Reviewerly/exHarmony huggingface dataset:
Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization

The crux of self-supervised video representation learning is to build general features from unlabeled videos. However, most recent works have mainly focused on high-level semantics and neglected lower-level representations and their temporal relationship which are crucial for general video understanding. To address these challenges, this paper proposes a multi-level feature optimization framework to improve the generalization and temporal modeling ability of learned video representations. Concretely, high-level features obtained from naive and prototypical contrastive learning are utilized to build distribution graphs, guiding the process of low-level and mid-level feature learning. We also devise a simple temporal modeling module from multi-level features to enhance motion pattern learning. Experiments demonstrate that multi-level feature optimization with the graph constraint and temporal modeling can greatly improve the representation ability in video understanding. Code is available at https://github.com/shvdiwnkozbw/Video-Representation-via-Multi-level-Optimization.
Greedy Shallow Networks: A New Approach for Constructing and Training Neural Networks

We present a greedy-based approach to construct an efficient single hidden layer neural network with the ReLU activation that approximates a target function. In our approach we obtain a shallow network by utilizing a greedy algorithm with the prescribed dictionary provided by the available training data and a set of possible inner weights. To facilitate the greedy selection process we employ an integral representation of the network, based onthe ridgelet transform, that significantly reduces the cardinality of the dictionary and hence promotes feasibility of the greedy selection. Our approach allows for the construction of efficient architectures which can be treated either as improved initializations to be used in place of random-based alternatives, or as fully-trained networks in certain cases, thus potentially nullifying the need for backpropagation training. Numerical experiments demonstrate the tenability of the proposed concept and its advantages compared to the conventional techniques for selecting architectures and initializations for neural networks.
A New Approach to Automatic Detection and Correction of Derivational Errors in L2 Russian

Learner corpora serve as one of the most valuable sources of statistical data on learners' errors. For instance, data from foreign-language learners’ corpora can be used for the Second Language Acquisition research. However, corpora representativity strongly depends on the quality of its error markup, which is most frequently carried out manually and thus presents a time-consuming and painstaking routine for the annotators. To make annotation process easier, additional tools, such as spellcheckers, are usually used. This paper focuses on developing a program for automatic correction of derivational errors made by learners of Russian as a foreign language. Derivational errors, which are not common for adult Russian native speakers (L1), but occur quite often in written texts or speech of Russian as foreign language learners (L2) [Chernigovskaya, Gor, 2000], were chosen as scope of our research because correction of such mistakes presents a formidable challenge for existing spellcheckers. Using the data from the Russian Learner Corpus (http://www.web-corpora.net/RLC/), we tested two already existing approaches to solve such kind of problems. The first one is based on a finite state automaton principle developed by Dickinson and Herring 2008, and it was test-ed as algorithm for derivational errors detection. The second one which relies on the Noisy Channel model by Brill and Moore, 2000, was used for studying errors correction. After we analyzed effectiveness of these tests, we developed our own system for autocorrection of derivational errors. In our program the algorithm of Dickinson and Herring was used as word-formation error detection module. The Noisy Channel model has been rejected, and we decided to use instead the Continuous Bag of Words FastText model, based on Harris distributional semantics theory [1954]. In addition, filtering rules have been developed for correcting frequent errors that the model is unable to handle. To restore automatically the correct grammatical word form, dictionary of word paradigms is used. Model results were validated on the data of Russian Learner Corpus.
Near-Optimal Quantum Algorithms for String Problems

We study quantum algorithms for several fundamental string problems, including Longest Common Substring, Lexicographically Minimal String Rotation, and Longest Square Substring. These problems have been widely studied in the stringology literature since the 1970s, and are known to be solvable by near-linear time classical algorithms. In this work, we give quantum algorithms for these problems with near-optimal query complexities and time complexities. Specifically, we show that: - Longest Common Substring can be solved by a quantum algorithm in $\tilde O(n^{2/3})$ time, improving upon the recent $\tilde O(n^{5/6})$-time algorithm by Le Gall and Seddighin (2020). Our algorithm uses the MNRS quantum walk framework, together with a careful combination of string synchronizing sets (Kempa and Kociumaka, 2019) and generalized difference covers. - Lexicographically Minimal String Rotation can be solved by a quantum algorithm in $n^{1/2 + o(1)}$ time, improving upon the recent $\tilde O(n^{3/4})$-time algorithm by Wang and Ying (2020). We design our algorithm by first giving a new classical divide-and-conquer algorithm in near-linear time based on exclusion rules, and then speeding it up quadratically using nested Grover search and quantum minimum finding. - Longest Square Substring can be solved by a quantum algorithm in $\tilde O(\sqrt{n})$ time. Our algorithm is an adaptation of the algorithm by Le Gall and Seddighin (2020) for the Longest Palindromic Substring problem, but uses additional techniques to overcome the difficulty that binary search no longer applies. Our techniques naturally extend to other related string problems, such as Longest Repeated Substring, Longest Lyndon Substring, and Minimal Suffix.
Rancang Bangun Sistem Informasi Desa Manulondo Berbasis Web

