Miracle-dz/UniFAR-FTUs huggingface dataset:
CORE: Cooperative Encryption with its Applications to Controllable Security Services

This article introduces a new concept of encryption, the cooperative encryption (CORE), to control encryption capability, i.e., only permitted (not any) users can encrypt messages with their public keys. In CORE, any message encrypted with a user public key can be decrypted only after it is matured by the security mediator (SEM). Compared with the other security controllable encryption systems, CORE can be more flexibly applied to various encryption systems, e.g., identity-based and attribute-based encryptions, supported by encrypting user private keys tightly. Moreover, CORE can directly revoke the corrupted SEM without re-issuing user private keys and prevent from producing a complete ciphertext, which is not cooperatively computed with the SEM, by users with the disclosed security mediated keys. Generally, CORE is of unique interest in fully controlling user encryption/decryption behaviors in controllable security services, and supporting distributed SEMs for large scale networks. This work also develops the framework of CORE, and proposes two constructions of CORE to identity-based and attribute-based encryptions, which are empowered to achieve two essential security properties, i.e., immediate revocation and unforgeable encryption. In addition, this work analyzes the security and performance of the proposed schemes. Overall, CORE provides a new notion of controllable security without involving any trust authority or key management server.
Nonlinear Optimization Using Discrete Variational Mechanics for Dynamic Maneuvers of a 3D One-Leg Hopper

We present an optimization-based motion planning framework for producing dynamically rich and feasible motions for a 3D one-leg hopper in challenging terrains. We formulate dynamic motion planning as a nonlinear optimization problem that computes position and orientation of the centroidal model, position of the limb, contact forces, contact locations, and timings of the gait in one unified framework. The dynamics are represented as a single rigid body, while the equations of motion are derived using discrete mechanics with a variational quaternion-based integrator for the orientation. We validate the capabilities by planning complex motions in three challenging tasks: jumping over an obstacle, leaping over a gap, and performing a somersault. All contact forces generated by the proposed optimization are verified with accurate numerical simulation to prove the feasibility of the generated agile motions with respect to the kinematic, dynamic, and environmental constraints.
A balanced hybrid ring oscillator for precise temperature compensation

A balanced hybrid ring oscillator (RO) is proposed in this paper for the precise temperature compensation. Different from the previous ROs with only one type of delay cell, the proposed RO consists of both the delay cells with PTAT (proportional to the absolute temperature) characteristics and those with CTAT (complementary to absolute temperature) characteristics. The optimal temperature compensated point can then be found by adjusting the number of PTAT and CTAT delay cells and the load capacitance of each delay cell. Both ideal and non-ideal models are developed to explain this temperature compensation mechanism. Finally, combining the opposite temperature characteristics of normal RO and current-starved RO, a 13.4 MHz on-chip clock circuit is implemented in 0.13um CMOS process, with 14.6 ppm/°C over the temperature range from -40°C to 80°C. The worst process variation is 1.19% and the supply variation is 0.06%/V.
Distributed Active Disturbance Rejection Control for Linear Multi-Agent Systems Over Directed Graph

In this paper, the consensus problem of multiagent systems (MAS) with unknown disturbances has been addressed, by a novel feedforward control protocol. A distributed observer-based protocol, which extendes active disturbance rejection control(ADRC) to MAS under directed graph, has been proposed. Firstly, the local states and disturbances are estimated simultaneously by extended state observers. Secondly, states and disturbances based on observers have been applied to control protocol, which avoids chatting phenomenon and lagged effects caused by existing discontinuous control protocals, and the assumption about disturbances is relaxed. Thirdly, the case that the derivative of disturbance is bounded is discussed. Finally, simulations validate the correctness and effectiveness of the proposed control protocal.
Leveraging Spatio-Temporal Soccer Data to Define a Graphical Query Language for Game Recordings

For professional soccer clubs, performance and video analysis are an integral part of the preparation and post-processing of games. Coaches, scouts, and video analysts extract information about strengths and weaknesses of their team as well as opponents by manually analyzing video recordings of past games. Since video recordings are an unstructured data source, it is a complex and time-intensive task to find specific game situations and identify similar patterns.In this paper, we present a novel approach to detect patterns and situations (e.g., playmaking and ball passing of midfielders) based on trajectory data. The application uses the metaphor of a tactic board to offer a graphical query language. With this interactive tactic board, the user can model a game situation or mark a specific situation in the video recording for which all matching occurrences in various games are immediately displayed, and the user can directly jump to the corresponding game scene. Through the additional visualization of key performance indicators (e.g., the physical load of the players), the user can get a better overall assessment of situations. With the capabilities to find specific game situations and complex patterns in video recordings, the interactive tactic board serves as a useful tool to improve the video analysis process of professional sports teams.
Query Answering On Uncertain Big RDF Data Using Apache Spark Framework

