style for conveying linguistic knowledge, in an indirect way, through conversations on several topics. A series of experiments was conducted using 80 recruited participants to evaluate the effect of implicit learning and the retention effect in a joining-in-type robot-assisted language learning system. The experimental results show positive effects of implicit learning and repetitive learning in general. Based on these experimental results, we propose an improved method, integrating implicit learning and tutoring with corrective feedback in an adaptive way, to increase performance in practical communication skills even for a wide variety of proficiency of L2 learners.
A Novel Local Human Visual Perceptual Texture Description with Key Feature Selection for Texture Classification

This paper proposes a novel local texture description method which defines six human visual perceptual characteristics and selects the minimal subset of relevant as well as nonredundant features based on principal component analysis (PCA). We assign six texture characteristics, which were originally defined by Tamura et al., with novel definition and local metrics so that these measurements reflect the human perception of each characteristic more precisely. Then, we propose a PCA-based feature selection method exploiting the structure of the principal components of the feature set to find a subset of the original feature vector, where the features reflect the most representative characteristics for the textures in the given image dataset. Experiments on different publicly available large datasets demonstrate that the proposed method provides superior performance of classification over most of the state-of-the-art feature description methods with respect to accuracy and efficiency.
Parallel Ant Colony Algorithm for Shortest Path Problem

During travelling, more and more information must be taken into account, and travelers have to make several complex decisions. In order to support these decisions, IT solutions are unavoidable, and as the computational demand is constantly growing, the examination of state-of-the-art methodologies is necessary. In our research, a parallelized Ant Colony algorithm was investigated, and a parameter study on a real network has been made. The aim was to inspect the sensibility of the method and to demonstrate its applicability in a multi-threaded system (e.g. Cloud-based systems). Based on the research, increased effectiveness can be reached by using more threads. The novelty of the paper is the usage of the processors’ parallel computing capability for routing with the Ant Colony algorithm.
A tree-based approach for English-to-Turkish translation

In this paper, we present our English-to-Turkish translation methodology, which adopts a tree-based approach. Our approach relies on tree analysis and the application of structural modification rules to get the target side (Turkish) trees from source side (English) ones. We also use morphological analysis to get candidate root words and apply tree-based rules to obtain the agglutinated target words. Compared to earlier work on English-to-Turkish translation using phrase-based models, we have been able to obtain higher BLEU scores in our current study. Our syntactic subtree permutation strategy, combined with a word replacement algorithm, provides a 67 % relative improvement from a baseline 12.8 to 21.4 BLEU, all averaged over 10-fold cross-validation. As future work, improvements in choosing the correct senses and structural rules are needed.
Hash Code Reconstruction for Fast Similarity Search

Learning to hash is a popular technique for fast similarity search on a large-scale image database. However, many hashing methods do not achieve satisfactory results because of the quantization loss in the straightforward binary code generation procedure. In order to address this problem, we propose a novel hash code reconstruction framework for existing unsupervised hashing methods. In our proposed approach, the hash codes are generated through reconstructing the original images with relaxed hamming vector representation, such that the final learned codes will be more approximate to characterize the intrinsic image structure. Moreover, our proposed hash code reconstruction algorithm is very efficient for computing, which can be generalized to various hashing methods. Extensive experiments are conducted on four public image datasets by incorporating our proposed scheme with different hashing methods, and the comparison results have shown that significant performance improvements can be achieved with minor additional time cost for fast similarity search task.
Sentiment Analysis Method based on Piecewise Convolutional Neural Network and Generative Adversarial Network

Text sentiment analysis is one of the most important tasks in the field of public opinion monitoring, service evaluation and satisfaction analysis under network environments. Compared with the traditional Natural Language Processing analysis tools, convolution neural networks can automatically learn useful features from sentences and improve the performance of the affective analysis model. However, the original convolution neural network model ignores sentence structure information which is very important for text sentiment analysis. In this paper, we add piece-wise pooling to the convolution neural network, which allows the model to obtain the sentence structure. And the main features of different sentences are extracted to analyze the emotional tendencies of the text. At the same time, the user’s feedback involves many different fields, and there is less labeled data. In order to alleviate the sparsity of the data, this paper also uses the generative adversarial network to make common feature extractions, so that the model can obtain the common features associated with emotions in different fields, and improves the model’s Generalization ability with less training data. Experiments on different datasets demonstrate the effectiveness of this method.
Quantum Spectrometry for Arbitrary Noise

