Reviewerly/exHarmony huggingface dataset:
The Acceptability Delta Criterion: Testing Knowledge of Language using the Gradience of Sentence Acceptability

Any test that promises to assess Human Knowledge of Language (KoL) for any statistically-based Language Model (LM) must meet three requirements: (1) comprehensive coverage of linguistic phenomena;(2) replicable and statistically-vetted human judgement data; and (3) test the LM's ability to track the gradience of sentence acceptability.To this end, we propose here the LI-Adger dataset: a comprehensive collection of 519 sentence types (4177 sentences) spanning the field of current generative linguistics, accompanied by attested and replicable human acceptability judgements (Sprouse and Almeida, 2012; Sprouse et al., 2013;Sprouse and Almeida, 2017).Finally, we posit the Acceptability Delta Criterion (ADC), an evaluation metric that tests how well a LM can track changes in human acceptability judgements across minimal pairs instead of testing whether the LM assigned a greater likelihood to the expert-labeled acceptable sequence of a minimal pair (S 1 > S 2 ).We benchmark six different BERT (Devlin et al., 2018) models and a baseline trigram model with the ADC.Although the best performing BERT model scores 94%, and the trigram scores 75% classification accuracy under the traditional metric, performance drops precipitously to 38% for BERT and 30% for the trigram model under the ADC.Adopting the ADC reveals how much harder it is for LMs to track the gradience of acceptability across minimal pairs.With this work, we propose and provide the three necessary requirements for a comprehensive linguistic analysis and test of the apparently Human KoL exhibited by LMs that we believe is currently missing in the field of Computational Linguistics.
Unsupervised Adverbial Identification in Modern Chinese Literature

In many languages, adverbials can be derived from words of various parts-of-speech.In Chinese, the derivation may be marked either with the standard adverbial marker DI, or the non-standard marker DE.Since DE also serves double duty as the attributive marker, accurate identification of adverbials requires disambiguation of its syntactic role.As parsers are trained predominantly on texts using the standard adverbial marker DI, they often fail to recognize adverbials suffixed with the non-standard DE.This paper addresses this problem with an unsupervised, rule-based approach for adverbial identification that utilizes dependency tree patterns.Experiment results show that this approach outperforms a masked language model baseline.We apply this approach to analyze standard and non-standard adverbial marker usage in modern Chinese literature.
Exploration of innovative and entrepreneurship Teaching Mode in Application-oriented Universities

In 2021, our university was selected as one of the first batch of universities supported by the construction of Application-oriented universities in Shandong Province.The construction of application-oriented universities has many contents and involves a wide range.However, in general, the cultivation of application-oriented talents is the core, and the key to the cultivation of applicationoriented talents is to improve students' application and practice ability and innovation ability.Therefore, how to cultivate students' ability of innovation and entrepreneurship in local applicationoriented universities has become an urgent problem for us to solve.This paper puts forward some suggestions on the teaching mode of innovation and entrepreneurship in application-oriented universities in our university, hoping to be of some help to the education mode of innovation and entrepreneurship in application-oriented universities.
LBP‐based progressive feature aggregation network for low‐light image enhancement

At night or in other low-illumination environments, optical imaging devices cannot capture details and color information in images accurately because of the reduced number of photons captured and the low signal-to-noise ratio. Consequently, the image is very noisy with low contrast and inaccurate color information, which affects human visual perception and creates significant challenges in computer vision tasks. Low-light image enhancement has great research value because it aims to reduce image noise and improve image quality. In this study, we propose an LBP-based progressive feature aggregation network (P-FANet) for low-light image enhancement. The LBP feature has insensitivity to illumination, and it contains rich texture information. In the network, we input the LBP feature into each iteration of the network in an accompanying manner, which helps to restore some detailed information of the low-light image. First, we input the low-light image into the dual attention mechanism model to extract global features. Second, the extracted different features enter the feature aggregation module (FAM) for feature fusion. Third, we use the recurrent layer to share the features extracted at different stages, and use the residual layer to further extract deeper features. Finally, the enhanced image is output. The rationality of the method in this study has been verified through ablation experiments. Many experimental results show that the method in this study has greater advantages in subjective and objective evaluations compared with many other advanced methods.
Types of Sentences Used by Male and Female Writers in Journal Article Abstracts

