Reviewerly/exHarmony huggingface dataset:
Computer-Aided Design as Language

Computer-Aided Design (CAD) applications are used in manufacturing to model everything from coffee mugs to sports cars. These programs are complex and require years of training and experience to master. A component of all CAD models particularly difficult to make are the highly structured 2D sketches that lie at the heart of every 3D construction. In this work, we propose a machine learning model capable of automatically generating such sketches. Through this, we pave the way for developing intelligent tools that would help engineers create better designs with less effort. Our method is a combination of a general-purpose language modeling technique alongside an off-the-shelf data serialization protocol. We show that our approach has enough flexibility to accommodate the complexity of the domain and performs well for both unconditional synthesis and image-to-sketch translation.
A Large-Scale Study of Machine Translation in Turkic Languages

Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of NMT. In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios. In addition to presenting an extensive analysis that identifies the bottlenecks towards building competitive systems to ameliorate data scarcity, our study has several key contributions, including, i) a large parallel corpus covering 22 Turkic languages consisting of common public datasets in combination with new datasets of approximately 1.4 million parallel sentences, ii) bilingual baselines for 26 language pairs, iii) novel high-quality test sets in three different translation domains and iv) human evaluation scores. All models, scripts, and data will be released to the public.
A Light-Weight White-Box Encryption Scheme for Securing Distributed Embedded Devices

Embedded devices distributed widely in sensor networks and the Internet of Things is used to collect and send data. Many of them are deployed unattended (for example, sensor nodes and tag readers), while others can be easily lost (for example, bracelets and smart watches). This distributed hardware can be captured and accessed unauthorized due to its physical nature. From a security perspective, they generally work in the context of a white square attack, where opponents have full visibility of deployed cryptographic systems deployments and full control over their implementations. Undoubtedly, it is a big challenge to deal with white box attacks on these devices. Current encryption algorithms for white box attack contexts require a large amount of memory space and are therefore unsuitable for embedded devices with limited resources. To meet this challenge, we suggest a new lightweight encryption plan to protect data confidentiality. Encryption is done using specialized confidential components, and the encryption algorithm requires a small amount of static data to store critical information. Also, this scheme supports only effective key update at a very small COST. The safety and cost of the proposed scheme have been theoretically analyzed with positive results, and extensive empirical assessments indicate that the new scheme meets the requirements of distributed hardware combined in terms of limited memory usage and low arithmetic cost.How to cite this article:Shridevi, Dakulagi V. A Light-Weight White-Box Encryption Scheme for Securing Distributed Embedded Devices. J Adv Res Embed Sys 2020; 7(3&4): 24-28.
On Ordinary Words of Standard Reed–Solomon Codes over Finite Fields

Reed–Solomon codes are widely used to establish a reliable channel to transmit information in digital communication which has a strong error correction capability and a variety of efficient decoding algorithm. Usually we use the maximum likelihood decoding (MLD) algorithm in the decoding process of Reed–Solomon codes. MLD algorithm relies on determining the error distance of received word. Dür, Guruswami, Wan, Li, Hong, Wu, Yue and Zhu et al. got some results on the error distance. For the Reed–Solomon code [Formula: see text], the received word [Formula: see text] is called an ordinary word of [Formula: see text] if the error distance [Formula: see text] with [Formula: see text] being the Lagrange interpolation polynomial of [Formula: see text]. We introduce a new method of studying the ordinary words. In fact, we make use of the result obtained by Y.C. Xu and S.F. Hong on the decomposition of certain polynomials over the finite field to determine all the ordinary words of the standard Reed–Solomon codes over the finite field of [Formula: see text] elements. This completely answers an open problem raised by Li and Wan in [On the subset sum problem over finite fields, Finite Fields Appl. 14 (2008) 911–929].
CRITICAL THINKING AND INFORMATION LITERACY

The article is dedicated to the critical thinking position in the structure of such an important competence of a contemporary person as information literacy. In the context of the information literacy analysis, the interconnection between the concepts of «information» and «knowledge» is studied. Based on the consideration of the critical thinking properties, its significance is revealed at each stage of information activity.
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