We present a technique for recovering the spectrum of a non-Markovian bosonic bath and/or non-Markovian noises coupled to a harmonic oscillator. The treatment is valid under the conditions that the environment is large and hot compared to the oscillator, and that its temporal autocorrelation functions are symmetric with respect to time translation and reflection---criteria which we consider fairly minimal. We model a demonstration of the technique as deployed in the experimental scenario of a nanosphere levitated in a Paul trap, and show that it would effectively probe the spectrum of an electric field noise source from $1{0}^{2}$ to $1{0}^{6}\text{ }\text{ }\mathrm{Hz}$ with a resolution inversely proportional to the measurement time. This technique may be deployed in quantum sensing, metrology, computing, and in experimental probes of foundational questions.
Fuzzy Clustering of Sequential Data

With the increase in popularity of the Internet and the advancement of technology in the fields like bioinformatics and other scientific communities the amount of sequential data is on the increase at a tremendous rate.With this increase, it has become inevitable to mine useful information from this vast amount of data.The mined information can be used in various spheres; from day to day web activities like the prediction of next web pages, serving better advertisements, to biological areas like genomic data analysis etc.A rough set based clustering of sequential data was proposed by Kumar et al recently.They defined and used a measure, called Sequence and Set Similarity Measure to determine similarity in data.However, we have observed that this measure does not reflect some important characteristics of sequential data.As a result, in this paper, we used the fuzzy set technique to introduce a similarity measure, which we termed as Kernel and Set Similarity Measure to find the similarity of sequential data and generate overlapping clusters.For this purpose, we used exponential string kernels and Jaccard's similarity index.The new similarity measure takes an account of the order of items in the sequence as well as the content of the sequential pattern.In order to compare our algorithm with that of Kumar et al, we used the MSNBC data set from the UCI repository, which was also used in their paper.As far as our knowledge goes, this is the first fuzzy clustering algorithm for sequential data.
Cooperative Hybrid Semi-Supervised Learning for Text Sentiment Classification

A large-scale and high-quality training dataset is an important guarantee to learn an ideal classifier for text sentiment classification. However, manually constructing such a training dataset with sentiment labels is a labor-intensive and time-consuming task. Therefore, based on the idea of effectively utilizing unlabeled samples, a synthetical framework that covers the whole process of semi-supervised learning from seed selection, iterative modification of the training text set, to the co-training strategy of the classifier is proposed in this paper for text sentiment classification. To provide an important basis for selecting the seed texts and modifying the training text set, three kinds of measures—the cluster similarity degree of an unlabeled text, the cluster uncertainty degree of a pseudo-label text to a learner, and the reliability degree of a pseudo-label text to a learner—are defined. With these measures, a seed selection method based on Random Swap clustering, a hybrid modification method of the training text set based on active learning and self-learning, and an alternately co-training strategy of the ensemble classifier of the Maximum Entropy and Support Vector Machine are proposed and combined into our framework. The experimental results on three Chinese datasets (COAE2014, COAE2015, and a Hotel review, respectively) and five English datasets (Books, DVD, Electronics, Kitchen, and MR, respectively) in the real world verify the effectiveness of the proposed framework.
Mimosa: Protecting Private Keys Against Memory Disclosure Attacks Using Hardware Transactional Memory

Cryptography is essential for computer and network security. When cryptosystems are deployed in computing or communication systems, it is extremely critical to protect the cryptographic keys. In practice, keys are loaded into the memory as plaintext during cryptographic computations. Therefore, the keys are subject to memory disclosure attacks that read unauthorized data from RAM. Such attacks could be performed through software exploitations, such as OpenSSL Heartbleed, even when the integrity of the victim system's binaries is maintained. They could also be done through physical methods, such as cold-boot attacks, even if the system is free of software vulnerabilities. This paper presents Mimosa, to protect RSA private keys against both software-based and physical memory disclosure attacks. Mimosa uses hardware transactional memory (HTM) to ensure that (a) whenever a malicious thread other than Mimosa attempts to read the plaintext private key, the transaction aborts and all sensitive data are automatically cleared with hardware, due to the strong atomicity guarantee of HTM; and (b) all sensitive data, including private keys and intermediate states, appear as plaintext only within CPU-bound caches, and are never loaded to RAM chips. To the best of our knowledge, Mimosa is the first solution to use transactional memory to protect sensitive data against memory attacks. However, the fragility of TSX transactions introduces extra cache-clogging denial-of-service (DoS) threats, and attackers could sharply degrade the performance by concurrent memory-intensive tasks. To mitigate the DoS threats, we further partition an RSA private-key computation into multiple transactional parts by analyzing the distribution of aborts, while (sensitive) intermediate results are still protected across transactional parts. Through extensive experiments, we show that Mimosa effectively protects cryptographic keys against attacks that attempt to read sensitive data in memory, and introduces only a small performance overhead, even with concurrent cache-clogging workloads.
Ergonomics and the translation process