ABSTRACTThis study analyzed the types of sentences used in the Abstracts of Journal Articles written by 10 Female and 10 Male fully sourced from the Book Program of The First Annual International Seminar UNIMED Medan: November 19, 2016. Of the 195 sentences, 102 of which were written by females, and 92 by Males, all types of sentences are present. The occurrence of the types of sentence used by Females is Simple Sentence (50.1%), Complex Sentence (37.1%), Complex - Compound Sentence (6.8%), and Compound Sentence (4.9%). By Males , Simple Sentence is (50.%), Complex Sentence (41%), Compound Sentence (5.4%) and Complex- Compound Sentence (3.3%). Thus, this proves that the frequency is only different in Complex-Compound, i.e. by Females in the third place, but by Males in the fourth and Compound in the third. Overall, it can be concluded that gender has no relation with sentence types.Key Words: Types of sentence; Female and Male; Abstracts
BOOKLET DEVELOPMENT OF THE OFFICE OF GENERAL EDUCATION AND INNOVATIVE ELECTRONIC LEARNING

The objective of this study is to study the development of the brochure of the Office of General Education and Electronic Learning Innovation, and to study the opinions of students on the brochure of the Office of General Education and Electronic Learning Innovation. The agency has produced a public relations brochure for various activities that the agency has conducted in the past and can also be kept as department data. The population used in this study are undergraduate students enrolled in general education courses for the 1/2018 academic year, consisting of 35,405 people used to collect data using questionnaires and statistics used to analyze data such as percentage, average and standard deviation. The results of the study 1) Make students aware of the opinions of the academic brochure, Office of General Education and Electronic Learning Innovation. 2) Know the opinions of students and apply various suggestions in order to improve the booklet to create more modern and interesting to readers, and 3) Develop the academic booklet for the Office of General Education and the innovation of electronic learning that is effective for students and those who are interested, easy to understand and make a good impression.
Morning Routines between Calm and Engaging: Designing a Smart Mirror

Frequently the main purpose of domestic artifacts equipped with smart sensors is to hide technology, like previous examples of a Smart Mirror show. However, current Smart Homes often fail to provide meaningful IoT applications for all residents’ needs. To design beyond efficiency and productivity, we propose to realize the potential of the traditional artifact for calm and engaging experiences. Therefore, we followed a design case study approach with 22 participants in total. After an initial focus group, we conducted a diary study to examine home routines and developed a conceptual design. The evaluation of our mid-fidelity prototype shows, that we need to study carefully the practices of the residents to leverage the physical material of the artifact to fit the routines. Our Smart Mirror, enhanced by digital qualities, supports meaningful activities and makes the bathroom more appealing. Thereby, we discuss domestic technology design beyond automation.
Eliciting Bias in Question Answering Models through Ambiguity

Deep learning models have shown great success in question answering (QA), however, biases in the training data may lead to them amplifying or reflecting inequity.To probe for bias in QA systems, we create two benchmarks for closed and open domain question answering, consisting of ambiguous questions and bias metrics.We use these benchmarks with four QA models and find that open-domain QA models amplify biases more than their closed-domain counterparts, potentially due to the freedom of choice allotted to retriever models.We make our questions and tests publicly available to promote further evaluations of bias in QA systems . 1
On the Hardness of Robust Classification

It is becoming increasingly important to understand the vulnerability of machine learning models to adversarial attacks. In this paper we study the feasibility of robust learning from the perspective of computational learning theory, considering both sample and computational complexity. In particular, our definition of robust learnability requires polynomial sample complexity. We start with two negative results. We show that no non-trivial concept class can be robustly learned in the distribution-free setting against an adversary who can perturb just a single input bit. We show moreover that the class of monotone conjunctions cannot be robustly learned under the uniform distribution against an adversary who can perturb $\omega(\log n)$ input bits. However if the adversary is restricted to perturbing $O(\log n)$ bits, then the class of monotone conjunctions can be robustly learned with respect to a general class of distributions (that includes the uniform distribution). Finally, we provide a simple proof of the computational hardness of robust learning on the boolean hypercube. Unlike previous results of this nature, our result does not rely on another computational model (e.g. the statistical query model) nor on any hardness assumption other than the existence of a hard learning problem in the PAC framework.
Mobile Proximity Channel Using Vibration