Bounding box (bbox) regression is a fundamental task in computer vision. So far, the most commonly used loss functions for bbox regression are the Intersection over Union (IoU) loss and its variants. In this paper, we generalize existing IoU-based losses to a new family of power IoU losses that have a power IoU term and an additional power regularization term with a single power parameter $\alpha$. We call this new family of losses the $\alpha$-IoU losses and analyze properties such as order preservingness and loss/gradient reweighting. Experiments on multiple object detection benchmarks and models demonstrate that $\alpha$-IoU losses, 1) can surpass existing IoU-based losses by a noticeable performance margin; 2) offer detectors more flexibility in achieving different levels of bbox regression accuracy by modulating $\alpha$; and 3) are more robust to small datasets and noisy bboxes.
Data Transaction Pricing Model for Energy Big Data Centers

This paper studies and proposes an energy data transaction method based on the combination of multi-layer hierarchical fusion fuzzy feature mapping method (MLHFFFM) and long short-term memory neural network (LSTM). First, analyze the big data collected to determine the type of missing data. On this basis, MLHFFFM and fuzzy C-means algorithm (FCM) are used to cluster electricity big data, and the LSTM neural network is trained based on the characteristic values of different users and historical data, and different pricing strategies are designed for different data types. Finally, the feasibility and reliability of the improved method proposed in this paper are verified through the data analysis and comparison experiment of the public data set.
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial

In this paper, we answer the question when inserting label noise (less informative labels) can instead return us more accurate and fair models. We are primarily inspired by two observations that 1) increasing a certain class of instances' label noise to balance the noise rates (increasing-to-balancing) results in an easier learning problem; 2) Increasing-to-balancing improves fairness guarantees against label bias. In this paper, we will first quantify the trade-offs introduced by increasing a certain group of instances' label noise rate w.r.t. the learning difficulties and performance guarantees. We analytically demonstrate when such an increase proves to be beneficial, in terms of either improved generalization errors or the fairness guarantees. Then we present a method to leverage our idea of inserting label noise for the task of learning with noisy labels, either without or with a fairness constraint. The primary technical challenge we face is due to the fact that we would not know which data instances are suffering from higher noise, and we would not have the ground truth labels to verify any possible hypothesis. We propose a detection method that informs us which group of labels might suffer from higher noise, without using ground truth information. We formally establish the effectiveness of the proposed solution and demonstrate it with extensive experiments.
THE INFLUENCE OF INSTRUCTIONAL VIDEO MEDIA ON STUDENT LEARNING OUTCOMES IN CLASS IV SUBJECTS SBdP AT SDN TONDO

Video learning media is an alternative that can increase student participation in learning, so that students can think actively. The formulation of the research problem is how the student learning outcomes using video media and student learning outcomes who do not use video media at SDN Tondo. Are there differences in student learning outcomes taught using video media with student learning outcomes taught not using video at SDN Tondo. How do students respond to learning using video media and student responses who do not use video media at SDN Tondo. The results showed that the learning outcomes of the experimental and control classes were categorized as very high, where the percentage of the experimental class was 86.63% and the control class was 77.53%. There are differences in the learning outcomes of the experimental and control classes in SBdP subjects, it can be seen with the sig-value t-test. (2-tailed) for the experimental class of 0.014, greater than the significance level of 0.005 (0.014> 0.005) as well as the sig results. (2-tailed) for the control class of 0.018, which is greater than the significance level of 0.005 (0.018> 0.005), which means that these two have significant differences. The t value of the Experiment class is 2.545, which is greater than the t table (1.70562) as well as the t value of the Control class, which is 2.467, which is greater than the t table (1.71714), meaning that there is a significant difference. Thus it can be concluded that H0 is rejected and H1 is accepted. From the research results, it was concluded that learning using video media could affect student learning outcomes in SBdP subjects in class IV SDN Tondo.
Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence

This paper proposes a transformer over transformer framework, called Transformerˆ2, to perform neural text segmentation. It consists of two components: bottom-level sentence encoders using pre-trained transformers, and an upper-level transformer-based segmentation model based on the sentence embeddings. The bottom-level component transfers the pre-trained knowledge learnt from large external corpora under both single and pair-wise supervised NLP tasks to model the sentence embeddings for the documents. Given the sentence embeddings, the upper-level transformer is trained to recover the segmentation boundaries as well as the topic labels of each sentence. Equipped with a multi-task loss and the pre-trained knowledge, Transformerˆ2 can better capture the semantic coherence within the same segments. Our experiments show that (1) Transformerˆ2$manages to surpass state-of-the-art text segmentation models in terms of a commonly-used semantic coherence measure; (2) in most cases, both single and pair-wise pre-trained knowledge contribute to the model performance; (3) bottom-level sentence encoders pre-trained on specific languages yield better performance than those pre-trained on specific domains.
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation

Tsetlin Machine (TM) is an interpretable pattern recognition algorithm based on propositional logic, which has demonstrated competitive performance in many Natural Language Processing (NLP) tasks, including sentiment analysis, text classification, and Word Sense Disambiguation. To obtain human-level interpretability, legacy TM employs Boolean input features such as bag-of-words (BOW). However, the BOW representation makes it difficult to use any pre-trained information, for instance, word2vec and GloVe word representations. This restriction has constrained the performance of TM compared to deep neural networks (DNNs) in NLP. To reduce the performance gap, in this paper, we propose a novel way of using pre-trained word representations for TM. The approach significantly enhances the performance and interpretability of TM. We achieve this by extracting semantically related words from pre-trained word representations as input features to the TM. Our experiments show that the accuracy of the proposed approach is significantly higher than the previous BOW-based TM, reaching the level of DNN-based models.
Image Enhancement for Colour Deficiency via Gamut Mapping

This paper describes a colour image enhancement method for those having colour-vision deficiencies. The proposed method can be divided into 3 stages. Firstly, a conversion relation between the wavelength shift (measured in nanometers) of a colour deficient observer (CDO) and the severity of colour deficiency was established. Secondly, the perceived colour gamut was built by applying the conversion relation. Finally, the original images were re-coloured by adopting a gamut mapping algorithm to map colours from the gamut of colour normal observer (CNO) to that of a CDO. Psychophysical experiments were then conducted to show the effectiveness of the method.
An Early Warning Model of Cybercrime Based on User Profile

With the development of telecom and Internet technologies, new types of crimes on telecom network have become one new normal thing. According to the <Report on the security of mobile phone in the first half of 2020>, The loss per victim has reached 10037RMB in the first half of 2020. Since population China is too large to be analyzed individually, and the current anti-fraud early warning mechanisms is lack of pertinence, this project believes that the comparison of group characteristics can effectively detect the type of user may be deceived and help the user successfully avoid risks. According to publicly available data from Weibo, the project proposed an early warning fraud scheme based on the user’s profile. First, obtain users’ data of Weibo through technologies such as web crawlers and construct user profile who has suffered with telecom fraud. Then, train the deep neural network on the multidimensional data set. The data set we had obtained was used as small sample which have label to train the scheme. On this basis, the project developed out a Wechat applet based on user profile which can give a warning of telecom network fraud. When users input their personal information on the client side, the server side database can conduct correlation analysis by itself, match the most likely type of fraud and give relevant cases to warn, so as to achieve precise warning.
Hash Layers For Large Sparse Models

We investigate the training of sparse layers that use different parameters for different inputs based on hashing in large Transformer models. Specifically, we modify the feedforward layer to hash to different sets of weights depending on the current token, over all tokens in the sequence. We show that this procedure either outperforms or is competitive with learning-to-route mixture-of-expert methods such as Switch Transformers and BASE Layers, while requiring no routing parameters or extra terms in the objective function such as a load balancing loss, and no sophisticated assignment algorithm. We study the performance of different hashing techniques, hash sizes and input features, and show that balanced and random hashes focused on the most local features work best, compared to either learning clusters or using longer-range context. We show our approach works well both on large language modeling and dialogue tasks, and on downstream fine-tuning tasks.
Differential Privacy Over Riemannian Manifolds