The translation process can be regarded as a complex system involving many agents, organizational factors such as workflow, communication processes, project management, job security, and translator status.Environmental factors in the physical sense (e. g. lighting, temperature, air quality, space) as well in the broader sense of the role of translation and translators in the economy and society as a whole can also influence the process.Viewing translation from an ergonomic perspective can provide an appropriate framework to understand the impact of such factors on the demanding bilingual activity that translators engage in.Because their work requires close attention and concentration, translators have to exert energy and ultimately cognitive resources to compensate for the distraction of any physical discomfort, delays in computer responsiveness, or frustration with organizational problems.In this article, the relevance of ergonomics and the implications of putting the translator and their translation processes in focus are discussed in light of recent research.
Drags: A compositional algebraic framework for graph rewriting

We are interested in a natural generalization of term-rewriting techniques to what we call drags, viz. finite, directed, ordered, rooted multigraphs, each vertex of which is labeled by a function symbol. To this end, we develop a rich algebra of drags that generalizes the familiar term algebra and its associated rewriting capabilities. Viewing graphs as terms provides an initial building block for rewriting with such graphs, one that should impact the many areas where computations take place on graphs.
Monitoring Kinerja User Akuntan Menggunakan Dashboard Pada Web Based Accounting Online di Perguruan Tinggi

Monitoring the performance of the accountant's user in an agency's Web Based Online Accounting is something that must be done so that its performance can be seen. Performance User accountants must be considered at all times to minimize errors made by the user. As well as monitoring the performance of employees in general, this monitoring is deemed necessary to prevent losses between the two parties, both the user and the provider of Web Based Accounting Online. The use of a dashboard on Web Based Online Accounting can be used to monitor user performance. Because the information presented on the dashboard can display things that happen to an Online Web Based Accounting account owned by an agency that includes actions to create, change and delete transactions, and can see if there are other users who log in. The method used in this study is to identify existing problems, then plan what should be done based on the data obtained, and design a system of information delivery to monitor performance. With the existence of this study in order to monitor the performance of employees as users of Web Based Online Accounting is expected to work optimally and not make mistakes that harm both parties.
Predicting ecosystem components in the Gulf of Mexico and their responses to climate variability with a dynamic Bayesian network model

The Gulf of Mexico is an ecologically and economically important marine ecosystem that is affected by a variety of natural and anthropogenic pressures. These complex and interacting pressures, together with the dynamic environment of the Gulf, present challenges for the effective management of its resources. The recent adoption of Bayesian networks to ecology allows for the discovery and quantification of complex interactions from data after making only a few assumptions about observations of the system. In this study, we apply Bayesian network models, with different levels of structural complexity and a varying number of hidden variables to account for uncertainty when modeling ecosystem dynamics. From these models, we predict focal ecosystem components within the Gulf of Mexico. The predictive ability of the models varied with their structure. The model that performed best was parameterized through data-driven learning techniques and accounted for multiple ecosystem components’ associations and their interactions with human and natural pressures over time. Then, we altered sea surface temperature in the best performing model to explore the response of different ecosystem components to increased temperature. The magnitude and even direction of predicted responses varied by ecosystem components due to heterogeneity in driving factors and their spatial overlap. Our findings suggest that due to varying components’ sensitivity to drivers, changes in temperature will potentially lead to trade-offs in terms of population productivity. We were able to discover meaningful interactions between ecosystem components and their environment and show how sensitive these relationships are to climate perturbations, which increases our understanding of the potential future response of the system to increasing temperature. Our findings demonstrate that accounting for additional sources of variation, by incorporating multiple interactions and pressures in the model layout, has the potential for gaining deeper insights into the structure and dynamics of ecosystems.
Characterizing Models of Informal Physics Programs