Data is often associated with uncertainty because the fusion of conflicting data sources, measurement inaccuracy, sampling discrepancy, outdated data sources. We address the problem of query answering over uncertain big data sources using Resource Description Framework (RDF) and ontologies, while computing the exact uncertainty measure of the answer. Therefore, the probability is embraced along the reasoning process when answering. the query. In this paper, we introduce a probabilistic approach for answering user queries that computes complete results by exploiting uncertain knowledge on data sources. We have designed algorithms that are ontological rules based to infer implicit data by combining saturation and query rewriting reasoning. To handle big data the algorithms are spark-based implementation.
Controller Design of Equivalent-Input-Disturbance System based on H-infinity

A methodology of the the Equivalent-Input-Disturbance control system based on H-infinity $(H_{\infty})$ is proposed in this paper. This design method aims to solve the problem that the parameters of the control system lack unified standard to set. It converts the controller design problem into a convex optimization problem with LMI constraints. The parameters of the control system are obtained by using the LMI toolbox in Matlab. Firstly, the model of the Equivalent-Input-Disturbance system containing state feedback controller is built; secondly, the controller design problem is transformed into a standard $H_{\infty}$ state-feedback design problem and a mathematical model of the transformed $H_{\infty}$ standard control model is established; finally, under the $H_{\infty}$ formulation, the state feedback gain is obtained by solving the LMI constraint inequations and the stability is considered based on the small gain theorem. Simulation results show that the proposed method is effective and easy to implement.
Capability Level at Faculty Information Technology Unit Using COBIT 5

Information technology (IT) is a very important part of an organization and is an investment value to make the organization better. The organization places IT as a matter that can support the achievement of the company's strategic plan to achieve the goals of the company's vision or mission and goals, as well as the Faculty of Science and Mathematics of the Diponegoro University (FSM UNDIP). FSM UNDIP has a vision to become a superior faculty based on research in the fields of science and mathematics and the development of its application. And one of the missions to achieve this vision is Enhancing governance efficient, accountable, transparent, fair, and integrated between sectors (good governance). IT governance in an organization aims to align IT strategy with the organization's business strategy. Without good and right governance, IT will cause problems and threats to the organization. This research aims to assess the capabilities of the IT governance process at FSM UNDIP. The framework used to assess IT governance that focuses on web-based services in FSM is this study using COBIT 5 on EDM03 and EDM04 domains, with the Self Assessment method. The results showed that the capability level of the IT governance process at FSM UNDIP in the EDM03 process was at Level 2 (Managed Process), which is an organization in carrying out IT, at this stage it has been successful and governance has been done well. Management of the planning, evaluation and adjustment processes is directed to better direction. While the EDM04 process is at level 1, the organization has successfully implemented IT processes and the goal of the IT process has been successfully achieved. The results provided are documents containing the governance of Risk Optimization and Resource Optimization management as a basis for risk and resource management references on the FSM UNDIP website. With the risk management and good resources will help organizations to make strategic decisions.
Digital Hardware Implementation of Gaussian Wilson–Cowan Neocortex Model

Hardware implementation of biological neural models can help in better understanding of the brain functionality, implementing cognitive tasks, and also studying the brain diseases. Gaussian Wilson–Cowan model as one of the well-known population-based models represents neuronal functionality in neocortex. In this paper, Gaussian Wilson–Cowan model is investigated in terms of its digital implementation feasibility. Digital model is proposed for the Gaussian Wilson–Cowan and examined from dynamical and timing behavior point of view. The evaluations indicate that the digitized model is able to reproduce the dynamical bifurcations as the original model is capable of. An efficient digital hardware system is given for the proposed model with minimum required resources using Verilog Hardware Description Language. Digital architectures are physically implemented on an Altera FPGA board. Experimental results show that the proposed circuits take maximum 2% of the available resources of a Stratix Altera board. In addition, static timing analysis indicates that the circuits can work in a maximum frequency of 244 MHz.
Deep Learning Forecasting Based on Auto-LSTM Model for Home Solar Power Systems