This research aimsto produce a village web-based information system design in Manulondo Village. This application contains the design of a village information system including village profiles, population information, and village potential information. This research uses the method waterfall. The research stages include data collection, analysis, design, coding, implementation testing. The trial using BlackBox Testing. That is by testing the user interface system to find out whether the user interface contained in this application can function properly as expected. That results of the software design in the form of a web-based Manulondo Village information system, consist of, serach for population information an village potential information.
Implementasi Algoritma Fuzzy Tsukamoto Untuk Diagnosis Penyakit Anemia (Studi Data: Rekam Medis Pasien Ibu RSIA Bunda Arif Purwokerto)

Lack of knowledge about the signs of anemia results in a high prevalence of anemia sufferers so that early detection is needed to diagnose anemia. Anemia is caused by a low hemoglobin condition in the human body. Low hemoglobin conditions can cause various symptoms, including fatigue, weakness, dizziness and others. The impact on anemia can reduce concentration, physical endurance and get sick easily. So it is necessary to detect early to diagnose anemia based on the symptoms experienced with maximum accuracy. Users only need to enter the value of symptoms experienced, namely the value of hb, bleeding and weakness, the system will calculate the symptom values using the Tsukamoto fuzzy algorithm. In calculations using the Tsukamoto fuzzy algorithm using the Python programming language, there are 4 stages, namely fuzzification, rule formation, inference engine and defuzzification. At the fuzzification stage, the input symptom value becomes a fuzzy value (0-1), then at the rule formation stage there are 18 rules of 3 symptoms and 3 diagnosis results. After obtaining a rule, it is followed by an inference engine that looks for the α-predicate value in each rule using the min function. After getting the α-predicate value, defuzzification is carried out to get the crisp value or the output value. With the multiple confusion matrix method, the accuracy value of the resulting data from the Tsukamoto fuzzy algorithm and prediction data is 85%. This can be used by the community to easily detect anemia early through the website. Keywords: Accuracy, Anemia, Diagnosis, Fuzzy Tsukamoto, Python
Rancang Bangun Sistem Informasi Administrasi Jasa Penitipan Hewan Berbasis Web

The development of the Animal Care Service Administration Information System is a data input system for Owners, Cats, Types of Packages. Owner data input system includes Owner's Name, Address, and Telephone Number. The cat input system includes Cat Name, Gender, Description, Weight, and Veterinarian Recommendation. The Package Type input system includes Package Type, Price, and Package Description. The purpose of the Veterinary Service Administration system is to facilitate the data management of the Animal Care Service Administration. The development of this system uses the system development life cycle method. The method includes several stages of the process, namely the system planning stage, the system analysis stage, the system design stage, the system implementation stage, the system evaluation stage, the system use and maintenance stage. The Animal Care Service Administration Information System is expected to facilitate the process of making officer reports so that they can produce accurate and effective data.
Neural Algorithmic Reasoners are Implicit Planners

Implicit planning has emerged as an elegant technique for combining learned models of the world with end-to-end model-free reinforcement learning. We study the class of implicit planners inspired by value iteration, an algorithm that is guaranteed to yield perfect policies in fully-specified tabular environments. We find that prior approaches either assume that the environment is provided in such a tabular form -- which is highly restrictive -- or infer "local neighbourhoods" of states to run value iteration over -- for which we discover an algorithmic bottleneck effect. This effect is caused by explicitly running the planning algorithm based on scalar predictions in every state, which can be harmful to data efficiency if such scalars are improperly predicted. We propose eXecuted Latent Value Iteration Networks (XLVINs), which alleviate the above limitations. Our method performs all planning computations in a high-dimensional latent space, breaking the algorithmic bottleneck. It maintains alignment with value iteration by carefully leveraging neural graph-algorithmic reasoning and contrastive self-supervised learning. Across eight low-data settings -- including classical control, navigation and Atari -- XLVINs provide significant improvements to data efficiency against value iteration-based implicit planners, as well as relevant model-free baselines. Lastly, we empirically verify that XLVINs can closely align with value iteration.
Searching for Replacement Classes

