Reviewerly/exHarmony huggingface dataset:
A Mobile Application for Self-Guided Study of Formal Reasoning

In this work, we introduce AXolotl, a self-study aid designed to guide students through the basics of formal reasoning and term manipulation. Unlike most of the existing study aids for formal reasoning, AXolotl is an Android-based application with a simple touch-based interface. Part of the design goal was to minimize the possibility of user errors which distract from the learning process. Such as typos or inconsistent application of the provided rules. The system includes a zoomable proof viewer which displays the progress made so far and allows for storage of the completed proofs as a JPEG or LaTeX file. The software is available on the google play store and comes with a small library of problems. Additional problems may be opened in AXolotl using a simple input language. Currently, AXolotl supports problems that can be solved using rules which transform a single expression into a set of expressions. This covers educational scenarios found in our first-semester introduction to logic course and helps bridge the gap between propositional and first-order reasoning. Future developments will include rewrite rules which take a set of expressions and return a set of expressions, as well as a quantified first-order extension.
Explicit Alignment of Requirements and Architecture in Agile Development

[Context & Motivation] Requirements and architectural components are designed concurrently, with the former guiding the latter, and the latter restricting the former. [Question/problem] Effective communication between requirements engineers and software architects is often experienced as problematic. [Principal ideas/results] We present the Requirements Engineering for Software Architecture (RE4SA) model with the intention to support the communication within the development team. In RE4SA, requirements are expressed as epic stories and user stories, which are linked to modules and features, respectively, as their architectural counterparts. Additionally, we provide metrics to measure the alignment between these concepts, and we also discuss how to use the model and the usefulness of the metrics by applying both to case studies. [Contribution] The RE4SA model employs widely adopted notations and allows for explicitly relating a system’s requirements and architectural components, while the metrics make it possible to measure the alignment between requirements and architecture.
Monitoring Counterfeit Drugs using CounterChain

With the advent of globalisation, the average Indian consumer now has the option to shop for a wide range of products across multiple channels. However, with this increase in product choices, one of the significant issues confronted by the industry today is the proliferation of counterfeit drugs in the marketplace. Drug, being a product which is widely used by the entire population irrespective of the economic class, it is crucial to ensure the authenticity of the drugs. Customers belonging to low-income groups tend to buy medicines and drugs from small local shops. In this scenario, it is difficult to ensure that the products are authentic as this class of customers has minimal exposure to the technological facilities. The existence of counterfeit drugs in the supply chain is considered extremely lethal to public health by the World Health Organization. World Health Organization claims that a significant portion of drugs consumed in low-income and middle-income countries is substandard, which leads to certain dangerous side effects and even deaths. Brand owners, online and offline retailers alike, have always struggled with addressing the issue of counterfeit drugs, finding an effective solution to this issue and to maintain consumer confidence in goods sold by them. Counterfeit drugs have an economy-wide impact on trade, investment and most importantly, on the health and safety of consumers. The lack of collaboration between various stakeholders like brand owners, intermediaries, retailers, etc. leaves the drug supply chain vulnerable to infiltration of counterfeit drugs. This paper introduces the concept of CounterChain, a blockchain based technology to implement a supply chain wide unified solution to increase supply chain visibility and consumer safety.
An End-to-End Trainable Multi-Column CNN for Scene Recognition in Extremely Changing Environment

Scene recognition is an essential part in the vision-based robot navigation domain. The successful application of deep learning technology has triggered more extensive preliminary studies on scene recognition, which all use extracted features from networks that are trained for recognition tasks. In the paper, we interpret scene recognition as a region-based image retrieval problem and present a novel approach for scene recognition with an end-to-end trainable Multi-column convolutional neural network (MCNN) architecture. The proposed MCNN utilizes filters with receptive fields of different sizes to have Multi-level and Multi-layer image perception, and consists of three components: front-end, middle-end and back-end. The first seven layers VGG16 are taken as front-end for two-dimensional feature extraction, Inception-A is taken as the middle-end for deeper learning feature representation, and Large-Margin Softmax Loss (L-Softmax) is taken as the back-end for enhancing intra-class compactness and inter-class-separability. Extensive experiments have been conducted to evaluate the performance according to compare our proposed network to existing state-of-the-art methods. Experimental results on three popular datasets demonstrate the robustness and accuracy of our approach. To the best of our knowledge, the presented approach has not been applied for the scene recognition in literature.
The Implementation of Contextual Teaching Learning (CTL) to Improve the Students’ Speaking Ability in Islamic Studies Course