Mobile phones are becoming a common tool for people to make the payments. The current peer-to-peer mobile based payment system allows two parties to use physical location-limited interaction between their devices to facilitate secure payment. This physical location-limited interaction is ensured by enabling wireless proximity through Near Field Communication (NFC) technology embedded in mobile phones. However, the NFC technology is vulnerable to relay attacks which is a very challenging security problem. This paper proposes a novel new proximity channel based on vibration for peer-to-peer mobile payment system. The built-in vibrator and accelerometer on the phone are used to send and receive the information. The communication ensures the payment is conducted at a close distance. This paper also compares and analyse the method with the other method such as the phone vibration data and encrypt One Time Password (OTP) as the vibration pattern for payment. The result shows that the proposed method is able retrieve the information successfully from the other smartphones and guarantees the transaction is safe from relay attacks and eavesdropping.
Gesture Based Robot Arm Control

Robotics is a constantly evolving field nowadays. A robot is a mechanical device that can perform physical tasks while being controlled and supervised by humans. Several robots have been created to perform risky tasks that are impossible for humans to perform directly. The robot arm is one of the most popular types. In this project, it is proposed to control a robot arm using finger and hand movements. However, the traditional way of gesture control employs sensor-based gloves to track motion, which is time and energy intensive due to its weight. To overcome this problem, using vision for motion tracking can be the most suited method at hand currently. The ML based computer vision method provides us with the real-time landmark on different points of the hand. With these landmarks we can calculate various mathematical parameters that can be used for the control of the robot arm. Movements that are landmarked, are processed and formulated using the mathematical parameters. The final output of mathematical parameters fed to the microcontroller. Signals from the microcontroller will be the desired input signal to the motors. The rotation of the robot arm is controlled using movement of the human arm. The end application of this project is to be as similar as human operation in dull, dirty and dangerous environments. The project's major goal is to protect those who come into direct contact with these environments. Bomb defusing, painting, welding, and other applications are a few examples of where this project is useful.
Fast, Accurate, and Automated 3D Reconstruction Using a Depth Camera Mounted on an Industrial Robot

Abstract 3D reconstruction technology is used in a wide variety of applications. Currently, automatically creating accurate pointclouds for large parts requires expensive hardware. We are interested in using low-cost depth cameras mounted on commonly available industrial robots to create accurate pointclouds for large parts automatically. Manufacturing applications require fast cycle times. Therefore, we are interested in speeding up the 3D reconstruction process. We present algorithmic advances in 3D reconstruction that achieve a sub-millimeter accuracy using a low-cost depth camera. Our system can be used to determine a pointcloud model of large and complex parts. Advances in camera calibration, cycle time reduction for pointcloud capturing, and uncertainty estimation are made in this work. We continuously capture point-clouds at an optimal camera location with respect to part distance during robot motion execution. The redundancy in pointclouds achieved by the moving camera significantly reduces errors in measurements without increasing cycle time. Our system produces sub-millimeter accuracy.
Extra-Class Activities to Support the Energy Education of Students of the Transport Operation Specialty

The demand of energy increases every day and constitutes one of the main worries for several fields of the science, especially for environmental issues.The knowledge of energetic sources and how they are used has a great importance for Cuban Education System and particularly for Technical and Professional Education.This article is based on previous studies about this issue and it deals with about some extra-class activities addressing to contribute to energy education in the Technical and Professional Education process of Transport Operation specialty.The extra-class activities were suggested to students from third-years of Transport Operation specialty.For developing this proposal, sequential methods were used.They consist of theoretical methods such as, analysis-synthesis, induction-deduction and empirical methods such as, scientific observation, interviews, surveys document review, and dialogue with specialists, as well as instruments for collecting, processing, analyzing and interpreting the data obtained.As sample, 65 participants were intentionally selected from "Calixto García Iñiguez" Technical School and University of Holguín in Holguín province.
A Study of Novel Education and Classicization of Ancient Chinese Novels in the Age of Fusion Media