Software developers must often replace existing components in their systems to adapt to evolving environments or tooling. While traditional code search systems are effective at retrieving components with related functionality, it is much more challenging to retrieve components that can be used to directly replace existing functionality, as replacements must account for more fundamental program properties such as type compatibility. To address this problem, we introduce ClassFinder, a system which given a query class Q, and a search corpus S, returns a ranked subset of classes that can replace Q and its functionality. ClassFinder produces afield and method mapping between the classes that can provide useful hints to a developer and can be used to effectively refine the ranking of candidate replacement classes. Our technique leverages the complementary strengths of a distributed embeddings-based search and type-based analysis, using the former to prune down candidates for an optimization-based approach based on the latter. ClassFinder retrieves replacement classes, along with a type-aware field/method mapping between classes. We evaluate ClassFinder on a search space of ~600thousand open sourceJava classes. Querying ClassFinder with 24 known Java classes provided meaningful replacement classes and mappings, in many cases producing complete mappings with functionally identical replacement classes.
Decidability and Complexity of Some Finitely-valued Dynamic Logics

Propositional Dynamic Logic, PDL, is a well known modal logic formalizing reasoning about complex actions. We study many-valued generalizations of PDL based on relational models where satisfaction of formulas in states and accessibility between states via action execution are both seen as graded notions, evaluated in a finite Łukasiewicz chain. For each n&gt;1, the logic PDŁn is obtained using the n-element Łukasiewicz chain, PDL being equivalent to PDŁ2. These finitely-valued dynamic logics can be applied in formalizing reasoning about actions specified by graded predicates, reasoning about costs of actions, and as a framework for certain graded description logics with transitive closure of roles. Generalizing techniques used in the case of PDL we obtain completeness and decidability results for all PDŁn. A generalization of Pratt's exponential-time algorithm for checking validity of formulas is given and EXPTIME-hardness of each PDŁn validity problem is established by embedding PDL into PDŁn.
Hierarchical Trajectory Planning for AGVs with a Local Adaptive Search Space

This paper presents a real-time and responsive trajectory planner for Autonomous Ground Vehicles (AGVs). We improve a state-based sampling search space method to generate well separated kinematically feasible trajectories according to the obstacle distribution ahead in a finer-scale range of vehicle. Meanwhile, the planner utilizes a cost function, which introduces a global guidance with the nonholonomic nature of AGVs, to ensure the optimality and responsivity for vehicles navigation in the dynamic environments. The experimental results demonstrate that the proposed planner has an improvement in generating superior and smoother trajectories, compared with the Global Adaptive Hierarchical Trajectory Planner (GAHTP). Simulation results show that the proposed method can provide safe and smooth trajectories in highly constrained semi-structured or unstructured environments.
Making Academic Security Management System Planning Document Based on ISO 27001: 2013 for Improving Information Security at AAK University Dynamics

Dinamika University is an educational institution engaged in information technology. One of the mision of undika is to create profesional, superior and competent human resource. To support the mission, Undika has a supporting unit in the administration field, namely the Academic Administration Secction and the Student Affairs Section (AAK). The solution that has been carried out by the AAK section in handling information security is related to 3 (three) basic prociples of information security namely Confidentiality, Integrity and Availability is still limited to wholeness and confidentiality which can only be meet the management and technical sides, the support provided is to compile documents related to information security control and the creation of Standard Operational Procedure (SOP) documents which include policy documents, work instructions and work records. The results of this study are risk management documents and the preparation of objective controls and security controls used to support the preparation of SOP documents based on technical categories consisting of 5 policy documents, 5 SOP, 5 work instructions, and 11 work records. Management consist of 1 policy document, 1 SOP, 1 work instruction and 1 work record. Operational 1 policy document, 1 SOP, 1 work instruction, and 3 work records
Construction of Advanced Applied Mechanical Professional Training Mode in Independent Colleges

In view of the lack of pertinently targeted mechanical major in independent colleges, the reform measures are put forward. Independent colleges should train senior application-oriented professionals. The paper puts forward some suggestions on reforming the existing teaching management system and points out that the reform of management and evaluation system should be strengthened. It is necessary to integrate the curriculum system and to strengthen the practical construction. Strengthening the construction of practical conditions is an important push to improve students' practical ability. It is also very important to build a scientific students' assessment and evaluation mechanism to motivate students' learning enthusiasm.
Interpretable noninterference measurement and its application to processor designs