Contextual Teaching Learning (CTL) has been widely implemented in ELT. However, little used it specifically in speaking class in Islamic studies course. This research, which was a four-cycle action research conducted at the fourth semester English Language Education students at an Islamic university in Lampung, Indonesia aimed to find out how CTL can improve speaking skills especially in the subject of public speaking which includes storytelling, speech, News casting and drama in Speaking for Islamic Studies course. The data were obtained qualitatively through interviews, observation and filed notes during the learning process and also quantitatively through speaking tests for four cycles. Data were then analyzed by comparing the results of the pre-test, 55. 63 and the results of the post test after taking action in each cycle, namely storytelling gained 60.45, speech reached 64.9, news casting was 66.70, and the final drama reached 73.2. In addition, students tended to feel enthusiastic to work together in study groups and also thought critically, especially when connecting understanding the concept of language with the appropriate language context during their involvement in the speaking class. In other words, CTL could improve the students’ speaking abilities and also the quality of their learning activities. Thus, CTL could be implemented as one of the effective methods of speaking learning, especially those related to the Islamic context.
On Design-Theoretic Aspects of Boolean and Vectorial Bent Function

There are two construction methods of designs from $(n,m)$-bent functions, known as translation and addition designs. In this paper we analyze, which equivalence relation for Boolean bent functions, i.e. $(n,1)$-bent functions, and vectorial bent functions, i.e. $(n,m)$-bent functions with $2\le m\le n/2$, is coarser: extended-affine equivalence or isomorphism of associated translation and addition designs. First, we observe that similar to the Boolean bent functions, extended-affine equivalence of vectorial $(n,m)$-bent functions and isomorphism of addition designs are the same concepts for all even $n$ and $m\le n/2$. Further, we show that extended-affine inequivalent Boolean bent functions in $n$ variables, whose translation designs are isomorphic, exist for all $n\ge6$. This implies, that isomorphism of translation designs for Boolean bent functions is a coarser equivalence relation than extended-affine equivalence. However, we do not observe the same phenomenon for vectorial bent functions in a small number of variables. We classify and enumerate all vectorial bent functions in six variables and show, that in contrast to the Boolean case, one cannot exhibit isomorphic translation designs from extended-affine inequivalent vectorial $(6,m)$-bent functions with $m\in\{ 2,3 \}$.
Confidence-Guided Self Refinement for Action Prediction in Untrimmed Videos

Many existing methods formulate the action prediction task as recognizing early parts of actions in trimmed videos. In this paper, we focus on predicting actions from ongoing untrimmed videos where actions might not happen at the very beginning of videos. It is extremely challenging to predict actions in such untrimmed videos due to ambiguous or even no information of actions in the early parts of videos. To address this problem, we propose a prediction confidence that assesses the decision quality of a prediction model. Guided by the confidence, the model continuously refines the prediction results by itself with the increasing observed video frames. Specifically, we build a Self Prediction Refining Network (SPR-Net) which incrementally learns the confidence for action prediction. SPR-Net consists of three modules: a temporal hybrid network, an incremental confidence learner, and a self-refining Gumbel softmax sampler. The temporal hybrid network generates the action category distributions by integrating static scene and dynamic motion information. The incremental confidence learner calculates the confidence in an incremental manner, judging the extent to which the temporal hybrid network should believe its prediction result. The self-refining Gumbel softmax sampler models the mutual relationship between the prediction confidence and the category distribution, which enables them to be jointly learned in an end-to-end fashion. We also present a sparse self-attention mechanism to encode local spatio-temporal features into the frame-level motion representation to further improve the prediction performance. Extensive experiments on five datasets (i.e., UT-Interaction, BIT-Interaction, UCF101, THUMOS14, and ActivityNet) validate the effectiveness of the proposed method.
CNNs for Fine-Grained Car Model Classification

This paper describes an end-to-end training methodology for CNN-based fine-grained vehicle model classification. The method relies exclusively on images, without using complicated architectures. No extra annotations, pose normalization or part localization are needed. Different full CNN-based models are trained and validated using CompCars [31] dataset, for a total of 431 different car models. We obtained a top-1 validation accuracy of 97.62% which substantially outperforms previous works.
Distributed Learning and Inference With Compressed Images