The Internet of things is widely used to provide a lot of useful services such as human health care, security systems and green energy monitoring. It contributes to the sustainable development of smart cities in order to manage and integrate renewable energies. The swift growth of Home Solar Power Systems (HSPS) has enabled a large-scale collection of time series data. As advanced tools, smart meters can ensure the timely reading of HSPS data, automating metering and producing fine-grained data. However, to ensure the dynamic data management and a better understanding of HSPS operations, it is crucial to analyze and forecast these digital records for decision-making and smart control. Up to now, deep learning algorithms have only been applied sparsely in the field of renewable energy power forecasting. In this paper, we apply an auto-configurable middleware based on a Long Short Term Memory (LSTM) model for several forecasting time dimensions to choose the significant timescale for learning setting. The results show that our deep-learning model has good performances compared to the Support Vector Machine (SVM) model for the whole proposed learning timescale. However, the Auto-Regressive Integrated Moving Average (ARIMA) seems better than our proposed auto-LSTM algorithm, but it takes much execution time for 15 min and 30 min-ahead forecasting. Thus, the day-ahead forecasting is the most efficient timescale in our case.
OBMA: Minimizing Bitmap Data Structure with Fast and Uninterrupted Update Processing

Software-based IP route lookup is one of the key components in Software Defined Networks. To address challenges on density, power and cost, Commodity CPU is preferred over other platforms to run lookup algorithms. As network functions become richer and more dynamic, route updates are more frequent. Unfortunately, previous works put less effort on fast incremental updates. On the other hand, The cache in CPU could be a performance limiter due to its small size, which requires algorithm designers to give high priority on storage efficiency in addition to time complexity. In this paper, we propose a new route lookup algorithm, OBMA, which improves update performance and storage efficiency while maintaining high lookup speed. The extensive experiments over real-word traces show that OBMA reduces the memory footprint to just 4.52 bytes/prefix, supports update speed up to 7.2 M/s which is 12.5 times faster than the state-of-the-art algorithm Poptrie. Besides, OBMA achieves up to 195.87 Mpps lookup speed with a single thread. Tests on comprehensive performance of lookup and update show that OBMA can sustain high lookup speed with update speed increasing.
Task Clustering-Based Energy-Aware Workflow Scheduling in Cloud Environment

In the recent years, the efficient management of Cloud Data Center (CDC) resources (computation, storage, and network) is a complex task due to over provisioning and overloading. Furthermore, to form an energy-aware data center with negligible impact on the workload performance is also a critical issue because over provisioned and overload system consumes a considerable amount of electric power. The compute-intensive scientific applications generate complex workload structure (workflow) whose successful execution in terms of energy-performance trade-off is still at its beginning. For this, the simulators are used to analyze and understand the behavior of diverse workload in a distributed environment. WorkflowSim is a popular simulator for workflow processing because of it presents scientific workflows in Directed Cyclic Graphs (DAG) format and offers task clustering (horizontal clustering, vertical clustering, and balanced clustering) for load balancing. In this work, task clustering is used for load balancing and we proposed a Power-aware MAX-MIN VM placement algorithm. Dynamic Voltage Frequency Scaling (DVFS) technique with On-Demand governor and Conservative governor are integrated into Power-aware MAX-MIN VM placement algorithm for reducing power consumption. The implementation results show that energy consumption is reduced considerably in balance, On-Demand, and Conservative approaches with respect to Simple approach.
PC-SENE: A node embedding based method for protein complex detection

With the accumulation of protein-protein interaction (PPI) datasets, various computational methods have been developed for identifying protein complexes from PPI networks. However, many exiting computational methods have their own limitations: supervised learning approaches need tedious effort for feature engineering and the quality measures used to guide the mining process of unsupervised methods have some drawbacks in reflecting the properties of a protein complex in PPI networks. In this work, we proposed a novel protein complex detection method, named PC-SENE. For given seeds, it uses alias sampling strategy based on protein node embedding similarities to select potential addable nodes, and makes use of a new conductance measure to decide whether to extend current candidate subgraph in order to find protein complexes. Intuitively, a well trained node embedding vector could preserve both the topological characteristics of the PPI network and the diversity of connectivity patterns of nodes in the network, and thus node embedding similarities can better reflect the relationship between nodes. The experimental results show the robustness and effectiveness of PC-SENE.
Enhancing Biped Locomotion on Unknown Terrain Using Tactile Feedback