There has been an increase of funding agencies' investment in informal science education in recent years, resulting in significant growth of the field.However, little research has been done in discipline-based education research to determine the impact of informal physics programs and what makes them successful.While structures exist to assess the impact of informal learning, those are not yet robust enough to rigorously assess which programs work and, more critically, why they work.In this study, we used a non-profit organization framework as a lens to evaluate the 'success' of three informal physics programs in achieving their objectives and overall vision.To determine the practices and structures that most influence the 'success' of these programs, we conducted interviews with directors and coordinators of the programs, hosted at R1 institutions and identified initial indicators that can increase chances of informal physics programs to be 'successful'.
A Real-Time Upper-Body Robot Imitation System

An upper-body robot imitation (UBRI) system is proposed and developed to enable the human upper body imitation by a humanoid robot in real time. To achieve the imitation of arm motions, a geometry-based analytical method is presented and applied to extracting the joint angles of the human and mapping to the robot. Comparing to the traditional numerical methods of inverse kinematic computations, the geometrical analysis method generates a lower computational cost and maintains good imitation similarity. To map the human head motions to the head of the humanoid robot, a face tracking algorithm is employed to recognize the human face and track the human head poses in real time. A hand extraction and hand state recognition algorithm is proposed to achieve the hand motion mapping. At last, the completion rate and similarity evaluation experiments are conducted to verify the effectiveness of the proposed UBRI system.
Food intake gesture monitoring system based-on depth sensor

Food intake gesture technology is one of a new strategy for obesity people managing their health care while saving their time and money. This approach involves combining face and hand joint point for monitoring food intake of a user using Kinect Xbox One camera sensor. Rather than counting calories, scientists at Brigham Young University found dieters who eager to reduce their number of daily bites by 20 to 30 percent lost around two kilograms a month, regardless of what they ate [1]. Research studies showed that most of the methods used to count bite are worn type devices which has high false alarm ratio. Today trend is going toward the non-wearable device. This sensor is used to capture skeletal data of user while eating and train the data to capture the motion and movement while eating. There are specific joint to be capture such as Jaw face point and wrist roll joint. Overall accuracy is around 94%. Basically, this increase in the overall recognition rate of this system.
Social Media, Violence and the Law: ’Objectionable Material’ and the Changing Countours of Hate Speech Regulation in India

With the advent of the internet and increasing circulation of hate speech, and material that has been linked to public order disturbances, there has been a shift in the legal discourse around hate speech. What has emerged, especially post the striking down of section 66A of the Information Technology Act, are categories such as ‘objectionable’, ‘provocative’ content. The focus has shifted from the content itself, what it says, and the intention of the author, to being able to pre-empt the circulation of such material. Law is increasingly invoked to prevent speech (through prior restraint) rather than post facto investigation and prosecutions. This in turn has given rise to a range of institutional mechanisms such as monitoring labs that are now part of policing practice. Additionally, civil society organizations are now collaborating with police to help trigger mechanisms to take content off internet platforms. Increasingly it is through keywords and algorithmic searches that the category of hate speech has been defined rather than traditional legal doctrine. In the words of Lawrence Lessig, code plays the role of law, and the architecture of the internet becomes policy. This paper will examine the issues outlined above relying heavily on a series of interviews with lawyers, policy analysts, journalists, academics, civil society activists, and police personnel conducted in Delhi, Bengaluru, Mumbai and Pune.
Pedagogy of Productive Failure: Navigating the Challenges of Integrating VR into the Classroom

The surge of popular interest in virtual reality (VR), largely driven by recent advances making the hardware and software for VR development and use accessible to average consumers, is showing all the signs of a durable trend. One of those signs is active uptake in the academy, both for research (where it was pioneered) and in the classroom (where its footing is less certain). To be sure, VR offers exciting and relatively underdeveloped pedagogical terrain for teachers looking to enhance their curricula by deploying technologies that might optimize the learning process. This article offers a critical reflection on one such effort, specifically, our academic team’s grant-funded digital humanities research project called Focused Associational Thinking-Virtual Reality (FAT-VR). The main premise of the project was to create a virtual reality environment where students could cultivate creative fluency in divergent thinking. Such competencies are thought to afford students with a means of “thinking things together” in the service of transdisciplinary inquiry and problem solving. This essay recounts how, as we attempted to “move forward” by harnessing VR, we often found ourselves going “backward” and “in circles” due to technological glitches and challenging student feedback. Putting our digital pedagogy project in conversation with phenomenological philosophy and critical theory, we offer a provocation on how forward motion can sometimes set us back pedagogically, and how disorienting experiences—even failure—can become productive.