Modern computer vision requires processing large amounts of data, both while training the model and/or during inference, once the model is deployed.Scenarios where images are captured and processed in physically separated locations are increasingly common (e.g.autonomous vehicles, cloud computing, smartphones).In addition, many devices suffer from limited resources to store or transmit data (e.g.storage space, channel capacity).In these scenarios, lossy image compression plays a crucial role to effectively increase the number of images collected under such constraints.However, lossy compression entails some undesired degradation of the data that may harm the performance of the downstream analysis task at hand, since important semantic information may be lost in the process.Moreover, we may only have compressed images at training time but are able to use original images at inference time (i.e.test), or vice versa, and in such a case, the downstream model suffers from covariate shift.In this paper, we analyze this phenomenon, with a special focus on vision-based perception for autonomous driving as a paradigmatic scenario.We see that loss of semantic information and covariate shift do indeed exist, resulting in a drop in performance that depends on the compression rate.In order to address the problem, we propose dataset restoration, based on image restoration with generative adversarial networks (GANs).Our method is agnostic to both the particular image compression method and the downstream task; and has the advantage of not adding additional cost to the deployed models, which is particularly important in resourcelimited devices.The presented experiments focus on semantic segmentation as a challenging use case, cover a broad range of compression rates and diverse datasets, and show how our method is able to significantly alleviate the negative effects of compression on the downstream visual task.
Towards classification of email through selection of informative features

Dodging spam filters by spammers has become a serious issue in classifying emails as spam or ham. An extremely high dimensionality of emails sent/received is one of the major characteristic of email classification. To train a classifier without decreasing its capability of prediction is important in classification of emails. Feature selection method plays an important role in identifying most relevant features to classify emails by inventing new techniques. The features are extracted based on the relationship established between the words of various classes and it helps to increase the probability of the words to represent informative features. The words can be either of positive or negative in nature. In this paper, relationship among the words present in subject and content of emails has been used to determine the nature of the word and then selected the most related words to form informative features from set of all words. These words are then used to generate the N-grams. Four different classifiers namely Decision tree, Multinomial Naive Bayes, Random Forest classifiers, Linear Support Vector Machine classifiers have been used to determine the performance of the selected N-Grams features. The experimental analysis has been performed over Ling-Spam email dataset.
Designing an Intelligent User Interface for Preventing Phishing Attacks

Most phishing sites are simply copies of real sites with slight features distorted or in some cases masqueraded. This property of phishing sites has made them difficult for humans and various anti-phishing techniques to detect. Also, the attacker community has proved itself able to quickly adapt to anti-phishing measures, mainly warning messages to help limit the effectiveness of phishing attacks and protect unsuspecting users. Despite the notable advances made in the last years by the active warning messages for phishing, this attack remains one of the most effective. In this paper we propose an intelligent warning message mechanism, that might limit the effectiveness of phishing attacks and that might increase the user awareness about related risks. It implements an intelligent behavior that, besides warning the users that a phishing attack is occurring, explains why the specific suspect site can be fraudulent.
Constructing Infinitary Quotient-Inductive Types

Abstract This paper introduces an expressive class of quotient-inductive types, called QW-types. We show that in dependent type theory with uniqueness of identity proofs, even the infinitary case of QW-types can be encoded using the combination of inductive-inductive definitions involving strictly positive occurrences of Hofmann-style quotient types, and Abel’s size types. The latter, which provide a convenient constructive abstraction of what classically would be accomplished with transfinite ordinals, are used to prove termination of the recursive definitions of the elimination and computation properties of our encoding of QW-types. The development is formalized using the Agda theorem prover.
Basic Guidelines, Principles and Psychological-Pedagogical Technologies of Creation of the Engineer of the Future

The article considers reflections on the principles and technologies of forming an engineer in modern reality and with a focus on the future.An engineer is analyzed as a professional who is directly involved in changing and creating the physical (and virtual) environment.The results of research on activity styles in psychology are used to explain the importance of training engineers based on an environmental resource approach to ensure the well-being of mankind. It can be achieved by creating and maintaining a flexible environment for engineers that adapts to a person and expands their capabilities by compensating for health limitations and (or) underdevelopment of professionally important qualities.The article formulates the principles of designing interdisciplinary interactive technologies (individualization, humanization, and ensuring the ability to make sustainable professional decisions) for creating and conducting interdisciplinary courses and presents modified individual psychological and pedagogical interactive technologies for solving modern educational problems in the process of training engineers.The possibilities of using psychological disciplines as a basis for creating interdisciplinary courses are discussed and demonstrated. The author substantiates the possibility of forming the ability to make sustainable professional decisions (based on the development of reflection of professional activity) through training in the format of an interdisciplinary course. The article presents the results of students’ self-assessment of General cultural and professional skills and competencies after completing the course to demonstrate the high efficiency of the applied technology. The experience of developing and implementing interdisciplinary courses is analyzed.
Face Attribute Modification Using Fine-Tuned Attribute-Modification Network