Human bipedal balance during standing and walking depends on several receptors including the cutaneous receptors in the glabrous skin of the foot sole. It has been shown in human-involved studies that the different areas of the sole have distinct sensitivities and serve a different purpose in both walking and standing. In humanoid robotics, the feedback to keep balance is mainly achieved using force-torque sensors mounted at the robot's ankles. Although these sensors can accurately estimate the center of pressure of a foothold, they cannot provide information about the pressure shape of the footprint and therefore can miss ill terrain conditions during locomotion. In this paper, we present a biologically inspired sole skin sensor based on the robot skin developed at our lab. The robot skin can enhance and complement the ankle force-torque sensors used in balancing and walking controllers by providing additional information that a force-torque sensor cannot produce. This additional information can be used to reconstruct the supporting polygon and the pressure footprint online. We present a case study where a force-torque sensor fails to detect the terrain conditions while the skin succeeds and the information is used to re-plan the footstep position.
Hardware Dithering: A Run-Time Method for Trojan Neutralization in Wireless Cryptographic ICs

We introduce a hardware dithering methodology for neutralizing Trojans in integrated circuits (ICs). The proposed approach seeks to make the operating point of an IC an unpredictable moving target during run time. Thereby, the ability of a Trojan to exploit the process variation margins, wherein hardware Trojans typically find breathing room to operate while remaining concealed, is significantly restricted. To demonstrate this hardware dithering concept, we leverage tuning knobs operating on the power and frequency characteristics of the transmission of a wireless cryptographic IC. These knobs are driven by a random number generator, thus forcing the circuit into a random walk in the space of its parametric performances while in normal operating mode. In essence, while the circuit remains within its operating specifications during this random walk, its exact operating point varies, thus muddying the waters for the adversary. Experimental results on the wireless cryptographic IC, which was designed and fabricated in a 0. 35μm CMOS technology, corroborate that hardware dithering imposes a significant and unpredictably dispersed bit error rate to the adversary, thereby impeding hardware Trojan operation.
Experiments on Fine Tuning Deep Learning Models With News Data For Tweet Classification

In this paper we present our experiments on tweet classification task. Because Twitter data is very noisy by its nature for including URLs, symbols, typos etc. it needs special treatment of text classification approaches. Also, availability of annotated Twitter data is rare except for sentiment analysis use cases. In this work we perform text classification on tweets for identifying whether a tweet belongs to one of the topics of culture, economy, politics, sports, or technology. Our approach shows that in the circumstance of having a very small amount of labeled tweet data we can classify tweets on high accuracy levels. To do this we first train a simple neural network with huge amount of news data. Then we apply basic fine-tuning steps on these models by using tweet data.
In-Memory Hadoop on Supercomputers Using Memcached-Like Nodes for Data Storage Only

In this paper, we propose an in-memory architecture called H-Memcached, which can solve the mismatch of local disks on modern supercomputers. It helps avoid out of memory and makes large-scale datasets fit into memory. Hadoop and Spark are good choices to run MapReduce and Machine learning algorithms, such as PageRank and K-means clustering. A supercomputer is a high-speed network cluster consisting of thousands of compute nodes where local disks are typically not available. All nodes share the same disk called the central disk, and disk I/O to that storage is expensive. To avoid disk I/O, inmemory Hadoop/Spark is required to fit into supercomputer environment. However, memory is limited on each node and out of memory happens when data does not fit into memory. In our proposal, a group of nodes are allocated and used as a storage device instead of the central disk. The uniqueness of H-Memcached is that some computation resources are used for data storage rather than running computation tasks. Nodes which are dedicated to data storage are less busy and give more memory for in-memory storage. Compared to the central disk on the FX10 supercomputer, our experimental results show that H-Memcached reduces execution time by 25% on average.
Simulation of Volatility Trading using Nikkei Stock Index Option based on Stock Bulletin Board

We developed a simulation program for trading Nikkei stock index options and verifies the validity of the volatility index (VIX) prediction model proposed by Suwa et al. (2017). We simulated two cases from 18 Nov. 2014 to 29 Jun. 2016. One case involved a benchmark of trading every day during that period and the other was in accordance with the buy/sell/hold instructions of Suwa et al.’s VIX prediction model. When using the call option butterfly spread according to their model’s instructions, profit increased from −3,926 to 536 yen. When using the put option butterfly spread according to their model’s instructions, profit increased from −4,818 to −799 yen. Therefore, Suwa et al.’s VIX prediction model is effective.
Improvement of General Inquirer Features with Quantity Analysis

General Inquirer is a word-affect association vocabulary having 11896 entries. Ranging from rectitude to expressiveness, it comes with a flavor of categories. Despite the extensive content, a mapping from "To be or not to be. " to "How much? " can be beneficial for word representation. In this work, we apply a method of window based analysis to obtain real valued General Inquirer attributes. Sentence Completion task is chosen to calculate the effectiveness of the operation. After whitening post-process, total cosine similarity convention is followed to concentrate on embedding improvement. Results indicate that our quantity focused variant is considerable.