Noninterference measurement quantifies the secret information that might leak to an adversary from what the adversary can observe and influence about the computation. Static and high-fidelity noninterference measurement has been difficult to scale to complex computations, however. This paper scales a recent framework for noninterference measurement to the open-source RISC-V BOOM core as specified in Verilog, through three key innovations: logically characterizing the core’s execution incrementally, applying specific optimizations between each cycle; permitting information to be declassified, to focus leakage measurement to only secret information that cannot be inferred from the declassified information; and interpreting leakage measurements for the analyst in terms of simple rules that characterize when leakage occurs. Case studies on cache-based side channels generally, and on specific instances including Spectre attacks, show that the resulting toolchain, called DINoMe, effectively scales to this modern processor design.
Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning.

In few-shot learning scenarios, the challenge is to generalize and perform well on new unseen examples when only very few labeled examples are available for each task. Model-agnostic meta-learning (MAML) has gained the popularity as one of the representative few-shot learning methods for its flexibility and applicability to diverse problems. However, MAML and its variants often resort to a simple loss function without any auxiliary loss function or regularization terms that can help achieve better generalization. The problem lies in that each application and task may require different auxiliary loss function, especially when tasks are diverse and distinct. Instead of attempting to hand-design an auxiliary loss function for each application and task, we introduce a new meta-learning framework with a loss function that adapts to each task. Our proposed framework, named Meta-Learning with Task-Adaptive Loss Function (MeTAL), demonstrates the effectiveness and the flexibility across various domains, such as few-shot classification and few-shot regression.
Secure Decision Making and Inference in Critical Systems

The use of digital components in critical systems creates vulnerabilities. Artificial Intelligence (AI) and Machine Learning (ML) can be used towards the monitoring of a system, because of their ability to handle big volumes of data. Specifically, the physical nature of a category of these systems – the cyber physical systems, (CPSs) – permits the embedding of the concept of inference and reinforcement learning. According to this, a system can perform decision making by selecting actions, which later provide certain rewards. This idea is implemented on a simple nuclear system, to determine whether the system has the ability to adjust to a specific power level by making decisions about other variables, such as control rod heights. Some preliminary results show that the system is able to adjust its state after some time has passed.
A Study of Conceptual Development on Internet-Based Academic Service (Internship Program Related) as A Manifestation of UNNES the Conservation University

An embodiment of the quality management system, minimizing the errors lead to complaining, Easiness, and Effectiveness to access or to run any kind of activity, also facilitating all people who are engaged in the academic system are some of the most important reasons why we should always improving the academic service quality.On another hand, an internship is one of the challenging programs that all university students must accomplish, because it is the integration of academic environment and real job ambient that involving not only lecturer and student but also the mentor, administration staffs, and other people like the client or customer.Moreover, since the pandemic Covid-19 has been attacking all over the globe, the manual administration things nowadays must adapt and migrate to an online service.Therefore, A new system that could become the platform to accommodate all the needs related to academic services especially for the internship program seems a promising solution.
Real Time Static Gesture Recognition using Time of Flight Camera

Hand gesture recognition is challenging task in machine vision due to similarity between inter class samples and high amount of variation in intra class samples. The gesture recognition independent of light intensity, independent of color has drawn some attention due to its requirement where system should perform during night time also. This paper provides an insight into dynamic hand gesture recognition using depth data and images collected from time of flight camera. It provides user interface to track down natural gestures. The area of interest and hand area is first segmented out using adaptive thresholding and region labeling. It is assumed that hand is the closet object to camera. A novel algorithm is proposed to segment the hand region only. The noise due to ToF camera measurement is eliminated by preprocessing algorithms. There are two algorithms which we have proposed for extracting the hand gestures features. The first algorithm is based on computing the region distance between the fingers and second one is about computing the shape descriptor of gesture boundary in radial fashion from the centroid of hand gestures. For matching the gesture the distance between two independent regions is computed for every row and column. Same process is repeated across the columns. The number of total region transitions are computed for every row and column. These number of transitions across rows and columns forms the feature vector. The proposed solution is easily able to deal with static and dynamic gestures. In case of second approach we compute the distance between the gesture centroid and shape boundaries at various angles from 0 to 360 degrees. These distances forms the feature vector. Comparison of result shows that this method is very effective in extracting the shape features and competent enough in terms of accuracy and speed. The gesture recognition algorithm mentioned in this paper can be used in automotive infotainment systems, consumer electronics where hardware needs to be cost effective and the response of the system should be fast enough.