In today’s new media environment, more and more communication contents have been digitized. Also because of digitization, traditional media and new media, which were previously well-defined services, have now merged, media fusion. In the age of media fusion, communication systems are updated more rapidly and more and more novels are being adapted into TV series. Literary education in ancient China has a long history and has played an important role in the development and dissemination of the ancient Chinese literature. Literary education refers to an educational behavior in which the educator and the educated acquire knowledge, enrich emotional experience, and obtain aesthetic pleasure through the reading, explanation, and acceptance of literary texts and then cultivate language ability and cultivate spirituality. There are many factors that promote the classicization of ancient Chinese fiction works. This thesis examines the relationship between fiction education and the classicization of ancient Chinese fiction works. The experiment shows that there are still many problems with the reading of ancient Chinese novels today; the number of respondents who have an average interest in reading ancient Chinese novels accounts for 51%, and only 12% have a high interest in reading. In terms of the choice of reading content, 16% of the students focus on reading literary masterpieces, 70% are inclined to reading young adult literature and campus literature, and 14% prefer to read romance martial arts novels, popular science books, and newspaper publications, etc.
LIMEcraft: handcrafted superpixel selection and inspection for Visual eXplanations

Abstract The increased interest in deep learning applications, and their hard-to-detect biases result in the need to validate and explain complex models. However, current explanation methods are limited as far as both the explanation of the reasoning process and prediction results are concerned. They usually only show the location in the image that was important for model prediction. The lack of possibility to interact with explanations makes it difficult to verify and understand exactly how the model works. This creates a significant risk when using the model. The risk is compounded by the fact that explanations do not take into account the semantic meaning of the explained objects. To escape from the trap of static and meaningless explanations, we propose a tool and a process called LIMEcraft. LIMEcraft enhances the process of explanation by allowing a user to interactively select semantically consistent areas and thoroughly examine the prediction for the image instance in case of many image features. Experiments on several models show that our tool improves model safety by inspecting model fairness for image pieces that may indicate model bias. The code is available at: http://github.com/MI2DataLab/LIMEcraft .
You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection

Can Transformer perform $2\mathrm{D}$ object-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the $2\mathrm{D}$ spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the naive Vision Transformer with the fewest possible modifications as well as inductive biases. We find that YOLOS pre-trained on the mid-sized ImageNet-$1k$ dataset only can already achieve competitive object detection performance on COCO, \textit{e.g.}, YOLOS-Base directly adopted from BERT-Base can achieve $42.0$ box AP. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through object detection. Code and model weights are available at \url{this https URL}.
From Farkas' Lemma to Linear Programming: an Exercise in Diagrammatic Algebra ((Co)algebraic pearls)

Farkas' lemma is a celebrated result on the solutions of systems of linear inequalities, which finds application pervasively in mathematics and computer science. In this work we show how to formulate and prove Farkas' lemma in diagrammatic polyhedral algebra, a sound and complete graphical calculus for polyhedra. Furthermore, we show how linear programs can be modeled within the calculus and how some famous duality results can be proved.
How concept drift can impair the classification of fake news

Fake news is a serious problem that can influence political choices, harm people's physical and mental health, promote treatments without scientific evidence, and even incite violence. Machine learning methods are one of the leading solutions that have been studied for filtering fake news automatically. However, most studies do not consider the dynamic nature of news, creating static models and evaluating them offline through the traditional holdout or cross-validation. These studies naively assume that news characteristics do not change over time and, therefore, the performance of offline models is preserved as time goes on. In this study, we show how concept drift can impair the classification of fake news. We aim to verify whether the conclusions obtained in studies that disregarded the dynamic nature of the news are sustained. We analyzed how the performance of methods trained in an offline fashion is affected by the news update over time, including concept drift due to impacting events like the Covid-19 pandemic and the United States presidential election. The results showed that the performance of offline models is over-optimistic. Incremental learning methods should be preferred because they can adapt to changes in textual patterns over time.
Control of the Group of Mobile Robots Without Pre-planned Route for Monitoring and Diagnostic Purposes

In this paper an approach is presented to solving the problem of control a heterogeneous group of mobile robots with a leader. In particular this approach has been applied for a group of unmanned aerial vehicles (UAVs) during monitoring (scanning) geographically distributed areas.A description of the solution to the problem is given for the subtask for determining the number of UAV subgroups required for scanning areas; for the subtask for distribution of scanning sub-areas between UAV subgroups; for the subtask for distribution of UAVs by subgroups; for the subtask for UAV distribution over scanning bands.The main attention is paid to the issues of working out pre-planned trajectories of movement of individual UAVs in well-known works dedicated the problem of group control of UAVs in solving various target tasks. Unlike these works in this paper an approach proposes to autonomous planning of trajectories which allows minimizing the intersection of trajectories and thereby reduce the risk of collisions of UAVs with each other during operation.