Multi-domain image-to-image translation with the desired attributes is an important approach for modifying single or multiple attributes of a face image, but is still a challenging task in the computer vision field. Previous methods were based on either attribute-independent or attribute-dependent approaches. The attribute-independent approach, in which the modification is performed in the latent representation, has performance limitations because it requires paired data for changing the desired attributes. In contrast, the attribute-dependent approach is effective because it can modify the required features while maintaining the information in the given image. However, the attribute-dependent approach is sensitive to attribute modifications performed while preserving the face identity, and requires a careful model design for generating high-quality results. To address this problem, we propose a fine-tuned attribute modification network (FTAMN). The FTAMN comprises a single generator and two discriminators. The discriminators use the modified image in two configurations with the binary attributes to fine tune the generator such that the generator can generate high-quality attribute-modification results. Experimental results obtained using the CelebA dataset verify the feasibility and effectiveness of the proposed FTAMN for editing multiple facial attributes while preserving the other details.
Single photon generation and non-locality of perfect W-state

We study the generation of single photon perfect W-state.An important aspect of this perfect W-state is that, it can be used for perfect teleportation and superdense coding, which are not achievable with maximally entangled W-state.Our scheme for generation involves entanglement between various path degrees of freedom of a single photon in a compact and weakly coupled integrated waveguide system, which can be fabricated precisely with femtosecond laser direct writing technique.These platforms are interferometrically stable, scalable, less sensitive to decoherence and ensures a very low loss factor of 0.1dBcm -1 during photon propagation and hence are ideal for generation of perfect W-state.In addition to generation of single photon perfect W-state we study its non local properties using theory of local elements of reality.
Semantics-Aware Inferential Network for Natural Language Understanding

For natural language understanding tasks, either machine reading comprehension or natural language inference, both semantics-aware and inference are favorable features of the concerned modeling for better understanding performance. Thus we propose a Semantics-Aware Inferential Network (SAIN) to meet such a motivation. Taking explicit contextualized semantics as a complementary input, the inferential module of SAIN enables a series of reasoning steps over semantic clues through an attention mechanism. By stringing these steps, the inferential network effectively learns to perform iterative reasoning which incorporates both explicit semantics and contextualized representations. In terms of well pre-trained language models as front-end encoder, our model achieves significant improvement on 11 tasks including machine reading comprehension and natural language inference.
Parameter tuning of big data platforms for performance optimization

The data processing platforms make use of distributed systems to process and store the big data efficiently. These big data platforms have more than hundreds of configurable parameters, which are currently tuned based on intuition and experience. Finding the optimum values from the set of exponential combinations of such values and relevant parameter selection is a tedious process. In the proposed work, this issue is addressed for three Apache big data platforms, namely Hadoop, Spark and Storm. The most significant parameters shortlisted using various feature selection approaches are tuned. Iterative runs of applications are executed for tuning these parameters and to identify the optimal value to examine the individual impact of resultant parameters. The empirical results depict significant reduction in job execution time of Hadoop and Spark and increase in number of tuples emitted for Storm thus showing the optimised performance of the data platforms.
Anomalies of the weight-based coherence measure and mixed maximally coherent states

As an analogy of best separable approximation (BSA) in the framework of entanglement theory, here we concentrate on the notion of best incoherent approximation, with application to characterizing and quantifying quantum coherence. From both analytical and numerical perspectives, we have demonstrated that the weight-based coherence measure displays some unusual properties, in sharp contrast to other popular coherence quantifiers. First, by deriving a closed formula for qubit states, we have showed the weight-based coherence measure exhibits a rich (geometrical) structure even in this simplest case. Second, we have identified the existence of mixed maximally coherent states (MMCS) with respect to this coherence measure and discussed the characteristic feature of MMCS in high-dimensional Hilbert spaces. Especially, we present several important families of MMCS by gaining insights from the numerical simulations. Moreover, it is pointed out that some considerations in this work can be generalized to general convex resource theories and a numerical method of improving the computational efficiency for finding the BSA is also discussed.
Research on English spoken semantic recognition machine learning model based on neural network and statistics fusion

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218.