In this work we consider the problem of releasing a differentially privatestatistical summary that resides on a Riemannian manifold. We present anextension of the Laplace or K-norm mechanism that utilizes intrinsic distancesand volumes on the manifold. We also consider in detail the specific case wherethe summary is the Fr\'echet mean of data residing on a manifold. Wedemonstrate that our mechanism is rate optimal and depends only on thedimension of the manifold, not on the dimension of any ambient space, whilealso showing how ignoring the manifold structure can decrease the utility ofthe sanitized summary. We illustrate our framework in two examples ofparticular interest in statistics: the space of symmetric positive definitematrices, which is used for covariance matrices, and the sphere, which can beused as a space for modeling discrete distributions.
Quantum Machine Learning for Earth observation

Our objectives are to employ the current methodologies and achievements made in emergent Quantum Computing to real-world problems in Earth observation; in particular, to enlarge and to broaden Earth Observation methodologies by using a D-Wave quantum annealer and a gate-based quantum computer.
Overview of the 2021 Key Point Analysis Shared Task

We describe the 2021 Key Point Analysis (KPA-2021) shared task on key point analysis that we organized as a part of the 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP 2021. We outline various approaches and discuss the results of the shared task. We expect the task and the findings reported in this paper to be relevant for researchers working on text summarization and argument mining.
CAMERA, a Cellular Automata Modulation for Estimation Replica Applications

The CAMERA method is present in the proposed paper, which not only capture image but also it produced audio signals existing in the captured image. This paper presents a method for developing a device which is helpful for the disabled persons who are unable to see environmental objects but they can listens voice. The primary objective of the proposed method is to extract existing objects of the image and produced relevant output in voice format without delaying. Conversion of voice output is very big challenge but the CAMERA method performs their functions in fastest manner. The proposed method uses various image related applications like segmentation, edge detection and corner detection for object extraction. The visibility disability persons can easily use the system due to user friendly nature and such type of persons can perform their daily functions easily without others help. The cellular automata approach is used in the proposed method to achieve objectives. The cellular automata uses frequency modulation to read all possible considerable cells of captured image to provide exact objects and provides desired output using amplitude modulation  without delaying any movement.
Evaluation of the timing covert channel capacity considering packet transfer time distribution

Lampson was the first to introduce a covert channel as a channel that was not designed for information transmission. The problem of information leakage via network covert channels has a large scale due to the facts that IP protocol is widely used and has a lot of features to use it for hidden information transmission. Usually covert channels are divided into two groups by transmission technic: storage and timing covert channels. In the paper authors provide brief survey for network timing and storage covert channels as well as methods of information leakage counteraction. According to best practices, information systems and infrastructure have an information security policy with the requirements about allowable level of covert channel capacity. However, to take a decision about any method activation it is important not to allow underestimation of covert channel capacity. For the effective prevention of information leakage via network covert channels authors suggest a way to assess timing covert channel capacity. Two binary timing channels have been investigated: on/off and channel based on inter packet intervals modulation. In on/off covert channel the sender sends a packet during a preliminarily agreed time interval to transmit the bit «1» and does not send to transmit the bit «0». In a covert channel based on inter packet intervals modulation the sender sends packets with different time intervals defining different bits. The scientific novelty consists in taking into account network load conditions while assessing maximum amount of information that can be stealthily transmitted from secure infrastructure to an illegitimate receiver beyond secure perimeter. Authors investigated cases when packet transfer time from the sender to the receiver in the network (PTT) is defined by normal and exponential distribution – the most common distribution according to current research. Covert channel capacity is evaluated as a function of covert channel parameters and parameters of the PTT distribution (DPTT). Conducted research shows that in case when secure officer does not take into account typical load for the network and DPTT type maximum covert channel capacity will most likely be underestimated. If allowable level of covert channel capacity is set up, obtained results allow to take right decision about activation of countermeasures to prevent information leakage.